Jinkyoo Park
Jinkyoo Park
Department of Industrial and Systems Engineering, KAIST
Verified email at - Homepage
Cited by
Cited by
Graph neural ordinary differential equations
M Poli, S Massaroli, J Park, A Yamashita, H Asama, J Park
arXiv preprint arXiv:1911.07532, 2019
Learning to schedule job-shop problems: representation and policy learning using graph neural network and reinforcement learning
J Park, J Chun, SH Kim, Y Kim, J Park
International Journal of Production Research 59 (11), 3360-3377, 2021
Dissecting neural odes
S Massaroli, M Poli, J Park, A Yamashita, H Asama
Advances in Neural Information Processing Systems 33, 3952-3963, 2020
Layout optimization for maximizing wind farm power production using sequential convex programming
J Park, K Law
Applied Energy 151, 320-334, 2015
Electromagnetic energy harvester with repulsively stacked multilayer magnets for low frequency vibrations
SD Kwon, J Park, K Law
Smart materials and structures 22 (5), 055007, 2013
A data-driven, cooperative wind farm control to maximize the total power production
J Park, KH Law
Applied Energy 165, 151-165, 2016
Multi-agent actor-critic with hierarchical graph attention network
H Ryu, H Shin, J Park
Proceedings of the AAAI Conference on Artificial Intelligence 34 (05), 7236-7243, 2020
Toward a generalized energy prediction model for machine tools
R Bhinge, J Park, KH Law, DA Dornfeld, M Helu, S Rachuri
Journal of manufacturing science and engineering 139 (4), 041013, 2017
Cooperative wind turbine control for maximizing wind farm power using sequential convex programming
J Park, KH Law
Energy Conversion and Management 101, 295-316, 2015
Demand-side management with shared energy storage system in smart grid
J Jo, J Park
IEEE Transactions on Smart Grid 11 (5), 4466-4476, 2020
Wind farm power maximization based on a cooperative static game approach
J Park, S Kwon, KH Law
Active and Passive Smart Structures and Integrated Systems 2013 8688, 204-218, 2013
Physics-induced graph neural network: An application to wind-farm power estimation
J Park, J Park
Energy 187, 115883, 2019
Learning collaborative policies to solve np-hard routing problems
M Kim, J Park
Advances in Neural Information Processing Systems 34, 10418-10430, 2021
Large‐eddy simulation of stable boundary layer turbulence and estimation of associated wind turbine loads
J Park, S Basu, L Manuel
Wind Energy 17 (3), 359-384, 2014
Bayesian Ascent: A Data-Driven Optimization Scheme for Real-Time Control With Application to Wind Farm Power Maximization
J Park, KH Law
IEEE Transactions on Control Systems Technology,, 1-14, 2016
Hypergraph convolutional recurrent neural network
J Yi, J Park
Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020
Classification of heart sound recordings using convolution neural network
H Ryu, J Park, H Shin
2016 Computing in cardiology conference (CinC), 1153-1156, 2016
An intelligent machine monitoring system for energy prediction using a Gaussian Process regression
R Bhinge, N Biswas, D Dornfeld, J Park, KH Law, M Helu, S Rachuri
2014 IEEE International Conference on Big Data (Big Data), 978-986, 2014
Hypersolvers: Toward fast continuous-depth models
M Poli, S Massaroli, A Yamashita, H Asama, J Park
Advances in Neural Information Processing Systems 33, 21105-21117, 2020
A generalized data-driven energy prediction model with uncertainty for a milling machine tool using Gaussian Process
J Park, KH Law, R Bhinge, N Biswas, A Srinivasan, DA Dornfeld, M Helu, ...
International Manufacturing Science and Engineering Conference 56833 …, 2015
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