• Journal of IKEEE
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• v.21 no.2
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• pp.158-161
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• 2017

In this paper, we propose Total RMS(Radiation Monitoring System) for nuclear and nuclear medicine. The proposed system can expand and control Stack Monitor, Area Monitor, and Water(Liquid) Monitor into one system, and can monitor the signals measured by each radiation detector in an integrated manner. The proposed system consists of a sensor module that detects the radiation, a display unit that displays the radiation dose near the radiation detection location, an alarm unit that reports the alarm when the detected radiation dose reaches the danger level, A Main Hub for collecting and storing the contents to the remote monitoring system, and an RMS Monitoring Unit for clearly displaying the measured radiation dose at the remote site. In order to evaluate the performance of Total RMS for the proposed nuclear and nuclear medicine field, it is confirmed that the measurement uncertainty is less than 8.5% and it operates normally within ${\pm}15%$ of the international standard.

• Journal of Advanced Marine Engineering and Technology
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• v.32 no.1
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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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• v.42 no.2
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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.