• ETRI Journal
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• v.46 no.3
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• pp.379-391
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• 2024

To meet increasing traffic requirements in mobile networks, small base stations (SBSs) are densely deployed, overlapping existing network architecture and increasing system capacity. However, densely deployed SBSs increase energy consumption and interference. Although these problems already exist because of densely deployed SBSs, even more SBSs are needed to meet increasing traffic demands. Hence, base station (BS) switching operations have been used to minimize energy consumption while guaranteeing quality-of-service (QoS) for users. In this study, to optimize energy efficiency, we propose the use of deep reinforcement learning (DRL) to create a BS switching operation strategy with a traffic prediction model. First, a federated long short-term memory (LSTM) model is introduced to predict user traffic demands from user trajectory information. Next, the DRL-based BS switching operation scheme determines the switching operations for the SBSs using the predicted traffic demand. Experimental results confirm that the proposed scheme outperforms existing approaches in terms of energy efficiency, signal-to-interference noise ratio, handover metrics, and prediction performance.

• Architectural research
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• v.26 no.1
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• pp.1-12
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• 2024

This study introduces a deep reinforcement learning (DRL)-based methodology for optimizing apartment space arrangements, addressing the limitations of human capability in evaluating all potential spatial configurations. Leveraging computational power, the methodology facilitates the autonomous exploration and evaluation of innovative layout options, considering architectural principles, legal standards, and client re-quirements. Through comprehensive simulation tests across various apartment types, the research demonstrates the DRL approach's effec-tiveness in generating efficient spatial arrangements that align with current design trends and meet predefined performance objectives. The comparative analysis of AI-generated layouts with those designed by professionals validates the methodology's applicability and potential in enhancing architectural design practices by offering novel, optimized spatial configuration solutions.

• Journal of the Korean Society of Marine Environment & Safety
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• v.15 no.3
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• pp.269-274
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• 2009

Recently, the construction of Liquefied Natural Gas Carriers(LNGC) is being promoted larger and larger depending on long voyage. In 1950 years, $5,000m^3$ class of LNGC had been changed to $71,500m^3$ class in 1973. and to $210,000-266,000m^3$ class in 2007. Especially, the system of main engines and cargo control, Re-liquefaction of natural gases have become possible in LNGC. This research deals with the LNG projects, world markets of energy and developing tendency of liquefied natural gas carriers.

• Korean Journal of Digital Imaging in Medicine
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• v.14 no.1
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• pp.1-6
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• 2012

This round of tests in patients with UGI and Esophagography data collected by national and international reference levels based on the original set of guidelines and fluoroscopy, through the provision of medical radiation exposure reduction and further optimization of Defense to realize that is intended. 359 names in our hospital underwent Esophagography 302 patients who underwent UGI average fluoroscopy time and number of images to calculate the average 21 cm Acryl phantom dose for 10 seconds and 20 seconds, average area dose and the area dose of 1 spot image, 5 spot consecutive images by measuring the patient dose and third quartile of the mean area dose was set seonryangin reference dose. Esophagography average patient dose was set to 30.05 $Gy{\cdot}cm^2$, DRL was set at a 25.37 $Gy{\cdot}cm^2$. Average dose of UGI patients were selected as 45.33 $Gy{\cdot}cm^2$, DRL was set at a 34 $Gy{\cdot}cm^2$. UGI patients with established average dose recommended in the 2008 national recommendation from the UGI examination with a dose of less than 49.7 $Gy{\cdot}cm^2$ seonryangin is evaluated. This Note examines the dose of self-aware through education recognizes the importance of dose reduction and examine if their efforts and further reduce patient dose could achieve optimization of the medical exposure is considered.

• Korean Journal of Digital Imaging in Medicine
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• v.11 no.1
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• pp.15-20
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• 2009

Far reducing medical radiation exposure and managing patient doses, Entrance surface doses(ESDs) were measured at Diagnostic Radiology Department in ASAN medical center, also we determined and compared with the Diagnostic Reference Level(DRL) of some other countries. ESDs were measured far the most common types of X-ray procedures, such as chest PA, lumbar spine AP, lumbar spine lateral, Pelvis AP, Skull PA. ESDs were measured by Glass dosimeter and Unfors Xi meter. Those were applied collimation center of phantom's entrance skin surface. The results of ESDs were compared Glass dosimeter with Unfors Xi meter. Those were measured within 5% statistical difference. It seemed well agreement at two devices. In most cases ESDs measured far the different types of X ray procedures were found to be lower than the DRL of IAEA, but ESDs on chest PA, lumbar spine AP, lumbar spine lateral, Pelvis AP, Skull PA were proximity ar excesses at DRL of advanced country. Through this study, we need an investigation and improvement at present diagnostic radiology exam system. Also, radiologists make an effort to reduce patient dose and having a technical skill.

• Progress in Medical Physics
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• v.30 no.4
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• pp.75-88
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• 2019

Diagnostic reference level (DRL) is employed to optimize the radiation doses of patients. The objective of this study is to review the DRLs for interventional procedures in Korea and abroad. Literature review was performed to investigate radiation dose index and measurement methodology commonly used in DRL determination. Dose area product (DAP) and fluoroscopy time within each major procedure category were systematically abstracted and analyzed. A wide variation was found in the radiation dose. The DAP values and fluoroscopy times ranged 0.01-3,081 Gy·㎠ and 2-16,878 seconds for all the interventional procedures, 8.5-1,679 Gy·㎠ and 32-5,775 seconds for the transcatheter arterial chemoembolization (TACE), and 0.1-686 Gy·㎠ and 16-6,636 seconds for the transfemoral cerebral angiography (TFCA), respectively. The DRL values of the DAP and fluoroscopy time were 238 Gy·㎠ and 1,224 seconds for the TACE and 189 Gy·㎠ and 686 seconds for the TFCA, respectively. Generally, the DRLs of Korea were lower than those of other developed countries, except for the percutaneous transluminal angioplasty with stent in arteries of the lower extremity (LE PTA and stent), aneurysm coil embolization, and Hickman insertion procedures. The wide variation in the radiation doses of the different procedures suggests that more attention must be paid to reduce unnecessary radiation exposure from medical imaging. Furthermore, periodic nationwide survey of medical radiation exposures is necessary to optimize the patient dose for radiation protection, which will ultimately contribute to patient dose reduction and radiological safety.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.9-15
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• 2023

Existing simple statistics-based recommendation systems rely solely on students' course enrollment history data, making it difficult to identify classes that match students' preferences. To address this issue, this study proposes a personalized major subject recommendation system based on deep reinforcement learning (DRL). This system gauges the similarity between students based on structured data, such as the student's department, grade level, and course history. Based on this information, it recommends the most suitable major subjects by comprehensively considering information about each available major subject and evaluations of the student's courses. We confirmed that this DRL-based recommendation system provides useful insights for university students while selecting their major subjects, and our simulation results indicate that it outperforms conventional statistics-based recommendation systems by approximately 20%. In light of these results, we propose a new system that offers personalized subject recommendations by incorporating students' course evaluations. This system is expected to assist students significantly in finding major subjects that align with their preferences and academic goals.

• 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.

• Journal of radiological science and technology
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• v.43 no.5
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• pp.353-357
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• 2020

IVR procedures are on the rise, and patient doses are on the rise. It is necessary to evaluate fluoroscopy dose in IVR procedure. Evaluate ESD on IVR equipment as a reference to DRL settings, I would like to present the direction of improvement in the ESD rate test criteria for fluoroscopy dose. The experimental method is measured with 6cc ionization chamber under the 20cm PMMA Phantom. Radiation is subject to abdominal procedure. The average dose rate of the incident surface was 21.6 ± 11.4 mGy/min. The highest dose equipment was 58.5 mGy/min, and there was no equipment exceeding the domestic standard of 100 mGy/min. However, there were five units above 50 mGy/min. To reduce fluoroscopy dose, it is recommended to reduce pulse rate, The dose increases as the image receptor ages. It is recommended to modify the domestic inspection criteria to 50 mGy/min.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1472-1479
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• 2024

Cyber-Physical Energy Systems (CPESs) integrate cyber and hardware components to ensure a reliable and safe physical power production and supply. Renewable Energy Sources (RESs) add uncertainty to energy demand that can be dealt with flexible operation (e.g., load-following) of CPES; at the same time, scenarios that could result in severe consequences due to both component stochastic failures and aging of the cyber system of CPES (commonly overlooked) must be accounted for Operation & Maintenance (O&M) planning. In this paper, we make use of Deep Reinforcement Learning (DRL) to search for the optimal O&M strategy that, not only considers the actual system hardware components health conditions and their Remaining Useful Life (RUL), but also the possible accident scenarios caused by the failures and the aging of the hardware and the cyber components, respectively. The novelty of the work lies in embedding the cyber aging model into the CPES model of production planning and failure process; this model is used to help the RL agent, trained with Proximal Policy Optimization (PPO) and Imitation Learning (IL), finding the proper rejuvenation timing for the cyber system accounting for the uncertainty of the cyber system aging process. An application is provided, with regards to the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED).