• 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 Information Processing Systems
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• v.14 no.6
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• pp.1307-1317
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• 2018

To deal with the problems of occlusion, pose variations and illumination changes in the object tracking system, a regression model weighted multi-templates mean-shift (MS) algorithm is proposed in this paper. Target templates and occlusion templates are extracted to compose a multi-templates set. Then, the MS algorithm is applied to the multi-templates set for obtaining the candidate areas. Moreover, a regression model is trained to estimate the Bhattacharyya coefficients between the templates and candidate areas. Finally, the geometric center of the tracked areas is considered as the object's position. The proposed algorithm is evaluated on several classical videos. The experimental results show that the regression model weighted multi-templates MS algorithm can track an object accurately in terms of occlusion, illumination changes and pose variations.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2007.11a
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• pp.256-259
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• 2007

In this paper, the combustion characteristics of hybrid rocket were studied with various port number of the cylindrical multi-port grain. For the regression rate case, as the port number increases, the both port regression rate and end-surface regression rate tend to increase. For the performance parameter case, as the port number increases, the O/F ratio tends to decreases and the specific impulse tends to increase.

• Journal of Korean Society on Water Environment
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• v.22 no.5
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• pp.919-925
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• 2006

The regression models for the water temperatures of Ban Byeon Stream and Yong Jeon stream were developed using multi-regression method. It was also investigated that the applicability of the stream temperature prediction to two-dimensional numerical simulation to predict the vertical water temperature in Imha Reservoir. Air temperature and dew point as independent variables were selected to be applicable to cases with the different variation of flow rates. The data division of water temperature using a cutoff flow rate of $20m^3/s$ was found to reduce the prediction error of the stream temperature. The mean absolute percent error of the numerical simulation results of the vertical water temperature in Imha Reservoir using the regression models was 11%, which was only 4.3% lager than the simulation result using the measured stream temperature. Therefore, the regression models of the stream temperatures using multi-regression method applied in this study could be applied to predict the vertical water temperature in Imha Reservoir with a good accuracy.

• 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 Propulsion Engineers Conference
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• 2008.11a
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• pp.247-250
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• 2008

An experimental investigation was conducted to clarify the combustion characteristics and merge characteristics of PMMA-GOX and PE-GOX hybrid motor using multi-port fuel grain configuration. The regression rate of multi-port fuel grain is higher than the regression rate of single-port fuel grain by thermal conduction and chamber pressure. The merge of multi-port has an effect on hybrid rocket performance by change of a combustion area.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2010.11a
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• pp.479-483
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• 2010

In this paper, a study for hybrid rocket combustion with cylindrical multi-port grain and swirl injector was performed to take advantage of regression rate. Change of the regression rate in the multi-port grain the placement of a swirl Injector experiments were performed. The results of multi-port grain using swirl injector were showed that the regression rate was increased compare with the shower head type injector.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.357-366
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• 2010

Many tree algorithms have been developed for regression problems. Although they are regarded as good algorithms, most of them suffer from loss of prediction accuracy when there are many noise variables. To handle this problem, we propose the multi-step GUIDE, which is a regression tree algorithm with a variable selection process. The multi-step GUIDE performs better than some of the well-known algorithms such as Random Forest and MARS. The results based on simulation study shows that the multi-step GUIDE outperforms other algorithms in terms of variable selection and prediction accuracy. It generally selects the important variables correctly with relatively few noise variables and eventually gives good prediction accuracy.

• 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 the Korean Operations Research and Management Science Society
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• v.28 no.3
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• pp.61-66
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• 2003

In many multiple regression analyses, the “multi-collinearity” problem arises since some independent variables are highly correlated with each other. Practically, the Ridge regression method is often adopted to deal with the problems resulting from multi-collinearity. We propose a better alternative method using iteration to obtain an exact least squares estimator. We prove the solvability of the proposed algorithm mathematically and then compare our method with the traditional one.