{"title":"基于LSSVM的煤矿安全等级预测","authors":"Desheng Liu, Zhiru Xu, Wei Wang, Lei Wang","doi":"10.1109/MVHI.2010.71","DOIUrl":null,"url":null,"abstract":"Coal mine disaster has a serious threat to production and safety, mine safety prediction is an extremely challenging problem from many perspectives. This paper describes a generic fusion model for coal mine safety combining information from several physically different sensors aiming to the detection, monitoring and crisis management of such natural hazards. A conduct model base on least squares support vector machine (LSSVM) is proposed. Experimental results from the coal mine sensors are presented","PeriodicalId":34860,"journal":{"name":"HumanMachine Communication Journal","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Prediction of Coal Mine Safety Level Based on LSSVM\",\"authors\":\"Desheng Liu, Zhiru Xu, Wei Wang, Lei Wang\",\"doi\":\"10.1109/MVHI.2010.71\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Coal mine disaster has a serious threat to production and safety, mine safety prediction is an extremely challenging problem from many perspectives. This paper describes a generic fusion model for coal mine safety combining information from several physically different sensors aiming to the detection, monitoring and crisis management of such natural hazards. A conduct model base on least squares support vector machine (LSSVM) is proposed. Experimental results from the coal mine sensors are presented\",\"PeriodicalId\":34860,\"journal\":{\"name\":\"HumanMachine Communication Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-04-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"HumanMachine Communication Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MVHI.2010.71\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Social Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"HumanMachine Communication Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MVHI.2010.71","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Social Sciences","Score":null,"Total":0}
Prediction of Coal Mine Safety Level Based on LSSVM
Coal mine disaster has a serious threat to production and safety, mine safety prediction is an extremely challenging problem from many perspectives. This paper describes a generic fusion model for coal mine safety combining information from several physically different sensors aiming to the detection, monitoring and crisis management of such natural hazards. A conduct model base on least squares support vector machine (LSSVM) is proposed. Experimental results from the coal mine sensors are presented
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
