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2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)最新文献

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Edge Detection Method Based on Hysteresis Connection and Prediction 基于迟滞连接与预测的边缘检测方法
2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2020-06-01 DOI: 10.1109/ITNEC48623.2020.9085213
Shao-qing Mo, Haiyun Gan, Rui Zhang, Ying Yan, Xiaofeng Liu
{"title":"Edge Detection Method Based on Hysteresis Connection and Prediction","authors":"Shao-qing Mo, Haiyun Gan, Rui Zhang, Ying Yan, Xiaofeng Liu","doi":"10.1109/ITNEC48623.2020.9085213","DOIUrl":"https://doi.org/10.1109/ITNEC48623.2020.9085213","url":null,"abstract":"Edge detection is a critical component of many vision systems, including object detectors and image segmentation algorithms. Based on the analysis of edge gradient changes and the defects of traditional edge detection algorithms, a novel edge detection method is proposed. Firstly, the edge set is initialized by gradient map binaryzation with a given threshold. Then the pixel adjacent to edge pixel will be joined to the edge set, if its gradient is higher than half of threshold or the gradient difference between this pixel and the adjacent strong edge is less than a given value. In order to go over the broken interval, this method predicts the edge extension direction using the local information of edge, and searches for discontinuous weak edges on this direction. The experimental results show that this method not only can extract complete edge segments, but also can detect the valid weak edge segment over the broken interval, and at the same time it can suppress the noise with the same gradient value.","PeriodicalId":235524,"journal":{"name":"2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126388735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
An Improved Multi-modal Data Decision Fusion Method Based on DS Evidence Theory 基于DS证据理论的改进多模态数据决策融合方法
2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2020-06-01 DOI: 10.1109/ITNEC48623.2020.9084828
Shengfu Lu, Peng Li, Mi Li
{"title":"An Improved Multi-modal Data Decision Fusion Method Based on DS Evidence Theory","authors":"Shengfu Lu, Peng Li, Mi Li","doi":"10.1109/ITNEC48623.2020.9084828","DOIUrl":"https://doi.org/10.1109/ITNEC48623.2020.9084828","url":null,"abstract":"The method from DS evidence theory based multi-modal information decision fusion uses the classification structure information which the correct and error classification information provided by the classifiers. These two types of information affect the fusion results of DS evidence theory. This paper proposes a new method(DShW) for correct and error classification information in the balanced classification structure information based on DS evidence theory. That is, a method based on inertia weight normalization is introduced in the confusion matrix. To adjust the specific gravity of correct and error classification in classification structure information by changing the size of the value h, so as to achieve the purpose of balancing correct and error classification information. By comparing with other classifiers, we find that the DShW method effectively improves the accuracy of decision fusion.","PeriodicalId":235524,"journal":{"name":"2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126423786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Bird's Nest Detection Method on Electricity Transmission Line Tower Based on Deeply Convolutional Neural Networks 基于深度卷积神经网络的输电铁塔鸟巢检测方法
2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2020-06-01 DOI: 10.1109/ITNEC48623.2020.9084814
Mengying Chen, Chen Xu
{"title":"Bird's Nest Detection Method on Electricity Transmission Line Tower Based on Deeply Convolutional Neural Networks","authors":"Mengying Chen, Chen Xu","doi":"10.1109/ITNEC48623.2020.9084814","DOIUrl":"https://doi.org/10.1109/ITNEC48623.2020.9084814","url":null,"abstract":"Birds nesting on electricity transmission line towers have potential hazards to the whole electricity transmission system. At present, it mainly relies on manual inspection to determine whether bird's nests exist, which not only has heavy workload, high missed detection rate, but also low efficiency. Therefore, in order to ensure the safe and reliable operation of the power grid system, eliminate hidden dangers in time, and reduce the adverse effects of bird activities on electricity transmission line towers, it is necessary to monitor and warn the nesting behavior in electricity transmission line towers. Therefore, in order to improve the efficiency and accuracy of bird's nest detection, a detection system for bird's nest is designed based onconvolutional neural networks technology taking common bird's nest pictures as samples and adopting CNN network structure. The comparative experiments proved that the model can effectively improve the identification accuracy of bird's nest on the electricity transmission line tower.","PeriodicalId":235524,"journal":{"name":"2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128056060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
A CU Fast Division Decision Algorithm with Low Complexity for HEVC 面向HEVC的低复杂度CU快速除法决策算法
2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2020-06-01 DOI: 10.1109/ITNEC48623.2020.9084705
Yaqi Liu, Airong Wei
{"title":"A CU Fast Division Decision Algorithm with Low Complexity for HEVC","authors":"Yaqi Liu, Airong Wei","doi":"10.1109/ITNEC48623.2020.9084705","DOIUrl":"https://doi.org/10.1109/ITNEC48623.2020.9084705","url":null,"abstract":"In this paper, a CU (coding unit) fast division decision algorithm with low complexity is designed for HEVC (high efficiency video coding). First, CU block is divided into multiple small blocks, then the texture features of CU are calculated. Finally, the current CU is determined whether it needs to be divided in advance based on the calculation result. Experimental results show that the algorithm can save 23.87% encoding time and an average bit rate increase by 0.065%, and the peak signal-to-noise ratio is increased by 0.018dB. Moreover, which means the method can effectively reduce the computational complexity of intra prediction.","PeriodicalId":235524,"journal":{"name":"2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128180681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Research on Website Phishing Detection Based on LSTM RNN 基于LSTM RNN的网站钓鱼检测研究
2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2020-06-01 DOI: 10.1109/ITNEC48623.2020.9084799
Yang Su
{"title":"Research on Website Phishing Detection Based on LSTM RNN","authors":"Yang Su","doi":"10.1109/ITNEC48623.2020.9084799","DOIUrl":"https://doi.org/10.1109/ITNEC48623.2020.9084799","url":null,"abstract":"In order to effectively detect phishing attacks, this paper designed a new detection system for phishing websites using LSTM Recurrent Neural Networks (RNN). LSTM has the advantage of capturing data timing and long-term dependencies. LSTM has strong learning ability, can automatically learn data characterization without manual extraction of complex features, and has strong potential in the face of complex high-dimensional massive data. Experimental results show that this model approach the accuracy of 99.1%, is higher than that of other neural network algorithms.","PeriodicalId":235524,"journal":{"name":"2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115965704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 17
Design and Implementation of Automation Deployment Tool for Power Grid Dispatching and Control System 电网调度控制系统自动化部署工具的设计与实现
2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2020-06-01 DOI: 10.1109/ITNEC48623.2020.9084827
Jun Hu, Bing Zhang, Jixi Lu, Liyan Xu, Meifang Hou
{"title":"Design and Implementation of Automation Deployment Tool for Power Grid Dispatching and Control System","authors":"Jun Hu, Bing Zhang, Jixi Lu, Liyan Xu, Meifang Hou","doi":"10.1109/ITNEC48623.2020.9084827","DOIUrl":"https://doi.org/10.1109/ITNEC48623.2020.9084827","url":null,"abstract":"To improve the quality and efficiency of software deployment of power grid dispatching and control system, an automation deployment tool is designed. In the overall system architecture, a JSON-RPC-based access service layer is designed to achieve high reuse of the back-end service under C/S and B/S architectures. This paper analyzes the structural characteristics of each module, and focuses on the implementation of JSON-RPC service and GUI under the C/S architecture. Finally, the function of the automation deployment tool is verified by testing.","PeriodicalId":235524,"journal":{"name":"2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132036607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Analysis on Measurement Method of Equipment Contribution Rate to the System-of-Systems 系统的设备贡献率测量方法分析
2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2020-06-01 DOI: 10.1109/ITNEC48623.2020.9084838
Shuai Liu, Xiaowei Chen, Xuejun Liao, Hongjiang Zhang
{"title":"Analysis on Measurement Method of Equipment Contribution Rate to the System-of-Systems","authors":"Shuai Liu, Xiaowei Chen, Xuejun Liao, Hongjiang Zhang","doi":"10.1109/ITNEC48623.2020.9084838","DOIUrl":"https://doi.org/10.1109/ITNEC48623.2020.9084838","url":null,"abstract":"How to measure the contribution rate effectively is the premise of analyzing and evaluating the contribution rate of the system. Based on the basic concept of contribution rate of the existing system, four measurement methods are given: incremental measurement method, ratio measurement method, satisfaction measurement method and cost-effectiveness measurement method, and their advantages and disadvantages are compared and analyzed.","PeriodicalId":235524,"journal":{"name":"2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132069659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Scene-Edge GRU for Video Caption 视频字幕的场景边缘GRU
2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2020-06-01 DOI: 10.1109/ITNEC48623.2020.9084781
Xin Hao, F. Zhou, Xiaoyong Li
{"title":"Scene-Edge GRU for Video Caption","authors":"Xin Hao, F. Zhou, Xiaoyong Li","doi":"10.1109/ITNEC48623.2020.9084781","DOIUrl":"https://doi.org/10.1109/ITNEC48623.2020.9084781","url":null,"abstract":"Recurrent neural networks for video caption have recently attracted widespread attention. It is essential for the video captioning task as it is involved in both the encoding phase and the text description generation phase of the video. However, the traditional encoding-decoding method ignores the scene switching in the video during the encoding phase. In this paper, we propose a video encoding scheme that can discover the structure of a video scene, so as to achieve variable length of the flexible encoding for the video. Unlike the classic encoding-decoding scheme, we propose a new GRU unit that recognizes discontinuities between video frames and enables end-to-end training without the need for additional annotation information. We evaluated our approach on two large datasets: the MPII movie description dataset, and the MSVD dataset. Experiments have shown that our method can find the appropriate level representation of the video and improve the best results of the movie description dataset.","PeriodicalId":235524,"journal":{"name":"2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132470101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Research on non-uniformity correction based on blackbody calibration 基于黑体标定的非均匀性校正研究
2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2020-06-01 DOI: 10.1109/ITNEC48623.2020.9085189
Shanshan Song, X. Zhai
{"title":"Research on non-uniformity correction based on blackbody calibration","authors":"Shanshan Song, X. Zhai","doi":"10.1109/ITNEC48623.2020.9085189","DOIUrl":"https://doi.org/10.1109/ITNEC48623.2020.9085189","url":null,"abstract":"The non-uniformity of digital response of the infrared temperature measurement system which based on infrared focal plane array (IRFPA) seriously affects the temperature accuracy and image quality. In this paper, three non-uniformity correction (NUC) methods are performed to eliminate the non-uniformity, namely, the one-point correction (OPC), two-point correction (TPC) and multi-point correction (MPC) algorithms. A high precision blackbody radiation source is used for calibration experiments. This paper also verifies that the difference in calibrated ambient temperature will affect the correction effect and the correction temperature. The NUC results are evaluated by two aspects of subjective and objective evaluation. The experimental results show that the TPC correction effect is similar to MPC and is superior to OPC method. The non-uniformity degree can be reduced 12.35% compared to the original non-uniformity.","PeriodicalId":235524,"journal":{"name":"2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132541733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
A Novel Self-Docking and Undocking Approach for Self-Changeable Robots 一种自变机器人自对接与自解对接的新方法
2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2020-06-01 DOI: 10.1109/ITNEC48623.2020.9085076
J.Q. Wu, C. Yuan, R. Yin, Wei Sun, W.J. Zhang
{"title":"A Novel Self-Docking and Undocking Approach for Self-Changeable Robots","authors":"J.Q. Wu, C. Yuan, R. Yin, Wei Sun, W.J. Zhang","doi":"10.1109/ITNEC48623.2020.9085076","DOIUrl":"https://doi.org/10.1109/ITNEC48623.2020.9085076","url":null,"abstract":"In this paper, an efficient self-docking and self-undocking approach that can be applied to any self-changeable robot is reported. The changeable robot is a concept to extend the concept of reconfiguration in literature, and the name is first coined in this paper. The new feature of the approach is the self-search of a target module with which the remaining robot docks with and then to complete the docking. The new feature is realized by developing modules on which there are built-in cameras and distance sensors. A deep learning method based on CNN (Convolutional Neural Network) was employed to enhance the intelligence level of the docking and undocking system. The experiment was carried out to test the effectiveness of the approach based on a proprietary self-changeable robot.","PeriodicalId":235524,"journal":{"name":"2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130105974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
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