• Journal of Ocean Engineering and Technology
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• v.26 no.2
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• pp.39-47
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• 2012

The objective of this study was to develop a strength prediction model using a polynomial regression analysis based on the experimental results obtained from ninety samples. As the results of a correlation analysis between various mixing factors and unconfined compressive strength using SPSS (statistical package for the social sciences), the governing factors in the strength of lightweight soil were found to be the crumb rubber content, bottom ash content,and water-cement ratio. After selecting the governing factors affecting the strength through the correlation analysis, a strength prediction model, which consisted of the selected governing factors, was developed using the polynomial regression analysis. The strengths calculated from the proposed model were similar to those resulting from laboratory tests (R2=87.5%). Therefore, the proposed model can be used to predict the strength of lightweight mixtures with various mixing ratios without time-consuming experimental tests.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.1021-1026
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• 2017

Mixture experiments are commonly encountered in many fields including food, chemical and pharmaceutical industries. In mixture experiments, measured response depends on the proportions of the components present in the mixture and not on the amount of the mixture. Statistical analysis of the data from mixture experiments has mainly focused on a continuous response variable. In the example of quantal response data in mixture experiments, however, the tumor incidence data have been analyzed in Chen et al. (1996) to study the effects of 3 dietary components on the expression of mammary gland tumor. In this paper, we compared the logistic regression models with linear predictors such as second degree Scheffe polynomial model, Becker model and Akay model in terms of classification accuracy.

• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.505-510
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• 2014

Normalized Difference Vegetation Index (NDVI) is a major indicator for monitoring climate change and detecting vegetation coverage. In order to retrieve NDVI, it is preprocessed using cloud masking and atmospheric correction. However, the preprocessed NDVI still has abnormally low values known as noise which appears in the long-term time series due to rainfall, snow and incomplete cloud masking. An existing method of using polynomial regression has some problems such as overestimation and noise detectability. Thereby, this study suggests a simple method using amoving average approach for correcting NDVI noises using SPOT/VEGETATION S10 Product. The results of the moving average method were compared with those of the polynomial regression. The results showed that the moving average method is better than the former approach in correcting NDVI noise.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.1
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• pp.43-50
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• 2003

Today, the demand of image acquisition systems grows as the multimedia applications go on increasing greatly. Among the systems, film scanner is one of the systems, which can acquire high quality and high resolution images. However due to the nonlinear characteristic of the light source and sensor, colors of the original film image do not correspond to the colors of the scanned image. Therefore color correction mr the scanned digital image is essential in the film scanner. In this paper, polynomial regression method is applied for the color correction to CIE $L^{*}$ $a^{*}$ $b^{*}$ color model data converted from RGB color model data. A1so a film scanner hardware with 12 bit color resolution for each R, G, B and 2400 dpi was implemented by using TMS320C32 DSP chip and high resolution line sensor. An experimental result shows that the average color difference ($\Delta$ $E^{*}$$_{ab}$ ) is reduced from13.48 to 8.46.6.6.6.6.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2002.11a
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• pp.13-19
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• 2002

Design and implementation methodologies on the performance evaluation system of two-channel ring-core Flux-Gate Compass (FG-Compass) are described, with evaluation procedures and methods based on the polynomial regression models. Performance evaluation system is consists of a step motor driving unit, a bearing transmitting unit and, evaluation programs using polynomial regression formulae. Through performance evaluation tests, total residual deviation tests, total residual deviation of $\pm$4$^{\circ}$ and eigen residual deviation of $\pm$2$^{\circ}$ are obtained from the FG-Compass. The result is more accurate values than the typical FG-Compass with eigen residual deviation of $\pm$4$^{\circ}$ and is provide a possibility to develop a high performance FG-Compass. In addition, the design methodology of a smart FG-Compass with the self estimation and correction of residual deviations is also discussed.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.2
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• pp.1-10
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• 2004

This paper proposes the method that design CLUT(color look-up table) simultaneously processing gamut mapping and color space conversion using only CLUT without complex computation. After CLUT is constructed using scanner gamut and printer gamut, the scanner gamut is extended to include original scanner gamut. This extended scanner gamut is used as input CIE $L^{*}$ $a^{*}$ $b^{*}$ values for CLUT. Then CMY values are computed by using gamut mapping. Input RGB image of scanner is converted into CIE $L^{*}$ $a^{*}$ $b^{*}$ by using regression function. CIE $L^{*}$ $a^{*}$ $b^{*}$ values of scanner are converted into CMY values without computation of additional gamut mapping using the proposed CLUT. In the experiments, the proposed method resulted in the similar color difference, but reduced the complexity computation than the direct computing method to process gamut mapping and color space conversion respectively.espectively.ively.

• Journal of Navigation and Port Research
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• v.26 no.5
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• pp.529-535
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• 2002

Design and implementation methodologies on the performance evaluation system of Two-Channel Ring-Core Flux-Gate Compass (TCRC FG-Compass) are described, with evaluation procedures and methods based on the polynomial regression models. Performance evaluation system consists of a step motor driving unit, a bearing transmitting unit and evaluation programs derived from polynomial regression formulae. Newly designed performance evaluation system enabled the accuracy of TCRC FG-Compass to be ascertained. It was confirmed that the size of residual deviation of TCRC FG-Compass is $2^{\circ}$, while that of the conventional one is $4^{\circ}$. In addition, the design methodology to the self estimation and correction of residual deviations is also discussed.

• Korean Journal of Remote Sensing
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• v.26 no.4
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• pp.403-410
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• 2010

NDVI(Normalized Difference Vegetation Index), which is broadly used as short-term data composite, is an important parameter for climate change and long-term land surface monitoring. Although atmospheric correction is performed, NDVI dramatically appears several low peak noise in the long-term time series. They are related to various contaminated sources, such as cloud masking problem and wet ground condition. This study suggests a simple method through harmonic analysis for reducing NDVI noise using SPOT/VGT NDVI 10-day MVC data. The harmonic analysis method is compared with the polynomial regression method suggested previously. The polynomial regression method overestimates the NDVI values in the time series. The proposed method showed an improvement in NDVI correction of low peak and overestimation.

• The Journal of Engineering Geology
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• v.31 no.1
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• pp.31-42
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• 2021

Database of Cut-slope management system (CSMS) has been constructed based on investigations of all slopes on the roads of the whole country. The investigation data is documented by human, so it is inevitable to avoid human-error such as missing-data and incorrect entering data into computer. The goal of this paper is constructing a prediction model based on several machine-learning algorithms to solve those imperfection problems of the CSMS data. First of all, the character-type data in CSMS data must be transformed to numeric data. After then, two algorithms, i.g., multinomial logistic regression and deep-neural-network (DNN), are performed, and those prediction models from two algorithms are compared. Finally, it is identified that the accuracy of DNN-model is better than logistic model, and the DNN-model will be utilized to improve data-quality.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.6B
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• pp.543-550
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• 2009

The traditional simple extrapolation type short term quantitative rainfall forecast can not realize the evolution of rainfall generating weather system. To overcome the drawback of the linear extrapolation type rainfall forecasting model, the history of a weather system from sequential weather radar information and a polynomial regression technique were used to generate forecast fileds of x-directional, y-directional velocities and radar reflectivity which considered the nonlinear behavior related to the evolution of weather systems. Results demonstrated that test statistics of forecasts using the developed model is better than that of 2-CAPPI forecast. However there is still a large room to improve the forecast of spatial and temporal evolution of local storms since the model is not based on a fully physical approach but a statistical approach.