# Anomaly Detection in Particulate Matter Sensor using Hypothesis Pruning Generative Adversarial Network

arXiv: https://arxiv.org/abs/1912.00583 · HTML: https://mlsanity.com/paper/1912.00583/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2019.

## Built on by (most cited first, 9 of 9)

- [PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies](https://mlsanity.com/paper/2303.10704/) (2023)
- [Neural Network Training Strategy to Enhance Anomaly Detection Performance: A Perspective on Reconstruction Loss Amplification](https://mlsanity.com/paper/2308.14595/) (2023)
- [Efficient Non-Compression Auto-Encoder for Driving Noise-based Road Surface Anomaly Detection](https://mlsanity.com/paper/2111.10985/) (2021)
- [Non-Compression Auto-Encoder for Detecting Road Surface Abnormality via Vehicle Driving Noise](https://mlsanity.com/paper/2103.12992/) (2021)
- [Excision And Recovery: Visual Defect Obfuscation Based Self-Supervised Anomaly Detection Strategy](https://mlsanity.com/paper/2310.04010/) (2023)
- [Constricting Normal Latent Space for Anomaly Detection with Normal-only Training Data](https://mlsanity.com/paper/2403.16270/) (2024)
- [Anomaly Detection Based on Multiple-Hypothesis Autoencoder](https://mlsanity.com/paper/2107.08790/) (2021)
- [Latent Vector Expansion using Autoencoder for Anomaly Detection](https://mlsanity.com/paper/2201.01416/) (2022)
- [Concise Logarithmic Loss Function for Robust Training of Anomaly Detection Model](https://mlsanity.com/paper/2201.05748/) (2022)

## Abstract (arXiv)

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.