2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)最新文献_第6页

Machine Learning Based Fault Type Identification In the Active Distribution Network 基于机器学习的有源配电网故障类型识别

2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2019-03-15 DOI: 10.1109/ITNEC.2019.8729054

B. Sun, Hengxu Zhang, Fang Shi

{"title":"Machine Learning Based Fault Type Identification In the Active Distribution Network","authors":"B. Sun, Hengxu Zhang, Fang Shi","doi":"10.1109/ITNEC.2019.8729054","DOIUrl":"https://doi.org/10.1109/ITNEC.2019.8729054","url":null,"abstract":"To realize the intelligent of the distribution network, it is necessary to identify the fault type accurately. This paper presents the fault type identification method based on machine learning in active distribution networks. The process of machine learning is divided into four steps: data preparation, data preprocessing, feature extraction and model training. When preparing data, a method of generating fault scenarios in the batch of simulation experiments is presented. The IEEE34 Bus System is built in PSCAD to complete the data preparation for machine learning. Variation multiples of voltage and current are extracted as the features to describe the fault type. Various machine learning models are trained by cross-validation method to get the accuracy of identification. The application of decision tree in fault type identification is presented in the form of a tree diagram. The result of fault type identification is shown by the confusion matrix of the decision tree. All the test results show that the proposed fault identifiers can identify all kinds of fault types in the distribution network.","PeriodicalId":202966,"journal":{"name":"2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"55 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":"121738578","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 Network Traffic Identification based on Machine Learning and Deep Packet Inspection 基于机器学习和深度包检测的网络流量识别研究

2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2019-03-15 DOI: 10.1109/ITNEC.2019.8729153

Bowen Yang, Dong Liu

引用次数: 21

NBI Snubber power control system and reliability design NBI缓冲器电源控制系统及可靠性设计

2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2019-03-15 DOI: 10.1109/ITNEC.2019.8729222

Lu Yang, Song Wang

引用次数: 0

Design of Intelligent Classroom Attendance System Based on Face Recognition 基于人脸识别的智能课堂考勤系统设计

2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2019-03-15 DOI: 10.1109/ITNEC.2019.8729496

Wenxian Zeng, Qinglin Meng, Ran Li

引用次数: 23

Recognizing Biomedical Named Entities by Integrating Domain Contextual Relevance Measurement and Active Learning 基于领域语境关联测量和主动学习的生物医学命名实体识别

2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2019-03-15 DOI: 10.1109/ITNEC.2019.8728991

Jiangfan Gao, Jianhui Chen, Shun Zhang, Xiaobo He, Shaofu Lin

{"title":"Recognizing Biomedical Named Entities by Integrating Domain Contextual Relevance Measurement and Active Learning","authors":"Jiangfan Gao, Jianhui Chen, Shun Zhang, Xiaobo He, Shaofu Lin","doi":"10.1109/ITNEC.2019.8728991","DOIUrl":"https://doi.org/10.1109/ITNEC.2019.8728991","url":null,"abstract":"Named entity recognition is a basic and core task of biomedical text mining. Comparing with other named entity recognition methods, methods based on domain relevance measurement need the smaller training corpora and entity samples and are appropriate for recognizing narrow-domain entities, which belong to a subdivision and small semantic class. However, how to obtain the high-quality target corpus set become a key issue. This paper propose a biomedicine named entity recognition method by integrating domain contextual relevance measurement and active learning. Firstly, binding with densitybased clustering and semantic distance measurement, the representative and informative contexts are selected to construct the target corpus set by an active learning approach. Secondly, the domain contextual relevance of candidate entities is calculated by using Domain the discrimination degree and domain dependence function for recognizing the target entities. Experimental results show that the proposed method can effectively reduce training time and improve the accuracy of entity recognition.","PeriodicalId":202966,"journal":{"name":"2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"26 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":"123818852","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: 4

Safety Helmet Wearing Detection Based On Deep Learning 基于深度学习的安全帽佩戴检测

2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2019-03-15 DOI: 10.1109/ITNEC.2019.8729039

Xitian Long, WenpengCui Cui, Zhe Zheng

引用次数: 35

Object Recognition in Remote Sensing Images Using Combined Deep Features 基于组合深度特征的遥感图像目标识别

2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2019-03-15 DOI: 10.1109/ITNEC.2019.8729392

Bitao Jiang, Xiaobin Li, Lu Yin, Wenzhen Yue, Shengiin Wang

{"title":"Object Recognition in Remote Sensing Images Using Combined Deep Features","authors":"Bitao Jiang, Xiaobin Li, Lu Yin, Wenzhen Yue, Shengiin Wang","doi":"10.1109/ITNEC.2019.8729392","DOIUrl":"https://doi.org/10.1109/ITNEC.2019.8729392","url":null,"abstract":"Object recognition, which is also referred as object classification or object type recognition, aims at discriminating object types in remote sensing images. With the availability of high resolution remote sensing images, object recognition attracts more and more attention. Different from traditional methods mainly using hand-crafted features, we propose an object recognition method that combines deep features extracted from a convolutional neural network (CNN) to recognize aircrafts and ships in remote sensing images. The proposed method consists of two stages. In the training stage, images of objects with different types and corresponding labels are exploited to fine-tune a pre-trained CNN. Convolutional features are extracted from a convolutional layer of the fine-tuned CNN and pooled by Fisher Vector, and fully-connected features are extracted from a fully-connected layer of the CNN. These features are combined by concatenation and used to train a support vector machine (SVM). In the test stage, the type of each object is determined by the trained SVM using its combined features. Experiments on two data sets collected from Google Earth demonstrate the effectiveness of our method.","PeriodicalId":202966,"journal":{"name":"2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"2008 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":"127310769","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: 11

Novel Modbus Adaptation Method for IoT Gateway 一种新的物联网网关Modbus适配方法

2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2019-03-15 DOI: 10.1109/ITNEC.2019.8729209

Feng Shu, Hanhua Lu, Yin Ding

引用次数: 6

Source Enumeration Based on Spatial Correlation Function for Independent/Dependent Sources 基于空间相关函数的独立/依赖源的源枚举

2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2019-03-15 DOI: 10.1109/ITNEC.2019.8729080

Qiao Su, Yimin Wei, Changliang Deng, Yue-hong Shen

引用次数: 0

Cloud Computing Task Scheduling Algorithm Based On Improved Genetic Algorithm 基于改进遗传算法的云计算任务调度算法

2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2019-03-15 DOI: 10.1109/ITNEC.2019.8728996

Fang Yiqiu, Xiaoyu Xia, Ge Junwei

引用次数: 30