IEMEK Journal of Embedded Systems and Applications (대한임베디드공학회논문지)

Institute of Embedded Engineering of Korea (대한임베디드공학회)
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Test and Evaluation of Fabric Classification Technology based on Deep Learning of Feedback Vibration

피드백 진동의 심층학습 기반 원단 분류 기술의 시험 및 평가

  • Received : 2021.02.03
  • Accepted : 2021.03.26
  • Published : 2021.04.30
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Abstract

This paper presents the test and evaluation results of the fabric classification technology using deep learning of feedback vibration occurring on fabric surfaces. Ten fabrics composed of different materials were selected for the classification test. To build a database for the design of an artificial intelligence model, feedback vibration measurement equipment with functions of fixed tension, contact load, and contact velocity control, was constructed and feedback vibration data on each fabric surface were collected under the same measurement conditions. Then, training and validation datasets were created with the collected feedback vibration data, and a deep learning architecture with convolutional neural networks was designed in the consideration of data characteristics. A deep learning model development program was established and the fabric classification model was derived with the training and validation dataset. In the end, a test system including an embedded system with the developed model was constructed in order to test and evaluate the performance of the fabric classification model. Results of the fabric classification test were summarized and analyzed by means of the confusion matrix. Finally, the performance of the integrated system was confirmed to have an accuracy of more than ninety percent in fabric classification with the developed model.

Keywords

Acknowledgement

본 논문은 한국생산기술연구원 기관주요사업과 한국연구재단 기초연구사업 (No. 2018R1D1A1B07043406)에서 지원하여 연구하였음.

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