• Fire Science and Engineering
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• v.17 no.1
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• pp.8-20
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• 2003

Natural gas has been supplied through underground pipelines and valve stations as a new city gas in Seoul. In contrast to its handiness the natural gas has very substantial hazards due to fires and explosions occurring from careless treatments or malfunctions of the transporting system. The main objectives of this study are to identify major hazards and to perform risk assessments after assessing reliabilities of the composing units in dealing with typical pipeline networks. there-fore two method, fault tree analysis ;1nd event tree analysis, are used here. Random valve stations are selected and considered its situation in location. The value of small leakage, large rupture, and no supply of liquefied natural gas is estimated as that of top event. By this calculation the values of small leakage are 3.29 in I)C valve station, 1.41 in DS valve station, those of large rup-lure are $1.90Times10_{-2}$ in DC valve station, $2.32$\times$10^{-2}$ in DS valve station, and those of no supply of LNG to civil gas company are $2.33$\times$10 ^{-2}$ , $2.89$\times$10^{-2}$ in each valve station. And through minimal cut set we can find the parts that is important and should be more important in overall system. In DC valve station one line must be added between basic event 26,27 because the potential hazard of these parts is the highest value. If it is added the failure rate of no supply of LNG is reduced to one fourth. In DS valve station the failure rate of basic event 4 is 92eye of no supply of LNG. Therefore if the portion of this part is reduced (one line added) the total failure rate can be decreased to one tenth. This analytical study on the risk assessment is very useful to prepare emergency actions or procedures in case of gas accidents around underground pipeline networks and to establish a resolute gas safety management system for loss prevention in Seoul metropolitan area.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.8 no.3
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• pp.85-96
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• 2013

Entrepreneurship courses and programs in Korean universities tend to increase steadily but seem to have some limitations. They are usually recognized as another domain of Business Administration. Entrepreneurship program is practical like Business Administration but should be much more interdisciplinary than that. Because Korean Entrepreneurship programs are in the early stage, they must be reinforced with factors such as education culture, faculties, curriculum and relationships with communities. This study aims to get some implications from Entrepreneurship programs have been run by universities in America in order to facilitate Entrepreneurship program in Korean universities. Based on 11 success factors found by our case universities' Entrepreneurship programs and Kauffman Campus, this study has drawn implications of critical success factors of Entrepreneurship programs as follow. First of all, because Entrepreneurship programs should focus on Entrepreneurship mind sets such as innovative idea generation and courage to overcome risk, it is more desirable that Entrepreneurship programs are introduced in all departments of universities such as Arts, Science and Engineering. These programs also need to take interdisciplinary approach and required to be opened from liberal arts course. In order to be sustained during all their academic careers, vision, mission and strategy for Entrepreneurship programs should be based on strong leadership and support of top leaders. Entrepreneurship culture of each university is also one of the most important success factors. Entrepreneurship programs not only as major programs but also as specific Entrepreneurship minors designed for departments such as Arts, Science and Engineering could be considered according to each university's situation. This study also suggest to make a motivation system for Entrepreneurship faculties, Ph d. programs for Entrepreneurship, communication network for Entrepreneurship programs and mentor system in community. To begin Entrepreneurship programs, it also needs to develop good education contents as many as possible. When it concerned with teaching method, project based 4 year program can be suggested to be effective and efficient. To introduce project based program that should be consistent till participants' graduation, university must prepare regulations to support team teaching, mentor and interdisciplinary cooperation. To dissipate the concept that Entrepreneurship is another version of management, this study support the idea that Entrepreneurship programs should be designed and run by independent and central-focused governance system, Entrepreneurship education center.

• Journal of Korean Tunnelling and Underground Space Association
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• v.15 no.5
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• pp.505-522
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• 2013

Previously, a new concept of indexing methodology has been proposed for quantitative assessment of tunnel collapse hazard level at each tunnel face with respect to the given geological data, design condition and the corresponding construction activity (Shin et al, 2009a). In this paper, 'linear' model, in which weights of influence factors are invariable, and 'non-linear' model, in which weights of influence factors are variable, are taken into account with some examples. Then, the 'non-linear' model is validated by using 100 tunnel collapse cases. It appears that 'non-linear' model allows us to have adapted weight values of influence factors to characteristics of given tunnel site. In order to make a better understanding and help for an effective use of the system, a series of operating processes of the system are built up. Then, by following the processes, the system is applied to a real-life tunnel project in very weak and varying ground conditions. Through this approach, it would be quite apparent that the tunnel collapse hazard indices are determined by well interlinked consideration of face mapping data as well as design/construction data. The calculated indices seem to be in good agreement with available electric resistivity distribution and design/construction status. In addition, This approach could enhance effective usage of face mapping data and lead timely and well corresponding field reactions to situation of weak tunnel faces.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Korean Journal of Environmental Agriculture
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• v.30 no.3
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• pp.252-260
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• 2011

BACKGROUND: Currently, national biomass inventory are being established for efficient management of the potential energy sources. Among the various types of biomass, agricultural wastes are considered to take the biggest portion of the total annual biomass generated in Korea, implying its importance. However, the currently estimated amount is not reliable because the old reference data are still used to estimate total annual amount of agricultural wastes. METHODS AND RESULTS: Therefore, to provide reliable estimation data, a correct conversion factor obtained by taking into account the current situation is required. For this, the current study was conducted to provide the conversion factors for each representative 8 crop through a field cultivation study. Also conversion factors for 18 crops were calculated using the average amount of each crop produced during 2004 and 2008, subsequently; total amount of agricultural wastes generated in 2009 was estimated using these conversion factors. The total biomass of rice straw and rice husk generated in 2009 were 6.5 and 1.1 million tons, respectively, which consist 75% of the total agricultural based wastes, while the total biomass of pepper shoots and apple pruning twigs were 1.0 and 0.6 million tons, respectively. Despite the high amount of rice-based biomass, their applicability for bio-energy production is low due to conventional utilization of these materials for animal feeds and beds for animal husbandry. In addition to exact estimation of the total biomass, temporal variations in both generated amount and the type of agricultural biomass materials are also important for efficient utilization; fruit pruning twigs (January to March); barley-, been-, and mustard-related waste materials (April to June); rice-related waste (September to October). CONCLUSION(s): Such information provided in this study can be used to establish a master plan for efficient utilization of the agricultural wastes on purpose of bio-energy production.

• 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.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of Navigation and Port Research
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• v.39 no.6
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• pp.439-450
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• 2015

This study objects to find characteristics in chemical tanker markets and to determine optimal chemical tanker size using a total shipping cost in main trading route of asia chemical tankers .Precedent studies of determination of the optimal ship size and case studies about chemical tankers was carried out and tried to introduce a cost model which is applicable to chemical tanker. This study is dependant on numerical analysis and involves scenario analysis to minimize sensitivity of results. This analysis shows as follows. First, 12,000DWT tanker is an optimal size on the 'Far East-Middle East' services, 9,000DWT tanker is a most competitive on the 'Far East-South East Asia' services and 3,000DWT tanker is a most economic size on the 'Inner Far East' services at average market situation. Second, the bigger size of chemical tanker, the more competitive advantage the tanker will obtain when bunker fuel prices rise. Small size ship gets more competitive during bunker prices down. Third, market fluctuation of time charter rate for chemical tanker is less than 20% against its average time charter hire which means less volatile. And tanker's competitiveness per each size is remained mostly same when time charterer rates rise at same proportion. Fourth, bigger size chemical tankers have cost advantages when tanker's quantity of each part cargo increase. And small-sized tanks are more competitive when part cargo scales decrease. For the last, ship's port stay strongly influences on the determination of the optical tanker size. When vessel has shorter port stay, bigger-sized tanker will be more competitive and even can be competitive if applies in short voyage as well.

• Journal of Distribution Science
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• v.7 no.4
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• pp.15-26
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• 2009

The internet has dramatically changed a way of business management and competition in the business environment. Especially, it stimulated not only to evolve online floral distribution service but also to change a phase of competition among floral retail stores in industry. And that also led to keen competition among IT service providers as well. This study is to examine how floral retail stores have been evolved and competed with the radical situation of the floral distribution industry through IT service in the aspect of business and information technology. In addition, the Typhoon Positioning Strategy(TPS), a strategy for the IT service positioning, is introduced from IT service provider's perspective. For IT service providers to create high business value and continuous service providing, IT service should be positioned on the customers' "core business" and developed to the level of "solution." The Typhoon Positioning Strategy(TPS) is a strategy for the IT service positioning, indicating that IT service should be positioned according to a Business Process-Service model with the consideration of business development direction, IT service trend, and user's IT capability. That is, IT service providers should find out customers' "core business" area first to provide a right IT service to the company, and the IT service provided should meet to the level of business solution. The capability of the IT solution users is also an important factor to be considered for the advanced IT service. There are four principles of the Typhoon Positioning Strategy(TPS). Principle 1) IT service provided should be an IT solution Map suitable for customer business processes. Principle 2) IT service provided should be able to support customer core business. Principle 3) IT service provided should be a business solution. Principle. 4) IT service provided should be applied differently according to the level of customer's IT capability.

• Journal of Korea Port Economic Association
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• v.34 no.4
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• pp.191-208
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• 2018

This paper focuses on the integration aspect of operators to determine an improvement strategy for the operating system to enhance competitiveness of Busan Port. This Study proposes the following alternatives: valuation standards for the integration of operators, the road map for the integration period, the scope and role setting of integrated operators' participation of Busan Port Authority(BPA), and the separation and linkage North Port and the New Port operators. First, the valuation standards for operator integration should be based on international standards. Additionally quantitative factors such as financial situation, business performance and participating companies' profitability, and the qualitative factors such as management ability, technology, and labor relations should be considered. Second, the timing of North Port's operator integration should be prioritized in the short term in conjunction with the commencement of its phase 2-4, 2-5, and 2-6. The integration of New Port operators should provide a road map for a relatively long-term perspective. Third, the participation of BPA' integrated operators should be considered in terms of publicity as a policy coordinator between terminals and by pursuing the profitability of entering into overseas business by fostering Korean global terminal operators. The scope and role of participation ensures that the experience and technology of the terminal operation business is maximized. Fourth, because physically intergrating the North Port' operator into a single corporate form is difficult, initially establishing a special purpose company to maximize the effect of the integrated operation is necessary. Then, the operators decided to convert to a holding company given the termination of the lease term contract with the State or BPA, and ultimately proposed a merger into a single corporation.