• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.26 no.5
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• pp.240-246
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• 2014

A fault detector was developed for heat exchangers of a hybrid heat pump (HP) for household. The proposed detector can be applied directly to raw operating data. It is to monitor a tracking error between a measured saturation temperature and its state observer. The observer was estimated from a state-space model simulating dynamics of a heat exchanger. The real hybrid HP was substituted with a dynamic simulator that implemented two-phased heat transfer and was validated by experimental data. And artificial fault data were generated using the simulator. Diagnosing the data showed the following. The residual calculated from the state observer error shows a relatively robust consistency with respect fouling level. The fault detector is practically useful because it detects a threshold fouling beyond which the performance starts to deteriorate significantly.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.4
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• pp.324-334
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• 2023

In this paper, the target simulator for RF signals was developed by using VST(Vector Signal Transceiver) and set by real-time signal processing SW programs. A function to process RF signals using FPGA(Field Programmable Gate Array) board was designed. The system functions capable of data processing, raw signals monitoring, target signals(simulated range, velocity) generating and RF environments data analyzing were implemented. And the characteristics of modulated signal were analyzed in RF environment. All function of programs for processing RF signal have options to store signal data and to manage the data. The validity of the signal simulation was confirmed through verification of simulated signal results.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.4
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• pp.351-359
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• 2009

We have developed a computer simulator fur cone-beam computed tomography (CBCT) based on the commercial Monte Carlo code, MCNP. All the functions to generate input files, run MCNP, convert output files to image data, reconstruct tomographs were realized in graphical user-interface form. The performance of the simulator was demonstrated by comparing with the experimental data. Although some discrepancies were observed due to the ignorance of the detailed physics in the simulation, such as scattered X-rays and noise in image sensors, the overall tendency was well agreed between the measured and simulated data. The developed simulator will be very useful for understanding the operation and the better design of CT systems.

• Archives of design research
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• v.16 no.2
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• pp.57-66
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• 2003

In order to develop color image, color simulation based on data processing techniques has been developed and applied to data interpretation tools or product design supporting systems. It has been a commonmethod to use image key words to search for data and provide color coordination samples that determine computer combination in computerized support systems until recently. However, this method does not reflect system designers and users taste or preference on making final choices of color coordination samples because the database was designed based on an assumption of standardized group that was established database from large scaled image evaluation research. In this study, we suggest a color coordination simulator that supports designer's final decision-making procedure on sample groups through the simulation of various color combination. The simulator allows communications with the system to explore a designer's color combination taste and preference, and provides a user for an efficient environment to judge the optimum result. The color coordination simulator was designed based upon Dual mapping model derived from a designer's thought process, and four steps of operations longrightarrowdefining color concept longrightarrowmaking color sample groupslongrightarrow simulation-determining ranking among final combination samples - will be assisting color design process.

• The KIPS Transactions:PartB
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• v.13B no.6 s.109
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• pp.615-624
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• 2006

The objective of this paper is to implement parallel multi-layer ANN(Artificial Neural Network) simulator based on the mobile agent system which is executed in parallel in the virtual parallel distributed computing environment. The Multi-Layer Neural Network is classified by training session, training data layer, node, md weight in the parallelization-level. In this study, We have developed and evaluated the simulator with which it is feasible to parallel the ANN in the training session and training data parallelization because these have relatively few network traffic. In this results, we have verified that the performance of parallelization is high about 3.3 times in the training session and training data. The great significance of this paper is that the performance of ANN's execution on virtual parallel computer is similar to that of ANN's execution on existing super-computer. Therefore, we think that the virtual parallel computer can be considerably helpful in developing the neural network because it decreases the training time which needs extra-time.

• Transactions of the Korean hydrogen and new energy society
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• v.23 no.4
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• pp.323-329
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• 2012

A thermal management system of a proton exchange membrane fuel cell is taken charge of controlling the temperature of fuel cell stack by rejection of electrochemically reacted heat. Two major components of thermal management system are heat exchanger and pump which determines required amount of heat. Since the performance and durability of PEMFC system is sensitive to the operating temperature and temperature distribution inside the stack, it is necessary to control the thermal management system properly under guidance of operating strategy. The control study of the thermal management system is able to be boosted up with hardware in the loop simulation which directly connects the plant simulation with real hardware components. In this study, the plant simulation of fuel cell stack has been developed and the simulation model is connected with virtual data acquisition system. And HIL simulator has been developed to control the coolant supply system for the study of PEMFC thermal management system. The virtual data acquisition system and the HIL simulator are developed under LabVIEWTM Platform and the Simulation interface toolkit integrates the fuel cell plant simulator with the virtual DAQ display and HIL simulator.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.5
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• pp.90-96
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• 2017

Due to the merit of having wide range with low cost, HF radar's ship detection and tracking research as maritime surveillance system has been recently studied. Many ship detection and tracking algorithms have been developed so far, however, performance comparison cannot be conducted properly because the states of target ships (such as moving path, size, etc.) differ from each study. In this paper, we propose a simulator based on compact HF radar, which generates data according to the size and moving path of target ship. Given the generated data with identical ship state, it is possible to conduct performance comparison. In order to validate the proposed simulator, the simulated data has been compared with real data collected by the SeaSonde HF radar sites. As a result, it has been shown that our simulated data resembles the real data. Therefore, the performance of various detection or tracking algorithms can be compared and analyzed respectively by using our simulated data.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.896-908
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• 2022

As a part of probabilistic risk (or safety) assessment (PRA or PSA) of nuclear power plants (NPPs), the primary role of human reliability analysis (HRA) is to provide credible estimations of the human error probabilities (HEPs) of safety-critical tasks. In this regard, it is vital to provide credible HEPs based on firm technical underpinnings including (but not limited to): (1) how to collect HRA data from available sources of information, and (2) how to inform HRA practitioners with the collected HRA data. Because of these necessities, the U.S. Nuclear Regulatory Commission and the Korea Atomic Energy Research Institute independently developed two dedicated HRA data collection systems, SACADA (Scenario Authoring, Characterization, And Debriefing Application) and HuREX (Human Reliability data EXtraction), respectively. These systems provide unique frameworks that can be used to secure HRA data from full-scope training simulators of NPPs (i.e., simulator data). In order to investigate the applicability of these two systems, two papers have been prepared with distinct purposes. The first paper, entitled "SACADA and HuREX: Part 1. The Use of SACADA and HuREX Systems to Collect Human Reliability Data", deals with technical issues pertaining to the collection of HRA data. This second paper explains how the two systems are able to inform HRA practitioners. To this end, the process of estimating HEPs is demonstrated based on feed-and-bleed operations using HRA data from the two systems.

• Progress in Medical Physics
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• v.20 no.2
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• pp.51-61
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• 2009

A Compton camera, which is based on the geometrical interpretation of Compton scattering, is a very promising gamma-ray imaging device considering its several advantages over the conventional gamma-ray imaging devices: high imaging sensitivity, 3-D imaging capability from a fixed position, multi-tracing functionality, and almost no limitation in photon energy. In the present study, a Monte Carlo-based, user-friendly Compton imaging simulator was developed in the form of a graphical user interface (GUI) based on Geant4 and $MATLAB^{TM}$. The simulator was tested against the experimental result of the double-scattering Compton camera, which is under development at Hanyang University in Korea. The imaging resolution of the simulated Compton image well agreed with that of the measured image. The imaging sensitivity of the measured data was 2~3 times higher than that of the simulated data, which is due to the fact that the measured data contains the random coincidence events. The performance of a stacking-structure type Compton camera was evaluated by using the simulator. The result shows that the Compton camera shows its highest performance when it uses 4 layers of scatterer detectors.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1181-1188
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• 2021

When abnormal events occur in a nuclear power plant, operators must conduct appropriate abnormal operating procedures. It is burdensome though for operators to choose the appropriate procedure considering the numerous main plant parameters and hundreds of alarms that should be judged in a short time. Recently, various research has applied deep-learning algorithms to support this problem by classifying each abnormal condition with high accuracy. Most of these models are trained with simulator data because of a lack of plant data for abnormal states, and as such, developed models may not have tolerance for plant data in actual situations. In this study, two approaches are investigated for a deep-learning model trained with simulator data to overcome the performance degradation caused by noise in actual plant data. First, a preprocessing method using several filters was employed to smooth the test data noise, and second, a data augmentation method was applied to increase the acceptability of the untrained data. Results of this study confirm that the combination of these two approaches can enable high model performance even in the presence of noisy data as in real plants.