Zobrazeno 1 - 10
of 205
pro vyhledávání: '"Kim, Juhyeon"'
Autor:
Kim, Juhyeon, Lee, Hyungeun, Yu, Seungwon, Hwang, Ung, Jung, Wooyul, Park, Miseon, Yoon, Kijung
Multivariate time series is prevalent in many scientific and industrial domains. Modeling multivariate signals is challenging due to their long-range temporal dependencies and intricate interactions--both direct and indirect. To confront these comple
Externí odkaz:
http://arxiv.org/abs/2311.12630
Autor:
Lee, Chanhui, Kim, Juhyeon, Jeong, Yongjun, Lyu, Juhyun, Kim, Junghee, Lee, Sangmin, Han, Sangjun, Choe, Hyeokjun, Park, Soyeon, Lim, Woohyung, Lim, Sungbin, Lee, Sanghack
Scaling laws have allowed Pre-trained Language Models (PLMs) into the field of causal reasoning. Causal reasoning of PLM relies solely on text-based descriptions, in contrast to causal discovery which aims to determine the causal relationships betwee
Externí odkaz:
http://arxiv.org/abs/2311.11212
We introduce Doppler time-of-flight (D-ToF) rendering, an extension of ToF rendering for dynamic scenes, with applications in simulating D-ToF cameras. D-ToF cameras use high-frequency modulation of illumination and exposure, and measure the Doppler
Externí odkaz:
http://arxiv.org/abs/2309.16163
Autor:
Reed, Albert W., Kim, Juhyeon, Blanford, Thomas, Pediredla, Adithya, Brown, Daniel C., Jayasuriya, Suren
Synthetic aperture sonar (SAS) measures a scene from multiple views in order to increase the resolution of reconstructed imagery. Image reconstruction methods for SAS coherently combine measurements to focus acoustic energy onto the scene. However, i
Externí odkaz:
http://arxiv.org/abs/2306.09909
We propose IBL-NeRF, which decomposes the neural radiance fields (NeRF) of large-scale indoor scenes into intrinsic components. Recent approaches further decompose the baked radiance of the implicit volume into intrinsic components such that one can
Externí odkaz:
http://arxiv.org/abs/2210.08202
Publikováno v:
Neurocomputing 555 (2023) 126643
Machine learning using quantum convolutional neural networks (QCNNs) has demonstrated success in both quantum and classical data classification. In previous studies, QCNNs attained a higher classification accuracy than their classical counterparts un
Externí odkaz:
http://arxiv.org/abs/2208.14708
Autor:
Kim, Juhyeon, Shah, Parth, Bhavsar, Raj, Lim, Dongbin, Seo, Sojin, Hyung, Jisung, Park, Sangmin, Kwon, Joseph Sang-Il
Publikováno v:
In Chemical Engineering Journal 1 November 2024 499
Publikováno v:
In Control Engineering Practice November 2024 152
Publikováno v:
In Automation in Construction October 2024 166
Publikováno v:
In Chemical Engineering Journal 1 May 2024 487