Výsledky vyhledávání

Report

Deep learning-based modularized loading protocol for parameter estimation of Bouc-Wen class models

Autor: Oh, Sebin, Song, Junho, Kim, Taeyong

This study proposes a modularized deep learning-based loading protocol for optimal parameter estimation of Bouc-Wen (BW) class models. The protocol consists of two key components: optimal loading history construction and CNN-based rapid parameter est

Externí odkaz: http://arxiv.org/abs/2411.02776

Zobrazit plný text záznamu

Report

Foundation Models for ECG: Leveraging Hybrid Self-Supervised Learning for Advanced Cardiac Diagnostics

Autor: Song, Junho, Jang, Jong-Hwan, Lee, Byeong Tak, Hong, DongGyun, Kwon, Joon-myoung, Jo, Yong-Yeon

Using foundation models enhanced by self-supervised learning (SSL) methods presents an innovative approach to electrocardiogram (ECG) analysis, which is crucial for cardiac health monitoring and diagnosis. This study comprehensively evaluates foundat

Externí odkaz: http://arxiv.org/abs/2407.07110

Zobrazit plný text záznamu

Report

MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detection

Autor: Song, Junho, Kim, Keonwoo, Oh, Jeonglyul, Cho, Sungzoon

Detecting anomalies in real-world multivariate time series data is challenging due to complex temporal dependencies and inter-variable correlations. Recently, reconstruction-based deep models have been widely used to solve the problem. However, these

Externí odkaz: http://arxiv.org/abs/2312.02530

Zobrazit plný text záznamu

Report

Efficient seismic reliability and fragility analysis of lifeline networks using subset simulation

Autor: Lee, Dongkyu, Wang, Ziqi, Song, Junho

Various simulation-based and analytical methods have been developed to evaluate the seismic fragilities of individual structures. However, a community's seismic safety and resilience are substantially affected by network reliability, determined not o

Externí odkaz: http://arxiv.org/abs/2310.10232

Zobrazit plný text záznamu

Report

Adaptive active subspace-based metamodeling for high-dimensional reliability analysis

Autor: Kim, Jungho, Wang, Ziqi, Song, Junho

To address the challenges of reliability analysis in high-dimensional probability spaces, this paper proposes a new metamodeling method that couples active subspace, heteroscedastic Gaussian process, and active learning. The active subspace is levera

Externí odkaz: http://arxiv.org/abs/2304.06252

Zobrazit plný text záznamu