• Journal of Educational Research in Mathematics
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• v.20 no.1
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• pp.73-84
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• 2010

Mathematical modeling is an important part of mathematics education since it can be used or created to find mathematical models to understand real life various situations. Most of mathematical modeling tasks taught and learned currently in secondary school mathematics classes need simple mathematical modelling with one or two variables and produce fixed solutions to the real life problems. But many real life problems involve various and complex variables which can be used to get more proper solutions. Constructing mathematical models to get more appropriate solutions from the real problems having various and complex variables is not easy. In this paper the researcher suggested a model to fit mathematical models to get more appropriate solutions and showed three examples to apply the model in solving real life problems which can be treated in the secondary school mathematics classrooms.

• International Journal of Reliability and Applications
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• v.7 no.1
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• pp.27-39
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• 2006

Many real world cases in material failure analysis do not follow perfectly the normal distribution. Forcing of the normality assumption may lead to inaccurate predictions and poor product quality. We examine the failure process of the internal bond (IB or tensile strength) of medium density fiberboard (MDF). We propose a forced censoring technique that closer fits the lower tails of strength distributions and better estimates extremely smaller percentiles, which may be valuable to continuous quality improvement initiatives. Further analyses are performed to build an accelerated common-shaped Weibull model for different product types using the $JMP^{(R)}$ Survival and Reliability platform. In this paper, a forced censoring technique is implemented for the first time as a software module, using $JMP^{(R)}$ Scripting Language (JSL) to expedite data processing, which is crucial for real-time manufacturing settings. Also, we use JSL to automate the task of fitting an accelerated Weibull model and testing model homogeneity in the shape parameter. Finally, a package script is written to readily provide field engineers customized reporting for model visualization, parameter estimation, and percentile forecasting. Our approach may be more accurate for product conformance evaluation, plus help reduce the cost of destructive testing and data management due to reduced frequency of testing. It may also be valuable for preventing field failure and improved product safety even when destructive testing is not reduced by yielding higher precision intervals at the same confidence level.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2008.10a
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• pp.74-77
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• 2008

Online shoppers are not able to try clothing on. Therefore, the pictures of clothing on the website play a significant role when shoppers make decision on their purchase. There are generally three different ways to show clothing at online shops. The first one is showing only clothing images, and the second one is showing the pictures that have actual fitting models wearing clothing on (In this case, Model's face is mostly not shown in the picture.), and the third is showing the pictures of professional fitting models who wear goods. The shopping malls adopt each of the different ways but little is known about affect on purchasing from these three ways. The aim of this study is to figure out how the online shopper's emotional status is affected by these three ways of presenting pictures of clothing. At first, we developed a set of adjective words of human emotion to set up the evaluation criteria for user's emotional status. Those adjectives are originally from the precedent research on human emotion. To cut 99 adjectives down to a proper number for the criteria, we conducted a preliminary survey, and finally, 5 adjectives are selected as appropriate criteria for evaluating users' emotional status while they are shopping. Those five adjectives are 'possess','sensual', 'unique', 'tasteful', and 'stylish'. Then, we conducted the main survey showing 10 kinds of cloth (each cloth was consist of 3 ways). And in the page of model images, we measured the model's preference for understanding the relation with customer's emotion criteria of the product. As a result of the test there was statistically significant difference between product only images and anonymous images, but there was no significant difference between anonymous images and model images. And the preference of the model and value of the emotion criteria have large correlation except 'unique' criteria. It is expected that the result in this study will help to build new marketing strategy which satisfy customers' emotion.

• Journal of The Korean Astronomical Society
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• v.37 no.4
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• pp.151-157
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• 2004

It is investigated quantitative relations between the magnetic storm magnitude and the solar wind parameters such as the Interplanetary Magnetic Field (hereinafter, IMF) magnitude (B), the southward component of IMF (Bz), and the dynamic pressure during the main phase of the magnetic storm with focus on the role of the interplanetary shock (hereinafter, IPS) in order to build the space weather fore-casting model in the future capable to predict the occurrence of the magnetic storm and its magnitude quantitatively. Total 113 moderate and intense magnetic storms and 189 forward IPSs are selected for four years from 1998 to 2001. The results agree with the general consensus that solar wind parameter, especially, Bz component in the shocked gas region plays the most important role in generating storms (Tsurutani and Gonzales, 1997). However, we found that the correlations between the solar wind parameters and the magnetic storm magnitude are higher in case the storm happens after the IPS passing than in case the storm occurs without any IPS influence. The correlation coefficients of B and $BZ_(min)$ are specially over 0.8 while the magnetic storms are driven by IPSs. Even though recently a Dst prediction model based on the real time solar wind data (Temerin and Li, 2002) is made, our correlation test results would be supplementary in estimating the prediction error of such kind of model and in improving the model by using the different fitting parameters in cases associated with IPS or not associated with IPS rather than single fitting parameter in the current model.

• Korean Journal of Construction Engineering and Management
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• v.19 no.5
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• pp.43-52
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• 2018

Generally, research on construction cost has been done mostly regarding its direct cost, thus model regarding indirect cost lacks attention. This research seeks to introduce a model to predict on-site overhead cost for apartment construction projects, which constitutes a big portion in Korean construction industry. We devised an equation of 9th degree via curve-fitting, using multiple on-site actual expense data, which can be used to calculate per-progress rate, per-day on-site overhead cost. We further show prospective usage of the model by applying it on construction projects sizing about 30 billion won. Regarding the fact that previous studies could not recognize pattern changes of a total on-site overhead cost, this model is worthy of its conveniency and thoroughness, as well as providing reasonal ground for its derivation in predicting on-site overhead cost of apartment construction projects.

• The Journal of Information Systems
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• v.28 no.3
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• pp.249-276
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• 2019

Purpose The purpose of this study is to develop a prediction model and decision rules for the elderly's suicidal ideation based on the Korean Welfare Panel survey data. By utilizing this data, we obtained many decision rules to predict the elderly's suicide ideation. Design/methodology/approach This study used classification analysis to derive decision rules to predict on the basis of decision tree technique. Weka 3.8 is used as the data mining tool in this study. The decision tree algorithm uses J48, also known as C4.5. In addition, 66.6% of the total data was divided into learning data and verification data. We considered all possible variables based on previous studies in predicting suicidal ideation of the elderly. Finally, 99 variables including the target variable were used. Classification analysis was performed by introducing sampling technique through backward elimination and data balancing. Findings As a result, there were significant differences between the data sets. The selected data sets have different, various decision tree and several rules. Based on the decision tree method, we derived the rules for suicide prevention. The decision tree derives not only the rules for the suicidal ideation of the depressed group, but also the rules for the suicidal ideation of the non-depressed group. In addition, in developing the predictive model, the problem of over-fitting due to the data imbalance phenomenon was directly identified through the application of data balancing. We could conclude that it is necessary to balance the data on the target variables in order to perform the correct classification analysis without over-fitting. In addition, although data balancing is applied, it is shown that performance is not inferior in prediction rate when compared with a biased prediction model.

• Applied Chemistry for Engineering
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• v.30 no.2
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• pp.178-185
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• 2019

Three spherical quaternary composites composed of metal/metal oxide/high explosive/oxidizer were prepared by a crystallization/agglomeration process. From the characteristics of composites by thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC), the shortening of the decomposition zone of high explosives in the quaternary composite was observed, which may be attributed to the autocatalytic reaction caused by $ClO_2$ or HCl which are ammonium perchlorate (AP) degradation products. The activation energy analysis showed that the activation energy abruptly decreases at the end of the decomposition zone of high explosives, and it was considered to be caused by $HNO_2$ which is common in decomposition products of high explosives. The activation energy predicted from complex pyrolysis results by the distributed activation energy model (DAEM) showed much better in accuracy than those by model-fitting methods such as Kissinger-Akahira-Sunose and Flynn-Wall-Ozawa models.

• Proceedings of the Korea Society for Simulation Conference
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• 1998.03a
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• pp.101-105
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• 1998

A large system predictor, which can perform prediction of sales trend in a huge number of distribution centers, is presented using neural predictive model. There are 20,000 number of distribution centers, and each distribution center need to forecast future demand in order to establish a reasonable inventory policy. Therefore, the number of forecasting models corresponds to the number of distribution centers, which is not possible to estimate that kind of huge number of accurate models in ERP (Enterprise Resource Planning)module. Multilayer neural net as universal approximation is employed for fitting the prediction model. In order to improve prediction accuracy, a sequential simulation procedure is performed to get appropriate network structure and also to improve forecasting accuracy. The proposed simulation procedure includes neural structure identification and virtual predictive model generation. The predictive model generation consists of generating virtual signals and estimating predictive model. The virtual predictive model plays a key role in tuning the real model by absorbing the real model errors. The complement approach, based on real and virtual model, could forecast the future demands of various distribution centers.

• Journal of Korean Society for Quality Management
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• v.26 no.3
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• pp.130-140
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• 1998

Studies present a guide to parameter estimation of software reliability models using SAS JMP. In this paper, we consider only software reliability growth model(SRGM), where mean value function has a S-shaped growth curve, such as Yamada et al. model, and ohba inflection model. Besides these stochastic SRGM, deterministic SRGM's, by fitting Logistic and Gompertz growth curve, have been widely used to estimate the error content of software systems. Introductions or guide lines of JMP are concerned. Estimation of parameters of Yamada et al. model and Logistic model is accomplished by using JMP. The differences between Yamada et al. model and Logistic model is accomplished by using JMP. The differences between Yamada et al. model and Logistic model is discussed, along with the variability in the estimates or error sum of squares. This paper have shown that JMP can be an effective tool I these research.

• ETRI Journal
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• v.28 no.1
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• pp.115-118
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• 2006

In this letter, a simple model parameter extraction methodology for an on-chip spiral inductor is proposed based on a wide-band inductor model that incorporates parallel inductance and resistance to model skin and proximity effects, and capacitance to model the decrease in series resistance above the frequency near the peak quality factor. The wide-band inductor model does not require any frequency dependent elements, and model parameters can be extracted directly from the measured data with some curve fitting. The validity of the proposed model and parameter extraction methodology are verified with various size inductors fabricated using $0.18\;{\mu}m$ CMOS technology.