• Journal of the Korean Solar Energy Society
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• v.34 no.5
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• pp.1-9
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• 2014

Daylight is very useful to control the indoor environment, and can save energy in buildings. So it is necessary to evaluate the daylighting performance of buildings. We proposed a simplified equation that can be used in the early stages of design. And we verified the equation by using the measured illuminance data from the 1/5 scale model. We compared the calculated indoor illuminances and measured illuminance including Daylight Factors of scale model in order to verify the applicability of the simplified equation, and proved the analyzed values are acceptable. When we have a target value of the Daylight Factor, we just have to determine the window area, transmittance of the glazing system, and indoor surface reflectance, then can achieve it with this simplified equation.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1990.10a
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• pp.79-84
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• 1990

The modal parameter estimations of linear multi-degree-of-freedom structural dynamic systems are carried out in time domain. For this purpose, the equation of motion is transformed into the autoregressive and moving average model with auxiliary stochastic input (ARMAX) model. The parameters of the ARMAX model are estimated by using the sequential prediction error method. Then, the modal parameters of the system are obtained thereafter. Experimental results are given for a 3-story building model subject to ground exitations.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2001.10a
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• pp.45-50
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• 2001

The purpose of this study was to estimate of soil loss form hillslope using WEPP(Water Erosion Prediction Project) model. WEPP model was developed for predicting soil erosion and deposition, fundamentally based on soil erosion prediction technology. The model for predicting sediment yields from single storms was applied to a tested watershed. Surface runoff is calculated by kinematic wave equation and infiltration is based on the Green and Ampt equation. Governing equations for sediment continuity, detachment, deposition, shear stress in rills, and transport capacity are presented. Tested watershed has an area of 0.6ha, where the runoff and sediment data were collected. The relative error between predicted and measured runoff was $-16.6{\sim}2.2%$, peak runoff was $-15.6{\sim}2.2%$ and soil loss was $-23.9{\sim}356.5%$.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.235-240
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• 2003

Methods to predict fatigue crack growth are compared in a quantitative manner for crack growth test data of 2024-T351 aluminum alloy under narrow and wide band random loading. In order to account for the effect of load ratio, crack closure model, Hater's equation and NASGRO's equation have been employed. Load interaction effect under random loading has been considered by crack closure model, Willenborg's model and Wheeler's model. The prediction method using the measured crack opening results provides the best result among the prediction methods discussed for narrow and wide band random loading data.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.10
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• pp.1785-1792
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• 2003

Methods to predict fatigue crack growth are compared in a quantitative manner for crack growth test data of 2024- T351 aluninum alloy under narrow and wide band random loading. In order to account for the effect of load ratio, crack closure model, Hater's equation and NASGRO's equation have been employed. Load interaction effect under random loading has been considered by crack closure model, Willenborg's model and Wheeler's model. The prediction method using the measured crack opening results provides the best result among the prediction methods discussed for narrow and wide band random loading data.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.32 no.6
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• pp.191-198
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• 1983

This paper describes the aplication of different model which can be used for short-term load prediction. The model is based on Bohlin's approach to first develop a load profile model representing the nominal load component and the Box-Jenkins approach is used to predict residuals. An on-line algorithm using Kalman Filter and Time Series is implemented for and hour-ahead prediction. In the Kalman Filter system equation and measurement equation were fixed and parameters of Time Series were varied week after week. A set of data for Korea Electric Power Corporation from April to June 1981 was used for the evaluation of the model. As the result of this simulation 1.2% rms error was acquired.

• Membrane and Water Treatment
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• v.12 no.2
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• pp.59-64
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• 2021

The rejection of sodium chloride (NaCl) and calcium chloride (CaCl2) single salt solutions were carried out for commercial nanofiltration NFDK membrane. Results showed that the NFDK membrane had a negative surface charge and had a higher observed rejection of 93.65% for calcium (Ca2+) ion and 78.27% for sodium (Na+) ions. Prediction of rejection for aqueous solutions of both salts was made using Donnan Steric Pore Model based on Extended Nernst-Planck Equation in addition to concentration polarization film theory. A MATLAB program was developed to execute the model calculations. Absolute Average Relative Error (% AARE) was found below 5% for real rejection of the NFDK membrane. This research could be used successfully to assess the membrane characterization parameter using a proposed procedure which can reduce the number of experiments.

• Nuclear Engineering and Technology
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• v.47 no.7
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• pp.859-866
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• 2015

The aim of this work is to investigate the transport mechanism of radioactive nuclides through the reverse osmosis (RO) membrane and to estimate its effectiveness for nuclide separation from radioactive liquid waste. An analytical model is developed to simulate the RO separation, and a series of experiments are set up to confirm its estimated separation behavior. The model is based on the extended Nernst-Plank equation, which handles the convective flux, diffusive flux, and electromigration flux under electroneutrality and zero electric current conditions. The distribution coefficient which arises due to ion interactions with the membrane material and the electric potential jump at the membrane interface are included as boundary conditions in solving the equation. A high Peclet approximation is adopted to simplify the calculation, but the effect of concentration polarization is included for a more accurate prediction of separation. Cobalt and cesium are specifically selected for the experiments in order to check the separation mechanism from liquid waste composed of various radioactive nuclides and nonradioactive substances, and the results are compared with the estimated cobalt and cesium rejections of the RO membrane using the model. Experimental and calculated results are shown to be in excellent agreement. The proposed model will be very useful for the prediction of separation behavior of various radioactive nuclides by the RO membrane.

• Proceedings of the Korea Concrete Institute Conference
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• 2002.05a
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• pp.997-1002
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• 2002

Traditional prediction models have been developed with a fixed equation form based on the limited number of data and parameters. If new data is quite different from original data, then the model should update not only its coefficients but also its equation form. However, artificial neural network (ANN) does not need a specific equation form. Instead of that, it needs enough input-output data. Also, it can continuously re-train the new data, so that it can conveniently adapt to new data. Therefore, the purpose of this paper is to develop the I-PreConS (Intelligent system for PREdiction of CONcrete Strength using ANN) that provides in-place strength information of the concrete to facilitate concrete form removal and scheduling for construction.

• Proceedings of the Korea Concrete Institute Conference
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• 2004.05a
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• pp.446-449
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• 2004

Traditional prediction models have been developed with a fixed equation from based on the limited number of data and parameters. If new data is quite different from original data, then the model should update not only its coefficients but also its equation form. However, artificial neural network dose not need a specific equation form. Instead of that, it needs enough input-output data. Also, it can continuously re-train the new data, so that it can conveniently adapt to new data. Therefore, the purpose of this study is to verify faith and application of prediction system of concrete strength using artificial neural networks through mock-up test.