bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on General Data Layer Components Model based on Data-Driven
Authors: Xiong Shiyong , Kong Jingjing , Zhou Yulong 
Published: July 2022

Abstract
With the continuous deepening of Web applications, more and more people in the front-end field have begun to pay attention to the design of the data layer, which puts forward more and more requirements for the versatility and operability of the data layer. Analyzing the data transmission process, it is found that the data layer has problems such as complex data requests, close data dependencies, and data display differences. In response to the above problems, this paper proposes a data-driven general data layer component model and develops this model with Vue components at the same time. Schema fusion implements a common data transfer mechanism. Based on the component model of the data layer, middleware such as adapters, assemblers, collectors, and converters are designed, and the data layer is operable, universal, and Configurable purpose.
Deep Convolutional Neural Networks for Skin Lesions Classification via Ensemble Strategy
Authors: Li Chang , Du Yue , Zhang Wenlong , Huang Daobin 
Published: July 2022

Abstract
The early diagnosis of skin cancer is an urgent problem in public health, which helps to improve the survival rate of patients. In order to improve the model generalization ability, we propose an ensemble model, which first uses multiple different neural network models to train each model on the training set and then integrates the output of different models to form a rich feature representation to classify skin lesions. The experimental results show that the proposed ensemble model is effective, and its performance is better than the single model, with an accuracy of 89.5%.
Combining Domain Knowledge and Deep Learning Methods for Vehicle Trajectory Prediction
Authors: Lu Lihui , Li Qizhong , Liu Jiyuan , Huang Miaohua 
Published: July 2022

Abstract
Predicting the future trajectory of surrounding agents is especially crucial for autonomous vehicles applied in dense traffic streams. Majority of the approaches presently implemented for vehicle trajectory prediction can be generally classified into domain knowledge-driven method and deep learning approach. Although domain priori knowledge such as traffic rules implementing in knowledge-driven method has realistic output, the interactive performance with other traffic agents is constrained. Conversely, data-driven approach can acquire superior interactive performance by training the model with huge amounts of data, but the trajectory prediction cannot completely satisfy kinematic constraints, especially to datasets the model has not been explicitly trained on. After carefully design and verification, we put forward a method that combining domain knowledge and deep learning model to output accurate and realistic trajectory prediction. Lastly, we evaluate the result on a public available dataset.
Research on improving transient voltagestability of energy storage system
Authors: Zhang Lan , Li Peiqiang , Xu Jiaqi 
Published: July 2022

Abstract
The transient overvoltage and voltage sags caused by DC blocking in HVDC transmission systems cause great harm to the stable operation of transmission equipment and power system. This paper analyzes the AC/DC equivalent circuit model of battery energy storage system, and deduces the calculation method of the transient voltage of the converter bus at the rectifier side when the remaining reactive power compensation capacity of the converter station is known. According to the results of regulating the transient voltage of the bus at the dc latched rear end of the energy storage station, the calculation method of reactive power of charge and discharge of the energy storage station is further derived. On this basis, the reactive power control method of regulating the transient voltage of the bus at rectifier side of the energy storage station is given. The power system comprehensive simulation program based on IEEE-39 node system is used to conduct simulation analysis to verify the correctness and effectiveness of the theoretical analysis, and the conclusion is drawn that the energy storage power station has a good regulating effect on the voltage fluctuation caused by DC blocking after being connected to the grid.
Mechanical Movement in Stamping Die Design Based on Computer Analysis
Authors: Ou Jingang 
Published: July 2022

Abstract
With the rapid development of the national economy, the supporting role of molds to the national industry is becoming more and more indispensable. The purpose of this paper is to study the control and application of mechanical motion in stamping die design based on computer analysis. According to the principle of stamping die, the related mechanical motion of the die in the stamping process and its definition method are sorted out, which lays a foundation for the mechanical motion control. The interference detection algorithm based on die stamping motion simulation is established, and the method of automatically acquiring CATIA digital-analog information is studied, which not only standardizes the die design process, but also provides a basis for the realization of simulation motion control. each movement model. The static interference detection method adopted by the system is determined, and the method of distinguishing normal interference and abnormal interference is analyzed. Combining the CATIA interference detection with the motion law and motion curve of the process parts, the analysis of the mechanical motion control system is completed, which is of great significance to improve the production efficiency of the stamping line and speed up the processing speed of the stamping parts.
Capacity Allocation Method of Economic Model Considering Hybrid Energy Storage Battery to Suppress Wind Power Fluctuation
Authors: Fan Jiangtao , Li Peiqiang , Zuo Mingle 
Published: July 2022

Abstract
This paper aims at the problem that the cost of energy storage in the wind power hybrid energy storage system exceeds the economic loss caused by wind curtailment. Firstly, the frequency allocation method using wavelet packet analysis divides the wind power into three parts: expected grid-connected power, high frequency power and low frequency power. The flywheel energy storage is used to smooth the high frequency power; the lithium battery is used to reduce the low frequency power. Secondly, an economic model of annual comprehensive cost is constructed based on initial investment, operation and maintenance, wind curtailment and energy shortage costs. Genetic algorithm is used to find the critical point of energy storage capacity allocation. Finally, through Matlab/Simulink simulation, it is verified that this capacity allocation method is economical and effective.
Motion Characteristic Detection of Operating Mechanism Based on Image Processing
Authors: Wang Wenbo , Jiang Jinghua , Ku Zhaoyu , Xing Xuanyu , Dong Huajun 
Published: July 2022

Abstract
It is important for the study of circuit breaker opening capacity to know how to accurately measure the movement characteristics of the actuator. In order to measure the motion characteristics of the actuator accurately, an image processing method based on LabVIEW platform is proposed in this paper to build a system for detecting the motion speed of the actuator. In this paper, a high-speed camera is used to capture the motion images of the circuit breaker during the opening process, and the VISION module in LabVIEW is used to locate the motion edges with high accuracy, calculate the motion parameters of the moving end during the motion process, and evaluate the opening capacity of the circuit breaker by combining the motion parameters. The experimental results show that the proposed method can achieve accurate detection of the motion parameters of the operating mechanism, and has the advantages of high accuracy and simple operation, which can provide strong technical support for the study of the breaking capacity of the circuit breaker.
UG-based research on unfolding of bent sheet metal parts
Authors: Chen Haiyang 
Published: July 2022

Abstract
In the process of sheet metal bending, there is a large deviation between the value of sheet metal unfolding calculation by UG software and actual processing. In order to solve this problem, the method of unfolding calculation by UG software is analyzed and compared with the traditional calculation by manual compensation. The relationship between the neutral factor value K in unfolding calculation by UG software and the compensation factor value B in calculation by manual compensation is obtained through formula derivation. Thus, the purpose of automatic unfolding calculation by UG software instead of manual compensation calculation is realized.
Research on Lane Detection of Intelligent Vehicle Based on Single Camera
Authors: Liu L , Tian M Z , Cheng Y 
Published: July 2022

Abstract
The detection of lane lines is the basis of lane departure warning system, adaptive cruise control and blind spot monitoring system, and its application research has attracted more and more attention from researchers. Based on computer vision, this paper designs a real-time lane detection and recognition algorithm. Firstly, the original image is pre-processed to remove the interference information and reduce the amount of information calculation. Then, according to the principle of edge detection technology and threshold segmentation technology, the possible lane line areas are segmented. Finally, based on the analysis of the lane structure and direction attributes, the lanes that match the attributes in the possible lane area are obtained. The experimental results show that the method has better accuracy, real-time and robustness than the traditional algorithm, and has certain reference significance for the development of auxiliary driving and unmanned driving.
Lightweight display of railway four-electricity BIM model based on glTF format and WebGL technology
Authors: Ding Y Y , Zheng Y S 
Published: July 2022

Abstract
Aiming at the problems of large volume of railway four-electricity BIM model, high computer performance requirements and low data interaction efficiency, a research method of lightweight display of railway four-electricity model based on glTF format and WebGL technology was proposed. This method realizes the lightweight display of the railway four-electricity BIM model on the browser side through the format conversion of the model, the development of the Three.js rendering engine, and the design of the user interaction function. The research results show that this method reduces the size of the model while completing the model format conversion, and realizes the lightweight display and user interaction functions of the model on the browser side. This is conducive to the further development of the railway four-electricity information visualization platform and promotes the utilization of the railway four-electricity BIM model.
Fault Diagnosis Method of Rolling Bearing Based on bCNN-LSTM Layout guide for Journal of Physics: Conference Series using Microsoft Word
Authors: Xiang Xuan , Cao Shaozhong , Yang Yanhong , Wei Xuhang 
Published: July 2022

Abstract
To address the present methods based on Rectified Linear Unit (ReLU) were prone to mean shift, a fault diagnostic optimization method was proposed that combined the Convolutional Neural Network (CNN) of bReLU activation function and Long Short-Term Memory (LSTM). The CNN layer based on a novel activation function bReLU was utilized to adaptively extract features, and enhanced feature extraction speed by adding a batch standardization layer between the convolution layer and the activation function. In addition, the LSTM layer was employed to learn features to classify rolling bearing faults. The accuracy of the enhanced method was greater than 99% on the single-channel fault dataset of Case Western Reserve University (CWRU), and it also has a simpler model structure and takes less time to train than other CNN-LSTM ways.
Influence of Energy Storage on System Transient Stability Based on SGEAC Analysis
Authors: Ma Ruohan , Deng Huiqiong , Qi Ning 
Published: July 2022

Abstract
In order to prevent the system from being paralyzed in a large area after being disturbed, it is particularly important to compensate the energy of the unstable area after being disturbed. Firstly, the quantitative integral of transient margin considering energy storage system is derived based on SGEAC method. Secondly, according to the approximate sensitivity of line transient margin, the storage position is determined. Finally, PSASP/UD module is used to build the transient energy storage battery model, and CEPRI-36 node system is used for a large number of simulations. The simulation results show that the energy storage system can inhibit the acceleration of power angle, improve the system transient stability margin, and play a positive role in improving the system transient stability.
Four-rail electromagnetic transmitter electromagnetic shielding structure analysis
Authors: Ren Shi-Da , Feng Gang , Liu Shao-Wei 
Published: July 2022

Abstract
Orbital electromagnetic launch is the most widely used type of electromagnetic launch method. The strong electromagnetic radiation and electromagnetic interference in the launch process will not only damage the electronic components inside the smart munition, but also produce magnetic leakage near the munition, which threatens the safety and stability of the weapon system. Therefore, this paper proposes two electromagnetic shielding body structures, and through finite element simulation analysis, comparing the magnetic field distribution before and after adding the shielding device, it can be seen that after setting the shielding body, the magnetic induction intensity inside the launch barrel decreases, and the magnetic induction intensity inside the double shielding layer is almost zero. This indicates that the provision of shielding body can play a role in weakening the magnetic field, which can effectively solve the influence of strong magnetism on the missile during the electromagnetic launch of the rail.
A Multifunctional Video Switching Matrix Design
Authors: Xu Ning , Li Erlei , Yang Xue 
Published: July 2022

Abstract
The use of Video Switching Matrix can greatly improve the utilization rate of internal space such as ships and submarines. This paper designs a multifunctional video switching matrix, which can realize 16-channel HDMI and VGA video signal input and 8-channel HDMI and VGA video signal output function. It can meet the complex scenes of HDMI and VGA integrated video input and output. This design realizes the conversion of input and output video formats through the front-end HDMI to VGA unit and the back-end VGA to HDMI unit. The middle RGB switching matrix is used to realize 16 input and 8 output selection of VGA video signal. It realizes the fusion of multi-channel dual-standard video switching. The module has the prospect of being applied to high reliability requirements in aerospace, navigation, and military industries.
Research on Two-Way Detection of YOLO V5s+Deep Sort Road Vehicles Based on Attention Mechanism
Authors: Gao Bo 
Published: July 2022

Abstract
YOLO v5 was born when Joseph Redmon, the father of YOLO, announced his retirement from the field of computer vision, and its structure was further optimized on the basis of YOLO v4. After the sort target tracking algorithm was proposed, the deep sort algorithm added the optimization of apparent features on the basis of the sort algorithm, which enables the model to be more suitable for practical situations and more mature for vehicle detection on traffic roads. Focusing on the new direction of road traffic detection, a single camera is used to detect and track dual-channel vehicles. In the process of experimental exploration, a “double vertical line” algorithm is proposed by introducing the attention mechanism into the YOLO v5 structure and combining with the deep sort target tracking algorithm, which provides a new thinking angle and flow chart for vehicle repeat counting. Finally, the detection results of vehicle identity and traffic flow are obtained through experiments, enabling vehicles to be detected and tracked in real time even on roads with a certain speed. In addition, the calculation law avoids the miscalculation of roads with forks, obtains excellent results on vehicle types and traffic flow, and uses id identity to give car information, which significantly improves the possibility that the model can be applied to practical problems.
Design of multi-protocol conversion module
Authors: Liu Dong , Cai Huilin 
Published: July 2022

Abstract
In view of the communication difficulties of CNC machine tools with only RS232 and RS485 interfaces in industry, this paper studies DNC technology and multi-protocol conversion technology, proposes to use CAN bus to build DNC system, establish multi-protocol conversion module communication model, and build software and hardware platform based on STM32F103VCT6 MCU. The CAN bus protocol, RS232 bus protocol and RS485 bus protocol CAN be converted to each other. The experimental results verify the accuracy and reliability of the multi-protocol conversion module, realize the two-way transmission of data, and solve the problem of communication difficulties between different protocols.
A Novel Multi-Task Face Super-Resolution Framework Embedding Degraded Augmented GAN Networks
Authors: Zhao Shuai , Sun Jifeng , Ou Hongshi , Lin Yibin 
Published: July 2022

Abstract
The training method that uses predefined degradation methods to provide training data for deep convolutional neural networks suffers from overfitting and image distortion when performing face super-resolution reconstruction. In this paper, we propose a multi-task facial super-resolution reconstruction framework embedded in a degraded augmented GAN network. Compared with the commonly used method of generating low-quality face images through pixel interpolation, this paper inversely embeds a generative adversarial network composed of residual coding blocks in the network. Through the feature learning ability of the network, the feature fitting of the face images collected in the natural scene is performed to generate training data that can more fully represent the real noise in the natural scene, thereby improving the noise reduction and image reconstruction capabilities of the network. In order to take full advantage of the task-specific features, the semantic features of the dataset are extracted through a pre-trained facial organ semantic detection framework and the data is used to add face semantic information in the super-resolution reconstruction network. The experimental results show that the model proposed in this study can achieve super-resolution reconstruction of ultra-low-resolution face images simply and efficiently, and achieve super-resolution face images with lower visual quality than other advanced methods with lower model complexity. It solves the problem of single style and distortion of face reconstruction images caused by the training method of the data set obtained by the usual predefined degradation.
EBP-A: a prediction method of protein backbone torsion angle based on convolutional neural network
Authors: Wen Yunguang 
Published: July 2022

AbstractProtein backbone torsion angles prediction from sequence is an important sub-problem in protein structure prediction. The prediction of protein backbone torsion angle can help to achieve an accurate prediction of protein structure and its function determination. Therefore, there are many methods for the prediction of protein backbone torsion angle. However, most existing methods are profile-based, and most proteins(more than 90%) have none or very few homologous sequences for generating evolutionary information. In addition, with the development of protein sequencing technology, the number of sequences in the protein sequence library continues to grow rapidly. To this end, this paper proposes a new embeddings-based prediction method, which takes the embedding feature of amino acid sequences as input, and designs a suitable network structure, to prediction protein backbone torsion angles. The experimental results on the Test2018 and Test2016 test datasets show that our method has higher prediction accuracy than existing prediction methods.
Visual relation detection based on pyramidal convolution and gated recurrent unit
Authors: Zhong Guanhua , Wei Huang 
Published: July 2022

Abstract
Visual relationship detection, not only needs to identify the targets in the image and their position, but also to identify the interrelationship between the targets, is a comprehensive task including object detection, positioning, image classification. In this paper, pyramid convolutional networks are embedded in the image feature extraction module, and three convolutional kernels of different scales and depths are used to increase the sensing field of the network and ensure multi-scale feature extraction. At the same time, a recurrent neural network is introduced, which combines the background information in the image to improve the accuracy of semantic relationship detection.
Optimal allocation strategy of energy storage to improve the voltage quality of distribution network
Authors: Fan Xiuhua , Li Peiqiang , Zuo Minglei 
Published: July 2022

Abstract
In order to alleviate the security and stability problems of distribution system caused by distributed generation participating in distribution network operation, a distribution network optimal configuration scheme considering operation strategy and investment benefit is proposed. Firstly, the comprehensive node sensitivity index is established to analyze and determine the installation position of the energy storage device; Secondly, the double-layer multi-objective energy storage optimal allocation model is solved by improving particle swarm optimization algorithm. The upper model uses economic indicators to determine the capacity, and the lower model uses technical indicators to determine the dynamic dispatching capacity. Finally, through the simulation and analysis of ieee-33 bus system, the effectiveness and economy of the proposed energy storage optimal allocation scheme are verified.
Trajectory tracking control of wheeled mobile robot based on improved LSTM-DDPG algorithm
Authors: Gou Wenyao , Liu Yan 
Published: July 2022

Abstract
This paper proposed a deep deterministic policy gradient (DDPG) algorithm based on the clipped double Q-learning method and long short-term memory (LSTM) neural network for the trajectory tracking control of wheeled mobile robots (WMR). Firstly, aiming at solving the problem of overestimating state-action value in the DDPG algorithm, a double critic network is introduced to approximate the value function, and the minimum value will be taken to calculate the target value of actor network update, avoiding the algorithm falling into local optimal. Then, to overcome the difficulty caused by partial observability in reinforcement learning control, the historical states information will be used to generate the current action by an actor, which adopts an LSTM neural network processing the temporal relations in those historical states. Finally, the reinforcement learning agent based on the improved LSTM-DDPG algorithm is designed as the kinematic controller of WMR and provides reference values for the dynamic controller (two independent PI controllers). Simulation results verify the effectiveness of this control scheme based on the proposed algorithm for WMR trajectory tracking control, even in the presence of external disturbances and model parameter uncertainties.