@article{ART002504180},
author={Doyeob Yeo and Ji-Hoon Bae and Jae Cheol Lee},
title={Unsupervised Learning-Based Pipe Leak Detection using Deep Auto-Encoder},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2019},
volume={24},
number={9},
pages={21-27},
doi={10.9708/jksci.2019.24.09.021}
TY - JOUR
AU - Doyeob Yeo
AU - Ji-Hoon Bae
AU - Jae Cheol Lee
TI - Unsupervised Learning-Based Pipe Leak Detection using Deep Auto-Encoder
JO - Journal of The Korea Society of Computer and Information
PY - 2019
VL - 24
IS - 9
PB - The Korean Society Of Computer And Information
SP - 21
EP - 27
SN - 1598-849X
AB - In this paper, we propose a deep auto-encoder-based pipe leak detection (PLD) technique from time-series acoustic data collected by microphone sensor nodes. The key idea of the proposed technique is to learn representative features of the leak-free state using leak-free time-series acoustic data and the deep auto-encoder. The proposed technique can be used to create a PLD model that detects leaks in the pipeline in an unsupervised learning manner. This means that we only use leak-free data without labeling while training the deep auto-encoder. In addition, when compared to the previous supervised learning-based PLD method that uses image features, this technique does not require complex preprocessing of time-series acoustic data owing to the unsupervised feature extraction scheme. The experimental results show that the proposed PLD method using the deep auto-encoder can provide reliable PLD accuracy even considering unsupervised learning-based feature extraction.
KW - Pipe leak detection;Acoustic signal;Unsupervised learning;Auto-encoder;Deep learning
DO - 10.9708/jksci.2019.24.09.021
ER -
Doyeob Yeo, Ji-Hoon Bae and Jae Cheol Lee. (2019). Unsupervised Learning-Based Pipe Leak Detection using Deep Auto-Encoder. Journal of The Korea Society of Computer and Information, 24(9), 21-27.
Doyeob Yeo, Ji-Hoon Bae and Jae Cheol Lee. 2019, "Unsupervised Learning-Based Pipe Leak Detection using Deep Auto-Encoder", Journal of The Korea Society of Computer and Information, vol.24, no.9 pp.21-27. Available from: doi:10.9708/jksci.2019.24.09.021
Doyeob Yeo, Ji-Hoon Bae, Jae Cheol Lee "Unsupervised Learning-Based Pipe Leak Detection using Deep Auto-Encoder" Journal of The Korea Society of Computer and Information 24.9 pp.21-27 (2019) : 21.
Doyeob Yeo, Ji-Hoon Bae, Jae Cheol Lee. Unsupervised Learning-Based Pipe Leak Detection using Deep Auto-Encoder. 2019; 24(9), 21-27. Available from: doi:10.9708/jksci.2019.24.09.021
Doyeob Yeo, Ji-Hoon Bae and Jae Cheol Lee. "Unsupervised Learning-Based Pipe Leak Detection using Deep Auto-Encoder" Journal of The Korea Society of Computer and Information 24, no.9 (2019) : 21-27.doi: 10.9708/jksci.2019.24.09.021
Doyeob Yeo; Ji-Hoon Bae; Jae Cheol Lee. Unsupervised Learning-Based Pipe Leak Detection using Deep Auto-Encoder. Journal of The Korea Society of Computer and Information, 24(9), 21-27. doi: 10.9708/jksci.2019.24.09.021
Doyeob Yeo; Ji-Hoon Bae; Jae Cheol Lee. Unsupervised Learning-Based Pipe Leak Detection using Deep Auto-Encoder. Journal of The Korea Society of Computer and Information. 2019; 24(9) 21-27. doi: 10.9708/jksci.2019.24.09.021
Doyeob Yeo, Ji-Hoon Bae, Jae Cheol Lee. Unsupervised Learning-Based Pipe Leak Detection using Deep Auto-Encoder. 2019; 24(9), 21-27. Available from: doi:10.9708/jksci.2019.24.09.021
Doyeob Yeo, Ji-Hoon Bae and Jae Cheol Lee. "Unsupervised Learning-Based Pipe Leak Detection using Deep Auto-Encoder" Journal of The Korea Society of Computer and Information 24, no.9 (2019) : 21-27.doi: 10.9708/jksci.2019.24.09.021