• Journal of the Korean Society of Marine Environment & Safety
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• v.19 no.5
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• pp.467-475
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• 2013

In order to seize quantitative materials as part of studies on measures for oil pollution prevention and control, the statistics of oil pollution incidents in Korean coastal waters for 10 years from 2003 to 2012 were analyzed with relation to the number of oil spills and the volume of oil spilt according to causes, sources and sea areas of spills. Total number and total volume of oil spills for 10 years were found to be 2,833 cases and 17,877 kL, respectively. 50.4 %(1,429 cases) of total number of oil spills were caused by negligence, although oil spillage due to negligence was 294 kL(1.7 %). While oil spillage caused by marine accidents was 17,400 kL(97.3 %), marine accidents accounted for 27.9 %(790 cases) of total number of oil spills. While negligence had a great influence on the number of oil spills, marine accidents had a huge impact on the amount of oil spilt. Fishing boats accounted for 42.7 %(1,210 cases) of the number of oil spills, and although oil tankers accounted for 9.2 %(261 cases) of the number of oil spills, oil spillage from oil tankers was 15,488kL(86.7 %). It means that oil tankers such as VLCC or ULCC may be the main sources of major oil spills and a few very large spills are responsible for a high percentage of the amount of oil spilt. While the number of oil spill incidents was closely related to the accidents of fishing boats, the volume of oil spilt was greatly affected by the major oil spill incidents of oil tankers such as M/T Hebei Spirit. The number and volume of oil spills were shown to be 1,613 cases(56.9 %) and 3,804 kL(21.3 %) in South Sea, 700 cases(24.7 %) and 13,501 kL(75.5 %) in West Sea, and 520 cases(18.2 %) and 572 kL(3.2 %) in East Sea of Korea, respectively. The highest number of oil spills was found in South Sea and the most volume of oil spilt was shown in West Sea of Korea for 10 years.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• International Journal of Highway Engineering
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• v.9 no.4
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• pp.33-43
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• 2007

Because usually signal controllers on the crosswalks of mid-block provide pedestrian signals every cycle based on the fixed signal plan, pedestrian signals are provided even when there is no pedestrian demand. Consequently, signal is operated inefficiently and this may cause drivels to experience useless delay or violate the signal. Even though recently pushbuttons have been installed to improve the efficiency of pedestrian signal control in the crosswalks of mid-block and the pedestrian safety. they are not spread out national-wide in Korea because of the cost of the pushbutton equipments and the lack of an acknowledgement of the efficiency of the pushbutton. In this study, the effectiveness of the pushbutton on saving the vehicle delay was verified through before and after study in 4 study sites using a traffic micro-simulation model, VISSIM. To evaluate the viability of the pushbutton, a benefit/cost analysis was also performed for 4 study sites. It was found that B/C ratio of all of 4 study sites was greater than 1. The sensitivity analysis for the traffic volume and pedestrian volume were performed to identify the impact of the both volume on the operation of pushbutton. And, a benefit/cost analysis was performed for all scenarios. It was found that when the pedestrian volume is greater than 90ped/h, the pedestrian signal was operated same as the fixed signal plan. That is, there is no benefit of pushbutton at all once the pedestrian volume is greater than 90ped/h. When the pedestrian volume is equal to or less than 90ped/h and the traffic volume is greater than 2,500veh/h, B/C ratio is greater than 1. Also it was found that as traffic volume increases and pedestrian volume decreases, the benefit increases. In this study, the criteria for installation of pushbutton on the crosswalks of mid-block are developed through the sensitivity analysis and benefit/cost analysis. The results of this study may be used as a criteria for expansion of pushbutton system.

• Journal of the Korean Society of Radiology
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• v.8 no.4
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• pp.211-216
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• 2014

Radium is rock or soil of crust or uranium of building materials after radioactivity collapse process are created colorless and odorless inert gas that accrue well in sealed space like basement. It inflow to lung circulate respiratory organ and caused lung cancer because of deposition of lung or bronchial tubes. In this study, the air in the elementary school classroom nongdoeul tonkatsu place of measured values were compared using the calculated annual internal radiation exposure. La tonkatsu exposure measured in primary school classroom at least five schools when you close the windows in the average floor 0.56mSv 2 floors ground floor windows when opened 0.384mSv 048mSv 3 floors, 2 floor levels of the same three layers 0.31mSv 0.296mSv the human exposure to radon and radiation on the first floor of 3 floors above ground in a lot of exposure was moderate. When you close the window from the first floor up 0.384mSv 056mSv 3 floors with a minimum annual radiation exposure due to natural radiation in the 16 to 23.3 percent minimum 2.4mSv accounted for. When I opened the window to the maximum annual radiation exposure 2.4mSv 0.296mSv 0.31mSv least a minimum of 12.3 to 12.91% accounted for Results suggest that more than five chodeunghakgyoeun La tonkatsu domestic radon measurements conducted below regulatory requirements and internal exposure has also fall within the normal range. People The less the radiation exposure to the human body because it reduces the impact in the classroom in elementary school vent windows often reduced to the maximum radon concentration in the air, if called tonkatsu be able to reduce radiation exposure for the immune system is weak and elementary will be helpful to experiment more in the future for the school authorities called tonkatsu investigation is done to him if the action to establish a more secure school building facilities is thought would be helpful.

• Asia pacific journal of information systems
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• v.20 no.1
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• pp.127-140
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• 2010

The benefit that a consumer derives from the use of a good often depends on the number of other consumers purchasing the same goods or other compatible items. This property, which is known as network externality, is significant in many IT related industries. Over the past few decades, network externalities have been recognized in the context of physical networks such as the telephone and railroad industries. Today, as many products are provided as a form of system that consists of compatible components, the appreciation of network externality is becoming increasingly important. Network externalities have been extensively studied among economists who have been seeking to explain new phenomena resulting from rapid advancements in ICT (Information and Communication Technology). As a result of these efforts, a new body of theories for 'New Economy' has been proposed. The theoretical bottom-line argument of such theories is that technologies subject to network effects exhibit multiple equilibriums and will finally lock into a monopoly with one standard cornering the entire market. They emphasize that such "tippiness" is a typical characteristic in such networked markets, describing that multiple incompatible technologies rarely coexist and that the switch to a single, leading standard occurs suddenly. Moreover, it is argued that this standardization process is path dependent, and the ultimate outcome is unpredictable. With incomplete information about other actors' preferences, there can be excess inertia, as consumers only moderately favor the change, and hence are themselves insufficiently motivated to start the bandwagon rolling, but would get on it once it did start to roll. This startup problem can prevent the adoption of any standard at all, even if it is preferred by everyone. Conversely, excess momentum is another possible outcome, for example, if a sponsoring firm uses low prices during early periods of diffusion. The aim of this paper is to analyze the dynamics of the adoption process in markets exhibiting network effects by focusing on two factors; switching and agent heterogeneity. Switching is an important factor that should be considered in analyzing the adoption process. An agent's switching invokes switching by other adopters, which brings about a positive feedback process that can significantly complicate the adoption process. Agent heterogeneity also plays a important role in shaping the early development of the adoption process, which has a significant impact on the later development of the process. The effects of these two factors are analyzed by developing an agent-based simulation model. ABM is a computer-based simulation methodology that can offer many advantages over traditional analytical approaches. The model is designed such that agents have diverse preferences regarding technology and are allowed to switch their previous choice. The simulation results showed that the adoption processes in a market exhibiting networks effects are significantly affected by the distribution of agents and the occurrence of switching. In particular, it is found that both weak heterogeneity and strong network effects cause agents to start to switch early and this plays a role of expediting the emergence of 'lock-in.' When network effects are strong, agents are easily affected by changes in early market shares. This causes agents to switch earlier and in turn speeds up the market's tipping. The same effect is found in the case of highly homogeneous agents. When agents are highly homogeneous, the market starts to tip toward one technology rapidly, and its choice is not always consistent with the populations' initial inclination. Increased volatility and faster lock-in increase the possibility that the market will reach an unexpected outcome. The primary contribution of this study is the elucidation of the role of parameters characterizing the market in the development of the lock-in process, and identification of conditions where such unexpected outcomes happen.

• Asia pacific journal of information systems
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• v.23 no.2
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• pp.21-39
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• 2013

As the utilization of information technology and the turbulence of technological change increase in organizations, the adoption of IT outsourcing also grows to manage IT resource more effectively and efficiently. In this new way of IT management technique, service level management(SLM) process becomes critical to derive success from the outsourcing in the view of end users in organization. Even though much of the research on service level management or agreement have been done during last decades, the performance of the service level management process have not been evaluated in terms of final objectives of the management efforts or success from the view of end-users. This study explores the relationship between SLM maturity and IT outsourcing success from the users' point of view by a analytical case study in four client organizations under an IT outsourcing vendor, which is a member company of a major Korean conglomerate. For setting up a model for the analysis, previous researches on service level management process maturity and information systems success are reviewed. In particular, information systems success from users' point of view are reviewed based the DeLone and McLean's study, which is argued and accepted as a comprehensively tested model of information systems success currently. The model proposed in this study argues that SLM process maturity influences information systems success, which is evaluated in terms of information quality, systems quality, service quality, and net effect proposed by DeLone and McLean. SLM process maturity can be measured in planning process, implementation process and operation and evaluation process. Instruments for measuring the factors in the proposed constructs of information systems success and SL management process maturity were collected from previous researches and evaluated for securing reliability and validity, utilizing appropriate statistical methods and pilot tests before exploring the case study. Four cases from four different companies under one vendor company were utilized for the analysis. All of the cases had been contracted in SLA(Service Level Agreement) and had implemented ITIL(IT Infrastructure Library), Six Sigma and BSC(Balanced Scored Card) methods since last several years, which means that all the client organizations pursued concerted efforts to acquire quality services from IT outsourcing from the organization and users' point of view. For comparing the differences among the four organizations in IT out-sourcing sucess, T-test and non-parametric analysis have been applied on the data set collected from the organization using survey instruments. The process maturities of planning and implementation phases of SLM are found not to influence on any dimensions of information systems success from users' point of view. It was found that the SLM maturity in the phase of operations and evaluation could influence systems quality only from users' view. This result seems to be quite against the arguments in IT outsourcing practices in the fields, which emphasize usually the importance of planning and implementation processes upfront in IT outsourcing projects. According to after-the-fact observation by an expert in an organization participating in the study, their needs and motivations for outsourcing contracts had been quite familiar already to the vendors as long-term partners under a same conglomerate, so that the maturity in the phases of planning and implementation seems not to be differentiating factors for the success of IT outsourcing. This study will be the foundation for the future research in the area of IT outsourcing management and success, in particular in the service level management. And also, it could guide managers in practice in IT outsourcing management to focus on service level management process in operation and evaluation stage especially for long-term outsourcing contracts under very unique context like Korean IT outsourcing projects. This study has some limitations in generalization because the sample size is small and the context itself is confined in an unique environment. For future exploration, survey based research could be designed and implemented.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• Asian Journal of Turfgrass Science
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• v.23 no.2
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• pp.219-228
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• 2009

The construction procedures and artificial turf maintenance program on golf course definitely influence on the distortion of its environment. Soil microbial communities in soil profile were affected directly by those practises on turf areas. In Jeju island, the environmental impact assessment has been required to apply the first quality class granular activated carbon(GAC), which has a high absorbent character to agricultural chemicals, on the soil profiles of golf green system to reduce the pesticide leaching to ground water. This research was carried out to analyze the changes of microbial communities and chemical properties on soil profiles where GAC had been applied at the construction stage at two golf courses in Jeju. The changes of soil microbial population and chemical properties associated with construction methods of soil profile and agrochemical management program were analyzed by monthly at the surface and sub-soil profiles during April through October, 2007. The total numbers of bacteria and fungi, soil moisture content, soil physio-chemical properties were measured on greens and fairways of the both golf courses with different GAC treatment on the green and fairway soil profiles. The results showed that GAC had positive effects on the water holding capacity, pH and EC, however, it did not improved the holding capacity of available nutrients ${NO_3}^-,{NH_4}^+$, and phosphorus by its sorption phenomenon. In microbial count test, the total numbers of bacteria and fungi showed a great variation during sampling dates. That may directly relate to the agrochemical application, however, the ratio of total bacterial number versus total fungus number showed a constant value on a sub-soil of 15~30cm depth. Thus, the construction method of GAC in soil profile, and application of fertilizer and pesticide, both impacted on the changes of microbial population. It's means that the construction method of soil profile and turf management using agro-materials might greatly affect on the turfgrass culture and the environment of golf course.

• Journal of Wetlands Research
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• v.23 no.4
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• pp.327-341
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• 2021

The riparian eco-belt is an efficient technique that can reduce non-point pollution sources in the basin and improve ecological connectivity and health. In Korea, a legal system for the construction and management of riparian eco-belts is in operation. However, it is currently excluded that rivers and floodplains in dam reservoir that are advantageous for buffer functions such as control of non-point pollutants and ecological habitats. Accordingly, this study presented and analyzed a plan to select a site for an integrated riparian ecol-belt that comprehensively evaluates the water quality and ecosystem characteristics of each dam floodplain and riparian zone for the Daecheong Dam basin in Geum River watershed. First, the Daecheong Dam basin was divided into 138 sub-basin with GIS, and the riparian zone adjacent to the dam floodplain was analyzed. Sixteen evaluation factors related to the ecosystem and water quality impact that affect the selection of integrated riparian eco-belt were decided, and weights for the importance of each factor were set through AHP analysis. The priority of site suitability was derived by conducting an integrated evaluation by applying weights to sub-basin by floodplains and riparian zone factors. In order to determine whether the sites derived through GIS site analysis are sutiable for actual implementation, five sites were inspected according to three factors: land use, pollution sources, and ecological connectivity. As a result, it was confirmed that all sites were appropriate to apply integrated riparian ecol-belt. It is judged that the riparian eco-belt site analysis technique proposed through this study can be applied as a useful tool when establishing an integrated riparian zone management policy in the future. However, it might be necessary to experiment various evaluation factors and weights for each item according to the characteristics and issues of each dam. Additional research need to be conducted on elaborated conservation and restoration strategies considering the Green-Blue Network aspect, evaluation of ecosystem services, and interconnection between related laws and policy and its improvements.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.113-137
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• 2021

According to the clear potential of mobile banking growth, many studies related to this are being conducted, but in Korea, it is concentrated on the analysis of technical factors or consumers' intentions, behaviors, and satisfaction. In addition, even though it has a strong customer base of 20s, there are few studies that have been conducted specifically for this customer group. In order for mobile banking to take a leap forward, a strategy to secure various perspectives is needed not only through research on itself but also through research on external factors affecting mobile banking. Therefore, this study analyzes impulsiveness, credit card use, and SNS addiction among various external factors that can significantly affect mobile banking in their 20s. This study examines whether the relationship between impulsiveness and mobile banking usage depends on whether or not a credit card is used, and checks whether a customer's impulsiveness is possible by examining whether a credit card is used. Based on this, it is possible to establish new standards for classification of marketing target groups of mobile banking. After finding out the static or unsuitable relationship between whether to use a credit card and impulsiveness, we want to indirectly predict the customer's impulsiveness through whether to use a credit card or not to use a credit card. It also verifies the mediating effect of SNS addiction in the relationship between impulsiveness and mobile banking usage. For this analysis, the collected data were conducted according to research problems using the SPSS Statistics 25 program. The findings are as follows. First, positive urgency has been shown to have a significant static effect on mobile banking usage. Second, whether to use credit cards has shown moderating effects in the relationship between fraudulent urgency and mobile banking usage. Third, it has been shown that all subfactors of impulsiveness have significant static relationships with subfactors of SNS addiction. Fourth, it has been confirmed that the relationship between positive urgency, SNS addiction, and mobile banking usage has total effect and direct effect. The first result means that mobile banking usage may be high if positive urgency is measured relatively high, even if the multi-dimensional impulsiveness scale is low. The second result indicates that mobile banking usage rates were not affected by the independent variable, negative urgency, but were found to have a significant static relationship with negative urgency when using credit cards. The third result means that SNS is likely to become addictive if lack of premeditation or lack of perseverance is high because it provides instant enjoyment and satisfaction as a mobile-based service. This also means that SNS can be used as an avoidance space for those with negative urgency, and as an emotional expression space for those with high positive urgency.