• Nuclear Engineering and Technology
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• v.48 no.1
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• pp.98-113
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• 2016

A reactor vessel level monitoring system measures the water level in a reactor during normal operation and abnormal conditions. A drop in the water level can expose nuclear fuel, which may lead to fuel meltdown and radiation spread in accident conditions. A level monitoring system mainly consists of a sensing line and pressure transmitter. Over a period of time boron sediments or other impurities can clog the line which may degrade the accuracy of the monitoring system. The aim of this study is to determine blockage in a sensing line using the energy of the composite signal. An equivalent Pi circuit model is used to simulate blockages in the sensing line and the system's response is examined under different blockage levels. Composite signals obtained from the model and plant's unblocked and blocked channels are decomposed into six levels of details and approximations using a wavelet filter bank. The percentage of energy is calculated at each level for approximations. It is observed that the percentage of energy reduces as the blockage level in the sensing line increases. The results of the model and operational data are well correlated. Thus, in our opinion variation in the energy levels of approximations can be used as an index to determine the presence and degree of blockage in a sensing line.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.9
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• pp.512-519
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• 2000

A Neural networks has been used for a expert system and fault diagnosis system. It is possible to nonlinear function mapping and parallel processing. Therefore It has been developing for a Diagnosis system of nuclear plower plant. In general Neural Networks is a static mapping but Dynamic Neural Network(DNN) is dynamic mapping.쪼두 a fault occur in system a state of system is changed with transient state. Because of a previous state signal is considered as a information DNN is better suited for diagnosis systems than static neural network. But a DNN has many weights so a real time implementation of diagnosis system is in need of a rapid network architecture. This paper presents a algorithm for RCP monitoring Alarm diagnosis system using Self Dynamic Neural Network(SDNN). SDNN has considerably fewer weights than a general DNN. Since there is no interlink among the hidden layer. The effectiveness of Alarm diagnosis system using the proposed algorithm is demonstrated by applying to RCP monitoring in Nuclear power plant.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.4_2
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• pp.673-686
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• 2022

Recently, as the importance of eco-friendly energy has increased hydrogen gas is in the spotlight as future energy. Due to its special properties, hydrogen gas is more difficult to detect requiring more precise sensing technology. The primary objective of this study is to design a concept of an underground hydrogen pipeline monitoring system. For this, the following research works are conducted sequentially; 1)selection of core technology for conceptual design, 2)state-of-the-art review, 3)design of a concept of the system. As a result, DAS(Distributed Acoustic Sensing), and DTS(Distributed Temperature Sensing) are selected as each core technology. Furthermore, a conceptual design of an underground hydrogen pipeline monitoring system is deducted. It is expected that the impact on the eco-friendly energy industry will be enormous due to the increasing interest in using hydrogen energy.

• Tribology and Lubricants
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• v.26 no.1
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• pp.73-77
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• 2010

One of the important trends for condition monitoring in the 21st century is the development of smart sensors that will permit the cost-effective continuous monitoring of key machine equipments. In this study, an integrated in-line oil monitoring sensor assigned for continuous in situ monitoring multiple parameters of oil performance is presented. The sensor estimates oil deterioration based on the information about chemical degradation, total contamination, water content of oil and oil temperature. The oil oxidation is estimated by "chromatic ratio", total contamination is measured by the changes in optical density of oil in three optical wave-bands ('Red', 'Green' and 'Blue') and water content is evaluated as relative saturation of oil by water. In order to evaluate the sensor's effectiveness, the sensor was applied to several used oil samples in steel making industry and the results were compared with those measured by standard test methods.

• Smart Structures and Systems
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• v.12 no.2
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• pp.209-233
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• 2013

With an annual growth rate of about 30%, wind energy systems, such as wind turbines, represent one of the fastest growing renewable energy technologies. Continuous structural health monitoring of wind turbines can help improving structural reliability and facilitating optimal decisions with respect to maintenance and operation at minimum associated life-cycle costs. This paper presents an integrated monitoring system that is designed to support structural assessment and life-cycle management of wind turbines. The monitoring system systematically integrates a wide variety of hardware and software modules, including sensors and computer systems for automated data acquisition, data analysis and data archival, a multiagent-based system for self-diagnosis of sensor malfunctions, a model updating and damage detection framework for structural assessment, and a management module for monitoring the structural condition and the operational efficiency of the wind turbine. The monitoring system has been installed on a 500 kW wind turbine located in Germany. Since its initial deployment in 2009, the system automatically collects and processes structural, environmental, and operational wind turbine data. The results demonstrate the potential of the proposed approach not only to ensure continuous safety of the structures, but also to enable cost-efficient maintenance and operation of wind turbines.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.5
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• pp.499-509
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• 2019

This paper discusses energy use monitoring and analysis as part of a study on a low-cost energy supply management system that links an existing database with weather information with no real-time monitor for energy demand of buildings using renewable energy, generator and energy storage systems. This study is targeted at small and medium-sized buildings and aims to monitor energy use with a small number of sensors at low cost by applying an energy management system (EMS). The present study can help overcome the limitations of high-cost EMS applied to large commercial and public buildings. We developed current, indoor temperature and human motion sensors and installed them in an office of a company in a sample building. Through these sensors, we analyzed energy use patterns and the effects of weather information and human motion on the energy use. Furthermore, we analyzed the correlations between the total KEPCO energy use of the sample building and weather by comparing these two data. The results showed that the office energy use of a company was more affected by human motions than by weather information. The comparison between the total energy use of the Building and weather information found that external temperature had an effect on the energy use.

• Journal of the Korean Solar Energy Society
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• v.30 no.3
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• pp.25-32
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• 2010

A real-time monitoring system was developed for transfer, receive, backup and analysis of wind power data at three wind farm(Hang won, Hankyung and Sung san) in Jeju. For this monitoring system a communication system analysis, a collection of data and transmission module development, data base construction and data analysis and management module was developed, respectively. These modules deal with mechanical, electrical and environmental problem. Especially, time series graphic is supported by the data analysis and management module automatically. The time series graphic make easier to raw data analysis. Also, the real-time monitoring system is connected with wind power forecasting system through internet web for data transfer to wind power forecasting system's data base.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.764-766
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• 1997

This paper presents a DSM Monitoring Methodology which is able to evaluate the impact of DSM programs under processing. For an effective assessment of DSM programs, it is foremost necessary to examine the market characteristics on the specific end-use appliance according to the customer's adoption of DSM programs. The Proposed Monitoring system contains a diffusion process of high efficient end-use considering major factors of DSM impact such as, price, price elasticity and lifetime of end-use apparatus or appliance. The case study shows the propriety and necessity of DSM monitoring in order to diagnose the current state and provide a baseline of DSM policy

• Journal of the Korean Society for Precision Engineering
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• v.12 no.5
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• pp.25-32
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• 1995

In highly automated milling process, in-process monitoring of the malfunction is indispensable to ensure efficient cutting operation. Among many malfunctions in milling process, chatter vibration deteriorates surface finish, tool life and productivity. In this study, the monitoring system of chatter vibration for face milling process is proposed and experimentally estimated. The monitoring system employs two types of sensor such as cutting force and acceleration in sensory detection state. The RMS value and band frequency energy of the sensor signals are extracted in time domain for the input patterns of neural network to reduce time delay in signal processing state. The resultes of experimental evaluation show that the system works well over a wide range of cutting conditions.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2158-2165
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• 2023

Marine radioactivity monitoring is critical for taking immediate action in case of unexpected nuclear accidents at nuclear facilities located near coastal areas. Especially when the level of contamination is not predictable, mobile monitoring systems will be useful for wide-area ocean radiation survey and for determination of the level of radioactivity. Here, we used a silicon photomultiplier and a high-efficiency GAGG crystal to fabricate a compact, battery-powered gamma spectroscopy that can be used in an ocean environment. The developed spectroscopy has compact dimensions of 6.5 × 6.5× 8 cm³ and weighs 560 g. We used LoRa, a low-power wireless protocol for communication. Successful data transmission was achieved within 1.4 m water depth. The developed gamma spectroscopy was able to detect radioactivity from a ¹³⁷Cs point source (3.7 kBq) at a distance of 20 cm in water. Moreover, we demonstrated an unmanned radioactivity monitoring system in a real sea by combining unmanned surface vehicle with the developed gamma spectroscopy. A hidden ¹³⁷Cs source (3.07 MBq) was detected by the unmanned system at a distance of 3 m. After successfully testing the developed mobile spectroscopy in an ocean environment, we believe that our proposed system will be an effective solution for mobile real-time marine radioactivity monitoring.