• The Transactions of the Korean Institute of Power Electronics
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• v.24 no.2
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• pp.99-104
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• 2019

Solar power generation systems require maximum power point tracking (MPPT) control to acquire maximum power using inefficient and high-cost PV modules. Most conventional MPPT algorithms are based on the slope-tracking concept. The perturb and observe (P&O) algorithm is a typical slope-tracking method. The two factors that determine the MPPT performance of P&O algorithm are the MPPT control period and the magnitude of the perturbation voltage. The MPPT controller quickly moves to the new maximum power point at insolation change when the perturbation voltage is set to large, and the error of output power will be huge in the steady state even when insolation is not changing. The dynamics of the MPPT controller can be accelerated even though the perturbation voltage is set to small when the MPPT control period is set to short. However, too short MPPT control period does not improve MPPT performance but consumes the MPPT controller resources. Therefore, analyzing the performance of the MPPT controller is necessary for actual insolation conditions in real weather environment to determine the optimum MPPT control period and the magnitude of the perturbation voltage. This study proposes an optimum MPPT control period that maximizes MPPT efficiency by measuring and analyzing actual insolation profiles in typical clear and cloudy weather in central Korea.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.4
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• pp.355-362
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• 2009

Sound source localization method based on the time delay of arrival(TDOA) is applied to many research fields such as a robot auditory system, teleconferencing and so on. When multi-microphones are utilized to localize the source in 3 dimensional space, the conventional localization methods based on TDOA decide the actual source position using the TDOAs from all microphone arrays and the detection measure, which represents the errors between the actual source position and the estimated ones. Performance of these methods usually depends on the number of microphones because it determines the resolution of an estimated position. In this paper, we proposed the localization method using spatially mapped GCC functions. The proposed method does not use just TDOA for localization such as previous ones but it uses spatially mapped GCC functions which is the cross correlation function mapped by an appropriate mapping function over the spatial coordinate. A number of the spatially mapped GCC functions are summed to a single function over the global coordinate and then the actual source position is determined based on the summed GCC function. Performance of the proposed method for the noise effect and estimation resolution is verified with the real environmental experiment. The mean value of estimation error of the proposed method is much smaller than the one based on the conventional ones and the percentage of correct estimation is improved by 30% when the error bound is ${\pm}20^{\circ}$.

• The Journal of Korean Medicine
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• v.45 no.1
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• pp.1-16
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• 2024

Introduction: The importance of clinical skills training in traditional Korean medicine education is increasingly emphasized. Since the clinical skills tests are high-stakes tests that determine success in national licensing exams, it is essential to develop reliable multifaceted analysis methods for clinical skills tests in actual education settings. In this study, we applied the multifaceted validity evaluation methods to the evaluation results of the cardiopulmonary resuscitation module to confirm the applicability and effectiveness of the methods. Methods: In this study, we used internal consistency, factor analysis, generalizability theory G-study and D-study, ANOVA, Kendall's tau, descriptive statistics, and other statistical methods to analyze the multidimensional validity of a cardiopulmonary resuscitation test in clinical education settings over the past three years. Results: The factor analysis and internal consistency analysis showed that the evaluation rubric had an unstable structure and low concordance. The G-study showed that the error of the clinical skills assessment was large due to the evaluator and unexpected errors. The D-study showed that the variance error of the evaluator should be significantly reduced to validate the evaluation. The ANOVA and Kendall's tau confirmed that evaluator heterogeneity was a problem. Discussion and Conclusion: Clinical skills tests should be continuously evaluated and managed for validity in two steps of pre-production and actual implementation. This study has presented specific methods for analyzing the validity of clinical skills training and testing in actual education settings. This study would contribute to the foundation for competency-based evidence-based education in practical clinical training.

• Journal of the Korea Society of Computer and Information
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• v.25 no.1
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• pp.29-36
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• 2020

Wear-leveling techniques and Error Correction Codes (ECCs) are essential for the improvement of the reliability and durability of flash memories. Low-Density Parity-Check (LDPC) codes have higher error correction capabilities than conventional ECCs and have been applied to various flash memory-based storage devices. Conventional wear-leveling schemes using only the number of Program/Erase (P/E) cycles are not enough to reflect the actual aging differences of flash memory components. This paper introduces an actual aging measurement scheme for flash memory wear-leveling using LDPC decoding information. Our analysis, using error-rates obtained from an flash memory module, shows that LDPC decoding information can represent the aging degree of each block. We also show the effectiveness of the wear-leveling based on the proposed scheme through wear-leveling simulation experiments.

• Journal of the Korean Society of Tobacco Science
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• v.35 no.1
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• pp.1-6
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• 2013

Near InfraRed Spectroscopy(NIRS) is a quick and accurate analytical method to measure multiple components in tobacco manufacturing process. This study was carried out to develop calibration equation of near infrared spectroscopy for the prediction of the amount of chemical components and hot water solubles(HWS) of reconstituted tobacco leaf. Calibration samples of reconstituted tobacco leaf were collected from every lot produced during one year. The calibration equation was formulated as modified partial least square regression method (MPLS) by analyzing laboratory actual values and mathematically pre-treated spectra. The accuracy of the acquired equation was confirmed with the standard error of prediction(SEP) of chemical components in reconstituted tobacco leaf samples, indicated as coefficient of determination($R^2$) and prediction error of sample unacquainted, followed by the verification of model equation of laboratory actual values and these predicted results. As a result of monitoring, the standard error of prediction(SEP) were 0.25 % for total sugar, 0.03 % for nicotine, 0.03 % for chlorine, 0.16 % for nitrate, and 0.38 % for hot water solubles. The coefficient of determination($R^2$) were 0.98 for total sugar, 0.97 for nicotine, 0.96 for chlorine, 0.98 for nitrate and 0.92 for hot water solubles. Therefore, the NIRS calibration equation can be applicable and reliable for determination of chemical components of reconstituted tobacco leaf, and NIRS analytical method could be used as a rapid and accurate quality control method.

• Journal of Korean Society of Water and Wastewater
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• v.30 no.3
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• pp.253-261
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• 2016

A variety of methods for detecting the location of an underground water pipeline are being used across the world; the current main methods used in South Korea, however, have the problems of low precision and efficiency and the limitations in actual application. On this, this study developed locator capable of detecting the location of a water pipe by the use of an IMU sensor, and technology for using the extended karman filter to correct error in location detection and to plot the location on the coordinate system. This study carried out a tract test and a road test as basic experiments to measure the performance of the developed technology and equipment. As a result of the straight line, circular and ellipse track tests, the 1750 IMU sensor showed the average error of 0.08-0.11%; and thus it was found that the developed locator can detect a location precisely. As a result of the 859.6-m road test, it was found that the error was 0.31 m in case the moving rate of the sensor was 0.3-0.6 m/s; and thus it was judged that the equipment developed by this study can be applied to long-distance water pipes of over 1 km sufficiently. It is planned to evaluate its field applicability in the future through an actual pipe network pilot test, and it is expected that locator capable of detecting the location of a water pipe more precisely will be developed through research for the enhancement of accuracy in the algorithm of location detection.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.37 no.3
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• pp.72-83
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• 2000

This paper presents the implementation of the position control system for 3 phase induction motor using reaching mode controller and neural networks. The reaching mode controller is used to bring the position error and speed error trajectories toward the sliding surface and to train neural networks at the first time. The structure of the reaching mode controller consists of the switch function of sliding surface. And feedforward neural networks approximates the equivalent control input using the reference speed and reference position and actual speed and actual position measured form an encoder and, are tuned on-line. The reaching mode controller and neural networks are applied to the position control system for 3 phase induction motor and, are compared with a PI controller through computer simulation and experiment respectively. The results are illustrated that the output of reaching mode controller is decreased and feedforward neural networks take charge of the main part for the control action, and the proposed controllers show better performance than the PI controller in abrupt load variation and the precise control is possible because the steady state error can be minimized by training neural networks.

• Journal of the Korean Society of Safety
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• v.37 no.6
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• pp.89-95
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• 2022

A method for measuring the length and slope of a temporary structure using an unmanned aerial vehicle (UAV) and 3D modeling method is proposed. The actual length and slope of the vertical member of the specimen were measured and compared with the measured values obtained by the proposed method for the specimens with and without the vertical protection net installed. Based on the result of measuring the length of the temporary structure specimen using the UAV and 3D modeling method, the measured value showed an error of 0.87% when compared to the actual length in the specimen without the vertical protection net installed. In addition, the error of the slope was 0.63°. It was thought that the proposed method could be usable for the purpose of finding parts in wrong installation state on the temporary structure and informing the manager in charge. However, in the case of the specimen with the vertical protection net, the measurement showed a 1.46% error in length and 2.77° difference in slope. Therefore, if a vertical protection net is to be installed in a temporary structure, the measurement accuracy should be improved by utilizing an image processing method, etc.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.6
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• pp.251-258
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• 2023

It is necessary to predict peak load accurately in order to supply electric power and operate the power system stably. Especially, it is more important to predict peak load accurately in winter and summer because peak load is higher than other seasons. If peak load is predicted to be higher than actual peak load, the start-up costs of power plants would increase. It causes economic loss to the company. On the other hand, if the peak load is predicted to be lower than the actual peak load, blackout may occur due to a lack of power plants capable of generating electricity. Economic losses and blackouts can be prevented by minimizing the prediction error of the peak load. In this paper, the latest deep learning model such as TCN is used to minimize the prediction error of peak load. Even if the same deep learning model is used, there is a difference in performance depending on the hyper-parameters. So, I propose methods for optimizing hyper-parameters of TCN for predicting the peak load. Data from 2006 to 2021 were input into the model and trained, and prediction error was tested using data in 2022. It was confirmed that the performance of the deep learning model optimized by the methods proposed in this study is superior to other deep learning models.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.141-146
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• 2018

Recently, location recognition technology has attracted much attention, especially for locating people or objects in an indoor environment without being influenced by the surrounding environment GPS technology is widely used as a method of recognizing the position of an object or a person. GPS is a very efficient, but it does not allow the positions of objects or people indoors to be determined. RFID is a technology that identifies the location information of a tagged object or person using radio frequency information. In this study, an RFID system is constructed and the position is measured using tags. At this time, an error occurs between the actual and measured positions. To overcome this problem, a neural network is trained using the measured and actual position data to reduce the error. In this case, since the number of read tags is not constant, they are not suitable as input values for training the neural network, so the neural network is trained by converting them into center-of-gravity inputs and median value inputs. This allows the position error to be reduce by the neural network. In addition, different numbers of trained data are used, viz. 50, 100, 200 and 300, and the correlation between the number of data input values and the error is checked. When the training is performed using the neural network, the errors of the center-of-gravity input and median value input are compared. It was found that the greater the number of trained data, the lower the error, and that the error is lower when the median value input is used than when the center-of-gravity input is used.