249 citations · 291 across the 31 of their papers we have counts for
33 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…
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 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…
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…
Quantum Multi-Agent Actor-Critic Neural Networks for Internet-Connected Multi-Robot Coordination in Smart Factory Management
Won Joon Yun, Jae Pyoung Kim, Soyi Jung +2
As one of the latest fields of interest in both academia and industry, quantum computing has garnered significant attention. Among various topics in quantum computing, variational…
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…
Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Coding
Won Joon Yun, Jae Pyoung Kim, Hankyul Baek +4
While witnessing the noisy intermediate-scale quantum (NISQ) era and beyond, quantum federated learning (QFL) has recently become an emerging field of study. In QFL, each quantum c…
Neural Architectural Nonlinear Pre-Processing for mmWave Radar-based Human Gesture Perception
Hankyul Baek, Yoo Jeong, Ha +3
In modern on-driving computing environments, many sensors are used for context-aware applications. This paper utilizes two deep learning models, U-Net and EfficientNet, which consi…
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…
Cooperative Multi-Agent Deep Reinforcement Learning for Reliable and Energy-Efficient Mobile Access via Multi-UAV Control
Chanyoung Park, Soohyun Park, Soyi Jung +2
This paper addresses a novel multi-agent deep reinforcement learning (MADRL)-based positioning algorithm for multiple unmanned aerial vehicles (UAVs) collaboration (i.e., UAVs work…
Spatio-Temporal Attack Course-of-Action (COA) Search Learning for Scalable and Time-Varying Networks
Haemin Lee, Seok Bin Son, Won Joon Yun +3
One of the key topics in network security research is the autonomous COA (Couse-of-Action) attack search method. Traditional COA attack search methods that passively search for att…
Slimmable Quantum Federated Learning
Won Joon Yun, Jae Pyoung Kim, Soyi Jung +3
Quantum federated learning (QFL) has recently received increasing attention, where quantum neural networks (QNNs) are integrated into federated learning (FL). In contrast to the ex…
Search Space Adaptation for Differentiable Neural Architecture Search in Image Classification
Youngkee Kim, Soyi Jung, Minseok Choi +1
As deep neural networks achieve unprecedented performance in various tasks, neural architecture search (NAS), a research field for designing neural network architectures with autom…
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…