Table of contents

Application development often faces problems such as demand changes, long cycle, and low efficiency. This paper attempts to solve such problems with automated generation technology. The article abstracts the design and development process of a typical application system; analyzes the key issues; then, after designing the overall architecture of the builder, it describes the design of interface presentation layer, control layer (business logic layer) and data model layer; finally this paper describes the challenges encountered by the builder and improvements in practice.

A model predictive control (MPC) algorithm is designed to solve problem in the steering control of dual electric drive tracked vehicle. Firstly, the driver manipulated model is constructed after analyzing tracked vehicle dynamics. Based on which, the predictive control of vehicle speed and yaw rate is achieved by establishing predictive model, rolling optimization, and correcting feedback. Finally, co-simulation model of vehicle multi-body dynamics and steering control is constructed to simulate the steering conditions of variable speed and variable radius. The simulation results show that the MPC algorithm can quickly and accurately track the target speed and yaw rate, and the output motor torque is smooth. Which can realize the stable steering of vehicles under various working conditions.

Based on Convolutional Neural Network, the paper presents a compact detection algorithm that can estimate the head pose from a single picture. Our method is based on soft stage wise regression. In order to reduce model complexity, three-dimensional detection of the "pitch, yaw, and roll" of the head posture adopts multi-level classification. Each level of classification requires only a small number of classification tasks and fewer neurons. In order to enhance the feature expression of the algorithm, the attention model is embedded. Attention model includes channel attention structure and spatial attention structure, enhancing the feature expression of the feature map in the two dimensions of the intermediate feature map channel and space. The attention model can be seamlessly integrated into the CNN architecture with low overhead. The experiment proves that the improved algorithm compares the method model proposed by Yang with a smaller complexity of 4.36M and an average absolute error of 0.7%~0.9%.

Dynamical properties of mechanical systems can be obtained with the vibration signals from the systems. However, for the influence of noises, it is difficult to accurately acquire the features. Therefore, de-noising operation is significant for vibration signal in the practical engineering. In order to resolve this problem, discrete wavelet transform (DWT) with hard threshold is used to remove noises from vibration signal. We provide the principle of DWT at first. And then, a sample signal is constructed with white noise is processed by the technique. As the result shown, the random noise can be effectively eliminated. This reflects that the de-noising method based on DWT the hard threshold is effective.

In order to solve the problem that the emotion recognition rate of single-mode physiological signals is not high in the physiological signals based emotion recognition, in this paper, we propose a convolutional recurrent neural network based method for multi-modal physiological signal emotion recognition task. The method used convolutional neural network to learn the spatial representations of multi-channel EEG signals and the Long Short-term Memory network to learn the temporal representations of peripheral physiological signals (EOG, EMG, GSR, RSP, BVP, and TMP). The two representations are combined for emotion recognition and classification. In the two emotion dimensions of Arousal and Valence, our experiments conducted on the open source dataset DEAP shows that, this method achieve 89.68% and 89.19% average accuracy in the EEG emotion classification, 63.06% and 62.41% average accuracy in the peripheral physiological signal emotion classification, 93.06% and 91.95% average accuracy in the combined feature emotion classification. The experimental results show that the convolutional recurrent neural network based method that we proposed efficiently extract multi-modal physiological signal feature to improve the emotion recognition performance.

Checkpoint technology can improve the efficiency of simulation backtracking. Existing check pointing tools are dedicated to solving system fault tolerance and load balancing problems, and have limited support for persistent data. The overhead of setting and restoring checkpoints for persistent data is critical for simulation backtracking. We designed LibIFC to deal with the problem of checkpoint overhead for persistent data in two aspects. Firstly, the incremental method is used to reduce the space cost of the checkpoint; secondly, a two-way recovery method is used to reduce the time cost of checkpoint recovery. The experimental results show that the above two strategies have achieved significant results in terms of space overhead and checkpoint recovery time overhead.

System of systems confrontation is the main form of modern warfare. In order to meet different modeling and simulation needs, a large number of evolution ideas of system of systems simulation have emerged. This paper gives two different definitions of evolution ways of system of systems simulation through induction and summary, and makes a comparative analysis from the perspective of evolution mechanism and application scope. Taking atypical parallel simulation system as an example, it gives different evolution ideas Design of simulation application framework under mechanism.

Particularly In the nowadays, under the environment of increasing severe weather, buildings become consumers of energy resources that cannot be ignored, the hvac is one of the most important energy consuming equipment in the building, it has great practical significance and practical guidance for energy consumption prediction and optimization to reduce overall energy consumption and cost. The Adaboost-BP model based on integrated learning algorithm can not only improve the prediction accuracy of BP neural network algorithm model, at the same time, the defects of BP neural network algorithm such as falling into local minimum and slow convergence speed can be corrected. Moreover, the integrated learning algorithm has low requirements for weak classifiers and almost no need to adjust its parameters, so it has a wide range of use and good robustness. The building cannot be ignored as energy resource consumers, and hvac, as one of the main energy consumption equipment in buildings, prediction and energy consumption in the energy saving optimization to reduce the overall energy consumption, reduce costs. The Adaboost-BP model based on integrated learning algorithm can not only improve the prediction accuracy of BP neural network algorithm, but also correct the defects of BP neural network algorithm such as falling into local minimum value and slow convergence speed. Moreover, the integrated learning algorithm has very low requirements for weak classifiers and almost no need to adjust its parameters, so it has a wide range of use and good robustness .In conclusion, the energy consumption forecasting and optimization scheduling based on data-driven have good effect to optimize the energy consumption structure of office buildings, save energy resources, reduce greenhouse gas emissions, and reduce the impact on the power grid caused by the increase of demand from users during the peak period of electricity consumption, also provides a design idea for distributed energy network design.

The wave state plays a vital role in the planning of marine activities. Predicting the wave significant height can prepare for the abnormal sea state in advance, which is of crucial influence to the marine activities. Unlike other scholars in the field of ocean research, this paper starting with analyzing artificial neural network model. By using the convolution neural network to model the measured wave height historical data, and to predict the ocean wave situation in the next six hours based on the measured data of wave height in the Beibu Gulf sea area. Using MAE, RMSE, PCC and etc. error evaluation methods, analyse the influence of convolution neural network on wave height prediction results in different layers is discussed and analyzed under different historical data input conditions. Finally, the network model is trained with the measured data in November and December 2018, and the results are analysed and compared.

With the development of information technology, the information process of power companies is accelerating. Power big data is the product of this information, and its related technologies play an increasingly important role. According to the power big data and related technologies, the visual display technology of power grid planning is also gradually developed. The application of power big data in power grid planning management is analysed and analysed. At the same time, the opportunities and challenges faced by power big data in the development process are analysed. Research provides ideas.

Power systems often suffer from various large disturbances during operation, especially grounding and short-circuit faults of operating lines, which may lead to transient instability of the system. In view of the fact that the existing relay protection is difficult to be fully applied to the power system with high permeability distributed energy, the machine learning algorithm is applied to the relay protection of the power system. Enhance the robustness of the model to noise; In the training, more weight is given to the unstable samples to balance the influence caused by the difference in the number of samples. In addition, a regular term is introduced into the loss function to control the complexity of the model and reduce over-fitting, thus adapting to various operating conditions of the power system. By comparing the difference between measured data and estimated data to detect bad data, the machine learning method is more intelligent than the traditional method. The research results show that the transient stability evaluation method based on incremental learning of support vector machine greatly reduces the learning time while maintaining the evaluation performance, and is a promising online learning algorithm for transient stability evaluation.

Smart grid is increasingly becoming the development trend of grid technology. Smart grid is an important part of smart grid. Power equipment is the core part of smart grid, and the normal or not of power equipment directly affects the safety and stability of the entire power system. Smart smart grid usually has relatively complete fault diagnosis and self-healing functions to improve the stability and reliability of the grid. The application and development of a practical fault monitoring and early warning system for power equipment is to carry out predictive maintenance of power equipment so that the equipment can operate more safely and reliably. Due to the time-varying nonlinearity, stochastic uncertainty and local observability of smart grid, it is difficult for traditional power system modeling and analysis methods to fully reflect the steady- state and transient characteristics of power system in the new form. In this paper, based on machine learning simulation technology, through the research of power equipment fault monitoring system in smart grid, provide early warning information and solutions for equipment management personnel maintenance.

With the continuous development of electric power systems, electric power companies have standardized the requirements for safety testing laboratories for salt spray test of electric power facilities in accordance with relevant national standards and specifications, and combined with the previous work experience of electric power companies, this paper analyses in detail the laboratory-based The characteristics and significance of standardized management of salt spray test of electric power facilities under standardized construction, and also elaborated the new requirements for laboratory construction. At the same time, it strengthened training, performed competence verification and comparison between laboratories, and strict In the aspects of good method confirmation, enhanced management, and enhanced standard dynamic management, relevant suggestions for strengthening the standardized management of safety testing laboratories in power facilities for salt spray testing were put forward. It is hoped that the test results of power facilities salt spray testing laboratories can be effectively improved. And accuracy to achieve sustainable development.

According to the control requirements of cleaning equipment, this paper designs corresponding electrical control system the design detection system is designed to realize horizontal and vertical positioning of cleaning mechanism with electromagnetic proximity switches. The control circuit is designed with a VB1 model PLC as main controller, the system control flow is analyzed to switch the steps of entering a pigsty, collecting pig manure, and returning and dumping pig manure.

Combined with an example of 750kV shunt reactor exception handling, the author used field test inspections and search methods to dismantle the plant and found that the cause of the reactor abnormality was an incident caused by impurities or foreign matter. Combining the historical operation data of the equipment, the analysis and comparison of the reactor's routine inspections and test results are performed. Combined with the simulation calculation of the internal insulation margin of the reactor, the judgment methods and conclusions are given, and the troubleshooting method and flow of the reactor abnormality are proposed, and put forward specific measures to strengthen the state monitoring of the network operation reactor.

The principles of 330kV power transformer long time induced voltage field test are introduced in the paper, the varieties of key elements on the test are analysed on GB/T1094.3 release version, some suggestions about test and schemes are suggested finally.

In order to accurately detect the partial discharge information inside GIS, this paper proposes a PD measurement method based on fluorescent fiber sensing system. Firstly, the typical physical model reflecting four typical insulation defects in GIS is perfected. The PD optical signal is obtained through the detection system, and the corresponding 3D map and its gray image are constructed. At the same time, for the simple fractal is not enough to accurately describe the characteristics of PD discharge mode, a multi-fractal spectrum algorithm for partial discharge gray image is proposed. Then, based on the extraction of each feature information in the multifractal spectrum, BP neural network based on improved conjugate gradient algorithm is selected as the classifier for feature recognition. The results show that the multifractal spectrum can effectively describe the degree of PD gray image inhomogeneity and its different levels of geometric structure, and can accurately identify the PD phenomenon caused by four typical insulation defects.

Optical method is a commonly used method for detecting partial discharge in electrical equipment. How to use photometric quantitative detection is one of the most urgent problems to be solved. Based on the in-depth analysis of the physical meaning of the integral value of the PD optical signal, it is concluded that the linear integral value of the signal is linear with the apparent discharge. The experimental results show that the integral value of the PD photometric signal at different polarities and different distances is linear with the apparent discharge. The larger the gap distance between the needle and the plate electrode is, the larger the slope is. The linear relationship between the average primary integrated value of the PD photometric signal and the average apparent discharge is different. With the increase of the applied voltage, the PD discharge intensity and the pulse repetition rate increase, and the phase interval of the PD becomes wider. The actual air gap volume of the PD becomes larger, and the variation interval becomes wider, that is, the apparent discharge amount distribution interval becomes wider, and the maximum value and the minimum value of the distribution interval become larger. Therefore, it is possible to obtain the PD apparent discharge amount by using the photometric signal, and it is effective and feasible to quantitatively detect the internal PD of the GIS by optical method.

In view of the extreme high temperature in the summer when the natural light plant factory runs, it is easy to cause the plant to die due to high temperature. The actual operation of the Shanghai Chongming Natural Light Plant Factory in the past two years is studied to explore various ways of cooling - water Energy-saving groundwater source heat pump cooling air treatment unit (ATU), wet curtain fan, etc. cooling characteristics in the semi-enclosed natural light plant factory in the summer high temperature weather. The results show that the typical high temperature typical day, the natural light plant factory of the water storage type groundwater source heat pump cooling ATU cooling system is 13.88°C lower than the average temperature of the natural light plant without the system. In high temperature weather, the natural light plant factory wet curtain fan system and water storage type groundwater source heat pump cooling ATU cooling system have better cooling effect, but the latter cooling effect is better, such as only the wet curtain fan cooling system The average indoor temperature of the natural light plant factory A5 is 30.88°C; the average temperature of the natural light plant factory A2 of the natural light plant factory A28 with only the water storage type groundwater source heat pump cooling ATU cooling system is 28.64°C, the average temperature of the natural light plant factory A2 without taking any cooling measures is 40.19°C.

In summer, it is easy to cause the plant to die in the natural light plant factory due to high temperature and high humidity. It is studied for the cooling dehumidification characteristics among various ways of cooling and dehumidification - water Energy- saving groundwater source heat pump cooling air treatment unit (ATU), wet curtain fan, mechanical ventilation, etc. in the semi-enclosed natural light plant factory in typical plum rain weather. The results show that, during the rainy season, the external environment is relatively low temperature. On a typical day, the average temperature is 28.10°C from 9:15 to 17:00 in daytime in the natural light plant factory with only mechanical ventilation. The indoor temperature is 31.1°C, and the average relative humidity is 81.61%, which can meet the needs of suitable temperature and humidity for cultivated crops. Compared with the normal opening of ATU, only the A area can save the operating cost of 337.34 yuan per day. Between 0:00 and 6:40 in the morning and 20:40 to 24:00 in the evening, the average relative humidity of the natural light plant with both mechanical and natural ventilation is 87.03%. Only mechanical ventilation is opened, and the relative humidity is 95.44%. Therefore, natural ventilation should be strengthened from night to next morning.

To improve the instability of light hydrocarbon gas supply station, the need for long-term maintenance and guard of staff, and improve the intelligence and automation of gas supply station, a perception terminal of light hydrocarbon gas generation device control system based on NB-IoT is designed. The data of liquid level, temperature, gas supply, and pressure are collected by STC15W4K56S4 low-power single-chip microcomputer. Based on the platform of the Internet of things, the remote transmission of data is completed by the BC95 module and local monitoring center. The experiment shows that the terminal can effectively transmit the collected data, respond to the command of the monitoring center, and complete the two-way communication between the terminal and the monitoring center, which has a high engineering value.

Various parameters in the process of diesel engine operation contain a lot of information. Through data mining, the inherent information of these parameters can be mined out to solve the problems of inaccurate diagnosis and time-consuming. In this paper, a fault diagnosis scheme for the diesel engine is proposed based on the K-means analysis and the back propagation (BP) neural network. K-means is used to cluster the data and BP neural network is designed to diagnose the running state of diesel engine. Then, the fault diagnosis scheme is optimized by principal component analysis (PCA) to simplify the raw data, which are clustered by K-means and set as the input of the BP neural network to establish the fault classification model. Through the analysis and comparison of the results of the two diagnosis algorithms, it shows that the optimized algorithm can extract data features more effectively, improve the diagnosis accuracy, and reduce the diagnosis time.

In order to continuous the advancement of technology, the development of Internet of Things technology will greatly promote the intelligent, we make information and automation of buildings, which is of great significance for promoting intelligent buildings. The modern intelligent building electrical energy-saving design measures can not only realize the energy-saving design, the real purpose is to save energy and reduce emissions, promote energy-saving emission reduction can promote China's sustainable development, implement scientific methods, and assist China's economic development. When designing, we designers should consider various factors and rationally design electrical energy-saving measures.

The heat transfer model of difference in temperature is used in the heat transfer analysis for platelet nosetip. The temperature distribution of platelet construction unit and transpiration cooling medium is got by analytical solution. Hot dipping depth is used for range of influence. The results show that the maximum temperature and hot dipping depth decrease with transpiration flux increasing, and decreasing trend of hot dipping depth is slowing. Hot dipping depth and the maximum temperature increase with the heat flux on the wall increasing, and increasing trend of hot dipping depth is slowing. Temperature increasing trend of platelet construction unit in heat-affected zone is slowing with thermal conductivity of structural material increasing. The temperature change tendency is basically unchanged with the length of distribution channel increasing, but only heat-affected zone of platelet move some distance to the exit. So simply increasing the length of the distribution channel does not improve the thermal protective effect.

With the development and advancement of technology, modern industry has made rapid progress in improving the combustion performance of automobile exhaust heat in improving the combustion performance of automobile engines and reducing harmful emissions of automobile engines. This technology can not only improve the energy utilization of vehicle fuels. Efficiency can also reduce environmental pollution. The spiral array heat exchanger is an important equipment for waste heat recovery of automobile exhaust gas, which can fully recycle this waste heat energy. Therefore, this paper studies the structure optimization of spiral array heat exchangers, and plays an important role in the recovery of exhaust gas heat recovery, optimization of structure and enhancement of its comprehensive heat transfer performance. It improves fuel economy, saves energy and protects the environment. Great significance.

The fair and reasonable method of cross-provincial transmission pricing can give the price and economic signal for the each trader. The models of cross-provincial transmission pricing and evaluation are based on power tracing is proposed in this paper. In the cross-provincial transmission pricing model the load is allocated no transmission cost when the load bus is as the same with the originate bus of one branch. And the cross-provincial transmission benefit is evaluated accurately by consumers' surplus through the relation between generation and load based up-stream tracing. And the power use of cross-provincial transmission grid for each trader is considered in this model.

When electric vehicles are loaded with wireless charging coils, engineering targets such as efficiency and power have always been the most significant targets for designers. In the previous research work, researchers designed and optimized the above relevant targets from the aspects of coil structure and magnetic core. However, the majority of these design and optimization schemes adopt a single-size coil to verification work, and the size often don't take the actual application environment into consideration, so it don't have engineering guidance significance and practical application value. In order to solve the gap and cooperate better with the application in engineering, this paper carries out a complete coil design method from the perspective of engineering design purposes and application environment. Firstly, the coil design conditions are constrained from the aspects of electric vehicle loading environment and design purpose, then the circuit and coil structure are analysed and selected, all the losses that may exist in reality are calculated, and the calculation method of the maximum winding length and minimum mutual inductance requirement of the coil is introduced. Finally, the maximum winding length index of the coil winding corresponding to the design purpose and application environment is formed, and the minimum mutual inductance design requirement is formed too. The design method of this paper is complete, and it can form different design schemes for different engineering design purposes (such as different power and efficiency). It has strong guidance to practical engineering and has high practical application value.

With the mass production and application of wireless charging system for electric vehicles (EV), and in the process of production and use of the device, it is inevitable that there will be offsets between the actual parameter and the theoretical parameter. This will lead to the deviation of the intrinsic frequency from the design value. Finally, the transmission performance decreases and the actual design requirements are not met. Therefore, based on SS resonance compensation topology and the key parameters in SAE standard, this paper analyzes the self-inductance allowable values at different power levels and frequency points. In addition, the range of allowable operating frequency bands under the inductance offset is derived, which plays a certain guiding role in the production of key devices and practical engineering applications.

A new type of loop antenna with core of fan-shaped plasma is designed. When the plasma frequency is about 4 times of the antenna frequency, the radiated power of loop antenna is enhanced greatly. Numerical analysis shows it is the resonance of electric field between plasma and dielectric of positive permittivity, which enhances the radiated power. Furthermore, the effects of parameters of antenna core on radiation characteristics are studied.

In order to promote the NEV manufacturers and the echelon using enterprises to implement the responsibility of wasted traction battery recycling, to standardize the construction of the traction battery recycling service outlets, and to improve the construction level of the traction battery recycling system, this paper studies and interprets the functional requirements of the recycling service outlets in combination with the present construction situation of the recycling service outlets, analyzes the problems existing in the construction of the recycling service outlets from the aspects of site construction and standard operation, and puts forward some specific suggestions.

The development of economic globalization makes the cooperation relationship between enterprises and suppliers closer. How to optimize suppliers becomes a research hotspot. This paper studies the selection process and selection method of quotient evaluation indicators based on the analysis of supplier data by using information visualization tools. The multi-dimensional data and analytic hierarchy process are used to evaluate the supplier's commercial value and product information. The example of z agricultural machinery company is used to verify the effectiveness and feasibility of the new scheme.

In order to solve the problem of low efficiency and high false detection rate of transformer label detection in the process of factory production, an algorithm of transformer label detection based on machine vision is proposed in combination with OpenCV function library. In the pre-processing stage, the image noise is filtered and the vertical edge information is highlighted, then the label region is located by morphological processing and contour extraction, and the label image is screened by SVM classifier. Finally, the label character is divided into a single contour and recognized by artificial neural network. The experimental results show that the accuracy of the algorithm is 94.0%, and the average time is 0.334s, which meets the requirements of the factory production line.

PTC electric heating is widely used in the air conditioning system of electric vehicles. This paper studied the PTC electric heater detection bench. First, introduced the research background and three keys of the detection bench control system: PWM board, serial port protocol, design of database. Then, elaborated the three key points respectively, including board structure, analysis of serial port protocol and design ideas of database. Finally, the detection bench was set up and tested repeatedly. The results of experiment shows that the bench achieved expected goals.

Satellite power system is an important system of satellites and it has a significant impact on safe and reliable operations of satellites. Researches on anomaly detection method of satellite power systems can improve the reliability of normal satellite operations. Fixed top and bottom limitations for telemetry parameters are often used in engineering to detect anomalies of telemetry parameters, but the detection thresholds do not change with time and can neither reflect the dynamic tendency of telemetry parameters nor anomalies that occur during dynamic changes of telemetry parameters. In order to solve this problem, this paper puts forward a dynamic detection method for finding anomalous satellite power supply telemetry parameters by comparing predicted values and real-time telemetry data by using dynamic characteristics of satellite power supply telemetry parameters and the Bayesian Model. This method can be used to verify telemetry data of voltage parameters of the storage battery of satellite actually in orbit. The results show that this method can effectively detect anomalies of satellite power supply.

Power system will occur chaotic oscillation when it is exist periodic load disturbances. As for it, the simplified mathematical model of interconnected two- machine second-order power system is established based on the modelling and analysis of nonlinear system theory, and the dynamic behaviour of chaotic oscillation in power system is analysed. According to the sliding mode control and fuzzy control theory, the fuzzy-based sliding mode control is proposed, it is also analysed and proved theoretically. Simulations results show that the proposed method can suppress the chaotic oscillation in power system effectively. It not only reduce the convergence time, but also reduced the chattering of the system, and has strong robustness.

Several defective plasma photonic crystals were designed based on crystal edge dislocations, and their transmission characteristics were studied using CST software. The transmission characteristics of PPC with different defects in TM mode are mainly discussed. Compare defective PPCs to non-defective PPCs and other defective PPCs. Defective plasma photonic crystals with the largest changes in PPC electromagnetic characteristics were obtained. This type of defective photonic crystals can create a band gap in a certain frequency band and control its movement through defect parameters.

This paper use the finite difference time domain (FDTD) algorithm to study the forbidden band characteristics of two-dimensional non-magnetized plasma photonic crystals. We analyse the obtained S-parameter from the perspective of frequency domain, and discuss the influence of the interlayer angle and distance of the photonic crystal, the polarization mode of the antenna and the plasma parameters on the band gap characteristics. The results show that when the photonic crystal has a certain plasma filling rate, different angles and distances between layers and different polarization modes can achieve modulation of transmission characteristics.

In this paper, the plasma density in the lateral discharge of the tip gap at atmospheric pressure was measured by Mach-Zehnder interferometry. Based on the offset of the fringe picture, the maximum electron density of the plasma was calculated to be about 1.76 × 10¹⁸cm⁻³. At the same time, the spatial distribution of plasma electron density and the change of the highest electron density with the applied voltage across the tip were obtained.

In this paper, a new type of ring-shaped plasma photonic crystal structure is proposed. The influence of the structure parameters on the photonic band gap is calculated by simulation. At the same time, the application possibility of the structure change transmission mechanism is discussed. The use of photonic crystals to achieve tunable forbidden bands is the focus of current research in the field of electromagnetics, and plasmas have been widely used in photonic crystal structures in recent years because of their special electromagnetic properties, density changes with frequency. In addition to studying the band gap regulation of photonic crystals by plasma frequency, it is also an important direction to explore the effects of different plasma photonic crystal shapes and periodic structure parameters on the mechanism of microwave transmission.

In the spray deposition system, there are problems such as inconvenient control methods and low adjustment accuracy for the substrate temperature. Based on the analysis of model, a predictive control method of BP neural network is proposed; and the established control model is tested in real time using STM32. The analysis of the results shows that the method is feasible and effective.

This paper presents a composite artificial electromagnetic structure that couples aplasma photonic crystal(PPC) with frequency selective surface (FSS). Plasma photonic crystals, as an artificial electromagnetic structure, have the effects of photon band gap and photon locality, but their cut-off frequency is affected by their characteristic scale, so we couple another artificial electromagnetic structure FSS, and expect it to have better electromagnetic properties and tuning capabilities. The S- parameters of this coupled artificial electromagnetic structure in the 6-18GHz frequency band were simulated and measured in a microwave darkroom, which proved that the structure has excellent electromagnetic characteristics.

In this paper, a multilayer conically squeezed GER (giant electrorheological fluid) damper combined the characteristics of shear mode and flow mode is studied. Based on the theory of fluid mechanics, this paper established a simplified mathematical model of damper and had experiments to verify it. The result shows a good agreement and an effective isolation ability. With the simple structure, this damper has tunable ability of damping and stiffness to adapt to different application requirements.

Nowadays, the safety of large areas has become the focus of attention. The gas leak will easily cause the terrible losses of lives and property if the appropriate measures are not taken immediately. Therefore, once the similar situation happens, the toxic gas must be dealt with timely to adjust the safe areas of the large space and ensure the necessary security of people. On the basis of the background, this paper introduces a method to study the relationship between the safe areas and the fan position by using the PIV (Particle Image Velocimetry).

The large-scale access of distributed generation has a great impact on load forecasting in substation-area, which means the load curve in the substation-area cannot reflect the real load of users. Firstly, this paper considers the impact of distributed generation on the load curve, and proposes a load forecasting model based on data cleaning and deep learning in the substation-area. Secondly, considering the data missing during communication and transmission, the KNN algorithm is adopted to complete the missing data before the data input. And then, Pearson correlation coefficients are used for the correlation analysis of the input factors related to distributed generation, and the data is trained through Long-short Term Memory in deep learning. Finally, verified by load data of some substation-area in Jiangsu Province, the prediction model established in this paper has good prediction accuracy and stability.

This paper proposes a design scheme of intelligent food cold chain system. Firstly, the functional structure of the system is defined based on the requirement of different subjects participating in the system. Then the system is designed from the technical perspective by introducing intelligent logistics support technology. Different from the existing food cold chain system, the system proposed in this paper is relatively macro, not specific to food categories; and it focuses on the whole process of the cold chain, instead of only considering some parts.

When considering the cascading failures or the robust performance of the power network, most of the researches in network science are based on the topology of the power network. However, according to some researches, only using the topology of the power network can not reasonably evaluate the robustness of the power network.Therefore, when considering the cascading failures in the power network, we must combine the electrical characteristics with the network topology to make a better evaluation. From the perspective of network science, the related research on cascading failures has made a lot of progress. In order to optimize the robustness of power network, the optimal topology of power network is designed under the cascading failure model combined with real electrical characteristics.

The continuous growth of power demand poses new challenge to the efficiency of power system. Therefore, distributed generation (DG) is widely concerned because it can provide clean, reliable and economic power. However, the location and sizing of DG connected to the grid shall be properly selected. In this paper, the voltage stability index and the line loss reduction rate are considered synthetically, and the two indexes are transformed into a single index (VSLR) to determine the optimal location of DG. According to this index, the node with high sensitivity and high average power loss reduction rate are selected as the optimum location of DG connected to the power grid, and the optimal access capacity of DG is determined by the algorithm proposed in this paper. The simulation experiments of IEEE30-bus are carried out with MATLAB\ Matpower toolbox, and the results are compared with the existing research results. The results show that the method of studying the optimal location and sizing of DG in this paper emerges better performance.

At present, with the size increase and complication of the grid, it has brought greater challenges to the stable operation of the grid; the author proposes a stability evaluation model of grid transient based on deep learning technology, which has high recognition speed and accuracy after being verified by practical examples.

As one of the important infrastructure of the country, it is very important to identify the key links of the power network and protect them to prevent the blackout. Based on the theory of complex network, this paper proposes a comprehensive multi-attribute identification method for key nodes of power grid. Firstly, the Pearson correlation coefficient is used to select the evaluation index according to the topological characteristics of the power grid, then the entropy weight method is used to get the final decision matrix, and finally adopt the comprehensive method to calculate the order of the important lines in the power grid. In order to compare the advantages and disadvantages of different recognition methods, we compare the evaluation results of electrical method, and further verify the effectiveness of the proposed method in IEEE39 system.

Due to high power load peaks and long durations in extreme weather conditions, as well as large-scale unscheduled electric vehicles charging, it may cause "peak plus peak" loading in local areas of the power grid, moreover, lead to transformer substations overload operation in local areas, which is not good for distribution safe and stable operation of the power grid. Considering the driver's willingness, this study proposes an orderly charging strategy based on the temperature control of electric vehicle on-board air conditioner. The proposed strategy first constructs the temperature control model for electric vehicle cabins with considering the temperature of entire electric vehicle and the special characteristics of urban roads. Then, multi-objective function is formed with minimum user charging cost and variance of grid load fluctuations. Finally, electric vehicles orderly charging period is optimized by combining with electric vehicle travel chains, charge constraints, time-of-use electricity prices, and substation capacity constraints. The results show that temperature-controlled dispatching can effectively reduce the electric vehicles disorderly charging load. It can serve the power grid "cut peaks and fill valleys" and smooth the overall grid load curve using an improved Lagrange relaxation algorithm to optimize electric vehicles charging period.

With the increasing globalization and the rapid growth of China's economy, the scale of environmental energy companies has continued to expand, and social requirements for financial management of environmental energy companies have become more stringent. In order to improve the efficiency and quality of financial management of energy-source enterprises, so as to ensure that the financial management of energy companies is in a normal operating state, thereby saving more costs for environmental-energy companies, it is necessary to analyse the current stage on the basis of reviewing the concept of informatization The potential problems of financial management of Chinese energy companies have been explored in depth by putting forward specific innovative measures. Therefore, the construction of a new type of financial information system for green environmental energy companies has emerged. The construction of this information system is beneficial to environmental energy companies. More efficient and clean development of large enterprises, and further improvement of financial information systems.

Firstly, the basic principle and characteristics of GIS vibration are analyzed and studied. It is pointed out that electromagnetic force and magnetostriction are two basic forms of GIS vibration, and both of them take 100Hz as the characteristic frequency. Then through the experiment, it is found that the frequency doubling characteristics of 200Hz and 300Hz can be excited by the loose mechanical fault. These conclusions provide an important basis for the field vibration test of GIS. Finally, a successful case of vibration test and analysis is described in detail, and the basic methods of measuring point arrangement and spectrum analysis are introduced.

In this paper, the feasibility of the fault diagnosis method based on the vibration signal in GIS is studied through the experiments of the correlation between the discharge fault and the vibration signal and the propagation characteristics of the vibration signal in GIS. The concept of pulse interval frequency is put forward. According to the experimental data, it is concluded that there is a strong correlation between the interval frequency of GIS discharge pulse and the frequency of vibration signal. At the same time, the vibration signal has regular propagation characteristics when passing through the basin insulator, cavity and T-shaped structure. The above two points strongly support the vibration test method can be used for fault diagnosis of GIS.

The Internet has massive data and a large number of relationships describing various entities and entities. HTML pages with semi-structured and structured data are important data sources for building knowledge graph. This paper proposes to construct a knowledge graph based on a template semi-automatically and construct a lure template by filling the template as a data source to realize the domain knowledge graph by extracting the triple data in the multi-data source through the attracting template. And build an application platform to provide a domain knowledge graph, reduce human participation to achieve efficient construction of knowledge graph.

Side channel attacks are well-known thread to ciphers in real world. In this paper, we give a CNN based profiled side channel attacks on AES cipher implemented on HI Silicon Kirin 620 SoC, which is a 1.2GHz multi-core platform. With 100,000 acquired side channel traces to train, the CNN have ability to recover real key byte under 1,000 traces, even though the traces are misaligned.

Biometric technology has played an important role in our lives. Finger vein recognition has many advantages as a new biometric technology. Different from iris, fingerprint or face, finger vein recognition is cheap, Non-contact, living. However, finger vein recognition is susceptible to many factors such as light environment, finger moving, etc. In order to solve these problems, in this paper we first we first propose a method for extracting region of interest for finger vein images based on a convolutional neural network. Secondly, we have integrated several common finger vein databases and manually labeled the region of interest. We also extended the dataset using data augmentation methods. Finally, for the transformed images, we calculate the IOU of the prediction box and the theoretical box to measure the robustness of the method and Experiments show that our method improves the performance of the recognition system.

With the development of information technology, the problem of privacy disclosure has become increasingly prominent. State Grid Corporation has a large number of sensitive data such as power marketing data, power customer data, personal electricity information, which has disclosure dangers during the production, transmission, storage, processing and sharing stage. This paper mainly studies the desensitization algorithm model of power consumption data based on anonymity and differential privacy technology, builds a general algorithm model and then realizes data desensitization according to each application scenario. The evaluation and analysis results show that the scheme can effectively protect the massive power marketing data, power customer data and personal power information from invasion and disclosure.

With the rapid increase of vehicles, real-time dynamic dispatch is an important means to alleviate traffic congestion. Game theory-based real-time vehicle scheduling algorithms are often used for dynamic adjustments, but traditional algorithms only switch signal phases for fixed phases during dynamic adjustment scheduling, and cannot achieve the comprehensiveness of real-time scheduling; or phases with large differences in traffic flow when setting the duration, the reasonable waiting time for the smaller traffic phase are not considered. In order to solve the real-time dynamic adjustment of signal lights, an improved algorithm based on game theory is designed to control the signal switch in the game mode, and the waiting time of vehicles at traffic intersections can be reduced by adding parameter values of lane waiting time changes. Through experimental comparison, the algorithm reduces the vehicle stay by 3.4% and the waiting time by 4.4% compared with the fuzzy algorithm of the polling mechanism.

In order to comprehensively measuring software credibility, a method for establishing credibility metric system is proposed in this paper. The method first determine the structure of the metric system, analysis the relationship between the established credibility evidence structure and the metric system structure. On the basis of correlation analyses result, a reasonable metric system construction method is established.

Heat transfer process in power system applications mainly refers to the gas in the cylinder through the combustion chamber wall and related parts pass the process of cooling medium, the heat it mainly includes the gas flow and combustion in cylinder, cylinder internal boundary layer heat transfer (gas and wall heat transfer), heated component of thermal load and thermal conductivity, cooling medium heat transfer, and a series of process. Because it is closely related to the working process of the power system, the lubrication cooling system and the structural strength design of components, the heat transfer and its thermal load will directly affect the dynamic performance, economy and reliability of the system. Therefore, the heat conduction of machining in the power system has always been an important field in the power system research.

The SEA medium frequency extension methods are described. Then, medium frequency extension methods (FE-SEA (finite element-statistical energy analysis), CI-FE/SEA (CHEBYSHEV interval finite-element/statistical energy analysis), ES-FE-SEA (Edge smoothing -finite element- statistical energy analysis)) are compared. In FE-SEA method, neutron structure assembly and modeling method, interval parameter uncertainty and hybrid interval parameter analysis, interval variables and the hybrid interval variable response method are emphatically expounded. And on this basis, the existing methods are compared and analyzed. Based on the differences among the CHEBYSHEV expansion CI-FE/SEA method, the edge smooth domain ES-FE-SEA method and the Taylor series expansion FE-SEA method in solving parameter interval range and solution precision, the hybrid and wide range interval algorithm combined existing methods is expected to develop.

This paper proposes a new nature based on deep deterministic policy gradient for deep reinforcement learning in continuous state and action space, which can greatly accelerate the emergence of artificial intelligence training and solving problem: state-action permissibility. The proposed method is integrated into the latest DDPG algorithm to guide its training and is applied to solve the lane keeping (steering control) problem in autonomous driving or automatic driving. Finally, the TORCS which is the open racing car simulator simulation software builds various simulation environments, including different tracks to verify the effectiveness of the algorithm. The results show that the proposed method can significantly speed up the algorithm training speed of the lane keeping task.

When using the finite element software ANSYS to numerically simulate the thermal effect of laser irradiation, the calculation results will show some differences with the different ways of meshing. The thermal effect of finite element model under different meshing modes when loaded in the form of heat flux is studied, which is based on the process of laser irradiation of metal circular plates. The effects of element size ratio and mesh density on the simulation result are analysed. By comparing the theoretical analytical solutions with the calculation results under different element size ratio conditions, the overall scheme of meshing is determined first, and then the influence of mesh density is further explored. The calculation results show that when the element size ratio is 1 and the overall element size is 0.25mm, the finite element simulation solution gets high precision, which is consistent with the actual.

Flexible DC transmission system has the advantages of small line loss, high reliability and strong controllability, and can reduce the frequency of system inactivity. In this paper, the voltage droop control method of flexible DC transmission system is studied in detail. Firstly, the principle and characteristics of the series structure, parallel structure and hybrid structure of multi-terminal flexible DC transmission system are introduced in detail. Based on this, the principle and characteristics of the control strategy of DC voltage droop control of the system are analyzed, and an improved voltage droop control strategy is proposed. Finally, a three-terminal flexible DC transmission system model is built on the Matlab/Simulink platform. The simulation results show that the control strategy plays a certain role in stabilizing DC, AC voltage, AC current and power balance control.

The inhibition of common-mode leakage current is the key problem to be solved in cascaded photovoltaic grid-connected inverter. To eliminate the common-mode leakage current of cascaded dual-buck grid-connected inverter, a three-phase cascade double-buck photovoltaic grid-connected inverter (TPCDBGCI) topology is proposed in this paper. Meanwhile, a new phase-shifting carrier SPWM (PSCSWPM) control strategy that can make the single phase common-mode voltage and three phase differential-mode voltage constant in the half cycle is presented. Then, the simplified model and operating modes of TPCDBGCI are analyzed, which show that common-mode leakage current of TPCDBGCI is effectively suppressed. Finally, the experimental results verified the correctness of TPCDBGCI.

During the operation of a substation, the requirements for the engineering design are very strict, requiring the cooperation of multiple professions, and the demand for engineering digitalization is also increasing. The three-dimensional digital technology is to effectively solve the problems encountered in the substation operation in the form of data representation and data storage, and provides reliable technical support and efficient concepts of technology for substation operation. This time, the main analysis of the specific application of 3D digital technology in the substation design, and combined with the actual situation of China's current substation, the development prospects of 3D digital design are analyzed and prospected.

To improve the quality and efficiency of inspection tasks, this paper studies the path planning optimization strategy for distribution equipment inspection of distribution networks. The comprehensive information model of each type of distribution equipment to be inspected is established by integrating the real-time monitoring information, geographic information and historic information effectively. The inspection path optimization model with an admissible cost function is established and solved by using the heuristic search A* algorithm. Comparison of the results of several inspection path planning strategies are provided to verify the effectiveness of the proposed strategy.

The on-site verification of electric energy meters has become an indispensable part of power system equipment maintenance. Recently, under the call of the State Grid Corporation of China to further optimize the business environment, in order to reduce the time of power outages for customers, the "non-interruption meter replacement" business is also actively promoted. A special wiring board is installed in the single-phase energy measurement box to ensure replacement. The meter does not lose power, but this measure has greatly affected the on-site verification of the energy meter. This article is based on the new environment of "uninterrupted power meter replacement", in-depth demonstration of the necessity, feasibility, and ease of development of a dedicated plug-in meter for the on-site verification of electric energy meters. Based on the premise of modularization, a special plug-in strip for the on-site verification of electric energy meters has been developed to ensure that the on-site verification of the electric energy meters is not affected.

Smart grid and transparent power grid require extensive collection of basic information of power grid, which puts forward higher requirements for intelligent measuring technique. In this paper, the micro intelligent current sensor is developed, including the design of the whole structure of the sensor, signal processing circuit, digital processing circuit, energy acquisition, wireless communication and other functional modules, and the IP and insulation protection design is completed. Finally, the long-term trial application in the field of low-voltage power distribution is carried out. The results show that the current sensor can work stably and reliably for a long time. It has the advantages of miniaturization, high integration, convenient installation and remote management. It can collect the real-time operation data of the distribution power grid, and provide sufficient information support for the construction of the transparent power grid.

In order to study the deformation and stress characteristics of the pile under the action of landslide thrust. Taking the Suo Er Tou landslide in Gansu Province, China as the engineering background, In the paper the numerical analysis model by establishing the finite difference software FLAC3D of geotechnical engineering is analysed the displacement and bending moment of the pile. The anti-slide pile adopts pile element, soil constitutive model uses Mohr-Coulomb. The results show that the displacement of the piles at the top of the pile and the section of the pile are basically equal, but the horizontal displacement values are not equal above the sliding surface. In the sliding surface, the bending moment of the front pile is smaller than the bending moment of the back pile, but the bending moment of the front pile at the top of the pile is larger than the bending moment of the back pile.

In order to solve the berth allocation problem and improve the efficiency of the dock, a mixed integer nonlinear programming model is built with the length of port stay. The genetic-harmony search algorithm is used to solve it for the first time. Finally, the algorithm and Lingo are used to solve the large-scale berth allocation problem at the same time. Under the same data of arrival ships, several groups of experiments are designed to verify the algorithm. The results show that the genetic-harmony search algorithm can be used to solve the berth allocation problem. On the basis of the consistency between the genetic-harmony search algorithm and Lingo, the genetic-harmony search algorithm is faster and more efficient.

In view of the false data injection attack of cyber physical systems (CPS), this paper aims to study the designing method of adaptive compensation controller for system executor when the system executor suffers from constant false data injection attack. The application of the adaptive compensation control algorithm to the false data injection attack shows that the designed adaptive law has the advantages of fast convergence speed and accurate parameter estimation. The compensation control algorithm can substantially compensate for the effects of false data injection attacks, and we can prove that all signals of the final closed-loop system are bounded and the state of the system can asymptotically converge to zero. Finally, we provide simulation examples to verify the effectiveness of the state feedback adaptive compensation controller proposed in this paper.

In this paper, the impact of distributed photovoltaic (PV) access on the power quality and fault cur-rent of distribution network is studied. First of all, the constant power output of photovoltaic power model is adopted and the influence of the access of distributed photovoltaic on distribution net-work voltage deviation and voltage harmonic are analyzed; secondly, the change of fault current is analyzed considering fault at the upstream, downstream and adjacent feeder of the distributed PV access location respectively; finally, practical example is calculated based on above theoretical analysis, and the maximum penetration of PV is calculated in comprehensive consideration of power quality and existing protection setting value.

This paper proposes an approach to optimize the fuel economy of a two-speed electric vehicle (EV) in car sharing business through Genetic Algorithm and ADVISOR co-simulation. The ADVISOR simulation model built including dynamics, fuel cell, storage battery and motor. Under NEDC working condition, the two-speed gearboxmodeled, modeled and the optimal combination of transmission ration and main reduction ratio is found by genetic algorithm to improve fuel economy.

In order to accurately assess the lost production of each wind turbine, a fusion method, which is the combination of Gaussian Distribution and bins of method (GD-BIN), was established. Firstly, Complete the first cleaning of operation data according to the operation characteristics and status logs of the unit. Further, GD-BIN algorithm is employed to establish the Wind Power Curve Model (WPCM), which can reflect the real performance of each unit. Thus, the operation data of the unit is delivered to WPCM to calculate theoretical power and lost power at each statistical moment. To verify the accuracy, it is applied in a wind farm in Northeast China and is compared to the other three current methods in the industry. Result shows that the proposed algorithm could effectively estimate lost production with a high accuracy.

Based on the theory of rigid flexible coupling, a cam manipulator used in an automatic production line is simulated. By comparing the differences between rigid body system and rigid flexible coupling system in various dynamic performance indexes, it can be seen that the positioning accuracy and stability of manipulator movement are affected by the elasticity of components. In the case of high accuracy requirements, the elasticity of members cannot be ignored. The simulation analysis provides the basis for the following optimization design and machining.

With the increase of load types and power consumption, the scale of power grids continues to expand. Traditional AC distribution networks face a series of problems, such as distributed power grid-connected power generation, low power quality, and DC power distribution technology. There are clear advantages in these areas. This paper first analyzes the advantages of analyzing flexible DC distribution networks, and clearly analyzes several common topologies of flexible DC distribution networks. Secondly, the distributed photovoltaic model is established, and its way of accessing the AC grid and DC grid is analyzed. It is learned that the DC distribution network has great economic significance. Finally, the four-terminal toroidal DC distribution network in different scenarios is simulated. The simulation results show that the DC distribution network can effectively adjust the voltage through the energy regulation of the energy storage module and the photovoltaic module, and quickly realize the power balance of the DC distribution network.

This paper mainly focuses on the problem of time-consuming and labor-intensive configuration of existing inspection objects. A rapid configuration method for inspection objects of substation inspection robots based on hierarchical maps is proposed. The method hierarchically characterizes the operating environment of the substation inspection robot, and establishes a hierarchical map including the distribution of obstacles in the substation, the distribution of the passable roads, and the semantics of the primary equipment, so as to realize the setting of the inspection points of the inspection objects on the map. The proposed method has been put into application.

Using AVL-BOOST to simulate the thermal fault of diesel engine, the principal component analysis method is used to analyze the thermal fault of diesel engine, and the three principal components are selected that can reflect the original variable 99.589% information as the input of BP neural network. The failure mode of the diesel engine is used as an output to construct a three-layer neural network prediction model. The results show that the PCA-BP neural network model can well diagnose the failure mode of diesel engines.

This paper proposes an approach to optimize the effect of air-cooling heat dissipation structure for electric vehicle lithium-ion battery pack through CFD simulation and Genetic Algorithm. A 3D model of air-cooling heat system of battery pack is calculated and built through CFD, and the proxy model of battery pack structure parameters and function is established by BP neural network. Then based on this proxy model and genetic algorithm, the three structural parameters with lower maximum temperatures and temperature differences are calculated.

This paper introduces the design and fabrication process of 1200V SiC trench Junction Barrier Schottky (JBS) diode, and tests the performance of the diode. The cell and terminal of diode structure both adopt trench structure, by simulation and early stage, the structural parameters of device cell and terminal structure are determined, and the fabrication of 1200V SiC trench JBS diode is completed through an intact technological process. The test results show that the leak current of the device under 1200V at room temperature is about 0.36uA, when the current is 20A, the forward voltage drop is 1.53V.

In order to improve the performance of permanent magnet synchronous motor (PMSM) servo system and suppress the influence of torque ripple on the control system, a robust current injection control method combining active disturbance rejection controller (ADRC) and current injection is proposed. We designed a current injection module to suppress the torque ripple of the motor and proposed an ADRC to improve the anti-disturbance performance of the system, which ensures the strong robustness and rapidity of the system. The simulation results show that compared with the conventional PI control and PI superimposed current injection, the proposed method can more effectively suppress the torque ripple, while improving the robustness and dynamic response of the system.

To predict the health status of lithium-ion batteries, long and short-term memory (LSTM) recurrent neural networks are used to build two types of battery SOH evaluation models. The discharge capacity is as input to a single feature model. While, the charge capacity, the charge time, the average charge temperature, the charge average voltage, the discharge temperature, and the discharge average voltage are as input to a multiple feature input model. The data samples are separated to training and test dataset. The test results show that the maximum absolute error of the LSTM-based model is less than 2%, which satisfies the industry standard (less than 5%). Meanwhile, this study transfers the NASA-based lithium-ion battery model to the University of Maryland lithium-ion battery data, which can reduce model training iterations and get good performance as well. The experimental results validate the effectiveness of transfer learning in the field of lithium-ion battery SOH prediction and provide a reference for future work in this field.

In order to solve the problem that the accuracy is not high when the traditional fuzzy morphological associative memories network (FMAM) is used to classify samples, based on quantum superposition and collapse, the quantum fuzzy morphological associative memories network (QFMAM) is proposed. In QFMAM, the structural element is constructed by the qubit system and the qubit probability represents corresponding membership degree to obtain adaptive structural elements. The samples are preprocessed first and then classified to ensure high classification accuracy. QFMAM, FMAM, support vector machine (SVM) and naive Bayes classifier (NBC) are used to classify simulation data and experimental signals of rolling bearings respectively. Comparing the performance of the four classifiers, it is obvious that the training time of QFMAM is far less than SVM and NBC, and the classification accuracy of the test samples by QFMAM is higher than that by the other three classifiers. QFMAM is very suitable for intelligent diagnosis of rolling bearing fault.

In order to achieve the goal of intelligent dispatching, the article analyses the requirements of the smart grid, and then applies the artificial intelligence multi-agent technology to the smart grid dispatching system to study the formal structure and communication cooperation of the multi-agent. Using SQL Server 2000 to build a power grid dispatching data model, set up different Agent function modules, do some research on the living environment and decision behaviour of functional Agents, improve the structure of Agent-based dispatching decision-making systems, and build an Agent-based The intelligent dispatching decision system based on technology provides a feasible solution for the dispatching decision system of the smart grid.

In recent years, with the development and progress of computer technology, its application has become more and more widely. Among them, the application of 3D technology in the the design industry is very common, which effectively improves the design efficiency and design quality. In the process of substation construction design, the application of 3D design technology to the design of substations can effectively improve the scientificity and effectiveness of the design scheme and avoid cross-design. In this paper, the relevant applications of 3D design technology in the design of substations are analyzed in detail in order to promote the further improvement of the design level of substations.

In sensor networks, as the data collection point of nodes in the cluster, cluster head is easy to be the key target of attack. Once the cluster head node is controlled by the attacker, it will have a fatal impact on the security of the network. In order to solve the above problem, this paper proposes a cluster head monitoring nodes secret selection protocol. It uses secret selection algorithm to select the cluster head monitoring node from the neighbor nodes of the cluster head node. Thus, the attacker can not confirm the monitoring node, it results that the cluster head node can not be attacked. The protocol uses the spatio-temporal correlation between cluster head and cluster head monitoring nodes. Once it finds that cluster head node is suspected anomaly, it reports immediately to sink node. The analysis of protocol security shows that the protocol can meet the energy efficiency of sensor network and ensure the security of the system.

A large amount of data will be generated during the daily use of equipment, which is of great value to equipment management, maintenance support and other activities. Firstly, the characteristics and connotation of digital twin were analyzed, and it is concluded that digital twin can create high fidelity virtual model of equipment in digital way during the whole life cycle of equipment. It can more accurately simulate, monitor, diagnose, predict and control the real state and behavior of equipment physical entities in the real environment. Secondly, the simulation process and method of equipment maintenance support based on digital twin technology were proposed. Finally, the application of digital twin technology in equipment life cycle management was analyzed.

The article introduces the working principle of ultrasonic based on the live detection technology of the reactor, and combines the actual application case of the ultrasonic test system to verify the feasibility of ultrasonic application in the field of live detection, and provide a basis for further research.

This paper proposes the off-design mathematical model of generation units for the optimal energy dispatch of the energy hubs (EHs). EHs are usually operated under off-design condition as the widely fluctuating energy demands of residence. In order to investigate the impacts of the off-design characteristics on the operation strategy of EHs, we test the off-design model in the integrated energy system with three EHs. The simulation result shows that ignoring the off-design characteristics of EHs may lead to the inaccurate result of the optimal energy dispatch of EHs.

With the improvement of residents' living standards, the cooling and heating load are increasing rapidly, and the influence of temperature on the unified scheduling electricity consumption is increasing. Based on the temperature gradient change, the monthly unified scheduling electricity consumption prediction research is carried out to quantify the influence of temperature on the unified scheduling electricity consumption, and the unified scheduling electricity consumption prediction is performed for a provincial power grid. The example further proves the feasibility and effectiveness of the prediction method.

In view of the similar structure between the distributed generation (DG) inverter and the active power filter (APF), it is of great significance to develop the DG inverters with harmonic suppression functions. The accurate tracking and rapid response to the phase and amplitude of the harmonic components is the guarantee of the effective use of the DG inverter to suppress the harmonic. In this paper, a harmonic suppression control method of DG inverter based on improved PI and repetitive control is proposed. This method can make the DG inverter output the active power of the fundamental wave by using the PI controller, and output the high precision harmonic current compensation by the repetitive controller. While considering the existence of a periodic delay in the repetitive controller, the feedforward control of harmonic components is added to speed up the dynamic response of harmonic suppression. Finally, the simulation results show that, compared with the traditional harmonic suppression method, the proposed control strategy can ensure the steady-state accuracy and dynamic response speed of the active output and harmonic suppression of the DG inverter while reducing the computational complexity.

In order to improve the feasibility and accuracy of steam cloud explosion overpressure prediction, a combination of factor analysis method and BP neural network method is proposed to propose an improved BP neural network prediction method. According to the original data of the main influencing factors of steam cloud explosion overpressure, the factor analysis method is used to reduce the dimensional data of the five steam cloud explosion overpressure factors, and obtain a common factor; replace the original with a common factor. As an input layer parameter of BP neural network, a steam cloud explosion overpressure prediction model is established by combining the factor analysis method with BP neural network method to predict the overpressure of steam cloud explosion. The improved BP neural network prediction method is verified by the example data. The final verification result is that the relative average error between the predicted and actual values of the 15 training samples is 2.51%, which proves that the improved BP neural network model with training has a good fitting effect. The relative errors of the five predicted samples were 0.77%, 1.34%, 2.07%, 3.96%, and 6.27%, both of which were less than 10%, which proved that the improved BP neural network prediction model has better prediction accuracy.

With the rapid development of all walks of life, heat exchangers have been used all over the various fields. Plate fin heat exchanger was concerned, because of its high heat transfer rate and the small size. Among them, the offset strip fin is called "high efficiency fin" because its special staggered arrangement structure can increase the turbulent flow of fluid and enhance heat transfer. In order to study the influence of flow and heat transfer in dislocation fins, the three-dimensional numerical simulation of LPD dislocation fins is carried out by numerical simulation. Firstly, the accuracy of the standard laminar flow model is verified by using the dislocation fins and experimental data provided by the literature. The average error between the calculated value of the heat transfer factor and the experimental value is 5.95%, and the average error between the calculated value and the experimental value of the friction factor is 2.02%. On this basis, the influence of flow parameters and structural parameters on the internal flow and heat transfer in the dislocation fins is analyzed.The results show that: (1) the smaller the fluid velocity, the lower the height of the dislocation fin; the smaller the tooth angle, the shorter the cut length; the greater the heat transfer factor j, the better the heat transfer performance;(2) The higher the fluid velocity, the higher the height of the dislocation fin, the smaller the tooth angle; the shorter the cut length, the smaller the friction factor f, the better the resistance characteristic;(3) the lower the fin height, the smaller the tooth angle; the shorter the cut length, the greater the temperature difference and the pressure drop with the length of the heat transfer section; the fin height has little effect on the velocity field, The flow distribution of the fluid flow in the flow channel is affected, the shorter the cutting length is, the faster the velocity field changes.

With the rapid development of ranging technology, people's requirements for ranging technology are more and more high. In some special occasions, such as live, high temperature, explosive and other inconvenient scenes, the importance of non-contact ranging technology is highlighted. As a non-contact measurement technology, laser ranging technology has faster measurement speed, higher measurement accuracy, longer measurement distance and stronger anti-interference ability compared with other measurement methods. However, at present, the measurement accuracy of laser ranging technology in China is not very high, and the design is relatively complex. Aiming at the above problems, this paper designs a spatial point target ranging system based on laser beam triangulation. The system uses the combination of laser ranging module and angle sensor module to measure the distance, uses the laser axis angle algorithm based on triangle method to calculate the distance, and uses the pyboard development platform to design the simultaneous acceptance and processing of multiple serial port data, and finally displays the measured data through the display. The experimental results show that the system can measure the distance between two points in space with relative error less than 1%, and it has the functions of circle and quadrilateral perimeter measurement, continuous measurement distance and data storage, which can meet the ranging requirements in various fields of engineering.

To diagnose fault of nonlinear components in analog circuits, a method of fault diagnosis based on Volterra series theory, high-order spectrum and kernel assurance criterion (KAC) analysis was proposed. Firstly, input and output signals of the analog circuit were used to identify a Volterrra model. Secondly, the kernel function was solved by the improved multi-pulse excitation method. The high-order spectrum and its slice method were used to qualitatively characterize the kernel function. Finally, the concept of KAC indicator was introduced to quantitatively compare the characteristics of soft faults in the analog circuit, and the fault degree information was extracted and diagnosed. Taking the Sallen-key filter circuit as the research object, the simulation of the method shows that the high-order spectrum and KAC index can be used to quantify the Volterra system with different fault levels, which can effectively distinguish analog circuit with different soft faults, and have certain practical significance.

Downlink scheduling of LTE packet scheduling has been widely studied, while uplink scheduling is rarely studied. Aiming at the problem that uplink scheduling can not guarantee the transmission of real-time traffic packets within the delay period, a new uplink scheduling algorithm is proposed, we establish the target integer linear programming model by the delay constraints of real-time traffic. And aiming at the problem that the bandwidth of low priority traffic is insufficient and the overall QoS is affected when multi-class traffic is processed, a new scheduling algorithm for multi-class traffic is proposed, which includes multi-class traffic scheduling algorithm for eNodeB and priority adjustment algorithm for UE. Through our algorithm, emergency UE is always scheduled first, the algorithm can also maximize MAC throughput and satisfy the minimum QoS constraints. It can also improve the problem that the bandwidth of low priority traffic is insufficient. The experimental results show that the proposed algorithm can significantly improve LTE uplink performance.

As one of the most important facilities in the power system, power transformers undertake the crucial tasks of voltage transformation, power distribution and transmission. In the process of operation, the power transformers may have discharges, overheating, insulation degradation, winding and core loosing, solid pollution of insulation oil and some other faults. In order to address the aforementioned issues, a novel fault diagnosis system for power transformer is proposed. Through using the acoustic characteristics of the power transformer and establishing the model of the neural network, the proposed system is demonstrated with high accuracy in the experiment.

Human activity recognition (HAR) has attracted considerable research attentions from all walks of life due to its application in human-computer interaction, smart medical treatment and smart home health care. There are various ways of using different motion capture methods for HAR. Among which, HAR based on inertial sensor signals has recently emerged as a challenging and hot research topic. In this paper, we presented a inertial sensor-based method for HAR. Firstly, the variance, mean, information entropy, peak, etc. were extracted from the inertial data as robust features of the actions. Then, the robust features were further processed by Principal Component Analysis (PCA) to reduce the dimensions of features. Finally, the features with different actions label were used to train the Extreme Learning Machine (ELM) and Support Vector Machine (SVM) model. The result can achieve an mean accuracy of 83.49%, it also verified the effectiveness of our method.

Traditional image stitching method based on SIFT algorithm that the stitching time is long and the efficiency is low, and the stitching effect is not ideal. In this paper, the location of the parasitic nest in the cuckoo algorithm is mutated, and an evolutionary reduction function is proposed for the step factor and the probability of discovery. On the basis of this, an improved cuckoo algorithm is proposed, and the edge points of the image are solved by this algorithm, and the comparison between the edge points of the image and the feature points in the SIFT algorithm is made. The results show that the time of image stitching is obviously reduced and the speed and quality of the splicing are improved obviously, and it can fully meet the needs of practical application.

This paper designs and implements a student-centered teaching evaluation system based on face recognition and pose estimation technology. Our work firstly combines classroom attendance and behavior analysis in an evaluation system. For checking attendance, we select student faces as the identification object, employing a multi-task cascaded convolutional networks (MTCNN) as a face detector and a deep learning network FaceNet to extract face features. Then the head pose information is analyzed using Ensemble of Regression Trees (ERT) algorithm, which is able to detect 68 key feature points of faces. At last, we design and implement the whole system, including designs of functional modules, service software, database and telecommunication of various parts. This system can check attendance and collect student behavior information automatically, enhancing the intelligent level of the learning and teaching system.

It is very difficult to obtain information in non-cooperative areas. Error correction coding analysis is one of the key and difficult links. The general analysis is highly demanding and highly dependent on the level of personal analysis. For this reason, we mainly study various deep learning methods, which are applied to scrambling codes, short constraint length convolutional codes, and short constraint length block codes and we also I want to rely on the development of deep learning now, to study and realize intelligence and automated analysis.

In recent years, the share of electric vehicles in the domestic automobile market is also increasing. When users meet the basic electricity consumption, they can use the electricity price given by the power company to reasonably allocate household energy and choose a more appropriate household energy layout, which can reduce energy consumption and save daily expenses. The purpose of this paper is to optimize the scheduling of household energy users, so as to improve the quality of power, save electricity expenditure, and obtain economic benefits. Based on the intelligent power optimization strategy and the environmental protection and energy conservation of electric vehicles, this paper analyzes the influence of V2G technology on the optimization strategy of household electricity consumption, and establishes the household electricity load model and electric vehicle model. For the whole system model, the scheduling time, power constraints and users' habit of using electric vehicles as constraints, the final scheduling scheme is obtained through dynamic planning algorithm. Finally, the simulation results verify the correctness of the model coordination control optimization method established in this paper.

While developing distributed generation, a large number of distributed power sources will affect the voltage stability of the grid. Firstly, the combination of theoretical analysis and formula deduction is adopted to study the factors affecting the voltage of the distributed power supply grid. Then the IEEE 33-node system is used as the simulation model to optimize the reactive power distribution of the distributed power distribution network. Search for the optimal reactive power output of the single power supply and achieve the minimum system voltage fluctuation.

The use of the re-entry energy meter is related to the on-site operating environment. First, the five-level quantitative standard is determined. Establish the correlation between the fault phenomenon and the operating environment, and use the software SPSS to analyze the correlation between the fault phenomenon and the operating environment. According to the size of the correlation, analyze the cause of the correlation. It provides a theoretical basis for the subsequent strengthening of quality supervision, management and production of electric energy meters.

To ensure the power quality and satisfaction of power users, at the same time to reduce power outage losses for power supply companies, live operations are increasingly used in operation and maintenance of the distribution networks, which poses a great challenge to operator safety. This paper systematically studies the safety management and control of live working in the distribution network, analyzes the safety risks in the live working process, proposes corresponding safety protection measures, and calculates the rank member of the index based on the subjective and objective weights, introduces the D-S evidence theory fusion principle to fuse the hierarchical membership of the indicators, and gets security risk assessment results based on the principle of maximum membership.

Designed and developed a set of intelligent storage system for the change back meter. The storage system supports various types of inbound and outbound tasks in the MDS system and the four-line one-library interface specification, including the transfer of the inbound and outbound warehouses, the returning of the warehouses, the returning of the warehouses, and the returning of the warehouses. "Device outbound interface". The warehousing system can receive multiple tasks simultaneously by receiving the MDS system, and has a queuing processing mechanism in the case of multi-task concurrency. The system has a complete operation and maintenance log, and automatically records information such as warehouse system inventory maintenance, manual operation, and software upgrade. Fully realize the process of asset management, back office management, and plan management management to achieve comprehensive measurement process management. The realization of process is more conducive to the orderly planning of measurement management, which is conducive to the assessment of work quality, reducing work handover and work errors, and improving work efficiency.

This paper provides an introduction to electricity tariff cross-subsidies and analyses the main forms and problems of the cross-subsidies of electricity tariff in China. According to the essence of power system reform, it provides some suggestions on how to find appropriate solutions to the problems mentioned in this paper in order to promote the electricity market reform.

The development and utilization of biomass energy based on crop biomass fuel power generation is of great significance for promoting China's ecological civilization construction, energy revolution and low-carbon economic development, and responding to the implementation of major national strategies such as global climate change. Based on the analysis of the status and trends of China's biomass energy development, this paper systematically analyzes the challenges China faces in developing biomass energy, and discusses the strategies and key technical directions for the future development of biomass energy. The research results can be Chinese students. The rapid development of material energy technology provides theoretical support.

In the electricity market where the renewable energy quota system is implemented on the electricity selling side, the electricity selling company can complete the quota of consumption and realize profit through electricity trading, consumption trading, green certificate trading and other forms. Based on the all day renewable energy generation curve trading mode, this paper puts forward the corresponding trading rules, which can effectively promote the active consumption of renewable energy in the power selling side. This paper comprehensively compares the trading income of electricity selling companies in electricity market, green certificate market and transferable load market. Aiming at the maximum profit, it studies the operating strategy of electricity selling companies, which can provide reference for multi market trading decision-making of electricity selling companies.

By considering the uncertainty of renewable energy in AC/DC active distribution network with power electronic transformer (PET), this paper proposes a linear robust optimized dispatch method with PET contained. Firstly, a steady-state model that only takes into account the characteristics of PET port is established. Secondly, we linearize the power flow equations of the AC/DC distribution network, and derive the expressions for the voltage safety at network nodes and for the current safety of lines, then establish the robust optimized dispatch model of the AC/DC distribution network containing PET. Finally, the nonlinear constraints such as port capacity in the network are linearized, and the robust optimized dispatch model is transformed into a quadratic programming model without uncertain variables using dual optimization theory, so that the model can be solved in an efficient and fast way. The two modified IEEE33 node systems have constructed the AC/DC active distribution network with PET. The simulation verified the accuracy and effectiveness of the proposed method.

In this paper, a data driven fault detection model of the generator based on improved nonlinear state estimation (INSET) method was established and the residuals of all the relevant parameters were predicted synchronously. According to the residual distribution characteristics the alarm rules were designed. In case studies, the fault was detected timely and exactly for avoiding serious accident. In addition, compared with other conventional algorithms, the results showed higher prediction accuracy and sensitivity and indicated the feasibility of the method.

Based on surveys and tests of railway stations in hot summer and cold winter zone in China, analyzed the status quo of railway station buildings. A performance analysis model of the Zhengzhou East Railway Station waiting hall was established to analyse the effects of scattered skylights and different aspect ratios on the lighting environment and energy consumption characteristics, provided reference for the design of Railway Stations in hot summer and cold winter zone.

Compared with the traditional content delivery network (CDN), content delivery based on cloud storage has the advantage of reducing storage costs while providing superior data security over common storage solutions. There has been a lot of research on content delivery technology, but existing research on content delivery technology is rarely used in cloud storage based CDN. In this paper, we analyze the charging and content delivery mechanism of cloud storage, then propose a reasonable network topology and cost model. Based on the analysis of existing heuristic algorithms such as user greedy and server greedy, an improved content delivery technology based on genetic algorithm in cloud storage is proposed, which effectively reduces the content delivery cost.

Through the analysis of the classic protocol IEEE 802.11a in OFDM system. An OFDM symbol synchronization algorithm based on Long Training Sequence (LTS) is studied. The symbol synchronization estimation using the LTS sequence symbol cross-correlation method can improve the two parameters of the correlation delay D and the synchronization point decision threshold. It reduces the interference of noise on the decision threshold under the Gaussian White Noise (AGWN) channel and the difficulty of FPGA implementation; improves the accuracy of symbol synchronization and the probability of detecting symbol synchronization under a low signal-to-noise ratio. Simulation experiments and FPGA hardware implementation show that compared with conventional algorithms, the system performance is improved.

Low frequency noise has the characteristics of slow attenuation, strong penetration and long propagation distance. Compared with other types of environmental noise with the same equivalent sound level, the subjective annoyance of low frequency noise in substation is higher, which easily leads to the problem of public complaints. In order to quantify people's subjective and objective feelings of noise, researchers established the relationship between subjective perception and objective parameters through human perception test. In this paper, the development history of low frequency noise human perception test method and the present situation are investigated, and a feasible human perception test method suitable for substation low frequency noise is put forward.

At present, the low frequency noise control limit standards are only suitable for indoor structural noise control, and it is urgent to establish standards or specifications for substation low frequency noise evaluation and limit value. According to the theory of spectrum gauge weight, the equivalent continuous A sound level decays the low frequency part of noise greatly, only from the aspect of loudness, it only considers the feeling of human hearing to noise, and fails to fully consider the subjective feeling of people's annoyance to low frequency noise. The method of low frequency noise weight correction needs to be further studied. In this paper, the evaluation method of low frequency noise and the control limit standard are fully investigated, which can be used as a reference for the formulation of low frequency noise limit standard in our country.

Hot water entering students' dormitories is the development direction of school hot water system, and air energy hot water system is one of the main forms of heat supply in colleges and universities hot water system. This paper first describes the composition of air energy hot water system and BOT construction mode, then analyses the main equipment selection and operation mode of the system, and finally shows the construction effect diagram of the system. The results show that it is necessary and feasible to construct the air energy hot water system in colleges and universities students' dormitories, which is worth popularizing.

This paper designs a solar energy automatic tracking system based on STC89C52. The photoelectric sensor collects the sunlight signal. After A/D conversion, the collected signal is sent to STC89C52. After data comparison, MCU sends out control signals. The step motor is used to control the level and pitch angle of the solar panel, so that it always faces the direction of sunlight, and realizes the sunlight tracking. The hardware design includes the minimum system of STC89C52 MCU, sunlight signal acquisition circuit, A/D conversion circuit, key circuit, motor drive circuit, etc. The software is written in C language, including main program, data acquisition subroutine, key reading subroutine, data comparison subroutine, etc. Through physical production and operation debugging, the design function is normal.