2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)最新文献

On the Distributed Jamming System of Covert Timing Channels in 5G Networks 5G网络隐时信道分布式干扰系统研究

2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2020-06-01 DOI: 10.1109/ICAICA50127.2020.9182534

Fangjian Shang, Xin Li, Di Zhai, Yang Lu, Donglei Zhang, Yuwen Qian

引用次数: 1

Fault diagnosis and analysis of water temperature sensor 水温传感器故障诊断与分析

2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2020-06-01 DOI: 10.1109/ICAICA50127.2020.9181868

Shengbo Zhou, Bingxian Li, Bin Wang

引用次数: 0

A Improved Parameter Design Method of LCL APF Interface Filter 一种改进的LCL APF接口滤波器参数设计方法

2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2020-06-01 DOI: 10.1109/ICAICA50127.2020.9182457

Lin Zhou, Zhiwen Liu, Yanchao Ji, Daoyuan Ma, Jianze Wang, Lingda Li

引用次数: 4

Design of Temperature Remote Monitoring System Based on STM32 基于STM32的温度远程监控系统设计

2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2020-06-01 DOI: 10.1109/ICAICA50127.2020.9182397

Zhiyuan Zhang, Y. Jin

引用次数: 5

Resolve out of Vocabulary with Long Short-Term Memory Networks for Morphology 用长短期记忆网络解决词法词汇不足问题

2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2020-06-01 DOI: 10.1109/ICAICA50127.2020.9182586

Yun Tang, Chuanxiang Tang, Caixin Zhu

引用次数: 2

Mining Technological Innovation Talents Based on Patent Index using t-SNE Algorithms*: Take the Field of Intelligent Robot as an Example 基于t-SNE算法的专利索引挖掘技术创新人才*——以智能机器人领域为例

2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2020-06-01 DOI: 10.1109/ICAICA50127.2020.9182541

Ning Zhao, Guohui Yang, Yang Cao

{"title":"Mining Technological Innovation Talents Based on Patent Index using t-SNE Algorithms*: Take the Field of Intelligent Robot as an Example","authors":"Ning Zhao, Guohui Yang, Yang Cao","doi":"10.1109/ICAICA50127.2020.9182541","DOIUrl":"https://doi.org/10.1109/ICAICA50127.2020.9182541","url":null,"abstract":"The purpose of this paper is to effectively evaluate the innovation ability and classification of technical talents in the intelligent robot field, and to be able to carry out adaptive learning and mining technical innovation talents according to the real-time change data corresponding to different indicators. Taking inventor's patent information retrieved and cleaned from DI database as research object, it constructs the evaluation index system of technological innovation talents. It reduces the dimension of the index and cluster automatically, shows the visual effect, and mines the similar technical innovation talents through t-SNE algorithm. For a large number of patent information data, machine learning algorithm improves the traditional recognition method. According to inventor similarity, automatic classification is realized. Combined with DWPI manual code mining, the corresponding innovators and members of the technical team in the intelligent robot technology field were found. According to the results of visual dimensional reduction, the specific inventors can be traced. Machine learning algorithm t-SNE can reduce dimension and analysis clustering. It breaks the limitations of artificial statistics, deals with the larger order of magnitude data, and analyzes data timely, accurate and intuitive.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"14 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":"122300122","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 the Key Technologies of Big Data Based High-speed Railway Permanent Way Data Asset Collection Platform 基于大数据的高速铁路常轨数据资产采集平台关键技术研究

2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2020-06-01 DOI: 10.1109/ICAICA50127.2020.9182429

Zhibo Cheng, Yanhua Wu, Taifeng Li, Zhengyang Zhao

{"title":"Research on the Key Technologies of Big Data Based High-speed Railway Permanent Way Data Asset Collection Platform","authors":"Zhibo Cheng, Yanhua Wu, Taifeng Li, Zhengyang Zhao","doi":"10.1109/ICAICA50127.2020.9182429","DOIUrl":"https://doi.org/10.1109/ICAICA50127.2020.9182429","url":null,"abstract":"It is of great significance to manage high-speed railway permanent way equipment data efficiently for the improvement of operational safety. This paper, by investigating the management status of high-speed railway permanent way equipment data, conducts a demand analysis on exploring big data applications of permanent way and proposes an overall framework for a High-speed Railway Permanent Way Data Asset Collection Platform based on the Railway Data Service Platform. After sorting out and designing the main functions of the platform from a technical and business perspective, this paper also researches some key technologies of the platform including data cleaning and management, full-text retrieval, and cold/warm/hot data storage strategies. Finally, this paper takes one of the highspeed railway permanent way subjects as an example, building a prototype system of the High-speed Railway Permanent Way Data Asset Collection Platform and verifies the feasibility of the overall framework and key technologies. Based on the proposed platform, performing typical permanent way equipment analysis and big data applications can provide data and decision-making support for reasonable guidance towards permanent way equipment maintenance and management.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"27 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":"116626855","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

Research on Portable Multi-Bar Code Recognizer Based on Digital Image Processing Technology 基于数字图像处理技术的便携式多条码识别器研究

2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2020-06-01 DOI: 10.1109/ICAICA50127.2020.9182615

Huang Huilin, Liu Tinghui

引用次数: 0

Construction of hierarchical knowledge graph based on deep learning 基于深度学习的分层知识图谱构建

2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2020-06-01 DOI: 10.1109/ICAICA50127.2020.9181920

Zuquan Peng, Huazhu Song, Xiaohan Zheng, Luotianhao Yi

{"title":"Construction of hierarchical knowledge graph based on deep learning","authors":"Zuquan Peng, Huazhu Song, Xiaohan Zheng, Luotianhao Yi","doi":"10.1109/ICAICA50127.2020.9181920","DOIUrl":"https://doi.org/10.1109/ICAICA50127.2020.9181920","url":null,"abstract":"With the continuous deepening of knowledge graph research, more and more knowledge is softened together, and the knowledge in professional fields is also emerging. Although people can quickly identify the knowledge they need based on their needs, machines cannot. There are many problems in the organization and application of traditional graphs, such as the inaccuracy of knowledge representation, which makes it difficult to obtain. The lack of clear knowledge layers causes a lot of irrelevant knowledge to appear after the query. The chaotic structure of knowledge in the graph causes query time-consuming. Therefore, considering the different layers of knowledge representation and the knowledge used to solve complex engineering problems, we propose to divide knowledge into three layers - basic knowledge, deep knowledge, and application knowledge and an agent-based hierarchical knowledge graph construction framework and methodology. The deep learning model method is used in the classification agent to realize the automatic division of knowledge type, pass the classification results to the corresponding knowledge agent. This knowledge agent is able to construct the hierarchical knowledge graph by the same layer knowledge or cross-layer knowledge. This method of constructing the hierarchical knowledge graph has practical significance in the application of the knowledge graph, which makes the knowledge graph have a wider application and practical value.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","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":"129038841","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

Research on Attack Mechanism using Attack Surface 基于攻击面的攻击机制研究

2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2020-06-01 DOI: 10.1109/ICAICA50127.2020.9182583

Xinlin Liu, Jianhua Huang, Weifeng Luo, Qingming Chen, Peishan Ye, Dingbo Wang

引用次数: 0