• Journal of Korean Association for Spatial Structures
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• v.4 no.3 s.13
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• pp.49-56
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• 2004

This paper predicted the shear deformation, such as strain of shear reinforcement and shear track width, of reinforced concrete (RC) members using Transformation Angle Truss Model (TATM) in order to apply to the shea, analysis of RC buildings. To check the validity of TATM for the shear deformation of RC beams, four RC beams with different shear span-to-depth ratios were cast, instrumented and tested. Observed results were compared with theoretical results by MCFT(Response-2000), RA-STM, FA-STM, and TATM. The proposed model, TATM, better predicted the relationships of the shear stress-strain of shear reinforcement and the shear stress-shear track width than other truss models.

• Journal of Korean Society of Forest Science
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• v.99 no.2
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• pp.204-212
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• 2010

This study was performed in 22 unthinned Larix olgensis plantations in northeast China. Data were collected on 95 sample trees of different canopy positions and the diameter at breast height ($d_{1.3}$) ranged from 5.7 cm to 40.2 cm. The individual tree models for the prediction of vertical distribution of live crown, branch and needle biomass were built. Our study showed that the crown, branch and needle biomass distributions were most in the location of 60% crown length. These results were also parallel to previous crown studies. The cumulative relative biomass of live crown, branch and needle were fitted by the sigmoid shape curve and the fitting results were quite well. Meanwhile, we developed the crown ratio and width models. Tree height was the most important predictor for crown ratio model. A negative competition factor, ccf and bas which reflected the effect of suppression on a tree, reduced the crown ratio estimates. The height-diameter ratio was a significant predictor. The higher the height-diameter ratio, the higher crown ratio is. Diameter at breast height is the strongest predictor in crown width model. The models can be used for the planning of harvesting operations, for the selection of feasible harvesting methods, and for the estimation of nutrient removals of different harvesting practices.

• Geomechanics and Engineering
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• v.21 no.6
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• pp.583-598
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• 2020

This study is attempted to propose a new hybrid artificial intelligence model called integrative genetic algorithm with multivariate adaptive regression splines (GA-MARS) for settlement prediction of shallow foundations on sandy soils. In this hybrid model, the evolution algorithm - Genetic Algorithm (GA) was used to search and optimize the hyperparameters of multivariate adaptive regression splines (MARS). For this purpose, a total of 180 experimental data were collected and analyzed from available researches with five-input variables including the bread of foundation (B), length to width (L/B), embedment ratio (D_f/B), foundation net applied pressure (q_net), and average SPT blow count (N_SPT). In further analysis, a new explicit formulation was derived from MARS and its accuracy was compared with four available formulae. The attained results indicated that the proposed GA-MARS model exhibited a more robust and better performance than the available methods.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.18 no.1
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• pp.1-5
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• 2005

A new model of plasma etch process was constructed by using a radial basis function network (RBFN). This technique was applied to an etching of silicon carbide films in a NF$_3$ inductively coupled plasma. Experimental data to train RBFN were systematically collected by means of a 2$^4$ full factorial experiment. Appropriateness of prediction models was tested with test data consisted of 16 experiments not pertaining to the training data. Prediction performance was optimized with variations in three training factors, the number of pattern units, width of radial basis function, and initial weight distribution between the pattern and output layers. The etch responses to model were an etch rate and a surface roughness measured by atomic force microscopy. Optimized models had the root mean-squared errors of 26.1 nm/min and 0.103 nm for the etch rate and surface roughness, respectively. Compared to statistical regression models, RBFN models demonstrated an improvement of more than 20 % and 50 % for the etch rate and surface roughness, respectively. It is therefore expected that RBFN can be effectively used to construct prediction models of plasma processes.

• Corrosion Science and Technology
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• v.4 no.3
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• pp.100-107
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• 2005

Reinforcing steel bars in reinforced concrete structures are protected from corrosion by passive film on the steel surface inside concrete with high alkalinity. However, when the passive film breaks down due to chloride ion ingressed into the RC structures, a corrosion initiates at the surface of steel bars. Then, internal pressure by volume expansion of corrosion products in reinforcing bars induces cracking and spalling of cover concrete, which reduces not only durability performance but also structural performance in RC structures. In this paper, a service life prediction of RC structures is carried out by using a micro-mechanics based corrosion model. The corrosion model is composed of a chloride penetration model to evaluate the initiation of corrosion and an electric corrosion cell model and an oxygen diffusion model to evaluate the rate and the accumulated amounts of corrosion. Then, a corrosion cracking model is combined to the models to evaluate critical amount of corrosion product for initiation cracking in cover concrete. By implementing the models into a finite element analysis program, a time and space dependent corrosion analysis and a service life prediction of RC structures due to chloride attack are simulated and the results of the analysis are compared with test results. The effect of crack width on the corrosion and the service life of the RC structures are analyzed and discussed.

• Korean Journal of Environmental Agriculture
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• v.39 no.3
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• pp.204-213
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• 2020

BACKGROUND: Since the number of crops cultivated in reclaimed land is huge, it is very difficult to quantify the total crop production. Therefore, a non-destructive method for predicting crop production is needed. Salt tolerant root vegetables such as red beets and sugar beet are suitable for cultivation in reclaimed land. If their underground biomass can be predicted, it helps to estimate crop productivity. Objectives of this study are to investigate maximum leaf length and weight of red beet, sugar beet, and turnips grown in reclaimed land, and to determine optimal model with regression analysis for linear and allometric growth models. METHODS AND RESULTS: Maximum leaf length, width, and root fresh weight of red beets, sugar beets, and turnips were measured. Ten linear models and six allometric growth models were selected for estimation of root fresh weight and non-linear regression analysis was conducted. The allometric growth model, which have a variable multiplied by square of maximum leaf length and maximum leaf width, showed highest R² values of 0.67, 0.70, and 0.49 for red beets, sugar beets, and turnips, respectively. Validation results of the models for red beets and sugar beets showed the R² values of 0.63 and 0.65, respectively. However, the model for turnips showed the R² value of 0.48. The allometric growth model was suitable for estimating the root fresh weight of red beets and sugar beets, but the accuracy for turnips was relatively low. CONCLUSION: The regression models established in this study may be useful to estimate the total production of root vegetables cultivated in reclaimed land, and it will be used as a non-destructive method for prediction of crop information.

• Journal of KIISE
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• v.44 no.9
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• pp.954-965
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• 2017

A stable power supply is very important for the maintenance and operation of the power infrastructure. Accurate power consumption prediction is therefore needed. In particular, a university campus is an institution with one of the highest power consumptions and tends to have a wide variation of electrical load depending on time and environment. For this reason, a model that can accurately predict power consumption is required for the effective operation of the power system. The disadvantage of the existing time series prediction technique is that the prediction performance is greatly degraded because the width of the prediction interval increases as the difference between the learning time and the prediction time increases. In this paper, we first classify power data with similar time series patterns considering the date, day of the week, holiday, and semester. Next, each ARIMA model is constructed based on the classified data set and a daily power consumption forecasting method of the university campus is proposed through the time series cross-validation of the predicted time. In order to evaluate the accuracy of the prediction, we confirmed the validity of the proposed method by applying performance indicators.

• Transactions of Materials Processing
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• v.20 no.8
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• pp.548-554
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• 2011

A Finite Element (FE)-based model is proposed for predicting the roll force in an edger. The model is developed on the basis of the hypothetical mode of rolling and the least-squares regression analysis from the result of the FE approach. This model reflects the effect of process variables affected by the roll force, and has three dimensionless parameters, I.e., shape factor, reduction ratio and width-to-thickness ratio. The model prediction compared satisfactorily with experiment observations.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.3
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• pp.440-449
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• 2003

Residual strength of fiber metal laminates after impact was studied. 3/4 lay up FML was fabricated using 4 ply prepreg, 2 ply aluminum sheets, and 1 ply steel sheet. Quasi isotropic ([0/45/90/-45]s) and orthotropic ([0/90/0/90]s) FRP were also fabricated to compare with FML. Impact test were conducted by using instrumented drop weight impact machine (Dynatup, Model 8250). Penetration load and absorbed energy of FML were superior to those of FRPs. Tensile tests were conducted to evaluate the residual strength after impact. Strength degradation of FML was less than that of FRP. This means that the damage tolerance of FML is excellent than that of FRP. Residual strength of each specimen was predicted by using Whitney and Nuismer(WN) Model. Impact damage area is assumed as a circular notch in WN model. Damage width is defined as the average of back face and top face damage width of each specimen. Average stress and point stress criterions were used to calculate the characteristic length. It is supposing that a characteristic length is a constant. The distribution of characteristic length shows that the assumption is reasonable. Prediction was well matched with experiment under both stress criterions.

• Journal of Power Electronics
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• v.16 no.3
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• pp.970-981
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• 2016

Leakage currents occur in pulse-width-modulated voltage source inverter (PWM-VSI)-fed permanent magnet synchronous motor (PMSM) drives for air-conditioners, which seriously affect system safety and operation performance. High accuracy modeling and prediction of leakage currents are key issues for the design and implementation of air-conditioning products. In this study, the generation mechanism of leakage currents is discussed. A systematic modeling approach of leakage currents is proposed, including the modeling of leakage current sources and leakage current paths. By using the proposed approach, the complete model of leakage currents in PWM-VSI-fed PMSM drives for air-conditioners has been developed based on the extraction of all parameters. A comparison between the simulated leakage currents based on the developed model and measured leakage currents in the outdoor unit of an air-conditioning product is conducted. The comparison verifies the effectiveness of the proposed modeling approach, and the developed model exhibits high accuracy within a wide frequency range.