• Journal of Biosystems Engineering
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• v.40 no.3
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• pp.296-304
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• 2015

Purpose: The balance between miscanthus production and its cost effectiveness depends greatly on its moisture content during post processing. The objective of this research was to measure the moisture content using a non-destructive and non-contact methodology for in situ applications. Methods: The moisture content of comminuted miscanthus was controlled using a closed chamber, a humidifier, a precision weigher, and a real-time monitoring software developed in this research. A CMOS sensor equipped with $50{\times}$ magnifier lens was used to capture magnified images of the conditioned materials with moisture content level from 5 to 30%. The hypothesis is that when light is incident on the comminuted particles in an inclined manner, higher moisture content results in light being reflected with a higher intensity. Results: A linear regression analysis for an initiative hypothesis based on general histogram analysis yielded insufficient correlations with low significance level (<0.31) for the determination coefficient. A significant relationship (94% confidence level) was determined at level 108 in a reverse accumulative histogram proposed based on a revised hypothesis. A linear regression model with the value at level 108 in the reverse accumulative histogram for a magnified image as the independent variable and the moisture content of comminuted miscanthus as the dependent variable was proposed as the estimation model. The calibrated linear regression model with a slope of 92.054 and an offset of 32.752 yielded 0.94 for the determination coefficient (RMSE = 0.2%). The validation test showed a significant relationship at the 74% confidence level with RMSE 6.4% (n = 36). Conclusions: To compensate the inconsistent significance between calibration and validation, an estimation model robust against various systematic interferences is necessary. The economic efficiency of miscanthus, which is a promising energy resource, can be improved by the real-time measurement of its crucial material properties.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.2
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• pp.363-370
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• 2022

The study aims to probe the impact of foreign ownership on Vietnamese listed firms' capital structure. This study employs panel data of 288 non-financial firms listed on the Ho Chi Minh City stock exchange (HOSE) and Ha Noi stock exchange (HNX) in 2015-2019. In this research, we applied a Bayesian linear regression method to provide probabilistic explanations of the model uncertainty and effect of foreign ownership on the capital structure of non-financial listed enterprises in Vietnam. The findings of experimental analysis by Bayesian linear regression method through Markov chain Monte Carlo (MCMC) technique combined with Gibbs sampler suggest that foreign ownership has substantial adverse effects on the firms' capital structure. Our findings also indicate that a firm's size, age, and growth opportunities all have a strong positive and significant effect on its debt ratio. We found that the firms' profitability, tangible assets, and liquidity negatively and strongly affect firms' capital structure. Meanwhile, there is a low negative impact of dividends and inflation on the debt ratio. This research has ramifications for business managers since it improves a company's financial resources by developing a strong capital structure and considering foreign investment as a source of funding.

• The KIPS Transactions:PartD
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• v.11D no.1
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• pp.163-172
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• 2004

In the software development project applying object-oriented development methodology, the research on the UCP(Use Case Point) as a method to estimate development effort is being carried on. The existing research proposes the linear model calculating the development effort that multiplies an invariant on AUCP(Adjusted Use Case Point) which applied technical and environmental factors. However, the statistical model that estimates the development effort using AUCP and UUCP(Unadjusted Use Case Point) is not being studied. The irrelevant relationship of the linear regression model, whose development period is increasing tremendously as the software size increases, is confirmed. Moreover, during the UCP calculating process, there can be errors in FP by applying the TCF(Technical Complexity Factor) and EF(Environmental Factor). This paper presents a non-linear regression model, that does not consider the TCF and EF, and that estimate the development effort from UUCP directly by utilizing the exponential function. An exponential function is selected among the linear, logarithm, polynomial, power, and exponential model via statistical evaluations of the models mentioned above.

• Journal of the Korean Data and Information Science Society
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• v.24 no.2
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• pp.267-275
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• 2013

Among data mining techniques, the association rule is the most recently developed technique, and it finds the relevance between two items in a large database. And it is directly applied in the field because it clearly quantifies the relationship between two or more items. When we determine whether an association rule is meaningful, we utilize interestingness measures such as support, confidence, and lift. Interestingness measures are meaningful in that it shows the causes for pruning uninteresting rules statistically or logically. But the criteria of these measures are chosen by experiences, and the number of useful rules is hard to estimate. If too many rules are generated, we cannot effectively extract the useful rules.In this paper, we designed a variety of non-linear regression equations considering all association thresholds between the number of rules and three interestingness measures. And then we diagnosed multi-collinearity and autocorrelation problems, and used analysis of variance results and adjusted coefficients of determination for the best model through numerical experiments.

• Preventive Nutrition and Food Science
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• v.1 no.1
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• pp.41-45
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• 1996

This study was conducted to develop the fermentation kinetic model for the prediction of acidity and pH changes in Kimchi as a function of fermentation temperatures. The fitness of the model was evaluated using traditional two-step method and an alternative non-linear regression method. The changes in acidity and pH during fermentation followed the pattern of the first order reaction of a two-step method. As the fermentation temperature increased from 4$^{\circ}C$ to 28, the reaction rates of acidity and pH were increased 8.4 and 7.6 times, respectively. The activation energies of acidity and pH were 16.125 and 16.003kcal/mole. The average activation energies of acidity and pH using a non-linear method were 16.006 by the first order and 15.813 kcal/mole by the zero order, respectively. The non-linear procedure had better fitting 개 experimental data of the acidity and pH than two-step method. The shelf-lives based on the time to reach the 1.0% of acidity were 33.1day at 4$^{\circ}C$ and 2.8 day 28$^{\circ}C$.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.1
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• pp.143-150
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• 2006

Box-Cox model and T-factor method have been widely used to measure economic depreciations for industrial property. The Box-Cox model which combines economic efficiency with depreciation pattern is here extended to the reliability function. To do so a Rayleigh distribution which has been used to estimate the reliability of current assets was chosen as an efficiency curve of marginal productivity. Such an approach provides the possibility to classify the efficiency curves into four categories. It is also possible to analyze the types of depreciation curves. Therefore, the power family of a non-linear Box-Cox model could be set at certain constant values, then the model can be transformed into a linear model to estimate the economic depreciation rates by utilizing the reliability function. Estimating the resultant linear regression equation requires minimal number of observations, while at the same time facilitating the test of hypothesis on depreciation rates.

• Journal of Biosystems Engineering
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• v.23 no.6
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• pp.561-570
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• 1998

The MLR(Multiple Linear Regression) models to estimate soluble solids content non-destructively were presented to make a selection of optimal photosensor utilized to measure the soluble solids content of apples. Visible and NIR absorbance in the 400 to 2498 nanometer(nm) wavelength region, soluble solids content(sugar content), hardness, and weight were measured for 400 apples(gala). Spectrophotometer with fiber optic probe was utilized for spectrum measurement and digital refractometer was used for soluble solids content. Correlation between absorbance spectrum and soluble solids content was analyzed to pick out the optimal wavelengths and to develop corresponding prediction model by means of MLR. For the coefficient of determination($R^2$) to be over 0.92, the MLR models out of the original absorbance were built based on 7 wavelengths of 992, 904, 1096, 1032, 880, 824, 1048nm, and the ones of the second derivative absorbance based on 5 wavelengths of 784, 1056, 992, 808, 872nm. The best model of the second derivative absorbance spectrum had $R^2$=0.91, bias= -0.02bx, SEP=0.28bx for unknown samples.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.159-159
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• 2022

Soil represents a substantial component within the global carbon cycle and small changes in the SOC stock may result in large changes of atmospheric CO₂ particularly over tens to hundreds of years. In this study, we aim to (i) evaluate the SOC stock in the topsoil 0 - 15 cm from soil physical and chemical characteristics and (ii) find the correlation of SOC and soil organic matter (SOM) for national-scale in South Korea. First of all, based on the characteristics of the soil to calculate the soil hydraulic properties, SOC stock is the SOC mass per unit area for a given depth. It depends on bulk density (BD-g/cm³), SOC content (%), the depth of topsoil (cm), and gravel content (%). Due to insufficient data on BD observation, we establish a correlation between BD and SOC content, sand content, clay content parameter. Next, we present linear and non-linear regression models of BD and the interrelationship between SOC and SOM using a linear regression model and determine the conversion factor for them, comparing with Van Bemmelen 1890's factor value for the country scale. The results obtained, helps managers come up with suitable solutions to conserve land resources.

• Journal of Wetlands Research
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• v.14 no.3
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• pp.341-352
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• 2012

The purpose of this study is to statistically project future probable rainfall and to quantitatively assess a future flood vulnerability using flood vulnerability model. To project probable rainfall under non-stationarity conditions, the parameters of General Extreme Value (GEV) distribution were estimated using the 1 yr data added to the initial 30 yr base series. We can also fit a linear regression model between time and location parameters after comparing the linear relationships between time and location, scale, and shape parameters, the probable rainfall in 2030 yr was calculated using the location parameters obtained from linear regression equation. The flood vulnerability in 2030 yr was assessed inputted the probable rainfall into flood vulnerability assessment model suggested by Jang and Kim (2009). As the result of analysis, when a 100 yr rainfall frequency occurs in 2030 yr, it was projected that vulnerability will be increased by spatial average 5 % relative to present.

• Industrial Engineering and Management Systems
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• v.8 no.3
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• pp.148-154
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• 2009

In recent days, large-sized flat-panel display (FPD) has been increasingly applied to computer monitors and TVs. Mura defects, appearing as low contrast or non-uniform brightness region, sometimes occur in manufacturing of the Thin-Film Transistor Liquid-Crystal Displays (TFT-LCD). Implementation of automatic Mura inspection methods is necessary for TFT-LCD production. Various existing Mura detection methods based on regression diagnostics, surface fitting and data transformation have been presented with good performance. This paper proposes an efficient Mura detection method that is based on a regression diagnostics using studentized residuals for automatic Mura inspection of FPD. The input image is estimated by a linear model and then the studentized residuals are calculated for filtering Mura regions. After image dilation, the proposed threshold is determined for detecting the non-uniform brightness region in TFT-LCD by means of monitoring the every pixel in the image. The experimental results obtained from several test images are used to illustrate the effectiveness and efficiency of the proposed method for Mura detection.