• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.905-906
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• 2008

This paper presents a method where the best focues points are calculated using line fitting. Two datasets are selected for each pixel based on the maximum value which is calculated using Laplacian operator. Then linear regression model is used to find lines that approximate these datasets. The best fit lines are found using least squares method. After approximating the two lines, their intersection point is calculated and weights are assigned to calculate the new value for the depth map.

• Food Science and Biotechnology
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• v.18 no.5
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• pp.1293-1297
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• 2009

In this study, drying kinetics of onion slices was examined in a laboratory scale vacuum dryer at an air temperature in a range of $50-70^{\circ}C$. Moisture transfer from onion slices was described by applying the Fick's diffusion model, and the effective diffusivity was calculated. Temperature dependency of the effective diffusivity during drying process obeyed the Arrhenius relationship. Effective diffusivity increased with increasing temperature and the activation energy for the onion slices was estimated to be 16.92 kJ/mol. The experimental drying data were used to fit 9 drying models, and drying rate constants and coefficients of models tested were determined by non-linear regression analysis. Estimations by the page and Two-term exponential models were in good agreement with the experimental data obtained.

• Journal of the Optical Society of Korea
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• v.1 no.1
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• pp.60-64
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• 1997

An image processing algorithm is presented that can identify the orientation as well as the left/right side (parity) of the digitized radiographs. The orientation was found by computing the mean square deviation between the sampled gray values along the center and their best-fit linear regression relations. The parity was determined by comparing the area difference between two thresholded images of the left and the right side around the heart, which is assumed to be around the center of the image. This method was tested with 86 images with their orientations intentionally rotated. The rotation was limited to multiples of 90 degrees, as this was the way the rotation is most likely to happen in the clinical environment. We obtained positive responses for 85 out of 86 images subject to the screening.

• Journal of Environmental Science International
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• v.2 no.4
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• pp.271-278
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• 1993

Optimal biodegradation kinetics models to the initial nonylphenol ethoxylates-30 concentration were investigated and had been fitted by the linear regression. Microorganisms capable of degrading nonylphenol ethoxylates-30 were isolated from sewage near Ulsan plant area by enrichment culture technique. Among them, the strain designated as EL-10K had the highest biodegradability and was identified as Pseudomonas from results of taxonomical studies. The optimal conditions for the biodegradation were 1.0 g/ι of nonylphenol ethoxylates-30 and 0.02 g/ι of ammonium nitrate at pH 7.0 and 3$0^{\circ}C$. The highest degradation rate of nonylphenol ethoxylates-30 was about 89% for 30 hours incubation on the optimal condition. Biodegradation data were fit by linear regression to equations for 3 kinetic models. The kinetics of biodegradation of nonylphenol ethoxylates was best described by first order model for 0.1 $\mu\textrm{g}$/ι nonylphenol ethoxylates-30 ; by Monod no growth model and Monod with growth model for 0.5 $\mu\textrm{g}$/mι and 1.0, 5.0 $\mu\textrm{g}$/mι, respectively.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.407-419
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• 2006

In this paper, we analyze household overdue loans in Korea which has been causing serious social and economical problems. We consider customers of Bank A in Korea and focus on overdue cash services which have been snowballing in the past few years. From analysis of overdue loans, one can predict possible delays for current customers as well as build a credit evaluation and risk management system for future customers. As a statistical analytical tool, we propose a two-stage Generalized Linear regression Model (GLM) which assumes a logistic model for presence/non-presence of overdue and a gamma model for the amount of overdue in the case of overdue. We perform goodness of fit test for the two-stage model and select significant explanatory variables in each stage of the model. It turns out that age, the amount of credit loans from other financial companies, the amount of cash service from other companies, debit balance, the average amount of cash service, and net profit are important explanatory variables relevant to overdue credit card cash service in Korea.

• The Korean Journal of Applied Statistics
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• v.21 no.4
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• pp.571-580
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• 2008

The logistic regression analysis is widely used in the area of customer relationship management and credit risk management. It is well known that the maximum likelihood estimation is not appropriate when multicollinearity exists among the regressors. Thus we propose the logistic principal components regression to deal with the multicollinearity problem. In particular, new method is suggested to select proper principal components. The selection method is based on the condition index instead of the eigenvalue. When a condition index is larger than the upper limit of cutoff value, principal component corresponding to the index is removed from the estimation. And hypothesis test is sequentially employed to eliminate the principal component when a condition index is between the upper limit and the lower limit. The limits are obtained by a linear model which is constructed on the basis of the conjoint analysis. The proposed method is evaluated by means of the variance of the estimates and the correct classification rate. The results indicate that the proposed method is superior to the existing method in terms of efficiency and goodness of fit.

• Journal of Korean Society of Transportation
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• v.17 no.5
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• pp.99-110
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• 1999

This study deals with the adaptability questions of O-D table estimation models. Its objectives are two-fold; (1) to estimate the characteristics of various O-D table estimation models(i.e. linear regression models. entropy models and statistic models) and (2) to find the model which estimates the O-D table with the best accuracy under the various data conditions. In Pursuing the above, this study gives the particular attentions to the test of the models, using the Sioux Falls network and equilibrium assignment method of MINUTP. The major findings are the followings. Firstly. it finds that the statistic models have the most goodness of fat among all models, if the required data are all Prepared. But it Presents that statistic models are the most sensitive against the underspecification and inconsistency problems of link data. Secondly, It shows that the linear regression models have the worst goodness of fat among all models. But the linear regression models are the most insensitive to the underspecification and inconsistency problems. Thirdly, THE/1 model of entropy model is sensitive against the underspecification and incon-sistency problems, but THE/2 model is insensitive. Finally, other informations like total volume, zonal Production and attraction volumes in 0-D table, help models to gain the better goodness of fit. Especially, in the statistic models. both the zonal production and attraction volume data are helpful to estimate the link volumes. It can be expected that the results dive some implications not only to the selection of optimal model under the various given data, but also to the development or modification of model.

• Journal of Korea Technology Innovation Society
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• v.11 no.1
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• pp.72-90
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• 2008

What has been the contribution of industrial innovation to economic growth? Typically, the issue has been approached with growth-accounting methods augmented to include a "stock of knowledge". An independent estimate of the rate of return to R&D is found in order to impute patents granted to the accumulation of knowledge. Griliches(1973) then uses a regression approach to assess the effect of an R&D variable on the computed TFP growth rate. The regression coefficient on the R&D variable would provide an estimate of the social rate of return to R&D. The related studies tend to show high social rates of return to R&D, typically in a range of 20 to 40 % per year. We need to provide multiple equation dynamic system for productivity and innovation in Korean economy in state space form. A wide range of time series models, including the classical linear regression model, can be written and estimated as special cases of a state space specification. State space models have been applied in the econometrics literature to model unobserved variables like productivity. Estimation produces the following results. Considering the goodness of fit, we can see that the evidence is strongly in favor of the range $0.120{\sim}0.135$ for the elasticity of TFP to R&D stock in the period between 1970's and the early 2000's.

• Korean Journal of Ecology and Environment
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• v.42 no.4
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• pp.452-464
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• 2009

The objectives of this study were to evaluate spatial and temporal variability of water quality in 10 reservoirs and identify the key nutrients (N, P) influencing chlorophyll-a (CHL) along with analysis of empirical models and zonal patterns of total phosphorus (TP) and CHL. We analyzed total nitrogen (TN), TP, CHL, water clarity (Secchi depth, SD), and evaluated potential limiting nutrient using ambient N:P ratios and previous criteria of ambient nutrients. Water clarity and CHL varied largely depending on the seasonal monsoon and type of reservoir, but trophic state was diagnosed as eutrophy, base on mean CHL in most reservoirs. The peak of TP did not match the contents of CHL due to rapid flushing during the high run-off period. In the reservoir of DR, regression coefficient in the $P_r$ was 0.510 but was 0.159 in the $M_o$, while the TP-CHL relation in the YR increased during the monsoon compared to the premonsoon. The regression coefficient in the $P_r$ was not statistically significant but the value of $M_o$ was 0.250. TP showed similar longitudinal zonal gradients among the reservoirs of DR, YR and JR. Empirical models of TP-CHL, based on overall data, showed that CHL was determined by phosphorus($R^2=0.244$, p=0.0019). Regression analysis of CHL-SD showed a stronger linear fit ($R^2=0.638$, p<0.001) than the TP-CHL model.

• Animal Bioscience
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• v.34 no.2
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• pp.163-171
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• 2021

Objective: Considering the importance of dairy farming and the negative effects of heat stress, more tolerant genotypes need to be identified. The objective of this study was to investigate the effect of heat stress via temperature-humidity index (THI) and diurnal temperature variation (DTV) in the genetic evaluations for daily milk yield of Holstein dairy cattle, using random regression models. Methods: The data comprised 94,549 test-day records of 11,294 first parity Holstein cows from Brazil, collected from 1997 to 2013, and bioclimatic data (THI and DTV) from 18 weather stations. Least square linear regression models were used to determine the THI and DTV thresholds for milk yield losses caused by heat stress. In addition to the standard model (SM, without bioclimatic variables), THI and DTV were combined in various ways and tested for different days, totaling 41 models. Results: The THI and DTV thresholds for milk yield losses was THI = 74 (-0.106 kg/d/THI) and DTV = 13 (-0.045 kg/d/DTV). The model that included THI and DTV as fixed effects, considering the two-day average, presented better fit (-2logL, Akaike information criterion, and Bayesian information criterion). The estimated breeding values (EBVs) and the reliabilities of the EBVs improved when using this model. Conclusion: Sires are re-ranking when heat stress indicators are included in the model. Genetic evaluation using the mean of two days of THI and DTV as fixed effect, improved EBVs and EBVs reliability.