Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data

Autor: Oh, YongKyung, Lim, Dongyoung, Kim, Sungil

Irregular sampling intervals and missing values in real-world time series data present challenges for conventional methods that assume consistent intervals and complete data. Neural Ordinary Differential Equations (Neural ODEs) offer an alternative a

Externí odkaz: http://arxiv.org/abs/2402.14989

Multi-modal lifelog data fusion for improved human activity recognition: A hybrid approach

Autor: Oh, YongKyung, Kim, Sungil

Publikováno v: In Information Fusion October 2024 110

Multichannel convolution neural network for gas mixture classification.

Autor: Oh, YongKyung¹ (AUTHOR), Lim, Chiehyeon¹ (AUTHOR), Lee, Junghye¹ (AUTHOR), Kim, Sewon² (AUTHOR), Kim, Sungil¹ (AUTHOR) sungil.kim@unist.ac.kr

Publikováno v: Annals of Operations Research. Aug2024, Vol. 339 Issue 1/2, p261-295. 35p.

Grid-Based Bayesian Bootstrap Approach for Real-Time Detection of Abnormal Vessel Behaviors From AIS Data in Maritime Logistics

Autor: Oh, YongKyung, Kim, Sungil

Publikováno v: IEEE Transactions on Automation Science and Engineering: A Publication of the IEEE Robotics and Automation Society; October 2024, Vol. 21 Issue: 4 p6680-6692, 13p

Time delay estimation of traffic congestion propagation due to accidents based on statistical causality.

Autor: Oh, YongKyung¹, Kwak, JiIn², Kim, Sungil^1,2 sungil.kim@unist.ac.kr

Publikováno v: Electronic Research Archive. 2023, Vol. 31 Issue 2, p1-17. 17p.