249 citations · 263 across the 26 of their papers we have counts for
10 papers · 1 filter
Handover Protocol Learning for LEO Satellite Networks: Access Delay and Collision Minimization
Ju-Hyung Lee, Chanyoung Park, Soohyun Park +1
This study presents a novel deep reinforcement learning (DRL)-based handover (HO) protocol, called DHO, specifically designed to address the persistent challenge of long propagatio…
Realizing Stabilized Landing for Computation-Limited Reusable Rockets: A Quantum Reinforcement Learning Approach
Gyu Seon Kim, JaeHyun Chung, Soohyun Park
The advent of reusable rockets has heralded a new era in space exploration, reducing the costs of launching satellites by a significant factor. Traditional rockets were disposable,…
Quantum Multi-Agent Reinforcement Learning for Autonomous Mobility Cooperation
Soohyun Park, Jae Pyoung Kim, Chanyoung Park +2
For Industry 4.0 Revolution, cooperative autonomous mobility systems are widely used based on multi-agent reinforcement learning (MARL). However, the MARL-based algorithms suffer f…
Two Tales of Platoon Intelligence for Autonomous Mobility Control: Enabling Deep Learning Recipes
Soohyun Park, Haemin Lee, Chanyoung Park +3
This paper presents the deep learning-based recent achievements to resolve the problem of autonomous mobility control and efficient resource management of autonomous vehicles and U…
Multi-Agent Reinforcement Learning for Cooperative Air Transportation Services in City-Wide Autonomous Urban Air Mobility
Chanyoung Park, Gyu Seon Kim, Soohyun Park +2
The development of urban-air-mobility (UAM) is rapidly progressing with spurs, and the demand for efficient transportation management systems is a rising need due to the multifacet…
Entropy-Aware Similarity for Balanced Clustering: A Case Study with Melanoma Detection
Seok Bin Son, Soohyun Park, Joongheon Kim
Clustering data is an unsupervised learning approach that aims to divide a set of data points into multiple groups. It is a crucial yet demanding subject in machine learning and da…
Coordinated Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Swarms in Autonomous Mobile Access Applications
Chanyoung Park, Haemin Lee, Won Joon Yun +2
This paper proposes a novel centralized training and distributed execution (CTDE)-based multi-agent deep reinforcement learning (MADRL) method for multiple unmanned aerial vehicles…
Workload-Aware Scheduling using Markov Decision Process for Infrastructure-Assisted Learning-Based Multi-UAV Surveillance Networks
Soohyun Park, Chanyoung Park, Soyi Jung +2
In modern networking research, infrastructure-assisted unmanned autonomous vehicles (UAVs) are actively considered for real-time learning-based surveillance and aerial data-deliver…
EQuaTE: Efficient Quantum Train Engine for Dynamic Analysis via HCI-based Visual Feedback
Soohyun Park, Won Joon Yun, Chanyoung Park +4
This paper proposes an efficient quantum train engine (EQuaTE), a novel tool for quantum machine learning software which plots gradient variances to check whether our quantum neura…
Quantum Multi-Agent Actor-Critic Networks for Cooperative Mobile Access in Multi-UAV Systems
Chanyoung Park, Won Joon Yun, Jae Pyoung Kim +4
This paper proposes a novel algorithm, named quantum multi-agent actor-critic networks (QMACN) for autonomously constructing a robust mobile access system employing multiple unmann…