• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권1호
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• pp.334-349
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• 2022

The effectiveness of Wi-Fi networks is greatly influenced by the optimization of contention window (CW) parameters. Unfortunately, the conventional approach employed by IEEE 802.11 wireless networks is not scalable enough to sustain consistent performance for the increasing number of stations. Yet, it is still the default when accessing channels for single-users of 802.11 transmissions. Recently, there has been a spike in attempts to enhance network performance using a machine learning (ML) technique known as reinforcement learning (RL). Its advantage is interacting with the surrounding environment and making decisions based on its own experience. Deep RL (DRL) uses deep neural networks (DNN) to deal with more complex environments (such as continuous state spaces or actions spaces) and to get optimum rewards. As a result, we present a new approach of CW control mechanism, which is termed as contention window threshold (CW_Threshold). It uses the DRL principle to define the threshold value and learn optimal settings under various network scenarios. We demonstrate our proposed method, known as a smart exponential-threshold-linear backoff algorithm with a deep Q-learning network (SETL-DQN). The simulation results show that our proposed SETL-DQN algorithm can effectively improve the throughput and reduce the collision rates.

• Nuclear Engineering and Technology
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• 제55권9호
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• pp.3277-3290
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• 2023

Nuclear power plant (NPP) operations with multiple objectives and devices are still performed manually by operators despite the potential for human error. These operations could be automated to reduce the burden on operators; however, classical approaches may not be suitable for these multi-objective tasks. An alternative approach is deep reinforcement learning (DRL), which has been successful in automating various complex tasks and has been applied in automation of certain operations in NPPs. But despite the recent progress, previous studies using DRL for NPP operations have limitations to handle complex multi-objective operations with multiple devices efficiently. This study proposes a novel DRL-based approach that addresses these limitations by employing a continuous action space and straightforward binary rewards supported by the adoption of a soft actor-critic and hindsight experience replay. The feasibility of the proposed approach was evaluated for controlling the pressure and volume of the reactor coolant while heating the coolant during NPP startup. The results show that the proposed approach can train the agent with a proper strategy for effectively achieving multiple objectives through the control of multiple devices. Moreover, hands-on testing results demonstrate that the trained agent is capable of handling untrained objectives, such as cooldown, with substantial success.

• 대한방사선기술학회지:방사선기술과학
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• 제47권3호
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• pp.167-173
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• 2024

This study purpose to establish an appropriate target exposure index(EIT) using dose area product(DAP) and exposure index(EI) based on chest radiography. First, the system response experiment was conducted with radiation quality of RQA5 to compare the dosimetry and dose area product of equipment. Next, EI and DAP were acquired and analyzed while varying the dose in the diagnostic at 70kVp using a human body model phantom. The signal to noise ratio(SNR) of the obtained results was analyzed in the diagnostic with in the diagnostic reference level(DRL) application range. The DRL at percentage 25% had a dose of 0.17 mGy and EI was 83, and at percentage 75% the dose was 0.68 mGy and EI was 344. As the dose increased, the SNR in the subdiaphragm increased. To set the EIT, calibration must first be performed using a dosimeter and set within the DRL range to reflect the needs of the medical institution.

• 대한방사선기술학회지:방사선기술과학
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• 제39권1호
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• pp.27-33
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• 2016

심장혈관 조영술과 인터벤션은 현대 성인병의 증가로 급격히 증가하고 있다. 심장혈관 인터벤션은 장시간 동일 부위에 방사선을 조사하는 검사로 방사선으로 인한 피부상해를 일으킬 수 있다. 본 연구에서는 의료기관의 심장혈관 인터벤션의 진단참조준위를 조사하여 환자의 피폭선량을 감소시키는 도구로 사용하고자 한다. 본 연구는 147명의 환자에서 심장혈관 조영술과 인터벤션을 대상으로 누적 투시시간, 누적 투시면적선량, 영상촬영을 위한 면적선량, 누적 면적선량, 공기커마, 동영상 수, 총 영상 수에 대한 정보를 획득하여 진단참조준위를 설정하였다. 심장혈관 조영술의 진단참조준위와 인터벤션의 진단참조준위에 해당하는 면적선량 값은 각각 $44.4 Gy{\cdot}cm2$와 $298.6Gy{\cdot}cm2$로 나타났고 투시시간에 대한 진단참조준위는 각각 191.5 sec와 1935.3 sec로 나타났다. 진단참조준위는 반드시 넘으면 안 되는 값은 아니다. 하지만 진단참조준위를 제정하여 의료 기관에서 사용하고 있는 선량의 참조 값을 설정하고 이를 검토하는 과정은 환자의 불필요한 피폭선량을 감소시키는데 기여할 것이다.

• 한국방사성폐기물학회:학술대회논문집
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• 한국방사성폐기물학회 2004년도 학술대회지
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• pp.289-289
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• 2004

• 한국방사성폐기물학회:학술대회논문집
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• 한국방사성폐기물학회 2005년도 춘계 학술대회
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• pp.543-544
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• 2005

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.47-49
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• 2022

Robot navigation has seen a major improvement since the the rediscovery of the potential of Artificial Intelligence (AI) and the attention it has garnered in research circles. A notable achievement in the area was Deep Learning (DL) application in computer vision with outstanding daily life applications such as face-recognition, object detection, and more. However, robotics in general still depend on human inputs in certain areas such as localization, navigation, etc. In this paper, we propose a study case of robot navigation based on deep reinforcement technology. We look into the benefits of switching from traditional ROS-based navigation algorithms towards machine learning approaches and methods. We describe the state-of-the-art technology by introducing the concepts of Reinforcement Learning (RL), Deep Learning (DL) and DRL before before focusing on visual navigation based on DRL. The case study preludes further real life deployment in which mobile navigational agent learns to navigate unbeknownst areas.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 춘계학술발표대회
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• pp.114-116
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• 2022

The advent of the Multi-access Edge Computing (MEC) paradigm allows mobile users to offload resource-intensive and delay-stringent services to nearby servers, thereby significantly enhancing the quality of experience. Due to erratic roaming of mobile users in the network environment, maintaining maximum quality of experience becomes challenging as they move farther away from the serving edge server, particularly due to the increased latency resulting from the extended distance. The services could be migrated, under policies obtained using Deep Reinforcement Learning (DRL) techniques, to an optimal edge server, however, this operation incurs significant costs in terms of service downtime, thereby adversely affecting service quality of experience. Thus, this study addresses the service mobility problem of deciding whether to migrate and where to migrate the service instance for maximized migration benefits and marginal service downtime.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 춘계학술발표대회
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• pp.147-150
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• 2022

최근 들어 다양한 제약 조건이 있는 스마트 시티나 스마트 팩토리와 같은 도메인들 내에서 태스크들을 효과적으로 처리하기 위해서 MEC 기술이 많이 사용되고 있다. 그러나 이러한 도메인에서 발생하는 복잡하고 동적인 시나리오는 기존의 휴리스틱이나 메타 휴리스틱 기법을 이용하여 해결하기엔 계산 복잡도가 증가하는 문제점을 가지고 있다. 따라서 최근 들어 이러한 문제점을 해결하기 위한 방법 중 하나로 강화학습과 딥러닝이 결합된 DRL 기법이 주목을 받고 있다. 본 연구는 스마트 팩토리 환경에서 종속성을 가진 태스크들이 실행시간과 태스크가 처리되는 MEC 서버들의 로드 표준편차를 최소화하는 태스크 스케줄링 기법을 제안한다. 모의실험을 통하여 제안 기법은 태스크가 증가하는 동적인 환경에서도 좋은 성능을 보임을 증명하였다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 추계학술발표대회
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• pp.624-627
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• 2023

Unmanned aerial vehicles are gaining in popularity with the development of science and technology, and are being used for a wide range of purposes, including surveillance, rescue, delivery of goods, and data collection. In particular, the ability to avoid obstacles during navigation without human oversight is one of the essential capabilities that a drone must possess. Many works currently have solved this problem by implementing deep reinforcement learning (DRL) model. The essential core of a DRL model is reward function. Therefore, this paper proposes a new reward function with appropriate action space and employs dueling double deep Q-Networks to train a drone to navigate in indoor environment without collision.