{"title":"基于离散小波变换、离散傅立叶变换和基于k-means聚类方法的人工神经网络的优质褐煤检测","authors":"S. A. Korkmaz, Furkan Esmeray","doi":"10.1109/ISDFS.2018.8355326","DOIUrl":null,"url":null,"abstract":"In this article, the lignite coal datas in the Kalburçayı area of the Sivas-Kangal Basin have been used. This original data obtained from Kalburçayı area of the Sivas-Kangal Basin consists of 66 observations in the lignite coal area, including lignite quality parameters such as moisture content, ash, sulfur content and calorific value. These lignite coal datas have been clustered in two group with k-means method according to calori values. This clustering lignite coal data is classified by the Artifical Neural Network (ANN) classifier. In addition, Discrete Fourier Transform (DFT) and Discrete Wavelet Transform (DWT) have been applied to coal data for ANN classifiers. DFT_ANN, DWT_ANN, and ANN classification success results are compared. The highest classification success rate was found by DWT_ANN method.","PeriodicalId":154279,"journal":{"name":"2018 6th International Symposium on Digital Forensic and Security (ISDFS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Quality lignite coal detection with discrete wavelet transform, discrete fourier transform, and ANN based on k-means clustering method\",\"authors\":\"S. A. Korkmaz, Furkan Esmeray\",\"doi\":\"10.1109/ISDFS.2018.8355326\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this article, the lignite coal datas in the Kalburçayı area of the Sivas-Kangal Basin have been used. This original data obtained from Kalburçayı area of the Sivas-Kangal Basin consists of 66 observations in the lignite coal area, including lignite quality parameters such as moisture content, ash, sulfur content and calorific value. These lignite coal datas have been clustered in two group with k-means method according to calori values. This clustering lignite coal data is classified by the Artifical Neural Network (ANN) classifier. In addition, Discrete Fourier Transform (DFT) and Discrete Wavelet Transform (DWT) have been applied to coal data for ANN classifiers. DFT_ANN, DWT_ANN, and ANN classification success results are compared. The highest classification success rate was found by DWT_ANN method.\",\"PeriodicalId\":154279,\"journal\":{\"name\":\"2018 6th International Symposium on Digital Forensic and Security (ISDFS)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-03-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 6th International Symposium on Digital Forensic and Security (ISDFS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISDFS.2018.8355326\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 6th International Symposium on Digital Forensic and Security (ISDFS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISDFS.2018.8355326","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Quality lignite coal detection with discrete wavelet transform, discrete fourier transform, and ANN based on k-means clustering method
In this article, the lignite coal datas in the Kalburçayı area of the Sivas-Kangal Basin have been used. This original data obtained from Kalburçayı area of the Sivas-Kangal Basin consists of 66 observations in the lignite coal area, including lignite quality parameters such as moisture content, ash, sulfur content and calorific value. These lignite coal datas have been clustered in two group with k-means method according to calori values. This clustering lignite coal data is classified by the Artifical Neural Network (ANN) classifier. In addition, Discrete Fourier Transform (DFT) and Discrete Wavelet Transform (DWT) have been applied to coal data for ANN classifiers. DFT_ANN, DWT_ANN, and ANN classification success results are compared. The highest classification success rate was found by DWT_ANN method.