2019 2nd International Conference on Innovation in Engineering and Technology (ICIET)最新文献_第7页

A New Multilevel Inverter for Grid Integration of Renewable Energy Sources 一种用于可再生能源并网的新型多级逆变器

2019 2nd International Conference on Innovation in Engineering and Technology (ICIET) Pub Date : 2019-12-23 DOI: 10.1109/ICIET48527.2019.9290691

M. Islam, M. Hasan, Shahidul Islam

引用次数: 1

A New Method of Improving Performance of Canny Edge Detection 一种提高Canny边缘检测性能的新方法

2019 2nd International Conference on Innovation in Engineering and Technology (ICIET) Pub Date : 2019-12-23 DOI: 10.1109/ICIET48527.2019.9290676

Tasnuva Tasneem, Zeenat Afroze

引用次数: 1

Performance Analysis of Deep Autoencoder and NCA Dimensionality Reduction Techniques with KNN, ENN and SVM Classifiers 基于KNN、ENN和SVM分类器的深度自编码器和NCA降维技术性能分析

2019 2nd International Conference on Innovation in Engineering and Technology (ICIET) Pub Date : 2019-12-11 DOI: 10.1109/ICIET48527.2019.9290722

M. Siddique, S. Sakib, Mohamed Abdur Rahman

{"title":"Performance Analysis of Deep Autoencoder and NCA Dimensionality Reduction Techniques with KNN, ENN and SVM Classifiers","authors":"M. Siddique, S. Sakib, Mohamed Abdur Rahman","doi":"10.1109/ICIET48527.2019.9290722","DOIUrl":"https://doi.org/10.1109/ICIET48527.2019.9290722","url":null,"abstract":"The central aim of this paper is to implement Deep Autoencoder and Neighborhood Components Analysis (NCA) dimensionality reduction methods in Matlab and to observe the application of these algorithms on nine unlike datasets from UCI machine learning repository. These datasets are CNAE9, Movement Libras, Pima Indians diabetes, Parkinsons, Knowledge, Segmentation, Seeds, Mammographic Masses, and Ionosphere. First of all, the dimension of these datasets has been reduced to fifty percent of their original dimension by selecting and extracting the most relevant and appropriate features or attributes using Deep Autoencoder and NCA dimensionality reduction techniques. Afterward, each dataset is classified applying K-Nearest Neighbors (KNN), Extended Nearest Neighbors (ENN) and Support Vector Machine (SVM) classification algorithms. All classification algorithms are developed in the Matlab environment. In each classification, the training test data ratio is always set to ninety percent: ten percent. Upon classification, variation between accuracies is observed and analyzed to find the degree of compatibility of each dimensionality reduction technique with each classifier and to evaluate each classifier performance on each dataset.","PeriodicalId":427838,"journal":{"name":"2019 2nd International Conference on Innovation in Engineering and Technology (ICIET)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114606406","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

Non-Intrusive Electrical Appliances Monitoring and Classification using K-Nearest Neighbors 基于k近邻的非侵入式电器监测与分类

2019 2nd International Conference on Innovation in Engineering and Technology (ICIET) Pub Date : 2019-11-22 DOI: 10.1109/ICIET48527.2019.9290671

Mohammad Mahmudur Rahman Khan, M. Siddique, S. Sakib

{"title":"Non-Intrusive Electrical Appliances Monitoring and Classification using K-Nearest Neighbors","authors":"Mohammad Mahmudur Rahman Khan, M. Siddique, S. Sakib","doi":"10.1109/ICIET48527.2019.9290671","DOIUrl":"https://doi.org/10.1109/ICIET48527.2019.9290671","url":null,"abstract":"Non-Intrusive Load Monitoring (NILM) is the method of detecting an individual device’s energy signal from an aggregated energy consumption signature [1]. As existing energy meters provide very little to no information regarding the energy consumptions of individual appliances apart from the aggregated power rating, the spotting of individual appliances’ energy usages by NILM will not only provide consumers the feedback of appliance-specific energy usage but also lead to the changes of their consumption behavior which facilitate energy conservation. B Neenan et al. [2] have demonstrated that direct individual appliance-specific energy usage signals lead to consumers’ behavioral changes which improves energy efficiency by as much as 15%. Upon disaggregation of an energy signal, the signal needs to be classified according to the appropriate appliance. Hence, the goal of this paper is to disaggregate total energy consumption data to individual appliance signature and then classify appliance-specific energy loads using a prominent supervised classification method known as K-Nearest Neighbors (KNN). To perform this operation, we have used a publicly accessible dataset of power signals from several houses known as the REDD dataset. Before applying KNN, data is preprocessed for each device. Then KNN is applied to check whether their energy consumption signature is separable or not. KNN is applied with K=5.","PeriodicalId":427838,"journal":{"name":"2019 2nd International Conference on Innovation in Engineering and Technology (ICIET)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127235574","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: 24