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pro vyhledávání: '"Lee Sangwon"'
Model updating of engineering systems inevitably involves handling both aleatory or inherent randomness and epistemic uncertainties or uncertainities arising from a lack of knowledge or information about the system. Addressing these uncertainties pos
Externí odkaz:
http://arxiv.org/abs/2410.03150
Publikováno v:
Computer Methods in Applied Mechanics and Engineering,Volume 429, 1 September 2024, 117148
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 autoencoder (VAE). The method facilitates an approximation of the likelihood
Externí odkaz:
http://arxiv.org/abs/2406.09051
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 histories based on generative adversarial networks (GANs). The proposed mode
Externí odkaz:
http://arxiv.org/abs/2404.15640
Publikováno v:
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, Vol. 10, No. 4, pp. 04024072, 2024
This study presents a novel approach to quantifying uncertainties in Bayesian model updating, which is effective in sparse or single observations. Conventional uncertainty quantification metrics such as the Euclidean and Bhattacharyya distance-based
Externí odkaz:
http://arxiv.org/abs/2404.03871
Publikováno v:
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 4860-4865
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 Behavior Analysis in-the-wild (ABAW) competition have been particularly i
Externí odkaz:
http://arxiv.org/abs/2403.13731
In robot-assisted minimally invasive surgery (RAMIS), optimal placement of the surgical robot base is crucial for successful surgery. Improper placement can hinder performance because of manipulator limitations and inaccessible workspaces. Convention
Externí odkaz:
http://arxiv.org/abs/2402.16101
This paper proposes TRAININGCXL that can efficiently process large-scale recommendation datasets in the pool of disaggregated memory while making training fault tolerant with low overhead. To this end, i) we integrate persistent memory (PMEM) and GPU
Externí odkaz:
http://arxiv.org/abs/2301.07492
Autor:
Lee, Sangwon1 (AUTHOR), Park, Eun Hye1,2 (AUTHOR), Jang, Bo Yun1,2 (AUTHOR), Kang, Ye Ji1 (AUTHOR), Jung, Kyu-Won3 (AUTHOR), Cha, Hyo Soung1,4 (AUTHOR) kkido@ncc.re.kr, Choi, Kui Son1,4 (AUTHOR) kschoi@ncc.re.kr
Publikováno v:
Scientific Reports. 9/29/2024, Vol. 14 Issue 1, p1-10. 10p.
Autor:
Kang, Hyung Cheol, Lee, Sangwon
Publikováno v:
In Research in International Business and Finance October 2024 72 Part B
Publikováno v:
In Fuel 15 September 2024 372