7 citations · 15 across the 18 of their papers we have counts for
6 papers · 1 filter
ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting
Myung Jin Kim, YeongHyeon Park, Il Dong Yun
This paper proposes a simple yet effective convolutional module for long-term time series forecasting. The proposed block, inspired by the Auto-Regressive Integrated Moving Average…
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…
Concise Logarithmic Loss Function for Robust Training of Anomaly Detection Model
YeongHyeon Park
Recently, deep learning-based algorithms are widely adopted due to the advantage of being able to establish anomaly detection models without or with minimal domain knowledge of the…
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…
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…
Comparison of RNN Encoder-Decoder Models for Anomaly Detection
YeongHyeon Park, Il Dong Yun
In this paper, we compare different types of Recurrent Neural Network (RNN) Encoder-Decoders in anomaly detection viewpoint. We focused on finding the model that can learn the same…