no code implementations • 28 Jun 2023 • Won Joon Yun, Samuel Kim, Joongheon Kim
The prodigious growth of digital health data has precipitated a mounting interest in harnessing machine learning methodologies, such as natural language processing (NLP), to scrutinize medical records, clinical notes, and other text-based health information.
no code implementations • 9 Feb 2023 • Chanyoung Park, Won Joon Yun, Jae Pyoung Kim, Tiago Koketsu Rodrigues, Soohyun Park, Soyi Jung, Joongheon Kim
This paper proposes a novel algorithm, named quantum multi-agent actor-critic networks (QMACN) for autonomously constructing a robust mobile access system employing multiple unmanned aerial vehicles (UAVs).
no code implementations • 31 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.
no code implementations • 23 Dec 2022 • Chanyoung Park, Haemin Lee, Won Joon Yun, Soyi Jung, Joongheon Kim
This paper proposes a novel centralized training and distributed execution (CTDE)-based multi-agent deep reinforcement learning (MADRL) method for multiple unmanned aerial vehicles (UAVs) control in autonomous mobile access applications.
no code implementations • 4 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.
no code implementations • 24 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
no code implementations • 12 Nov 2022 • Won Joon Yun, Hankyul Baek, Joongheon Kim
In recent years, the field of quantum science has attracted significant interest across various disciplines, including quantum machine learning, quantum communication, and quantum computing.
no code implementations • 30 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.
no code implementations • 18 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.
no code implementations • 29 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.
no code implementations • 26 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.
no code implementations • 2 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.
no code implementations • 22 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.
no code implementations • 20 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).
no code implementations • 26 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.
1 code implementation • 20 Mar 2022 • Won Joon Yun, Yunseok Kwak, Jae Pyoung Kim, Hyunhee Cho, Soyi Jung, Jihong Park, Joongheon Kim
This paper extends and demonstrates the QRL to quantum multi-agent RL (QMARL).
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 19 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.
no code implementations • 17 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.
no code implementations • 15 Jan 2022 • Won Joon Yun, Soohyun Park, Joongheon Kim, MyungJae Shin, Soyi Jung, David A. Mohaisen, Jae-Hyun Kim
This paper creates a case where the UAVs with CCTV-cameras fly over the city area for flexible and reliable surveillance services.
no code implementations • 26 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.
no code implementations • 5 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.
no code implementations • 5 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.
no code implementations • 19 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.
no code implementations • 16 Aug 2021 • Yunseok Kwak, Won Joon Yun, Soyi Jung, Jong-Kook Kim, Joongheon Kim
The emergence of quantum computing enables for researchers to apply quantum circuit on many existing studies.
no code implementations • 10 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.
no code implementations • 2 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.
no code implementations • 22 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.
no code implementations • 21 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.