2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS)最新文献_第9页

Feature Learning for Bird Call Clustering 鸟类叫声聚类的特征学习

2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2018-12-01 DOI: 10.1109/ICIINFS.2018.8721418

Harshita Seth, Rhythm Bhatia, Padmanabhan Rajan

引用次数: 3

A Novel Passive Islanding Detection Methods Using Wavelet Transform for Grid Connected PV System 一种基于小波变换的并网光伏系统被动孤岛检测方法

2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2018-12-01 DOI: 10.1109/iciinfs.2018.8721382

Omkar Koduri, S. S. Duvvuri, Sagiraju Dileep Kumar Varma

{"title":"A Novel Passive Islanding Detection Methods Using Wavelet Transform for Grid Connected PV System","authors":"Omkar Koduri, S. S. Duvvuri, Sagiraju Dileep Kumar Varma","doi":"10.1109/iciinfs.2018.8721382","DOIUrl":"https://doi.org/10.1109/iciinfs.2018.8721382","url":null,"abstract":"This paper presents new passive islanding detection methods for a grid-connected PV (GCPV) system. In the first proposed approach, the 3-phase voltage (p.u) at the point of common coupling (PCC) is extracted and calculates the mean voltage samples. This is passed through the wavelet transform, decomposed up to the level-6(d6) coefficient and then it obviously localizes the event and as a result detects the islanding condition. Finally, to calculate the energy and standard deviation (STD) at level-6(d6) for 2-cycles mean voltage samples it will discriminate islanding condition from the non-islanding condition. The second proposed approach, the negative sequence voltage is considered at PCC. This is passed through the wavelet transform and successfully detects the islanding state. This method also calculates the energy and standard deviation (STD) at level-4(d4) for one cycle of negative sequence voltage it will discriminate islanding situation from the non-islanding situation. The proposed passive methods are tested on a GCPV system with islanding and non-islanding (normal operation and abrupt load change) situation. These techniques are turn out the extremely successfully in islanding detection at PCC for GCPV system. The test GCPV system and simulations are developed in MATLAB/Simulink Software.","PeriodicalId":397083,"journal":{"name":"2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134107124","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

Critical analysis of classification techniques for precision agriculture monitoring using satellite and drone 卫星与无人机精准农业监测分类技术关键分析

2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2018-12-01 DOI: 10.1109/ICIINFS.2018.8721422

Ankush Agarwal, A. Singh, Sandeep Kumar, Dharmendra Singh

引用次数: 12

Datacenter Workload Classification and Characterization: An Empirical Approach 数据中心工作负载分类和表征:一种实证方法

2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2018-12-01 DOI: 10.1109/ICIINFS.2018.8721402

V. S. Shekhawat, A. Gautam, A. Thakrar

{"title":"Datacenter Workload Classification and Characterization: An Empirical Approach","authors":"V. S. Shekhawat, A. Gautam, A. Thakrar","doi":"10.1109/ICIINFS.2018.8721402","DOIUrl":"https://doi.org/10.1109/ICIINFS.2018.8721402","url":null,"abstract":"Datacenter traffic has increased significantly due to rising number of web applications on Internet. These applications have diverse Quality of Service (QoS) requirements making datacenter management a complex task. For a datacenter the amount of resources required for a given resource type (computing, memory, network and storage) is termed as workload. In cloud datacenters, workload classification and characterization is used for resource management, application performance management, capacity sizing, and for estimating the future resource demand. An accurate estimation of future resource demand helps in meeting QoS requirements and ensure efficient resource utilization. Thus modeling and characterization of datacenter workloads becomes necessary to meet performance requirements of applications in a cost-efficient manner. In this paper, a methodology to classify datacenter workloads and characterize them based on resource usage is proposed. Two different workloads have been used, one is Google Cluster Trace (GCT) dataset and other is Bit Brains Trace (BBT) dataset. Seven different machine learning algorithms for workload classification have been used. Workload distribution is estimated in a mix of heterogeneous applications for both GCT and BBT. The seven machine learning algorithms have been compared on the basis of their classification accuracy. Finally, an algorithm to estimate the importance of different attributes for classification is proposed in this paper.","PeriodicalId":397083,"journal":{"name":"2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122681878","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: 13

Development of HMI based Multi-stresses Aging Facility for Polymeric Insulators 基于人机界面的聚合物绝缘子多应力老化装置的研制

2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2018-12-01 DOI: 10.1109/ICIINFS.2018.8721413

B. S. Reddy, S. Nandi

引用次数: 0

Satellite Image Edge Detection using Fractional order method 基于分数阶方法的卫星图像边缘检测

2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2018-12-01 DOI: 10.1109/ICIINFS.2018.8721376

Shivangi Godbole, G. Phadke

引用次数: 0