The World Health Organization provides guidelines for managing the particulate matter (PM) level because a higher PM level represents a threat to human health. To manage the PM level, a procedure for measuring the PM value is first needed. We use a PM sensor that collects the PM level by laser-based light scattering (LLS) method because it is more cost effective than a beta attenuation monitor-based sensor or tapered element oscillating microbalance-based sensor. However, an LLS-based sensor has a higher probability of malfunctioning than the higher cost sensors. In this paper, we regard the overall malfunctioning, including strange value collection or missing collection data as anomalies, and we aim to detect anomalies for the maintenance of PM measuring sensors. We propose a novel architecture for solving the above aim that we call the hypothesis pruning generative adversarial network (HP-GAN). Through comparative experiments, we achieve AUROC and AUPRC values of 0.948 and 0.967, respectively, in the detection of anomalies in LLS-based PM measuring sensors. We conclude that our HP-GAN is a cutting-edge model for anomaly detection.

中文翻译：

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使用假设修剪生成对抗网络的颗粒物传感器异常检测

世界卫生组织提供了管理颗粒物 (PM) 水平的指南，因为较高的 PM 水平代表着对人类健康的威胁。要管理 PM 水平，首先需要一个测量 PM 值的程序。我们使用 PM 传感器通过基于激光的光散射 (LLS) 方法收集 PM 水平，因为它比基于 β 衰减监测器的传感器或基于锥形元件振荡微量天平的传感器更具成本效益。然而，与成本较高的传感器相比，基于 LLS 的传感器发生故障的可能性更高。在本文中，我们将整体故障，包括奇怪的值收集或丢失的收集数据视为异常，我们旨在检测异常以维护 PM 测量传感器。我们提出了一种新的架构来解决上述目标，我们称之为假设修剪生成对抗网络（HP-GAN）。通过对比实验，我们在基于 LLS 的 PM 测量传感器的异常检测中分别实现了 0.948 和 0.967 的 AUROC 和 AUPRC 值。我们得出结论，我们的 HP-GAN 是用于异常检测的尖端模型。