Table of contents

Health care systems are crucial to people's lives, so we established a quantitative model of multi-index health care assessment system to evaluate health care systems of different countries objectively. Firstly, we adopted the entropy weight method to calculate the weight of each indicator and built a linear function to derive the scoring results of the system in each country. Secondly, we used the incomplete induction method for the secondary screening of indicators, and the weight of selected seven indicators are: 0.16 0.11 0.13 0.14 0.19 0.15 0.12. Thirdly, we compared different countries' health care systems. Compared to the America's health care system, Japan had a better one, while China had a poorer one, and the scores of them are: 71.23 77.87 38.96. Finally, we proposed two suggestions to U.S. and predicted its improvement effects by BP neural network. Given the ease of implementation, cost and other issues, the latter is considered more appropriate.

In this paper, in order to improve the accuracy of handwritten number recognition, a convolutional neural network based on noise reduction and stack auto coding is formed by combining the denoising auto coding algorithm with stack auto coding algorithm. In this paper, the MNIST dataset is preprocessed first, then the noise reduction and stack auto coding network layer are constructed, and finally the stack auto coding algorithm based on the improved denoising auto coding is used to train and test the dataset. Experimental data show that the proposed method achieves low loss value and effectively improves the recognition accuracy in the MNIST test set.

In recent years, the rapid expansion of urban distribution business and the increase use of traditional fuel logistics trucks have resulted in a lot of environmental, traffic and resource problems. "Green logistics" has become the direction of urban distribution transformation, and replacing fuel vehicles with pure electric logistics vehicles has become a way to meet the requirements of urban end distribution. In this paper, the characteristics of electric logistics vehicles are combined with the constraints of cargo capacity to meet the demand for sufficient power and driving range in the distribution process. Charging operations are carried out using charging piles at fixed locations when necessary, and the time windows of customers at each node are considered and penalty factors are set so that the distribution path of electric logistics vehicles can be well planned, which makes the total logistics cost lowest.

Road traffic markings are made of glass beads embedded in paint. To analyze the effects of glass-bead radius, embedment depth, correlated color temperature of light sources, and incident angle on the reflective performance of glass beads, a numerical model is established using the geometric optics method, effective light conditions are analyzed, calculation methods for optical power devised, and non-standard planes of incident light calculated. Calculations were also carried out. Results show that the retroreflective properties of glass beads are positively correlated with the correlated color temperature of the light source and radius of the beads. The glass-bead radii, embedment depths, and incident angles have little effect on their reflective performance. Moreover, the correlated color temperature of the light source is an important factor affecting the accuracy of measuring glass-bead reflective performance. When using retro reflectometers, standard retro reflectance-measuring devices for retro reflectance measurement, and other equipment to evaluate the reflective performance of road traffic markings, the correlated color temperature of the light source of the equipment should be as close as possible to 2856 K.

The frequent cross-ship communication has raised the complexity of ship network, therefore raised the requirements of monitoring system on the complicated local network on board. The traditional monitoring system is expensive and can hardly monitor cross-OS local network. Aimed at the specifications of ship network, this paper puts forward using embedded web server to design monitoring system. Through designing monitoring layer with LibPcap, designing application layer with CGI and designing user layer with JavaScript and REST, this paper finish the complete monitoring system and high improves the performance on latency and threat identification success rate.

In today's society, severe traffic congestion is one of the main reasons hindering social development. However, China's existing urban transportation system has been basically completed, and it is impossible to make major adjustments. By combining the current fast-developing cloud computing and intelligent algorithms, the original traffic control system is optimized and improved. This system will start from two aspects to optimize the city's traffic system. On the one hand, it establishes an intelligent signal light device to sort out the city's traffic situation, thereby greatly improved the congestion at various intersections; on the other hand, it uses cloud data as the basis of vehicle-mounted mobile terminal users. Real-time traffic routed formulation, reasonable allocation of urban traffic resources.

At present, with the steady progress of industrialization and information technology in China, lots of high-tech, including artificial intelligence, such as financial robot and face recognition system, are gradually coming into the public view. This paper points out the constituent elements and design logic of artificial intelligence, starting from the background of the development and application of related technologies. Meanwhile, based on this artificial logic, assisted by voice recognition, image recognition, data analysis and other processes, it is applied to neural network construction, data mining, intelligent information system and other fields. Finally, taking one of the fields as an example, this article deeply analyzes the possible effect and impact of the application of these technologies, which provides a theoretical basis for the large-scale promotion of artificial intelligence technology.

When mobile robots use odometer method to locate and map simultaneously in large-scale indoor scenes, there are some problems, such as large accumulated error of odometer, low positioning accuracy due to unreliable information, and large difference between the mapping results and the real environment. In view of the above problems, this paper proposes a method to correct the accumulated error of odometer with the aid of AR code artificial landmark, so as to meet the requirements of high-precision positioning and mapping of mobile robots. Using the particle filter algorithm based on Rao-Blackwellized to perform SLAM in indoor corridors, the comparison of experimental results proves that the method based on AR code landmarks can obtain high-precision positioning when the robot is running at a long distance, and the mapping results are similar to the real environment, the trajectory accuracy of the mobile robot is improved by about 8%.

The Golden Delicious apple shelf-life prediction model based on electronic nose was built by partial least squares regressions (PLSR). Sensory quality, such as, weight loss, texture, color changes and electronic nose were studied. The correlation between the electronic nose sensors and the sensory quality of Golden Delicious apples were analyzed to select the appropriate sensors for the construction of the shelf-life model. The results of the shelf-life prediction model on Golden Delicious apples building by PLSR showed that R² of training set was 0.86, RMSEC was 3.94. The R² of prediction set reached to 0.98, and the model was relatively accurate.

Sharing of general aviation pilots is beneficial to the growth of pilots. It is beneficial to achieve the balance between supply and demand of general aviation pilots and reduce the cost to a certain extent. It is beneficial to improve the flight safety level of general aviation enterprises. With the continuous deepening of the reform of "distribution suit", there are fewer and fewer policy constraints on navigation development, which creates a favorable policy environment for navigation to "fly". However, under the background of loose policies, China's navigation development still faces many challenges, among which the high transaction cost of navigation industry is an important reason restricting navigation development. Among them, the cost of pilots accounts for the vast majority of the labor cost.

The article uses the holistic, comprehensive and dynamic diversified and complex thinking mode of big data system theory. The article creates a generalized linear model for the value of each technical and tactical performance index and the game result in each score-balanced game. To define the linear relationship between the technical and tactical performance indicators of the game and the probability of winning the game; finally, adopt the data series inference method to define the significance of the linear relationship between each technical and tactical performance indicator and the probability of winning the game. The research found that the information provided by the model created in the paper can be used in football practice such as game performance evaluation, opponent information detection, selection and modification of training preparation plans.

with the rapid development of information technology, how to use big data for effective analysis has become the focus of various industries. Big data will inevitably bring data noise and data redundancy. The traditional big data preprocessing methods do not take into account the functional relationship between variables, and the commonly used big data modeling tools can not effectively model big data with complex nonlinear relationships. This paper puts forward a prediction method combining DEA and RBF, uses DEA to preprocess big data to select the effective date, then uses RBF to model the data, and compares the prediction accuracy with the original environmental management fund data without pre-processing. The results show that the DEA-RBF method has higher prediction efficiency and better prediction accuracy.

This paper is based on the classification decision tree algorithm, based on the Spark cloud platform, through the construction of the decision tree algorithm, and setting AUC parameters, comprehensive comparison of the support vector machine algorithm, and Logistic regression. The results show that the Spark cloud platform has the best robustness, Better realize data forecasting.

With the development of the Internet, public opinion monitoring emphasizes the need to obtain a more accurate mood prediction model, so as to grasp the public opinion tendency and thinking process more accurately. In addition, the need to process real-world datasets of sentiment analysis without using sampling techniques can introduce noise into the data being used. In this paper, we propose an extraction method based on compact interval valued intuitionistic fuzzy rules for the classification of news comment pages based on IVFS, for modeling and predicting real-world Sentiment Analysis applications. The web pages are partitioned based on DOM tree, and the emotional tendency of news comments is classified by the improved intuitionistic fuzzy reasoning algorithm. Finally, this method is used to extract news comment web pages. To test the quality of our recommendations, we will present an experimental study that includes a dataset of 9 news pages with comments. We will demonstrate that this method is superior to the original C4.5 decision tree, Type 1 and Region 1, which are preprocessed.5. Decision tree. Type 1 corresponds to the interval value fuzzy and approximates the original fuzzy classifier. Our approach avoids a lot of unnecessary trouble and provides interpretable models that allow for more accurate results.

The measurement accuracy of MEMS gyroscope is relatively low. Starting from the software level, the multi-sensor information fusion technology of MEMS gyroscope array (MGA) is used to reduce the drift of MEMS gyroscope. Firstly, a signal acquisition and processing system for communicating with ADXRS810 gyroscope based on field-programmable gate array (FPGA) is designed. Secondly, the collected drift data of the gyroscope array is preprocessed. long-term drift trend terms are obtained by ensemble empirical mode decomposition (EEMD) and the linear function fitting, and the trend terms are filtered out to obtain a smooth and normal drift signal. Then, the data fusion model based on time series analysis is built for the gyroscope array and the error analysis is performed by Allan variance. Finally, based on the AR (1) model, the moving horizon optimization Kalman filter method is used to filter the drift data of the gyroscope array. The experiment shows that when the time domain length N=3, the bias instability of gyroscope 1, 2, 3 and 4 decreases from 9.716 o/h, 8.5682 o/h, 13.484 o/h and 26.414 o/h to 1.2922 o/h, 0.61147 o/h, 1.4184 o/h and 1.6964 o/h, respectively, and the average noise coefficient decreases by more than 85%. Compared with the ordinary Kalman filter method, the bias instability has been significantly improved.

Receiver Autonomous Integrity Monitoring (RAIM) method is widely used in the field of aviation navigation for its advantages of fast response, low cost and wide coverage. The performance of traditional RAIM is not as good as that of fault detection, and the calculation is complex. With the development of multi-constellation integrated positioning technology, the number of visible satellite is gradually increasing. Although the performance of traditional RAIM method has been improved, but it is not large, and the amount of calculation has increased greatly. In order to solve the above problems, an optimized grouping RAIM method is proposed. Through uniform grouping, fault detection can be directly used to complete fault identification, which reduces a lot of calculation and improves the efficiency and performance. The simulation results of graphics and data show that the algorithm is feasible and effective.

Nowadays, the demand for data analysts has increased dramatically. First the job requirements of data analyst are downloaded from online recruitment website ChinaHR.com and zhaopin.com, then the information of the recruitments are analyzed by text mining method such as word segmentation, word frequency statistics and visualization technology. At last we summarized the required skills for data analyst. We found that compared with Python, the company is inclined to use R for data analysis, the recruitment requirements of every type of company are different. Our findings can provide reference for schools and guide individual professionals to assess and advance their competencies.

With the development of science and technology, artificial intelligence has become one of the main driving forces for the development of modern society, and has also injected new vitality into the future development of libraries. Based on the high efficiency and scientific of artificial intelligence technology, the technology has been widely used in intelligent search and reasoning, machine learning and human imitation. In the "internet plus" era, the information service carried out by libraries has changed to a new intelligent information service mode. With the support of new technologies, the intelligent library has gradually become a new form of library development, showing the characteristics of digitalization, networking, intelligence and mobility. Artificial intelligence technology has changed the management mode, management content and service mechanism of libraries, and promoted the transformation of traditional libraries towards smart libraries. This paper first expounds the basic concepts of artificial intelligence and smart library, then analyses the constituent elements of smart library, and finally focuses on the construction method of digital resource system of smart library.

By understanding the international research hotspots in this field, we hope to provide reference information for health big data researchers to grasp the research hotspots and frontiers. By searching the relevant literature in the field of international health big data research in the Web of Science database, using the method of bibliometrics to draw a visual map. Results A total of 3850 articles were retrieved. Since 2013, the number of health big data research papers has shown a rapid growth. Harvard Medical School, University of Michigan and Stanford University are the research institutions with the largest number of papers in this field. The United States is the most active in the field of big data research, with 1506 papers, far higher than other countries, followed by China, the United Kingdom and South Korea. International health big data research hotspots include research on Intelligent wearable devices, design and development of health big data monitoring system, and application of health big data in precision medicine. The research reveals the characteristics and hotspots of international health big data research field, and provides some references for the development direction of this field in China.

There are many problems in the intelligent car driving on the slope, such as pause, tire slip and so on. This system takes msp432p401r single chip microcomputer as the control core, combines with motor differential, photoelectric cell array, and controls through memory algorithm and PID speed control algorithm, designs an intelligent electric car, and realizes the function of ramp driving and accurate stop. At the same time, in the process of experimental analysis, it can memorize the track, extract effective information from various working conditions, and then complete the accurate control System. After the final debugging, the system has realized the effect of non-stop and non-skid driving on the 0-35° slope surface, and can accurately arrive at the parking point and stop at the set time.

Relying on the centralized operation feedback mode of cloud computing, the data processing capacity is improved, but the delay and energy consumption of discrete manufacturing industry are increased. A resource scheduling method for discrete manufacturing based on edge computing architecture is proposed. Based on the establishment of big data multi cluster edge cloud framework, the task priority of discrete manufacturing industry is determined. According to the task priority of each production link, the objective plan of resource scheduling and unloading is established to achieve real-time and efficient task resource scheduling. The experimental results show that the stability is higher than 78%, the real-time response rate is fast and the performance is better.

Considering the problem of not providing energy model in OPNET, a modeling method in WSN is presented. First the modeling process of energy model and the way of its realization are analyzed. Then by introducing RM battery model with higher precision in describing the battery discharge, the energy model in OPNET is established. The results of simulation shows that the modeling method is valid and a better estimation of node lifetime can be achieved using RM model.

This paper proposes a method based on deep learning to improve the efficiency of children's English learning. Firstly, the migration method is used to improve the accuracy of Resnet18 model. Then the accuracy of the model was further improved by adding channel attention mechanism. Finally, the model fine-tuning method is used to reduce the computational load of the model, so as to facilitate the transplantation to embedded devices and improve the efficiency of the classification model. By comparing with the classical classification model, it is found that the classification model proposed in this paper has higher accuracy and fewer parameters, so it is suitable for transplantation into the English teaching of children.

Aiming at the cluster network composed of BA scale-free network, this paper proposes two one-way driving inter-cluster interconnection methods. One inter-cluster interconnection method is connected to the generous node of the driven sub-network, referred to as BDN mode. The other is a small node connected to the driven subnet, referred to as SDN mode. Using the second eigenvalue and the synchronization dynamics to compare the synchronization capabilities of cluster networks in different inter-cluster interconnection modes, it is found that the cluster networks interconnected by BDN mode have stronger synchronization capabilities.

In recent years, the rapid growth of vehicles has led to a great increase in the demand for parking spaces, unmanaged car parks makes it difficult to efficiently utilize the limited parking resources. To solve this problem, an intelligent parking lock system based on the Internet of things (IoT) is designed and implemented in this paper. Our design incorporates the IoT technology, such as LORA wireless network, to carry out real-time monitoring of the parking space and offer remote control ability of the parking lock. The application of our smart parking lock includes time sharing rental of parking space. Our design can improve utilization of the parking space and help to ease the problem of parking space shortage.

With the strengthening of macro-control of the real estate industry and the intensification of market competition, it is of great significance for the steady development of the industry to establish an accurate and effective early-warning mechanism for enterprise financing risk. Taking the A-share real estate listed companies in China in 2019 as target, this paper collects financial information of the relevant companies from 2010 to 2019, supplemented the risk sample data from 2005 to 2010, and reduced classification imbalance by removing discrete points and using SMOTE. When the financing risk evaluation system of listed real estate companies is constructed with capital as the entry point, the stochastic forest algorithm is used to finally select five important characteristic dimensions, namely, current ratio, equity financing ratio, operating income, current liability ratio and receivable turnover ratio. This paper establishes SVM early warning model, PSO-GA-BP optimization model and KNN early warning model to predict financing risks of real estate companies. It is concluded that, by comparing their applicability and advantages and disadvantages, SVM after feature screening and sample processing has better performance in the real estate financing risk early warning, which can provide some references for enterprise decision makers, investors and regulatory authorities.

Aspect-Oriented Programming (AOP) can effectively solve the code-tangling and code-scattering which are caused by crosscutting attributes, However, much work is presented on the programming and the implementation phase, and few works have addressed the early model stages. In this paper, we do research on the requirements engineering phase of software by using AOP technology. First in the requirements analysis phase, we identified the functional requirements and non-functional requirements by the separation of concerns, then to realize the functional requirements with components and the non-functional requirements with aspects. Then we represent the components and aspects by combining with the formal language Aspect-Z which have characteristics of accurate description; As actions accompany with conflicts while two or more aspects effect on the same joint point synchronously, we propose a method to solve by defining aspects priority level.

The non-functional requirements of the software system usually cross the whole system, and the object-oriented software development method can lead to the entanglement and dispersion of the code. ASECT-Z proposed a formal method for non-functional requirements, it is the formal specification for the extension of Z language. The language based on set theory and mathematical logic, with strict logic, and the formal specification of reasoning. In this paper, we describe a library management system based on Aspect-Z, and then give a formal proof of the formal specification for formal verification.

Cloud service centers have the problems such as untrusted virtual machines and frequent untrusted migration caused by instantaneous peak. In this paper, the trusted virtual machine is measured and the dynamic exponential smoothing prediction algorithm is used to filter the migration requests, to ensure that a trusted virtual machine will migrate at the trusted migration time. The trustworthiness of the service level agreement (SLA) for cloud services is maintained, and the efficiency of the utilization of cloud service center resources is improved.

VOI (Virtual Operating System Infrastructure), a cloud desktop technology, is popular in educational area recently, especially in colleges, because of its many advantages. It widely used in the laboratory which main equipment are computers, such as computer rooms and language laboratory. Language lab equipped computers acts as an important actor for language teaching in many colleges. Constructing a laboratory with high ratio of quality and value is the focus of many laboratory researchers. The study explores the requirement of teaching and learning for labs, surveys market and several other colleges, and designs how to construct NWAFU language labs based on VOI, compared with other prevalent technologies on market, from requirement, cost, deployment and management aspects. The study provides a reference for other type laboratory's construction and gives some suggestions to a decision maker.

In view of the fact that the subjective determination method will be affected by natural and man-made factors, it has high uncertainty. In order to further improve the accuracy of the algorithm, considering the comprehensive effect of various factors has become the key to solving the problem. Therefore, the paper first uses the analytic hierarchy process to initially determine the weights, uses the fuzzy inference model to make preliminary predictions based on the similarity of the impact factor matrix, and then we use the Topsis-fuzzy comprehensive evaluation model to identify the impact factors, and screen out the reasonable predictors for the second time prediction. Finally, we use an example to test the effect of the fuzzy inference model based on the Topsis-fuzzy comprehensive evaluation model factor identification, and compare it with the BP model of the fuzzy optimization neural network for ice forecasting. The results show that the TOPSIS-fuzzy comprehensive evaluation model factor identification basis the above fuzzy inference model has higher prediction accuracy and better effect.

Emotional information recognition is an important application in the field of artificial intelligence. This paper analyzes the existing speech emotion recognition system, based on the improved neural network strategy to improve the accuracy of speech emotion recognition, through the combination of convolution neural network (CNN) and long-term and short-term neural network (LSTM), the emotional data information is successfully recognized, and the better accuracy is obtained.

The prediction of rice yield is of great significance for improving rice yield and preventing disasters. At present, there is no exact and effective solution to the problem of rice yield prediction, and there are many factors affecting the rice yield, which may lead to large errors in the prediction results. Based on the rice yield data of 81 counties in Guangxi Province of China for three years, a long short time memory network (LSTM) model was established and used to predict rice yield under the influence of meteorological factors. The results show that the performance of the recurrent neural network with LSTM structure is better than that of the standard recurrent neural network, which solves the problems of gradient dispersion and gradient explosion of RNN model, and enables the model to learn long-term laws. At the same time, the traditional machine learning model is established to compare with LSTM model. The neural network model can better mine the information hidden in the data and make full use of the rules in the data. Combined with meteorological factors, the test shows that LSTM model has higher prediction accuracy. It provides a reference for rice yield prediction.

The Beidou satellite navigation system is currently one of the most advanced navigation systems on an international scale. The purpose of this work is to design a system framework based on the Beidou navigation system for the positioning and tracking of children. The novel system designed in this work includes a Beidou satellite system, a tracker assembly mounted on the sole of a child's shoe, a guardian's cell phone terminal and a monitoring computer terminal. In particular, the present work creates a new tracker assembly that is communicatively connected to the guardian's mobile phone terminal and the monitoring computer terminal through the Beidou satellite system, respectively. This paper designs and implements an effective monitoring and tracking of children's location based on Beidou navigation, which is worth promoting and applying

In wireless communications, rapid acquisition of synchronization parameters is essential. Very High Frequency (VHF) Data Exchange System (VDES), a new maritime communication system developed on the basis of traditional Automatic Identification System (AIS), uses time division multiple access (TDMA) and belongs to burst communication. Therefore, synchronization is the top priority of VDES research. VDES includes the terrestrial part and satellite part, named as VDE-TER and VDE-SAT, and the two parts have different channel conditions. This paper studies synchronization algorithms for VDE-TER and VDE-SAT respectively on the basis of different channel conditions and focuses on the derivation, the theoretic analysis and performance simulation of the algorithms.

This paper takes the determinants of the academic social media using frequency of Chinese young scholars as the research topic, aiming to get the causal relationship between the factors and the academic social media using frequency of young scholars. Based on the Expectation-Conformation Model, the Technology Acceptance Model and existed research results, this paper constructs an analysis framework according to two elements of which are users' characteristics and perception effects. By using Linear Regression and Qualitative Comparative Analysis, this paper makes a comparative analysis of the following factors; they are literature acquisition, recommendation function, effect of use, purpose of use and discipline. The conclusion of Linear Regression is contrary to the empirical theory and practical knowledge, which means the weak applicability of this method to this research topic. While Qualitative Comparative Analysis can be more reasonable to analyze the configuration that led young researchers to use academic social media more frequently.

The dependence between insurance businesses greatly influences the evaluation of insurance companies' overall reserves. Because of the heterogeneity of risks, the dependence between businesses is often not exactly the same. In order to analyze the asymmetric dependence relationship, an asymmetric dependence structure based on hierarchical Archimedean Copula function is proposed. It is assumed that the loss distribution of business lines follows Log-normal, Gamma and Weibull distribution. And then a hierarchical Copula dependence structure among different businesses is established. By the empirical analysis of the historical data of CPIC's automobile insurance, transportation insurance, enterprise property insurance and liability insurance, the optimal 4-dim hierarchical Copula structure is selected to analyze the dependence among the four. The empirical results show that automobile insurance and transportation insurance have the strongest dependence, followed by enterprise property insurance and liability insurance. At the same time, based on the obtained hierarchical structure, reserve assessment and risk measurement can be carried out, which provides an insight for analyzing the dependence among multivariate analysis.

We have divided rebuilding food systems into two phases, one of which is to maximize productivity in a way that maximizes sustainability and economic benefits. Another is to minimize hunger and food insecurity, with regard to equity. Firstly, we use Euclidean Metri and Complete Connection Method for cluster analysis according to the production efficiency and production structure of various foods. Then the countries are classified into countries with different production structures and productivity. Secondly, we quantified sustainability indicators to make standard deviations and risks to food production. Using models, principal component analysis, and factor analysis, we figure out how to distribute food among countries to maximize food productivity worldwide. Thirdly, the model is used to analyze the coefficient of food insecurity and the degree of hunger in each country, so as to obtain the utility level of food distribution in each country. The level of food utility in developed countries is relatively flat, while that in underdeveloped countries is steeper. So, the utility of food distribution in less developed countries is higher than that in developed countries. Therefore, when we measure the total utility of food, we multiply the utility difference of the insecurity factor by the quantity of food mixture and add the original food utility level to maximize the food utility level.

The theme revolves around the automatic scoring of subjective questions. The key technologies involved in the whole automatic scoring process are discussed in this paper, analyzes the advantages, disadvantages and application scope of different calculation methods for text similarity to understand the development trend of this field. It is helpful for further research on the automatic scoring method of subjective questions based on Word2vec improved model.

With the rapid development of information technology and network, we have ushered in a new round of communication technology called fifth generation communication technology (5G). The arrival of 5G has brought new opportunities and challenges to all walks of life. As a document information center, the library is the carrier of collecting, sorting and collecting document resources. As a librarian, we should keep up with the pace of the times, take practical and effective management methods to combine 5G with the construction of smart library, and establish efficient, intelligent, interconnected and convenient smart library services.

Aiming at the problem of large deviation of the recognition results of the traditional recognition system, this article carried out the design and research of the intelligent recognition system of the football curve ball movement trajectory. The hardware structure design includes the acceleration sensor selection, the angular motion detection device selection and the power supply circuit design, and the software structure design includes the motion trajectory data filtering processing and the football curve ball motion speed displacement recognition. Experiments show that, compared with the traditional system, the design system has a smaller deviation of the recognition results, significantly fewer recognition errors, and higher accuracy.

Under the background of artificial intelligence education, the application of RobotArt industrial robot simulation software in university teaching can make teaching "low carbon", which not only saves training equipment, effectively reduces teaching costs, and greatly improves the learning efficiency of industrial robot programming. The thesis combines virtual simulation technology and practical operation in the training workshop to quickly improve the teaching effect, reduce the hidden safety hazards in the training process, and enable students to quickly grasp the key points of teaching, and at the same time, it can also reduce the teaching difficulty and work of the training teachers.

In recent years, with the development and progress of science and technology, all kinds of water and underwater vehicles have been gradually integrated into all kinds of commercial and civil marine operations, which are self-energy, autonomous control, flexible and convenient, and can be widely used in fish monitoring, population migration, environmental protection, early warning, underwater survey, etc., while better attitude adjustment and positioning methods can effectively promote the promotion of all kinds of submersible vehicles.

With the rapid development of artificial intelligence technology and the rapid progress of big data technology, based on the theory of artificial intelligence fusion systems based on the lives of college students, and the use of advanced technology, university courses have also expanded in multiple directions. A reasonable and scientific development model of electronic technology design. Derived by using electronic data as a carrier to optimize the derivative tools of university courses and improve performance in many aspects such as learning and sports. Using the empirical formula of the Big Data Fourier algorithm, the manual verification was verified through the statistical comparison and analysis of the competitive auxiliary review system. The application of intelligent systems in the fusion system of university curriculum derivatives and artificial intelligence algorithms based on Internet big data play an active role in the fusion system of university curriculum derivatives.

At present, in China's higher education institutions, with the rapid progress of software engineering theory, this paper adopts the signal monitoring system method and derives the mathematical analog signal strength monitoring system. It is optimized in combination with the software engineering wearable system. By using the heart rate acquisition module to improve the performance, using the temperature acquisition module empirical formula, and finally through the software engineering statistical comparison analysis, it is proved that the student basic signal monitoring system has a significant effect and can play a role in preventing students from fainting or sudden death. positive effect.

In recent years, with the rapid advancement of multi-mode teaching theory, this paper adopts an interactive large classroom method and derives from the interaction of mathematics. In combination with online course optimization, the performance is improved through the use of a multi-mode system and the use of interactivity The principle and empirical formula, and finally through the comparative analysis of mathematical statistics, proved that the human-computer interaction mathematical algorithm has a significant effect, "problem English human-computer interaction mathematical algorithm plays an active role in online courses.

With the rapid advancement of database theory, this paper uses mathematical algorithm modeling methods, and derives the mathematical XXX derivation. In combination with Android system optimization, the performance is improved through the use of Java language and MySQL database, and XXX empirical formulas are used. Finally, after AP ' s continuous dynamic interactive communication and tracking statistical comparative analysis, it proves that the physical fitness database algorithm has significant effects. It shows that the real-time health information and exercise effect of the college student's physical fitness database can help college students develop long-term exercise habits and health. Plays a positive role.

This article uses artificial intelligence machine vision technology to design a method for judging violations of passing passes. First, use Adaptive Enhanced Variant Algorithm (GAB) to train the classification model to complete the detection of sports (badminton and racket); combine the detection area to extract the sphere center coordinates are used as the target point, and the detected racket part is constructed as the target area. Then the position relationship between the two is defined, and the judgment key frame is extracted according to the relative position change relationship between the target point and the target area; finally, the image angle representation method is used to establish the state judgment model takes the state of the racket in the judgment key frame as an input, and quantizes the output result as an angle value, so as to realize the judgment on whether to serve the ball.

With the rapid advancement of the theory of teamwork offensive tactics, this article uses computer mathematical statistics and derives from mathematical computer mathematics. In the combination of tactical characteristics, the penalty area scoring optimization model research, through the use of teamwork is the offense The main form of organization before scoring improved performance, using the mid-speed confrontation empirical formula, and finally through calculation and statistical comparison and analysis, it proved that the middle-speed statistical algorithm has a significant effect, and it explained the calculation of the team cooperation attack tactics to organize the middle-speed confrontation. Statistics play a positive role.

Advances in information technology have promoted the development of smart cities. Under the umbrella of building smart cities, the computer digital surveying and mapping geographic information industry builds an important spatial information infrastructure for smart cities by building a smart city spatio-temporal information cloud platform, and provides intelligent spatio-temporal carriers for smart cities. Based on this research background, the paper analyzes the positioning of computer digital geographic information in the construction of smart cities, and proposes the concept of geographic platform and its value in smart cities: spatial information resource sharing, ubiquitous information support and collaborative management.

This paper designs a self-examination platform based on big data, which consists of three layers, introduces the functions of each part of the top-level visual interface, and expounds the dimensions and the realization of the contents of each function module. The Support Algorithm of face recognition and the flow of video clip are introduced. The Support Algorithm and data mining algorithm of face recognition have been verified in practice, and the effect achieves the expected goal. After further improvement, it will be applied to the Chengdu Smart Travel Project, which will play a positive role in promoting the wisdom of Chengdu smart travel.

Passive acoustic localization is an important interdisciplinary research branch which combines acoustics, communication and signal processing. Passive acoustic positioning system, as an important part of battlefield sensing and monitoring system, has been paid more and more attention. Due to the disturbance of battlefield and other environments and the existence of measurement errors, the positioning accuracy often cannot meet the requirements. Passive acoustic positioning system, as an important part of battlefield sensing and monitoring system, has been paid more and more attention. Due to the disturbance of battlefield and other environments and the existence of measurement errors, the positioning accuracy often cannot meet the requirements. In multi-sensor passive location, the measured data of each array element is not only related, but also compensatory and redundant, so some people use the optimal data fusion algorithm as post-processing algorithm to improve the positioning accuracy. In this paper, the positioning accuracy of ground passive acoustic positioning system is analyzed based on feature matching method, and some useful conclusions are drawn.

The warehousing logistics mode of "pre-warehouse plus terminal distribution" is a decentralized resource allocation scheme around the logistics industry at the end of distribution, supporting the development of New Retail, a data-driven extensive retail form centered on consumer experience. Firstly, taking the stand-alone pre-warehouse as the research object, this paper analyzed the distribution characteristics of new retail fresh goods, the consumption structure of demand and the functional characteristics of the pre-warehouse. Secondly, the extended CFLP (Capacitated Facility Location Problem) model was established with processing fee, freshness loss, time penalty and warehouse service capacity. In addition, the one-to-one distribution relationship between demand point and storage facilities in the traditional CFLP model was changed to many-to-many in order to improve the overall facility utilization. Finally, an improved coding genetic algorithm was adopted to solve the nonlinear mixed 0-1 programming model. Therefore, location, matching and distribution volume were showed at the same time. The results of the example demonstrated that the model gave priority to meet the timeliness and fully utilized the facility capacity.

With the development of computer information technology, the learning methods of college students have gradually changed, and the Internet-based online learning methods are becoming more and more prosperous. This article uses big data analysis to explore the reasons for the application of the economic management network learning platform in colleges and universities and the impact of these reasons on the learning effect. Finally, some suggestions are put forward for the construction of the network learning platform. The research results not only provide new ideas for the continuous development of online learning platforms, but also have practical guiding significance for the improvement of teaching quality in colleges and universities.

Biomechanics can help people understand the work of the human body and organs, as well as the changes of various effects under the action of external loads, to carry out artificial control and prediction. Bone biomechanics is an important branch of biomechanics, and bone materials are divided into four hierarchical structures, starting from the lowest molecular level, to woven bones, lamellar bones, and Haversian bones, then a higher level of large woven bones and primary woven bones, Harversian bone system and laminar bones, at last to the macroscopic level compact bones and cancellous bones. The analysis of bone mechanical properties focuses on the elasticity modulus parameters, the elasticity modulus of dry bones is usually greater than that of wet bones, and the elasticity modulus of compact bones is usually greater than that of cancellous bones. As well as the stress relaxation and creep behavior of compact bone, the 8 microstructure parameters of cancellous bone. Combined with the mechanical analysis of fracture and internal fixation in clinical medicine, the connection method between the steel plate and the bone, the position of the steel plate relative to the bending direction, the selection of materials and other factors affect the mechanical properties, so that various factors need to be paid attention to in the analysis of surgery.

Clustering analysis of traffic flow time series can improve the accuracy of traffic flow prediction, which is the basis of traffic planning. The correlations between traffic flow series imply that there are some potential patterns of traffic flow movements. In this work, a clustering framework based on multi-scale analysis of traffic flow time series is proposed to seek these potential patterns. The framework includes the selection of the optimum algorithm, the construction of quantitative indicators to evaluate the clustering effect, and the visual inspection. The experimental results of 24-hour long-term traffic flow time series clustering on large-scale road network in Shenzhen indicate that the model can clearly distinguish different classes of traffic flow time series.

Operating suburban trains on existing railways has been highly valued in China due to the advantages of activating railway resources, reducing construction costs and improving the overall transportation efficiency. As a result, it is proposed that 'operating suburban trains on existing railways' is the key task of improving urban rail transit network. Recently, south section of existing Xinyi-Changxing Railway has experienced low passenger and freight volume, idle capacity and underutilized corridor resources. To take fully advantages of existing railway corridor resource, the study provides 5 schemes to operate suburban trains on the existing south section of Xinyi-Changxing Railway: Scheme A. Operating DMU trains on existing railway; Scheme B. Open reserved stations + operating DMU trains; Scheme C. Open reserved stations + electrification + operating EMU trains; Scheme D. Open reserved stations + electrification + operating EMU trains + increasing designed speed to 160km/h; Scheme E. Electrification + constructing a double track. The study could provide reference for the further research on operating suburban trains on the existing railways.

Through the force analysis of the flexible needle puncture process, the corresponding mechanical model is established, and the main factors that cause the flexible needle bending deformation are analysed, and the bending model of the flexible needle puncture process is initially established. By discretizing the bending model, combined with experimental verification, a relatively complete bending model is obtained. Based on the RRT algorithm, the goal guidance algorithm, the optimal path selection algorithm, etc. are proposed, combined with the bending model of the flexible needle, and an improved RRT algorithm suitable for the flexible needle insertion method is established. Through simulation analysis and combined with experimental verification, it is concluded that path planning based on the improved RRT algorithm can bypass important human organs and soft tissues more flexibly, accurately puncture the target position, and achieve relatively safe surgery.

In the music history, there are plenty of splendid music artists. musicians in a certain genre may bring some varieties other genres, or influenced by them. this paper constructs a complex network model based on directional weighted graph. Specifically, we calculate the edge of the graph by referring the ideology of information entropy to describe the direct influence on two certain artists. Then, we take the 100 most influential artists and analyze their influence to get a direct influence square matrix. After that, we utilize the DEMATEL method to process this square matrix, and calculate the scale of the comprehensive influence square matrix about these musicians. This article uses Euclidean distance as the similarity metric, and measures the similarity between artists through 11 indicators. In order to facilitate the calculation, this paper uses the principal component analysis method to reduce the dimensionality of these indicators. In order to determine the influence relationship between genres, this paper uses the k-means algorithm to compare and analyze the clusters of artists with more than 10 followers.

With the continuous development of computer network technology and the large-scale popularization of smart phones, mobile phone palm technology has also ushered in a golden period of rapid development. The development and use of mobile terminals provide a broad and convenient stage for the development of APP technology applications. Based on Android phones, the development and application of mobile phone APP software can effectively improve development efficiency, promote some development activities not to be restricted by time and space, improve the flexibility of the development system, and meet the development needs of the new era. Based on this, this article deeply analyzes the background of developing Android mobile phone APP software on the computer basis, from the aspects of hardware, software and operation, detailed research on software development and application, promotes the popularization of related mobile phone software technology and product promotion, and develops mobile phones for the construction of computers. The APP mode laid the foundation.

This article uses a factor analysis model to conduct an empirical analysis using data analysis. The data comes from a questionnaire survey. The subjects of the survey are college students in Guangxi, China. The research topic is the dual "3+1" training model for compound talents. Based on the SPSS analysis that KMO=0.812 and spherical Bartlett=0.000, the contribution rates of the extracted 8 common factor variances are: H =13.487%, H₂ =8.535%, H₃ =8.303%, H₄ =6.152%, H_s =5.814%, H₆ =5.622%, H₇ =4.925%, H₈ =4.870%, the above data results indicate: government support and social support, school publicity, government and university implementation efforts, student satisfaction with the training model, students' willingness for future development, etc. The Factor Analysis Model provides reference and enriches the theoretical basis.

Traditional target-following robots are not only easy to lose targets, but also unable to solve all kinds of unexpected situations in time. Based on the above problems, this paper designs a multi-robot following system based on target recognition. The system can complete the task of target following through image recognition and processing and PID algorithm control. While completing target following, it can also complete the cooperative control of multi-robots by constructing local networks among robot systems, which makes the system have better processing ability for various situations. After experimental tests, the robot system has higher following robustness and practical value.

As electric vehicles enter people's lives in large numbers, their behavior of disorderly charging and discharging will have a seriously negative impact on the safety and economic operation of the power system. This study proposes an electric vehicle charging and discharging load model under the constraint of joint benefits on the both power supply and demand sides. In this model, we set the minimum variance of grid load peak-to-valley values and the maximum benefit for users as the objective function, and take into account the randomness of electric vehicle starting charging time and duration as well as the grid capacity limitation. The main innovation point is that based on the fruit fly optimization algorithm, it proposes an intelligent orderly charging and discharging strategy with vehicle-to-grid (V2G). Following this, we compare several common charging models for electric vehicles with simulations, and conclude that the proposed optimization model can best reduce the target of peak-to-valley difference in grid load and also charging and discharging costs, thus demonstrating the feasibility and effectiveness of the model and strategy.

This paper studies a time-varying formation control algorithm based on the unmanned intelligent vehicle (UIV) system. First, modeling the multi-agent system. Reference [4] gives the time-varying formation control algorithm of multi-agent under changing communication topology and necessary and sufficient conditions for the establishment of the algorithm. The simulation verifies the effectiveness of the algorithm. On this foundation, based on the consensus theory, this paper proposes a formation algorithm in the case of a formation reference center movement and realizes multiple time-varying formations of a multi-agent system through simulation. The simulation results show that the algorithm proposed in this paper can quickly realize formation under different formation requirements and the algorithm has better control effect and robustness.

In order to solve the problems such as poor diversity and poor convergence ability of the offspring population of the NSGA-II Algorithm in the vehicle production scheduling problem, an improved shop scheduling algorithm based on NSGA-II is proposed. The improved NSGA-ii Algorithm mainly focuses on the crossover and mutation of the traditional NSGA-II Algorithm, and proposes a new improved self-adaptive Crossover and mutation operator. By comparing the individual crowding degree with the average crowding degree of the population, and combining the iterative evolution process of the population, in order to avoid blind orientation and to improve the convergence speed of the population, the genetic probability is correlated with the individuals and the evolution iteration times of the population, and a new uniform evolution strategy is proposed to select the individuals of the population through adaptive hierarchical selection, in order to improve the quality of the solution, the problem of the poor diversity of the offspring population was solved. The improved NSGA-II Algorithm is used to carry out the experimental simulation analysis. The effectiveness of the proposed algorithm is verified by comparing the optimization results before and after the improvement.

According to the characteristics of flexible job shop scheduling (FJPS), a mathematical model was established to minimize the maximum completion time, and an improved genetic algorithm was proposed to solve the problem. A variety of heuristic methods are used to improve the quality of the initial solution. The parallel double-chain encoding is designed and the optimal insertion method is proposed to improve the quality of the solution. Two crossover methods, namely IPOX crossover and multi-point crossover, are adopted to inherit the excellent genes from the parent generation and balance the global development ability of the algorithm. In different coding layers, a variety of variation methods were used to maintain the diversity of the population. The local development ability of the algorithm is enhanced by variable neighborhood search. Finally, by solving the Brandimarte standard example and comparing with other algorithms, the feasibility and effectiveness of the proposed algorithm are verified.

An improved genetic algorithm is proposed to solve the traveling salesman problem. On the basis of traditional genetic algorithm, greedy algorithm is introduced to initialize the population. The genetic parameters are adjusted adaptively from two aspects of genetic evolution algebra and individual fitness function value, which can speed up the optimization speed and prevent the optimization from falling into local optimum. In mutation operation, adaptive algorithm is used to select mutation operator to improve the quality of variation and search effect of the algorithm; after individual evolution, one-way evolutionary reversal operation is introduced to improve the chance of inheriting parent and parent genes, and improve the ability of algorithm to search for optimal solution.

Based on the combination of ethnic minority costume design and computer information technology, the first is to promote the reform and innovation of ethnic minority costume design; Second, computer information technology plays an auxiliary role in ethnic minority costume design. The two complement each other. They not only enhance the expression of costumes in the innovation and reform, but also better promote ethnic minority costumes, which can be applied to more occasions and attract more people's attention.

Facial expressions carry the emotional state of human beings and are one of the most powerful signals for humans to express themselves. Therefore, the research of facial expression recognition has very important significance and broad application prospects. However, in complex scenes, the blurring of facial images, the interference of redundant information, and the similarity of expressions will cause unsatisfactory expression recognition. In response to these shortcomings, this article adds a convolutional attention module to the basic Resnet network structure, infers the attention map in turn along two independent dimensions (channel and space), performs adaptive feature optimization, captures expression feature information, and reduces human faces. The interference of redundant information; and the introduction of island loss ISlandloss classification to optimize the distance between classes, so that the network can learn more discriminative features, and improve the discrimination of expressions. Compared with the existing mainstream facial expression classification algorithms, this method has certain advantages in objective evaluation indicators. The experimental results on FER2013 show that the accuracy of this algorithm is 73.85%, which is higher than 71.23% of the Resnet network.

Logistics enterprises attach great importance to customer satisfaction. This paper focuses on customer satisfaction, and innovatively designs the measurement formula of customer satisfaction, which provides a theoretical basis for the construction of the model. In addition, a multi-objective planning distribution route optimization model with fuzzy time window is constructed to further seek the minimum transportation cost and maximum satisfaction. The improved particle swarm optimization algorithm is used to obtain the optimal solution on MATLAB 2018A software. By comparing the original path with the optimal path, the applicability and effectiveness of the model under the condition of customer satisfaction are verified, which provides some reference value for the improvement of other enterprises.

The invasion of exotic species should be paid attention to because of its severe consequences, and governments have constructed monitoring and tracking system to collect people's sightings of species. For example, Asian giant hornet, an exotic species for North American countries was first discovered in September, 2019. Interpretations based on received reports are of essential to master the current situation of species and to predict its spread. However, there is a considerable number of mistake reports, it is necessary to select the most likely positive reports out of mountains of reports to be detailly investigate first. In this paper, two main problems are focused: interpretation of historical reports and strategy of prioritization. First of all, this paper preprocesses the image data based on Gaussian filtering and convolution neural network to make it as balanced as possible. In addition, the image consists of a wide range of array data. Proper treatment can improve the efficiency of analysis. Then, construct the GPSM model (Gray Predicting Spread Model) based on gray prediction to measure the spread of the pest. From the historical positive sighting location provided in the dataset, the GPSM model is able to output the predicted next location following the previous principle. If the distance between predicted location and current outbreak area are significant, a spread can be concluded. Tests for the GPSM model shows the model has a very high level of precision

Basic farmland is the essence of cultivated land. To completely solve China's food security problems and achieve stable growth of agricultural production in China, it is one of the most effective ways to build high-standard basic farmland. Therefore, this paper evaluates the high standard basic farmland. This paper evaluates the natural quality of basic farmland through the construction of mathematical models. The total cultivated land area in the hilly area of Faku County is 153061.02 hm2, while the natural quality score of cultivated land in the hilly area of Faku County is between 51 and 86. Among the equal-valued intervals, the maximum cultivated land area between ≥60-70 is 129110.25 hm2, accounting for 84.35% of the total cultivated land in the hilly area of the county, followed by the area between ≥70-80, which is 9069.44 hm2. The total area of cultivated land in the hilly area of the county is 5.92%, followed by the interval of ≥ 80 points, <60 interval, the area is 8095.61 hm2, and the ratio of 6787.72 hm2 accounts for 5.29% and 4.43% of the total cultivated land in the hilly area of the county. In general, the natural quality of cultivated land in the hilly area of Faku County is moderate.

Health management system of airborne missiles has a large amount and variety of knowledge, but such technical documents are lack of effective management in the knowledge level. According to the technical characteristics of knowledge in the field of airborne missile health management, a HTA analysis model (Hierarchy, time, and activity analysis model) based on multi-dimensional fuzzy ontology in the health management field of airborne missiles is constructed, and ontology description language OWL is introduced to express knowledge. It provides a theoretical and methodological basis for the dynamic management of health knowledge in the field of condition monitoring, prediction and evaluation, fault diagnosis, technical service.

The h index of universities can quantitatively calculate the scale of high-level scientific research personnel in universities, and evaluate the academic influence of universities from a new perspective. It has the advantages of simplicity, intuition, and visibility, but there are also some problems. Based on this research background, the paper uses big data statistical methods to conduct an in-depth theoretical analysis of the problems existing in the h-index of universities. The results show that the h index and other ranking indexes are effective in the academic evaluation of colleges and universities. And the correlation analysis between their comprehensive rankings and various evaluation indicators shows that the correlation between the rankings of various indexes and the comprehensive rankings is higher than that of SCI included rankings.

For the general nonlinear constrained optimization model, this article will propose a new nonlinear Lagrange function, discuss the properties of the function at the KKT point, and prove that under appropriate conditions, the iterative point generated by the dual algorithm based on the function The column is locally convergent, and then an error estimate of the solution related to the penalty parameter is given. This provides a new way to solve nonlinear constrained optimization problems.

Sports tourism resources are divided into two types: sports tourism natural resources and sports tourism human resources. The natural resources of sports tourism are divided into landform resources and water resources. The human resources of sports tourism are divided into folk customs and events, venues and buildings. The thesis explores the advantages and disadvantages of Chinese existing sports tourism information system through the combination of big data and sports tourism. At the same time, we analyzed and established the hierarchical structure of the sports tourism information system big data platform and the logical structure of the sports tourism data warehouse, and constructed a sports tourism information data model system based on big data analysis, laying a foundation for further research on the sports tourism information system based on big data.

The standingpile has been applied quite extensive in our country, which is one of the three major structure forms of the quay. Take the internal force calculation of wharf track beam in Jiangsu Taizhou for example, this paper studied the bar system finite element method processing method of rigid support and elastic support. Using different bearing support mode to calculate the track beam internal force, the results show that the corresponding internal force of different models has big differences. The negative bending moment and shear force of point support model are large. The effect of support width and elastic support was considered in the continuous beam model of elastic distribution supporting and should be considered prior to other supporting. In the process of similar design, the bearing width can be increased. This is conducive to the uniform distribution of internal force, and make the structure more reasonable.

The whole process of concrete slump experiment was simulated with Particle Flow Code 3 Dimensions (PFC3D) software based on discrete element simulation. The simulation results are in good agreement with the macroscopic results obtained from field experiments, indicating that the use of PFC software for concrete had the feasibility of slump experiment simulation. The software simulation results have a good observation on the speed and direction of the particles in the process of concrete slump.

Neuron reconstruction is an important step to study the relationship between brain neuron morphology and function. Existing neuron reconstruction algorithms have achieved relatively good results under certain conditions. In the context of whole brain research, the conditions are more demanding, so the previous methods cannot guarantee high-quality results in any brain image data. This paper proposes a fusion framework for automatic reconstruction, which is dedicated to improving the applicability of traditional reconstruction algorithms and obtaining high-quality neuron morphological data. Experiments show that this framework is more flexible than traditional reconstruction methods and has broad application prospects in the current research background.

Keyword search is still the most effective means for users to obtain information. In the era of massive data, more and more structured and semi-structured data can be directly accessed by users. Different from the web data and text data, structured data and unstructured data contain a lot of semantic information, which can improve the accuracy of search results. Based on the theory of information entropy, we calculates the information entropy of semantic information to determine the semantic relevance of keywords, and then uses the degree of relevance between keywords and semantics, finally constructs a semantical model based on information entropy. Final experiments prove the effectiveness of our model.

As a kind of mathematical geometry education software, dynamic geometry software is an application software that can interactively create and manipulate geometric objects. Its vivid image changes and colorful geometric objects provide teachers with an efficient Geometry teaching tool. Comparing and analyzing the existing geometry software, we found that there is a curve smoothness problem in its trajectory drawing. In this article, we propose the uniform point selection method based on the principle of the trajectory generation geometric method. Under the premise of ensuring rendering efficiency, extensive case studies have shown that the smoothness of the trajectory curve generated by this method has been improved.

In order to better improve the shooting percentage, this article proposes mathematical modeling and statistical analysis of the offensive line measurement of the basketball's vertical height, horizontal speed, and vertical speed when shooting. Studies have found that excellent shooters can get a high and stable shooting percentage when using dominant hands. Excellent shooters usually like to use a variety of shooting postures. The thesis also develops an interactive and intelligent basketball tactics teaching software system to enable students to initially establish a perceptual understanding of basketball skills and tactics, and improve the teaching effect of teachers explaining basketball training techniques and tactics.

The US election will have a certain impact on the US economy. In this paper, different index systems are established to analyze the influence of different factors and American economy. After establishing the index system, this paper will use machine learning regression algorithm to fit the data in recent decades. We choose a variety of models, including ordinary linear regression model, Bayesian ridge regression model, elastic network regression model, support vector machine regression model and gradient enhanced regression model. In this paper, a prediction model based on machine learning is established without considering the epidemic situation. First of all, preprocess the data; secondly, determine the index system; then, based on the regression algorithm of machine learning, compare and select the optimal model for data fitting and prediction; finally, use linear regression to predict the indicators of the next four years.

The current world food system gives priority to efficiency and profitability, creating some environmental and inequality problems in the world. In order to re-optimize our food system, we have developed a food system model to adjust the efficiency, profitability, fairness and sustainability of the food system. First of all, we describe the positive and negative effects of food systems optimized for fairness and sustainability, respectively. We have established the index system, and on the basis of the existing data, we use the grey prediction and fitting method to obtain the future data of each index. After that, we divide the previous 12 indicators into benefit category and cost category, and use entropy method to get the weight of indicators that affect benefit and cost each year. We use the "scale factor" method to quantify indicators that are difficult to measure, build models to calculate cost and benefit priorities for changing the world food system, and chart changes that benefit cost ratios in different years.

Considering that the current model of the world food system puts efficiency and profit first, many people still go hungry and seriously pollute the environment. In this paper, the multiple nonlinear regression method is used to optimize the existing food system. Firstly, the food security evaluation system is established, and the global food security situation in recent years is obtained by calculating FSSI. Then, the model of influencing factors of food security is established, and the relationship between food system security index and various factors is obtained by multiple nonlinear regression, on this basis, fairness and sustainability index are obtained by the same method, and the existing food system is optimized. After analyzing the current situation of countries with different standards, we have formulated a series of intelligent optimization policies for them. Finally, by comparing the change trend of the index before and after optimization, it is found that the optimized index can reach the ideal value more quickly.

The advancement of Internet information technology and the strong demand for private finance have promoted the vigorous development of Internet financial netw ork lending. However, due to the complexity of the network environment and the volat ility of the financial market, Internet finance has a higher risk. Aiming at the particular ity of Chinese Internet financial big data platform, this article uses Internet lending as a case to screen out 15 financial risk indicators. The article uses the principle of game t heory to set the principal component factor as a variable, and the operation of the platf orm in 2018 as the dependent variable to establish a financial risk early warning syste m for the online lending platform. At the same time, the paper cited cloud computing t o optimize its performance, and designed a financial risk management and control syst em based on cloud computing. Construct system test links to test system performance. With the same amount of data and the same test platform, the computing performance of this system is better than that of the original system.

The thesis combines the national policy on the targeted poverty alleviation system and follows the adaptive efficiency principle of rural development, and designs a system that promotes the coordinated operation of government mechanisms and market economic mechanisms in the form of a new type of professional farmer training. The system combines and analyses the characteristics and needs of new-type professional farmer trainee skills training, and establishes a new-type professional farmer training platform and operating mechanism through information technology. The paper designs a new online training platform for professional farmers based on the spring cloud distributed system structure, which provides convenience for new professional farmers to participate in education and training.

The paper analyses the status quo of the construction of the teaching staff of undergraduate colleges and universities in Jilin Province, expounds the irrational structure of the teaching staff, the low professional autonomy of teachers, the instability of the teaching staff, the lack of humane care for teachers in society and schools, and the teaching staff. Poor management and other issues. Based on the actual needs of undergraduate colleges in Jilin Province, this thesis uses the. NET framework, ASP.NET and SQL Server technology to develop a university teacher management information system based on the B/S structure. The system includes basic teacher file management, discipline construction management, the functions of teaching and research management, scientific research management, faculty management and system management have reached the goal of networked and electronic management of the school's teacher information. The purpose of the thesis is to improve the construction level of teachers in private colleges and universities in Jilin Province.

Based on a questionnaire survey of economics students at school, 290 students majoring in economics were selected as the survey sample, and a nonlinear fractional differential equation model was constructed to comprehensively score the innovation ability of school students. The study found that the innovative spirit of economics students is more active, but the innovative skills are relatively scarce. In this regard, it is proposed to promote the reform of the education system, improve the implementation and evaluation mechanism, focus on theory and practice, promote the application of innovative abilities, improve the quality of teaching, cultivate a team of learning teachers, cultivate students' independent thinking ability, strengthen students' logical thinking ability, and promote "industry-university-research" Suggestions on base training model.

The paper proposes the use of support vector machines to establish a financial early warning system and a method for financial risk monitoring, gives a financial evaluation index system and its quantitative methods, and uses the classification capabilities of support vector machines to establish a financial early warning model. At the same time, the paper explores and studies the combination of enterprise group's financial risk identification, assessment, response and management information system, and proposes an enterprise group's financial risk control information system. Through experimental comparison, the model based on the support vector machine has good consistency with the experimental data, which is in line with the expected results.

With the rapid advancement of artificial intelligence theory, this paper adopts a multi-intelligent agent collaboration method and derives through data mining. In combination with content filtering methods and intelligent agent learning optimization, it improves the high performance by using a personalized information service system architecture. The performance of the library system of vocational colleges. According to the difference of readers' interest, it matches the results of traditional document retrieval, effectively filtering out readers' demand information, reducing the time for readers to search for required information, improving reader retrieval efficiency, realizing information push of similar users, and realizing "information looking for people".

A new algorithm based on the combination of chaotic sequences and immune differencing is proposed to improve the population diversity capability of the immune differencing algorithm in order to improve the conventional immune differencing algorithm for solving the signal-to-noise optimal problem. The method uses chaotic sequence immune differencing for population expansion, and the accuracy of the algorithm is analysed. In order to test the effectiveness of the algorithm, it is applied to a function optimisation problem. Simulation results show that the method has a lower number of iterations and better accuracy.

The informationization of high-speed railway is of great significance to the operation and maintenance of high-speed trains. Through the operation and maintenance data communication based on wireless public network, the ground monitoring system can know the real-time status of trains in time, which can facilitate the daily arrangement of inbound inspection and maintenance of trains and improve the operation and maintenance efficiency of high-speed trains. The reliability of data transmission in wireless sensor networks is low under the influence of resource constraints and environmental factors. However, the reliability of data transmission is an important index to evaluate the network performance in application fields such as national defense, military affairs and industrial control. Based on data mining technology, this paper analyzes the real-time, reliability and security of data transmission according to the characteristics of digital mobile communication used for data transmission in railway running and train automatic control. Combined with the characteristics of wireless channel in the environment of intelligent railway transportation system, this paper puts forward concrete measures to improve the performance of wireless link and concrete methods to judge whether wireless transmission can meet the requirements of IRTS environment.

To balance the performance and complexity for frequency estimation of sinusoid wave using DFT-based methods, an efficient estimation algorithm based on improved Quinn is proposed. The amplitude and phase information are used to interpolate which ensures the accuracy of the frequency shift direction. A method of piecewise frequency shift is performed, and it has the characteristics of higher precision and lower computational costs. Simulation results show that the judgment of direction factor is more correct using Quinn, the Root Mean Square Error (RMSE) of this method is closer to Cramer-Rao Lower Bound (CRLB) under the conditions of different frequencies and SNRs comparing with Quinn and other improved algorithms, and it can attain remarkable decrease of computational cost which facilitates the algorithm implementation.

This paper studies and analyzes the influence of cab layout elements such as vehicle seat, steering wheel, mirror and other adjustment variables on driving posture. The prediction model of adjustment variables of each arrangement element and key elements of driving posture is also established. Based on the multivariate factor regression analysis of the experimental data, the variables were adjusted according to the layout elements of the vehicle cab, and the corresponding driver's posture and human physical characteristics were predicted. At the same time, this paper collects new experimenter data, evaluates and verifies the prediction effect of the model, and analyzes the shortcomings and limitations of the prediction model.

Given data captured from multiphase flow industrial process which has non-stationary and dynamic characteristics, it's difficult to predict the dominant variables precisely via traditional soft sensor models. In this paper, we propose RP-LSTM (Recurrent Pattern Aware Long Short Term Memory) model, a soft sensing method that takes both dynamic of time-sequence and pattern mining of industrial process into account. In this way, we can fully utilize limit model capacity, and enhance model's knowledge on recurrent data stream patterns. Experiments on real data set demonstrate that RP-LSTM out-performs traditional sequence deep learning models by a significant margin.

This article proposes an automatic grammatical correction method for typos and word order errors that may occur in Chinese writing by beginners. The thesis first constructs heuristic rules and expands the corpus by analyzing the characteristics of different grammatical error types in the data set. Secondly, when the paper uses classification methods to detect grammatical errors, it extracts sentence-level binary and ternary part-of-speech combinations, n-gram models based on part-of-speech statistics and other three types of features to construct single classification and ensemble classification models, and then use convolutional neural the network constructs classification models from different angles. Finally, when the paper adopts the method based on sequence labeling for grammatical error detection, it mainly uses dependency syntax tree features, and realizes grammatical error detection by constructing a conditional random field model. This method can automatically detect grammatical errors while also identifying the sentence the location of the error. On this basis, the paper implements a simple Chinese grammatical error automatic detection system, which can provide help for the optimization of questions and answers in the question-and-answer system

The traditional measurement model cannot meet the processing requirements of non-linear and high-noise time series data. A two-layer simple recurrent unit (DSRU) prediction model based on BPSO and SO-PMI sentiment analysis algorithm is proposed. First, the model uses the SO-PMI algorithm to calculate the stock market sentiment tendency measurement index. Then the BPSO algorithm is used to extract the characteristics of stock technical indicators with greater stock price correlation. Finally, the DSRU network model is used to predict the stock price. Taking Shanghai Pudong Development Bank stock as an example, the results show that the model has a significant improvement in prediction accuracy, reaching 65.84%, with an average absolute error of 0.0541, which proves the superiority and effectiveness of the method.

This paper illustrates the meaning of different database recovery modes, represented by the select into command, and verifies the differences in the redo data produced by bulk operations in different database recovery modes with a detailed experimental procedure.

In view of the present intelligent and practicability of the small soccer robot system is still at the lower level, to improve and optimize the small soccer robot system performance, from the visual model to optimize the design of the decision system physical error control three perspective, based on the visual subsystem, decision-making subsystem and physical subsystems through the research of the three subsystems respectively through the model establishment and design improvement strategy is put forward, and USES the SOM simulation software to verify the simulation experiment and data results show that based on visual decision-making and physical three subsystems of the modified method is helpful to improve the overall performance of soccer robot system and intelligent level.

In view of the high bottleneck of current partial discharge detection technology, strong professionalism and heavy detection tasks, the partial discharge process is greatly affected by the equipment operating environment and condition factors. The paper proposes a switching device partial discharge multiphysics based on edge computing. Research on perception and state tracking technology. The article aims at the edge computing technology of multi-physical partial discharge signal feature extraction, the construction of multi-physical tracking over-limit early warning model, and the development and trial production of intelligent gateway technology. The research found that the edge computing algorithm proposed in the paper can effectively reduce the cost and power consumption under the premise of ensuring the performance of the detection system.

In the increasingly developing logistics industry, the application of intelligent logistics cars is becoming more and more common. This paper mainly introduces a design scheme of intelligent logistics cars based on the STM32F103ZET6 single-chip microcomputer and OpenMV programmable camera. Its main process is as follows. First, through OpenMV, the car can scan a QR code to read relevant information of the task. Then, OpenMV recognizes the color of the object, and the single-chip microcomputer controls the manipulator arm to grab the object of the corresponding color. Next, the car will carry the object to the designated position along the designated route. Finally, the process is repeated several times until the delivery task is completed. The study of intelligent cars has very important practical value and theoretical significance.

With the development of urbanization, the problem of urban traffic congestion is becoming more and more serious. An improved k-means clustering algorithm was proposed to solve the problem that the traditional k-means clustering center could easily be affected by the clustering center and fall into the local optimal solution. Based on the big data of New York City taxis, the operational characteristics are analyzed. The experimental results show that the improved K-means clustering algorithm has a better clustering analysis effect in terms of hot demand for taxis.

In order to study the algorithm of Tibetan Jiuqi, a new kind of chess in Chinese computer game, there are too many possibilities to be considered in solving the problems of falling pieces, and the influence of falling pieces on Xingzi and Tiezi. On the basis of forming different chess patterns as the main source of raising pieces, this paper puts forward a design scheme which takes the Monte Carlo algorithm randomly as the core and the final evaluation algorithm as the secondary one, and takes a variety of possibilities in Tibetan chess as a breakthrough. Get different benefits from different chess types to cooperate with the final evaluation algorithm to get the assignment of each replicated chessboard. It is proved by the program computer game that the program is complete and the three stages of Tibetan chess can be completed successfully. The experiment shows that the Monte Carlo algorithm is very suitable for the AI development of Tibetan chess.

The existing implicit feedback collaborative algorithm directly uses sparse binary social trust information to assist recommendation, which has serious data sparse problems and does not have the influence of deep integration of social trust information. In response to the above problems, the paper proposes an algorithm that uses a denoising autoencoder to deeply integrate user implicit feedback data and social information. At the same time, a cloud computing-based social trust information data mining system architecture is constructed, and its modules are described. This framework is conducive to solving the problems of high data complexity and large amount of data encountered in current data mining, and can significantly improve the performance of data mining.

As one of the mainstream equipment of fully mechanized mining equipment, shearer is a large complex system with the functions of breaking coal and transporting coal. It is an important milestone for China's coal industry to move towards modernization and mechanization. In order to adapt to the complex working environment and better meet the needs of social development, the optimization of spatial structure and bearing capacity of coal machine has become the focus of research. Based on genetic algorithm, taking MG500/1170-AWD1 electric haulage shearer as an example, this paper takes the weight function of maximizing the bearing capacity and minimizing the volume as the optimization objective to optimize the structure of the cutting part transmission system, and analyses the rationality of the scheme by comparing the objective function before and after optimization, so as to provide technical support for the R & D department.

Aiming at the problem of furnace temperature curve, this paper uses Fourier's law of heat conduction and Newton's law of cooling to establish a heat transfer model to obtain the temperature changing law of the welding area and the furnace temperature curve; uses the single-objective optimization model to establish the maximum conveyor belt passing speed and find out the optimal temperature for each temperature zone; the greedy algorithm and traversal search are used to obtain the undetermined parameters of the dual-objective model through local optimization. In response to question 1, it is required to study the temperature change in the center of the welding area. First, we set the heat transfer model from heat conduction law and convection heat transfer law. The interface of the reflow furnace is two-dimensional: the conveyor belt direction is the x-axis and the vertical direction of the reflow furnace is the y-axis. The energy balance method is used to find the relationship between the x and y-axis heat conduction to establish a differential equation, after then we use Newton Cooling Law and the temperature distribution in the reflow furnace and establish the difference equation of the welding zone temperature. This paper uses the numerical calculation to solve the differential equation sets, obtaining the statistics of the temperatures at intervals of 0.5s. The theoretical furnace temperature curve is drawn as well. The second problem requires the maximum furnace passing speed of the conveyor belt after the temperature of each zone was determined and the constraints of the process limit must be met. A single-objective optimization model was established in this paper and the maximum conveyor belt speed was taken as the objective function. Without exceeding the constraints of the problem, the maximum conveyor belt passing speed was 92.6cm/min after the computer finished the programme. For problem three, it is required to find out the suitable temperature of each zone and the belt-speed, making the area minimized which is covered by the temperature curve exceeding 217°C and under the peak point. This paper establishes a single-objective optimization model, where the objective function is the minimum coverage area; the constraints are the same as the five constraints above in question 2. We use the programming software to search the feasible solution: the temperature of the preheating zone is 167 °C, the temperature of the constant temperature zone 6 is 185.1°C, the temperature of the constant temperature zone 7 is 225°C, the temperature of the reflow zone is 259.2°C, and the passing speed of the conveyor belt is 91.2cm/min. For question four, it is required to make the furnace temperature curve as symmetrical to the peak value as possible while considering the coverage area. This paper defines the symmetry coefficient to quantify the peak symmetry. On this basis, a dual-objective optimization model is first established, and then the coverage area in the objective is converted into constraint conditions, which is transformed into a single-objective optimization problem, and the unknown parameters are finally determined. The full traversal search needs to determine 5 parameters so the calculation is time-consuming. Therefore, we first use the greedy algorithm to solve some of the local optimal parameters, and then use the full traversal search to simplify the calculation. The final result: the temperature of the preheating zone is 169.5°C, the temperature of the constant temperature zone 6 is 185°C, the temperature of the constant temperature zone 7 is 225°C, the temperature of the reflow zone is 258.7°C, the furnace passing speed of the conveyor belt is 90.4cm/min. Finally, the model established in this paper is discussed and analyzed, the model established is comprehensively evaluated, and the conclusion is made.

Rotor profile is a critical factor affecting the performance of Roots vacuum pumps. Hence, research on rotor profiles of Roots vacuum pumps is important. The rotor profiles are mainly composed of arcs, involutes, cycloids, epicycloid, and combined curves. Moreover, the two rotor profiles form a pair of conjugate curves. To obtain a parabolic rotor profile, transformations of three coordinate systems are established to derive equations and analyze the parabolic curve and its conjugate curve. Additionally, the parameters of the parabolic rotor profile are discussed to determine their ranges and the relationship between the performance of a parabolic rotor profile and the independent parameters. The results indicate that the area efficiency of the parabolic rotor profile is greater than the area efficiency of the arc rotor profile for the same ratio of the top circle radius to pitch circle radius.

At present, the modeling of mixed-point connection is mainly based on the assumption of infinite structure without considering the influence of boundary factors on the modeling and analysis results. This paper reviews the modeling methods of mixed-point connection model. At the same time, the conventional modeling methods of hybrid line connection is studied for the hybrid line connection model, and some new research topics are proposed for some problems of hybrid point and hybrid line connection modelling.

Since the traditional deterministic method and classical statistical energy method have little room for improvement, more and more researches gradually turn to the combination of the two methods to solve the low and medium frequency "acoustic vibration" problem. However, in the application of mixed FE-SEA model, researchers found that when the coupling interface between FEA substructure and SEA statistical substructure is in the form of continuous interface, a very large finite element grid needs to be established at the boundary of the mixed model, which leads to huge time-consuming problem in the calculation of medium and low frequency of the mixed model. To solve this problem, the hybrid FE-SEA model needs to be improved and modified. In this paper, some common hybrid model correction methods, including deterministic subsystem edge gradient correction, are reviewed to provide theoretical support for the comprehensive study of hybrid model correction.

Based on the technical study of a composite vertical tail, the mechanical properties of honeycomb sandwich structure at the leading edge of the composite were studied fundamental computational analysis. The stress calculation and modal analysis of sandwich beam/plate structure are carried out by using engineering algorithm and two finite element modeling methods. The comparative analysis of the results shows that the two modeling methods can effectively simulate the mechanical properties of composite sandwich structure. In the actual analysis of complex structures, a simple 2D shell element modeling method can be used for preliminary simulation analysis.

Community search is to explore valuable target community structure from a large social network. In real community, every point has a geographic location information, and many edges are uncertain. Such a network is called spatial uncertain network. In order to meet the existing needs, this paper studies the community search in the spatial uncertain network so that the searched communities can be relatively close in the geographical location and the relationship between each other is also very close. Based on the spatial uncertain graph, this paper proposes K-R-τ community, and proposes a linear algorithm and a two-dimensional algorithm for community search in the spatial uncertain network. A large number of experiments are carried out on several real datasets, and the results show that the algorithm proposed in this paper is effective and accurate.

Today in our world, the food system is very unstable despite its high efficiency and profitability. More and more problems and even tragedies are caused by the instability of today's food system. Therefore, the stability evaluation and optimization of the food system need to be implemented as soon as possible. A basic model named PEFS is established for better understangdings of our global food system based on the mechanism analysis of the food supply chain. Its output is the efficiency and profitability of any food system. We can use the PEFS model to simulate the global food system. Relying on the collected data, the model gives the profitability and efficiency of food systems in 162 countries around the world. The visualized map is shown in Figure 4. Among them, Guatemala is the country with the highest profitability, reached 0.812. Argentina is the country with the highest effciency, reached 0.854.

Aiming at the heat conduction equation, the heat conduction model is a very typical example of the partial differential equation. This paper focuses on the development and preliminary knowledge of the partial differential equation, the establishment of the heat conduction model and the solution of the heat conduction model. The partial differential equation with fractional Laplace operator is a kind of typical fractional partial differential equation, which has important applications in the fields of science and engineering. Fractional Laplace operator is a kind of nonlocal quasi-differential operator, which is the infinitesimal generator of L é vy steady-state process. It is essentially different from the classical Laplace operator, which leads to the disappearance of some classical properties, which generally brings difficulties to the study of this kind of problems. It is very difficult to solve the explicit solution of partial differential equation with fractional Laplace operator. In order to improve the accuracy and speed of solving partial differential equation, a high precision method for solving partial differential equation based on deep learning network is proposed. A multilayer radial basis function neural network is constructed and its structural layers are determined. By introducing subnetworks into each layer of the multilayer radial basis function neural network, a composite multilayer radial basis function neural network is constructed to improve the real function approximation performance and operation accuracy of the multilayer radial basis function neural network. The high-precision compound multi-layer radial basis function neural network is used to solve the partial differential equation. By giving a specific example of solving the partial differential equation, the solution effect of the method is tested. The results show that the method has very high solution accuracy under the four-layer network structure, which can improve about 1.5 orders of magnitude compared with the first layer. Under different number of training samples, it has higher solution accuracy and speed, and the comprehensive performance is superior. Therefore, it is a practical but challenging work to study the existence of solutions of partial differential equations with fractional Laplace operators. This paper mainly describes the research progress and trends of the existence of solutions of several classes of partial differential equations with fractional Laplace operators, including some of the work done by the author in this field in recent years.

In the context of global economic integration, the economic links between countries are getting closer and closer, and international direct investment is an important link to connect the economies of various countries. In recent years, with the rapid development of China's economy and the further improvement of the level of opening to the outside world, China's IFDI (foreign direct investment) and OFDI (outbound direct investment) have developed rapidly. At the same time, the contradiction of China's industrial structure has gradually emerged, and it is urgent to promote economic intensive development and industrial structure upgrade. Based on this research background, the thesis puts IFDI, OFDI and China 's industrial structure upgrade into the same theoretical analysis framework, and explores the mechanism of two-way FDI to promote industrial structure upgrade from the perspective of technology spillover; Panel data of several provinces (municipalities), through the construction of simultaneous equation models, empirical analysis of the impact of two-way FDI technology spillovers on the optimization and upgrading of China's industrial structure.

With the rapid development of science and technology in the world today, the development of the Internet is even more remarkable. Especially under the stimulation of continuous breakthroughs in the computer field, the network economy, as an important part of the knowledge economy, is growing rapidly and has become a new driving force for world economic growth. In order to meet the needs of the development of the network economy, traditional enterprises should implement e-commerce to be able to quickly respond to customer needs, improve business processes with customer relationships, provide personalized services, shorten sales cycles and sales costs, increase revenue, and develop markets, to enhance the competitiveness of enterprises.

Financial innovation is of great significance to the long-term and stable development of commercial banks and the entire financial market. Reasonable and effective innovation under the current changing financial situation is of great significance to the development of the overall banking industry and financial market, but to varying degrees and Different ways of innovation often produce different results. The article uses the cloud model to comprehensively evaluate the financial innovation indicators of the five state-owned commercial banks. By taking into account the characteristics of ambiguity and randomness, and using the characteristics of the cloud, the evaluation results are more objective and authentic.

With the rapid development of China's film industry, the public's enthusiasm for watching films has growing, and the contribution rate of the film industry to the economy has increasing year by year. Based on the research at home and abroad, this paper takes the box office of China's films as the dependent variable, selects a series of indicators of China's film box office for grey correlation analysis, and selects seven related indicators as independent variables, including the number of fans of heroine, the number of fans of hero, the box office of the first week, the number of fans of director, Baidu Index, average ticket price and film type. The influencing factors calculated by grey correlation degree are taken as the input of BP neural network, and the box office of China's films is taken as the output of the network to predict the box office of China's films. The results show that the prediction model based on BP neural network is more accurate than the classical Multiple Linear Regression model. This model can predict to a certain extent, and the prediction effect is good. It can predict the overall trend of film box office and provide reference for investment enterprises.

The China-EU freight train is a physical link connecting the Eurasian continent in the new era and a new bridge for expanding China-EU economic and trade cooperation. Since its opening in 2011, China-EU freight trains have developed rapidly. Since 2015, it has been normalized and fixed, and now it has entered a stage of steady growth. Based on the trade gravity model, this paper calculates the trade gravity coefficients between China and the rest of the world in 2015 and 2019, and then establishes the analysis and construction of China-EU freight trains and cross-border transport models. The combined optimization of intercontinental transport scheme and regional transport scheme is constructed, which is composed of tractor, semi-trailer and intercontinental railway intermodal transport. The transport network of this model consists of two types of nodes: multimodal transport station and highway transport station. Based on the double difference regression analysis of gravity coefficient, it is concluded that the central attractiveness of trade between China and the rest of the world is relatively low in the past, but under the overall downward trend of trade attractiveness between China and the rest of the world, the China-EU train has significantly promoted the growth of trade in recent years, which is attractive.

The rapid economic development and high incidence of crises in emerging economies make them play an increasingly important role in the transmission of global financial risks. As a new trend in the development of the financial industry, the development of green finance also indicates the direction of the future development of the financial industry. Faced with the increasingly concerned environmental issues, it is necessary for emerging economies, mainly represented by developing countries, to learn from the experience of developed countries in this field. In this paper, principal component analysis (PCA) and factor analysis (FPM) are applied to study the factors influencing financial fragility of emerging economies at different stages of development, and then the degree of fragility is analyzed and compared. The empirical results show that there are differences in the influencing factors of financial fragility in emerging economies at different stages of development. For middle- and low-income economies, credit security, social stability and the development of capital market are the priorities they should consider at present. For the Middle- and high-income economies, we should be alert to the malignant changes in a certain indicator. For high-income economies, macroeconomic stability is the focus of their financial security. The research results provide a basis for the emerging economies to reduce economic vulnerability according to local conditions, which is of great significance to avoid the outbreak of global financial crisis.

The relationship between AI language processing and human language analysis should not be delayed as AI application is in its blossom. The analysis of the cultural and social background in which the language is produced will better the application of natural language processing in AI. The study of interpersonal function, individuation and multimodality in Black English in the novel "The Color Purple" might provide implications for the relationship between language and society in terms of AI language processing and discourse analysis.

At present, people's food demand in the late stage of the new coronavirus is affected by many aspects and various factors, and the current prediction method is affected by redundant data, which leads to poor accuracy of the prediction results. In addition, in order to prevent the mismatch between food supply and demand in the event of major health events, the prediction model was selected in the big data environment and the prediction accuracy of the mathematical model was studied. Through the improvement of various prediction models and the comparison of prediction accuracy, it is found that the improved grey Verhulst has higher accuracy and is more suitable for short-term prediction.

To improve the performance and meet the rising tourist demand of luxury hotels, Crown Resorts Ltd. proposed to implement a new project, which is supposed to build a 6-star hotel resort with VIP gaming facilities in an iconic building at Barangaroo South (Jones Lang LaSalle 2013). Because the goal of Crown plans to build a luxury hotel with a casino, so the company requires a large amount of capital to launch the new project. The best financial solution to raise capital is to issue the bond. Therefore, according to the current market condition and previous experience of issuing Crown Note I & Crown Note II, here are our recommendations for the most beneficial strategy to issue a new bond--Crown Note III.

China's economy has entered a new normal, and the industrial structure must be upgraded in order to realize the transformation of economic growth kinetic energy. Sichuan Province, as the economic leader in western China, needs to accelerate the optimization of its industrial structure. Therefore, this paper makes an empirical analysis of the factors that affect the upgrading of Sichuan's industrial structure, and puts forward countermeasures and suggestions to promote the optimization of Sichuan's industrial structure.

In this paper, first of all, the optimal FCM algorithm was used to classify the economic development level of each sub-region. Secondly, the principal component analysis method was employed to comprehensively score and rank the economic development level of each sub-region. Thirdly, based on the classification and ranking, the differences in regional economic development were analyzed. Taking the economic disparities of western provinces as an example, the research showed that this algorithm could effectively comprehensively analyze the economic development disparities of western provinces and well reflect the characteristics of economic development of each province. Therefore, it was an improved and feasible method for analyzing the differences in economic development level of sub-regions within a large region.

As a natural enemy of the European honeybee, the Chinese great steller's wasp can have a serious impact on local bee populations, so they are considered an agricultural pest. After the emergence of these pests, countries must decide how to prioritize their limited resources for follow-up investigations. In this paper, a model was established to predict the emergence of wild silkworm, which could be used to provide strong support for pest management. when predicting how pests spread over time, we built a GM model to predict the spread of Vespa mandarinia from the spatial and temporal distribution. When predicting the geographic location of Vespa mandarinia, we set up a two-dimensional GM model using longitude and latitude as two variables to predict the longitude and latitude of the next Vespa mandarinia. At the same time, when predicting the occurrence time of Vespa mandarinia, we formed a sequence of the annual 12-month report times of previous years, and still used GM model for data processing and prediction.

Thin films play an important role in modern science and technology. With the development of optical fibre communication technology and laser technology, thin films are used more and more widely in this field. The application of optical film in optical communication and laser technology is introduced systematically. The application of optical films in optical communications basically covers a variety of conventional optical films. Based on this research background, the paper mainly analyses the optical and physical properties of zinc oxide nano-films and discusses its application in optical communication.

Photonic crystal fiber has a special light guiding mechanism and structural tunability, which can produce singular dispersion characteristics and high nonlinearity, which provides new conditions for the research in the field of nonlinear fiber optics. Due to the combined action of a variety of nonlinear optical effects, photonic crystal fibres with different structural parameters and transmission characteristics can produce rich nonlinear spectra under different pump light pulse parameters. We use the photonic crystal fiber cladding section to conduct nonlinear optical experimental research, and obtain the broadband spectral output of soliton wave and dispersive wave. The theoretical analysis and experimentally measured spectra include blue-shifted dispersion waves in the visible light band near 0.5 μm, residual pump light in the 0.82 μm band, soliton waves in the 1.1 μm band, and red-shifted broadband dispersive waves near 2 μm. Corresponding to this, the formation and redshift of solitons can be clearly seen in the time domain, and there is no more oscillation secondary peak in the femtosecond transmission pulse.

Both nonlinear effects and dispersion are important factors that affect the transmission of optical pulses in optical fibres. In the anomalous dispersion region, the mutual balance of dispersion and nonlinear effects can achieve optical soliton transmission. Starting from the nonlinear Schrodinger equation of the optical pulse, under the condition of ignoring the dispersion, the nonlinear phase shift and frequency chirp of the Gaussian optical pulse under the nonlinear effect of self-phase modulation (SPM) are derived and simulated; the numerical simulation of the SPM the spectrum of Gaussian optical pulses with different maximum phase shifts without chirp and with positive chirp. The study found that the fusion loss of the fibre obtained by this algorithm is less than 0.11dB than that of the traditional single-mode fibre, and the additional bending loss caused by the wavelength range of 1460nm to 1625nm after winding 100 times on a 60mm cylinder is less than 0.02dB/km.

CAPS is an integrated system of navigation and communication. The orbit position of AsiaSat-3S satellite has been pushed from 105.5E to 147.5E. It has become a SIGSO satellite, which plays a certain role in improving the constellation DOP of CAPS navigation system. According to the characteristic parameters of AsiaSat-3S satellite (mainly including orbit parameters, polarization parameters, frequency offset parameters, etc.), this paper expounds the changes of azimuth, elevation angle and polarization angle of 16-meter paraboloid antenna in Tianjin pointing and tracking AsiaSat-3S satellite in CAPS ground system, and analyzes the dynamic changes of frequency offset of AsiaSat-3S satellite transponder through the spontaneous receiving of navigation signals The parameter model prediction of CAPS repeater navigation system has a good reference value.

In the technology of satellite positioning, positioning observation equation is constructed by measuring pseudo distance between the satellite and the receiver and the satellite position coordinates from navigation message, then the equation is solved and positioning results are obtained. The algorithm proposed in this paper is to use total least squares method to calculate initial value, and then someone carry out the Taylor series expansion on the basis of it, and then use weighted least squares in the iterative process to get the calculation results. Finally, user positioning is achieved. The algorithm is simulated by using observation series of Fangshan GNSS station in Beijing, and the simulation results are compared with the results obtained by the least squares algorithm. The results show that the satellite positioning algorithm based on total least squares is more accurate and faster in positioning calculation.

The DE (Development Ephemeris) series of planetary ephemeris released by NASA's Jet Propulsion Laboratory (JPL) have been widely used in deep-space navigation, interplanetary exploration, analysis and reduction of high-precision astronomical observations, and calibrations of gravitational theories etc. This work takes solar astronomic positions as an example to analyze precision of the JPL DE431 planetary ephemeris data. The results show that the DE431 ephemeris has high precision, which can be guaranteed within 0.08 milliarcseconds (mas), and can be used to carry out high-precision calculation of planetary positions.

Benefit from the developed of multi-channel and multi-mode navigation SoC devices, the satellite-borne GNSS receiver scheme with highly integrated SoC platform as the baseband core device has gradually become the mainstream. Based on the NS962 device developed by Space Star Technology CO., LTD, the redundancy design of hardware double backup is proposed. This design is composed of baseband processing module, power management module and interface module. Results show that: the prototype weighs about 3.15kg; size is 198mm × 190mm × 89mm; power consumption is about 17W. It has the capability to process BD B1I/B3I, GPS L1CA/L1P/L2P single and the reliability over 8 years meets the requirement of >0.985. This design provides a new choice for the miniaturized and low-power GNSS receivers.

Global Navigation Satellite System (GNSS) signals to the Earth's surface are weak, vulnerable to interference, and require significant infrastructure investment to keep them operating. In order to get rid of the dependence on GNSS navigation system and overcome the problems such as shadow occlusion and malicious interference, opportunity signal is used to realize positioning to make up for the shortcomings in this aspect. Aiming at the limitation of coverage and availability of land-based opportunity signals at present, the opportunity signals of low-orbit satellites have the advantages of high signal power, wide coverage and no need to build additional infrastructure, etc. Therefore, space-based opportunity signal positioning by using low-orbit satellite system can be used as an effective backup or supplement to GNSS system. In this paper, the signal communication system of low orbit satellite is studied deeply, and the Doppler frequency information is obtained by using the single tone signal of the pilot frequency of low orbit satellite, and the mathematical model of instantaneous Doppler positioning and its geometric factor of accuracy is established, so as to realize the positioning ability of opportunity signal of low orbit satellite. The simulation results show that the positioning accuracy is better than 200 m using the actual low orbit satellite signals. The research results have reference significance for the theoretical research and application of location technology based on opportunity signal. By studying the communication system of low orbit satellite signal, the mathematical model of instantaneous Doppler positioning is established, and the method of obtaining Doppler frequency information by using the single tone signal of low orbit satellite signal and combining the satellite orbit information calculated by the orbit prediction model to realize low orbit satellite positioning is proposed. The measured data verification results show that the positioning accuracy of 100 meters can be achieved by using the LORB opportunity signal, and the research results have reference significance for the theoretical research and application of positioning technology based on the LORB opportunity signal.

According to the characteristics of BDS B1C signal, there are many sub peaks in the autocorrelation characteristics, which leads to the ambiguity of B1C signal acquisition. In this paper, the characteristics of B1C signal, acquisition strategy and the ambiguity of different acquisition strategies are analyzed. Based on ASPeCT algorithm, PMF and FFT is implemented in FPGA of navigation receiver. The B1C signal is generated by spiren gss9000 satellite signal simulator, and a test scenario is built for the navigation receiver, which proves the effectiveness of B1C signal acquisition.

GNSS receiver technologies allied in high orbit altitudes has developed rapidly in recent years and has been used in orbit on many spacecraft. In this paper, TJS-5 Satellite is taken as an example to illustrate the design scheme of the high orbit navigation receiver based on the SoC platform, which takes the characteristics of the weak signals of GEO orbit navigation into consideration, and adopts the high sensitivity acquisition technology and the fast signal acquisition technology. Finally, the 19-day in-orbit data of TJS-5 Satellite are analyzed. The number of available satellites is between 4 and 8, the position accuracy of orbit determination is 15.8133m and the velocity is 0.012108m/s, which meets the needs of autonomous positioning and precise orbit determination in the whole orbit period of the satellite. The calculated results are compared with those of TJS-2 satellite.

At present, the analytical method (reliability prediction) is mainly used to calculate the equipment reliability of electronic equipment system. But reliability verification and evaluation of electronic equipment, which is highly reliable, needs a lot of time and economic costs. In this paper, a reliability simulation algorithm based on Monte Carlo method is proposed to verify and evaluate the reliability of highly reliable GNSS receiver. Which can meet the requirements and greatly reduce the project cost.

In order to realize the intelligent control of the agricultural facility greenhouse environment cluster, the paper designs and implements an agricultural facility greenhouse environment cluster monitoring system based on RS485 bus and Modbus protocol. The system consists of on-site data acquisition and control part, remote monitoring centre monitoring software part and communication protocol part. We use the RS485 bus with Modbus protocol to realize the communication between the monitoring centre host and the microcontrollers of each greenhouse. The paper uses the BP neural network PID control algorithm to achieve the intelligent control of the agricultural greenhouse environment. Use Qt software to design a human-computer interaction interface to realize remote control of the greenhouse cluster. The experimental results show that the system can correctly collect the temperature, humidity and light intensity of the agricultural greenhouse environment, and can effectively control the environmental parameters of each greenhouse through the action of the control relay.

Forests have ecological functions such as purifying the atmosphere, fixing carbon and releasing oxygen, conserving water sources, preventing wind and sand invading, and harbouring biodiversity. At the same time, they provide human beings with materials and energy, and thus are important for economic and societal development. However, in our national power grid system, trees might cause interference on power transformation lines. Furthermore, if these circumstances cannot be responded timely, huge economic losses would happen. Therefore, research of forest growth models can more accurately predict the growth and the dynamics of forests, as well as provide a reference for the power warning system. This study conducts a forecast simulation study on the growth of eucalyptus in Guangzhou province, China, proposes a multivariate statistical analysis to predict tree species growth, and then compares its performances with traditional Logistic model results. We found that our multivariate statistical model can effectively consider various environmental influencing factors, thus predict tree height excellently (R²=0.9776), so as provide new foundations for tree height prediction.

In the traditional single-epoch kinematic positioning, the positioning performance is relatively poor due to the less prior constraint information and lower success rate of ambiguity fixation. Accordingly, this paper proposes a single-epoch kinematic positioning method using Doppler relative velocity information constraint, that is, using Doppler observation information of rover station and base station for relative velocity measurement, and then using rover's prior coordinates and velocity information to predict the coordinates of the current epoch, considering the advantages of higher accuracy of this method for coordinate update and more robustness of traditional single-point positioning, the corresponding strategy for single-epoch ambiguity resolution is further introduced to improve the positioning performance. The experimental results show that the proposed method can improve the accuracy of the float solution and ambiguity fixation rate, especially when the satellite geometry is poor and the observation conditions are obscured.

In this paper, we analyze and forecast China's future pension industry. We first use the fitting algorithm to predict the number of elderly population in the future. Then BP neural network is used to predict the willingness of the elderly at all levels to stay in pension institutions, and then the market demand scale of the number of elderly service beds in China in the future is obtained. Finally, we get that in 2023, the demand for elderly care beds in China is expected to reach 16.7 million, and urban beds will dominate, accounting for 86.83% of all beds.

Scientific data sharing has become an important activity to promote modern research findings in which helps to reduce costs and save time for data collection, but it also brings certain privacy issues when using scientific data. First, based the network and literature survey method to described the basic conceptual of scientific data sharing, and discussed the issues of privacy violation in scientific data sharing during the data lifecycle. Second, tried to propose a privacy protection model and framework to prevent privacy violation. Finally, provided some suggestions for the current privacy protection in scientific data sharing from different perspectives. The study contributes to the scientific data sharing by shedding light on how to protect privacy of scientific data sharing through the privacy protection model and framework.

This article has been retracted by the authors following an admission that this article uses work from [1] without attribution. IOP Publishing has investigated in line with the COPE guidelines, and agree the article should be retracted. The authors agree to this retraction and apologise to the original creator of the work.

1. Shi Kaiming 2019 Research on Movement Modeling and Path Planning of Flexible Cannula Needle Based on Finite Deformation Theory PhD Thesis Harbin University of Science and Technology, China (https://t.cnki.net/kcms/detail?v=zDWWPGpj7XNIYVmjPzotLQQrqE5suL8UT58pa0eYSzNWTc6cTneXhheuT3eZOa2hfsihnencE2errhffb1l--WQrpYCErNwN730qt90Gb_CdjKxkPFYC1Ppuj1cZoBQyYGNMZRph70O-WGDNoSWGdk1rtHYrxJSM&uniplatform=%20NZKPT)

Retraction published: 03 December 2021