• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.549-552
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• 2023

현재 전력망에서는 불규칙하거나 낭비되는 전력 문제를 해결하기 위한 한 방법으로 ESS(Energy Storage System)를 활용하는 방법이 많은 관심을 받고 있다. 본 연구에서는 업종별로 시간대에 따라 요금을 다르게 부과하는 배전망 시스템에서, 배터리를 보다 경제적으로 사용하는 동시에 여유 용량을 유지하도록 하는 DQN 기반 강화학습 기법을 제안하였다. 또한, 업종별로 다른 전력 소비 패턴을 에이전트의 동작성과 함께 그 성능을 분석하고 비교하였다.

• Journal of Energy Engineering
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• v.27 no.2
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• pp.26-31
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• 2018

This paper introduces a study to improve the thermal efficiency of the district heating user control facility based on reinforcement learning. As an example, it is proposed a general method of constructing a deep Q learning network(DQN) using deep Q learning, which is a reinforcement learning algorithm that does not specify a model. In addition, it is also introduced the big data platform system and the integrated heat management system which are specialized in energy field applied in processing huge amount of data processing from IoT sensor installed in many thermal energy control facilities.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1153-1160
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• 2021

This paper proposes a routing algorithm that determines the optimal path using deep reinforcement learning in software-defined networks. The deep reinforcement learning model for learning is based on DQN, the inputs are the current network state, source, and destination nodes, and the output returns a list of routes from source to destination. The routing task is defined as a discrete control problem, and the quality of service parameters for routing consider delay, bandwidth, and loss rate. The routing agent classifies the appropriate service class according to the user's quality of service profile, and converts the service class that can be provided for each link from the current network state collected from the SDN. Based on this converted information, it learns to select a route that satisfies the required service level from the source to the destination. The simulation results indicated that if the proposed algorithm proceeds with a certain episode, the correct path is selected and the learning is successfully performed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.61-67
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• 2023

The rise of 5G and the proliferation of smart devices have underscored the significance of multi-access edge computing (MEC). Amidst this trend, interest in effectively processing computation-intensive and latency-sensitive applications has increased. This study investigated a novel task offloading strategy considering the probabilistic MEC environment to address these challenges. Initially, we considered the frequency of dynamic task requests and the unstable conditions of wireless channels to propose a method for minimizing vehicle power consumption and latency. Subsequently, our research delved into a deep reinforcement learning (DRL) based offloading technique, offering a way to achieve equilibrium between local computation and offloading transmission power. We analyzed the power consumption and queuing latency of vehicles using the deep deterministic policy gradient (DDPG) and deep Q-network (DQN) techniques. Finally, we derived and validated the optimal performance enhancement strategy in a vehicle based MEC environment.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.45-47
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• 2022

Mobile edge computing (MEC), which enables mobile terminals to offload computational tasks to a server located at the user's edge, is considered an effective way to reduce the heavy computational burden and achieve efficient computational offloading. In this paper, we study a multi-user MEC system in which multiple user devices (UEs) can offload computation to the MEC server via a wireless channel. To solve the resource allocation and task offloading problem, we take the total cost of latency and energy consumption of all UEs as our optimization objective. To minimize the total cost of the considered MEC system, we propose an DRL-based method to solve the resource allocation problem in wireless MEC. Specifically, we propose a Asynchronous Advantage Actor-Critic (A3C)-based scheme. Asynchronous Advantage Actor-Critic (A3C) is applied to this framework and compared with DQN, and Double Q-Learning simulation results show that this scheme significantly reduces the total cost compared to other resource allocation schemes

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.803-812
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• 2022

With the increasing number of mobile device users worldwide, utilizing mobile edge computing (MEC) devices close to users for content caching can reduce transmission latency than receiving content from a server or cloud. However, because MEC has limited storage capacity, it is necessary to determine the content types and sizes to be cached. In this study, we investigate a caching strategy that increases the hit ratio from small base stations (SBSs) for mobile users in a heterogeneous network consisting of one macro base station (MBS) and multiple SBSs. If there are several SBSs that users can access, the hit ratio can be improved by reducing duplicate content and increasing the diversity of content in SBSs. We propose a Deep Q-Network (DQN)-based caching strategy that considers time-varying content popularity and content redundancy in multiple SBSs. Content is stored in the SBS in a divided form using maximum distance separable (MDS) codes to enhance the diversity of the content. Experiments in various environments show that the proposed caching strategy outperforms the other methods in terms of hit ratio.

• Journal of Platform Technology
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• v.11 no.6
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• pp.21-27
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• 2023

In this paper, we introduce a reinforcement learning-based algorithm for personalized tourist path recommendations. The algorithm employs a reinforcement learning agent to explore tourist regions and identify optimal paths that are expected to enhance tourism experiences. The concept of tourism experience is defined through points of interest (POI) located along tourist paths within the tourist area. These metrics are quantified through aggregated evaluation scores derived from reviews submitted by past visitors. In the experimental setup, the foundational learning model used to find tour paths is the Deep Q-Network (DQN). Despite the limited availability of historical tourist behavior data, the agent adeptly learns travel paths by incorporating preference scores of tourist POIs and spatial information of the travel area.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.537-540
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• 2018

본 논문은 Jetson_TX2(임베디드 보드)의 ROS(Robot Operating System)기반으로 맵 지도를 작성하고, SLAM 및 DQN(Deep Q-Network)을 이용한 목적지까지의 이동명령(목표 선속도, 목표 각속도)을 자이로센서로 측정한 현재 각속도를 이용하여 Cortex-M3의 기반의 MCU(Micro Controllor Unit)에 하달하여 엔코더(encoder) 모터에서 측정한 현재 선속도와 자이로센서에서 측정한 각속도 값을 이용하여 PID제어를 통한 실내 자율주행 서비스 로봇.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.1
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• pp.19-29
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• 2021

In the Port Logistics system, Container Stowage planning is an important issue for cost-effective efficiency improvements. At present, Planners are mainly carrying out Stowage planning by manual or semi-automatically. However, as the trend of super-large container ships continues, it is difficult to calculate an efficient Stowage plan with manpower. With the recent rapid development of artificial intelligence-related technologies, many studies have been conducted to apply enhanced learning to optimization problems. Accordingly, in this paper, we intend to develop and present a Deep Q-Learning Network model for the Master Stowage planning of Container ships.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.110-113
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• 2022

인터넷을 통해 주위 사물과 연결된 차량은 사용자에게 편리성을 제공하기 위해 다양한 콘텐츠를 요구하는데 클라우드로부터 가져오는 시간이 비교적 오래 걸리기 때문에 차량과 물리적으로 가까운 위치에 캐싱하는 기법들이 등장하고 있다. 본 논문에서는 기반 시설이 밀집하게 설치된 도시 환경에서 maximum distance separable(MDS) 코딩을 사용해 road side unit(RSU)에 캐싱하는 방법에 대해 연구하였다. RSU의 중복된 서비스 커버리지 지역을 고려하여 차량의 콘텐츠 요구에 대한 RSU hit ratio를 높이기 위해 deep Q-learning(DQN)를 사용하였다. 실험 결과 비교 알고리즘보다 hit raito 측면에서 더 높은 성능을 보이는 것을 증명하였다.