Young-Keun Kim

Associate Professor

Handong Global University

South Korea

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Young-Keun Kim is an Associate Professor at Handong Global University in South Korea. His research areas focus on advanced methodologies in LiDAR technology, semi-supervised learning for industrial inspection, and innovative engineering solutions such as small-scale bladeless wind turbines. He has published several articles on topics including noise-robust fault diagnosis and enhancements in 3D object detection for autonomous driving environments.

Articles (18)

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2025

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2024

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2024

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2023

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2023

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2023

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2023

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2023

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2023

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2022

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2022

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2022

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2022

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2022

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2021

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2021

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2021

Collaborators (1)

Wonsuk Jung

Chungbuk National University

SOUTH KOREA

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Young-Keun Kim

Research Interests

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Articles (18)

LiDAR Extrinsic Calibration Without Accessing 3-D Point Cloud Data: A New Approach for LiDAR-Vehicle Alignment Inspection

A Novel Small-Scale Bladeless Wind Turbine Using Vortex-Induced Vibration and a Discrete Resonance-Shifting Module

Physics-Informed Time-Frequency Fusion Network With Attention for Noise-Robust Bearing Fault Diagnosis

Automatic One-Shot LiDAR Alignment Inspection System Using NIR Camera

Friction variable rubber pad using magnetorheological elastomer for robot grippers

Semi-supervised learning for steel surface inspection using magnetic flux leakage signal

Semi-supervised learning for steel surface inspection using magnetic flux leakage signal

Time-Frequency Multi-Domain 1D Convolutional Neural Network with Channel-Spatial Attention for Noise-Robust Bearing Fault Diagnosis

Time-Frequency Multi-Domain 1D Convolutional Neural Network with Channel-Spatial Attention for Noise-Robust Bearing Fault Diagnosis

Up-Sampling Method for Low-Resolution LiDAR Point Cloud to Enhance 3D Object Detection in an Autonomous Driving Environment

Up-Sampling Method for Low-Resolution LiDAR Point Cloud to Enhance 3D Object Detection in an Autonomous Driving Environment

Effects of Carbon Nanotube and Graphene Oxide Incorporation on the Improvements of Magneto-Induced Electrical Sensitivity of Magneto-Rheological Gel

State Estimation and Motion Prediction of Vehicles and Vulnerable Road Users for Cooperative Autonomous Driving: A Survey

Automatic Inspection System for Automotive LiDAR Alignment Using a Cubic Target

Evaluation of (CNT@CIP)-Embedded Magneto-Resistive Sensor Based on Carbon Nanotube and Carbonyl Iron Powder Polymer Composites

A standalone tunable vibration absorber with self-sensing magnetorheological elastomer

Developing a Compressed Object Detection Model Based on YOLOv4 for Deployment on Embedded GPU Platform of Autonomous System

A novel LiDAR sensor alignment inspection system for automobile productions using 1-D photodetector arrays

Collaborators (1)

Wonsuk Jung

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