• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.1
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• pp.32-40
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• 2018

In this research, a method of deriving statistical weight prediction formula which is used during the conceptual design phase was studied and it was programmed using Microsoft Excel and verified by applying to jet transport aircraft. The database was built while referencing the variables of conventional wing weight estimation formulas and it was used for modeling the jet transport wing weight regression equation. The model was evaluated using the K-fold cross validation method to solve the overfitting problem of the model.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.4
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• pp.268-273
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• 2000

Plasma models are crucial to equipment design and process optimization. A radial basis function network(RBFN) in con-junction with statistical experimental design has been used to model a process plasma. A 2$^4$ full factorial experiment was employed to characterized a hemispherical inductively coupled plasma(HICP) in characterizing HICP, the factors that were varied in the design include source power, pressure, position of shuck holder, and Cl$_2$ flow rate. Using a Langmuir probe, plasma attributes were collected, which include typical electron density, electron temperature. and plasma potential as well as their spatial uniformity. Root mean-squared prediction errors of RBEN are 0.409(10(sup)12/㎤), 0.277(eV), and 0.699(V), for electron density, electron temperature, and Plasma potential, respectively. For spatial uniformity data, they are 2.623(10(sup)12/㎤), 5.704(eV) and 3.481(V), for electron density, electron temperature, and plasma potential, respectively. Comparisons with generalized regression neural network(GRNN) revealed an improved prediction accuracy of RBFN as well as a comparable performance between GRNN and statistical response surface model. Both RBEN and GRNN, however, experienced difficulties in generalizing training data with smaller standard deviation.

• Computers and Concrete
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• v.15 no.2
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• pp.215-229
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• 2015

The emerging technology of self compacting concrete, fiber reinforcement together reduces vibration and substitute conventional reinforcement which help in improving the economic efficiency of the construction. The objective of this work is to find the regression model to determine the response surface of mix proportioning Steel Fiber Reinforced Self Compacting Concrete (SFSCC) using statistical investigation. A total of 30 mixtures were designed and analyzed based on Design of Experiment (DOE). The fresh properties of SCC and mechanical properties of concrete were studied using Response Surface Methodology (RSM). The results were analyzed by limited proportion of fly ash, fiber, volume combination ratio of two steel fibers with aspect ratio of 50/35: 60/30 and super plasticizer (SP) dosage. The center composite designs (CCD) have selected to produce the response in quadratic equation. The model responses included in the primary stage were flowing ability, filling ability, passing ability and segregation index whereas in harden stage of concrete, compressive strength, split tensile strength and flexural strength at 28 days were tested. In this paper, the regression model and the response surface plots have been discussed, and optimal results were found for all the responses.

• Journal of the Ergonomics Society of Korea
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• v.19 no.3
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• pp.23-37
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• 2000

Usability is one of the most important factors that affect customers' decision to purchase a product. Several studies have been conducted to model the relationship between the product design variables and the product usability. Since there could be hundreds of design variables to be considered in the model, a variable screening method is required. Traditional variable screening methods are based on expert opinions (Expert screening) in most Kansei engineering studies. Suggested in this study are statistical methods for screening important design variables by using the principal component regression(PCR), cluster analysis, and partial least squares(PLS) method. Product variables with high effect (PCR screening and PLS screening) or representative variables (Cluster screening) can be used to model the usability. Proposed variable screening methods are used to model the usability for 36 audio/visual products. The three analysis methods (PCR, Cluster, and PLS) show better model performance than the Expert screening in terms of $R^2$, the number of variables in the model, and PRESS. It is expected that these methods can be used for screening the product design variables efficiently.

• Membrane and Water Treatment
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• v.9 no.5
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• pp.363-371
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• 2018

In this work, it was investigated the usability of artificial bee colony (ABC) and genetic algorithm (GA) in modeling adsorption of Co(II) onto drinking water treatment sludge (DWTS). DWTS, obtained as inevitable byproduct at the end of drinking water treatment stages, was used as an adsorbent without any physical or chemical pre-treatment in the adsorption experiments. Firstly, DWTS was characterized employing various analytical procedures such as elemental, FT-IR, SEM-EDS, XRD, XRF and TGA/DTA analysis. Then, adsorption experiments were carried out in a batch system and DWTS's Co(II) removal potential was modelled via ABC and GA methods considering the effects of certain experimental parameters (initial pH, contact time, initial Co(II) concentration, DWTS dosage) called as the input parameters. The accuracy of ABC and GA method was determined and these methods were applied to four different functions: quadratic, exponential, linear and power. Some statistical indices (sum square error, root mean square error, mean absolute error, average relative error, and determination coefficient) were used to evaluate the performance of these models. The ABC and GA method with quadratic forms obtained better prediction. As a result, it was shown ABC and GA can be used optimization of the regression function coefficients in modeling adsorption experiments.

• Food Science of Animal Resources
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• v.37 no.1
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• pp.139-146
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• 2017

Response surface methodology (RSM) is a useful set of statistical techniques for modeling and optimizing responses in research studies of food science. In the analysis of response surface data, a second-order polynomial regression model is usually used. However, sometimes we encounter situations where the fit of the second-order model is poor. If the model fitted to the data has a poor fit including a lack of fit, the modeling and optimization results might not be accurate. In such a case, using a fullest balanced model, which has no lack of fit, can fix such problem, enhancing the accuracy of the response surface modeling and optimization. This article presents how to develop and use such a model for the better modeling and optimizing of the response through an illustrative re-analysis of a dataset in Park et al. (2014) published in the Korean Journal for Food Science of Animal Resources.

• Journal of the Korean Geographical Society
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• v.51 no.2
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• pp.219-233
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• 2016

In social science fields, statistical models are used almost exclusively for causal explanation, and explanatory modeling has been a mainstream until now. In contrast, predictive modeling has been rare in the fields. Hence, we focus on constructing the predictive non-parametric model, instead of the explanatory model. Gangnam-gu, Seoul was chosen as a study area and we collected single-family house sales data sold between 2011 and 2014. We applied non-parametric models proposed in machine learning area including generalized additive model(GAM), random forest, multivariate adaptive regression splines(MARS) and support vector machines(SVM). Models developed recently such as MARS and SVM were found to be superior in predictive power for house price estimation. Finally, spatial autocorrelation was accounted for in the non-parametric models additionally, and the result showed that their predictive power was enhanced further. We hope that this study will prompt methodology for property price estimation to be extended from traditional parametric models into non-parametric ones.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.108-110
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• 2006

A prediction model of charge concentration of silicon nitride (SiN) thin films was constructed by using neural network and genetic algorithm. SIN films were deposited by plasma enhanced chemical vapor deposition and the deposition process was characterized by means of $2^{6-1}$ fractional factorial experiment. Effect of five training factors on the model prediction performance was optimized by using genetic algorithm. This was examined as a function of the learring rate. The root mean squared error of optimized model was 0.975, which is much smaller than statistical regression model by about 45%. The constructed model can facilitate a Qualitative analysis of parameter effects on the charge concentration.

• International Journal of Highway Engineering
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• v.14 no.6
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• pp.121-129
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• 2012

PURPOSES : Traffic accidents at intersections have been increased annually so that it is required to examine the causations to reduce the accidents. However, the current existing accident models were developed mainly with non-linear regression models such as Poisson methods. These non-linear regression methods lack to reveal complicated causations for traffic accidents, though they are right choices to study randomness and non-linearity of accidents. Therefore, to reveal the complicated causations of traffic accidents, this study used structural equation methods(SEM). METHODS : SEM used in this study is a statistical technique for estimating causal relations using a combination of statistical data and qualitative causal assumptions. SEM allow exploratory modeling, meaning they are suited to theory development. The method is tested against the obtained measurement data to determine how well the model fits the data. Among the strengths of SEM is the ability to construct latent variables: variables which are not measured directly, but are estimated in the model from several measured variables. This allows the modeler to explicitly capture the unreliability of measurement in the model, which allows the structural relations between latent variables to be accurately estimated. RESULTS : The study results showed that causal factors could be grouped into 3. Factor 1 includes traffic variables, and Factor 2 contains turning traffic variables. Factor 3 consists of other road element variables such as speed limits or signal cycles. CONCLUSIONS : Non-linear regression models can be used to develop accident predictions models. However, they lack to estimate causal factors, because they select only few significant variables to raise the accuracy of the model performance. Compared to the regressions, SEM has merits to estimate causal factors affecting accidents, because it allows the structural relations between latent variables. Therefore, this study used SEM to estimate causal factors affecting accident at urban signalized intersections.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.311-321
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• 2002

Transfer function model(TFM) capturings conditional heteroscedastic pattern is introduced to analyze stochastic regression relationship between the two time series. Nonlinear ARCH concept is incorporated into the TFM via threshold ARCH and beta- ARCH models. Steps for statistical analysis of the proposed model are explained along the lines of the Box & Jenkins(1976, ch. 10). For illustration, dynamic analysis between KOSPI and NASDAQ is conducted from which it is seen that threshold ARCH performs the best.