Development of the Abnormal Tension Pattern Recognition Module for Twisted Yarn Based on Deep Learning Edge Computing

IF 1.3 Q3 ENGINEERING, MULTIDISCIPLINARY
None Chuan-Pin Lu, None Yan-Long Huang, None Po-Jen Lai
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引用次数: 0

Abstract

This study aims to develop an artificial intelligence module for recognizing abnormal tension in textile weaving, The module can be used to address the time-consuming and inaccurate issues associated with traditional manual methods. Long short-term memory (LSTM) recurrent neural networks as the algorithm for identifying different types of abnormal tension are employed in this module. This study focuses on training and validating the model using five common patterns. Additionally, an approach involving the integration of plug-in modules and edge computing in deep learning is employed to achieve the research objectives without altering the original system architecture. Multiple experiments were conducted to search for the optimal model parameters. According to the experimental results, the average recognition rate for abnormal tension is 97.12%, with an average computation time of 46.2 milliseconds per sample. The results indicate that the recognition accuracy and computation time meet the practical performance requirements of the system.
基于深度学习边缘计算的捻纱异常张力模式识别模块的开发
本研究旨在开发一个人工智能模块来识别纺织品织造过程中的异常张力,该模块可用于解决传统手工方法耗时和不准确的问题。该模块采用长短期记忆(LSTM)递归神经网络作为识别不同类型异常张力的算法。本研究着重于使用五种常见模式来训练和验证模型。此外,在不改变原有系统架构的情况下,采用了一种将插件模块和边缘计算集成到深度学习中的方法来实现研究目标。通过多次实验寻找最优模型参数。实验结果表明,该算法对异常张力的平均识别率为97.12%,平均计算时间为46.2毫秒/个样本。结果表明,识别精度和计算时间均满足系统的实际性能要求。
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
CiteScore
2.80
自引率
0.00%
发文量
18
审稿时长
12 weeks
期刊介绍: The IJETI journal focus on the field of engineering and technology Innovation. And it publishes original papers including but not limited to the following fields: Automation Engineering Civil Engineering Control Engineering Electric Engineering Electronic Engineering Green Technology Information Engineering Mechanical Engineering Material Engineering Mechatronics and Robotics Engineering Nanotechnology Optic Engineering Sport Science and Technology Innovation Management Other Engineering and Technology Related Topics.
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