  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.
