Table of contents

It is with great pleasure and honor that we present the proceedings of the 16th International Conference on Computer and Electrical Engineering (ICCEE2023). 2023 the 16th International Conference on Computer and Electrical Engineering was held in Xi'an, China on June 23-25, 2023, which is sponsored by Chang'an University. Chang'an University is a university located in Xi'an, China. It was one of the Double First Class University Plan and former "211 Project" key development universities and is directly under the administration of the Ministry of Education. It is a Chinese state Double First Class University identified by the central government of China.

ICCEE2023 has grown to become a prestigious platform for researchers, scholars, and industry professionals to exchange knowledge and ideas in the field of computer and electrical engineering. It received an overwhelming response, with researchers from around the globe submitting their valuable contributions. The organizing committee meticulously reviewed each submission, ensuring the highest quality and relevance to the conference theme. This year, all presenters have presented and discussed topics in their respective research areas. The conference was held for 3 days in hybrid way. It includes onsite sign-up and online test ZOOM on the first day, and then followed with a wonderful array of 7 guest speeches along with 3 onsite sessions, 1 poster session and 3 online sessions during June 24-25.

After the conference, we have compiled a comprehensive collection of research papers that cover a wide range of topics, including but not limited to image detection algorithm and calculation, signal detection and recognition, system monitoring and functional control in electrical systems.

We would like to express our sincere gratitude to all the authors who submitted their work and to the diligent reviewers who dedicated their time and expertise to ensure the quality of the papers. Their efforts have been instrumental in shaping the content of this proceedings volume.

We would also like to extend our heartfelt appreciation to the conference sponsor-Chang'an University. All their supports and commitment to promoting research and innovation in computer and electrical engineering are truly commendable. And unwavering commitment and hard work coming from the committee are truly commendable and deserve the utmost recognition. Their contributions have been invaluable and have made a significant impact on the overall outcome of the event.

Lastly, we would like to thank all the conference attendees for their active participation and insightful discussions. It is through your contributions that this conference has become a vibrant platform for knowledge exchange and collaboration.

We hope that the proceedings of the 16th International Conference on Computer and Electrical Engineering will serve as a valuable resource for researchers, scholars, and industry professionals, facilitating further advancements in the field.

Best regards,

Conference Program Chair

Yisheng An

Chang'an University, China

list of Committees, Statement Of Peer Review are available in the pdf

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing.

• Type of peer review: Double Anonymous

• Conference submission management system: Morressier

• Number of submissions received: 69

• Number of submissions sent for review: 58

• Number of submissions accepted: 40

• Acceptance Rate (Submissions Accepted / Submissions Received × 100): 58

• Average number of reviews per paper: 1.03

• Total number of reviewers involved: 21

• Contact person for queries:

Name: Letian Huang

Email: huanglt_uestc@outlook.com

Affiliation: ICCEE 2023 Organizing Committee

In response to the challenges posed by the high overhead and low detection efficiency of traditional SDN, a novel approach has been proposed to detect DDoS attacks. This cooperative method leverages information entropy and deep learning techniques to divide the detection task between the data plane and control plane. An advanced CNN-BiLSTM model with batch normalization and attention mechanism is utilized to identify DDoS attack traffic. The results of experiments demonstrate that this method offers superior accuracy, detection rate, and false alarm rate compared to prior approaches. Moreover, the switch-controller collaborative detection method proposed in this research reduces the occupancy rate of CPU, in contrast to the conventional single point detection method.

Detecting dangerous goods in security images is a challenging task. To overcome the challenges of localization difficulty and directional feature loss of contraband in X-ray images, our proposed solution, R3Det, employs the Convolutional Block Attention Module (CBAM). By integrating ResNeSt into the original detector, our detector includes a soft attention mechanism to redistribute weights among feature channels. This enhances the network's ability to extract important features and facilitates extraction of target objects features under complex backgrounds. Subsequently, we introduced the spatial and channel attention mechanism during the connection between the backbone and the Feature Pyramid Network (FPN), enabling the model to focus on significant features while ignoring complex background information, then the following Feature Refinement Module to achieve feature alignment in a pixel-by-pixel manner. Our approach successfully achieved rotating target detection in the background of complex X-ray images. Through end-to-end training, our proposed method achieves a 2.6% improvement over the original detector, with a mean Average Precision (mAP) of 86.7%. Notably, our approach showed remarkable results in detecting sensors, pressure, and firetrackers. Now, we have deployed our proposed method on actual security machines for hazardous material detection tasks.

In order to enhance the storage efficiency of drug traceability code information on the blockchain and improve the extraction capability of drug traceability codes on drug packaging, a detection algorithm based on an enhanced version of YOLOv5 is proposed for the drug production and transportation scenario. The proposed algorithm introduces the SPD-Conv module into the backbone network, thereby enhancing the network's ability to extract detailed feature information. Additionally, the CA attention mechanism is incorporated into the Neck of the network, providing the network with superior feature fusion capabilities. Furthermore, the activation function in the network is replaced with the LeakyReLU activation function, reducing computational requirements during training and inference. This replacement improves the model's test accuracy and detection speed. Experimental evaluation, conducted on a self-built dataset, demonstrates the effectiveness of the improved YOLOv5 model. The results indicate a compression of model parameters from 6.14M to 2.71M, an increase in mAP@.5 from 82.4% to 93.7%, and a boost in detection speed from 32FPS to 51FPS. These findings establish the superior performance of the enhanced model compared to the original version.

Vehicle detection in foggy weather plays an indispensable role in the field of intelligent transportation. This article proposes an improved YOLOv5 vehicle detection model based on the problems of insufficient detection accuracy and high fault tolerance of most algorithms in foggy weather. First, the AOD-Net network is used for defogging preprocessing of the original image. Then, the SE attention mechanism is fused in the C3 module of the Backbone feature extraction backbone network to adaptively allocate weight information, enhance the attention to important features, and reduce the impact of noise and irrelevant information. Finally, BiFPN is used in the Neck feature fusion network to replace the original PANet and enhance the model's feature fusion ability. Experiments are conducted on the Cityscapes and RTTS datasets, and the results show that the improved YOLOv5 algorithm in this article has significant improvements in precision, recall rate, and average precision mean compared to the original model, with increases of 8.4%, 9.5%, and 9.2%, respectively. It can better adapt to vehicle detection tasks in foggy weather.

To develop algorithms capable of automatically detecting and evaluating the authenticity of images and videos, researchers have focused on false image detection algorithms. These algorithms aim to identify the authenticity of images by distinguishing between real images and forged ones generated using false generation algorithms. In this paper, the main focus is on implementing a single-frame authenticated image detector using the concept of migration learning. The detector utilizes Inception ResNet v2, a target classification network pre-trained on a self-built military scene dataset. To enhance the dataset, a series of graphical enhancement algorithms are employed, enabling the classification network to learn the crucial differences between real and forged images. Additionally, Focal Loss is introduced to balance the dataset for various GAN-based image generation algorithms. As a result, the final forged target image detector achieves an impressive classification accuracy of 0.8908 on a large-scale sample test set.

A flame detection algorithm with improved YOLOv7 is proposed to address the problems that small targets in flame detection are easy to be missed and misdetected, and are easily disturbed by bright objects during detection. In the improved algorithm, the SE attention module is introduced to enhance the perception of the model on the channel; ConvNeXt is used to retain the features of small target flames for small targets among flame images; Employing K-means ++ clustering algorithm to gain the anchor points that match the flame size more to enhance the detection accuracy.The results testify that the improved algorithm we proposed in this paper is higher than the YOLOv7 in terms of average accuracy and precision.

Aiming at the problems of insufficient or excessive local brightness enhancement, color distortion, and excessive noise in the existing low-light image enhancement algorithms, a low-light image enhancement method combining attention mechanism and global illumination estimation is proposed. First, the illumination distribution map of low illumination image is obtained through the illumination distribution estimation network coupled with the attention gate mechanism. Then, the weight of the light distribution map is learned in the feature attention module. Finally, the image details are fused by the detail reconstruction module to create improved image. According to the experimental results, this method may effectively improve image brightness, contrast, and color in subjective visual effects while also enhancing objective evaluation indicators like PSNR, SSIM, and MSE when compared to some conventional methods.

Rain will significantly lower image quality, which will undoubtedly have an impact on how well outdoor computer vision systems operate, such as autonomous driving. This paper proposes a two-branch deep neural network consisting of an attention guided U-Net and Vision Transformer, which could capture intra-field details and cross-patch relationships to obtain good global and local deraining effects. In order to ensure both branches pose positive effects on the target derain task, we specifically design a patch boot image module to achieve complemented feature fusion, which adaptively selects informative intra-field regions guided by the patch-wise importance map. Moreover, Wasserstein distance between the prediction and reference image is applied as the objective function to pursue better image quality measurement. In comparison to existing algorithms, the suggested method may more effectively remove rain and restore the background details, according to qualitative and quantitative results on the public datasets Rain200L and Rain200H. On the aforementioned datasets, the peaks of the structural similarity (SSIM) and signal-to-noise ratio (PSNR) were 32.55/0.9476 and 26.12/0.8826, respectively.

With the rapid development of socio-economy, railway safety operation has attracted increasing attention. Currently, the most widely used crack detection method on railways is still ultrasonic testing. However, the traditional ultrasonic testing method has certain blind zones and cannot detect cracks on both sides of the rail foot. This paper uses infrared non-destructive testing equipment to detect the temperature distribution on the surface of the rail foot, obtains the infrared image of the temperature distribution on the surface of the rail foot, and uses iterative threshold segmentation method to segment the infrared image. The skeleton of rail cracks is extracted from the temperature information of the infrared image, and the length of rail cracks is calculated based on the coordinate temperature information at the edge of the rail. This improves the accuracy of calculating the geometric characteristics of rail foot cracks.

Super-resolution reconstruction (SR) of dental computed tomography (Dental CT) images is a innovative and challenging task. To address the limitations of Dental CT in obtaining high-resolution (HR) images due to equipment constraints and noise interference, we propose a Dental CT SR method called C-SwinIR based on SwinIR. Firstly, the self-calibrated convolutions network (SCNet) is introduced to solve the problem of detail loss in shallow feature graphs and improve the ability to recover details. Subsequently, the cross-shaped windows (CSWin) self-attention transformer structure is used to replace the original transformer structure, which improves the ability of the model to obtain context information. Eventually, the integration of efficient channel attention (ECA-Net) module effectively realizes the local cross-channel interaction, accelerates the model convergence and solves the problem of gradient explosion in training. Experimental results show that our proposed method is superior to the original SwinIR network by achieving a higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) by 0.287 and 0.003 respectively. Additionally, it decreases mean squared error (MSE) by 1.108. By using this method, clinicians can obtain clearer details and textures from Dental CT, which effectively assist in diagnosis.

Hyperspectral imaging (HSI) is a competitive remote sensing technique used in various fields such as land cover mapping and environmental monitoring. Each hyperspectral imaging (HSI) scene is comprised of numerous narrow and contiguous spectral bands, rendering the extraction of information from HSI data cubes a challenging and computationally intricate endeavor. Convolutional neural networks (CNNs) have garnered widespread adoption for HSI classification due to their impressive performance. Nevertheless, the substantial number of internal parameters within CNNs engenders high computational and memory requirements, resulting in inefficient floating-point operations per second (FLOPS), particularly when faced with frequent memory access and an abundance of operators. To address this issue, this paper proposes a novel framework named Fast Inference and Channel Attention-based Network (FCA-Net). Specifically, the framework introduces a lightweight convolutional layer and a channel attention mechanism (CAM) to enhance the extraction of spatial and spectral information within the network. The proposed FCA-Net significantly reduces computational costs while maintaining reliable classification results and can perform fast processing on GPU or even CPU, making it a promising option for embedded systems. Furthermore, the optimized global computational cost, including reduced demand for compute power and memory, results in lower energy consumption, which has previously been proven advantageous for improving deep model performance.

This paper replicates the experiment presented in the work of Xu et al. [1], and examines errors in the generated captions. The analysis of the identified errors aims to provide deeper insight into the underlying causes. This study also encompasses subsequent experiments aiming at investigating the feasibility of rectifying these errors via a post-processing stage. Image recognition and object detection models, as well as a language probability computational model were explored. The findings presented in this paper aim to contribute towards the overarching objective of Explainable Artificial Intelligence (XAI), thereby providing potential pathways to improve image captioning.

Face clustering is primarily a method for grouping a large number of face images and has important applications in the fields of label-free face image annotation and image management. Traditional machine learning clustering algorithms do not work well on face image data and are unable to learn effectively on complex face image features. Recent research has turned to the use of graph convolutional neural networks (GCNs) to learn contextual information from neighbourhood features between face images for inference, which can significantly improve performance. Unlike the conventional link prediction of face data points and confidence prediction between vertices by using GCNs, for this paper a similarity graph based face clustering algorithm is proposed. Construction of subgraphs by K-nearest neighbour algorithm, Learn the similarity between the k-nearest neighbor subgraphs of each face instance by constructing subgraphs with the k-nearest neighbor algorithm. Using a bottom-up clustering strategy to merge subgraphs to complete the clustering, this algorithm improves the clustering results on complex face features by 20% and 7% compared to traditional machine learning methods such as K-means and DBSCAN respectively, improving the clustering accuracy and reducing the computational complexity.

In view of the digital image in the process of acquisition, storage, processing and transmission due to the camera equipment, compression degree and transmission bandwidth caused by various distortion problems, this paper summarizes the image quality evaluation methods. Firstly, two methods of image quality evaluation, namely subjective evaluation and objective evaluation, are introduced, and then the relationship between subjective evaluation and objective evaluation is analyzed. Finally, the image quality evaluation method is successfully applied to the image fusion processing test, which provides ideas for the subsequent research on image quality evaluation methods.

To improve the detection level of aggregate shape for automated road use, Per-Optuna-LightGBM model for aggregate shape classification is proposed. Collect aggregate images using industrial camera and extract 48 morphological feature parameters. A feature importance analysis method based on Spearman Correlation and Permutation Importance is proposed to remove redundant factors and select the feature parameters of aggregate morphology. Based on cross-validation, an optimized Optuna-LightGBM model is trained based on the constructed dataset. Compared with GS-XGBoost algorithm, the Optuna-LightGBM model can classify the shape of aggregates more accurately and efficiently. The accuracy value of the proposed model is 82.5%, which increased by 4% compared to before optimization. The proposed model can efficiently classify the shape of aggregates which meet the design requirements, also provide a certain foundation for automated classification of aggregate shapes.

As society moves towards the digital age, data security has become an increasingly important issue in wireless communication. To address this problem, the use of encryption techniques has become crucial. In this we studied the Triple Data Encryption Standard (3DES) from theory to practical application, and proposes a wireless spread spectrum system utilizing Direct Sequence Spread Spectrum (DSSS) technology, Quadrature Phase Shift Keying (QPSK) modulation, and 3DES encryption/decryption for secure and reliable data transmission. The system is able to efficiently recover the received signal, even in the presence of interference and noise, as demonstrated by the significantly lower Bit Error Rate (BER) of the received signal compared to the original transmitted binary sequence. The effectiveness of the system is demonstrated through simulation results, indicating that it offers a promising solution for wireless communication applications where secure and reliable data transmission is essential.

To solve the issue that the characteristic frequencies of the broken rotor bar fault (BRBF) are always submerged by the fundamental component during the stator current spectrum analysis in light loads, this paper proposes an effective approach based on the instantaneous power signal and the local maximum synchrosqueezing transform (LMSST). By multiplying the current and voltage, the instantaneous power signal can provide more fault features and enhance fault component amplitudes. The LMSST method is used to obtain a time-frequency analysis with more concentrated energy, and more fault characteristics are extracted from the reconstructed signal, which helps to accurately identify the motor BRBF.

This paper addresses the source localization for mobile robots under the assumption of spatio-temporal invariance. Two kriging-based source localization methods, namely global traversal and adaptive gradient extremum, are proposed and implemented using limited sampling data. The global traversal method controls the robot to traverse the whole region according to a fixed trajectory, and finally fits the information distribution of the sources in the whole target region, so as to obtain the location and number of sources. The adaptive gradient extremum method initially controls the robot to collect data by traversing the target search area. Simultaneously, it uses the collected data to fit the source distribution across the entire region. Then, it utilizes gradient and extremum principles to determine the next target point iteratively, eventually reaching the positions of the sources. The simulation results show that the global traversal can obtain more field distribution information, and the adaptive gradient extremum method is more efficient.

While online car-hailing provides fast and convenient services, it has the same problems in the process of 'seeking customers' as traditional taxis. With the research goal of the demand for online ride-hailing, an online ride-hailing demand forecast model based on G & LSTM is proposed. Firstly, grey relational degree analysis and other methods are used to analyze which and how the relevant factors affect the demand for online car-hailing. Secondly, the demand prediction model of online car-hailing based on GRU&LSTM is trained an adjusted. Through the adjustment and comparison experiment, the values of each parameter in the model are properly adjusted. Finally, based on the running data of Chengdu, the model proposed is verified and evaluated. The experimental results show that the GRU&LSTM prediction model has good prediction effect. By comparing the prediction effect of weighting data and unweighting data, it can be seen that weighted data has better prediction effect.

Traffic accident case named entity recognition, which helps mine key information in traffic accident texts, plays a vital role in downstream tasks such as the construction of knowledge graphs in road traffic and intelligent policing. In this paper, we construct a named entity recognition model based on the EDE (Entity Data Enhancement)-ERNIE-Bidirectional Gated Recurrent Unit Network (BiGRU)-Conditional Random Field (CRF) to address the current situation of low traffic accident case data and poor recognition of long-text entities. First, the amount of accident case data is enhanced using the entity random substitution method. Next, the text data of traffic accident cases are characterized as a dynamic word vector using the ERNIE pretraining model. Then, the BiGRU network learns the long-distance dependency relationship in the text to enhance the effect of the model on long-text entity recognition. Finally, the result sequence is constrained by the CRF layer to realize the named entity recognition model. The experimental part uses data related to real traffic accident cases in a domestic area. The data enhancement method increases the data volume three times compared to the original data volume. Experimental results show that the EDE-ERNIE-BiGRU-CRF model achieves better F1 values, recall and precision achieved better performance than the entity recognition methods of BERT-BiGRU-CRF, ERNIE-BiGRU-CRF, ERNIE-BiLSTM-CRF, ERNIE-CRF, ERNIE, BiGRU-CRF, ROBERTA-wwm-ext-BiGRU-CRF and verify its effectiveness for entity recognition in traffic accident cases.

Cloud-network integration(CNI) is an inevitable choice to enhance the supply capacity of industrial Internet. In traditional industrial control systems, there are problems such as insufficient depth of perception, insufficient connectivity and insufficient analytical predictability. Firstly, an industrial CNI control architecture is proposed in this paper. Then a software-defined PLC integrated development & runtime environment based on the cloud framework is developed and deployed on a private cloud platform. An efficient 5G private network is created to facilitate wireless omnidirectional connectivity of cloud PLC. The problems of complex industrial field circuits and difficult to unify protocols are solved through 5G intelligent terminal devices. Finally, the cloud control architecture and software platform are successfully applied to the remote centralized control of the tundish preparation system in a steel enterprise's two steelmaking plants. The results shows that the innovative system architecture meet the requirements for high-performance access, transmission, computing and storage in industrial plants, which achieves the cloud control of entire process industrial automation production.

Dynamic adjustment of resource supply according to users' resource load is one of the important technologies to achieve efficient management of cloud computing resources. In order to accurately obtain users' demand for resource load in the future, based on quantum particle swarm optimization (QPSO), a prediction model QVMD_AM_LSTM was proposed to optimize variational mode decomposition (VMD) and to add attention mechanism to AM_LSTM. A comparative experiment was conducted on the open-source dataset cluster-trace-v2018 from Alibaba Cloud. The outcomes show that compared with LSTM, AM-LSTM, GRU-LSTM, Refined-LSTM, Stacked-LSTM and other existing prediction models, the mean square error of the QVMD_AM_LSTM model proposed in this article decreases by 8-14, and the correlation coefficient rises by 6%-11%. QVMD_AM_LSTM model has higher prediction accuracy.

Aiming at the problem that fixed speed limit and static regular scheduling of single-type bus cannot meet the actual passenger flow demand with uneven distribution of time and space. By designing a dynamic speed limit rule of full load rate, based on the integrated scheduling of multiple types of bus models, taking the total passenger cost and bus scheduling cost as the overall optimization objective, and considering various constraints comprehensively, OBLESGA (Opposition-based learning elite strategy genetic algorithm) was designed to solve the problem, and the data of Beijing 563 route downlink was taken as an example for simulation experiment. The results show that OBLESGA can improve the convergence rate and solution accuracy of GA. At the same time, the optimized scheduling can reduce the cost of passenger congestion by 86.61 percent and the cost of passenger waiting times by 40.65 percent compared to traditional scheduling, while only increasing operating costs by 26.57 percent.

Real-time and accurate traffic flow prediction is crucial for improving the safety, stability, and efficiency of intelligent transportation system. Considering that traffic flow prediction methods rarely analyze from the perspective of the road network, in this paper, a spatial-temporal traffic flow prediction model based on the combination of graph attention network (GAT) and bidirectional gated recurrent unit (BiGRU) neural network is proposed. Firstly, GAT is used to analyze the complex topology of the road network, effectively obtaining the spatial features of the road network. Secondly, BiGRU is used to learn the dynamic changes of traffic flow data, effectively obtaining the temporal features. Thirdly, the obtained spatial-temporal features are output by the fully connected layer to complete the prediction of future traffic flow. Finally, the model is validated and evaluated on the California highway dataset. The experimental results show that the accuracy of GAT-BiGRU model is better than other benchmark models in predicting future traffic flows transformation, especially in long-term prediction.

The monitoring of cabin air pressure, temperature, humidity, microgravity and other environmental parameters is very important for the long-term and reliable operation of large manned spacecraft. Based on the characteristics of 1553B bus, a design method of regional integrated environmental measurement for the space station was proposed, which realized the high precision and reliable measurement of environmental parameters such as pressure, temperature, humidity and microgravity of china space station. The measurement data can accurately reflect the environmental changes of astronauts' in-orbit living, working and extravehicular activities, which verifies the effectiveness of the design. It provides the necessary new principle application verification for higher precision pressure measurement in lunar landing missions.

In this paper, an extended Kalman filtering algorithm based on coupled velocity model (CVM) is proposed for extended object tracking under distributed sensor networks. This algorithm tracks an object using measurements collected by multiple sensor nodes, and then obtains a global solution based on weighted Kullback-Leibler divergence. Simulation results display that the improved method can effectively track the extended object.

In this paper, for extended object tracking in the case of mutation of irregular stars convex shape, using Random Hypersurface Model (RHM ) to model the object, then the target shape is expressed as parameter by radial function. Considering that using circle as a prior shape information requires a long filter consumption time, the RHM-CC-IOU-UKF algorithm proposed in this paper uses the center contour method to correct the prior shape information of the object, and then uses the Intersection over Union (IOU) method to improve the object tracking accuracy in the mutation case. The object estimation shape is updated when combined with a simple filtering algorithm. Eventually, the effectiveness of this algorithm is demonstrated by simulation experiments in two scenarios.

The design scheme for a digital twin of the pneumatic classifier is proposed, which is associated with the physical entity. The key technologies such as visualization design and data acquisition are analysed in order to realize the digital management of the pneumatic classifier and operating process and monitor flow field characteristics and particle motion based on data analysis according to the digital twin. It aims to realize real-time monitoring, simulation and prediction of the classification performance of the complex pneumatic classification systems, and provide practical guidance for the optimal design of pneumatic classifier.

In order to solve the problem of tracking single extended targets in a cluttered environment, this thesis proposes a B-spline based probabilistic data association extended target tracking method (ET-BS-PDA). Firstly, the state of the extended target is modelled using B-sample strips, secondly, all events associated with the extended target will be counted based on valid measurements, and the probability of the associated events will be computed using the full probability principle. Finally, we use probabilistic data association algorithms to update state and covariance of extended targets and verify effectiveness of extended target tracking algorithms in cluttered environments through simulations.

Despite the increasing popularity of self-driving cars, researchers in the transportation industry continues to prioritize accident prevention and safety improvement. This study inspects the influence of driving experience and gender on the driver's gaze and pupil behaviors while descending a downhill. The data in this paper was collected by equipping ten subjects Tobii Pro glasses II in a series of experiments designed to track their gaze and pupil behavior in a downhill road section. The statistical and Python-based data analysis shows that novice drivers pay more attention to the instrument panel on the downhill road, and their pupils' diameter is larger at both the top and bottom of the hill. In addition, female drivers pay more attention to the instrument panel than male drivers do during downhill tasks. Finally, more accurate formulas can be obtained by considering the experience and gender of drivers through logistics regression. This study has important theoretical significance for discovering the driver's target search process and improving the driver assistance system.

Factors such as the stochastic nature of loads in energy systems make it difficult to optimize the operation of integrated energy systems. To address these problems, an energy system economy optimization scheme based on the PEC-DDPG is proposed. Firstly, exponential moving average (EMA) is introduced into deep deterministic policy gradient (DDPG) algorithm, and prioritized experience replay (PER) is added into the experience pool to prioritize the experience to improve the learning efficiency of algorithm, and the overestimation existing in a single Critic network is solved by using multi-Critic structure. Next, the energy system optimization model is constructed, and the appropriate observation states, decision actions and reward functions are selected. Finally, simulations using energy system data of a region show that the optimization of PEC-DDPG is better than the operational optimization of DDPG algorithm.

Building a New Power System with new energy as the main body is the main theme of modern energy system construction and energy for a period of time in the future. It is an inevitable choice to promote the clean and low-carbon development of electric power. Through artificial intelligence technology, we can solve the problems of strong volatility and difficult regulation caused by the diversity of new energy sources, and promote the establishment of a green, efficient, flexible, interactive, safe and controllable New Power System. This paper sorts out the challenges and deficiencies faced by New Power System, summarizes the application characteristics of artificial intelligence in various industries, analyzes the development and characteristics of artificial intelligence applications, combines the application hotspots of artificial intelligence New Power System, studies the application scenarios of artificial intelligence in the construction, operation and maintenance of New Power System, and puts forward suggestions on the research direction.

This article proposes a method for determining the scale and location of Distributed Photovoltaic (DPV)under the operational constraints of low-voltage transformer areas. Firstly, a low-voltage transformer areas operation model and a DPV output model were established. Determine the access nodes and rated capacity of the DPV using the indicators of minimizing voltage fluctuations and minimizing line loss rate in the optimized configuration model; Then, the maximum capacity of DPV that can be installed in the transformer area is determined with the goal of maximizing the photovoltaic penetration rate in DPV accessible nodes; Use Marine Predators Algorithm(MPA) to solve nonlinear problems in the model. Finally, simulation analysis was conducted using an actual low-voltage transformer area as an example to demonstrate the effectiveness of the proposed model and method, ensuring the safe and stable operation of the low-voltage transformer area when connected to a high proportion of photovoltaic energy.

Self-consistent energy system (SCES) that integrates volatile renewable energy, challenges power system operation of highway service areas. How to evaluate its resilience and energy efficiency is a key issue for the entire SCES. In this paper, we investigate the assessment approach of SCES. Based on the electricity consumption model consisting of different functional sections of SCES in service areas, by deriving the corresponding three-level indicators from the different functional sections, we propose a new SCES assessment approach from four aspects: reliability, availability, maintainability, and safety (RAMS). Simulation results show that the proposed approach can comprehensively and accurately assess the RAMS of SCES, provides data support for improving the resilience and energy efficiency of SCES, and gives a guide for the development of SCES.

As one of the traditional research subjects of power system, load forecasting has always been a hot research direction of related experts and scholars. This paper uses an extended algorithm combining the advantages of adaptive K-means algorithm and distributed clustering algorithm, improves the traditional K-means algorithm, and uses LSTM algorithm to build a load prediction model. LSTMS can learn the advantages of long distance time series dependence to recognize load patterns from the horizontal (time dimension). The simulation results show that the LSTM algorithm based on Adam optimizer improves the accuracy of load prediction, and the proposed algorithm is verified.

In the PWM rectifier, the rationality of the AC side filter inductance design directly affects the performance of the whole system. It not only limits the power output, but also affects the dynamic and static response of the current loop. It has an important influence on the four quadrant stable operation of the motor, the isolation of the grid electromotive force and the harmonic suppression of the current. According to the waveform characteristics of the sinusoidal current, the maximum change rate of the current occurs at the zero-crossing point. In this paper, the inductor is designed to meet the requirement of the change rate of the current near the zero-crossing point, so as to meet the tracking requirement of the current at any time in the whole cycle. In addition, for the design of the AC side inductance, this paper comprehensively designs from the perspective of meeting the requirements of power index and meeting the requirements of suppressing harmonic current.

Oilfield pumping units are the main production machinery in the oilfield, and the electricity consumed accounts for about 60% of the oilfield's electricity consumption. The requirements of the pumping unit for the electric motor are high starting torque, high efficiency, and a wide economic operating range. If an induction motor is used, in order to meet the requirements of high starting torque of the pumping unit, it is necessary to configure a large capacity induction motor. However, during normal operation, the average load rate is low, mostly under light or no-load operation, the efficiency and power factor are low, resulting in a large amount of waste of electrical energy. In this paper, the permanent magnetization of the induction motor is proposed. The stator remain unchanged, only grooves (straight/inclined) are cut on the outer surface of the rotor to place permanent magnets. The original asynchronous motor is converted to a permanent magnet motor. It can be used in actual oilfield exploitation engineering.

This paper focuses on the study of energy control for LVDC distribution systems based on maintaining a stable DC bus voltage. The system includes photovoltaic (PV) arrays, energy storage, and AC/DC loads. The Boost converter is used in the PV array to switch freely between MPPT and constant voltage modes. The MPPT control is applied to the PV modules to ensure maximum power output, and the energy storage unit adjusts the output to stabilize the DC bus voltage and maintain system power balance. The implementation of the proposed control strategy is in a MATLAB/Simulink simulation model to verify its correctness and effectiveness. The simulation results show that the control strategy can maintain a stable DC bus voltage, and improve the efficiency of LVDC system. The study provides valuable insights into the development of efficient and reliable energy control strategies for LVDC power distribution systems.

This paper proposes an adaptive monitoring method for wideband, multimodal, and time-varying oscillations in power systems with high penetration of renewable energy. The method is based on the sliding windowed interpolation FFT (SWIFFT) technique and adaptive threshold spectral filtering method to accurately identify the amplitude, frequency, phase, and damping coefficients of the oscillation modes in each segment. Additionally, the time-domain relationships of these parameters are established, enabling real-time monitoring of the oscillations. Finally, the proposed method is tested using a 5-mode example signal and a simulation system measurement signal. The results demonstrate that this method can accurately identify the parameters of complex oscillation signals, enabling real-time monitoring of oscillations. It exhibits flexibility in adapting to various oscillation scenarios and meets the signal characteristic requirements in modern power systems.

The threat of harmonic pollution has become increasingly serious with the rapid increase in the scale and complexity of distribution networks. However, the harmonic source variety of distribution networks and the phenomena about harmonic characteristics become vague after harmonic mixture because harmonic governance and responsibility division cause much inconvenience. To address this point, a distribution network mixed-harmonic separation method based on characteristic vector is developed in this study. This method realizes the division of the current working state, harmonic emission levels, and harmonic governance responsibility of each harmonic source by equivalent analysis and reverse thinking. The results are verified through an experiment.