• Journal of Korea Technology Innovation Society
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• v.10 no.3
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• pp.431-457
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• 2007

The main purpose of this study is to analyze the determinants of innovation in the medium core firms that belong to components and materials industry. For this purpose, we introduce the Schumpeterian hypothesis as a theoretical background at first. According to the Schumpeterian hypothesis, large firms in concentrated markets are likely to have more innovative activities. That means, firm site and market structure are the main determinant of innovation. Then, we propose other economic factors that have been considered to have effects on firms' innovative activities in previous studies. Those factors are export, profit, growth rate, R&D expenditure and capital intensity. In order to analyze the determinants of innovation, we estimate whether firm size, market structure, export, profit, growth rate, R&D expenditure and capital intensity affect to the possibility of creating innovation in medium core firms. In order to do this, our study uses survey data from 'Korean Innovation Survey(2005)' conducted by STEPI as well as utilizes the probit model as an analytical method. According to the empirical results, firm size has a positive relationship with innovative activities of medium core firms but market concentration does not. We find the negative correlation between market concentration and innovative activities in this study. Thus, was have to say that we do not fully support the Schumpeterian hypothesis in this case. Among other variables, profit and R&D expenditure are estimated to have positive relationship with innovative activities, while export and capital intensity are estimated to have negative relationship with innovative activities. In case of growth rate, we do not find any significant relationship with innovative activities. In conclusion, larger firm size, higher market competition, more access to the financial market and additional R&D investment would facilitate innovative activities of medium core firms. However, we have to review the relationship between export and innovative activities that has been estimated in this study. While the estimated effect of export on innovative activities can be explained by the own characteristics of medium core firms that produce and supply capital goods to final manufacturer, we have address this issue in the future.

• Nutrition Research and Practice
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• v.9 no.6
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• pp.650-657
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• 2015

BACKGROUND/OBJECTIVES: The aim of this study was to estimate average total fat and fatty acid intakes as well as identify major food sources using data from the Korea National Health and Nutrition Examination Survey (KNHANES) VI-1 (2013). SUBJECTS/METHODS: Total fat and fatty acid intakes were estimated using 24-hour dietary recall data on 7,048 participants aged ${\geq}3years$ from the KNHANES VI-1 (2013). Data included total fat, saturated fatty acid (SFA), monounsaturated fatty acid (MUFA), polyunsaturated fatty acid (PUFA), n-3 fatty acid (n-3 FA), and n-6 fatty acid (n-6 FA) levels. Population means and standard errors of the mean were weighted in order to produce national estimates and separated based on sex, age, income, as well as residential region. Major food sources of fat, SFA, MUFA, PUFA, n-3 FA, and n-6 FA were identified based on mean consumption amounts of fat and fatty acids in each food. RESULTS: The mean intake of total fat was 48.0 g while mean intakes of SFA, MUFA, PUFA, n-3 FA, and n-6 FA were 14.4 g, 15.3 g, 11.6 g, 1.6 g, and 10.1 g, respectively. Intakes of MUFA and SFA were each higher than that of PUFA in all age groups. Pork was the major source of total fat, SFA, and MUFA, and soybean oil was the major source of PUFA. Milk and pork were major sources of SFA in subjects aged 3-11 years and ${\geq}12years$, respectively. Perilla seed oil and soybean oil were main sources of n-3 FA in subjects aged ${\geq}50years$ and aged < 50 years, respectively. CONCLUSIONS: Estimation of mean fatty acid intakes of this study using nationally represented samples of the Korean population could be useful for developing and evaluating national nutritional policies.

• Nutrition Research and Practice
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• v.14 no.4
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• pp.384-400
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• 2020

BACKGROUND/OBJECTIVES: Dietary factors are important contributors to cardiometabolic and cancer mortality. We examined the secular trends of nine dietary factors (fruits, vegetables, whole grains, nuts and seeds, milk, red meat, processed meat, sugar-sweetened beverages, and calcium) and the associated burdens of cardiometabolic and cancer mortality in Korea using representative cross-sectional survey data from 1998 to 2016. SUBJECTS/METHODS: Using dietary data from Korean adults aged ≥ 25 years in the Korea National Health and Nutrition Examination Survey (KNHANES), we characterized secular trends in intake levels. We performed comparative risk assessment to estimate the population attributable fraction and the number of cardiometabolic and cancer deaths attributable to each dietary factor. RESULTS: A total of 231,148 cardiometabolic and cancer deaths were attributable to nine dietary risk factors in Korea from 1998 to 2016. Suboptimal intakes of fruits and whole grains were the leading contributors. Although the intakes of fruits, vegetables, and whole grains moderately improved over time, the intake levels in 2016 (192.1 g/d, 225.6 g/d, and 10.9 g/d, respectively) remained far below the optimal levels. Deaths attributable to the low intakes of nuts and seeds (4.5 g/d), calcium (440.5 mg/d), and milk (37.1 g/d) and the high intakes of red meat (54.7 g/d), processed meat (4.7 g/d), and sugar-sweetened beverages (33.0 g/d) increased since 1998. Compared with older age groups (≥ 45 years), more unfavorable changes in dietary patterns were observed in the younger population aged 25-44 years, including more sharply increased intakes of processed meat. CONCLUSIONS: We observed improvement in the intakes of fruits, vegetables, and whole grains and unfavorable changes in the intakes of processed meat and sugar-sweetened beverages over the past few decades. Our data suggest that to reduce the chronic disease burden in Korea, more effective nutritional policies and interventions are needed to target these dietary risk factors.

• The Journal of Industrial Distribution & Business
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• v.11 no.7
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• pp.29-40
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• 2020

Purpose: In modern society, many urban problems are occurring, such as aging, hollowing out old city centers and polarization within cities. In this study, we intend to apply big data and machine learning methodologies to predict depression symptoms in the elderly population early on, thus contributing to solving the problem of elderly depression. Research design, data and methodology: Machine learning techniques used random forest and analyzed the correlation between CES-D10 and other variables, which are widely used worldwide, to estimate important variables. Dependent variables were set up as two variables that distinguish normal/depression from moderate/severe depression, and a total of 106 independent variables were included, including subjective health conditions, cognitive abilities, and daily life quality surveys, as well as the objective characteristics of the elderly as well as the subjective health, health, employment, household background, income, consumption, assets, subjective expectations, and quality of life surveys. Results: Studies have shown that satisfaction with residential areas and quality of life and cognitive ability scores have important effects in classifying elderly depression, satisfaction with living quality and economic conditions, and number of outpatient care in living areas and clinics have been important variables. In addition, the results of a random forest performance evaluation, the accuracy of classification model that classify whether elderly depression or not was 86.3%, the sensitivity 79.5%, and the specificity 93.3%. And the accuracy of classification model the degree of elderly depression was 86.1%, sensitivity 93.9% and specificity 74.7%. Conclusions: In this study, the important variables of the estimated predictive model were identified using the random forest technique and the study was conducted with a focus on the predictive performance itself. Although there are limitations in research, such as the lack of clear criteria for the classification of depression levels and the failure to reflect variables other than KLoSA data, it is expected that if additional variables are secured in the future and high-performance predictive models are estimated and utilized through various machine learning techniques, it will be able to consider ways to improve the quality of life of senior citizens through early detection of depression and thus help them make public policy decisions.

• Journal of Chest Surgery
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• v.32 no.6
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• pp.549-555
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• 1999

Background: Patients who are considered for major pulmonary resection are generally evaluated by spirometry and clinical assessment to predict morbidity and mortality. Despite this, none has yet proved to be a convenient and reliable estimate of risk. Material and Method: A retrospective analysis was performed in 167 patients who were diagnosed for lung cancer, bronchiectasis, pulmonary tuberculosis, and other benign pulmonary disease, and who underwent major lung resections. The relationship of 25 preoperative or postoperative variables to 19 postoperative events were classified into categories as operative mortality, pulmonary or cardiovascular morbidity, and other morbidity was assessed. Logistic regression analysis and $\chi$2 analysis were used to identify the relationship of the operative risk factors to the grouped postoperative complications. Result: The best single predictor of complications was the percent predicted postoperative diffusing capacity (pulmonary morbidity, p<0.009; cardiovascular morbidity, p<0.003: overall morbidity, p<0.004). Conclusion: The diffusing capacity of the lung for carbon monoxide was an important predictor of postoperative complications than the spirometry, and it usually should be a part of the evaluation of patients being considered for pulmonary resection.

• Journal of Korean Society of Transportation
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• v.31 no.4
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• pp.67-75
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• 2013

The main objective of the research is to analyse the efficiency of bus route operation system which is comprised of Individual Operation System and Joint Operation System. While the reformation of bus route operation system has been usually considered as a service upgrade strategy for inner-city bus operation, it is comparatively difficult to have background information for policy establishment due to shortage of analytical research output. This study adopts Data Envelopment Analysis technique to estimate efficiency of 101 bus routes in Ulsan Metropolitan City which operates two types of operation systems at the same time as of 2009. The analysis results tells that the reason of inefficiency lies on pure technical factor for J.O.G., and scale factor for I.O.G.. It is also confirmed that there is bus route which inefficiently operated in I.O.G and city government needs to prepare the specific plan to upgrade pure technique efficiency before reformation of bus route operation system for successful installation.

• Journal of the Ergonomics Society of Korea
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• v.34 no.3
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• pp.199-215
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• 2015

Objective: The purpose of this study is to find the level of physical relaxation of individual subject by monitoring psychophysiological biofeedback to different types of mattresses. And, the study also aims to find a protocol to make a selection of the best mattress based on the measured information. Background: In Korea, there are an increasing number of people using western style bed. However, they are often fastidious in choosing the right mattress for them. In fact, people use their past experience with their old mattress as well as the spontaneous experience they encounter in a show room to finally decide to buy a bed. Method: Total five mattresses were tested in this study. After measuring the elasticity of the mattresses, they were sorted into five different classes. Physiological and psychological variables including Electromyography (EMG), heart rates (HR), oxygen saturations (SaO2) were used. In addition, the peak body pressure concentration rate was used to find uncomfortably pressured body part. Finally, the personal factors and subjective satisfaction were also examined. A protocol was made to select the best mattress for individual subject. The selection rule for the protocol considered all the variables tested in this study. Results: The result revealing psychological comfort range of 0.68 to 0.95, dermal comfort range of 3.15 to 6.07, back muscle relaxation range of 0.25 to 1.64 and personal habit range of 2.0 to 3.4 was drawn in this study. Also a regression model was developed to predict biofeedback with the minimal use of biofeedback devices. Moreover results from the proposed protocol with the regression equation and subjective satisfaction were compared with each other for validation. Ten out of twenty subjects recorded the same level of relaxation, and eight subjects showed one-level difference while two subjects showed two-levels difference. Conclusion: The psychophysiological variables and suitability selection process used in this study seem to be used for selecting and assessing ergonomic products mechanically or emotionally. Application: This regression model can be applied to the mattress industry to estimate back muscle relaxation using dermal, psychophysiology and personal habit values.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.4
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• pp.93-101
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• 2004

A face boundary can be approximated by an ellipse with five-dimensional parameters. This property allows an ellipse detection algorithm to be adapted to detecting faces. However, the construction of a huge five-dimensional parameter space for a Hough transform is quite unpractical. Accordingly, we Propose a selectively attentional Hough transform method for detecting faces from a symmetric contour in an image. The idea is based on the use of a constant aspect ratio for a face, gradient information, and scan-line-based orientation decomposition, thereby allowing a 5-dimensional problem to be decomposed into a two-dimensional one to compute a center with a specific orientation and an one-dimensional one to estimate a short axis. In addition, a two-point selection constraint using geometric and gradient information is also employed to increase the speed and cope with a cluttered background. After detecting candidate face regions using the proposed Hough transform, a multi-layer perceptron verifier is adopted to reject false positives. The proposed method was found to be relatively fast and promising.

• Journal of Acupuncture Research
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• v.35 no.3
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• pp.129-137
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• 2018

Background: The purpose of this study was to determine the effect of Postural Yinyang correction of the temporomandibular joint (functional cerebrospinal therapy) on temporomandibular disorder. Methods: Medical records of 21 outpatients were reviewed who were diagnosed with temporomandibular joint disorder, unspecified (K0769) and treated at the Department of Acupuncture & Moxibustion, Dong-Eui University Korean Medicine Hospital from May $1^{st}$, 2017 to April $30^{th}$, 2018. Patients received more than 10 treatments of upper cervical manipulation and performed self-exercise therapy more than 3 times a day and wore an accurate balancing appliance in the oral cavity for more than 8 hours per day. To estimate the efficacy, visual analogue scale (VAS), numerical rating scale (NRS), maximum mouth opening (MMO), symptom intensity scale (SIS), max SIS (MSIS), symptom frequency scale (SFS), mandibular function impairment questionnaire (MFIQ) and 5-point Likert scale were used. Results: NRS and MSIS were significantly improved during each period. VAS, MMO, SIS, and SFS were significantly improved during each period, except the period from the $8^{th}$ to $10^{th}$ visit. MFIQ score was significantly improved during the period from the $1^{st}$ to $10^{th}$ visit. In the 5-point Likert scale, the results showed a high patient satisfaction with the treatment. Conclusion: These results showed that functional cerebrospinal therapy using an accurate balancing appliance, may be useful for reducing the symptoms of temporomandibular disorder.

• Journal of Global Scholars of Marketing Science
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• v.13
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• pp.209-229
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• 2004

Although the site evaluation factors that affect the navigation pattern are well documented, the attempt to explore the difference in the relationship between navigation pattern and site evaluation factors by post hoc segmentation approach has been relatively rare. For this purpose, this study constructs the structure equation model using web-evaluation data and log file of a community site with 300,000 members. And then it applies the structure equation model to each segment. Each segment is identified by mixture model. Mixture model is to unmix the sample, to identify the segments, and to estimate the parameters of the density function underlying the observed data within each segment. The study examines the opportunity to increase GFI, using mixture model which supposes heterogeneous groups in the users, not through specification search by modification index from structure equation model. This study finds out that AGFI increases from 0.819 at total sample to 0.927, 0.930, 0.928, 0.929 for each 4 segments in the case of the community site. The results confirm that segment level approach is more effective than model modification when model is robust in terms of theoretical background. Furthermore, we can identify a heterogeneous navigation pattern and site evaluation variation in the community website at segment level.