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1,876 results on '"Lee, Jehee"'

1. Bidirectional GaitNet: A Bidirectional Prediction Model of Human Gait and Anatomical Conditions

Author

Park, Jungnam, Park, Moon Seok, Lee, Jehee, and Won, Jungdam

Subjects
Computer Science - Graphics, Computer Science - Artificial Intelligence, I.3, I.6
Abstract

We present a novel generative model, called Bidirectional GaitNet, that learns the relationship between human anatomy and its gait. The simulation model of human anatomy is a comprehensive, full-body, simulation-ready, musculoskeletal model with 304 Hill-type musculotendon units. The Bidirectional GaitNet consists of forward and backward models. The forward model predicts a gait pattern of a person with specific physical conditions, while the backward model estimates the physical conditions of a person when his/her gait pattern is provided. Our simulation-based approach first learns the forward model by distilling the simulation data generated by a state-of-the-art predictive gait simulator and then constructs a Variational Autoencoder (VAE) with the learned forward model as its decoder. Once it is learned its encoder serves as the backward model. We demonstrate our model on a variety of healthy/impaired gaits and validate it in comparison with physical examination data of real patients.

Published
2023
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3. Genomic prediction of survival traits in the response of olive flounder (Paralichthys olivaceus) to viral hemorrhagic septicemia virus: Comparing machine learning models and traditional approaches

Author

Liyanage, D.S., Lee, Sukkyoung, Yang, Hyerim, Lim, Chaehyeon, Omeka, W.K.M., Sandamalika, W.M. Gayashani, Udayantha, H.M.V., Kim, Gaeun, Hanchapola, H.A.C.R., Ganeshalingam, Subothini, Jeong, Taehyug, Oh, Seong-Rip, Won, Seung-Hwan, Koh, Hyoung-Bum, Kim, Mun-Kwan, Jones, David B., Massault, Cecile, Jerry, Dean R., and Lee, Jehee

Published
2025
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6. Functional characterization of peroxiredoxin 5 from yellowtail clownfish (Amphiprion clarkii): Immunological expression assessment, antioxidant activities, heavy metal detoxification, and nitrosative stress mitigation

Author

Rodrigo, D.C.G., Udayantha, H.M.V., Liyanage, D.S., Omeka, W.K.M., Kodagoda, Y.K., Hanchapola, H.A.C.R., Dilshan, M.A.H., Ganepola, G.A.N.P., Warnakula, W.A.D.L.R., Kim, Gaeun, Kim, Jeongeun, Lee, Jihun, Wan, Qiang, and Lee, Jehee

Published
2025
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8. Insights into the functional properties of thioredoxin domain-containing protein 12 (TXNDC12): Antioxidant activity, immunological expression, and wound-healing effect in yellowtail clownfish (Amphiprion clarkii)

Author

Dilshan, M.A.H., Omeka, W.K.M., Udayantha, H.M.V., Liyanage, D.S., Rodrigo, D.C.G., Warnakula, W.A.D.L.R., Hanchapola, H.A.C.R., Kodagoda, Y.K., Ganepola, G.A.N.P., Kim, Jeongeun, Kim, Gaeun, Lee, Jihun, Jeong, Taehyug, Lee, Sukkyoung, Wan, Qiang, and Lee, Jehee

Published
2024
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10. Characterization of tumor necrosis factor receptor-associated factor 2 (TRAF2) in red-spotted grouper (Epinephelus akaara): In vivo and in vitro investigation of its role in the regulation of antiviral immunity and cell death

Author

Tharanga, E.M.T., Nadarajapillai, Kishanthini, Warnakula, W.A.D.L.R., Kim, Gaeun, Lim, Chaehyeon, Yang, Hyerim, Jayasinghe, J.D.H.E., Jeyakanesh, Jeganathan Tharshan, Sirisena, D.M.K.P., Arachchi, U.P.E., Wan, Qiang, and Lee, Jehee

Published
2025
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12. Human Motion Control of Quadrupedal Robots using Deep Reinforcement Learning

Author

Kim, Sunwoo, Sorokin, Maks, Lee, Jehee, and Ha, Sehoon

Subjects
Computer Science - Robotics
Abstract

A motion-based control interface promises flexible robot operations in dangerous environments by combining user intuitions with the robot's motor capabilities. However, designing a motion interface for non-humanoid robots, such as quadrupeds or hexapods, is not straightforward because different dynamics and control strategies govern their movements. We propose a novel motion control system that allows a human user to operate various motor tasks seamlessly on a quadrupedal robot. We first retarget the captured human motion into the corresponding robot motion with proper semantics using supervised learning and post-processing techniques. Then we apply the motion imitation learning with curriculum learning to develop a control policy that can track the given retargeted reference. We further improve the performance of both motion retargeting and motion imitation by training a set of experts. As we demonstrate, a user can execute various motor tasks using our system, including standing, sitting, tilting, manipulating, walking, and turning, on simulated and real quadrupeds. We also conduct a set of studies to analyze the performance gain induced by each component.

Published
2022

13. Generative GaitNet

Author

Park, Jungnam, Min, Sehee, Chang, Phil Sik, Lee, Jaedong, Park, Moonseok, and Lee, Jehee

Subjects
Computer Science - Graphics, Computer Science - Machine Learning
Abstract

Understanding the relation between anatomy andgait is key to successful predictive gait simulation. Inthis paper, we present Generative GaitNet, which isa novel network architecture based on deep reinforce-ment learning for controlling a comprehensive, full-body, musculoskeletal model with 304 Hill-type mus-culotendons. The Generative Gait is a pre-trained, in-tegrated system of artificial neural networks learnedin a 618-dimensional continuous domain of anatomyconditions (e.g., mass distribution, body proportion,bone deformity, and muscle deficits) and gait condi-tions (e.g., stride and cadence). The pre-trained Gait-Net takes anatomy and gait conditions as input andgenerates a series of gait cycles appropriate to theconditions through physics-based simulation. We willdemonstrate the efficacy and expressive power of Gen-erative GaitNet to generate a variety of healthy andpathologic human gaits in real-time physics-based sim-ulation., Comment: 12 pages, 6 figures and 1 table

Published
2022

24. Functionality-Driven Musculature Retargeting

Author

Ryu, Hoseok, Kim, Minseok, Lee, Seunghwan, Park, Moon Seok, Lee, Kyoungmin, and Lee, Jehee

Subjects
Computer Science - Graphics
Abstract

We present a novel retargeting algorithm that transfers the musculature of a reference anatomical model to new bodies with different sizes, body proportions, muscle capability, and joint range of motion while preserving the functionality of the original musculature as closely as possible. The geometric configuration and physiological parameters of musculotendon units are estimated and optimized to adapt to new bodies. The range of motion around joints is estimated from a motion capture dataset and edited further for individual models. The retargeted model is simulation-ready, so we can physically simulate muscle-actuated motor skills with the model. Our system is capable of generating a wide variety of anatomical bodies that can be simulated to walk, run, jump and dance while maintaining balance under gravity. We will also demonstrate the construction of individualized musculoskeletal models from bi-planar X-ray images and medical examinations., Comment: 15 pages, 20 figures

Published
2020
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25. Understanding the Stability of Deep Control Policies for Biped Locomotion

Author

Park, Hwangpil, Yu, Ri, Lee, Yoonsang, Lee, Kyungho, and Lee, Jehee

Subjects
Computer Science - Graphics, Computer Science - Machine Learning
Abstract

Achieving stability and robustness is the primary goal of biped locomotion control. Recently, deep reinforce learning (DRL) has attracted great attention as a general methodology for constructing biped control policies and demonstrated significant improvements over the previous state-of-the-art. Although deep control policies have advantages over previous controller design approaches, many questions remain unanswered. Are deep control policies as robust as human walking? Does simulated walking use similar strategies as human walking to maintain balance? Does a particular gait pattern similarly affect human and simulated walking? What do deep policies learn to achieve improved gait stability? The goal of this study is to answer these questions by evaluating the push-recovery stability of deep policies compared to human subjects and a previous feedback controller. We also conducted experiments to evaluate the effectiveness of variants of DRL algorithms., Comment: 11 pages, 12 figures and 3 tables

Published
2020

29. Identification of candidate variants and genes associated with temperature tolerance in olive flounders by Genome-Wide Association Study (GWAS)

Author

Udayantha, H.M.V., Lee, Sukkyoung, Liyanage, D.S., Lim, Chaehyeon, Jeong, Taehyug, Omeka, W.K.M., Yang, Hyerim, Kim, Gaeun, Kim, Joungeun, Lee, Jihun, Nadarajapillai, Kishanthini, Ganeshalingam, Subothini, Park, Cheong-Uk, Lee, Jiwon, Oh, Seong-Rip, Gong, Po, Jang, Yeounghwan, Hyun, Jaemin, Park, Aejeon, Koh, Hyoung-Bum, Kim, Mun-Kwan, Jones, David B., Massault, Cecile, Jerry, Dean R., and Lee, Jehee

Published
2023
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41. Effect of the Pauli exclusion principle in the electric dipole moment of $^9$Be with $|\Delta S|=1$ interactions

Author

Lee, Jehee, Yamanaka, Nodoka, and Hiyama, Emiko

Subjects
Nuclear Theory, High Energy Physics - Phenomenology
Abstract

We calculate the contribution of the $|\Delta S|=1$ $K$ meson exchange process generated by the Cabibbo-Kobayashi-Maskawa matrix to the electric dipole moment (EDM) of the $^9$Be nucleus by considering the $\alpha n - \alpha \Lambda$ channel coupling. It is found that the effect of the Pauli exclusion principle is not important intermediate $S=-1$ state, and that the result is consistent with the EDM of $^9$Be calculated with the $|\Delta S|=1$ interactions as a perturbation without considering the nucleus-hypernucleus mixing. Our result suggests that the effect of the $|\Delta S|=1$ interactions is neither suppressed nor enhanced in nuclei, if the difference of binding energies between the nucleus and the hypernucleus is small compared to the hyperon-nucleon mass difference., Comment: 9 pages, 10 figures

Published
2018
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