• 전기전자학회논문지
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• 제21권2호
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• pp.158-161
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• 2017

본 논문에서는 원자력 및 핵의학 분야용 Total RMS(Radiation Monitoring System)를 제안한다. 제안하는 시스템은 Stack Monitor, Area Monitor, Water(Liquid) Monitor 등을 하나의 시스템으로 확장 및 제어할 수 있는 장비로 각 방사선 검출기에서 측정된 신호를 통합 모니터링 할 수 있다. 제안하는 시스템은 해당 방사선을 검출하는 Sensor Module, 방사선 검출 위치에 인접한 곳에서 방사선량을 디스플레이 하는 Display Unit, 검출된 방사선량이 위험 수준에 도달시 경보를 알리는 Alarm Unit, 각 모니터링에서 측정된 방사선량들을 취합하고 저장하여 원격 감시 시스템에 내용을 전달하는 Main Hub, 원격지에서 측정된 방사선량 상태를 일목요연하게 디스플레이 하는 RMS Monitoring Unit 등으로 구성된다. 제안된 원자력 및 핵의학 분야용 Total RMS의 성능을 평가하기 위하여 공인시험기관에서 실험한 결과, 측정 불확도가 8.5%이하로 측정되어 국제 표준인 ${\pm}15%$ 이하에서 정상동작 됨이 확인되었다.

• Journal of Advanced Marine Engineering and Technology
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• 제32권1호
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• pp.153-161
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• 2008

Recently, the research and development of the fish cage in open sea from domestic is active and it is almost complement with commercial scale test on 'seogwipo'. But domestic technology is not sufficient of management RMS(Remote Monitoring System) with advanced country, so it is difficult to maintenance and monitor fish cage in open sea. This paper introduces a fish cage environmental monitoring system for measuring pH, DO and temperature. A signal is treated with DAU(Data Acquisition Unit) by a wireless communication method and transfers data to host computer for data processing. These data are graphically shown on the monitor with LabVIEW program and logged on the data processing system in the form of file.

• Nuclear Engineering and Technology
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• 제42권2호
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• pp.219-230
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• 2010

In nuclear power plants (NPPs), periodic sensor calibrations are required to assure that sensors are operating correctly. By checking the sensor's operating status at every fuel outage, faulty sensors may remain undetected for periods of up to 24 months. Moreover, typically, only a few faulty sensors are found to be calibrated. For the safe operation of NPP and the reduction of unnecessary calibration, on-line instrument calibration monitoring is needed. In this study, principal component-based auto-associative support vector regression (PCSVR) using response surface methodology (RSM) is proposed for the sensor signal validation of NPPs. This paper describes the design of a PCSVR-based sensor validation system for a power generation system. RSM is employed to determine the optimal values of SVR hyperparameters and is compared to the genetic algorithm (GA). The proposed PCSVR model is confirmed with the actual plant data of Kori Nuclear Power Plant Unit 3 and is compared with the Auto-Associative support vector regression (AASVR) and the auto-associative neural network (AANN) model. The auto-sensitivity of AASVR is improved by around six times by using a PCA, resulting in good detection of sensor drift. Compared to AANN, accuracy and cross-sensitivity are better while the auto-sensitivity is almost the same. Meanwhile, the proposed RSM for the optimization of the PCSVR algorithm performs even better in terms of accuracy, auto-sensitivity, and averaged maximum error, except in averaged RMS error, and this method is much more time efficient compared to the conventional GA method.