{"title":"利用混合深度卷积神经网络进行压电材料的损伤检测和超声波行为预测","authors":"Prashant Vishnu Bhosale, Sudhir D Agashe","doi":"10.1080/10584587.2023.2296312","DOIUrl":null,"url":null,"abstract":"In this work the material is first checked for its health by using point contact method. Then the behavioural characteristic of the material is found once the material is decided as healthy one. Af...","PeriodicalId":13686,"journal":{"name":"Integrated Ferroelectrics","volume":"22 1","pages":""},"PeriodicalIF":0.7000,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Damage Detection and Prediction of Ultrasonic Behaviours in Piezoelectric Materials Using Hybrid Deep Convolutional Neural Network\",\"authors\":\"Prashant Vishnu Bhosale, Sudhir D Agashe\",\"doi\":\"10.1080/10584587.2023.2296312\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work the material is first checked for its health by using point contact method. Then the behavioural characteristic of the material is found once the material is decided as healthy one. Af...\",\"PeriodicalId\":13686,\"journal\":{\"name\":\"Integrated Ferroelectrics\",\"volume\":\"22 1\",\"pages\":\"\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2024-02-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Integrated Ferroelectrics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/10584587.2023.2296312\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Integrated Ferroelectrics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/10584587.2023.2296312","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Damage Detection and Prediction of Ultrasonic Behaviours in Piezoelectric Materials Using Hybrid Deep Convolutional Neural Network
In this work the material is first checked for its health by using point contact method. Then the behavioural characteristic of the material is found once the material is decided as healthy one. Af...
期刊介绍：
Integrated Ferroelectrics provides an international, interdisciplinary forum for electronic engineers and physicists as well as process and systems engineers, ceramicists, and chemists who are involved in research, design, development, manufacturing and utilization of integrated ferroelectric devices. Such devices unite ferroelectric films and semiconductor integrated circuit chips. The result is a new family of electronic devices, which combine the unique nonvolatile memory, pyroelectric, piezoelectric, photorefractive, radiation-hard, acoustic and/or dielectric properties of ferroelectric materials with the dynamic memory, logic and/or amplification properties and miniaturization and low-cost advantages of semiconductor i.c. technology.
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
