A Novel End-to-End Ensemble UNet Network Based on Edge Computing for Tool Wear Detection-Reference-Cited by-同舟云学术

A Novel End-to-End Ensemble UNet Network Based on Edge Computing for Tool Wear Detection

Published:2024-03-22 Issue: Volume:10 Page:403-408
ISSN:
Container-title:Proceedings of the 2024 3rd Asia Conference on Algorithms, Computing and Machine Learning
language:
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Author:

Chen Jianwen¹^ORCID,Tsai Meng-Shiun²^ORCID,Lin Wan-Ju³^ORCID,Hung Che-Lun⁴^ORCID

Affiliation:

1. Department of Computer Science, National Tsing Hua University, Taiwan

2. Department of Mechanical Engineering, National Taiwan University, Taiwan

3. Department of Mold and Die Engineering, National Kaohsiung University of Science and Technology, Taiwan

4. Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taiwan

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3654823.3654896

Reference15 articles.

1. Automated classification of blood loss from transurethral resection of the prostate surgery videos using deep learning technique;Chen Jian-Wen;Applied Sciences,2020

2. ResUNet++: An Advanced Architecture for Medical Image Segmentation

3. Wan-Ju Lin, Jian-Wen Chen, Jian-Ping Jhuang, Meng-Shiun Tsai, Che-Lun Hung, Kuan-Ming Li, and Hong-Tsu Young. 2021. Integrating object detection and image segmentation for detecting the tool wear area on stitched image. Scientific reports 11, 1 (2021), 19938.

4. Sequential spindle current-based tool condition monitoring with support vector classifier for milling process;Lin Xiankun;The International Journal of Advanced Manufacturing Technology,2017

5. Zepeng Liu, Zi-Qiang Lang, Yufei Gui, Yun-Peng Zhu, Hatim Laalej, and David Curtis. 2023. Vibration Signal-based Tool Condition Monitoring Using Regularized Sensor Data Modelling and Model Frequency Analysis. IEEE Transactions on Instrumentation and Measurement (2023).