• Magazine of the Korea Concrete Institute
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• v.12 no.2
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• pp.34-40
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• 2000

• Proceedings of the Korean Reliability Society Conference
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• 2006.05a
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• pp.75-75
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• 2006

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.3
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• pp.371-375
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• 2013

A new model about output power prediction of PV module with various tilted angles and cell to cell distances has been proposed in this paper. Light intensity arrived on a solar cell could be changed by characteristics of PV module materials. Refractive indices, thickness and absorption coefficients of glass, EVA, solar cell and Backsheet are used to predict output. Also, the incident angle of light is changed 0 to 90[$^{\circ}$] and cell to cell distances are 5, 10 15[mm]. Two types of light incident on a solar cell are considered which are direct to a solar cell and reflected from Backsheet. The intensity of the incident light directly into the solar cell is reduced through glass and EVA about 17.5[%] in theoretical way. It has an error of 2.26[%] compared with experimental result. The results for compare theoretical with experimental data is validated within the error of 6.3[%]. This paper would be a research material to predict output power when the PV module is installed outdoor or a building.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.93-99
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• 2019

In this paper, we propose a smart meter that uses GRU model, which is one of artificial neural networks, for the efficient energy management. We collected power consumption data that train GRU model through the proposed smart meter. The implemented smart meter has automatic power measurement and real-time observation function and load control function through power consumption prediction. We determined a reference value to control the load by using Root Mean Squared Error (RMS), which is one of performance evaluation indexes, with 20% margin. We confirmed that the smart meter with automatic load control increases the efficiency of energy management.

• Journal of Korean Society of Disaster and Security
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• v.14 no.2
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• pp.35-42
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• 2021

In order to produce electric vehicle demand forecasting information, which is an important element of the plan to expand charging facilities for electric vehicles, a model for predicting electric vehicle demand was proposed using Exponential Smoothing. In order to establish input data for the model, the monthly power demand of cities and counties was applied as independent variables, monthly electric vehicle charging stations, monthly electric vehicle charging stations, and monthly electric vehicle registration data. To verify the accuracy of the electric vehicle power demand prediction model, we compare the results of the statistical methods Exponential Smoothing (ETS) and ARIMA models with error rates of 12% and 21%, confirming that the ETS presented in this paper is 9% more accurate as electric vehicle power demand prediction models. It is expected that it will be used in terms of operation and management from planning to install charging stations for electric vehicles using this model in the future.

• The Transactions of the Korean Institute of Power Electronics
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• v.7 no.2
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• pp.158-164
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• 2002

This paper presents a proper parallel control method using a conventional control and a repetitive control for improving the output voltage waveform of uninterruptable power supply. Although first-order prediction control method shows a good characteristics to rectifier load, it is not sufficient to reduce steady state errors generated in nonlinear loads such as rectifier loads and phase controled loads. So we also employed a repetitive control method. A repetitive control method can eliminate steady state errors in the distorted output voltage caused by cyclic loads. The presented control scheme is verified through simulation and experiment. Experimental results Implemented on a single phase PWM inverter equipped with a LC output filter with 3 kVA, 60 Hz are shown.

• Journal of Biosystems Engineering
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• v.16 no.2
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• pp.142-147
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• 1991

Because combine cylinders account for up to 80 percent of the power requirements to harvest grain, it is important to have a reliable method to predict and analyze power consumption. An equation was derived to meet the prediction needs. The equation contains the variables number of bars on the cylinder, concave clearance, concave length, thickness of the feed material, feed rate, and cylinder speed. Indirectly, cylinder diameter was also considered. The derived equation was verified to be a reliable function for three of the variables and the equation was judged to be a reliable power prediction equation.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.1
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• pp.37-42
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• 2000

The radio noise produced by corona discharge in high voltage transmission lines is one of the most important line design considerations. Therefore it is necessary for transmission line designers to pre-evaluate radio noise using prediction formulas or field test results. In this paper, more accurate and useful formulas for predicting radio noise during fair and foul weathers in high voltage AC transmission lines were proposed through comparison with the existing formulas. Also it was verified by comparing with the long-term measured data from operating lines that the proposed formulas are more accurate. The proposed prediction formulas are developed by the applications of nonlinear least squares optimization method to radio noise database collected from lines throughout the world.

• Journal of the Optical Society of Korea
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• v.20 no.1
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• pp.199-203
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• 2016

An enzyme-free optical method is proposed for estimating high concentrations of glucose in a glucose-lactose mixture, based on a predictive equation that takes advantage of the reflective optical power observed at two discrete wavelengths. Compared to the conventional absorption spectroscopy method based on Beer's Law, which is mainly valid for concentrations below hundreds of mg/dL, the proposed scheme, which relies on reflection signals, can be applied to measure higher glucose concentrations, of even several g/dL in a glucose-lactose mixture. Two probe wavelengths of 1160 and 1300 nm were selected to provide a linear relationship between the reflective power and pure glucose/lactose concentration, where the relevant linear coefficients were derived to complete the predictive equation. Glucose concentrations from 2 to 7 g/dL in a glucose-lactose mixture were efficiently estimated, using the established predictive equation based on monitored reflective powers. The standard error of prediction was 1.17 g/dL.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.12
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• pp.107-116
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• 2008

The excitation force of a powertrain is one of major sources for the interior noise of a vehicle. This paper presents a novel approach to predict the interior noise caused by the vibration of the power rain by using the hybrid TPA (transfer path analysis) method. Although the traditional transfer path analysis (TPA) is useful for the identification of powertrain noise sources, it is difficult to modify the structure of a powertrain by using the experimental method for the reduction of vibration and noise. In order to solve this problem, the vibration of the power rain in a vehicle is numerically analyzed by using the finite element method (FEM). The vibration of the other parts in a vehicle is investigated by using the experimental method based on vibrato-acoustic transfer function (VATF) analysis. These two methods are combined for the prediction of interior noise caused by a power rain. Throughout this research, two papers are presented. This paper presents a simulation of the excitation force of the power rain exciting the vehicle body based on numerical simulation. The other paper presents a prediction of interior noise based on the hybrid TPA, which uses the VATF of the car body and the excitation force predicted in this paper.