• KSCE Journal of Civil and Environmental Engineering Research
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• v.6 no.2
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• pp.23-34
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• 1986

Multiple regression equation was derived for estimation of slump loss in ready mixed concrete and usefulness of the equation was verfied by field test. Factors affecting slump loss were examined and analyzed for transport distance and transport time of ready mixed concrete. The analysis showed that wait and discharge time of ready mixed concrete in job site caused difficulty in the slump control. To determine the influence of the other factors such as mix proportion, temperature of concrete, and dosage of admixture, experimental tests were performed. Generally, there was no significant difference in slump loss according to cement content and initial slump level. For one retarder, more slump loss was found, but difference according to dosage of admixture was not recognized.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.1631-1636
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• 2003

The purpose of this study is to analyze the phenomena of the thermally-induced vibration for the flexible space structure due to abrupt change of radiation heating circumstance using the numerical analyze and experiment test. In order to verify this structure, numerical approaches on the simplified flexible tube were compared with experimental test results at the ground experimental facility In this analyze, it was found that the thermal deformation occurs firstly due to fast radiation heating of flexible structure and then the thermally-induced vibration would be induced due to small periodic change of temperature. According to comparison of numerical and experimental result, in case of no tip mass, the first mode vibration by the numerical analyze was O.78Hz same as that of the experimental result However in case of increase tip-masses of 8g l6g, 50g and 100g, the first modes vibration theoretical analyze were 1.75Hz, 1.3Hz, 0.87Hz and O.73Hz, in decrease trend respectively and those by experimental test were 234Hz, 1.5Hz, O.78Hz and O.78Hz in decrease trend respectively Although using the simpled equation for the estimation, the estimation results were similar to experimental results.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.697-702
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• 2016

To control permanent magnet synchronous motors smoothly, it is important to know the exact parameter values of the stator resistance, various inductances, and the flux linkage of the permanent magnet. In practice, these parameters vary due to a variable operating point, temperature change, or a fault. This paper proposes a MRAS (Model Reference Adaptive System) based parameter estimator and adaptive control scheme. Owing to the non-linearity of the system equation with respect to these parameters, although many schemes proposed previously assumed that some parameters are known, all the parameters were assumed to be unknown. The simulation results revealed the effectiveness of the proposed algorithm.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.16 no.3
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• pp.41-53
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• 2013

The study for the estimation potential natural vegetation was estimated the occurrence probability distribution using geographic information system(GIS) in Bukhansan National Park. Correlation and factor analysis were analyzed to estimate probability distribution. Coefficients were calculated by logistic regression analysis. Correlation coefficients were significantly at the 0.01 level. Commonality of elevation, annual mean temperature, warmth index and potential evapotranspiration were high value, but topographic index was low value. Communities of over the 0.3 points distribution probability, Quercus mogolica communities were the largest area, 76,940,900 $m^2$, Pinus densiflora communities area was 860,800 $m^2$, Quercus acutissima communities area was 500,100 $m^2$ and Quercus variabilis communities area was 1,000 $m^2$, but Q. aliena, Q. serrata, Carpinus laxiflora and Zelcova serrata communities was not appeared. Therefore, potential national vegetation of Bukhansan national park was likely to be Q. mongolica community, P. densiflora community, Q. acutissima community and Q. variabilis community.

• Journal of Korean Society of Water and Wastewater
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• v.25 no.4
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• pp.547-554
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• 2011

In small-scale water systems, the measurement of quality of raw water in running water is generally implemented when the quality of water is stable and frequency of measurement is low. However, units such as water temperature and pH, which are easily monitored, are frequently measured. In establishing an improvement plan for a water treatment system, the range of concentration of the target material present in the raw water of the running water provides relevant information. If the concentration of target material can be specified by the quality of water of data items that are measured daily, inverse estimation of the range of concentration is possible as well. In this paper, we took note of manganese in the raw water from Ogasawara-mura, Tokyo, and estimated the manganese concentration in the raw water of the running water for the past five years. Based on the results obtained, we have proposed a manganese removal system, considering the current situation and geographical conditions of Ogasawara-mura.

• Magazine of the Korean Society of Agricultural Engineers
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• v.36 no.2
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• pp.88-95
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• 1994

This paper is to evaluate the applicability of neural networks to the estimation of evapotranspiration. Two neural networks were developed to forecast daily evapotranspiration of the rice crop with back-propagation and counter-propagation algorithm. The neural network trained by back-propagation algorithm with delta learning rule is a three-layer network with input, hidden, and output layers. The other network with counter-propagation algorithm is a four-layer network with input, normalizing, competitive, and output layers. Training neural networks was conducted using daily actual evapotranspiration of rice crop and daily climatic data such as mean temperature, sunshine hours, solar radiation, relative humidity, and pan evaporation. During the training, neural network parameters were calibrated. The trained networks were applied to a set of field data not used in the training. The created response of the back-propagation network was in good agreement with desired values and showed better performances than the counter-propagation network did. Evaluating the neural network performance indicates that the back-propagation neural network may be applied to the estimation of evapotranspiration of the rice crop. This study does not provide with a conclusive statement as to the ability of a neural network to evapotranspiration estimating. More detailed study is required for better understanding and evaluating the behavior of neural networks.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.17 no.2
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• pp.173-182
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• 2005

The Performance of U-tube ground heat exchanger for geothermal heat Pump systems depends on the thermal properties of the soil, as well as grout or backfill materials in the borehole. In-situ tests provide a means of estimating some of these properties. In this study, in-situ thermal response tests were completed on two vertical boreholes, 130 m deep with 62 mm diameter high density polyethylene U-tubes. The tests were conducted by adding a monitored amount of heat to water over a $17\~18$ hour period for each vertical boreholes. By monitoring the water temperatures entering and exiting the loop and heat load, overall thermal conductivity values of grout/soil formation were determined. Two parameter estimation models for evaluation of thermal response test data were compared when applied on the same temperature response data. One model is based on line-source theory and the other is a numerical one-dimensional finite difference model. The average thermal conductivity deviation between measured data and these models is of the magnitude $1\%$ to $5\%$.

• Journal of the Semiconductor & Display Technology
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• v.6 no.1 s.18
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• pp.53-57
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• 2007

In this paper, the intelligent estimation algorithm is developed for residual quantity estimate of lithium secondary cell and we suggest the control algorithm to get battery SOC through thermal modeling of electric cell. Lithium secondary cell gives cycle life, charge characteristic, discharge characteristic, temperature characteristic, self-discharge characteristic and the capacity recovery rate etc. Therefore, we make an accurate estimate of the capacity of battery according to thermal modeling to know the capacity of electric cell that is decreased by various special quality of lithium secondary cell. And we show effectiveness through comparison of value as result that use simulation and fuzzy logic.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.1
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• pp.79-87
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• 2015

A thermochemical equilibrium model was constructed for predicting composition of synthesis gas in biomass gasification. The model included estimation of equilibrium constants using Gibbs free energy. After constructing the model, the results were compared with the experimental values and predictions from a previous model. Gas compositions were reasonably well agreed with them and showed effects of operational and fuel condition. When the reaction temperature increased, the lower heating values decreased due to the decrease in CH4 concentrations. The methane concentrations were lower than those observed in experimental results. The model was used to predict the gas composition and heating values for the cases of mixed fuel of charcoal and un-dry woodchips. Although downdraft gasifiers require fuels less than 15% of moisture contents, the model results indicated that the mixed fuel with charcoal and woodchips which had over 25% of moisture contents could be used in the downdraft gasifiers. It might be explained by increase in energy density resulting from mixing charcoal. The results imply that the efforts and costs for drying biomass fuels could be reduced by mixing charcoal or fuels with higher calorific values.

• Journal of Ocean Engineering and Technology
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• v.20 no.4 s.71
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• pp.24-30
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• 2006

Different methods exist for the estimation of thermaldeformation of plates in the line heating process. These are based on the assumption of residual strains in the heat-affected zone, known as the method of inherent strains, or simulated relations between heating conditions and residual deformations. The purpose of this paper is to develop a simulator of thermal deformation in the line heating, using the artificial neural network. Curvature deformations for the plate-forming are investigated, which can be used as a prime deformation parameter in the process. The curvature of plates are calculated using the approximation of plate surface by NURBS. Line heating experiments for 11 specimens of different thickness and heating conditions were performed. Two neural networks predicting the maximum temperature and curvature deformations at the heating line are studied. It was concluded that the thermal deformations predicted by the neural network can be used in a line heating simulator, which is considered an attractive and practical alternative to the existing methods.