• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2237-2250
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• 2021

The analog-based Ex-core Neutron Flux Monitoring System (ENFMS) in Korean Nuclear Power Plants (NPPs) has been performing its intended functions successfully for a long time. On the other hand, the primary concern with the extended use of analog systems is the aging effect, such as mechanical failures, environmental degradation, and obsolescence. The transition to a digital-based Man-Machine Interface Systems (MMIS) in Korea and other countries has been accelerating, but some systems are still analog-based IC systems, such as the ENFMS in APR1400 NPPs. Digitalized ENFMS can become a reality using computers and microprocessors owing to the progress in digital electronics and information technology. This paper presents the result of the first phase of the research on the digitalization of the ENFMS signal processing electronics for NPPs operated or produced in Korea. It has two main parts: (1) review engineering bases of ex-core neutron flux monitoring system, including nuclear engineering, instrumentation techniques, and analog and digital signal processing techniques, and (2) analysis of analog signal processing electronics of ENFMS for OPR1000 and APR1400 power plants. They are prerequisite to the second phase of the research which is the detailed implementation of the digitalization.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.374-379
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• 2016

On-line process monitoring schemes are designed to give early warnings of process faults. In the artificial intelligence and machine learning fields, reliable approaches have been utilized, such as kernel-based nonlinear techniques. This work presents a kernel-based empirical monitoring scheme with a small sample problem. The measurement data of normal operations are easy to collect, whereas special events or faults data are difficult to collect. In such situations, noise filtering techniques can be helpful in enhancing the process monitoring performance. This can be achieved by the preprocessing of raw process data and eliminating unwanted variations of data. In this work, the performance of several monitoring schemes was demonstrated using three-dimensional batch process data. The results showed that the monitoring performance was improved significantly in terms of the detection success rate.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.63-68
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• 2006

As a part of efforts to enhance construction technology, it is essential to obtain competitive technology which is future-oriented. In this paper, the current status of structural health monitoring techniques is reviewed. Also, ubiquitous system is expected in its use for further development and applications in construction.

• Journal of the Korean Society for Nondestructive Testing
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• v.25 no.6
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• pp.421-430
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• 2005

Brillouin time-domain analysis in optical fibres is a novel technique making possible a distributed measurement of temperature and strain over long distance and will deeply modify our view about monitoring large structures, such as dams, bridges, tunnels and pipelines, Optical fibre sensing will certainly be a decisive tool for securing dangerous installations and detecting environmental and industrial threats.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.2116-2117
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• 2008

This system has successfully captured long intermittent discharge signals that hadn't been detected through conventional techniques, and solved the problem successfully. The results demonstrate that UHF technique has great advantages for on-line PD monitoring of transformers. By adopting the peak detection technique, it becomes easy and effective for the transplantation of the phase-resolved pattern recognition technique from conventional method to UHF method, and then to realize continuous on-line monitoring, source characterization and trending analysis.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.39-45
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• 2000

This paper describes the fundamental investigations on the in-process monitoring techniques focused on Acoustic Emission(AE) based on analytical method. Experiments were conducted on a CNC lathe using conventional carbide insert tools under various cutting conditions. As the result of this study a suggestion is given about the multi-purpose use of AE-signals detected with a single sensor for the monitoring of tool wear, built-up edge and chatter vibration in turning process.

• Nuclear Engineering and Technology
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• v.44 no.4
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• pp.393-404
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• 2012

After the Fukushima nuclear accident in 2011, there has been increasing concern regarding severe accidents in nuclear facilities. Severe accident scenarios are difficult for operators to monitor and identify. Therefore, accurate prediction of a severe accident is important in order to manage it appropriately in the unfavorable conditions. In this study, artificial intelligence (AI) techniques, such as support vector classification (SVC), probabilistic neural network (PNN), group method of data handling (GMDH), and fuzzy neural network (FNN), were used to monitor the major transient scenarios of a severe accident caused by three different initiating events, the hot-leg loss of coolant accident (LOCA), the cold-leg LOCA, and the steam generator tube rupture in pressurized water reactors (PWRs). The SVC and PNN models were used for the event classification. The GMDH and FNN models were employed to accurately predict the important timing representing severe accident scenarios. In addition, in order to verify the proposed algorithm, data from a number of numerical simulations were required in order to train the AI techniques due to the shortage of real LOCA data. The data was acquired by performing simulations using the MAAP4 code. The prediction accuracy of the three types of initiating events was sufficiently high to predict severe accident scenarios. Therefore, the AI techniques can be applied successfully in the identification and monitoring of severe accident scenarios in real PWRs.

• Journal of Soil and Groundwater Environment
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• v.28 no.spc
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• pp.18-39
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• 2023

Rapid development of geophysical exploration and hydrogeologic monitoring techniques has yielded remarkable increase of datasets related to groundwater systems. Increased number of datasets contribute to understanding of general aquifer characteristics such as groundwater yield and flow, but understanding of complex heterogenous aquifers system is still a challenging task. Recently, applications of data science technique have become popular in the fields of geophysical explorations and monitoring, and such attempts are also extended in the groundwater field. This work reviewed current status and advancement in utilization of data science in groundwater field. The application of data science techniques facilitates effective and realistic analyses of aquifer system, and allows accurate prediction of aquifer system change in response to extreme climate events. Due to such benefits, data science techniques have become an effective tool to establish more sustainable groundwater management systems. It is expected that the techniques will further strengthen the theoretical framework in groundwater management to cope with upcoming challenges and limitations.

• Journal of Conservation Science
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• v.31 no.4
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• pp.395-402
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• 2015

This study was considered utilization of hyperspectral image analysis for monitoring. Accordingly we applied to stone cultural properties to data correction methods, image classification techniques, NDVI computation techniques using hyperspectral image. As the results, hyperspectral image analysis was possible making detailed deterioration map, accurate calculation of deterioration rate, mapping of normalized difference vegetation index on the basis of reflectance of each materials. Therefore, hyperspectral image analysis will be used for effective monitoring techniques of stone cultural heritages.

• Journal of Soil and Groundwater Environment
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• v.25 no.2_spc
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• pp.1-15
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• 2020

Monitoring and assessing terminal electron accepting processes (TEAPs) are one of the most important steps to remediate contaminated sites via various in-situ techniques. TEAPs are a part of the microbial respiration reactions. Microorganisms gain energy from these reactions and reduces pollutants. Monitoring TEAPs enables us to predict degradability of contaminants and degradation rates. In many countries, TEAPs have been used for characterization of field sites and management of groundwater wells. For instance, US Environmental Protection Agency (EPA) provided strategies for groundwater quality and well management by applying TEAPs monitoring. Denmark has also constructed TEAPs map of local unit area to develop effective groundwater managing system, particularly to predict and assess nitrogen contamination. In case of Korea, although detailed soil survey and groundwater contamination assessment have been employed, site investigation guidelines using TEAPs have not been established yet. To better define TEAPs in subsurface environments, multiple indicators including ion concentrations, isotope compositions and contaminant degradation byproducts must be assessed. Furthermore, dissolved hydrogen concentrations are regarded as significant evidence of TEAPs occurring in subsurface environment. This review study introduces optimal sampling techniques of groundwater and dissolved hydrogen, and further discuss how to assess TEAPs in contaminated subsurface environments according to several contamination scenarios.