{"title":"基于提升小波包的埋地管道盗油信号能量特征提取","authors":"Li Ying-chun, Qin Xue, Fu Xing-jian","doi":"10.1109/ICAIE.2010.5641105","DOIUrl":null,"url":null,"abstract":"The system to collect stress wave signal of oil theft was briefly introduced, and data acquisition steps on-the-spot were given. According to the different energy distribution features that stress wave signal exhibits on wavelet domain, a new analyzed method based on the lifting scheme wavelet packet was presented. In the method, the stress signal was decomposed with lifting wavelet packet transform and the energy proportion in each sub-band is calculated. Analyses of experimental results show that identification of oil theft signal can be done through the differences of energy distribution features. The method, which can be computed fast with a simple implementation, provides a new approach for identification of oil theft signal.","PeriodicalId":216006,"journal":{"name":"2010 International Conference on Artificial Intelligence and Education (ICAIE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Energy features extraction of oil theft signal in buried pipeline based on lifting wavelet package\",\"authors\":\"Li Ying-chun, Qin Xue, Fu Xing-jian\",\"doi\":\"10.1109/ICAIE.2010.5641105\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The system to collect stress wave signal of oil theft was briefly introduced, and data acquisition steps on-the-spot were given. According to the different energy distribution features that stress wave signal exhibits on wavelet domain, a new analyzed method based on the lifting scheme wavelet packet was presented. In the method, the stress signal was decomposed with lifting wavelet packet transform and the energy proportion in each sub-band is calculated. Analyses of experimental results show that identification of oil theft signal can be done through the differences of energy distribution features. The method, which can be computed fast with a simple implementation, provides a new approach for identification of oil theft signal.\",\"PeriodicalId\":216006,\"journal\":{\"name\":\"2010 International Conference on Artificial Intelligence and Education (ICAIE)\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 International Conference on Artificial Intelligence and Education (ICAIE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAIE.2010.5641105\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Artificial Intelligence and Education (ICAIE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAIE.2010.5641105","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Energy features extraction of oil theft signal in buried pipeline based on lifting wavelet package
The system to collect stress wave signal of oil theft was briefly introduced, and data acquisition steps on-the-spot were given. According to the different energy distribution features that stress wave signal exhibits on wavelet domain, a new analyzed method based on the lifting scheme wavelet packet was presented. In the method, the stress signal was decomposed with lifting wavelet packet transform and the energy proportion in each sub-band is calculated. Analyses of experimental results show that identification of oil theft signal can be done through the differences of energy distribution features. The method, which can be computed fast with a simple implementation, provides a new approach for identification of oil theft signal.