Damage Detection and Prediction of Ultrasonic Behaviours in Piezoelectric Materials Using Hybrid Deep Convolutional Neural Network

IF 0.7 4区工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC

Integrated Ferroelectrics Pub Date : 2024-02-08 DOI:10.1080/10584587.2023.2296312

Prashant Vishnu Bhosale, Sudhir D Agashe

引用次数: 0

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...

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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

在这项工作中，首先使用点接触法检测材料的健康状况。一旦确定材料是健康的，就会发现材料的行为特征。之后...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Integrated Ferroelectrics 工程技术-工程：电子与电气

CiteScore

1.40

自引率

0.00%

发文量

179

审稿时长

3 months

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.