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김유성 프로필 사진

김유성

KIM, YUSUNG

소프트웨어융합대학

연구분야

2016

2026

연도별 연구 활동 현황

전체 30건 중 1번부터 10번까지의 결과를 표시합니다.

2026

Article

Learning-based Channel Estimation and Beamforming Framework for Battery-Free Backscatter Communications

2025

Article

Self-supervised risk factor model using dual Recurrent State Space Models

Article

Implementation of reinforcement learning for enhanced pressure control in a 190,000-barrel crude distillation unit: The first full-scale commercial deployment

Article

Graph-Assisted Stitching for Offline Hierarchical Reinforcement Learning

Article

Graph-Assisted Stitching for Offline Hierarchical Reinforcement Learning

Article

Temporal Distance-aware Subgoal Generation for Offline Hierarchical Reinforcement Learning

2024

Article

Cross-Domain Semantic Segmentation on Inconsistent Taxonomy Using VLMs

Article

Novelty-aware Graph Traversal and Expansion for Hierarchical Reinforcement Learning

Article

Self-supervised One-Stage Learning for RF-based Multi-Person Pose Estimation

Article

Prediction of intraoperative hypotension using deep learning models based on non-invasive monitoring devices

상세 보기

김유성

연구분야

자료유형

발행연도

키워드

언어

Learning-based Channel Estimation and Beamforming Framework for Battery-Free Backscatter Communications

Self-supervised risk factor model using dual Recurrent State Space Models

Implementation of reinforcement learning for enhanced pressure control in a 190,000-barrel crude distillation unit: The first full-scale commercial deployment

Graph-Assisted Stitching for Offline Hierarchical Reinforcement Learning

Graph-Assisted Stitching for Offline Hierarchical Reinforcement Learning

Temporal Distance-aware Subgoal Generation for Offline Hierarchical Reinforcement Learning

Cross-Domain Semantic Segmentation on Inconsistent Taxonomy Using VLMs

Novelty-aware Graph Traversal and Expansion for Hierarchical Reinforcement Learning

Self-supervised One-Stage Learning for RF-based Multi-Person Pose Estimation

Prediction of intraoperative hypotension using deep learning models based on non-invasive monitoring devices