• International Area Studies Review
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• v.20 no.1
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• pp.23-51
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• 2016

This study analyzed the expected economic effects of the Korea-GCC FTA and sought strategies for industrial cooperation. To see the economic effects of Korea-GCC FTA, we analysed the effect of the oil tariff reduction of economy by Vector Autoregression(VAR) model. The estimation results shows that following the abolishment of the tariff on crude oil imports, GDP, GNI and consumption are expected to grow by 0.212%, 0.389% and 0.238%, respectively. Meanwhile, investment, export and import are estimated to drop by 0.462%, 0.413% and 0.342%, respectively. As for prices, producer prices are to rise by 6.356%p, whereas consumer prices fall by 2.996%p. In short, the Korea-GCC FTA and resultant abolishment of the tariff on crude oil imports followed by the decline in crude oil prices will result in declining prices whilst macroeconomic indices, such as GDP, GNI and consumption, will increase exerting positive effects on domestic economic growth. Also, it is necessary to proactively respond to GCC member states' industrial diversification policies for FTA-based industrial cooperation to diversify the sources of crude oil and natural gas imports for further resource risk management.

• Journal of the Korean Society for information Management
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• v.39 no.3
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• pp.165-193
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• 2022

It can be said that digital technology is at the center of the change in the modern work environment. In particular, in general public institutions that prove their work with records produced by business management systems and document production systems, the record management system is also the work environment itself. Gimpo City applied for the 2021 public cloud leading project of the National Information Society Agency (NIA) to proactively respond to the 4th industrial revolution technology era and implemented a public cloud-based AI-OCR technology enhancement project with 330 million won in support of 330 million won. Through this, it was converted into data beyond the limitations of non-electronic records limited to search and image viewing that depend on standardized index values. In addition, a 98% recognition rate was realized by applying a new technology called AI-OCR. Since digital technology has been used to improve work efficiency, productivity, development cost, and record management service levels of internal and external users, we would like to share the direction of enhancing expertise in the record management and implementation of work environment innovation.

• Journal of the Korean Institute of Rural Architecture
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• v.25 no.4
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• pp.9-16
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• 2023

In this study, the status and characteristics of fine dust and its impact on neighboring areas were investigated to proactively respond to the government's environmental regulations expected in the future and to minimize the damage by the fine dust generated at construction waste intermediate treatment plants. In addition, since there are no such plants that can affect the surroundings with no houses or other waste treatment sites nearby, an independently located construction waste intermediate treatment plant was selected to compare the characteristics of fine dust with that from the construction waste intermediate treatment sites located in the downtown area. The conclusions of the study are as follows. (1) The measurement results of PM10 at 4 points in the plant showed that the location where the crushing facility was operating had an elevated level of fine dust at 80㎍/m³ on average and a maximum of 124㎍/m³, and the level rose to 110㎍/m³ at points where vehicles frequent. (2) The PM2.5 measurement results inside the plant showed that the average concentration of the reference point was 16㎍/m³ and the maximum value was 20㎍/m³, which was distributed within the management standard. (3) It was found that the average concentration of PM10 in the nearby area ranged from 28 to 38㎍/m³, which was similar to or lower than 36㎍/m³ of the reference point. Therefore, the concentration of the fine dust generated in the plant had a negligible effect on the increase in concentration of fine dust in nearby areas. (4) The heavy metal contents were measured from the filter paper collected from the plant. The PM10 was found to be about 14 to 26ng/m³, and PM 2.5 was 25 to 28ng/m³, which was the average of domestic atmospheric concentrations. (5) The SEM-EDX analysis results showed that the PM10 contained Si and O around 40% similarly for both. The SiO2, a component of silica occupied the most and C was present as CaCO3, which was assumed to be a limestone component. The remaining components included NaO, Al2O3, and CaO as trace oxides. (6) The SEM-EDX analysis results showed that the PM 2.5 contained 5 to 7% of Cl, which is a chlorine ion, and a small amount of K was detected at 2.51% in the sample from the shutdown plant.

• Journal of Korean Home Economics Education Association
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• v.35 no.4
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• pp.13-31
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• 2023

The purpose of this study was to develop a career education program linked to home economics in middle school to improve adolescents' ability to respond to the rapidly changing future society. The research procedure was conducted in four steps: Analysis, Design, Development, and Evaluation. In the analysis step, related prior studies were analyzed to identify the units and contents that linked home economics and career education. In the design step, learning topics and contents according to the design thinking process were selected and the overall program process was designed to cultivate entrepreneurship based on the textbook analysis results. In the development step, the goals and achievement standards of school career education linked to home economics were set for each class, and a total of eight teaching and learning plans, twenty-three types of teaching and learning materials, and expert validity verification questionnaires were developed. In the evaluation step, the validity of the developed program was verified by nine experts. The developed program was verified for overall programs, and the validity of the program was 0.94. It is expected that the career education program linked to home economics will contribute to foster the adolescents' entrepreneurship so they can design their future on their own and allow them to manage their life proactively.

• Journal of Food Hygiene and Safety
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• v.32 no.3
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• pp.179-186
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• 2017

For more efficient and proactive safety control of imported food, new trend in U.S. is emerging, which assesses the food safety control systems of exporting countries using Systems Recognition Assessment Tool and helps ensure safety of imported foods. This study examines trends in development and application of assessmemnt tool and country assessment reports in U.S. where an active discussion on this issue is in progress. The expert interviews were also conducted. U.S. Systems Recognition Assessment Tool was developed by FDA to recognize the potential value in leveraging the expertise of foreign food safety systems and help ensure safety of imported food. The tool is comprised of ten standards and provides an objective framework for determining the robustness of trading partners' overall food safety systems. Using its own tool, the U.S. FDA conducted a preliminary assessment of the food safety control systems of New Zealand and Canada. According to the U.S.-New Zealand and the U.S.-Canada assessment reports, the overall structure of the systems was similar between the countries. In summarizing the opinions of experts, such a trend in National Food Safety Control System Assessment may be utilized in the sanitary assessment and the control of imported food border inspection frequency before importing food. It would contribute to more effective distribution of national budget and increased public trust. Additionally, international collaboration as well as securing of qualified experts and sufficient budget appear to be crucial to further increase the utility of National Food Safety Control Systems Assessment. In conclusion, firstly, it is critically important for the competent authority of South Korea to proactively respond to international trend in National Food Safety Control System Assessment by identifying the details of its background, assessment purpose, core assessment elements, and assessment procedures. Secondly, it is necessary to identify and complement the weaknesses of Korea's food safety control system by reviewing it with U.S. Systems Recognition Assessment Tool. Thirdly, by adapting the assessment results from imported countries' food safety control systems to the imported food inspection intensity, the resources previously used in inspecting the imported food from accredited countries can be redistributed to inspecting the imported food from unaccredited countries, and it would contribute to more efficient imported food safety control. Fourthly, the competent authority of South Korea should also consider developing its own assessment tool designed to reflect the unique characteristics of its food safety control system and international guidelines.

• 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.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.125-141
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• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. 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, total misclassification cost is more affected by FNE rather than FPE. 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 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.5
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• pp.93-104
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• 2019

The entrepreneurship can be a source of national growth potential as behavioral tendencies of people who seek innovation, take risks of failure, and proactively respond to opportunities. In particular, in the economic situation of Korea where growth has been stagnated, it is necessary to strengthen the entrepreneurship of women which is relatively lower than men's in order to activate the start-up and economic participation of the whole people. In this regard, this study focuses not only on gender differences in entrepreneurship but also on the hidden impact of social contexts that cause gender differences in entrepreneurship. Specifically, this study examined the moderating effects of childbirth, a factor that reflects the social context of Korea in the relationship between gender and entrepreneurship. According to the results of the model that includes the interaction effect of these variables in addition to the independent effects of gender and childbirth, the gender effect disappeared, while the significant effect of both the childbirth variable and the interaction variable of gender and childbirth were confirmed. Furthermore, according to additional analysis, which identified the differences in entrepreneurship by creating four types of treatment groups based on gender and childbirth status, entrepreneurship was significantly lower in the 'female and childbirth' group than in all other groups. The difference between the remaining treatment groups was not statistically significant. These results indicate that differences in entrepreneurship levels between men and women overlap not with the unique trait of men and women, but with the social contextual effects of Korea, where women are under the full burden of childbirth and parenting. This study suggests implications that effective policy measures to promote women's entrepreneurship or economic activity should be taken by taking into account the social context of Korea that suppresses women's entrepreneurial behavior.