{"title":"小波神经网络在管道缺陷信号检测中的应用","authors":"Runjing Zhou, Fei Zhang","doi":"10.1109/ICNC.2007.263","DOIUrl":null,"url":null,"abstract":"Aiming at denoising to detection signal of the flaw in the long transporting pipe, the way of denoising based on wavelet neural network is present, and signal processing of ultrasonic detection application in long pipeline is described. Making use of self-learning characteristic of wavelet neural network, this way reduces wave loss. This method has the good effect and may acquire exact location and amplitude of the flaw. It is great significance for signal processing of ultrasonic detection.","PeriodicalId":250881,"journal":{"name":"Third International Conference on Natural Computation (ICNC 2007)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of Signal Detection for Pipeline Flaw Based on Wavelet Neural Network\",\"authors\":\"Runjing Zhou, Fei Zhang\",\"doi\":\"10.1109/ICNC.2007.263\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aiming at denoising to detection signal of the flaw in the long transporting pipe, the way of denoising based on wavelet neural network is present, and signal processing of ultrasonic detection application in long pipeline is described. Making use of self-learning characteristic of wavelet neural network, this way reduces wave loss. This method has the good effect and may acquire exact location and amplitude of the flaw. It is great significance for signal processing of ultrasonic detection.\",\"PeriodicalId\":250881,\"journal\":{\"name\":\"Third International Conference on Natural Computation (ICNC 2007)\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-08-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Third International Conference on Natural Computation (ICNC 2007)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNC.2007.263\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Third International Conference on Natural Computation (ICNC 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNC.2007.263","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of Signal Detection for Pipeline Flaw Based on Wavelet Neural Network
Aiming at denoising to detection signal of the flaw in the long transporting pipe, the way of denoising based on wavelet neural network is present, and signal processing of ultrasonic detection application in long pipeline is described. Making use of self-learning characteristic of wavelet neural network, this way reduces wave loss. This method has the good effect and may acquire exact location and amplitude of the flaw. It is great significance for signal processing of ultrasonic detection.