249 citations · 263 across the 26 of their papers we have counts for
29 papers
From LLM Plans to Authorized Motion: Execution-Bound Authorization for Excavators
Mehdi Heydari Shahna, Seihun Kim, Soyi Jung +3
An LLM-directed excavator can execute mechanically feasible motion whose destination or authority no longer matches the operator's approval. We propose execution-bound authorizatio…
IL-ACT: Imitation Learning with Adaptive Cartesian Tracking Control for a 30-ton Excavator
Mehdi Heydari Shahna, Seihun Kim, Soyi Jung +3
Autonomous excavator control is challenged by coupled kinematics, actuation lag, and uncertainty. We propose imitation learning and adaptive Cartesian tracking (IL-ACT), a novel mo…
How Can Quantum Deep Learning Improve Large Language Models?
Emily Jimin Roh, Hyojun Ahn, Samuel Yen-Chi Chen +2
The rapid progress of large language models (LLMs) has transformed natural language processing, yet the challenge of efficient adaptation remains unresolved. Full fine-tuning achie…
Hallucination-Aware Generative Pretrained Transformer for Cooperative Aerial Mobility Control
Hyojun Ahn, Seungcheol Oh, Gyu Seon Kim +3
This paper proposes SafeGPT, a two-tiered framework that integrates generative pretrained transformers (GPTs) with reinforcement learning (RL) for efficient and reliable unmanned a…
Quantum Multi-Agent Reinforcement Learning for Cooperative Mobile Access in Space-Air-Ground Integrated Networks
Gyu Seon Kim, Yeryeong Cho, Jaehyun Chung +4
Achieving global space-air-ground integrated network (SAGIN) access only with CubeSats presents significant challenges such as the access sustainability limitations in specific reg…
Quantum Neural Network Software Testing, Analysis, and Code Optimization for Advanced IoT Systems: Design, Implementation, and Visualization
Soohyun Park, Joongheon Kim
This paper introduces a novel run-time testing, analysis, and code optimization (TACO) method for quantum neural network (QNN) software in advanced Internet-of-Things (IoT) systems…
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…
Situation-Aware Deep Reinforcement Learning for Autonomous Nonlinear Mobility Control in Cyber-Physical Loitering Munition Systems
Hyunsoo Lee, Soohyun Park, Won Joon Yun +2
According to the rapid development of drone technologies, drones are widely used in many applications including military domains. In this paper, a novel situation-aware DRL- based…
Software Simulation and Visualization of Quantum Multi-Drone Reinforcement Learning
Chanyoung Park, Jae Pyoung Kim, Won Joon Yun +3
Quantum machine learning (QML) has received a lot of attention according to its light training parameter numbers and speeds; and the advances of QML lead to active research on quan…
Multi-Agent Deep Reinforcement Learning for Efficient Passenger Delivery in Urban Air Mobility
Chanyoung Park, Soohyun Park, Gyu Seon Kim +3
It has been considered that urban air mobility (UAM), also known as drone-taxi or electrical vertical takeoff and landing (eVTOL), will play a key role in future transportation. By…
Age-of-Information Aware Contents Caching and Distribution for Connected Vehicles
Soohyun Park, Chanyoung Park, Soyi Jung +2
To support rapid and accurate autonomous driving services, road environment information, which is difficult to obtain through vehicle sensors themselves, is collected and utilized…
Self-Configurable Stabilized Real-Time Detection Learning for Autonomous Driving Applications
Won Joon Yun, Soohyun Park, Joongheon Kim +1
Guaranteeing real-time and accurate object detection simultaneously is paramount in autonomous driving environments. However, the existing object detection neural network systems a…
Tutorial on Course-of-Action (COA) Attack Search Methods in Computer Networks
Seok Bin Son, Soohyun Park, Haemin Lee +3
In the literature of modern network security research, deriving effective and efficient course-of-action (COA) attach search methods are of interests in industry and academia. As t…
Quality-Aware Real-Time Augmented Reality Visualization under Delay Constraints
Rhoan Lee, Soohyun Park, Soyi Jung +1
Augmented reality (AR) is one of emerging applications in modern multimedia systems research. Due to intensive time-consuming computations for AR visualization in mobile devices, q…
AoI-Aware Markov Decision Policies for Caching
Soohyun Park, Soyi Jung, Minseok Choi +1
We consider a scenario that utilizes road side units (RSUs) as distributed caches in connected vehicular networks. The goal of the use of caches in our scenario is for rapidly prov…