• Journal of Information Processing Systems
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• 제16권5호
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• pp.1001-1007
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• 2020

In recent years, machine learning algorithms are continuously being used and expanded in various fields, such as facial recognition, signal processing, personal authentication, and stock prediction. In particular, various algorithms, such as deep learning, reinforcement learning, and Q-learning, are continuously being improved. Among these algorithms, the expansion of deep learning is rapidly changing. Nevertheless, machine learning algorithms have not yet been applied in several fields, such as personal authentication technology. This technology is an essential tool in the digital information era, walking recognition technology as promising biometrics, and technology for solving state-space problems. Therefore, algorithm technologies of deep learning, reinforcement learning, and Q-learning, which are typical machine learning algorithms in various fields, such as agricultural technology, personal authentication, wireless network, game, biometric recognition, and image recognition, are being improved and expanded in this paper.

• East Asian mathematical journal
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• 제38권1호
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• pp.95-105
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• 2022

In this paper, we consider a quadratic matrix equation Q(X) = AX² + BX + C = 0 where A, B, C ∈ ℝ^n×n. A new approach is proposed to find solutions of Q(X), using the novel structure of the information processing system. We also present some numerical experimetns with Artificial Neural Network.

• Smart Structures and Systems
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• 제32권4호
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• pp.219-233
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• 2023

Crack width is an important indicator to evaluate the health condition of the concrete structure. The crack width is measured by manual using crack width gauge commonly, which is time-consuming and laborious. In this paper, we have proposed a fast and simplified crack width quantification method via deep Q learning and geometric calculation. Firstly, the crack edge is extracted by using U-Net network and edge detection operator. Then, the intelligent decision of is made by the deep Q learning model. Further, the geometric calculation method based on endpoint and curvature extreme point detection is proposed. Finally, a case study is carried out to demonstrate the effectiveness of the proposed method, achieving high precision in the real crack width quantification.

• 한국인터넷방송통신학회논문지
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• 제23권6호
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• pp.61-67
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• 2023

5G의 도래와 스마트 디바이스의 급격한 증가는 멀티 액세스 엣지 컴퓨팅(MEC)의 중요성을 부각시켰다. 이런 흐름 속에서, 특히 계산 집약적이고 지연시간에 민감한 애플리케이션의 효과적인 처리가 큰 관심을 받고 있다. 본 논문에서는 이러한 도전 과제를 해결하기 위해 확률적인 MEC 환경을 고려한 새로운 태스크 오프로딩 전략을 연구한다. 먼저 동적인 태스크 요청 빈도와 불안정한 무선 채널 상태를 감안하여 차량의 전력 소모와 지연시간을 최소화하는 방안을 제시한다. 그리고 심층 강화학습(DRL) 기반의 오프로딩 기법을 중심으로 연구를 진행하였고, 로컬 연산 및 오프로딩 전송 전력 사이의 최적의 균형을 찾기 위한 방법을 제안한다. Deep Deterministic Policy Gradient (DDPG)와 Deep Q-Network (DQN) 기법을 활용하여 차량의 전력 사용량과 큐잉 지연시간을 분석하였다. 이를 통해 차량 기반의 MEC 환경에서의 최적의 성능 향상 전략을 도출 및 검증하였다.

• 반도체디스플레이기술학회지
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• 제22권3호
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• pp.112-118
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• 2023

As technology advances, the need for enhanced preparedness against cyber-attacks becomes an increasingly critical problem. Therefore, it is imperative to consider various circumstances and to prepare for cyber-attack strategic technology. This paper proposes a method to solve network security problems by applying reinforcement learning to cyber-security. In general, traditional static cyber-security methods have difficulty effectively responding to modern dynamic attack patterns. To address this, we implement cyber-attack scenarios such as 'Tiny Alpha' and 'Small Alpha' and evaluate the performance of various reinforcement learning methods using Network Attack Simulator, which is a cyber-attack simulation environment based on the gymnasium (formerly Open AI gym) interface. In addition, we experimented with different RL algorithms such as value-based methods (Q-Learning, Deep-Q-Network, and Double Deep-Q-Network) and policy-based methods (Actor-Critic). As a result, we observed that value-based methods with discrete action spaces consistently outperformed policy-based methods with continuous action spaces, demonstrating a performance difference ranging from a minimum of 20.9% to a maximum of 53.2%. This result shows that the scheme not only suggests opportunities for enhancing cybersecurity strategies, but also indicates potential applications in cyber-security education and system validation across a large number of domains such as military, government, and corporate sectors.

• 한국멀티미디어학회논문지
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• 제25권2호
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• pp.145-156
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• 2022

Nowadays, the complexity of object tracking models among hardware applications has become a more in-demand duty to complete in various indeterminable environment tracking situations with multifunctional algorithm skills. In this paper, we propose a virtual city environment using AirSim (Aerial Informatics and Robotics Simulation - AirSim, CityEnvironment) and use the DQN (Deep Q-Learning) model of deep reinforcement learning model in the virtual environment. The proposed object tracking DQN network observes the environment using a deep reinforcement learning model that receives continuous images taken by a virtual environment simulation system as input to control the operation of a virtual drone. The deep reinforcement learning model is pre-trained using various existing continuous image sets. Since the existing various continuous image sets are image data of real environments and objects, it is implemented in 3D to track virtual environments and moving objects in them.

• 한국인터넷방송통신학회논문지
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• 제21권5호
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• pp.215-219
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• 2021

본 논문에서는 스마트 빌딩 시스템과 전력망이 각각의 전력거래 희망가격을 제안하고 조정하는 양방향 전력거래 협상 기법에 심층 강화학습 기법을 적용한 전력거래 기법을 제안한다. 심층 강화학습 기법 중 하나인 deep Q network 알고리즘을 적용하여 스마트 빌딩과 전력망의 거래 희망가격을 조정하도록 하였다. 제안하는 심층 강화학습 기반 양방향 전력거래 협상 알고리즘은 학습과정에서 평균 43.78회의 협상을 통해 가격 협의에 이르는 것을 실험을 통해 확인하였다. 또한, 본 연구에서 설정한 협상 시나리오에 따라 스마트 빌딩과 전력망이 거래 희망가격을 조정하는 과정을 실험을 통해 확인하였다.

• 산업경영시스템학회지
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• 제47권2호
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• pp.74-83
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• 2024

The threat of North Korea's long-range firepower is recognized as a typical asymmetric threat, and South Korea is prioritizing the development of a Korean-style missile defense system to defend against it. To address this, previous research modeled North Korean long-range artillery attacks as a Markov Decision Process (MDP) and used Approximate Dynamic Programming as an algorithm for missile defense, but due to its limitations, there is an intention to apply deep reinforcement learning techniques that incorporate deep learning. In this paper, we aim to develop a missile defense system algorithm by applying a modified DQN with multi-agent-based deep reinforcement learning techniques. Through this, we have researched to ensure an efficient missile defense system can be implemented considering the style of attacks in recent wars, such as how effectively it can respond to enemy missile attacks, and have proven that the results learned through deep reinforcement learning show superior outcomes.

• 한국인터넷방송통신학회논문지
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• 제23권6호
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• pp.33-39
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• 2023

최근 비행 애드-훅 네트워크(Flying Ad-hoc Network) 환경에서 강화학습을 이용한 통신 성능 개선과 이동성 모델 설계에 관한 연구가 진행되고 있다. 무인항공기(UAV)에서의 이동성 모델은 움직임을 예측하고 제어하기 위한 핵심요소로 주목받고 있다. 본 논문에서는 무인항공기가 운용되는 3차원 가상 환경을 구현하고, 무인항공기의 경로 최적화를 위해 푸리에 기저 함수 근사를 적용한 Q-learning과 DQN 두 가지 강화학습 알고리즘을 적용하여 모델을 설계 및 성능을 분석하였다. 실험 결과를 통해 3차원 가상 환경에서 DQN 모델이 Q-learning 모델 대비 최적의 경로 탐색에 적합한 것을 확인하였다.

• 반도체디스플레이기술학회지
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• 제16권3호
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• pp.127-130
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• 2017

In this research, we present a path planning method for an autonomous flight of unmanned aerial vehicles (UAVs) through reinforcement learning under simulated environment. We design the simulator for reinforcement learning of uav. Also we implement interface for compatibility of Deep Q-Network(DQN) and simulator. In this paper, we perform reinforcement learning through the simulator and DQN, and use Q-learning algorithm, which is a kind of reinforcement learning algorithms. Through experimentation, we verify performance of DQN-simulator. Finally, we evaluated the learning results and suggest path planning strategy using reinforcement learning.