2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET)最新文献_第2页

An Improved Simplified Successive-Cancellation List Decoding of Polar Codes 一种改进的简化极化码连续消列译码

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906321

Yu Guo, Shufeng Li, Mingyu Cai

引用次数: 0

Automatic Current Transformer Verification Technology for High-Speed Railway Power Based on Edge-Cloud Collaborative Computing 基于边缘云协同计算的高速铁路电力电流互感器自动验证技术

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906363

Lin Yang, Zhi-Long Zhang, Shoumo Wang, Wen-Ting Zhang, Rui Dai, Yin-Bo Liu

{"title":"Automatic Current Transformer Verification Technology for High-Speed Railway Power Based on Edge-Cloud Collaborative Computing","authors":"Lin Yang, Zhi-Long Zhang, Shoumo Wang, Wen-Ting Zhang, Rui Dai, Yin-Bo Liu","doi":"10.1109/CCET55412.2022.9906363","DOIUrl":"https://doi.org/10.1109/CCET55412.2022.9906363","url":null,"abstract":"Current transformer is a key power equipment for high-speed railway power, with high risk for the operator. Based on the architecture of edge-cloud collaborative computing, an automatic high voltage transformer verification technology is proposed considering the applicability in various fields. a vehicle mounted verification device is designed, on which the verification computing at edge side can be realized. The working state model of current transformer is built based on the closed-loop ARMAX model. Combing the ARMAX Model identification with MOEA/D tendency guidance, the characteristic of fault mode for high voltage transformer is proposed. The comparative validation experiment is conducted for 30 consecutive days at a 15VA current transformer and a3.75VA current transformer. The results show that the estimated value of ratio error can reflect actual state changes, which can be used to identify the fault status of the high voltage current transformer. The automatic high voltage transformer verification is achieved with more security for the operator, and it has the value of promotion and application.","PeriodicalId":329327,"journal":{"name":"2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125366662","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

Grasshopper Optimization Algorithm Based on Adaptive Curve and Reverse Learning 基于自适应曲线和反向学习的蚱蜢优化算法

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906356

Yu Zhang, Jinhong Li

引用次数: 0

A Modeling Method of Cyberspace Security Structure Based on Layer-Level Division 基于分层划分的网络空间安全结构建模方法

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906354

Yuwen Zhu, Lei Yu

引用次数: 0

Design of Data Processing Software for Raindrop Spectrometer Based on LabVIEW 基于LabVIEW的雨滴光谱仪数据处理软件设计

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906353

Xu Yefeng, Jiao Ruili, Huang Minsong

引用次数: 0

Study on the Relationship between TMT’s Overconfidence and Green Innovation TMT过度自信与绿色创新的关系研究

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906365

Wang Lan, Zhang Ruimin

引用次数: 0

Research on Fault Identification Method of Power System Communication Network Based on Deep Learning 基于深度学习的电力系统通信网络故障识别方法研究

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906322

Yuting Wang, Ting Hao, Hai Wang

{"title":"Research on Fault Identification Method of Power System Communication Network Based on Deep Learning","authors":"Yuting Wang, Ting Hao, Hai Wang","doi":"10.1109/CCET55412.2022.9906322","DOIUrl":"https://doi.org/10.1109/CCET55412.2022.9906322","url":null,"abstract":"With the communication network scale, the increasing bandwidth and complexity of the constant improvement of the quality of network service, and user requirements, an urgent need to intelligent communication system of the current high speed communication network for effective and reliable management, and fault management is becoming more difficult and important than ever, when the network produces a fault or failure, Many thousands of alarms are generated in a short period of time, so analyzing the signals of these alarms becomes more complicated. Some existing alarm analysis systems have some shortcomings, such as poor scalability, difficulty in dealing with complex situations, and lack of learning ability. This paper proposes a method of fault identification and alarm correlation analysis based on deep learning algorithm. Combined with deep reinforcement learning technology, a sleep scheduling strategy based on multi-level is designed to reduce energy consumption, and its effectiveness is verified by simulation. Experimental results show THAT this method can overcome the limitations of common alarm correlation analysis methods, and create favorable conditions for improving the efficient utilization of spectrum resources in private networks.","PeriodicalId":329327,"journal":{"name":"2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133242604","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

Knowledge Graph Completion Based on Graph Attention Networks and Text Information 基于图注意网络和文本信息的知识图补全

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906358

Shen Hong, Heng Qian, Yongchao Gao, Hongli Lyu

{"title":"Knowledge Graph Completion Based on Graph Attention Networks and Text Information","authors":"Shen Hong, Heng Qian, Yongchao Gao, Hongli Lyu","doi":"10.1109/CCET55412.2022.9906358","DOIUrl":"https://doi.org/10.1109/CCET55412.2022.9906358","url":null,"abstract":"In knowledge graphs (KGs), there exist some unsolved problems such as incomplete data, hidden information with incomplete mining and so on. In the most completion models, the information of the triples in the KG is generally utilized, but the neighborhood information and rich entity description information are not included in the triples. In this paper, the knowledge graph completion (KGC) method is improved based on graph attention networks (GATs) with text information by using the neighborhood information of aggregated triples and entity description information. And the embedding capability of semantic information is enhanced in KGs. First, the feature vector of entity description information is extracted by the Bi-LSTM model and concatenated with the entity embedding in the triples. Then the joint vectors are trained by GATs to aggregate the neighborhood information. Next, the KGC task is realized by a decoder. Finally, the effectiveness of the proposed method is verified by the link prediction experiments in the public datasets FB15K-237 and WNISRR and comparison is investigated with several other existing methods. The test results show that most of the indicators in the two datasets are improved. Furthermore, it is proved that the model combined with multi-source information has better representation ability for entities, which can further improve the accuracy and comprehensive performance of KGC tasks.","PeriodicalId":329327,"journal":{"name":"2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116249461","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

Research on Hybrid Intelligence Wargame Method 混合智能战棋方法研究

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906386

Xin Jin, Xinnian Wang, Ran Ding, Yunchao Wu

引用次数: 0

Spaceborne SAR Time-Series Images Change Detection Based on Log-Ratio Operator 基于对数比算子的星载SAR时序图像变化检测

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906401

Wenjie Shen, Yunzhen Jia, Yanping Wang, Yun Lin, Y. Li

引用次数: 0