• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.143-148
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• 2024

Traffic congestion has caused issues in various forms such as the environment and economy. Recently, an intelligent transport system (ITS) using artificial intelligence (AI) has been focused so as to alleviate the traffic congestion problem. In this paper, we propose a reinforcement learning-based traffic signal control algorithm that can smooth the flow of traffic while reducing discomfort levels of drivers and pedestrians. By applying the proposed algorithm, it was confirmed that the discomfort levels of drivers and pedestrians can be significantly reduced compared to the existing fixed signal control system, and that the performance gap increases as the number of roads at the intersection increases.

• 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.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.12
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• pp.437-444
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• 2022

Recently, research on mobile edge services has been conducted to handle computationally intensive and latency-sensitive tasks occurring in wireless networks. However, MEC, which is fixed on the ground, cannot flexibly cope with situations where task processing requests increase sharply, such as commuting time. To solve this problem, a technology that provides edge services using UAVs (Unmanned Aerial Vehicles) has emerged. Unlike ground MEC servers, UAVs have limited battery capacity, so it is necessary to optimize energy efficiency through load balancing between UAV MEC servers. Therefore, in this paper, we propose a load balancing technique with consideration of the energy state of UAVs and the mobility of vehicles. The proposed technique is composed of task offloading scheme using genetic algorithm and task migration scheme using Q-learning. To evaluate the performance of the proposed technique, experiments were conducted with varying mobility speed and number of vehicles, and performance was analyzed in terms of load variance, energy consumption, communication overhead, and delay constraint satisfaction rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.1026-1031
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• 2022

Cognitive Radio Ad-Hoc Networks (CRAHNs) enable to overcome the shortage of frequency resources due to the increase of radio services. In order to avoid interference with the primary user in CRANH, channel sensing to check the idle channel is required, and when the primary user appears, the time delay due to handover should be minimized through fast idle channel selection. In this paper, throughput was improved by reducing the number of channel sensing and preferentially sensing a channel with a high probability of being idle, using reinforcement learning. In addition, we proposed a multi-channel MAC (Medium Access Control) protocol that can minimize the possibility of collision with the primary user by sensing the channel at the time of data transmission without performing periodic sensing. The performance was compared and analyzed through computer simulation.

• Journal of the Korean Institute of Gas
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• v.27 no.2
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• pp.49-56
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• 2023

As AI (Artificial Intelligence)-related technologies are highly developed due to the 4th industrial revolution, cases of applying AI in various fields are increasing. The main reason is that there are practical limits to direct processing and analysis of exponentially increasing data as information and communication technology develops, and the risk of human error can be reduced by applying new technologies. In this study, after collecting the data received from the 'remote potential measurement terminal (T/B, Test Box)' and the output of the 'remote rectifier' at that time, AI was trained. AI learning data was obtained through data augmentation through regression analysis of the initially collected data, and the learning model applied the value-based Q-Learning model among deep reinforcement learning (DRL) algorithms. did The AI that has completed data learning is put into the actual city gas supply area, and based on the received remote T/B data, it is verified that the AI responds appropriately, and through this, AI can be used as a suitable means for electricity management in the future. want to verify.

• Review of KIISC
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• v.33 no.2
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• pp.31-37
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• 2023

소인수분해와 같이 매우 큰 경우의 수를 탐색하고 연산하며 비교하는 작업에서 강점을 가지는 양자컴퓨터는현재 사용되는 암호체계를 붕괴시킬 수 있다는 점에서 위협이 될 수 있다. 하지만 화학, 머신러닝과 같은 분야에서는 대단히 큰 혁신을 가져올 차세대 컴퓨터로 주목받고 있으며, IBM, Google, Amazon과 같은 세계적인 IT 기업들이 이러한 양자컴퓨터 관련 연구개발에 적극적으로 투자하고 있다. 본 고에서는 양자컴퓨터의 최근 개발 현황과 양자컴퓨팅을 위한 플랫폼인 IBM Qiskit, Google Cirq, ProjectQ, Amazon Braket, Microsoft Azure Quantum, Intel Quantum SDK에 대해 알아보고자 한다.

• Review of KIISC
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• v.34 no.2
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• pp.21-27
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• 2024

양자컴퓨터는 매우 많은 경우의 수를 탐색하고 연산하는 데에 있어 이점을 가지며, 이는 소인수분해와 같은 작업에서 기존 컴퓨팅을 능가할 수 있다. 이러한 능력으로 인해 양자컴퓨터는 현재 사용되는 암호체계를 위협할 수 있다. 또한, 화학, 머신러닝 등 다양한 분야에서 혁신을 가져올 수 있는 차세대 컴퓨팅 환경으로 주목받고 있다. 현재 IBM, Google, Amazon 등의 세계적인 IT 기업들이 이 분야의 연구 및 개발에 적극적으로 투자하고 있으며 본고에서는 양자컴퓨터의 최근 개발현황과 양자컴퓨팅을 위한 플랫폼인 IBM Qiskit, Google Cirq, ProjectQ, Amazon Braket, Microsoft Azure Quantum, Intel Quantum SDK, Pennylane에 대해 알아보고자 한다.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.4
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• pp.231-241
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• 2006

Creating controllable, responsive avatars is an important problem in computer games and virtual environments. Recently, large collections of motion capture data have been exploited for increased realism in avatar animation and control. Large motion sets have the advantage of accommodating a broad variety of natural human motion. However, when a motion set is large, the time required to identify an appropriate sequence of motions is the bottleneck for achieving interactive avatar control. In this paper, we present a novel method for training avatar behaviors from unlabelled motion data in order to animate and control avatars at minimal runtime cost. Based on machine learning technique, called Q-teaming, our training method allows the avatar to learn how to act in any given situation through trial-and-error interactions with a dynamic environment. We demonstrate the effectiveness of our approach through examples that include avatars interacting with each other and with the user.

• Journal of Convergence for Information Technology
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• v.11 no.1
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• pp.12-19
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• 2021

For strengthening the national defense, the function of tactical network is essential. tactics and strategies in wartime situations are based on numerous information. Therefore, various reconnaissance devices and resources are used to collect a huge amount of information, and they transmit the information through tactical networks. In tactical networks that which use contention based channel access scheme, high-speed nodes such as recon aircraft may have performance degradation problems due to unnecessary channel occupation. In this paper, we propose a learning-backoff method, which empirically learns the size of the contention window to determine channel access time. The proposed method shows that the network throughput can be increased up to 25% as the number of high-speed mobility nodes are increases.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.377-383
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• 2021

Recently, in accordance with the 4th industrial revolution, The use of autonomous mobile robots for flexible logistics transfer is increasing in factories, the warehouses and the service areas, etc. In large factories, many manual work is required to use Simultaneous Localization and Mapping(SLAM), so the need for the improved mobile robot autonomous driving is emerging. Accordingly, in this paper, an algorithm for mapless navigation that travels in an optimal path avoiding fixed or moving obstacles is proposed. For mapless navigation, the robot is trained to avoid fixed or moving obstacles through Deep Q Network (DQN) and accuracy 90% and 93% are obtained for two types of obstacle avoidance, respectively. In addition, DQN requires a lot of learning time to meet the required performance before use. To shorten this, the target size change algorithm is proposed and confirmed the reduced learning time and performance of obstacle avoidance through simulation.