Version 1
: Received: 3 January 2020 / Approved: 4 January 2020 / Online: 4 January 2020 (06:03:08 CET)
Version 2
: Received: 13 February 2020 / Approved: 14 February 2020 / Online: 14 February 2020 (05:23:15 CET)
How to cite:
Park, Y.H.; Park, W.S.; Kim, Y.B. Anomaly Detection in Particulate Matter Sensor Using Hypothesis Pruning Generative Adversarial Network. Preprints2020, 2020010028. https://doi.org/10.20944/preprints202001.0028.v2
Park, Y.H.; Park, W.S.; Kim, Y.B. Anomaly Detection in Particulate Matter Sensor Using Hypothesis Pruning Generative Adversarial Network. Preprints 2020, 2020010028. https://doi.org/10.20944/preprints202001.0028.v2
Park, Y.H.; Park, W.S.; Kim, Y.B. Anomaly Detection in Particulate Matter Sensor Using Hypothesis Pruning Generative Adversarial Network. Preprints2020, 2020010028. https://doi.org/10.20944/preprints202001.0028.v2
APA Style
Park, Y.H., Park, W.S., & Kim, Y.B. (2020). Anomaly Detection in Particulate Matter Sensor Using Hypothesis Pruning Generative Adversarial Network. Preprints. https://doi.org/10.20944/preprints202001.0028.v2
Chicago/Turabian Style
Park, Y.H., Won Seok Park and Yeong Beom Kim. 2020 "Anomaly Detection in Particulate Matter Sensor Using Hypothesis Pruning Generative Adversarial Network" Preprints. https://doi.org/10.20944/preprints202001.0028.v2
Abstract
World Health Organization (WHO) provides the guideline for managing the Particulate Matter (PM) level because when the PM level is higher, it threats the human health. For managing PM level, the procedure for measuring PM value is needed firstly. We use Tapered Element Oscillating Microbalance (TEOM)-based PM measuring sensors because it shows higher cost-effectiveness than Beta Attenuation Monitor (BAM)-based sensor. However, TEOM-based sensor has higher probability of malfunctioning than BAM-based sensor. In this paper, we call the overall malfunction as an anomaly, and we aim to detect anomalies for the maintenance of PM measuring sensors. We propose a novel architecture for solving the above aim that named as Hypothesis Pruning Generative Adversarial Network (HP-GAN). We experimentally compare the several anomaly detection architectures to certify ours performing better.
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received:
14 February 2020
Commenter:
Park YeongHyeon
Commenter's Conflict of Interests:
Author
Comment:
Author information has changed. Due to internal circumstances, one of the authors is removed. Also, the detail description of the manuscript is changed.
Commenter: Park YeongHyeon
Commenter's Conflict of Interests: Author