Search Results for author: Joongheon Kim

Found 54 papers, 4 papers with code

Quantum Multi-Agent Actor-Critic Networks for Cooperative Mobile Access in Multi-UAV Systems

no code implementations9 Feb 2023 Chanyoung Park, Won Joon Yun, Jae Pyoung Kim, Tiago Koketsu Rodrigues, Soohyun Park, Soyi Jung, Joongheon Kim

This paper proposes a novel quantum multi-agent actor-critic networks (QMACN) algorithm for autonomously constructing a robust mobile access system using multiple unmanned aerial vehicles (UAVs).

Multi-agent Reinforcement Learning

Situation-Aware Deep Reinforcement Learning for Autonomous Nonlinear Mobility Control in Cyber-Physical Loitering Munition Systems

no code implementations31 Dec 2022 Hyunsoo Lee, Soohyun Park, Won Joon Yun, Soyi Jung, Joongheon Kim

Thus, our proposed autonomous nonlinear drone mobility control algorithm utilizes situation-aware components those are implemented with a Raycast function in Unity virtual scenarios.


Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Coding

no code implementations4 Dec 2022 Won Joon Yun, Jae Pyoung Kim, Hankyul Baek, Soyi Jung, Jihong Park, Mehdi Bennis, Joongheon Kim

While witnessing the noisy intermediate-scale quantum (NISQ) era and beyond, quantum federated learning (QFL) has recently become an emerging field of study.

Federated Learning Image Classification

Software Simulation and Visualization of Quantum Multi-Drone Reinforcement Learning

no code implementations24 Nov 2022 Chanyoung Park, Jae Pyoung Kim, Won Joon Yun, Soohyun Park, Soyi Jung, Joongheon Kim

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 quantum multi-agent reinforcement learning (QMARL).

Multi-agent Reinforcement Learning Quantum Machine Learning +2

Multi-Agent Deep Reinforcement Learning for Efficient Passenger Delivery in Urban Air Mobility

no code implementations13 Nov 2022 Chanyoung Park, Soohyun Park, Gyu Seon Kim, Soyi Jung, Jae-Hyun Kim, Joongheon Kim

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.

reinforcement-learning Reinforcement Learning (RL)

Quantum Split Neural Network Learning using Cross-Channel Pooling

no code implementations12 Nov 2022 Won Joon Yun, Hankyul Baek, Joongheon Kim

In recent years, quantum has been attracted by various fields such as quantum machine learning, quantum communication, and quantum computers.

Federated Learning Privacy Preserving +2

Neural Architectural Nonlinear Pre-Processing for mmWave Radar-based Human Gesture Perception

no code implementations7 Nov 2022 Hankyul Baek, Yoo Jeong, Ha, MinJae Yoo, Soyi Jung, Joongheon Kim

In modern on-driving computing environments, many sensors are used for context-aware applications.

Projection Valued Measure-based Quantum Machine Learning for Multi-Class Classification

no code implementations30 Oct 2022 Won Joon Yun, Hankyul Baek, Joongheon Kim

In recent years, quantum machine learning (QML) has been actively used for various tasks, e. g., classification, reinforcement learning, and adversarial learning.

Multi-class Classification Quantum Machine Learning

Seq2Seq-SC: End-to-End Semantic Communication Systems with Pre-trained Language Model

no code implementations27 Oct 2022 Ju-Hyung Lee, Dong-Ho Lee, Eunsoo Sheen, Thomas Choi, Jay Pujara, Joongheon Kim

While semantic communication is expected to bring unprecedented communication efficiency in comparison to classical communication, many challenges must be resolved to realize its potential.

Language Modelling Semantic Similarity +1

Learning Location from Shared Elevation Profiles in Fitness Apps: A Privacy Perspective

no code implementations27 Oct 2022 Ulku Meteriz-Yildiran, Necip Fazil Yildiran, Joongheon Kim, David Mohaisen

To preserve the privacy of users while allowing sharing, several of those platforms may allow users to disclose partial information, such as the elevation profile for an activity, which supposedly would not leak the location of the users.

Image Classification

3D Scalable Quantum Convolutional Neural Networks for Point Cloud Data Processing in Classification Applications

no code implementations18 Oct 2022 Hankyul Baek, Won Joon Yun, Joongheon Kim

Moreover, a quantum convolutional neural network (QCNN) is the quantum-version of CNN because it can process high-dimensional vector inputs in contrast to QNN.

Cooperative Multi-Agent Deep Reinforcement Learning for Reliable and Energy-Efficient Mobile Access via Multi-UAV Control

no code implementations3 Oct 2022 Chanyoung Park, Haemin Lee, Won Joon Yun, Soyi Jung, Carlos Cordeiro, Joongheon Kim

The reliable mobile access services can be achieved in following two ways, i. e., (i) energy-efficient UAV operation and (ii) reliable wireless communication services.

Self-Configurable Stabilized Real-Time Detection Learning for Autonomous Driving Applications

no code implementations29 Sep 2022 Won Joon Yun, Soohyun Park, Joongheon Kim, David Mohaisen

In addition, we demonstrate the self-configurable stabilized detection with YOLOv3-tiny and FlowNet2-S, which are the real-time object detection network and an optical flow estimation network, respectively.

Autonomous Driving object-detection +2

Scalable Quantum Convolutional Neural Networks

no code implementations26 Sep 2022 Hankyul Baek, Won Joon Yun, Joongheon Kim

With the beginning of the noisy intermediate-scale quantum (NISQ) era, quantum neural network (QNN) has recently emerged as a solution for the problems that classical neural networks cannot solve.

Spatio-Temporal Attack Course-of-Action (COA) Search Learning for Scalable and Time-Varying Networks

no code implementations2 Sep 2022 Haemin Lee, Seok Bin Son, Won Joon Yun, Joongheon Kim, Soyi Jung, Dong Hwa Kim

One of the key topics in network security research is the autonomous COA (Couse-of-Action) attack search method.

Quantum Multi-Agent Meta Reinforcement Learning

no code implementations22 Aug 2022 Won Joon Yun, Jihong Park, Joongheon Kim

Although quantum supremacy is yet to come, there has recently been an increasing interest in identifying the potential of quantum machine learning (QML) in the looming era of practical quantum computing.

Meta-Learning Meta Reinforcement Learning +4

Slimmable Quantum Federated Learning

no code implementations20 Jul 2022 Won Joon Yun, Jae Pyoung Kim, Soyi Jung, Jihong Park, Mehdi Bennis, Joongheon Kim

Quantum federated learning (QFL) has recently received increasing attention, where quantum neural networks (QNNs) are integrated into federated learning (FL).

Federated Learning

Search Space Adaptation for Differentiable Neural Architecture Search in Image Classification

no code implementations5 Jun 2022 Youngkee Kim, Soyi Jung, Minseok Choi, Joongheon Kim

As deep neural networks achieve unprecedented performance in various tasks, neural architecture search (NAS), a research field for designing neural network architectures with automated processes, is actively underway.

Image Classification Neural Architecture Search

Tutorial on Course-of-Action (COA) Attack Search Methods in Computer Networks

no code implementations27 May 2022 Seok Bin Son, Soohyun Park, Haemin Lee, Joongheon Kim, Soyi Jung, Donghwa Kim

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.

reinforcement-learning Reinforcement Learning (RL)

SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks

no code implementations26 Mar 2022 Won Joon Yun, Yunseok Kwak, Hankyul Baek, Soyi Jung, Mingyue Ji, Mehdi Bennis, Jihong Park, Joongheon Kim

However, applying FL in practice is challenging due to the local devices' heterogeneous energy, wireless channel conditions, and non-independently and identically distributed (non-IID) data distributions.

Distributed Computing Federated Learning

Feasibility Study of Multi-Site Split Learning for Privacy-Preserving Medical Systems under Data Imbalance Constraints in COVID-19, X-Ray, and Cholesterol Dataset

1 code implementation21 Feb 2022 Yoo Jeong Ha, Gusang Lee, MinJae Yoo, Soyi Jung, Seehwan Yoo, Joongheon Kim

It seems as though progressively more people are in the race to upload content, data, and information online; and hospitals haven't neglected this trend either.

Privacy Preserving

Quantum Distributed Deep Learning Architectures: Models, Discussions, and Applications

no code implementations19 Feb 2022 Yunseok Kwak, Won Joon Yun, Jae Pyoung Kim, Hyunhee Cho, Minseok Choi, Soyi Jung, Joongheon Kim

Although deep learning (DL) has already become a state-of-the-art technology for various data processing tasks, data security and computational overload problems often arise due to their high data and computational power dependency.

Two-stage architectural fine-tuning with neural architecture search using early-stopping in image classification

no code implementations17 Feb 2022 Youngkee Kim, Won Joon Yun, Youn Kyu Lee, Soyi Jung, Joongheon Kim

In many deep neural network (DNN) applications, the difficulty of gathering high-quality data in the industry field hinders the practical use of DNN.

Image Classification Neural Architecture Search +1

Neural Myerson Auction for Truthful and Energy-Efficient Autonomous Aerial Data Delivery

no code implementations29 Dec 2021 Haemin Lee, Sean Kwon, Soyi Jung, Joongheon Kim

In this paper, multiple delivery drones compete to offer data transfer to a single fixed-location surveillance drone.

Parallelized and Randomized Adversarial Imitation Learning for Safety-Critical Self-Driving Vehicles

no code implementations26 Dec 2021 Won Joon Yun, MyungJae Shin, Soyi Jung, Sean Kwon, Joongheon Kim

The RAIL is a novel derivative-free imitation learning method for autonomous driving with various ADAS functions coordination; and thus it imitates the operation of decision maker that controls autonomous driving with various ADAS functions.

Autonomous Driving Imitation Learning +1

Communication and Energy Efficient Slimmable Federated Learning via Superposition Coding and Successive Decoding

no code implementations5 Dec 2021 Hankyul Baek, Won Joon Yun, Soyi Jung, Jihong Park, Mingyue Ji, Joongheon Kim, Mehdi Bennis

To address the heterogeneous communication throughput problem, each full-width (1. 0x) SNN model and its half-width ($0. 5$x) model are superposition-coded before transmission, and successively decoded after reception as the 0. 5x or $1. 0$x model depending on the channel quality.

Federated Learning

Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks

no code implementations5 Dec 2021 Hankyul Baek, Won Joon Yun, Yunseok Kwak, Soyi Jung, Mingyue Ji, Mehdi Bennis, Jihong Park, Joongheon Kim

By applying SC, SlimFL exchanges the superposition of multiple width configurations that are decoded as many as possible for a given communication throughput.

Federated Learning

Spatio-Temporal Split Learning for Autonomous Aerial Surveillance using Urban Air Mobility (UAM) Networks

no code implementations15 Nov 2021 Yoo Jeong Ha, Soyi Jung, Jae-Hyun Kim, Marco Levorato, Joongheon Kim

This paper utilizes surveillance UAVs for the purpose of detecting the presence of a fire in the streets.

Stable Marriage Matching for Traffic-Aware Space-Air-Ground Integrated Networks: A Gale-Shapley Algorithmic Approach

no code implementations17 Oct 2021 Hyunsoo Lee, Haemin Lee, Soyi Jung, Joongheon Kim

In keeping with the rapid development of communication technology, a new communication structure is required in a next-generation communication system.

A Reliable, Self-Adaptive Face Identification Framework via Lyapunov Optimization

no code implementations2 Sep 2021 Dohyeon Kim, Joongheon Kim, Jae young Bang

Realtime face identification (FID) from a video feed is highly computation-intensive, and may exhaust computation resources if performed on a device with a limited amount of resources (e. g., a mobile device).

Face Identification

Spatio-Temporal Split Learning for Privacy-Preserving Medical Platforms: Case Studies with COVID-19 CT, X-Ray, and Cholesterol Data

no code implementations20 Aug 2021 Yoo Jeong Ha, MinJae Yoo, Gusang Lee, Soyi Jung, Sae Won Choi, Joongheon Kim, Seehwan Yoo

Since the centralized server does not need to access the training data and trains the deep neural network with parameters received from the privacy-preserving layer, privacy of original data is guaranteed.

Computed Tomography (CT) Privacy Preserving

Trends in Neural Architecture Search: Towards the Acceleration of Search

no code implementations19 Aug 2021 Youngkee Kim, Won Joon Yun, Youn Kyu Lee, Soyi Jung, Joongheon Kim

In modern deep learning research, finding optimal (or near optimal) neural network models is one of major research directions and it is widely studied in many applications.

Neural Architecture Search reinforcement-learning +1

Spatio-Temporal Split Learning

no code implementations13 Aug 2021 Joongheon Kim, Seunghoon Park, Soyi Jung, Seehwan Yoo

This paper proposes a novel split learning framework with multiple end-systems in order to realize privacypreserving deep neural network computation.

Joint Pilot Design and Channel Estimation using Deep Residual Learning for Multi-Cell Massive MIMO under Hardware Impairments

no code implementations10 Aug 2021 Byungju Lim, Won Joon Yun, Joongheon Kim, Young-Chai Ko

After that, a deep learning based pilot design is proposed to minimize the mean square error (MSE) of LMMSE channel estimation, which is utilized to the joint pilot design and channel estimator for transfer learning approach.

Transfer Learning

Quantum Neural Networks: Concepts, Applications, and Challenges

no code implementations2 Aug 2021 Yunseok Kwak, Won Joon Yun, Soyi Jung, Joongheon Kim

Quantum deep learning is a research field for the use of quantum computing techniques for training deep neural networks.

Quantum Scheduling for Millimeter-Wave Observation Satellite Constellation

no code implementations2 Aug 2021 Joongheon Kim, Yunseok Kwak, Soyi Jung, Jae-Hyun Kim

In beyond 5G and 6G network scenarios, the use of satellites has been actively discussed for extending target monitoring areas, even for extreme circumstances, where the monitoring functionalities can be realized due to the usage of millimeter-wave wireless links.


Distributed and Autonomous Aerial Data Collection in Smart City Surveillance Applications

no code implementations25 Jul 2021 Haemin Lee, Soyi Jung, Joongheon Kim

The data those are produced from surveillance cameras in aerial devices such as unmanned aerial networks (UAVs) are needed to be transferred to ground stations for secure data analysis.

Joint Mobile Charging and Coverage-Time Extension for Unmanned Aerial Vehicles

no code implementations27 Jun 2021 Soohyun Park, Won-Yong Shin, Minseok Choi, Joongheon Kim

To overcome this, we need to characterize a new type of drones, so-called charging drones, which can deliver energy to MBS drones.


Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication

no code implementations22 May 2021 Won Joon Yun, Byungju Lim, Soyi Jung, Young-Chai Ko, Jihong Park, Joongheon Kim, Mehdi Bennis

In this article, we study the problem of air-to-ground ultra-reliable and low-latency communication (URLLC) for a moving ground user.

Graph Attention reinforcement-learning +1

Multi-Agent Deep Reinforcement Learning using Attentive Graph Neural Architectures for Real-Time Strategy Games

no code implementations21 May 2021 Won Joon Yun, Sungwon Yi, Joongheon Kim

In real-time strategy (RTS) game artificial intelligence research, various multi-agent deep reinforcement learning (MADRL) algorithms are widely and actively used nowadays.

Graph Attention Reinforcement Learning (RL) +2

On the Performance of Generative Adversarial Network (GAN) Variants: A Clinical Data Study

no code implementations21 Sep 2020 Jaesung Yoo, Jeman Park, An Wang, David Mohaisen, Joongheon Kim

Generative Adversarial Network (GAN) is a useful type of Neural Networks in various types of applications including generative models and feature extraction.

A Tutorial on Quantum Convolutional Neural Networks (QCNN)

5 code implementations20 Sep 2020 Seunghyeok Oh, Jaeho Choi, Joongheon Kim

The second study introduces a method to improve the model's performance by adding a layer using quantum computing to the CNN learning model used in the existing computer vision.

Quantum Physics

Adversarial Imitation Learning via Random Search

no code implementations21 Aug 2020 MyungJae Shin, Joongheon Kim

As a result, research on imitation learning, which learns policy from a demonstration of experts, has begun to attract attention.

Imitation Learning reinforcement-learning +1

XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning

no code implementations9 Jun 2020 MyungJae Shin, Chihoon Hwang, Joongheon Kim, Jihong Park, Mehdi Bennis, Seong-Lyun Kim

User-generated data distributions are often imbalanced across devices and labels, hampering the performance of federated learning (FL).

Data Augmentation Federated Learning +1

Quantum Approximation for Multi-Scale Scheduling

1 code implementation arXiv 2020 Jaeho Choi, Seunghyeok Oh, Joongheon Kim

Then, for the given MWIS, the proposed QAOS designs the Hamiltonian of the problem.


Multi-Agent Deep Reinforcement Learning for Cooperative Connected Vehicles

no code implementations8 Jan 2020 Dohyun Kwon, Joongheon Kim

Millimeter-wave (mmWave) base station can offer abundant high capacity channel resources toward connected vehicles so that quality-of-service (QoS) of them in terms of downlink throughput can be highly improved.

reinforcement-learning Reinforcement Learning (RL)

Deep ensemble network with explicit complementary model for accuracy-balanced classification

no code implementations10 Aug 2019 Dohyun Kim, Kyeorye Lee, Jiyeon Kim, Junseok Kwon, Joongheon Kim

The average accuracy is one of major evaluation metrics for classification systems, while the accuracy deviation is another important performance metric used to evaluate various deep neural networks.

Classification General Classification

Randomized Adversarial Imitation Learning for Autonomous Driving

no code implementations13 May 2019 MyungJae Shin, Joongheon Kim

With the evolution of various advanced driver assistance system (ADAS) platforms, the design of autonomous driving system is becoming more complex and safety-critical.

Autonomous Driving Imitation Learning

A Personalized Preference Learning Framework for Caching in Mobile Networks

no code implementations15 Apr 2019 Adeel Malik, Joongheon Kim, Kwang Soon Kim, Won-Yong Shin

Under our model, we consider a single-hop-based device-to-device (D2D) content delivery protocol and characterize the average hit ratio for the following two file preference cases: the personalized file preferences and the common file preferences.

Collaborative Filtering

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