bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on Optimization Allocation of Aircraft Repair Line Resources Based on BSWPA
Authors: Li Shenshen , Chen Jie , Li Na 
Published: December 2022

Abstract
This paper abstracts the aircraft overhaul operation process into a directed network. Based on the analysis of the network structure, the calculation method of the operation time is given. Under the premise that the total resources and types of resources have been determined, an aircraft repair line resource optimization allocation model has been established to achieve the shortest single-machine repair time and find the optimal quantity ratio of various resources. According to the characteristics of the optimal allocation of aircraft repair line resources, the Binary Smart Wolf Pack Algorithm is used to solve the problems. Finally, the model and solution method are verified by a numerical example.
Research on Automatic Replacement Tool of Linear Insulator for Live Working in distribution network
Authors: Feng Yu , Wu Shaolei , Wu Kai , Wang Bin , Gong Fuxing 
Published: December 2022

Abstract
Aiming at the problems of high risk, high labor intensity, and complex operation processes in the existing manual replacement of insulators, an automatic replacement tool for linear insulators was developed. The device can be used for the quick replacement of linear insulators under live working conditions. Firstly, a mechanical structure with high positioning accuracy and strong stability was designed according to the shape of the linear insulator and the simulation of the artificial live working process. Secondly, the static simulation of the clamping mechanism of the device was carried out. The results show that the strength of the clamping mechanism is high, and the maximum elastic deformation is 4.952 mm, which meets the requirement of the structure design. Finally, the relevant simulation test of the automatic replacement tool was carried out at the operation site. The experimental results show that the designed mechanical structure is light and convenient, which can quickly complete the automatic disassembly, installation, and fastening of linear insulators. The designed mechanical structure also effectively improves the efficiency of maintenance operations and has high promotion and application value.
Traffic analysis of two-layer coupled networks
Authors: Zhang Min 
Published: December 2022

Abstract
Since many systems in the real world are structured and characterized by multilayer networks, more and more scholars have started to investigate the coupling mechanism in multilayer network. In a network, a node’s betweenness centrality reflects the load better than the node degree metric. This paper suggest a new coupling method based on the betweenness centrality of nodes in the network. Connections are made from the middle position according to the order of node betweenness. The results demonstrate that Middle-betweenness coupling makes more even distribution of traffic in the network and an increase in network capacity, as well as an increase in network throughput, compared to Assortative coupling and Disassortative coupling.
Enabling Li3.75Si alloy coupled with Argyrodite Electrolyte with Desirable Performance for All-solid-state Batteries
Authors: Jia Linan , Guo Bangjun , Zhang Xi 
Published: December 2022

Abstract
With excellent safety and outstanding energy density, all-solid-state batteries is of a prospect for surpassing liquid Li+ batteries. Li6PS5Cl, exhibits desirable ductility and good Li+ conduction property, which is the most commonly used solid-state electrolyte. Herein, Li3.75Si alloys have been prepared by high energy milling technology as an anode to couple with NCM811 cathode and demonstrated an outstanding capacity. During the charging/discharging process, a microstructural defect surface was obtained, and a large impedance interface was verified by EIS tests.
Silicon-metal and silicon-metal oxides composite anodes material for lithium-ion batteries
Authors: Chen Xuming , Zhang Hanyi , Zhao Tianhao 
Published: December 2022

Abstract
Lithium-ion batteries (LIBs) with higher specific energy as well as longer cycling performance have been rapidly evolving in recent years. Silicon as a kind of anode material in LIBs is arousing more and more attention owned to its ultrahigh specific energy and abundant supply. However, the pulverization during lithiation and de-lithiation of silicon always causes a considerable reduction in capacity. Several investigations have been carried out to tackle this problem by using silicon composites with nanostructural design. This study focuses on the most recent research on Si-metal alloy and Si-metal oxide composites, which show significant promise for improving the cycle life of LIBs. Their nanostructures will also be examined to propose new research avenues for silicon-based anodes.
Cause analysis of corrosion and burst of water wall of utility boiler
Authors: Sun Yunfei , Chen Hao , Yun Feng 
Published: December 2022

Abstract
During the operation of a boiler in a power plant, the water wall tube burst and leaked. In order to find out the reason of tube rupture, a comprehensive experimental study was made on the sample with macro morphology, chemical composition, metallography, SEM and EDS. The results show that due to the high sulfur content in the combustion medium of the boiler, a large amount of sulfate containing sulfur elements will deposit on the outer wall of the water wall tube during the long-term operation. In the high temperature environment of the boiler, the thermal corrosion of sulfate deposit, that is, high temperature sulphur corrosion, is formed on the side of the water wall wall steel tube, which leads to the continuous thinning of the wall thickness. And finally, explosion leakage occurs under the action of the internal high-temperature and high-pressure medium.
A Reliability Improvement Technology of Transformer Internal Inspection Robot and Its Control System
Authors: Pan YueHong , Wang Lei , Wang XiaoDong , Fu Zhao , Hu Tao 
Published: December 2022

Abstract
The oil-immersed transformer internal inspection robot has played a good role in the inspection of large power transformers, effectively reducing the working intensity of staff and improving the automation level of power equipment inspection. However, in practical work, this kind of internal inspection robot often has various quality problems and a high failure rate. Aiming at the various complex environmental stresses faced by the internal detection robot, this paper analyzes the performance failure mechanism and completes the research on the reliability improvement method of the internal inspection robot and its control system, which effectively improves the reliability of the internal inspection manipulator.
A Flicker Source Location Method Based on Wide-Area Information
Authors: Wei Xiaowei , Li Wanxin , Xu Hao , Zheng Xiaoxiang , Chen Fang 
Published: December 2022

Abstract
The voltage fluctuation and flicker level are important indicators for evaluating power quality. Correct identification of flicker sources can effectively help to improve power quality. To solve the problems of insufficient accuracy and lack of propagation path analysis in current flicker source location research, a flicker source location method based on wide-area information is proposed. Based on the frequency-amplitude binary characterization, the main components of the voltage data from each bus node are used to determine the main frequency of the voltage fluctuation. By comprehensively comparing the voltage data of each node at the main frequency, the flicker source can be located. Simulations in PSCAD/EMTDC verify that the flicker source in the regional power grid is accurately detected and the flicker propagation path is identified by the proposed method.
Reactive Power Optimization of Active Distribution Network with Distributed Generation
Authors: Gong Jianfeng , Han Yiming , Zhang Bin , Jin Panlong , Xu Pengfei , Feng Xue 
Published: December 2022

Abstract
Taking an active distribution network with distributed generation as an example, this paper designs a reactive power optimization scheme for the operation of a power grid to reduce the node loss in the operation of an active distribution network. The objective function of voltage optimization is established with the minimum active power loss as the optimization objective. According to the stable operation requirements of an active distribution network, the constraint conditions for the objective function are designed. The improved particle swarm optimization algorithm is introduced to search the particles in the effective workspace of the distribution network. Optimal position of the particles is located in this way. According to the solution set, a reactive power convergence model is constructed. According to the operation state of the active distribution network, the switching times of power equipment are set. The output value of the distributed generation in the power grid is taken as the reactive power optimization target to realize the distribution network’s dynamic reactive power optimization. Taking a large power generation enterprise in the pilot area as the research object, the application experiment is designed. The practice proves that this approach can act in reducing the loss of the distribution network.
Application of Support Vector Machines for Breast Calcification Cluster Detection and Mass Classification
Authors: Huang Yucheng , Chen Guangshun , Chen Jiyun , Li Dalin , Liang Yanchun , Du Wei 
Published: December 2022

Abstract
Breast cancer has become “the most common cancer worldwide”, and it is an urgent problem in the medical field to improve the detection rate of breast cancer through early diagnosis and treatment. Based on the current research on medical image processing, our work selects breast images from the MIAS database for pre-processing, segmentation, texture feature extraction and classification, and conducts an in-depth investigation and experimental validation on the theoretical basis of support vector machine algorithm, kernel function selection, and key parameter optimization. The results show the effectiveness of support vector machines in detecting abnormal areas and classifying masses in breast images and provide research ideas for breast cancer diagnosis.
Epilepsy Detection Method Based on the Time-gated Feature Network
Authors: Wang Xiaoli , Jin Yuanshang , Han Qiuyue , Cui Jie , Lin Zechuan 
Published: December 2022

Abstract
Epilepsy is a nervous system disease, which is caused by abnormal discharge of brain neurons. The clinical manifestations are generalized seizures, clonus, loss of consciousness, and shock. An electroencephalogram (EEG) can accurately capture the changes in EEG activities. Therefore, EEG signals are used to detect seizures. In this paper, an epilepsy detection model based on a time-gated feature network (TFGN) is proposed. Firstly, the original EEG signal is preprocessed, and the preprocessed signal is sent into the TFGN detection model which integrates feature extraction, feature selection, and classification to obtain the detection results of epilepsy. Through the verification of data from different ages and channels, the detection accuracy of the TFGN detection model is higher than that of the traditional detection model, and the validity and comprehensiveness of the TFGN detection model are verified.
A Fourier Ptychographic Microscopy Reconstruction Method Based on SwinIR Physical Model
Authors: Wang Xiaoli , Piao Yan , Jin Yuanshang , Li Jie , Han Qiuyue , Yu Jinyang 
Published: December 2022

Abstract
Fourier ptychographic microscopy (FPM) computational imaging is a newly developed microscopic computational imaging technology in recent years, which can realize large field-of-view (FOV) and high-resolution microscopic imaging. In this paper, the SwinIR physical model is introduced to solve the problems of a slightly long reconstruction time and poor reconstruction effect of the FPM reconstruction algorithm based on physical learning. Different from the traditional CNN network structure, this physical model completes the reconstruction of images by introducing modular design and feature fusion. Through a series of simulation experiments on ideal images and real images, it is proved that the reference physical model has better reconstruction quality than the comparison algorithm.
Lightweight Task Coordination of LEO Satellite Cluster based on Distributed Reinforcement Learning
Authors: Qin Junxiang , Li Xuan , Ma Xiaotian , Yang Jun 
Published: December 2022

Abstract
Low earth orbit (LEO) mega-constellation is in the stage of rapid construction, which is expected to serve more regions and apply to more scenarios, such as regional navigation enhancement, emergency communication, collaborative detection, etc. For such typical regional tasks, the construction of temporary LEO satellite clusters is an effective way to improve the quality of temporary services. However, LEO satellites move too fast, and relying on ground-based manual control can no longer meet the mission requirements. The cluster task coordination based on reinforcement learning can effectively improve the autonomous performance of the cluster, but it is difficult to apply to satellites, especially LEO satellites with extremely limited resources. Based on this, this paper proposes a lightweight collaboration method for LEO satellite constellation based on distributed reinforcement learning, which optimizes resources on the premise of ensuring the independent realization of task requirements. In this paper, the Q-learning reinforcement learning algorithm is used to compare the performance of the centralized Q-learning algorithm and distributed AC algorithm. The experiment of the star cluster cooperative detection task shows that the convergence time of the proposed algorithm is 81.5% less than that of the centralized algorithm, and it can run on embedded raspberry pi. It shows that the algorithm is expected to be applied to cluster task coordination of LEO satellites.
Accuracy Analysis of Oblique Photogrammetry Measurement in 3D Modeling of Power Line Selection Design
Authors: Liang LiHeng , Yang ZhenGuo , Wang YaGuang , Jia GaoYi , Sun BaoZhong 
Published: December 2022

Abstract
The measurement of power line selection in the field is one of the key works of route design. The traditional manual measurement is inefficient, and ground objects are prone to losses and leakage, often resulting in frequent line changes. With the promotion of 3D real scene work, power line design has also entered the 3D era. In this study, taking the line design near Dengtai Village in Dalian as an example, the UAV is used to carry a multi-viewer camera to obtain three-dimensional information on the ground surface. The power supply line is generally selected in the field far away from residential areas. The characteristics of weak texture bring certain difficulties to the Oblique photogrammetry method. A few control points and checkpoints are arranged to control the accuracy of the ground model, and the accuracy analysis and evaluation are carried out. The results show that the UAV Oblique photography technology has obvious advantages in the process of three-dimensional model construction. The maximum errors of the model result in X, Y, and Z directions are -0.1820 m, -0.1200 m, and -0.3860 m, respectively, the mean square error in the plane is ± 0.0595 m, and the mean square error in elevation is ± 0.2198 m. The 3D model accuracy can meet the actual needs of 1:2000 mapping, thus verifying the feasibility and reliability of the UAV Oblique photography technology to build a high-precision real scene three-dimensional digital model in the application of power line selection measurement.
Refinement Bilateral Segmentation Network for Semantic Segmentation in Traffic Scenes
Authors: Lin Xin , Sun RongChun 
Published: December 2022

Abstract
Semantic segmentation technology in traffic scenes can help vehicles make accurate analyses and positioning of the road ahead. In traffic scenarios, the trade-off between real-time performance and the accuracy of semantic segmentation is particularly important. This paper proposes a lightweight deep convolutional network, which can be applied to traffic scenes to complete accurate semantic segmentation tasks, considering both real-time performance and accuracy. The Cross Channel Attention Fusion Mechanism proposed in this paper can better integrate the context information and improve accuracy. The Depth-wise Separable Pyramid Module proposed based on the feature pyramid idea can improve the segmentation accuracy and effectively trade off the real-time performance.
Fourier Ptychographic Microscopy Reconstruction Method Based on Residual Transfer Networks
Authors: Wang Xiaoli , Piao Yan , Li Jie , Yu Jinyang 
Published: December 2022

Abstract
Fourier ptychographic microscopy reconstruction mostly adopts the traditional alternating iterative phase recovery method and optimization method, which has high computational complexity, high redundancy of image acquisition data, low reconstruction quality and high time consumption. In this paper, the model of residual transfer networks based on Resnet152 is proposed for Fourier ptychographic microscopy reconstruction, the learning process of deep convolution neural network is introduced, and the image reconstruction method based on deep learning realizes the end-to-end reconstruction of low-resolution images to high-resolution images. Through comparative experiments and analysis, the residual network can overcome the gradient explosion, make the feature information more complete and efficient, and the incremental up-sampling reconstruction network has higher image quality, lower computational complexity and shorter running time.
Design and Implementation of an Imaging System Based on the sCMOS Sensor
Authors: Rong ZhiWei , Chen Lei 
Published: December 2022

Abstract
sCMOS sensor has broken through the technical bottleneck of high noises and low quantum efficiencies of traditional CMOS sensors. It has the advantages of high sensitivity, high dynamic ranges, low dark current, low readout noises, low power consumption, and high integration, and has gradually become the preferred detector for low-noise imaging systems. Therefore, this paper developed an imaging system with high sensitivity, a high frame rate, low noises, and low power consumption based on a sCMOS sensor. The system adopts a sCMOS image sensor GSENSE400BSI which is very suitable for the requirements of low noises and a high dynamic range in the scientific imaging field as the photosensitive element and XILINX SPARtan-6 FPGA as the control core. Two pieces of high-speed DDR3 are configured to realize ping-pong cache of image data. In the thermal vacuum experimental verification, the total power consumption of the imaging system in the imaging mode is less than 2.2W, and the temperature rise of the sensor is less than 0.1°C. During the in-orbit verification, the imaging system runs stably and the imaging status of the system is good. It has achieved effective monitoring of space targets and high-energy ray bursts and other astronomical phenomena. The image indicators meet the application requirements of commercial space-based telescopes.
Research on the Construction Method of Driving Conditions with Optimal Deviation Based on Fuzzy C-Means Clustering
Authors: Liu Dongmei , Zhang Jianqiang , Li Xuejun , Quan Linfei , Hu Xiaodong , Xing Kefan 
Published: December 2022

Abstract
To construct a driving condition that conforms to the actual situation, this paper firstly removes, interpolates, and smooths the original data, and obtains several kinematic segments according to the division standard of kinematic segments. Then, extract the feature parameters and analyze the principal components of the kinematic segments obtained by division, and retain the principal components that can describe most of the features. Finally, the FCM clustering method is used to analyze the retained principal components, and the congestion, relative congestion, and relative unblocked and unblocked conditions are obtained. Finally, the comprehensive driving conditions are synthesized by the deviation optimization method, and their rationality and scientificity are verified by simulation analysis.
Dual-flow Spatio-temporal Separation Network for Lip Reading
Authors: Huang An , Zhang Xueyi 
Published: December 2022

Abstract
Lip reading is a task of predicting the corresponding language information in a silent video, which has attracted a lot of attention in recent years. Its key is to capture temporal and spatial features from lip motion videos and decode them. In the past, lip reading methods based on deep learning mostly adopt the form of spatio-temporal series connection, which first extracts spatial features, and then carries out global time-domain modeling on this basis. The spatial information extracted by the current approach is insufficient. To get more abundant spatio-temporal video representation and fully integrate the features from different viewpoints, this paper proposes a novel lip motion feature extraction framework, Dual-flow Spatio-temporal Separation Network (DSSN). Specifically, we adopt an end-to-end double tower structure to model the temporal information and spatial information respectively, and carry out feature fusion through collaborative learning. Finally, we evaluate our proposed model on the OuluVS2 lip reading dataset. Experiments show that our method outperforms baseline models.
Study for Identity Losses in Image-to-Image Domain Translation with Cycle-Consistent Generative Adversarial Network
Authors: Liu Sizhe 
Published: December 2022

Abstract
The effect of identity loss in image-to-image domain translation using generative adversarial networks (GAN) remains largely uncertain given its wide application in many successful GAN architectures, e.g., CycleGAN. In this study, negative identity regularization and idempotence regularization are identified from several related studies, and a novel loss, positive identity loss which limits the generative model’s output on the input of positive samples from the source domain, is defined. Results from identity regularized GANs and those without are compared and analyzed. This study concludes that: 1) negative identity loss is helpful in regularizing CycleGAN’s tendency to apply inversible but non-identity transform on non-features such as coloration and background landscapes; 2) positive identity loss significantly increases resolution, details, and affinity with the original image on top of negative identity loss; 3) idempotence loss has positive but limited impacts on output image quality.
Research on Road Condition Sensing Technology based on Vision and Radar Information
Authors: Guo Yishan , Li Mingqiu , Li Mingqiu 
Published: December 2022

Abstract
In the process of intelligent vehicle driving, due to the complexity of the environment, a single sensor or multiple homogeneous sensors cannot completely perceive the traffic environment around the intelligent vehicle. Therefore, it is necessary to study the information fusion scheme of different sensors and make use of the advantages of each sensor to make up for the deficiency of a single sensor, so as to realize the function of cooperation and mutual compensation between multiple sensors. In this paper, millimeter-wave radar and camera are selected as sensors for an intelligent vehicle to sense the environment ahead. Firstly, in the case of time and space synchronization of the two types of signals, fusion based on the cross-ratio of detection frames is used to find the region of common interest of radar and camera for fusion. Then the fusion based on data association is adopted. The experimental results show that the method based on radar and camera data can effectively realize accurate target recognition and location, and can meet the requirements of real-time accuracy in the application of intelligent vehicles.