• Journal of the Korean Society of Food Science and Nutrition
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• v.33 no.2
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• pp.433-438
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• 2004

This study was conducted to develop a sweet pumpkin to intermediate materials for various processed foods and dried food having high quality. Factorial experiment design with three variables having three levels was adapted and response surface methodology was used to determine optimum conditions for osmotic dehydration of sweet pumpkin. The moisture content, weight reduction, moisture loss and solid gain after osmotic dehydration increased according to increasement of immersion temperature, concentration and time. The effect of concentration was more significant than that of temperature and time at given conditions. Sugar concentration and vitamin C content increased in accordance with increasement of immersion temperature, concentration and time during osmotic dehydration. Hardness was increased by increasing immersion time. The regression models showed very significant values and high correlation coefficients (R2) above 0.91, excepting hardness. The optimum condition for osmotic dehydration was 23$^{\circ}C$, 52$^{\circ}C$Brix and 80 min at the constricted conditions such as 60∼70% moisture content, above 3 mg/100 g vitamin C and more than 10 kg/$\textrm{cm}^2$ hardness.

• Molecular & Cellular Toxicology
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• v.2 no.2
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• pp.134-140
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• 2006

Because shipyard workers are involved with various manufacturing process in shipyard industry, and they are exposed to many kinds of hazardous materials. Especially, painting workers were exposed polycyclic aromatic hydrocarbons (PAH). This study was conducted to assess the exposure status of PAH based on job-exposure matrix. We investigated the effect of genetic polymorphism of xenobiotic metabolism enzymes involved in PAH metabolism on levels of urinary metabolite. A total of 93 shipbuilding workers were recruited in this study. Questionnaire variables were age, sex, use of personal protective equipment, smoking, drinking, and work duration. The urinary metabolite was collected in the afternoon and corrected by urinary creatinine concentration. The genotypes of CYP1A1, CYP2E1, GSTM1, GSTT1 and UGT1A6 were investigated by using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) methods with DNA extracted from venous blood. Urinary 1-OHP levels were significantly higher in direct exposured group (spray and touch-up) than indirect exposed group. Urinary 1-OHP, concentration of the high exposure with wild type of UGT1A6 was significantlyhigher than that of the high exposure with other UGT1A6 genotype. In multiple regression analysis of urinary 1-OHP, the regression coefficient of job grade was statistically significant (p<0.05) and UGT1A6 was not significant but a trend (p<0.1). The grade of exposure affected urinary PAH concentration was statistically significant. But genetic polymorphism of xenobiotics metabolism enzymes was not statistically significant. Further investigation of genetic polymorphism with large sample size is needed.

• Journal of the Korean Society of Food Science and Nutrition
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• v.35 no.4
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• pp.499-506
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• 2006

Response surface methodology was used to investigate the cookie manufacturing process with black rice flour. A three- variable, three-level central composite design was employed where the independent variables were the amount of black rice flour ($0{\sim}20%$), baking time ($10{\sim}14 min$), and sugar type (sucrose, aspartame and oligosaccarides). pHs of dough and cookie tended to increase with the addition of black rice flour. Moisture content of dough slightly increased with tile addition of black rice flour but nearly affected by baking time. Spread factor increased with the addition of black rice flour and it was more evident in the samples prepared with sucrose. L*-value decreased but a*-value increased significantly with the addition of black rice flour. Generally the amount of black rice flour in the sample did not affect the textural characteristics of cookie. As the amount of black rice and baking time increased, sensory flavor became stronger. Sensory sweetness as well as hardness increased but sensory color became darker with the addition of black rice flour. In addition, the response surface models developed in this study for most of physicochemical and sensory characteristics of black rice cookie were adequate.

• Journal of the Economic Geographical Society of Korea
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• v.26 no.3
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• pp.238-255
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• 2023

In this study, we derived the characteristic factors of the cluster as measurable variables, and attempted to clarify the characteristics of the apparel sewing areas in Changsin-dong, Doksan-dong, and Jangwi-dong. Based on these results, a comparative analysis was conducted to see how the demand for the government's support policy differs for each agglomeration area. Materials were collected through face-to-face questionnaires targeting tenant companies in the three regions. As a result of the analysis, Changsin-dong was identified as an "innovative growth type," Doksan-dong as a "networking type," and Jangwi-dong as a "specialized localization type." As a result of the research on policy demands, the policy demands of the three agglomerations appeared different, but Changsin-dong preferred capacity building, Doksan-dong preferred information provision, and Jangwi-dong favored policy means of benefit. It was confirmed that even among clusters of the same apparel sewing industry, the formation process and characteristics are different, and as a result, the demand for policy instruments is also different. Policy recommendations include understanding the characteristics and policy demands of each agglomeration area through periodic fact-finding surveys, and recommending the establishment and implementation of differentiated support policies that match the characteristics of each agglomeration area.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.6
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• pp.201-214
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• 2023

This study attempted to analyze the impact of individual entrepreneurial capabilities on entrepreneurial intention targeting small and medium-sized business employees, and sought to confirm the mediating effect of entrepreneurial efficacy and entrepreneurial mentoring between entrepreneurial capabilities and entrepreneurial intention. The sub-variables of entrepreneurship competency were analyzed separately into creativity, problem solving, communication, and marketing. 368 questionnaires collected from employees at small and medium-sized manufacturing companies located across the country were used for empirical analysis. A parallel dual mediation model with no causal relationship between parameters was used for empirical analysis using SPSS v26.0 and PROCESS macro v4.2. As a result of the analysis, first, among the start-up competencies, creativity, communication, and marketing were confirmed to have a significant positive (+) effect on start-up efficacy. Second, among the start-up competencies, creativity, communication, and marketing were tested to have a significant positive influence on start-up mentoring. Third, both startup efficacy and startup mentoring were found to have a significant positive influence on startup intention. Fourth, among start-up capabilities, creativity and marketing were confirmed to have a significant positive (+) effect on start-up intention. Fifth, startup efficacy and startup mentoring were found to have a mediating effect on startup intention except for problem solving among startup competencies. As a result, it was confirmed that in order to strengthen the intention to start a business among small and medium-sized business employees, start-up efficacy and start-up mentoring are important factors, and that marketing and creativity have an important influence among individual start-up capabilities, so education and prior preparation for these are necessary. As follow-up research, it will be necessary to apply multivariate models, analyze time series data, research considering external environmental factors, and test the difference between start-up capabilities and performance considering detailed population characteristics.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Journal of Venture Innovation
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• v.3 no.2
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• pp.41-76
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• 2020

This study confirmed factors affecting smart factory technology acceptance through empirical analysis. It is a study on what factors have an important influence on the introduction of the smart factory, which is the core field of the 4th industry. I believe that there is academic and practical significance in the context of insufficient research on technology acceptance in the field of smart factories. This research was conducted based on the Unified Theory of Acceptance and Use of Technology (UTAUT), whose explanatory power has been proven in the study of the acceptance factors of information technology. In addition to the four independent variables of the UTAUT : Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions, Government Assistance Expectancy, which is expected to be an important factor due to the characteristics of the smart factory, was added to the independent variable. And, in order to confirm the technical factors of smart factory technology acceptance, the Task Technology Fit(TTF) was added to empirically analyze the effect on Behavioral Intention. Trust is added as a parameter because the degree of trust in new technologies is expected to have a very important effect on the acceptance of technologies. Finally, empirical verification was conducted by adding Innovation Resistance to a research variable that plays a role as a moderator, based on previous studies that innovation by new information technology can inevitably cause refusal to users. For empirical analysis, an online questionnaire of random sampling method was conducted for incumbents of domestic small and medium-sized enterprises, and 309 copies of effective responses were used for empirical analysis. Amos 23.0 and Process macro 3.4 were used for statistical analysis. For accurate statistical analysis, the validity of Research Model and Measurement Variable were secured through confirmatory factor analysis. Accurate empirical analysis was conducted through appropriate statistical procedures and correct interpretation for causality verification, mediating effect verification, and moderating effect verification. Performance Expectancy, Social Influence, Government Assistance Expectancy, and Task Technology Fit had a positive (+) effect on smart factory technology acceptance. The magnitude of influence was found in the order of Government Assistance Expectancy(β=.487) > Task Technology Fit(β=.218) > Performance Expectancy(β=.205) > Social Influence(β=.204). Both the Task Characteristics and the Technology Characteristics were confirmed to have a positive (+) effect on Task Technology Fit. It was found that Task Characteristics(β=.559) had a greater effect on Task Technology Fit than Technology Characteristics(β=.328). In the mediating effect verification on Trust, a statistically significant mediating role of Trust was not identified between each of the six independent variables and the intention to introduce a smart factory. Through the verification of the moderating effect of Innovation Resistance, it was found that Innovation Resistance plays a positive (+) moderating role between Government Assistance Expectancy, and technology acceptance intention. In other words, the greater the Innovation Resistance, the greater the influence of the Government Assistance Expectancy on the intention to adopt the smart factory than the case where there is less Innovation Resistance. Based on this, academic and practical implications were presented.

• Archives of design research
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• v.18 no.4 s.62
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• pp.95-104
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• 2005

The purpose of this study is to estimate the determinants of product design and analyze the impacts of product design on quality competitiveness, product reliability, and consumer satisfaction in an attempt to provide a foundation for the theory of design management. For this empirical analysis, this study has derived the relevant measurement variables from a survey on 400 Korean manufacturing firms during the period of $August{\sim}October$ 2003. The empirical findings are summarized as follows: First, the determinants of product design are very significantly (at p<0.001) estimated to be the R&D capability, the level of R&D expenditure, the level of innovative activities(5S, TQM, 6Sigma, QC, etc.). This empirical result can support Pawar and Driva(1999)'s two principles by which the performance of product design and product development can be simultaneously evaluated in the context of CE(concurrent engineering) of NPD(newly product development) activities. Second, the hypothesis on the causality: product design${\rightarrow}$quality competitiveness${\rightarrow}$customer satisfaction${\rightarrow}$customer loyalty is very significantly (at p<0.001) accepted. This implies that product design positively affects consumer satisfaction, not directly but indirectly, by influencing quality competitiveness. This empirical result of this study can also support the studies of for example Flynn et al.(1994), Ahire et at.(1996), Afire and Dreyfus(2000) which conclude that design management is a significant determinant of product quality. The aforementioned empirical results are important in the following sense: the empirical result that quality competitiveness plays a bridging role between product design and consumer satisfaction can reconcile the traditional debate between QFD(quality function development) approach asserted by product developers and conjoint analysis maintained by marketers. The first empirical result is related to QFD approach whereas the second empirical result is related to conjoint analysis. At the same time, the empirical results of this study can support the rationale of design integration(DI) of Ettlie(1997), i.e., the coordination of the timing and substance of product development activities performed by the various disciplines and organizational functions of a product's life cycle. Finally, the policy implication (at the corporate level) from the empirical results is that successful design management(DM) requires not only the support of top management but also the removal of communication barriers, (i.e. the adoption of cross-functional teams) so that concurrent engineering(CE), the simultaneous development of product and process designs can assure product development speed, design quality, and market success.

• Korean Journal of Food Science and Technology
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• v.30 no.2
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• pp.348-355
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• 1998

The developments of various processed foods and the high quality dried fruits, in particular, are urgently needed for the enhancement of fruit consumption and their competitive values. Therefore, in this study, three variables by three level factorial design and response surface methodology were used to determine optimum conditions for osmotic dehydration of kiwifruit. The relationships of moisture losses, solid gains, weight reductions, sugar contents, titratable acidities and vitamin C contents depending on changes with temperature, sugar concentration and immersion time were investigated. The moisture loss, solid gain, weight reduction and reduction of moisture content after osmotic dehydration were increased as temperature, sugar concentration and immersion time increased. The effect of concentration was more significant than those of temperature and time on mass transfer. Sugar content was increased by increasing sugar concentration, temperature, immersion time during osmotic dehydration. Titratable acidity and vitamin C content were increased by decreasing temperature, immersion time and increasing concentration during osmotic dehydration. The regression models showed a significant lack of fit (P>0.05) and were highly significant with satisfying values of $R^2$. At the given conditions such as $66{\sim}69%$ moisture content, above $24^{\circ}Brix$ sugar content and more than 23 mg% vitamin C, the optimum condition for osmotic dehydration was $37^{\circ}C,\;55^{\circ}Brix$ and 1.5 hour.

• Journal of Preventive Medicine and Public Health
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• v.36 no.2
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• pp.93-100
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• 2003

Objectives : Because shipyard workers are involved with various manufacturing process, they are exposed to many kinds of hazardous materials. Welders especially, are exposed to bisphenol-A (BPA) during the welding and flame cutting of coated steel, This study was conducted to assess the exposure status of the endocrine disrupter based on the job-exposure matrix. The effects of the genetic polymorphism of xenobiotic enzyme metabolisms involved in the metabolism of BPA on the levels of urinary metabolite were investigated. Methods : The study population was recruited from a shipyard company in the f province. A total of 84 shipbuilding workers 47 and 37 in the exposed and control groups, respectively, were recruited for this study. The questionnaire variables included, age, sex, use of personal protective equipment, smoking, drinking and work duration. The urinary metabolite was collected in the afternoon and correction made for the urinary creatinine concentration. The of the CYP1A1, CYP2E1 and UGT1A6 genotypes were investigated using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) methods with the DNA extracted from venous blood. Results : The urinary BPA level in the welders group was significantly higher than in the control group (p<0.05). The urinary BPA concentration with the wild type UGT1A6 was higher than the other UGT1A6 genotypes, but with no statistical significant. From themultiple regression analysis of the urinary BPA, the regression coefficient for job grade was statistically significant (p<0.05). Conclusions : The grade of exposure to BPA affected the urinary BPA concentration was statistically significant. However, the genetic polymorphisms of xenobiotics enzyme metabolism were not statistically significant. Further investigation of the genetic polymorphisms with a larger sample size is needed.