• Safety and Health at Work
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• v.3 no.4
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• pp.278-286
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• 2012

Objectives: Authors investigated the pattern of the rate of occupational injuries and illnesses (ROII) at the level of enterprises in order to build a network for exchange of experience and knowledge, which would contribute to workers' safety and health through safety climate of workplace. Methods: Occupational accidents were analyzed at the manufacturing work site unit. A two step clustering process for the past patterns regarding the ROII from 2001 to 2009 was investigated. The ROII patterns were categorized based on regression analysis and the patterns were further divided according to the subtle changes with Mahalanobis distance and Ward's linkage. Results: The first clustering of ROII through regression analysis showed 5 different functions; 29 work sites of the linear function, 50 sites of the quadratic function, 95 sites of the logarithm function, 62 sites of the exponential function, and 54 sites of the sine function. Fourteen clusters were created in the second clustering. There were 3 clusters in each function categorized in the first clustering except for sine function. Each cluster consisted of the work sites with similar ROII patterns, which had unique characteristics. Conclusion: The five different patterns of ROII suggest that tailored management activities should be applied to every work site. Based on these differences, the authors selected exemplary work sites and built a network to help the work sites to share information on safety climate and accident prevention measures. The causes of different patterns of ROII, building network and evaluation of this management model should be evaluated as future researches.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.1
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• pp.8-17
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• 2014

In this research, we employed various data mining techniques to build predictive models for win-loss prediction in Korean professional baseball games. The historical data containing information about players and teams was obtained from the official materials that are provided by the KBO website. Using the collected raw data, we additionally prepared two more types of dataset, which are in ratio and binary format respectively. Dividing away-team's records by the records of the corresponding home-team generated the ratio dataset, while the binary dataset was obtained by comparing the record values. We applied seven classification techniques to three (raw, ratio, and binary) datasets. The employed data mining techniques are decision tree, random forest, logistic regression, neural network, support vector machine, linear discriminant analysis, and quadratic discriminant analysis. Among 21(= 3 datasets${\times}$7 techniques) prediction scenarios, the most accurate model was obtained from the random forest technique based on the binary dataset, which prediction accuracy was 84.14%. It was also observed that using the ratio and the binary dataset helped to build better prediction models than using the raw data. From the capability of variable selection in decision tree, random forest, and stepwise logistic regression, we found that annual salary, earned run, strikeout, pitcher's winning percentage, and four balls are important winning factors of a game. This research is distinct from existing studies in that we used three different types of data and various data mining techniques for win-loss prediction in Korean professional baseball games.

• The Korean Journal of Food And Nutrition
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• v.35 no.6
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• pp.473-480
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• 2022

This study aimed to develop an optimal processing method for the production of apple-mango jelly for domestic suppliers, by analyzing the quality attributes of the jelly. According to the central composite design, a total of 11 experimental points were designed including the content of apple-mango juice (X₁), and the sugar content (X₂). The responses were analyzed including the color values (CIE Lab and color difference), physicochemical properties (water activity, sweetness, pH, and total acidity), and textural properties (hardness and gel strength). Regression analysis was conducted, except for total acidity, and showed no significant difference for all the experimental points (p<0.05). Quadratic model was derived for all responses with an R square value ranging from 0.8590 to 0.9978. Based on regression model, the appropriate mixing ratio of apple-mango jelly was found to be 31.11% of apple mango juice and 14.65% of sugar. Through this study, the possibility for developing jelly product using apple-mango was confirmed, and it is expected that these findings will contribute to the improvement of the agricultural industry.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.4
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• pp.1-10
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• 2023

As a result of climate change, non-point source pollution (NPS) from farmland with the steep slope during the rainy season is expected to have a significant impact on the water system. This study aimed to evaluate the effect of furrow mulching using alfalfa and PAM (Polyacrylamide) materials for each rainfall event, while considering the load characteristics of NPS. The study was conducted in Wanju-gun, Jeollabuk-do, in 2022, with a testbed that had a slope of 13%, sandy loam soil, and maize crops. The testbed was composed of four plots: bare soil (Bare), No mulching (Cont.), Vegetation mulching (VM), and PAM mulching (PM). Runoff was collected from each rainfall event using a 1/40 sampler and the NPS load was calculated by measuring the concentrations of SS, T-N, T-P, and TOC. During farming season, the reduction efficiency of NPS load was 37.1~59.5% for VM and 38.2~75.7% for PM. The analysis found that VM had a linear regression correlation (R²=0.28~0.86, P-value=0.01~0.1) with elapsed time of application, while PM had a quadratic regression correlation (R²=0.35~0.80, P-value=0.1). These results suggest that the selection of furrow mulch materials and the appropriate application method play a crucial role in reducing non-point pollution in farmland. Therefore, further studies on the time-series reduction effect based on the application method are recommended to develop more effective preemptive reduction technologies.

• Animal Bioscience
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• v.37 no.5
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• pp.817-825
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• 2024

Objective: The aim of this study was to identify suitable polynomial regression for modeling the average growth trajectory and to estimate the relative development of the rib eye area, scrotal circumference, and morphometric measurements of Guzerat young bulls. Methods: A total of 45 recently weaned males, aged 325.8±28.0 days and weighing 219.9±38.05 kg, were evaluated. The animals were kept on Brachiaria brizantha pastures, received multiple supplementations, and were managed under uniform conditions for 294 days, with evaluations conducted every 56 days. The average growth trajectory was adjusted using ordinary polynomials, Legendre polynomials, and quadratic B-splines. The coefficient of determination, mean absolute deviation, mean square error, the value of the restricted likelihood function, Akaike information criteria, and consistent Akaike information criteria were applied to assess the quality of the fits. For the study of allometric growth, the power model was applied. Results: Ordinary polynomial and Legendre polynomial models of the fifth order provided the best fits. B-splines yielded the best fits in comparing models with the same number of parameters. Based on the restricted likelihood function, Akaike's information criterion, and consistent Akaike's information criterion, the B-splines model with six intervals described the growth trajectory of evaluated animals more smoothly and consistently. In the study of allometric growth, the evaluated traits exhibited negative heterogeneity (b<1) relative to the animals' weight (p<0.01), indicating the precocity of Guzerat cattle for weight gain on pasture. Conclusion: Complementary studies of growth trajectory and allometry can help identify when an animal's weight changes and thus assist in decision-making regarding management practices, nutritional requirements, and genetic selection strategies to optimize growth and animal performance.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.51 no.5
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• pp.386-395
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• 2006

The core questions for determining nitrogen topdress rate (Npi) at panicle initiation stage (PIS) are 'how much nitrogen accumulation during the reproductive stage (PNup) is required for the target rice yield or protein content depending on the growth and nitrogen nutrition status at PIS?' and 'how can we diagnose the growth and nitrogen nutrition status easily at real time basis?'. To address these questions, two years experiments from 2001 to 2002 were done under various rates of basal, tillering, and panicle nitrogen fertilizer by employing a rice cultivar, Hwaseongbyeo. The response of grain yield and milled-rice protein content was quantified in relation to RVIgreen (green ratio vegetation index) and SPAD reading measured around PIS as indirect estimators for growth and nitrogen nutrition status, the regression models were formulated to predict PNup based on the growth and nitrogen nutrition status and Npi at PIS. Grain yield showed quadratic response to PNup, RVIgreen around PIS, and SPAD reading around PIS. The regression models to predict grain yield had a high determination coefficient of above 0.95. PNup for the maximum grain yield was estimated to be 9 to 13.5 kgN/10a within the range of RVIgreen around PIS of this experiment. decreasing with increasing RVIgreen and also to be 10 to 11 kgN/10a regardless of SPAD readings around PIS. At these PNup's the protein content of milled rice was estimated to rise above 9% that might degrade eating quality seriously Milled-rice protein content showed curve-linear increase with the increase of PNup, RVIgreen around PIS, and SPAD reading around PIS. The regression models to predict protein content had a high determination coefficient of above 0.91. PNup to control the milled-rice protein content below 7% was estimated as 6 to 8 kgN/10a within the range of RVIgreen and SPAD reading of this experiment, showing much lower values than those for the maximum grain yield. The recovery of the Npi applied at PIS ranged from 53 to 83%, increasing with the increased growth amount while decreasing with the increasing Npi. The natural nitrogen supply from PIS to harvest ranged from 2.5 to 4 kg/10a, showing quadratic relationship with the shoot dry weight or shoot nitrogen content at PIS. The regression models to estimate PNup was formulated using Npi and anyone of RVIgreen, shoot dry weight, and shoot nitrogen content at PIS as predictor variables. These models showed good fitness with determination coefficients of 0.86 to 0.95 The prescription method based on the above models predicting grain yield, protein content and PNup and its constraints were discussed.

• Korean Journal of Soil Science and Fertilizer
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• v.12 no.1
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• pp.15-23
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• 1979

In the first paper of the series the five soil fertility factors were evaluated by means of principal component analysis and varimax method. They are interpreted as representing, 1) skeletal available phosporus status, 2) organnic matter status, 3) salt status 4) base status, and 5) free oxide status. In order to resynthesize such fragmented information for the overall soil fertility evaluation, the method of multiple regression analysis was adopted, using the five factor scores and yield data for Korean paddy soils as independent and dependent variables respectively. As test of linear models with different combinations of independent variables the results of t-test of regression coefficient were revealed that the organic matter status (FII) has no relevance to the yield of paddy and that the free oxides and salt supply has by it self only an insignificant contribution to the yield. The multiple correlation coefficient (R) revealed its multiple regression analysis was as low as 0.43. Introduction of quadratic terms to the linear model bettered the result. Thus multiple correlation coefficient (R) was increased as 0.59. Therefore, a coefficient of determination 0.35 was obtained by a quadratic model with interaction terms among the five fertility constituents. Generally we think that the fertility factor has more contribution to raise the rice yield in paddy and that the failure of yield prediction by fertility factor scores was caused by one of follows; 1) the roughness of the yield inspection, and 2) missextraction of fertility constituents. The second step in this study, assuming that the residuals by multiple regression analysis were due to factors other than soil fertility, we can now proceed to predicting the yield from the field characters with the classified fertility groups by means of Hayashi's theory of quantification No. 1. Such variables as fertility groups (FTYG), water availability (WATER), soil drainage (DRNG), climatic zone (CLIZ), surface soil's stickiness (STCKT), surface soil's dry consistence (DCNST), and surface soil's texture (FTEXT) are taken up as the explanatory variables. The quantification appears reasonable; the well to extremely well in soil drainage, very sticky of surface soil, inefficiency in water availability, coarse texture, and very hard to extremely hard dry consistence in soil are detrimental to the rice yield. The R was as high as 0.90 for the set of variables. But the given explanatory variables in this study were not quite effective in explaining rice yield. The method developed seems to be promising only if properly collected data are available. Conditions that should be satisfied in the yield inspection obtained from common cultivator for the purpose of deriving a prediction equation were put forward.

• Bulletin of the Korean Chemical Society
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• v.29 no.8
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• pp.1549-1553
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• 2008

A molecular imprinted polymer (MIP) using (+)catechin ((+)C) as a template and acrylamide (AM) as a functional monomer was prepared. Acetonitrile was used as the porogen with ethylene glycol dimethacrylate (EGDMA) as the crosslinker and 2,2'-azobis(isobutyronitrile) (AIBN) as the initiator. The adsorption isotherms in the MIP were measured and the parameters of the equilibrium isotherms were estimated by linear and nonlinear regression analyses. The linear equation for original concentration and adsorpted concentrations was then expressed, and the adsorption equilibrium data were correlated into Langmuir, Freundlich, quadratic, and Langmuir Extension isotherm models. The mixture compounds of (+)C and epicatechin (EC) show competitive adsorption on specific binding sites of the (+)catechin-MIP. The adsorption concentrations of (+)C, epicatechin (EC), epicatechin gallate (ECG), and epigallocatechin gallate (EGCG), on the (+)catechin-molecular imprinted polymer were compared. Through the analysis, the (+)catechin-molecular imprinted polymer showed higher adsorption ability than blank polymer which was synthesized molecular imprinted polymer without (+)catechin. Furthermore, the competitive Langmuir isotherms were applied to the mixture compounds of (+)C and EC.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.12
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• pp.1960-1965
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• 2001

In the practical design, it is important to extract the design space information of a complex system in order to optimize the design because the design contains huge amount of design conflicts in general. In this research FEA (finite element analysis) has been successfully implemented and integrated with a statistical approach such as DOE (design of experiments) based RSM (response surface model) to optimize the thermal deformation of an automotive disk brake. The DOE is used for exploring the engineer's design space and for building the RSM in order to facilitate the effective solution of multi-objective optimization problems. The RSM is utilized as an efficient means to rapidly model the trade-off among many conflicting goals existed in the FEA applications. To reduce the computational burden associated with the FEA, the second-order regression models are generated to derive the objective functions and constraints. In this approach, the multiple objective functions and constraints represented by RSM are solved using the sequential quadratic programming to archive the optimal design of disk brake.

• Journal of Biosystems Engineering
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• v.41 no.1
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• pp.34-42
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• 2016

Purpose: This study was performed to improve the drying quality and drying rate of ginseng slices by combining microwave and far-infrared drying techniques. Methods: Based on single-factor experiments and analyses, a quadratic regression orthogonal rotation combination design was adopted to study the effects of the moisture content at the conversion point between the microwave and far-infrared techniques, the ginseng slice thickness and the far-infrared drying temperature on the chip drying time, the surface color difference value, the nutritional composition and the surface shrinkage rate index. Results: Compared to the far-infrared drying alone, the combined microwave and far-infrared drying resulted in an increase in the saponin content of the ginseng slices and reductions in the drying time, surface color difference, and shrinkage rate. Conclusions: We established a mathematical model of the relationships between the surface shrinkage rate index and the experimental factors using the multi-objective nonlinear optimization method to determine the optimal parameter combination, which was confirmed to be the following: microwave and far-infrared moisture contents of 65%, a ginseng slice thickness of 1 mm, and a far-infrared drying temperature of $54^{\circ}C$.