• Proceedings of the Korean Nuclear Society Conference
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• 1998.05a
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• pp.264-270
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• 1998

This research broadens the prime concern of nuclear power plant operations from safe performance to both economic and safe performance. First emergency diesel generator is identified as one of main contributors for the lost plant availability through the review of plants forced outage records. The framework of an integrated architecture for performing modern on-line condition for operational availability improvement is configured in this work. For the development of the comprehensive sensor networks for complex target systems, an integrated methodology incorporating a structural hierarchy, a functional hierarchy, and a fault-system matrix is formulated. The second part of our research is development of intelligent diagnosis and maintenance advisory system, which employs Bayesian Belief networks (BBNs) as a high level reasoning tool incorporating inherent uncertainty use in probabilistic inference. Our prototype diagnosis algorithms are represented explicitly through topological symbols and links between them in a causal direction. As new evidence from sensor network development is entered into the model especially, our advisory of system provides operational advice concerning both availability and safety, so that the operator is able to determine the likely modes, diagnose the system state, locate root causes, and take the most advantageous action. Thereby, this advice improves operational availability

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.695-706
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• 2014

Partial discharge (PD) measurements have emerged as a dominant investigative tool for condition monitoring of insulation in high voltage equipment. But the major problem behind them the PD signal is severely polluted by several noises like White noise, Random noise, Discrete Spectral Interferences (DSI) and the challenge lies with removing these noise from the onsite PD data effectively which leads to preserving the signal for feature extraction. Accordingly the paper is mainly classified into two parts. In first part the PD signal is artificially simulated and mixed with white noise. In second part the PD is measured then it is subjected to the proposed denoising techniques namely Translation Invariant Wavelet Transform (TIWT). The proposed TIWT method remains the edge of the original signal efficiently. Additionally TIWT based denoising is used to suppress Pseudo Gibbs phenomenon. In this paper an attempt has been made to review the methodology of denoising the PD signals and shows that the proposed denoising method results are better when compared to other wavelet-based approaches like Fast Fourier Transform (FFT), Discrete Wavelet Transform (DWT), by evaluating five different parameters like, Signal to noise ratio, Cross-correlation coefficient, Pulse amplitude distortion, Mean square error, Reduction in noise level.

• Nuclear Engineering and Technology
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• v.27 no.2
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• pp.234-247
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• 1995

Trace elements in airborne particulates were analyzed by instrumental neutron activation analysis (INAA) under the optimum analytical condition. Neutron irradiation for sample was done at the irradiation hole(neutron flux 1$\times$10$^{13}$ n/$\textrm{cm}^2$.s) of TRIGA MARK-III research reactor in the Korea Atomic Energy Research Institute. For the verification of the analytical method, NIST SRM-1648 and NIES CRM No.8 ore chosen and analyzed. The accuracy and precision of the analysis of 40 and 24 trace elements in the samples were compared with the certified and reported values, respectively. The analytical method was found to be reliable enough when the analytical data of NIES sample were compared with those of different counties. In the analytical result of two or both of standard reference materials, relative standard deviation wes within the 15％ except a few elements and the relative error was within the 10％. We used this method to analyze 30 trace elements in airborne particulates collected with the high volume air sampler(PM-10) at too different locations and also confirmed the possibility to use this method as a routine monitoring tool to find out environmental pollution sources.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.6
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• pp.1-8
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• 2019

Modern weapon systems are multifunctional, with capabilities for executing complex missions. However, they are required to be highly reliable, which increases their total cost of ownership. Because it is necessary to produce the best results within a limited budget, there is an increasing interest in development, acquisition, and maintenance costs. Consequently, there is a need for tools that calculate the lifecycle costs of weapons systems development to facilitate decision making. In this study, we propose a cost calculation function based on the Markov process simulator-a reliability, availability, and maintainability analysis tool developed by applying the Markov-Monte Carlo method-as an alternative to these requirements to facilitate decision-making in systems development.

• The Korean Journal of Quaternary Research
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• v.18 no.2 s.23
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• pp.105-106
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• 2004

The NDVI(normalized difference vegetation index) dataset is unique or main tool to assess the global, multi seasonal, multi annual, and multi spectral changes over the World. These features are useful for environmental studies in particular, for the vegetation coverage monitoring of the country as Mongolia, where are large pastureland and pastoral animal husbandry, which dependent on natural conditions. Pasture vegetation cover is changing accordingly with both of global climate change and anthropogenic effect or human impacts. Using past 20 years (1982-2001) NDVI derived from NOAA satellite, its dynamical trend has been decreased in all natural zones differently. Also applied the method named "Two Years Differences" which could calculate the number of years with increased or decreased NDVI values at the same place. From May to September have occurred the 9 years maximum decreases of NDVI over Mongolia, but it obtained differently in spatial and temporal scale. In 24.4 ? 32.7% of all territory occurred one year decrease of NDVI and in 18% occurred more than 3 years frequent decrease of NDVI. According to the linear trend of NDVI and in 18% occurred more than 3 years frequent decrease of NDVI dynamics over 69% of whole territory of Mongolia NDVI values had been decreased due to both natural and human induced impacts to the pasture condition. In this paper also included some results of the integrated analyses of NOAA/NDVI and ground truth data over Monglia separately by natural zones.

• Journal of the Korean Society of Radiology
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• v.85 no.3
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• pp.549-565
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• 2024

MRI is a crucial tool for diagnosing adenomyosis and identifying its related pathologies. To accurately diagnose adenomyosis, it is necessary to recognize both the typical MRI findings and atypical features of the condition. Recently, a standardized classification system has been developed to facilitate precise presurgical diagnosis of adenomyosis and to determine the appropriate treatment method. Differentiating between various subtypes based on MRI-based classification and identifying different MRI phenotypes can aid in categorizing patients with adenomyosis into specific treatment groups and monitoring their response to therapy.

• The KIPS Transactions:PartB
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• v.9B no.5
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• pp.663-672
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• 2002

In this paper, Using an automatic tracking system, behavior of an aquatic insect, Chironomus sp. (Chironomidae), was observed in semi-natural conditions in response to sub-lethal treament of a carbamate insecticide, carbofuran. The fourth instar larvae were placed in an observation cage $(6cm\times{7cm}\times{2.5cm)}$ at temperature of $18^\circ{C}$ and the light condition of 10 time (light) : 14 time (dark). The tracking system was devised to detect the instant, partial movement of the insect body. Individual movement was traced after the treatment of carbofuran (0.1ppm) for four days 2days : before treatment, 2 days : after treatment). Along with the other irregular behaviors, "ventilation activity", appearing as a shape of "compressed zig-zag", was more frequently observed after the treatment of the insecticide. The activity of the test individuals was also generally depressed after the chemical treatment. In order to detect behavioral changes of the treated specimens, wavelet analysis was implemented to characterize different movement patterns. The extracted parameters based on Discrete Wavelet Transforms (DWT) were subsequently provided to artificial neural networks to be trained to represent different patterns of the movement tracks before and after treatments of the insecticide. This combined model of wavelets and artificial neural networks was able to point out the occurrence of characteristic movement patterns, and could be an alternative tool for automatically detecting presences of toxic chemicals for water quality monitoring. quality monitoring.

• Earthquakes and Structures
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• v.17 no.5
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• pp.511-519
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• 2019

The concrete gravity dam is one of the most important parts of the nation's infrastructure. Besides the benefits, the dam also has some potentially catastrophic disasters related to the life of citizens directly. During the lifetime of service, some degradations in a dam may occur as consequences of operating conditions, environmental aspects and deterioration in materials from natural causes, especially from dynamic loads. Cumulative Absolute Velocity (CAV) plays a key role to assess the operational condition of a structure under seismic hazard. In previous researches, CAV is normally used in Nuclear Power Plant (NPP) fields, but there are no particular criteria or studies that have been made on dam structure. This paper presents a method to calculate the limitation of CAV for the Bohyeonsan Dam in Korea, where the critical Peak Ground Acceleration (PGA) is estimated from twelve sets of selected earthquakes based on High Confidence of Low Probability of Failure (HCLPF). HCLPF point denotes 5% damage probability with 95% confidence level in the fragility curve, and the corresponding PGA expresses the crucial acceleration of this dam. For determining the status of the dam, a 2D finite element model is simulated by ABAQUS. At first, the dam's parameters are optimized by the Minitab tool using the method of Central Composite Design (CCD) for increasing model reliability. Then the Response Surface Methodology (RSM) is used for updating the model and the optimization is implemented from the selected model parameters. Finally, the recorded response of the concrete gravity dam is compared against the results obtained from solving the numerical model for identifying the physical condition of the structure.

• Journal of the Korean Society for Nondestructive Testing
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• v.31 no.4
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• pp.360-366
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• 2011

The Elasto-magnetic sensor is applied to detect the local cross-sectional loss of steel cables in this study while it was originally developed for measuring the tensile force in the previous works. To verify the feasibility of the proposed damage detection technique, steel bars which have 4-different diameters were fabricated and the output voltage value was measured at each diameter by the E/M sensor. Optimal input voltage and working point are chosen so that the linearity and resolution of results can ensure through repeated experiments, and then the E/M sensor was measured the output voltage values at the damage points of steel bar specimen that was applied the 4 types of damage condition based on the selected optimal experimental condition. This proposed approach can be an effective tool for steel cable health monitoring.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.239-240
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• 2012

With advancements in semiconductor device technologies, manufacturing processes are getting more complex and it became more difficult to maintain tighter process control. As the number of processing step increased for fabricating complex chip structure, potential fault inducing factors are prevail and their allowable margins are continuously reduced. Therefore, one of the key to success in semiconductor manufacturing is highly accurate and fast fault detection and classification at each stage to reduce any undesired variation and identify the cause of the fault. Sensors in the equipment are used to monitor the state of the process. The idea is that whenever there is a fault in the process, it appears as some variation in the output from any of the sensors monitoring the process. These sensors may refer to information about pressure, RF power or gas flow and etc. in the equipment. By relating the data from these sensors to the process condition, any abnormality in the process can be identified, but it still holds some degree of certainty. Our hypothesis in this research is to capture the features of equipment condition data from healthy process library. We can use the health data as a reference for upcoming processes and this is made possible by mathematically modeling of the acquired data. In this work we demonstrate the use of recurrent neural network (RNN) has been used. RNN is a dynamic neural network that makes the output as a function of previous inputs. In our case we have etch equipment tool set data, consisting of 22 parameters and 9 runs. This data was first synchronized using the Dynamic Time Warping (DTW) algorithm. The synchronized data from the sensors in the form of time series is then provided to RNN which trains and restructures itself according to the input and then predicts a value, one step ahead in time, which depends on the past values of data. Eight runs of process data were used to train the network, while in order to check the performance of the network, one run was used as a test input. Next, a mean squared error based probability generating function was used to assign probability of fault in each parameter by comparing the predicted and actual values of the data. In the future we will make use of the Bayesian Networks to classify the detected faults. Bayesian Networks use directed acyclic graphs that relate different parameters through their conditional dependencies in order to find inference among them. The relationships between parameters from the data will be used to generate the structure of Bayesian Network and then posterior probability of different faults will be calculated using inference algorithms.