What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
Jong-Hwan Kim mainly investigates Robot, Artificial intelligence, Control theory, Mobile robot and Evolutionary computation.
His work on Social robot as part of general Robot study is frequently linked to Self-governance, therefore connecting diverse disciplines of science.
The concepts of his Artificial intelligence study are interwoven with issues in Multi-objective optimization and Computer vision.
His Control theory research includes elements of Control engineering, Humanoid robot and Motion control.
His study in Mobile robot is interdisciplinary in nature, drawing from both Sliding mode control and Simulation.
His Evolutionary computation research is multidisciplinary, relying on both Evolutionary algorithm and Knapsack problem.
His most cited work include:
- Quantum-inspired evolutionary algorithm for a class of combinatorial optimization (1086 citations)
- Sliding mode control for trajectory tracking of nonholonomic wheeled mobile robots (507 citations)
- Genetic quantum algorithm and its application to combinatorial optimization problem (489 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Robot, Control theory, Mobile robot and Evolutionary algorithm are his primary areas of study.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning, Computer vision and Pattern recognition.
His work deals with themes such as Task and Human–computer interaction, which intersect with Robot.
The study incorporates disciplines such as Control engineering and Motion control in addition to Control theory.
His work on Mobile robot is being expanded to include thematically relevant topics such as Motion planning.
His Evolutionary algorithm research integrates issues from Evolutionary computation, Genetic algorithm, Theoretical computer science and Selection.
He most often published in these fields:
- Artificial intelligence (46.22%)
- Robot (32.95%)
- Control theory (19.22%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (46.22%)
- Robot (32.95%)
- Human–computer interaction (12.13%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Robot, Human–computer interaction, Computer vision and Humanoid robot.
His studies deal with areas such as Machine learning and Pattern recognition as well as Artificial intelligence.
His Robot research includes themes of Task, Episodic memory and Adaptive resonance theory.
His Computer vision study deals with Visual odometry intersecting with RGB color model.
His Humanoid robot research incorporates themes from Robot end effector, Simulation, Motion planning and Trajectory.
His research in Optimization problem focuses on subjects like Evolutionary algorithm, which are connected to Algorithm.
Between 2014 and 2021, his most popular works were:
- Effective Background Model-Based RGB-D Dense Visual Odometry in a Dynamic Environment (59 citations)
- Visual Odometry Algorithm Using an RGB-D Sensor and IMU in a Highly Dynamic Environment (26 citations)
- Short-Range Radar Based Real-Time Hand Gesture Recognition Using LSTM Encoder (24 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
Jong-Hwan Kim focuses on Artificial intelligence, Robot, Computer vision, Motion planning and Adaptive resonance theory.
In his study, Normalization, Class and Dependency is inextricably linked to Machine learning, which falls within the broad field of Artificial intelligence.
He interconnects Object, Task and Human–computer interaction in the investigation of issues within Robot.
He has researched Computer vision in several fields, including Artificial neural network and Visual odometry.
His Motion planning research is multidisciplinary, incorporating elements of Rate of convergence and Mathematical optimization.
The Mobile robot study which covers Evolutionary algorithm that intersects with Differential evolution, Particle swarm optimization and Evolutionary computation.
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