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pro vyhledávání: '"Kim, Jin Sung"'
Autor:
Kim, Kwanyoung, Oh, Yujin, Park, Sangjoon, Byun, Hwa Kyung, Lee, Joongyo, Kim, Jin Sung, Kim, Yong Bae, Ye, Jong Chul
Recent advances in AI foundation models have significant potential for lightening the clinical workload by mimicking the comprehensive and multi-faceted approaches used by medical professionals. In the field of radiation oncology, the integration of
Externí odkaz:
http://arxiv.org/abs/2311.15876
Autor:
Oh, Yujin, Park, Sangjoon, Byun, Hwa Kyung, Cho, Yeona, Lee, Ik Jae, Kim, Jin Sung, Ye, Jong Chul
Target volume contouring for radiation therapy is considered significantly more challenging than the normal organ segmentation tasks as it necessitates the utilization of both image and text-based clinical information. Inspired by the recent advancem
Externí odkaz:
http://arxiv.org/abs/2311.01908
This paper proposes Koopman operator-based Stochastic Model Predictive Control (K-SMPC) for enhanced lateral control of autonomous vehicles. The Koopman operator is a linear map representing the nonlinear dynamics in an infinite-dimensional space. Th
Externí odkaz:
http://arxiv.org/abs/2310.10214
Publikováno v:
2024 American Control Conference
This paper proposes a method for uncertainty quantification of an autoencoder-based Koopman operator. The main challenge of using the Koopman operator is to design the basis functions for lifting the state. To this end, this paper builds an autoencod
Externí odkaz:
http://arxiv.org/abs/2309.09419
This paper proposes a Recurrent Neural Network (RNN) controller for lane-keeping systems, effectively handling model uncertainties and disturbances. First, quadratic constraints cover the nonlinearities brought by the RNN controller, and the linear f
Externí odkaz:
http://arxiv.org/abs/2309.08852
This paper proposes a local path planning method with a reachable set for Automated vertical Parking Systems (APS). First, given a parking lot layout with a goal position, we define an intermediate pose for the APS to accomplish reverse parking with
Externí odkaz:
http://arxiv.org/abs/2308.05992
Classification Method of Road Surface Condition and Type with LiDAR Using Spatiotemporal Information
This paper proposes a spatiotemporal architecture with a deep neural network (DNN) for road surface conditions and types classification using LiDAR. It is known that LiDAR provides information on the reflectivity and number of point clouds depending
Externí odkaz:
http://arxiv.org/abs/2308.05965
Autor:
Layden, David, Mazzola, Guglielmo, Mishmash, Ryan V., Motta, Mario, Wocjan, Pawel, Kim, Jin-Sung, Sheldon, Sarah
Publikováno v:
Nature 619, 282-287 (2023)
Sampling from complicated probability distributions is a hard computational problem arising in many fields, including statistical physics, optimization, and machine learning. Quantum computers have recently been used to sample from complicated distri
Externí odkaz:
http://arxiv.org/abs/2203.12497