• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.04a
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• pp.240-243
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• 2000

An apparel products retrieval system was proposed in which users can refer to products using Kansei evaluation values. The system adopts relevance feedback using history of the retrieval to learn the tendency of user evaluation. The system is based on a vector space retrieval model using products images expression as semantic scales. The system makes a query from user inputting information and retrieves closest products from the database. Revising algorithms of the difference method. linear multiple regression performed to investigate the effectiveness and criteria of the search. As a result of evaluation of the accuracy, it was found that the linear multiple regression and the neural network models are effective for the retrieval considering the individual Kansei.

• The Korean Journal of Applied Statistics
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• v.25 no.3
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• pp.503-512
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• 2012

Detecting outliers in a linear regression model eventually fails when similar observations are classified differently in a sequential process. In such circumstances, identifying clusters and applying certain methods to the clustered data can prevent a failure to detect outliers and is computationally efficient due to the reduction of data. In this paper, we suggest to implement a clustering procedure for this purpose and provide examples that illustrate the suggested procedure applied to the Hadi-Simonoff (1993) method, reverse Hadi-Simonoff method, and Gentleman-Wilk (1975) method.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.31-38
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• 2003

A simple method is proposed to detect the number of change points and test the location and size of multiple change points with jump discontinuities in an otherwise smooth regression model. The proposed estimators are based on a local linear regression fit by the comparison of left and right one-side kernel smoother. Our proposed methodology is explained and applied to real data and simulated data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.4
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• pp.887-896
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• 2015

This study estimates future AADT using historical AADT and socio-economic factors in isolated area. Multiple linear regression method by socio-economic factors are lower MAPE and higher R-square than using historical AADT. Analysis of socio-economic factors influence AADT in isolated typical areas, varied socio-economic factors influence on AADT. In isolated coastal areas, oil price influence on AADT. AADT forecasting model in isolated area is excellent when analysising $R^2$ and MAPE. It is assume that estimation of AADT in isolated area using multiple linear regression is accurate because of a little passed traffic volume and traffic volume fluctuation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.5
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• pp.1809-1827
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• 2013

Recently we need to take various measures to prepare for extreme flood that occur due to climate change. It is important that establish flood forecasting system to prepare flood over non-structure measures. The objective of this study is to develop superior real-time flood forecasting model by comparing the Neuro-fuzzy model and the multiple linear regression model. The Neuro-fuzzy model and the multiple linear regression model are established using same input data and applied for various flood events in Nakdong basin. The results show that the Neuro-fuzzy model can carry out flood forecasting results more accurately than the multiple linear regression model. This study can contribute to the establishment of a high accuracy flood information system that secure lead time in Nakdong basin.

• Journal of Korea Water Resources Association
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• v.55 no.3
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• pp.229-243
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• 2022

The effects of monthly meteorological data measured at 11 stations in South Korea on pan coefficient were analyzed to develop the four types of multiple linear regression models for estimating pan coefficients. To evaluate the applicability of developed models, the models were compared with six previous models. Pan coefficients were most affected by air temperature for January, February, March, July, November and December, and by solar radiation for other months. On the whole, for 12 months of the year, the effects of wind speed and relative humidity on pan coefficient were less significant, compared with those of air temperature and solar radiation. For all meteorological stations and months, the model developed by applying 5 independent variables (wind speed, relative humidity, air temperature, ratio of sunshine duration and daylight duration, and solar radiation) for each station was the most effective for evaporation estimation. The model validation results indicate that the multiple linear regression models can be applied to some particular stations and months.

• Korean Journal of Remote Sensing
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• v.35 no.4
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• pp.503-516
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• 2019

We have estimated the vertical column density (VCD) of formaldehyde (HCHO) on a global scale using a multiple linear regression method (MRM) with Ozone Monitoring Instrument (OMI) and Moderate-Resolution Imaging Spectroradiometer (MODIS) data. HCHO VCDs were estimated in regions of biogenic, pyrogenic, and anthropogenic emissions using independent variables, including $NO_2$ VCD, land surface temperature (LST), an enhanced vegetation index (EVI), and the mean fire radiative power (MFRP), which are strongly correlated with HCHO. To evaluate the HCHO estimates obtained using the MRM, we compared estimates of HCHO VCD data measured by OMI ($HCHO_{OMI}$) with those estimated by multiple linear regression equations (MRE) ($HCHO_{MRE}$). Good MRM performances were found, having the average statistical values (R = 0.91, slope = 1.03, mean bias = $-0.12{\times}10^{15}molecules\;cm^{-2}$, percent difference = 11.27%) between $HCHO_{MRE}$ and $HCHO_{OMI}$ in our study regions where high HCHO levels are present. Our results demonstrate that the MRM can be a useful tool for estimating atmospheric HCHO levels.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.173-179
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• 2000

Genetic algorithm(GA) is applied for detecting multiple outliers. GA is a heuristic optimization tool solving for near optimal solution. We compare the performance of GA and the other diagnostic measures commonly used for detecting outliers in regression model. The results show that GA seems to have better performance than the others for the detection of multiple outliers.

• KIEAE Journal
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• v.9 no.1
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• pp.107-113
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• 2009

The energy cost is resulted from the energy use. Its sources are divided into some types and depended on the building use or energy-use type. The energy cost should be affected by the amount of the energy use. The cost could be calculated to consider various factors such as the insulation, heating type, building shape and others. But it can not consider all of the affect factors to the energy cost and need to categorize the factors to the condition for estimating the cost. In this paper, it aimed at providing the estimation model in linear equation and multiple linear regression, utilizing the building exterior condition and management characteristics in apartment housing. Its survey are conducted in two parts of management characteristics and building exterior condition. The correlation analysis is conducted to get rid of the multicolinearity among the inputted factors. The number of linear equation model is 11 and includes the 1st, 2nd and 3rd equation function, power function and others. Among these, it suggested the 2nd and 3rd function and power function in terms of the statistics. In multiple linear regression model, the building volume and management area are inputted to the estimation.

• Journal of the Society of Naval Architects of Korea
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• v.51 no.6
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• pp.530-538
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• 2014

The weight estimation of floating offshore structures such as FPSO, TLP, semi-Submersibles, Floating Offshore Wind Turbines etc. in the preliminary design, is one of important measures of both construction cost and basic performance. Through both literature investigation and internet search, the weight data of floating offshore structures such as FPSO and TLP was collected. In this study, the weight estimation model was suggested for FPSO. The weight estimation model using non-linear regression analysis was established by fixing independent variables based on this data and the multiple regression analysis was introduced into the weight estimation model. Its reliability was within 4% of error rate.