• Journal of Korean Society for Atmospheric Environment
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• v.8 no.1
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• pp.29-37
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• 1992

The risk of benzo(a)pyrene for cancer in the ambient air of Seoul was assessed by using the extrapolation methods. The average daily lifetime exposure of benzo(a)pyrene in the ambient air of Seoul was calculated at 6.97-24.30ng/$m^2$/day, which was based on the occurrence analysis of benzo(a)pyrene in the residential(Bull Kwang Dong) and traffic areas(Shin Chon) of Seoul. Using the dose scaling based on body surface area in comparisons of toxicity for extrapolation from animal to human and mathematical models from the high dose region, the low-dose risk was estimated. The response probabilities were estimated by the tolerance distribution models; Probit, Logit and Weibull model. They were consistent with the observed ones at experimental dose region. The unit risk estimates of these models were too low to be used. One-hit and multistage model to prove more conservative risk was selected. As a redult, the lifetime unit risk of benzo(a)pyrene for cancer and virtually safe dose were calculated; One-hit model provided the risk 2.8 $\times 10^{-7}$ and 3.4ng/$m^3$, respectively and multistage model provided 5.2 $\times 10^{-7}$ and 1.9ng/$m^3$ as the more conservatives. The lifetime excess risk estimates of benzo(a)pyrene for cancer were calculated at 0.37-1.30 persons/million persons by one-hit model and 0.69-2.41 persons/million persons by multistage model, which was considered in without virtual risk.

• Journal of the Korean Society of Clothing and Textiles
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• v.26 no.5
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• pp.606-617
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• 2002

The purpose of this study is to develop a decision model that helps manufacturers and retailers determine the optimal timing of markdown in order to maximize their profit. An optimal timing decision model was developed based on three steps; conjoint measurement, scenario analysis and simulation. Data were collected from the sample of 149 out of 170 undergraduate and graduate students in Seoul in 1997. From the Jeans market, 8 brands; Levi's, lee, Guess, Calvin Klein, Pintos, Get used, MFG, and Basic, were selected as competitors for this study. In the conjoint measurement, respondents estimated the level of preference, from 1 to 100, for each item in which brand, price, style, and colors were used to explain product characteristics. Then, in order to reflect competitive situation in Jeans market, four types of scenarios were developed. In each scenario, simulations were applied to decide optimal timing of markdowns that leads to maximal profitability and sales volume. The profit was calculated based on the equation; Profit = Jean's market volume x market share of each brand - cost, where market volume was obtained by integral calculus for market utility function, and market share by logit value of part-worth from the conjoint analysis. For the purpose of the parsimony of the research, costs and the level of markdown were fixed to 30% of the regular price. In results, the optimal timing decision model identified 3 different types of brands. The brands that do not need to take markdown were Ievi's, MFG, and Basic Jeans characterized by the highest brand power and the highest price zone. The brands that needed to take early markdowns were Guess, Lee, Calvin Klein, and Get Used with the intermediate level of brand power and price. The brand that need late markdown was Pintos with the weakest brand power among the competitors and the lowest price. The optimal range of markdown remains for further research.

• Journal of Korea Trade
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• v.25 no.2
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• pp.111-133
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• 2021

Purpose - Although remittance payment in international trade settlements has played a bigger role in recent years, scant research is being done. This study is to zero in on analyzing determinants of international trade payments focused on remittance by constructing a payment prediction model. Design/methodology - This study categorizes the types of trade payments into advance remittance, post remittance, linked remittance, letter of credit, and mixed payment, and analyzes these after constructing a logit model. For empirical analysis, 147 survey data were collected for export manufacturers in Korea, and binominal logistic regression analysis was used to analyze the type of payment method the exporter chooses for trade transactions. Findings - The likelihood of choosing advance remittance increased as the exporters had non-recovery experiences with payments, and decreased as the market power of importers increased. The possibility of post remittance increased when the export amount was large and the character of the buyer was reliable. In the case of linked remittance, it was highly likely to be selected when payment efficiency was important in trade settlement. In addition, when competition among companies in the global market is intense and market uncertainty is high, the possibility of using a letter of credit decreases. It was also found that the greater the export amount, the greater the possibility of choosing advance remittance, and even if the transaction period was longer, exporters using a letter of credit continued to use it. Originality/value - Despite the high proportion of remittances in international trade settlements, it has been hard to find studies that reflect the practical characteristics of remittances. This study classified the types of remittance into advance remittance, post remittance, and linked remittance, and built a trade payment prediction model by adding a letter of credit and mixed payment. In addition, the originality of this study is recognized in that a logistic model was constructed and meaningful results were derived.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.157-173
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• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.6
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• pp.59-76
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• 2019

Most technology-based entrepreneurship(TBE) go through an process of decline or disappear without overcoming the valley of death(VoD). The purpose of this study is to identify the growth dimension of TBE and to test the influence of firms activities on firms growth over time. This study identified the two-dimensional growth dimension divided by size and profit through exploratory factor analysis(EFA) of a number of growth indicators. Then, we defined the discrete state of growth firm in four states, divided by size and profit, and five states, including the closure of business. Multi-nomial logit model is used to predict the effect of TBE activities on a discrete state of growth firm(size×profit, closure of business) based on multiple independent variables. The independent variables are based on five representative firms activities: employment, marketing, R&D, financial activities, and general management activities. The growth stage of TBE over time has been categorized into three stages: early stage, middle stage, and late stage of business, taking into account the main periods during which the survival rate of startups sharply decreases. The analytical data of this study was based on the secondary data of the start-up supporting companies of government and public institutions. The subjects of analysis were TBE within 10 years. As a result of the empirical analysis, the employment and marketing activities of TBE show that early and mid-term activities had an effect on the state of firms growth. However, if there is a difference, employment activities have both positive and negative effects, while marketing activities have only a positive effect on size and profit growth. And besides, R&D activities, financial activities, and general management activities throughout the entire process of firms growth were found to be firms activities that have both positive and negative effects on firms growth. In addition, the age of the founder, the firms' industry, and the geographic location of the firms, which are general characteristics of the company, were found to have a distinctive effect on the growth status of the firms according to the growth stage.

• Korean Journal of Social Welfare
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• v.64 no.3
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• pp.51-77
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• 2012

This study examines the scale of occurrence of Catastrophic Health Expenditure, and identifies the factors influencing Catastrophic Health Expenditure among disabled elderly households. Catastrophic Health Expenditure is defined by when the households' health care spending out of ability to pay exceeds 10%, 20%, 30%, and 40%. This study used the 2008, 2009, and 2010 surveys of the Panel Survey of Employment for the Disabled(PSED) to explore how gender, age, spouse, the level of education, the degree of disability, the type of disability, disability duration, subjective health status, chronic disease, the number of household members, the proportion of disabled households, the proportion of working households, the proportion of aged households, the type of poverty, household income, net asset, determine Catastrophic Health Expenditure among disabled elderly households. The study examines the frequency of Catastrophic Health Expenditure with 726 households, and conducted the panel logit model. The empirical results show that Catastrophic Health Expenditures are significantly related to age, spouse, the type of disability, subjective health status, chronic disease, the number of households, the proportion of disabled households, the proportion of aged households, the type of poverty. This study showed that the health care safety net in South Korea was insufficient for disabled elderly households and that a policy should be established in ordered to protect disabled elderly households from occurrence of Catastrophic Health Expenditure.

• Journal of Agricultural Extension & Community Development
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• v.20 no.4
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• pp.1105-1147
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• 2013

To investigate farmers' satisfaction level and factors influencing on various agricultural and rural policies, an interview survey has been carried out in Jeonnam and Jeonbuk agricultural area, and collected 750 survey questionnaires from farmers. Satisfaction level was low in average ranging from 2.71 to 3.09 in five point Likert-scale on 22 agricultural-rural related policies. Ordered logit model results showed that satisfaction level decreased when farmers are older, had higher income, and had higher number of attendance in agricultural education programme. In addition, satisfaction level decreased when farms had main source of income from rice farming, dry-field farming, livestock farming, or facility horticulture. Lower satisfaction level was also related to location of farm. On the contrary, satisfaction level increased when the farmer had greater owned land. Among 22 agricultural and rural policies, practices that farmers prefer include Direct Payment for Rice Farming Income Compensation, Environment-Friendly Farming Service, Farmland Banking Services in order. Since direct payment policies that farmers prefer may not contribute in development of agriculture, policies can induce both farm income and agricultural development may increase farmer satisfaction level and finally resolve the income gap between the urban workers and farmers.

• Journal of the Korean association of regional geographers
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• v.22 no.3
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• pp.553-565
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• 2016

Population aging has been an increasing social issue and the elderly health has become one of the most urgent public attentions in Korea. The aims of this paper are to compare the subjective health status according to the personal characteristics, social networks, and daily leisure activities of the urban-rural elderly, and to analyze the influencing factors of their subjective health status. Using 2011 elderly survey data, ordered logit Model was established to extract influencing factors of the elderly health status. The results show that socioeconomic and demographic characteristics of individual as well as frequent social contacts and daily activities within neighborhood environments influence the level of health status of the elderly. The most significant factors affecting the elderly health are personal economic conditions such as the education achievement level and household income. The elderly who visit an elderly welfare center in Seoul has almost 1.82 times higher odds of increasing health status level than not to visit an elderly welfare center. This study may give some important policy implications of the elderly health promotion strategy in urban-rural communities.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.6D
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• pp.549-556
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• 2012

Longitudinal tunnels are defined as tunnels with length of over 1km. Because of Korea's topographical conditions and as safety measures for linear design, many tunnels are inevitably being constructed in Korea. The number of longitudinal tunnels constructed on expressways amounted to 104 as of the end of 2010 with a total length of 192km. Given the increasing demand for tunnels and the increasing length of tunnels, a safety evaluation of longitudinal tunnels needs to be conducted. As such, this study selected design elements, transportation environment and delineation system as elements to check and tried to determine factors influencing road crashes. For this, tunnels have been classified based on history of crashes; ones with crashes and ones without crashes and statistically meaningful explanatory variables were selected. By using these variables, a logit model was development in order to better grasp the factors that directly and strongly influence crashes. The result, related to crashes as well as the analysis were utility tunnel interior materials of driving lane and passing lane, which are related to driver's visibility, lateral width widening to consolidate space in a tunnel, and annual average daily traffic (AADT) per lane. These results may be used in the future as analysis indicators when drawing up plans to prevent crashes in longitudinal tunnels.

• The Journal of the Korea Contents Association
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• v.18 no.4
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• pp.444-453
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• 2018

The study addresses the analysis on the financial determinants of corporate research and development (R&D) expenditure in finance. Overall level of R&D spending was estimated as one of the top-tier on a global basis and a majority of the expenditure was invested by large domestic firms in private sector. Consequently, financial factors that influence R&D intensity were empirically tested in the first hypothesis by using conditional quantile regression model for firms listed in KOSPI stock market in the post-era of the global financial turmoil. Firms in the groups of high- and low-R&D intensity were statistically compared to detect financial differences in the second hypothesis which was accompanied by the test of multi-logit model that included firms without R&D outlay. Concerning the results of the hypothesis tests, R&D spending of the prior fiscal year, firm size, business risk and advertising expense overall showed statistically significant impacts to determine the level. As an extended study of [1] that had examined financial factors of R&D intensity at the macro-level, the results of the present study are anticipated to contribute to maximizing shareholders' wealth in advance or emerging capital markets, when applied to find an optimal level of R&D expenditure.