• Korean Journal of Remote Sensing
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• v.38 no.4
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• pp.327-341
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• 2022

Cloud removal is often required to construct time-series sets of optical images for environmental monitoring. In regression-based cloud removal, the selection of an appropriate regression model and the impact analysis of the input images significantly affect the prediction performance. This study evaluates the potential of Gaussian process (GP) regression for cloud removal and also analyzes the effects of cloud-free optical images and spectral bands on prediction performance. Unlike other machine learning-based regression models, GP regression provides uncertainty information and automatically optimizes hyperparameters. An experiment using Sentinel-2 multi-spectral images was conducted for cloud removal in the two agricultural regions. The prediction performance of GP regression was compared with that of random forest (RF) regression. Various combinations of input images and multi-spectral bands were considered for quantitative evaluations. The experimental results showed that using multi-temporal images with multi-spectral bands as inputs achieved the best prediction accuracy. Highly correlated adjacent multi-spectral bands and temporally correlated multi-temporal images resulted in an improved prediction accuracy. The prediction performance of GP regression was significantly improved in predicting the near-infrared band compared to that of RF regression. Estimating the distribution function of input data in GP regression could reflect the variations in the considered spectral band with a broader range. In particular, GP regression was superior to RF regression for reproducing structural patterns at both sites in terms of structural similarity. In addition, uncertainty information provided by GP regression showed a reasonable similarity to prediction errors for some sub-areas, indicating that uncertainty estimates may be used to measure the prediction result quality. These findings suggest that GP regression could be beneficial for cloud removal and optical image reconstruction. In addition, the impact analysis results of the input images provide guidelines for selecting optimal images for regression-based cloud removal.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.12 no.4
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• pp.87-92
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• 2013

In this study, we carried out the turning of plastic mold steel(STAVAX) with whisker reinforced ceramic tool(WA1) and analyzed ANOVA(Analysis of Variance) test. Multi-regression analysis was performed to find influential factors to surface roughness and to derive regression equation. Results are follows: From ANOVA test and confidence interval analysis of surface roughness, We found that influential factors to surface roughness was feed rate, cutting speed and depth of cut in order. From multi-regression analysis, we derived regression equation of STAVAX. it's coefficient of determination($R^2$) was 0.945 and It means that regression equation is significant. From experimental verification, we confirmed that surface roughness was predictable by regression equation. Compared with former research, we confirmed that increase of feed rate is the main cause of the growing of surface roughness and cutting force.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.10a
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• pp.47-52
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• 2002

There are many factors in machine tool error. These are thermal deformation, geometric error, machine's part assembly error, error caused by tool bending. Among them thermal error is 70% of total error of machine tool . Prediction of thermal error is very difficult. because of nonlinear tendency of machine tool deformation. In this study, we tried thermal error prediction by using multi regression analysis.

• Health Policy and Management
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• v.25 no.1
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• pp.31-39
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• 2015

Background: The purpose of this study is comparison of the results between regression and multi-level analysis to find out factors influencing outcome indicators (in-hospital death, length of stay, and medical charges) of stroke patients. Methods: By using patient sample data of Health Insurance Review & Assessment Service, patients admitted with stroke were selected as survey target and 15,864 patients and 762 hospitals were surveyed. Results: For the results of existing regression analysis and multi-level analysis, models were assessed through model suitability index value and as a result, the value of results of multi-level analysis decreased compared to the results of regression, showing it is a better model. Conclusion: Factors influencing in-hospital death of stroke patients were analyzed and as a result, intra-class correlation (ICC) was 13.6%. In factors influencing length of stay, ICC was 11.4%, and medical charges, ICC was 17.7%. It was found that factors influencing the outcome indicators of stroke patients may vary in every hospital. This study could carry out more accurate analysis than existing research findings through analysis of reflecting structure at patient level and hospital level factors and analysis on random effect.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.6
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• pp.36-41
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• 2012

In this study, we turning plastic mold steel (STAVAX) against cutting speed, depth of cut, feed rate using whisker reinforced ceramic tool (WA1). To predict cutting force, analyze principal, radial, feed force with multi-regression analysis. Results are follows: From the analysis of variance, affected factor to cutting force feed rate, depth of cut, cutting speed in order and cutting speed was very small affect to cutting force. From multi-regression analysis, we extracted regression equation and the coefficient of determination$(R^2)$ was 0.9, 0.88, 0.856 at principal, radial and feed force. It means regression equation is significant. From the experimental verification, it was confirmed that principal, radial and feed force was predictable by regression equation.

• Communications for Statistical Applications and Methods
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• v.28 no.3
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• pp.267-279
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• 2021

A regression with multi-dimensional responses is quite common nowadays in the so-called big data era. In such regression, to relieve the curse of dimension due to high-dimension of responses, the dimension reduction of predictors is essential in analysis. Sufficient dimension reduction provides effective tools for the reduction, but there are few sufficient dimension reduction methodologies for multivariate regression. To fill this gap, we newly propose two fused slice-based inverse regression methods. The proposed approaches are robust to the numbers of clusters or slices and improve the estimation results over existing methods by fusing many kernel matrices. Numerical studies are presented and are compared with existing methods. Real data analysis confirms practical usefulness of the proposed methods.

• Journal of Electrical Engineering and information Science
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• v.1 no.1
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• pp.45-50
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• 1996

This paper mainly describes the wind load assumption of 765kV transmission towers. We analyzed wind velocity data a meteorological observatories to get the wind velocity of 50 years return period by using Gumbel I type extreme value distribution. By multi-correlative regression analysis method, wind velocity at no observation site was obtained. Reference dynamics wind pressure map was obtained from above analysis and the wind pressure was classified as three regio in high temperature season.

• Fashion & Textile Research Journal
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• v.10 no.3
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• pp.316-321
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• 2008

The purpose of this study is to find what male consumers value in their clothing behaviors, as well as to investigate how the consumers' shopping values and store VM impact on their clothing purchase behaviors. For data collection, research questionnaires were responded by 202 male students living in Busan. The collected data were analyzed according to the frequency-factor analysis using SPSS for win 10.1 Package, the factor analysis using Varimax, reliability analysis, and multi-regression analysis. The results of this study are as follows; First, the shopping values were composed of hedonic, utilitarian, and economic value, and VM was divided into store facility, store image, layout, and fashion information. Second, multi-regression analysis was conducted to find the impact of consumers' shopping values on their clothing purchase behaviors. The result showed that the hedonic shopping value and utilitarian shopping value significantly affected the consumers' clothing purchase behaviors, while economics shopping value did not show any statistical significance. Third, multi-regression analysis was conducted to find the impact of store VM on consumers' clothing purchase behaviors. The result showed that store image, layout, and fashion information had a significant impact on consumers' clothing purchase behaviors.

• Journal of the Korean Institute of Landscape Architecture
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• v.29 no.4
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• pp.91-99
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• 2001

This study was carried out to investigate the effects of mowing height, rolling, N-fertilizing, and season on green speed(i.e., ball-roll distance) for developing and implementing a program of increasing green speed in Korean golf courses. Data were subjected to multi-regression analysis using SPSSWIN(Statistical Package for the Social Science), which collected from Yong-Pyong golf course greens selected to investigate. The results was as follows. 1) The multi-regression analysis of mowing height, rolling times, and N-fertilizer application rates on spring green speed was as follows; $Y_1$(spring green speed)=4.287+0.155X$_1$(rolling times)-0.131X$_2$(the amount of N-fertilizing)-0.251X$_3$(mowing height). 2) The multi-regression analysis of mowing height, rolling times, and N-fertilizer application rates on summer green speed was as follows; $Y_2$(summer green speed)=4.833-0.423X$_3$(mowing height)+0.146X$_1$(rolling times)-0.107X$_2$(the amount of N-fertilizing). 3) The multi-regression analysis of mowing height, rolling times, and N-fertilizer application rates on fall green speed was as follows; $Y_3$(fall green speed)=4.651-0.383X$_3$(mowing height)+0.142X$_1$(rolling times)-0.103X$_2$(the amount of N-fertilizing). 4) As mowing height was lowered by 1mm, green speed increased by 0.251~0.423m. As rolling times increased by 1(one), green speed increased by0.142~0.15m. As the amount of N-fertilizing increased by 1g/$m^2$, green speed decreased by 0.103~0.131m. The season also affected green speed. In comparison with spring green speed, summer green speed decreased by 0.145m and fall green speed decreased by 0.144m.

• Korean Journal of Construction Engineering and Management
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• v.16 no.2
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• pp.29-37
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• 2015

Multi-household buildings are efficiently maintained from the mid- and long-term viewpoint according to the long-term repair coverage system etc. On the other hand, public buildings are not systematically maintained due to a lack of past maintenance cost data and inefficient budget plans, among other problems. Targeting public buildings in Incheon, this study analyzed operation costs variables. To verify the analysis results, they underwent a correlation analysis and a multi-regression analysis. With regard to public buildings electricity, gas and tap water cost, the influence power of the served life, floor area, and workforce were analyzed, revealing that electricity cost was highly correlated with workforce, while gas and tap water cost were correlated with tap water cost. Also, the correlation analysis results were verified through a multi-regression analysis, and a maintenance cost estimation model was presented using a regression equation.