bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research and Implementation of SSH Proxy System for Filtering and Auditing
Authors: Wang Zhuxiao , Zhang Jiaming , Wu Kehe , Li Wei , Cui Wenchao 
Published: September 2021

Abstract
With the rapid development of information technology and network, the informatization level of enterprises is constantly improving, and the security of information system is becoming more and more important. Not only the external intrusion needs to be effectively resisted, but the operation and maintenance of internal personnel also needs to be under security control. Starting from the internal security of enterprise information system, this paper designs and implements the SSH protocol operation and maintenance audit system based on libssh. The forwarding module of the system can meet the requirements of SSH protocol operation and maintenance, and set command parsing, filtering and recording modules, which can correctly identify the commands executed by users and server data, and addition to filter the forbidden commands. In addition, the setting of replay and monitoring module can record the whole process of personnel’s operation, so as to ensure that the auditors can monitor in real time or replay afterwards. The system ensures the security of the enterprise internal information system.
Research on remote data access and visualization for long pulse experiment based on web technologies
Authors: Fan Mengqi , Gu Yu , Cao Tengfei , Zhang Xiaodan 
Published: September 2021

Abstract
Model Drive System plus (MDSplus) is an efficient and convenient data storage and management software designed by MIT specifically for pulse experiments. After years of development, MDSplus has become a data management system that integrates data acquisition, storage and processing of complex scientific data. However, with the development of the experimental pulse, long pulse experiment has now become the main mode of pulse-type experiment. Compared with the short pulse experiment, the data acquisition under the long pulse experiment speed is faster, the experiment time is longer, and the amount of data is larger. In view of the fact that most of the existing visualization systems are based on client/server (C/S) architecture, which can only display local visualization. Therefore, In order to enrich the way for experimenters to obtain data, this paper designs a visual platform based on browser/server (B/S) that using the WAMP (Windows+apache+MySQL+php) architecture to build the server. The bootstrap framework is used to build the front-end. Before the visualization data transmission, the original data is processed by segmentation technology to reduce server load and improve transmission utilization so that experimenters can query and obtain the required data remotely through the platform.
Service Innovation of Industrial Big Data Based on Industry 4.0 Architecture
Authors: Cheng Xiaolei , Zhang Pinghua 
Published: September 2021

Abstract
The service innovation based on the cyber-physical system is a trend in the future manufacturing industry. In this article, the servicing functions involved in the manufacturing industry under the environment of industrial big data were analysed. In addition, the combination of the needs, environment and services of industrial big data were described. Furthermore, the intelligent forecasting tools to manage industrial big data, increase the transparency of industrial production, and improve work efficiency were proposed to adopt for the application of service innovation.
Key frame extraction for Human Action Videos in dynamic spatio-temporal slice clustering
Authors: Sima Mingjun 
Published: September 2021

Abstract
Detecting representative frames in videos based on human actions is quite challenging because of the combined factors of human pose in action and the background. This paper proposed a key frame extraction algorithm based on dynamic spatio-temporal slice clustering. This algorithm firstly uses the dynamic spatio-temporal slice position selection method based on the human mask heat map to calculate the position of slice to realize the dynamic selection of slice positions, then complete the extraction of spatio-temporal slice images. After clustering the spatio-temporal slice images, this method extracts key frames according to the clustering results. The experimental results prove the validity of spatio-temporal slice location selection method, the proposed algorithm can effectively solve the problems of information redundancy and key information missing in existing methods. We conduct experiments on a challenging human action dataset UCF101 and show that our method can detect key frames with high accuracy.
Design and Implementation of Hotel Big Data Analysis Platform Based on Hadoop and Spark
Authors: Chi Dianwei , Tang Chunhua , Yin Chen 
Published: September 2021

Abstract
In order to provide decision support for user travel and hotel management planning, this paper proposes the design and implementation of a hotel big data analysis platform based on Hadoop and Spark technology, using hotel basic data and user comment data as data sources. First, we design a crawler program to collect basic data and user comment data of city hotels in Qingdao, Shandong Province, and upload them to the hadoop storage platform, use Spark for big data preprocessing, use Hive to build a big data platform data warehouse, and finally use echarts to analyze the results and make decisions perform a visual display.
An illumination direction estimation algorithm of face images
Authors: Chen MeiLing 
Published: September 2021

Abstract
Illumination is one of the important factors affecting the accuracy of face recognition. This paper proposes an illumination direction estimation algorithm of face images and it can be applied to the non-frontal face image. In order to eliminate the influence of face’s albedo, the algorithm use SfSNet to get the shading image. The algorithm divides the face region into eight regions by using five landmarks in face and estimate the illumination direction by analyzing the average brightness of each regions. Experiment on YaleB dataset shows that the algorithm can estimate the illumination direction correctly in most cases.
Research on Modulation Recognition of Communication Signal
Authors: Liu Shukun , Gao Yifei , Wang Yuanshi , Ren Yifei , Huang Yixuan 
Published: September 2021

Abstract
Modular identification of radio communication signals is widely used in commercial software’s as well as the country’s defence. With the phenomenal development of modern telecommunications in various countries. The process of converting data into radio waves by adding information to an electronic or optical carrier signal becomes more sophisticated and variable. As a result, there has been a great deal of research and development into signal interpretation, and a number of effective schemes have been devised. For Signal Modulation, recent research has used a variety of techniques such as clustering, neural networks, automatic modulation, and analytical parameters. A quick description and summary of signal recognition approaches is offered in this study. We also reviewed contemporary technological hurdles, as well as the future direction of modulation.
An improved HOG-SVM recognition algorithm of power equipment with infrared images
Authors: Qian Yucheng , Zhen Chao , Wang Mengjun , Han Sheng , Li Jiaxuan , Zhao Changwei , Zhu Taiyun 
Published: September 2021

Abstract
Infrared image recognition of power equipment is a necessary prerequisite and a key step to using the infrared image for equipment defect detection, while one of the difficulties is to solve the problem of low accuracy of equipment recognition, which is caused by the difficulty of extracting equipment feature quantity caused by different rotation Angle and scale of equipment image. In this paper, 12 kinds of common power equipment, such as lightning arresters, current transformers, voltage transformers, circuit breakers, and insulators, are taken as the research objects. An improved HOG-SVM algorithm for infrared image recognition of power equipment with rotation invariance is proposed. Experimental results show that the proposed method can effectively compensate for the shortcoming of the HOG algorithm without rotation invariance, and the average recognition accuracy of 12 types of equipment reaches 92.14%, which significantly improves the recognition accuracy of infrared images of power equipment, and verifies the effectiveness and practicability of the proposed algorithm.
Design and Implementation of an Attention-Based Interpretable Model for Document Classification
Authors: Ye Zuoxian 
Published: September 2021

Abstract
With the rapid development of artificial intelligence and machine learning, deep learning technology has been successfully applied to various tasks in the field of natural language processing. However, the erroneous decisions produced by the deep learning model and the characteristics of its black box cause users to doubt its decisions. We urgently need the model to have the ability to make rationalized explanations of its decisions, that is, interpretability. In this paper, we propose an attention-based interpretable model for document classification, which aims to have a better model performance and at the same time have better interpretability. Experiments show that our classification model is close to the performance of other mainstream models. Also, the attribution score explanation provided by the interpretation method are effective.
Relation Aware Attention for Penson Re-identification
Authors: Song Junyu , Li Kaifang , Hui Guancheng , Zhang Miaohui 
Published: September 2021

Abstract
Person re-identification (Re-ID) means matching people across different camera views based on different locations. It is challengeing for person images where there are background clutter, pose variations, illumination changes, etc. Attention mechanisms have become attractive for person re-identification algorithms as they aim at strengthening discriminative features, which accord with the purpose of person re-id, i.e., learning discriminative features for diffirent pedestrains. Previous approches mostly learn attention by useing local convolutions which have limited receptive filds. In this paper, an Relation Aware Attention(RAA) module is proposed to address this issue. RAA infers attention maps along two dimensions, channel and spatial, then them are mutiplied to the feature as the output map. For each feature position, RAA harvests the pairwise relationship with others as its response. Furthermore, in order to grasp the structure information of global scope and the local apperance information, we stack the relations and the feature to lean the final attention with a convlutional model. We designe the experiment and compare it with the existing benchmark. The resulits show that our attention model can increas the abillity of feature representation.
Video Human Action Recognition with Channel Attention on ST-GCN
Authors: Zhang Deyuan , Wang Haoguang , Weng Chao , Shi Xiangbin 
Published: September 2021

Abstract
Action recognition based on human skeleton information is a hot research topic in the field of computer vision, and ST-GCN graph convolutional network is widely used to extract spatial and temporal features of human skeleton to represent the human skeleton structure. However, in the process of extracting features, the weights on each channel of the feature are the same, so it is difficult to effectively discriminate the useful features from the useless ones. In this paper, we propose Channel Attention module, which learns the importance of each feature channel to perform human action recognition more effectively. Experimental results on Kinetics and NTU-RGB+D datasets show that Channel Attention module can achieve better accuracy.
State-of-health prediction of lithium-ion battery based on improved gate recurrent unit
Authors: Lin Jingdong , Wang Chang , Yan Guansong 
Published: September 2021

Abstract
State of health (SOH) prediction of lithium-ion batteries is still a very important issue in evaluating the safety and reliability of battery-powered systems. This paper uses the lithiumion battery data in NASA Ames Research Center for research, analyzes the correlation between the relevant feature data, selects the data the most relevant to the capacity through the threshold, and uses it as the input of the neural network. We combine Particle Swarm Optimization (PSO) algorithm and Gate Recurrent Unit (GRU) to form PSO-GRU, and use the PSO-GRU method to find the time step and the number of neurons for the best prediction effect. The experimental results show that, compared with the LSTM method, the PSO-GRU method has higher prediction accuracy and has fewer weight parameters for the neural network training model.
UAV Inspection Technology Based on Lightweight Edge Computing Framework
Authors: Qiu Zifeng , Wang Yahan , Gao Feng , Liu Sha , Zhang Junkai , Yang Fuchen , Hu Yan 
Published: September 2021

Abstract
At present, unmanned aerial vehicle (UAV) is usually used to capture the ground images for power inspections, then transmit the images to the fixed ground station for analysis. That method is not conducive to the rapid positioning of key parts or timely treatment. At the same time, the automatic sensing of power inspections mainly adopts the targets detection and classification algorithms based on deep learning, which has a large amount of calculation, and the processor installed in the UAV terminal is difficult to achieve the effect of real-time detection. In order to improve the accuracy and real-time of key targets detection and classification of power facilities in the inspection process, a lightweight edge computing framework AirNet is proposed. In AirNet, simple linear iterative clustering (SLIC) algorithm and direct convolution method are used to optimize the UAV input image to simplify multiple granularity feature information and improve the accuracy of the algorithm. Real time intelligent analysis of the algorithm model is carried out in UAV terminal, and the key parts of transmission towers, houses and vehicles are selected for test. The results show that the algorithm can achieve 64 ms detection speed and 85% accuracy on Huawei Atlas 200 chip equipment.
An H
∞
H
- optimization approach to fault detection for UAV
Authors: Hao Liu 
Published: September 2021

Abstract
Unmanned aerial vehicles (UAVs) have good maneuverability and have been widely used in many fields. Therefore, it is of great significance to detect faults in the UAV flight control system. In this paper, a method based on H
∞
H
- optimization is proposed for the constant gain fault detection of UAV flight control system actuators, and the failure of UAV elevator and throttle lever parts are used as the example for simulation verification. The results show that the proposed method can realize the quick fault detection of UAV flight control system.
Research on Application of Edge Computing in Real-time Environmental Monitoring System
Authors: Teng Yunhao , Cui Jiapo , Jiang Wenrong 
Published: September 2021

Abstract
In order to solve the problem of insufficient data processing ability and high delay of cloud computing in environmental monitoring system, combined with edge computing technology, this paper designs an environmental monitoring system which can process data in real time. The system uses the Baetyl edge computing frame to build an edge node on Raspberry Pi, the monitoring node collects environmental data such as temperature and humidity and sends the data to the edge node. The edge node will upload the monitoring data to Baidu Intelligent Cloud Platform after processing. The test results show that edge computing has obvious advantages in the application of environmental monitoring.
A Research on Advanced Technology of Target Detection in Unmanned Driving
Authors: Wang Bosi , Wang Renjie , Tang Bin , Cai Linfeng , Zhong Nianbing , Zhao Mingfu , Long Zourong 
Published: September 2021

Abstract
Unmanned driving leads the development of smart cities and safe transportation. It relies on a large amount of complex data generated during driving. This paper reviews the state-of-the-art research status of data collection and analysis techniques. We divide the data required for unmanned driving into two types: traffic scene data and driving behavior data. Firstly, by fully considering the different needs of enterprises, universities and related institutions, we have fully studied the algorithms proposed by scholars all over the world. Then we introduce mainstream 2D and 3D target detection algorithms based on RGBD and laser point clouds. Finally, the article introduces some of the most widely used urban road data sets.
Design of intelligent sampler for large flow total suspended particulates
Authors: Lin Haiping , Liu Shibo , Fu Zhongjie , Huang Bingqiang , Yao Jiana 
Published: September 2021

Abstract
For the current nuclear radiation problem in society, it is very important to monitor the content of radionuclides in the atmosphere. The large flow total suspended particulate (TSP) sampler is the key equipment to monitor radionuclides. In this paper, a large flow total suspended particulate intelligent sampler is developed, which has high sampling accuracy, stable and reliable operation, and forms a complete set of automatic membrane changing particulates matter analyzer with analyzer and membrane sending mechanism, realizing 24-hour unattended automatic membrane changing sampling and analysis, and is used to collect suspended particulate matter in air in radiation monitoring stations and scientific research institutes at all levels to analyze the radionuclides: total α, β and γ and so on.
Unsupervised defect detection based on the pseudo-defect generation
Authors: Feng Jie-kang 
Published: September 2021

Abstract
With the progress of industrial production, more and more products need to be tested to ensure product quality. Detecting and locating surface defects have become a challenging and practical problem. In previous researches, the supervised training needs a lot of manual annotation data and defective product data. And the unsupervised method based on image inpainting can’t reconstruct complex images with high precision. In this paper, we proposed an unsupervised defect detection method based on the pseudo-defect generation to solve the problem of insufficient defective samples and the detecting accuracy problem. We conducted experiments on the AITEX dataset which get 93.3% DR, 3.2% FAR, and 35.5% MIOU. And it also shows outstanding effects in real industrial scenes.
The Real-Time Elevator Monitoring System Based on Multi-sensor Fusion
Authors: Guo Yanbin , Liu Yingbin , Zhang Xiaoyong , Wang Guoping 
Published: September 2021

Abstract
In recent years, the number of elevators has increased dramatically as the increase of high-rise buildings. The safety monitoring of elevators, a matter of people’s security, becomes more and more important. This paper proposed a real-time elevator monitoring system, which can rapidly and accurately diagnose the faults and abnormal states of the elevator based on the real-time operation data acquired from multi sensors. The proposed multi-sensor fusion algorithm effectively ensured the stability and reliability of the system. The real-time monitoring result was easily accessed using web client on various platforms via the local network, which also can be sent to the cloud platform realizing the remote access. The test result of the system demonstrated its usefulness.
Comprehensive Evaluation Method of Driving Behavior Based on Neural Network
Authors: Li Jiayao , Li Li , Jiao Ge , Zhao Xianjing , He Yuhong 
Published: September 2021

AbstractIn recent years, the number of traffic accidents in the world has increased sharply. Reasonable mining of the OBD data generated in the process of vehicle driving will help to improve traffic safety. However, the existing driving behavior scoring models have the following shortcomings: the indexes are not comprehensive enough, and the selection of weights is not objective. In order to solve these shortcomings, this paper proposes 16 driving behavior indexes and related definitions; the neural network is used to construct the model to distribute the weight reasonably; a new comprehensive evaluation model of driving behavior is constructed. The feasibility and efficiency of the model are verified by experiments on real vehicle OBD data sets.
A Fabric Defect Detection System Based Improved YOLOv5 Detector
Authors: Wang Ying , Hao Zhengyang , Zuo Fang , Pan Shanshan 
Published: September 2021

Abstract
Fabric defect detection is a key part of product quality assessment in the textile industry. It is important to achieve fast, accurate and efficient detection of fabric defects to improve productivity in the textile industry. For the problems of irregular shapes and many small objects, an improved YOLOv5 object detection algorithm for fabric defects is propose. In order to improve the detection accuracy of small objects, the ASFF(Adaptively Spatial Feature Fusion) feature fusion method is adopted to improve the PANet’s bad effect on multi-scale feature fusion. The transformer mechanisms can enhance fused features, allowing the network to focus on useful information. Experimental results show that the mean average precision of the improved YOLOv5 object detection algorithm in fabric defect map detection is 71.70%. The improved algorithm can quickly and accurately improve the accuracy of fabric defect detection and the accuracy of defect localization.
Research on fuzzy PID droop Control method for DC microgrid in island mode
Authors: Wang Qianru , Wang Caixia , Gu Ji-peng 
Published: September 2021

Abstract
In order to solve the problem of difficult power supply in remote mountainous areas and islands, based on the traditional drooping control of DC microgrid, this paper introduces fuzzy PID to realize the self-adjustment of drooping coefficient, which solves the problem of voltage instability of DC bus caused by external environmental conditions and load mutation. MATLAB/SIMULINK simulation results show that the fuzzy PID droop control can effectively reduce the voltage drop, maintain the bus voltage stability, and realize the current sharing of all distributed power sources in the DC microgrid under the load switching condition.