• Journal of the Korean Solar Energy Society
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• v.39 no.3
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• pp.59-66
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• 2019

New and renewable energy forecasts are key technology to reduce the annual operating cost of new and renewable facilities, and accuracy of forecasts is paramount. In this study, we intend to build a model for the prediction of short-term solar power generation for 1 hour to 3 hours. To this end, this study applied two time series technique, ARIMA model without considering seasonality and SARIMA model with considering seasonality, comparing which technique has better predictive accuracy. Comparing predicted errors by MAE measures of solar power generation for 1 hour to 3 hours at four locations, the solar power forecast model using ARIMA was better in terms of predictive accuracy than the solar power forecast model using SARIMA. On the other hand, a comparison of predicted error by RMSE measures resulted in a solar power forecast model using SARIMA being better in terms of predictive accuracy than a solar power forecast model using ARIMA.

• Journal of Power Electronics
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• v.18 no.1
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• pp.45-55
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• 2018

It is usually difficult for dual-active-bridge (DAB) dc-dc converters to operate efficiently at light loads. This paper presents an in-depth analysis of a DAB with triple-phase-shift (TPS) control under the light load condition to overcome this problem. A kind of operating mode which is suitable for light load operation is analyzed in this paper. First, an analysis of the zero-voltage-switching (ZVS) constraints for the DAB converter has been carried out and a reasonable dead-band setting method has been proposed. Secondly, the basic operating characteristics of the converter are analyzed. Third, under the condition of satisfying the ZVS constraints, both the reactive power and the root mean square (RMS) value of the current are simultaneously minimized and a particle swarm optimization (PSO) algorithm is employed to analyze and solve this optimization problem. Lastly, both simulations and experiments are carried out to verify the effectiveness of the proposed method. The experimental results show that the converter can effectively achieve ZVS and improved efficiency.

• The Transactions of the Korean Institute of Power Electronics
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• v.22 no.2
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• pp.150-158
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• 2017

This study proposes a new pulse-width modulation switching pattern for the low conduction loss of a three-level neutral point clamped (NPC)-based dual-active bridge (DAB) converter. The operational principle for a bidirectional power conversion is a phase-shift modulation. The conventional switching method of the three-level NPC-based DAB converter shows a symmetric switching pattern. This method has a disadvantage of high root-mean-square (RMS) value of the coupling inductor current, which leads to high conduction loss. The proposed switching method shows an asymmetrical pattern, which can reduce the RMS value of the inductor current with lower conduction loss than that of the conventional method. The performance of the proposed asymmetrical switching method is theoretically analyzed and practically verified using simulation and experiment.

• Transactions of the Korean hydrogen and new energy society
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• v.31 no.5
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• pp.436-443
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• 2020

The performance prediction model of a solid oxide fuel cell stack has been developed using deep neural network technique, one of the machine learning methods. The machine learning has been received much interest in various fields, including energy system mo- deling. Using machine learning technique can save time and cost requried in developing an energy system model being compared to the conventional method, that is a combination of a mathematical modeling and an experimental validation. Results reveal that the mean average percent error, root mean square error, and coefficient of determination (R2) range 1.7515, 0.1342, 0.8597, repectively, in maximum. To improve the predictability of the model, the pre-processing is effective and interpolative machine learning and application is more accurate than the extrapolative cases.

• Nuclear Engineering and Technology
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• v.31 no.2
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• pp.192-201
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• 1999

This paper proposes an improved signal processing strategy for accurate feedwater flowrate estimation in nuclear power plants. It is generally known that ∼2% thermal power errors occur due to fouling Phenomena in feedwater flowmeters. In the strategy Proposed, the noises included in feedwater flowrate signal are classified into rapidly varying noises and gradually varying noises according to the characteristics in a frequency domain. The estimation precision is enhanced by introducing a low pass filter with the wavelet analysis against rapidly varying noises, and an autoassociative neural network which takes charge of the correction of only gradually varying noises. The modified multivariate stratification sampling using the concept of time stratification and MAXIMIN criteria is developed to overcome the shortcoming of a general random sampling. In addition the multi-stage robust training method is developed to increase the quality and reliability of training signals. Some validations using the simulated data from a micro-simulator were carried out. In the validation tests, the proposed methodology removed both rapidly varying noises and gradually varying noises respectively in each de-noising step, and 5.54% root mean square errors of initial noisy signals were decreased to 0.674% after de-noising. These results indicate that it is possible to estimate the reactor thermal power more elaborately by adopting this strategy.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.3043-3050
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• 2022

In Korea, all nuclear power plants (NPPs) participate in annual performance tests including in vivo measurements using the FastScan, a stand type whole body counter (WBC), manufactured by Canberra. In 2018, all Korean NPPs satisfied the testing criterion, the root mean square error (RMSE) ≤ 0.25, for the whole body configuration, but three NPPs which participated in an additional lung configuration test in the fission and activation product category did not meet the criterion. Due to the low resolution of the FastScan NaI(Tl) detectors, the conventional peak analysis (PA) method of the FastScan did not show sufficient performance to meet the criterion in the presence of interfering radioisotopes (RIs), ¹³⁴Cs and ¹³⁷Cs. In this study, we developed an artificial neural network (ANN) to improve the performance of the FastScan in the lung configuration. All of the RMSE values derived by the ANN satisfied the criterion, even though the photopeaks of ¹³⁴Cs and ¹³⁷Cs interfered with those of the analytes or the analyte photopeaks were located in a low-energy region below 300 keV. Since the ANN performed better than the PA method, it would be expected to be a promising approach to improve the accuracy and precision of in vivo FastScan measurement for the lung configuration.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.5
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• pp.338-347
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• 2005

A computer-based low back muscle evaluation system was designed to simultaneously acquire, process, display, quantify, and correlate electromyographic(EMG) activity with muscle force, and range of motion(ROM) in the lumbar muscle of human. This integrated multi-channel system was designed around notebook PC. Each channel consisted of a time and frequency domain block, and T-F(time-frequency) domain block. The captured data in each channel was used to display and Quantify : raw EMG, histogram, zero crossing, turn, RMS(root mean square), variance, mean, power spectrum, median frequency, mean frequency, wavelet transform, Wigner-Ville distribution, Choi-Williams distribution, and Cohen-Posch distribution. To evaluate the performance of the designed system, the static and dynamic contraction experiments from lumbar(waist) level of human were done. The experiment performed in five subjects, and various parameters were tested and compared. This system could equally well be modified to allow acquisition, processing, and analysis of EMG signals in other studies and applications.

• Wind and Structures
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• v.35 no.5
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• pp.297-307
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• 2022

Over the past few decades, the use of wind tunnels has been increasing as a result of the rapid growth of cities and the urge to build taller and non-typical structures. While the accuracy of a wind tunnel study on a tall building requires several aspects, the precise extraction of wind pressure plays a significant role in a successful pressure test. In this research study, a low-cost expandable synchronous multi-pressure sensing system (SMPSS) was developed and validated at Ryerson University's wind tunnel (RU-WT) using electronically scanning pressure sensors for wind tunnel tests. The pressure system consists of an expandable 128 pressure sensors connected to a compact data acquisition and a host workstation. The developed system was examined and validated to be used for tall buildings by comparing mean, root mean square (RMS), and power spectral density (PSD) for the base moments coefficients with the available data from the literature. In addition, the system was examined for evaluating the mean and RMS pressure distribution on a standard low-rise building and were found to be in good agreement with the validation data.

• Journal of Korean Medicine Rehabilitation
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• v.19 no.2
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• pp.241-250
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• 2009

Objectives : To study autonomic nervous system dysfunction of Low Back Pain(LBP) patients, using spectral analysis of Heart Rate Variability(HRV). Methods : HRV of 190 patients was measured and seperated into two groups, those with LBP(n=95) and healthy controls(n=95). HRV was measured by SA-6000(Medicore, Korea) for 5 minutes after 5 minutes' resting. Results : 1. Mean heart rate(MHRT) of the experimental group was slightly higher than that of the control group, but did not show significant difference(P=0.428). The square root of the mean squared differences of successive normal-to-normal intervals(RMSSD), logarithmic very low frequency power(Ln VLF) and low frequency power/high frequency power ratio(LH/HF ratio) were not significantly low between experimental group and control group(P=0.16, 0.130, 0.537). 2. The standard deviation of all the normal-to-normal intervals(SDNN), logarithmic total power(Ln TP), logarithmic low frequency power(Ln LF) and logarithmic high frequency power(Ln HF) were significantly low between experimental group and control group(P=0.03, 0.005, 0.001, 0.007). 3. Ln LF of acute group was significantly low compared with those of chronic group(P= 0.039). Conclusions : This study suggests the activity and imbalance of autonomic nervous system in LBP is low. Also sympathetic nervous system of acute LBP is lower than that of chronic LBP. Further study of HRV related to LBP is needed in the clinical medicine.

• The Journal of Korean Obstetrics and Gynecology
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• v.28 no.3
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• pp.30-39
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• 2015

Objectives This study aims to analyze Heart Rate Variability (HRV) in women after missed abortion compared with healthy women Methods We studied 35 women who visited Kang-Nam Kyung-Hee Korean Hospital after missed abortion from 01 January 2012 to 28 February 2015 (missed abortion group) and 35 normal women visiting medical examination center from 1 January 2014 to 31 December 2014 (Normal Group). We measured HRV of each women and investigated the difference of HRV between two groups. Results The standard deviation of NN intervals (SDNN) in Missed abortion group is lower than normal group. The square root of the mean square difference of successive NNs (RMSSD) in Missed abortion group is lower than normal group. However there was no significant difference. Total Power (TP) and Low frequency power (LF) is significantly lower in Missed abortion group compared with normal group. High frequency power (HF), Very low frequency power (VLF) and LF/HF ratio in missed abortion group is lower than normal group. but There was no significant difference. Conclusions Missed abortion group is lower in function of overall autonomic nervous system, especially sympathetic nerve.