• Journal of the Korean Statistical Society
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• v.28 no.1
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• pp.35-44
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• 1999

We derive a new upper bound of convolution type for the median-unbiased estimators with respect to an arbitrary unimodal utility functions. We also obtain the necessary and sufficient condition for the attainability of the information bound. Applications to general MLR(Monotone Likelihood Ratio) model and censored survival data re discussed as examples.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.6
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• pp.550-557
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• 2005

The multivariate statistical analysis methods, using both multiple linear regression(MLR) and partial least square(PLS), have been applied to predict the reactor properties and the product quality of a direct esterification reactor for polyethylene terephthalate(PET) synthesis. On the basis of the set of data including the flow rate of water vapor, the flow rate of EG vapor, the concentration of acid end groups of a product and other operating conditions such as temperature, pressure, reaction times and feed monomer mole ratio, two multi-variable analysis methods have been applied. Their regression and prediction abilities also have been compared. The prediction results are critically compared with the actual plant data and the other mathematical model based results in reliability. This paper shows that PLS method approach can be used for the reasonably accurate prediction of a product quality of a direct esterification reactor in PET synthesis process.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1406-1420
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• 2017

Artificial neural networks (ANNs) play an important role in the fields of function approximation, prediction, and classification. ANN performance is critically dependent on the input parameters, including the number of neurons in each layer, and the optimal values of weights and biases assigned to each neuron. In this study, we apply the particle swarm optimization method, a popular optimization algorithm for determining the optimal values of weights and biases for every neuron in different layers of the ANN. Several regression models, including general linear regression, Fourier regression, smoothing spline, and polynomial regression, are conducted to evaluate the proposed method's prediction power compared to multiple linear regression (MLR) methods. In addition, residual analysis is conducted to evaluate the optimized ANN accuracy for both training and test datasets. The experimental results demonstrate that the proposed method can effectively determine optimal values for neuron weights and biases, and high accuracy results are obtained for prediction applications. Evaluations of the proposed method reveal that it can be used for prediction and estimation purposes, with a high accuracy ratio, and the designed model provides a reliable technique for optimization. The simulation results show that the optimized ANN exhibits superior performance to MLR for prediction purposes.

• Journal of Soil and Groundwater Environment
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• v.18 no.3
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• pp.119-133
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• 2013

In the present study, several groundwater vulnerability assessment methods were applied to an agricultural area of Gumma in Korea. For the groundwater intrinsic vulnerability assessment, the performance of DRASTIC, SINTACS and GOD models was compared and an ensemble approach was suggested. M-DRASTIC and multi-linear regression (MLR) models were applied for the groundwater specific vulnerability assessment to nitrate of the study site. The correlation coefficient between the nitrate concentration and M-DRASTIC index was as low as 0.24. The result of the MLR model showed that the correlation coefficient is 0.62 and the areal extents of livestock farming and upland field are most influential factors for the nitrate contamination of groundwater in the study site.

• Near Infrared Analysis
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• v.1 no.1
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• pp.31-35
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• 2000

Near-infrared reflectance spectroscopy(NIRS) was used to determine the humic acids in soil samples from the fields of different crops and land-use over Youngnam and Honam regions in Korea. An InfraAlyzer 500 scanning spectrophotometer was obtained near infrared relectance spectra of soil at 2-nm intervals from 1100 to 2500nm. Multiple linear regression(MLR) or partial least square regression (PLSR) was used to evaluate a NIRS method for the rapid and nondestructive determination of humic acid, fulvic acid and its total contents in soils. The raw spectral data(log 1/R) can be used for estimating humic acid, fulvic acid and its total contents in soil by MLR procedure between the content of a given constituent and the spectral response of several bands. In which the predicted results for fulvic acid is the best in the constituents. The new spectral data are converted from the raw spectra by PLSR method such as the first derivative of each spectrum can also be used to predict humic acid and fulvic acid of the soil samples. A low SEC, SEP and a high coefficient of correlation in the calibration and validation stages enable selection of the best manipulation. But a simple calibration and prediction method for determining humic acid and fulvic acid should be selected under similar accuracy and precision of prediction. NIRS technique may be an effective method for rapid and nondestructive determination for humic acid, fulvic acid and its total contents in soils.

• Journal of Biosystems Engineering
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• v.22 no.3
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• pp.333-342
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• 1997

The objective of this research was to develop model equations for measuring rice milling ratio by using visible / HIR spectroscopy. Twelve kinds of brown rice(n = 149) were milled to obtain various milling ratio ranged from 86% to 94%. Visible/NIR spectra were collected with a spectrophotometer with sample transport module. The reflectance and transmission spectra were measured in the range of 400~2, 500nm and 600~1, 400nm, respectively, with 2 nm intervals. Multiple linear regression(MLR), Partial least square (PLS), and Artificial neural network(ANN) were used to develop models. Model developed with reflectance spectra showed better prediction results then those with transmission spectra. The MLR model with six-wavelength obtained from first derivative spectra gave to the best results for measuring the rice milling ratio(SEP = 0.535, , $r^2$ = 0.980). The PLS model(SEP = 0.604, $r^2$= 0.976) and ANN model(SEP = 0.566, $r^2$= 0.978) also can be used to determine the rice milling ratio effectively.

• Journal of the Korean Institute of Gas
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• v.17 no.1
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• pp.35-41
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• 2013

In this study, flame retardancy of polyurethane foam and phenolic foam were investigated by addition of phosphorous flame retardants. The thermal degradation behavior of polyurethane foam and phenolic foam in the presence of flame retardants has been studied by thermogravimetric analysis(TGA). Heat release rate(HRR), mean HRR, mass loss rate(MLR), total smoke released(TSR) and limited oxygen index(LOI) were tested by cone calorimeter. From the test results, Phenolic foam showed low HRR, MLR and TSR than polyurethane foam.

• Geomechanics and Engineering
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• v.22 no.1
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• pp.81-95
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• 2020

Measurement of the unconfined compressive strength (UCS) of the rock is critical to assess the quality of the rock mass ahead of a tunnel face. In this study, extensive field studies have been conducted along 3,885 m of the new Nagasaki tunnel in Japan. To predict UCS, a hybrid model of artificial neural network (ANN) based on genetic algorithm (GA) optimization was developed. A total of 1350 datasets, including six parameters of the Measurement-While- Drilling data and the UCS were considered as input and output parameters respectively. The multiple linear regression (MLR) and the ANN were employed to develop contrast models. The results reveal that the developed GA-ANN hybrid model can predict UCS with higher performance than the ANN and MLR models. This study is of great significance for accurately and effectively evaluating the quality of rock masses in tunnel engineering.

• Food Science and Preservation
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• v.7 no.2
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• pp.155-159
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• 2000

Apple fruit grading is largely dependant on skin color degree. This work reports about the possibility of nondestructive assessment of apple fruit color using infrared(NIR) reflectance spectroscopy. NIR spectra of apple fruit were collected in wavelength range of 1100~2500nm using an InfraAlyzer 500C(Bran+Luebbe). Calibration as calculated by the standard analysis procedures MLR(multiple linear regression) and stepwise, was performed by allowing the IDAS software to select the best regression equations using raw spectra of sample. Color degree of apple skin was expressed as 2 factors, anthocyanin content by purification and a-value by colorimeter. A total of 90 fruits was used for the calibration set(54) and prediction set(36). For determining a-value, the calibration model composed 6 wavelengths(2076, 2120, 2276, 2488, 2072 and 1492nm) provided the highest accuracy : correlation coefficient is 0.913 and standard error of prediction is 4.94. But, the accuracy of prediction result for anthocyanin content determining was rather low(R of 0.761).

• Journal of Construction Engineering and Project Management
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• v.3 no.2
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• pp.58-65
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• 2013

Earthwork activities are typically performed by heavy duty diesel (HDD) construction equipment that consumes large quantities of diesel fuel use and emits large quantities of pollutants, including nitrogen oxides (NOx), particulate matters (PM), hydrocarbon (HC), carbon monoxide (CO), and carbon dioxide ($CO_2$). This paper presents the framework for a model that can be used to estimate the production rate, activity duration, total fuel use, and total pollutants emissions for earthwork activities. A case study and sensitivity analysis for an excavator performing excavations are presented. The tool is developed by combining the multiple linear regressions (MLR) approach for modeling the productivity with the EPA's NONROAD model. The excavator data from RSMeans Heavy Construction Data were selected to build the productivity model, and emission factors of all type of pollutants from NONROAD model were used to estimate the total fuel use and emissions. The MLR model for the productivity rate can explain 92% of the variability in the data. Based on the model, the fuel use and emissions of excavator increase as the trench depth increase, but as the bucket size increase, the fuel use and emissions decrease.