bluuebunny/crossref_metadata_2025 huggingface dataset:
Ultra-wideband and high-performance microwave vortex beam generator based on single-layer Pancharatnam–Berry metasurface with stacked-arc-shaped meta-atoms
Authors: Dong Hexin , Yang Xiaoqing , Su Piqiang , Gu Dezhen , Long Shanshan , Li Meng , Zhai Xiaoyu
Published: July 2022

Vortex beam carrying orbital angular momentum (OAM) reveals essential application potential in regimes ranging from communication to high-resolution imaging due to the capability to expand channel capacity. However, many current methods for generating vortex beams suffer from narrow bandwidth, low efficiency, and high profile, which undoubtedly will limit their practical application. In this paper, a microwave ultra-wideband single-layer Pancharatnam–Berry metasurface is proposed to convert incident plane waves into vortex waves. The proposed novel stacked-arc-shaped meta-atoms of the metasurface can realize high-performance spin angular momentum-OAM conversion with the efficiency of more than 83% within a considerable bandwidth from 5.85 to 19.56 GHz (fractal bandwidth of 108%), which achieves high-efficiency operation within a wider bandwidth compared to some other wideband metasurface-based vortex beam generator. Three metasurface prototypes for generating vortex beams with the topological charges of l = ±2, −1, −3 respectively are designed. In addition, a metasurface for generating a vortex beam with the topological charge of l = ±2 is fabricated as an example to demonstrate the feasibility and high performance of our vortex beam generator. The generated vortex beams with the mode order of ±2, −1, −3 both in the near-field and the far-field zone are simulated theoretically and the generated vortex beam with the mode order of l = −2 is measured experimentally. The results of the simulation and experiment coincide with each other, which verified the proposed vortex beam generation method successfully. The proposed vortex beam generator is valuable and user-friendly in practical application because of the advantages of low profile, ultra-wide bandwidth, high efficiency, and easy fabrication.
Evaluation of the blackbody radiation shift of an Yb optical lattice clock at KRISS
Authors: Heo Myoung-Sun , Kim Huidong , Yu Dai-Hyuk , Lee Won-Kyu , Park Chang Yong
Published: July 2022

As optical clocks are improved to reach the frequency uncertainty below the 10−17 level, the frequency shift due to the blackbody radiation (BBR) has been one of the major systematic effects hindering further improvement. To evaluate the BBR shift of an Yb optical lattice clock at KRISS, we installed an in-vacuum BBR shield and made radiation thermometry using a black-coated-sphere thermal probe. After we quantitatively measured the conduction loss of the thermal probe and the effects of all the external radiation sources, we determined the temperature at the atom trap site with an uncertainty of 13 mK, which corresponds to an uncertainty of 0.22 mHz in the clock frequency (a fractional frequency of 4.2 × 10−19). The total uncertainty of the BBR shift including the atomic response is 9.5 × 10−19.
New impedance bridge at LNE for the two terminal-pair AC resistor calibration up to 20 kHz
Authors: Ouameur Mohamed , Vasconcellos Renata , Agazar Mohamed , Thévenot Olivier , Patois Emmanuel
Published: July 2022

This paper presents the evolution of LNE’s impedance bridge (Wheatstone bridge) used to calibrate AC resistors defined in the two terminal-pair configuration. A new automated Wheatstone bridge, based on four resistances, was developed to operate in the full complex plane for resistances ranging from 400 Ω to 2 MΩ and for frequencies up to 20 kHz. A commercial dual precision arbitrary waveform generator supplies the bridge and PXI modules achieve the balance. Phase and frequency synchronization solutions between the arbitrary waveform generator source and the PXI modules were used. The choice of these instruments allows the bridge to be easily automated and to benefit from recent progress in the development of digital-analog converters, in particular those with high frequency sampling rates and resolution. The bridge is mainly dedicated to realize the periodic calibration of LNE’s standard resistors, used for routine customer calibrations. The traceability of LNE’s standard resistors is ensured, firstly, by the calibration of a 1 kΩ resistor by comparison to a calculable resistor up to 20 kHz. Then, this resistor is used as a reference element to ensure the traceability of all LNE’s standard resistors using the new bridge. The validation was done by comparing the results obtained, for some resistors with very low drift, with those obtained during calibrations performed in 1983, 1995 and 2009. For resistors of 1 kΩ in a 1:1 ratio, the expanded uncertainty of series resistance variation and argument are, respectively, less than 1.3 µΩ Ω−1 (k = 1) and 1.2 µrad (k = 1), up to 20 kHz.
Flexible coding scheme for robotic arm control driven by motor imagery decoding
Authors: Ai Qingsong , Zhao Mengyuan , Chen Kun , Zhao Xuefei , Ma Li , Liu Quan
Published: July 2022

Objective. Brain computer interface (BCI) technology is an innovative way of information exchange, which can effectively convert physiological signals into control instructions of machines. Due to its spontaneity and device independence, the motor imagery (MI) electroencephalography (EEG) signal is used as a common BCI signal source to achieve direct control of external devices. Several online MI EEG-based systems have shown potential for rehabilitation. However, the generalization ability of the current classification model of MI tasks is still limited and the real-time prototype is far from widespread in practice. Approach. To solve these problems, this paper proposes an optimized neural network architecture based on our previous work. Firstly, the artifact components in the MI-EEG signal are removed by using the threshold and threshold function related to the artifact removal evaluation index, and then the data is augmented by the empirical mode decomposition (EMD) algorithm. Furthermore, the ensemble learning (EL) method and fine-tuning strategy in transfer learning (TL) are used to optimize the classification model. Finally, combined with the flexible binary encoding strategy, the EEG signal recognition results are mapped to the control commands of the robotic arm, which realizes multiple degrees of freedom control of the robotic arm. Main results. The results show that EMD has an obvious data amount enhancement effect on a small dataset, and the EL and TL can improve intra-subject and inter-subject model evaluation performance, respectively. The use of a binary coding method realizes the expansion of control instructions, i.e. four kinds of MI-EEG signals are used to complete the control of 7 degrees of freedom of the robotic arm. Significance. Our work not only improves the classification accuracy of the subject and the generality of the classification model while also extending the BCI control instruction set.
ICoRD: iterative correlation-based ROI detection method for the extraction of neural signals in calcium imaging
Authors: Kang Seongtak , Park Jiho , Kim Kyungsoo , Lim Sung-Ho , Kim Samhwan , Choi Joon Ho , Rah Jong-Cheol , Choi Ji-Woong
Published: July 2022

Objective. In vivo calcium imaging is a standard neuroimaging technique that allows selective observation of target neuronal activities. In calcium imaging, neuron activation signals provide key information for the investigation of neural circuits. For efficient extraction of the calcium signals of neurons, selective detection of the region of interest (ROI) pixels corresponding to the active subcellular region of the target neuron is essential. However, current ROI detection methods for calcium imaging data exhibit a relatively low signal extraction performance from neurons with a low signal-to-noise power ratio (SNR). This is problematic because a low SNR is unavoidable in many biological experiments. Approach. Therefore, we propose an iterative correlation-based ROI detection (ICoRD) method that robustly extracts the calcium signal of the target neuron from a calcium imaging series with severe noise. Main results. ICoRD extracts calcium signals closer to the ground-truth calcium signal than the conventional method from simulated calcium imaging data in all low SNR ranges. Additionally, this study confirmed that ICoRD robustly extracts activation signals against noise, even within in vivo environments. Significance. ICoRD showed reliable detection from neurons with a low SNR and sparse activation, which were not detected by conventional methods. ICoRD will facilitate our understanding of neural circuit activity by providing significantly improved ROI detection in noisy images.
Ultra-high-resolution time-frequency analysis of EEG to characterise brain functional connectivity with the application in Alzheimer’s disease
Authors: Cao Jun , Zhao Yifan , Shan Xiaocai , Blackburn Daniel , Wei Jize , Erkoyuncu John Ahmet , Chen Liangyu , Sarrigiannis Ptolemaios G
Published: July 2022

Objective. This study aims to explore the potential of high-resolution brain functional connectivity based on electroencephalogram, a non-invasive low-cost technique, to be translated into a long-overdue biomarker and a diagnostic method for Alzheimer’s disease (AD). Approach. The paper proposes a novel ultra-high-resolution time-frequency nonlinear cross-spectrum method to construct a promising biomarker of AD pathophysiology. Specifically, using the peak frequency estimated from a revised Hilbert–Huang transformation (RHHT) cross-spectrum as a biomarker, the support vector machine classifier is used to distinguish AD from healthy controls (HCs). Main results. With the combinations of the proposed biomarker and machine learning, we achieved a promising accuracy of 89%. The proposed method performs better than the wavelet cross-spectrum and other functional connectivity measures in the temporal or frequency domain, particularly in the Full, Delta and Alpha bands. Besides, a novel visualisation approach developed from topography is introduced to represent the brain functional connectivity, with which the difference between AD and HCs can be clearly displayed. The interconnections between posterior and other brain regions are obviously affected in AD. Significance. Those findings imply that the proposed RHHT approach could better track dynamic and nonlinear functional connectivity information, paving the way for the development of a novel diagnostic approach.
Resource Operation Intelligent Scheduling Algorithm and System Based on Integrated 5G Public Private Network Communication Network
Authors: Lu Huiying , Yan Xin
Published: July 2022

With the development of the economy, users’ demands for communication systems continue to increase. At the same time, due to the development of technologies such as the Internet of Things, big data, and cloud computing, Internet users’ requirements for communication systems are also increasing. Network application scenarios such as in-vehicle networking, transmission of ultra-high-definition video, and real-time data transmission are emerging one after another. The breakthrough development of 5G communication technology has opened a chapter in the 5G era and has become the key to solving the problem of cellular communication technology. However, in the architecture of advanced networks, it is still a big problem to solve the problem of load balancing and realize the reasonable scheduling of network resources. At the same time, it has also become a hot spot of current research. This paper studies the resource allocation problem in the 5G communication network. Based on the analysis of its communication principle, the communication simulation system in the corresponding scenario is built and improved. At the same time, in order to solve the resource allocation under the new SDN architecture in the core network To solve the problem, a slice resource allocation scheme based on reinforcement learning is designed, and a slice resource allocation algorithm based on DQN (Deep Q-Network) is proposed. By testing the system functions, the superiority of the proposed algorithm in relevant scenarios is verified.
Classification Model of 3D Point Cloud Based on Linked Adaptive Graph Convolution
Authors: Yu Ying , Zhang Jun
Published: July 2022

A three-dimensional point cloud classification model LAGCN based on adaptive graph convolution is proposed to solve the problem that PointNet only extracts feature information from isolated points but not from neighborhood structure. Firstly, the three-dimensional point cloud is transformed into undirected graph structure, which is used to obtain the neighborhood information of point cloud adaptively, and the classification accuracy is improved by incorporating residual structure. In the classification experiment, this paper trains and tests on ModelNet40 dataset, and compares the classification accuracy with VoxNet, PointNet and DGCNN models. The classification accuracy of this model is better than that of the above models.
Research on Maximum Power Tracking of Grey Wolf Optimization Algorithm Introducing Levy Flight
Authors: He Sijiang , Li Hao
Published: July 2022

In the case of partial shadow, the power-voltage characteristic curve of photovoltaic array output will show multi-peak phenomenon, the traditional maximum power point tracking method is easy to fall into the local maximum power point, resulting in the loss of output power. Aiming at the common problems of traditional swarm intelligence optimization algorithms, such as slow convergence speed, large oscillation amplitude, and easy to fall into local optimum, a control method of gray wolf optimization algorithm with Levy flight was proposed. The algorithm uses GWO’s excellent ability of fast convergence speed and high solution accuracy to quickly converge to the vicinity of the maximum power point, and then uses CSA to achieve a stable state near the maximum power point. The power fluctuation is smaller. The simulation statistics show that compared with the traditional The improved gray wolf optimization algorithm has higher solution accuracy, shorter tracking time, and improves the output efficiency of the photovoltaic array.
Research on Dimension Reduction Algorithm of Prediction Model Input Variables Based on Principal Component Analysis
Authors: Shi Lei
Published: July 2022

The selection of input variables of neural network prediction model is very important. If there are too many input variables, the input layer nodes of the network will increase and we need an exponential number of training samples which can easily lead to “disaster of dimension”. According to the characteristics of so many influencing factors of cervical spinal cord injury and intertrochanteric fracture, principal component analysis is used to screen the input variables of neural network prediction model in this paper. And in this method, the principal component factor with the greatest contribution is selected as the input node of the neural network. Then it not only comprehensively considers the various information contained in the input variables, but also compresses the number of input variables to the greatest extent, so as to simplify the topology of the neural network. At the same time, the training time is greatly shortened. Simulation results show that the method is effective and feasible.
Unknown network attack detection method based on reinforcement zero-shot learning
Authors: Wang Hui , Wang Yifeng , Guo Yuanbo
Published: July 2022

With the increasing growth of zero-day attacks, traditional machine learning-based network intrusion detection systems (NIDS) are difficult to cope with a large amount of unknown network attacks without labeled data. To this end, this paper proposes a new unknown network attack detection method, which combines zero-shot learning algorithm with reinforcement learning algorithm. First, the feature vector in traffic data and the semantic vector in threat intelligence are encoded in the hidden space by variational autoencoder, so that the two modalities are matched in the hidden space, and then the hidden features of known class and unknown class are used to input into the training classifier to get the predicted of the labeling results, and then use asynchronous advantage actor-critic based algorithm to learn and correct the attack results that are misclassified by the variational autoencoder to improve the accuracy of detection. Experiments prove that the proposed method in this paper achieves more than 95% accuracy on four unknown network attack classes that are difficult to identify, namely MAILBOMB, U2R, POD, and SNAPATTACK, which illustrates the effectiveness and feasibility of the method.
Optical Remote Sensing Image Object Detection Model Based On Improved YOLOv5s
Authors: Zhou Qikai , Zhang Wei , Li Ruizhi , Wang Jin , Zhen Shuhui , Niu Fu
Published: July 2022

Aiming at the problem of error and omission caused by the complex image background and the high similarity between the remote sensing objectt and the background in remote sensing image detection, an improved model of remote sensing image detection based on YOLOv5s is proposed. The dual attention mechanism of channel and space is added to the convolutional layer of the trunk feature extraction network and the feature fusion network, which enhances the features with high correlation of the objectt to be detected and suppresses the features with low correlation, and improves the ability of the model to extract the features of remote sensing objectts in a complex background, so as to improve the detection accuracy of the algorithm. In this study, a comparative experiment was conducted on the NWPU VHR-10 dataset, and the results showed that the average accuracy of the proposed model was 94% when the dataset was turned over and the ratio was 0.5, which was 2.3% higher than that of the original YOLov5s, which verified the effectiveness of the proposed method.
Bi-LSTM-attention Based on ACNN Model for Disfluency Detection
Authors: Tian Xin , Fang Bei , He Juhou , He Xiuqing
Published: July 2022

Disfluencies are self-corrections in spontaneous speech, including filled pauses, repetitions, repairs and false starts. The task of disfluency detection is to identify these disfluencies phenomena in spoken speech and make them consistent with written text. Recent research has applied machine learning and deep learning approaches to disfluency recognition and classification. In addition, long distance dependence is one of the core issues in disfluency detection. In order to solve this problem, most existing approaches had combined plenty of hand-crafted features and words as input. However, hand-crafted model features need lots of time and energy. Some studies reduced the dependence on hand-crafted features, but they ignored the dependence between long sentences. In this article, we consider disfluency detection as a problem of sequence labeling, then apply Bi-LSTM and attention mechanisms to disfluency detection. In particular, on the basis of obtaining the dependency of rough copy by Auto-correlational neural network (ACNN), we improve the ACNN model to handle the long-term dependency, so that we can better capture dependency between words and words. In other words, our method can not only find the rough copy relationship between sentences without additional hand-crafted features, but also obtain the dependency relationship between long sentences. Experiments on commonly used English Switchboard test sets show that our approach achieves good performance compared to previous models that used texts only without other hand-crafted features.
Detection of road crack defects based on improved YOLOv5s model
Authors: Zhang Pingjun , Li Wenwen , Weng Yue
Published: July 2022

In order to meet the requirements of road crack defect detection accuracy and detection speed, a road crack defect detection algorithm based on the improved YOLOv5s network model is proposed. First, the Backbone area of the YOLOv5s model is improved, and the self-correcting convolution SCConv module is embedded in the Backbone area as a feature extraction network, which can better integrate multi-scale feature information; secondly, the Neck area of the YOLOv5s model is improved. SE-Net attention The module is added to the Neck area to improve the retention of defect feature information and strengthen the learning of feature information; finally, the Prediction area of the YOLOv5s model is improved to delete the 80×80 feature map branch suitable for detecting objects of smaller size, thereby reducing the complexity of the model and improving the Detection speed, the improved YOLOv5s network model is obtained. Using the improved YOLOv5s model to train and test on the road crack defect dataset, the experiments show that the improved YOLOv5s network models precision (P) is increased by 4.91%, the recall rate (R) is increased by 4.79%, and the average precision rate (mAP) is increased by 5.42%, the model size is reduced by 1.04MB, indicating that the improved YOLOv5s model has improved detection accuracy and detection speed.
Research on Rule Matching Model Based on Spark
Authors: Xiong Shiyong , Jiang Kaiwen , Wu Rongsen
Published: July 2022

Rule engines are widely used in engineering and academic fields because they can flexibly separate facts and rules from the rule matching process. Since the rule matching process is very time-consuming, and the traditional rule matching uses single-computer operation when many facts and rules exceed the computer’s memory and computational capacity limit, it will cause the application to crash and paralyze. To solve the above problems, this paper investigates the Spark framework and Rete algorithm to take advantage of Spark’s in-memory computation to alleviate the time-consuming problem of the traditional rule matching process. A high-performance distributed rule matching model is designed by combining the actual rule matching scenario and the development process. In addition, according to the form of rules and facts in the actual scenario, this paper effectively divides the matching process and improves the scalability of the rule matching model.
Discrete particle swarm algorithm with Q-Learning for solving flexible job shop scheduling problem with parallel batch processing machine
Authors: Song Libo , Liu Chang , Shi Haibo
Published: July 2022

For the flexible job shop scheduling problem with parallel batch processing machines (FJSP-PBPM), the mathematical model of the problem is defined, and an improved enhanced discrete particle swarm algorithm is proposed to solve this problem. In terms of global search, an improved discrete particle swarm algorithm is adopted, and effective particle velocity and position update formulas are designed; in the local search, in order to speed up the convergence speed and avoid local convergence, it is integrated into the q-learning method in reinforcement learning. The state space, action space, reward function, and Q-table update methods that meet the scheduling problem are designed, and the proposed hybrid algorithm achieves an effective balance between global search and local search capabilities. At the same time, according to the characteristics of FJSP-PBPM, effective process sequence scheduling rules and batch machine selection rules are designed respectively to improve the search ability of the algorithm. Through the standard FJSP benchmark problem and the production data of a transformer manufacturer that meets the characteristics of the FJSP-PBPM problem, the validity and feasibility of the proposed algorithm are verified.
Research on General Data Layer Components Model based on Data-Driven
Authors: Xiong Shiyong , Kong Jingjing , Zhou Yulong
Published: July 2022

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Protein 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

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

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

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.