2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)最新文献_第9页

Research on Safety Programmable Controller Based on Dual-CPU Architecture 基于双cpu架构的安全可编程控制器研究

2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2019-03-15 DOI: 10.1109/ITNEC.2019.8729401

Zhiwei Du, Guanghe Cheng, Maoli Wang, R. Sun, Kai Liu

引用次数: 0

A Relevance Inference Model of Electronic Evidence Based on Case Characteristics 基于案例特征的电子证据关联推理模型

2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2019-03-15 DOI: 10.1109/ITNEC.2019.8729430

Zhijun Li, Ning Wang

引用次数: 1

Study on Single Chip Integrated Methane Sensor based on MEMS Process 基于MEMS工艺的单片机集成甲烷传感器研究

2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2019-03-15 DOI: 10.1109/ITNEC.2019.8729394

Kai Zhang, Hongquan Zhang, Ben Mao, Mikolai Mukhurov

引用次数: 0

Research and Application of Intelligent Internet of Vehicles Model Based on Fog Computing 基于雾计算的智能车联网模型研究与应用

2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2019-03-15 DOI: 10.1109/ITNEC.2019.8729045

Baoling Qin, Jiejian Cai, Yunshi Luo, F. Zheng, Jianwei Zhang, Qiao Luo

{"title":"Research and Application of Intelligent Internet of Vehicles Model Based on Fog Computing","authors":"Baoling Qin, Jiejian Cai, Yunshi Luo, F. Zheng, Jianwei Zhang, Qiao Luo","doi":"10.1109/ITNEC.2019.8729045","DOIUrl":"https://doi.org/10.1109/ITNEC.2019.8729045","url":null,"abstract":"Fog computing, IoT and big data are hot topics in the research of computer application technology at home and abroad nowadays. With the high-speed application of Internet of Things, the data generated by edge devices grows rapidly, and data that is valid or useless is sent to the cloud, which cause data loss, network congestion, and severe shortage of bandwidth resources. A architecture based on fog computing and its distributed computing solution are proposed to solve these problems. Taking full advantages of architectures based on fog computing to build an intelligent Internet of Vehicles application system is to give better play to its “de-centralized” function and characteristics of fog computing, make its system run fast, stably and accurately, optimize the system structure, make it more simple, modular and intelligent, at the same time, reduce the network deployment and operation costs, make the network deployment between vehicles and vehicles, roads and roadside equipment more reasonable, reliable and more economical, provide theoretical basis and scientific experimental basis for intellienge Internet of Vehicles applications, of which the research has social and economic value and significance.","PeriodicalId":202966,"journal":{"name":"2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133507370","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: 8

Protecting Data Privacy for Permissioned Blockchains using Identity-Based Encryption 使用基于身份的加密保护许可区块链的数据隐私

2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2019-03-15 DOI: 10.1109/ITNEC.2019.8729244

Mengcan Zhang, Shen Wang, P. Zhang, Li He, X. Li, Shenshen Zhou

引用次数: 8

A Hybrid OSD-SC Decoding Algorithm for Polar Codes 一种极化码的混合OSD-SC译码算法

2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2019-03-15 DOI: 10.1109/ITNEC.2019.8729275

Yusheng Xing, G. Tu

引用次数: 1

The flywheel fault detection based on Kernel principal component analysis 基于核主成分分析的飞轮故障检测

2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2019-03-15 DOI: 10.1109/ITNEC.2019.8729163

Gan-hua Li, Jian-cheng Li, Ya-ni Cao, Min-qiang Xu, Keqiang Xia, Jun Wei, Baojun Lan, Li Dong

{"title":"The flywheel fault detection based on Kernel principal component analysis","authors":"Gan-hua Li, Jian-cheng Li, Ya-ni Cao, Min-qiang Xu, Keqiang Xia, Jun Wei, Baojun Lan, Li Dong","doi":"10.1109/ITNEC.2019.8729163","DOIUrl":"https://doi.org/10.1109/ITNEC.2019.8729163","url":null,"abstract":"Simulink model is built according to the mathematic model of flywheel system which is a closed-loop system. Considering the fault modes and the corresponding fault parameters of flywheel, the training data and testing data for two faults are collected from the Simulink model. And then the Kernel Principal Component Analysis (KPCA) is proposed to analysis the correlation of five parameters in the flywheel system. The faults of flywheel will cause some abnormal changes of testing data. This method can solve the problem of lack of fault knowledge and complex mathematic modeling. The behaviors of flywheel can be learned from the training data and the correlation is the interactions of five parameters in the flywheel system. However, the variable correlation is classified as two types, such as the nonlinear and linear relationship. The Principal Component analysis (PCA) is used to build the linear model of training data. The chosen five variables is a nonlinear relationship. In order to demonstrate the effectiveness of proposed algorithm, it is necessary to compare the detections results of KPCA with the results of PCA. Numerical simulation results show that the SPEKPCA index can detect the faults of flywheel without complex mathematical modeling, and better than the detection results of PCA model.","PeriodicalId":202966,"journal":{"name":"2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128071506","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: 6

Fault Detection for Mechanical Arm Systems: An Sliding Mode Observer Approach 机械臂系统的故障检测:滑模观测器方法

2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2019-03-15 DOI: 10.1109/ITNEC.2019.8729344

Tongli Jia, Yunqing Liu, Jiaqi Li

引用次数: 0

Recognition of Robot Based on Attention Mechanism and Convolutional Neural Network 基于注意机制和卷积神经网络的机器人识别

2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2019-03-15 DOI: 10.1109/ITNEC.2019.8728976

Hexi Li, Jihua Li

{"title":"Recognition of Robot Based on Attention Mechanism and Convolutional Neural Network","authors":"Hexi Li, Jihua Li","doi":"10.1109/ITNEC.2019.8728976","DOIUrl":"https://doi.org/10.1109/ITNEC.2019.8728976","url":null,"abstract":"In order to accurately recognize the working targets in complex environments, an industrial robot vision model based on attention mechanism and deep learning is proposed. The model uses an improved attention mechanism to achieve fast focusing of the target, and employ a 10-layer-convolutional neural network (CNN) which combines local connection with full connection to accomplish target recognition. The local connection of CNN consists of three convolution layers and three sub-sampling layers, the convolution layers are used for feature extraction and the sub-sampling layers are used to reduce network nodes. The full connection layer of CNN is composed of input layer, hidden layer and output layer as a classifier for target recognition. More than 1000 target images are sampled for CNN network training. The effects of different CNN network structure parameters on the model are analyzed in order to satisfy the rapidity and reliability of robot vision. The test results show that the combination of improved attention mechanism and CNN model can achieve the fast focusing and accurate recognition of working targets in the robot system, the proposed model is can be applied to the visual navigation of industrial robots.","PeriodicalId":202966,"journal":{"name":"2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125869831","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

ITNEC 2019 Author Index ITNEC 2019作者索引

2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2019-03-01 DOI: 10.1109/itnec.2019.8729506

引用次数: 0