7 papers
Frequency of Interest-based Noise Attenuation Method to Improve Anomaly Detection Performance
YeongHyeon Park, Myung Jin Kim, Won Seok Park
Accurately extracting driving events is the way to maximize computational efficiency and anomaly detection performance in the tire frictional nose-based anomaly detection task. Thi…
Latent Vector Expansion using Autoencoder for Anomaly Detection
UJu Gim, YeongHyeon Park
Deep learning methods can classify various unstructured data such as images, language, and voice as input data. As the task of classifying anomalies becomes more important in the r…
Anomaly Detection Based on Multiple-Hypothesis Autoencoder
JoonSung Lee, YeongHyeon Park
Recently Autoencoder(AE) based models are widely used in the field of anomaly detection. A model trained with normal data generates a larger restoration error for abnormal data. Wh…
Self-Weighted Ensemble Method to Adjust the Influence of Individual Models based on Reliability
YeongHyeon Park, JoonSung Lee, Wonseok Park
Image classification technology and performance based on Deep Learning have already achieved high standards. Nevertheless, many efforts have conducted to improve the stability of c…
The CNN-based Coronary Occlusion Site Localization with Effective Preprocessing Method
YeongHyeon Park, Il Dong Yun, Si-Hyuck Kang
The Coronary Artery Occlusion (CAO) acutely comes to human, and it highly threats the human's life. When CAO detected, Percutaneous Coronary Intervention (PCI) should be conducted…
Anomaly Detection in Particulate Matter Sensor using Hypothesis Pruning Generative Adversarial Network
YeongHyeon Park, Won Seok Park, Yeong Beom Kim
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…