18 citations · 33 across the 10 of their papers we have counts for
10 papers
Finite element model updating of building structures under seismic excitation: A parallelized latent space-based Bayesian framework
Taro Yaoyama, Sangwon Lee, Minoru Matsubara +3
Enhancing seismic fragility and risk assessment of nuclear power plants relies on accurate prediction of reactor building responses to seismic hazards, which can be further improve…
Transfer Learning-Based Surrogate Modeling for Nonlinear Time-History Response Analysis of High-Fidelity Structural Models
Keiichi Ishikawa, Yuma Matsumoto, Taro Yaoyama +2
In a performance based earthquake engineering (PBEE) framework, nonlinear time-history response analysis (NLTHA) for numerous ground motions are required to assess the seismic risk…
Waveform-Based Probabilistic Seismic Hazard Analysis Using Ground-Motion Generative Models
Yuma Matsumoto, Taro Yaoyama, Sangwon Lee +2
In probabilistic seismic hazard analysis (PSHA), the exceedance probability of a ground-motion intensity measure (IM) is typically evaluated. However, in recent years, dynamic resp…
CrimEdit: Controllable Editing for Counterfactual Object Removal, Insertion, and Movement
Boseong Jeon, Junghyuk Lee, Jimin Park +4
Recent works on object removal and insertion have enhanced their performance by handling object effects such as shadows and reflections, using diffusion models trained on counterfa…
Data augmentation using diffusion models to enhance inverse Ising inference
Yechan Lim, Sangwon Lee, Junghyo Jo
Identifying model parameters from observed configurations poses a fundamental challenge in data science, especially with limited data. Recently, diffusion models have emerged as a…
Latent Space-based Stochastic Model Updating
Sangwon Lee, Taro Yaoyama, Masaru Kitahara +1
Model updating of engineering systems inevitably involves handling both aleatory or inherent randomness and epistemic uncertainties or uncertainities arising from a lack of knowled…
Bayesian Structural Model Updating with Multimodal Variational Autoencoder
Tatsuya Itoi, Kazuho Amishiki, Sangwon Lee +1
A novel framework for Bayesian structural model updating is presented in this study. The proposed method utilizes the surrogate unimodal encoders of a multimodal variational autoen…
Site-Specific Ground Motion Generative Model for Crustal Earthquakes in Japan Based on Generative Adversarial Networks
Yuma Matsumoto, Taro Yaoyama, Sangwon Lee +2
We develop a site-specific ground-motion model (GMM) for crustal earthquakes in Japan that can directly model the probability distribution of ground motion acceleration time histor…
Latent Space-Based Likelihood Estimation Using a Single Observation for Bayesian Updating of a Nonlinear Hysteretic Model
Sangwon Lee, Taro Yaoyama, Yuma Matsumoto +2
This study presents a novel approach to quantifying uncertainties in Bayesian model updating, which is effective in sparse or single observations. Conventional uncertainty quantifi…
Emotion Recognition Using Transformers with Masked Learning
Seongjae Min, Junseok Yang, Sangjun Lim +3
In recent years, deep learning has achieved innovative advancements in various fields, including the analysis of human emotions and behaviors. Initiatives such as the Affective Beh…