• Proceedings of the Korean Nuclear Society Conference
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• 2004.05a
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• pp.225.1-225.1
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• 2004

• Proceedings of the Korean Nuclear Society Conference
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• 2005.05a
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• pp.1239-1240
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• 2005

• Proceedings of the Korean Nuclear Society Conference
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• 2003.10a
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• pp.209-209
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• 2003

• Proceedings of the Korean Nuclear Society Conference
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• 1999.10a
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• pp.92.1-92
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• 1999

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.305-306
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• 2007

본 논문은 차세대 원전 신고리 #3, 4호기 NSSS(Nuclear Steam Supply System) 제어계통의 검증시스템을 개발하기 위한 설계개념에 관한 것이다. 차세대 원전 신고리 #3, 4호기는 KHNP(Korea Hydro & Nuclear Power Co., Ltd.)가 개발한 APR1400(Advanced Power Reactor 1400 [MWe])을 적용하는 최초의 원자력 발전소이다. APR1400은 3세대 원자력발전소로 인정받고 있으며, APR1400 원자력발전소의 안전한 운영을 위하여 I&C(Instrumentation and Control)시스템이 디지털 표준 플랫폼으로 설계되었다[2]. 특히, 차세대 원전 신고리 #3, 4호기의 비안전계통(제어 감시 및 경보계통)은 WEC (Westinghouse Electric Company)의 DCS(Distributed Control System) 상용 단일 플랫폼으로 구성될 예정이다. 우리는 신고리 #3, 4호기의 제어계통 중에서 NSSS(Nuclear Steam Supply System) 제어계통의 검증시스템을 개발하기 위하여 Stimulated Simulator의 방법론을 적용하여 "Simulator"라는 설계 개념을 정립하였다. 현재 원자력발전소 NSSS 제어계통의 DCS Stimulator 개발을 위하여 차세대 원전 신고리 #3, 4호기에 시설될 WEC의 DCS와 Simulation 서버 그리고 I/O 설비를 구축 중에 있으며, 원자력발전소 현장 기기 모델링 소프트웨어와 I/O 설비간의 인터페이스를 위한 동신 소프트웨어도 개발하고 있다.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1181-1188
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• 2021

When abnormal events occur in a nuclear power plant, operators must conduct appropriate abnormal operating procedures. It is burdensome though for operators to choose the appropriate procedure considering the numerous main plant parameters and hundreds of alarms that should be judged in a short time. Recently, various research has applied deep-learning algorithms to support this problem by classifying each abnormal condition with high accuracy. Most of these models are trained with simulator data because of a lack of plant data for abnormal states, and as such, developed models may not have tolerance for plant data in actual situations. In this study, two approaches are investigated for a deep-learning model trained with simulator data to overcome the performance degradation caused by noise in actual plant data. First, a preprocessing method using several filters was employed to smooth the test data noise, and second, a data augmentation method was applied to increase the acceptability of the untrained data. Results of this study confirm that the combination of these two approaches can enable high model performance even in the presence of noisy data as in real plants.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.7
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• pp.1314-1319
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• 2010

The excitation control system of an emergency diesel generator is classified as a kind of safety-related system. Compared with other control systems in a power plant, this system is required to be more reliable and have better performance. KEPCO Research Institute successfully developed the excitation control system for a diesel generator. This paper presents its field test results by using a real time simulator on a nuclear power plant.

• Journal of the Korea Safety Management & Science
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• v.17 no.3
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• pp.215-219
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• 2015

The purpose in this study is to investigate CT number difference between conventional CT and CT simulator. It shows good correlation in CT number on the muscle, bone, and air. However, in the liver, lungs and water, the low correlation was detected. This result can become the good index for the direction of the distribution of dose difference research between CT equipment for using the computerized radiation therapy planning system.

• Proceedings of the Korea Society for Simulation Conference
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• 2005.05a
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• pp.129-133
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• 2005

This paper describes the methodology of connecting MASTER (Multi-purpose Analyzer for Static and Transient Effects of Reactors) to simulator system, system configuration, and previous test. The actual simulator environment for Youngkwang Unit1 has been developed. It is impossible for the simulator server to execute MASTER code by limitation of computer performance. So, environment of distributed system was developed, and it had a synchronization task. As MASTER and simulator module should be synchronized in different device, the connection of communication was tested and verified.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.896-908
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• 2022

As a part of probabilistic risk (or safety) assessment (PRA or PSA) of nuclear power plants (NPPs), the primary role of human reliability analysis (HRA) is to provide credible estimations of the human error probabilities (HEPs) of safety-critical tasks. In this regard, it is vital to provide credible HEPs based on firm technical underpinnings including (but not limited to): (1) how to collect HRA data from available sources of information, and (2) how to inform HRA practitioners with the collected HRA data. Because of these necessities, the U.S. Nuclear Regulatory Commission and the Korea Atomic Energy Research Institute independently developed two dedicated HRA data collection systems, SACADA (Scenario Authoring, Characterization, And Debriefing Application) and HuREX (Human Reliability data EXtraction), respectively. These systems provide unique frameworks that can be used to secure HRA data from full-scope training simulators of NPPs (i.e., simulator data). In order to investigate the applicability of these two systems, two papers have been prepared with distinct purposes. The first paper, entitled "SACADA and HuREX: Part 1. The Use of SACADA and HuREX Systems to Collect Human Reliability Data", deals with technical issues pertaining to the collection of HRA data. This second paper explains how the two systems are able to inform HRA practitioners. To this end, the process of estimating HEPs is demonstrated based on feed-and-bleed operations using HRA data from the two systems.