The Role of Information Seeking Behavior in Newcomers' Organizational Socialization

Lu, Chien-Chung; Lo, Shin-Hung; Liaw, Gou-Fong; Hsu, Chin-Tien

doi:10.1109/icicic.2008.572

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…Regarding the automated monitoring of yarn tension, Lu and Liaw [17] proposed a method that utilizes Gaussian density distribution for maximum similarity comparison to identify abnormal tension types. In this study, the abnormal tension data is measured using tension sensors installed on the twister machine with a signal sampling frequency of 0.01 Hz, and real-time data analysis and storage are conducted by the QAI system produced by Yu-Hwa Co. Ltd.…”

Section: Introductionmentioning

confidence: 99%

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

Chuan-Pin Lu,

Yan-Long Huang,

Po-Jen Lai

2023

Int. j. eng. technol. innov.

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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.

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Section: Introductionmentioning

confidence: 99%