• Journal of Wetlands Research
• /
• v.9 no.1
• /
• pp.69-78
• /
• 2007

Constructed wetlands and other aquatic systems have been successfully used for waste and wastewater treatment in either temperate or tropical regions. To treat waste or wastewater in a sustainable manner, the integrated eco-engineering designs are explained in this paper with 2 case studies: (i) a combination of vertical-flow constructed wetland (CW) with plant irrigation systemfor fecal sludge management and (ii) integrated CW units with landscaping at full-scale application for domestic wastewater treatment. The pilot-scale study of fecal sludge management employed 3 vertical-flow CW units, each with a dimension of $5{\times}5{\times}0.65m$ (width ${\times}$ length ${\times}$ media depth) and planted with cattails (Typha augustifolia). At the solid loading rate of 250 kg total solids (TS)/$m^2.yr$ and a 6-day percolate impoundment, the CW system could achieve chemical oxygen demand (COD), TS and total Kjeldahl nitrogen (TKN) removal efficiencies in the range of 80 - 96%. The accumulated sludge layers of about 80 - 90 cm was found at the CW bed surface after operating the CW units for 7 years, but no clogging problem has been observed. The CW percolate was applied to 16 irrigation Sunflower plant (Helianthus annuus) plots, each with a dimension of $4.5{\times}4.5m$ ($width{\times}length$). In the study, the CW percolate were fed to the treatment plots at the application rate of 7.5 mm/day but the percolate was mixed with tap water at different ratio of 20%, 80% and 100%. Based on a 1-year data of 3-crop plantation were experimented, the contents of Zn, Mn and Cu in soil of the experimental plots were found to increase with increasing in CW percolate ratios. The highest plant biomass yield and oil content of 1,000 kg/ha and 35%, respectively, were obtained from the plots fed with 20% or 50% of the CW percolate, whereas no accumulation of heavy metals in the plant tissues (i.e. leaves, stems and flowers) of the sunflower is found. In addition to the pilot-scale and field experiments, a case study of the integrated CW systems for wastewater treatment at Phi Phi Island (a Tsunami-hit area), Krabi province, Thailand is illustrated. The $5,200-m^2$ CW systems on Phi Phi Island are not only for treatment of $400m^3/day$ wastewater from hotels, households or other domestic activities, but also incorporating public consultation in the design processes, resulting in introducing the aesthetic landscaping as well as reusing of the treated effluent for irrigating green areas on the Island.

• Journal of KIISE:Databases
• /
• v.29 no.1
• /
• pp.44-57
• /
• 2002

Multiple spatial queries are defined as two or more spatial range queries to be executed at the same time. The primary processing of internet-based map services is to simultaneously execute multiple spatial queries. To improve the throughput of multiple queries, the time of disk I/O in processing spatial queries significantly should be reduced. The declustering scheme of a spatial dataset of the MIMD architecture cannot decrease the disk I/O time because of random seeks for processing multiple queries. This thesis presents query scheduling strategies to ease the problem of inter-query random seeks. Query scheduling is achieved by dynamically re-ordering the priority of the queued spatial queries. The re-ordering of multiple queries is based on the inter-query spatial relationship and the latency of query processing. The performance test shows that the time of multiple query processing with query scheduling can be significantly reduced by easing inter-query random seeks as a consequence of enhanced hit ratio of disk cache.

• Journal of the Korean earth science society
• /
• v.30 no.6
• /
• pp.787-798
• /
• 2009

A new apparatus was presented in order to help understand the concept of the Coriolis force which is essential in understanding the trajectories of the atmospheric current and the tide of seawater. In the apparatus proposed in high-school textbooks, since the slide from which the ball is released is set outside the rotating disk, it was not possible to interpret, with the trajectory of the ball on the disk, the motion of the atmosphere and the current of seawater occurring as a result of the Coriolis force. In order to resolve such problem, a new apparatus was developed in which the slide was set on the disk and rotated with the disk. Experiments were carried out using both the existing apparatus and the new apparatus, and the results were compared. While, in the experiment performed with the existing apparatus, it was difficult to analyze the trajectory of ball because the motion of the ball was not smooth when it hit the disk, in the experiment with the new apparatus it was much easier to analyze the trajectories. It was also possible to compare the trajectories when the initial velocity of the ball was varied.

• Korean Journal of Health Education and Promotion
• /
• v.26 no.5
• /
• pp.115-127
• /
• 2009

Objectives: To compare alcohol secondhand effects among US and Korean students. Methods: Nationally representative 4-year colleges of two countries were involved in this cross-national comparison study. Data from the 2001 U.S. College Alcohol Study and the 2003 Korean College Alcohol Study came from 120 colleges in 38 U.S. states and the District of Columbia and 60 colleges in Korea. Randomly selected 4-year college students from the U.S. (10,924) and Korea (2,385) participated in the study using self-reports of alcohol use and perceptions of drinking as assessed by College Alcohol Study questionnaires. Results: Korean students were tend to more likely to have being a victim of sexual assault or date rape, having to take care of drunken students and finding vomit in the hall or bathroom of residence, than US students, while US students were tend to more likely to have being insulted or humiliated, having a serious argument and quarrel, being pushed, hit, or assaulted, having study/sleep interrupted, and experiencing an unwanted sexual advance than Korean students. Conclusion: In general, US students were more likely to suffer interrelationship problems after drinking while Korean students were more likely to have physical and individual drinking related problems.

• Korean Journal of Construction Engineering and Management
• /
• v.10 no.6
• /
• pp.146-153
• /
• 2009

The year 2008 was a hard year for Korea's construction companies. The real estate downturn resulted in halting new construction and stopping existing work, and inflation of global oil prices caused price hikes in rebar and concrete materials. As a solution to reducing the budget, the newly appointed government announced plans to increase low cost bid contracts from 10 billion to 30 billion won. When such economical and political factors negatively impact the construction market, projects based on low cost contracts are the hardest hit. Many problems already inherent in low cost bid contracts become accentuated. Consequently, this provides an opportune time to actually study and analyze the issues in these projects. This paper introduces the findings made from investigating four projects struggling to make ends meet in the year 2008. Results show that flow of cash (i.e., liquidity), or lack thereof, was the root cause which in turn was hampered by failed mechanisms for design changes, material inflation. Attributing cash flow risk to the bottom of the production structure (i.e., small business subcontractors) was also a problem within the industry. Contractors need a better way to prepare against material price fluctuations, and owners need to assist in expediting payment during times of extreme downturn.

• Cross-Cultural Studies
• /
• v.30
• /
• pp.7-29
• /
• 2013

A movie which brought its material from a historical character or incidents in the past was produced by a story suggestion through a historical fact. It is because Shakespeare created a story based on a mythical element related with his life in the plot which was written from the script of the play and was on the show in the cinemas of London. It is an obvious fact that the historical drama of this movie was intentionally modified and the fictional story was added to episodes in order to create a dramatic effect. However, reflecting historical backgrounds and cultural aspects accurately through a historical study would also be an important factor. Therefore, the backgrounds and aspects presented in this movie are a kind of storytelling which was reconstructed as if a historian added his opinion to historical facts like a discourse. A historical background in was a story about Shakespeare who worked at the theater in London as a writer in 1593 the period of England Reneissance. The movie included the working and playwriting of Shakespeare who is a main character. This indicated not only the environment of the theater and literature during the reign of Queen Elizabeth I but also historical aspect in the early modern industrial society in England. This movie, that is, described that time as a recreation such as a cultural acceptance and an achievement of an initial capitalism in Renaissance in the life of characters. In particular, the factor of theaters flourishing during the Renaissance was because a newly emerging class, bourgeoisie, who held the capital emerging from a policy for middle class led to a box office hit through founding theaters and drama company and selling tickets and performing plays by themselves. Like this, the movie depicted the time led by plays to a industrialization. Moreover, Social aspects in the late 1500s were revealed in this movie through a depiction of the cinemas and the city of London. The depiction of the city of London reflected a social situation of an initial capitalism rapidly developed in trade and commerce. The social aspects such as conflicts between social classes based on getting richer and poorer, mammonism, a corrupted love between the male and the female, a immortality with growing brothels, religious and political conflicts with the foundation of the church in England were closely linked with characters' daily routine at that time in London and were reflected in this work overall. The reason why we highlight characters' job and custom like this in the movie is that these are ideationally inherent in a critical mind from people at that moment. The historical background and reproduction of the image depicted in the movie were focused on characters' daily routine and indicated the problem mentally and independently exposed in the form of initial capitalism.

• Strategy21
• /
• s.32
• /
• pp.192-221
• /
• 2013

In order to deal with the current economic crisis, the U.S. government, as a part of its austerity fiscal policy, implemented a budget sequester. The sequester will hit the U.S. defense budget the hardest, and as a result will most likely put the security of the international community in jeopardy. The U.S. will have to cut 46 billion dollars from its original 525 billon defense spending in 2013. And by the year 2022, will have to cut 486.9 billion dollars. Such an astronomical decrease in the U.S. defense spending will inevitably burden the friendly nations. According to recent studies, pirate related incidents in Somalia, where piracy is most active, has declined from its 226 incidents to 76 incidents per year in 2012, a 66% drop from previous years'. However, piracy threats as well as those related to firearms still remain and thus participants of anti-piracy operations, namely the U.S., U.K., France, Canada, NCC, EUNAVFOR, and NATO, are facing a problem of declining forces. Considering the current situation as well as rising expectations from the international community, Republic of Korea, a supporter of NCC's maritime security operation, not to mention its foremost duty of securing its sea, is at a stage to re-examine its operational picture. Such action will be a good opportunity for Republic of Korea to build the trust and live up to the international community's expectation. To quote from the network theory, although in relation to other friendly nations participating in the anti-piracy operation, Republic of Korea currently remains at a single cell level, this opportunity will certainly develop Korea to a 'node' nation in which power and information would flow into. Through this expansion of operational capability, Republic of Korea will be able to exert more influence as a more developed nation. Currently however, not only is the single 4,500 ton class destroyer deployed in Somalia a limited unit to further expand the scale and amount of force projection in the area, but also the total of six 4,500 ton class destroyers ROK feet possess is at a high fatigue degree due to standard patrolling operations, midshipman cruise and the RIMPAC exercise. ROK fleet therefore must consider expanding the number of ships deployed along with either deploying combat support ships or constructing logistics support site in the African region. Thus, by expanding its operational capabilities and furthermore by abiding to the rightful responsibilities of a middle power nation, Republic of Korea will surely earn its respect among the members of the international community.

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

• Asia pacific journal of information systems
• /
• v.19 no.2
• /
• pp.139-155
• /
• 2009

For the last few decades, many studies have tried to explore and unveil venture companies' success factors and unique features in order to identify the sources of such companies' competitive advantages over their rivals. Such venture companies have shown tendency to give high returns for investors generally making the best use of information technology. For this reason, many venture companies are keen on attracting avid investors' attention. Investors generally make their investment decisions by carefully examining the evaluation criteria of the alternatives. To them, credit rating information provided by international rating agencies, such as Standard and Poor's, Moody's and Fitch is crucial source as to such pivotal concerns as companies stability, growth, and risk status. But these types of information are generated only for the companies issuing corporate bonds, not venture companies. Therefore, this study proposes a method for evaluating venture businesses by presenting our recent empirical results using financial data of Korean venture companies listed on KOSDAQ in Korea exchange. In addition, this paper used multi-class SVM for the prediction of DEA-based efficiency rating for venture businesses, which was derived from our proposed method. Our approach sheds light on ways to locate efficient companies generating high level of profits. Above all, in determining effective ways to evaluate a venture firm's efficiency, it is important to understand the major contributing factors of such efficiency. Therefore, this paper is constructed on the basis of following two ideas to classify which companies are more efficient venture companies: i) making DEA based multi-class rating for sample companies and ii) developing multi-class SVM-based efficiency prediction model for classifying all companies. First, the Data Envelopment Analysis(DEA) is a non-parametric multiple input-output efficiency technique that measures the relative efficiency of decision making units(DMUs) using a linear programming based model. It is non-parametric because it requires no assumption on the shape or parameters of the underlying production function. DEA has been already widely applied for evaluating the relative efficiency of DMUs. Recently, a number of DEA based studies have evaluated the efficiency of various types of companies, such as internet companies and venture companies. It has been also applied to corporate credit ratings. In this study we utilized DEA for sorting venture companies by efficiency based ratings. The Support Vector Machine(SVM), on the other hand, is a popular technique for solving data classification problems. In this paper, we employed SVM to classify the efficiency ratings in IT venture companies according to the results of DEA. The SVM method was first developed by Vapnik (1995). As one of many machine learning techniques, SVM is based on a statistical theory. Thus far, the method has shown good performances especially in generalizing capacity in classification tasks, resulting in numerous applications in many areas of business, SVM is basically the algorithm that finds the maximum margin hyperplane, which is the maximum separation between classes. According to this method, support vectors are the closest to the maximum margin hyperplane. If it is impossible to classify, we can use the kernel function. In the case of nonlinear class boundaries, we can transform the inputs into a high-dimensional feature space, This is the original input space and is mapped into a high-dimensional dot-product space. Many studies applied SVM to the prediction of bankruptcy, the forecast a financial time series, and the problem of estimating credit rating, In this study we employed SVM for developing data mining-based efficiency prediction model. We used the Gaussian radial function as a kernel function of SVM. In multi-class SVM, we adopted one-against-one approach between binary classification method and two all-together methods, proposed by Weston and Watkins(1999) and Crammer and Singer(2000), respectively. In this research, we used corporate information of 154 companies listed on KOSDAQ market in Korea exchange. We obtained companies' financial information of 2005 from the KIS(Korea Information Service, Inc.). Using this data, we made multi-class rating with DEA efficiency and built multi-class prediction model based data mining. Among three manners of multi-classification, the hit ratio of the Weston and Watkins method is the best in the test data set. In multi classification problems as efficiency ratings of venture business, it is very useful for investors to know the class with errors, one class difference, when it is difficult to find out the accurate class in the actual market. So we presented accuracy results within 1-class errors, and the Weston and Watkins method showed 85.7% accuracy in our test samples. We conclude that the DEA based multi-class approach in venture business generates more information than the binary classification problem, notwithstanding its efficiency level. We believe this model can help investors in decision making as it provides a reliably tool to evaluate venture companies in the financial domain. For the future research, we perceive the need to enhance such areas as the variable selection process, the parameter selection of kernel function, the generalization, and the sample size of multi-class.

• Journal of Intelligence and Information Systems
• /
• v.16 no.4
• /
• pp.67-84
• /
• 2010

In terms of business, forecasting is a work of what is expected to happen in the future to make managerial decisions and plans. Therefore, the accurate forecasting is very important for major managerial decision making and is the basis for making various strategies of business. But it is very difficult to make an unbiased and consistent estimate because of uncertainty and complexity in the future business environment. That is why we should use scientific forecasting model to support business decision making, and make an effort to minimize the model's forecasting error which is difference between observation and estimator. Nevertheless, minimizing the error is not an easy task. Case-based reasoning is a problem solving method that utilizes the past similar case to solve the current problem. To build the successful case-based reasoning models, retrieving the case not only the most similar case but also the most relevant case is very important. To retrieve the similar and relevant case from past cases, the measurement of similarities between cases is an important key factor. Especially, if the cases contain symbolic data, it is more difficult to measure the distances. The purpose of this study is to improve the forecasting accuracy of case-based reasoning approach using fuzzy relation and composition. Especially, two methods are adopted to measure the similarity between cases containing symbolic data. One is to deduct the similarity matrix following binary logic(the judgment of sameness between two symbolic data), the other is to deduct the similarity matrix following fuzzy relation and composition. This study is conducted in the following order; data gathering and preprocessing, model building and analysis, validation analysis, conclusion. First, in the progress of data gathering and preprocessing we collect data set including categorical dependent variables. Also, the data set gathered is cross-section data and independent variables of the data set include several qualitative variables expressed symbolic data. The research data consists of many financial ratios and the corresponding bond ratings of Korean companies. The ratings we employ in this study cover all bonds rated by one of the bond rating agencies in Korea. Our total sample includes 1,816 companies whose commercial papers have been rated in the period 1997~2000. Credit grades are defined as outputs and classified into 5 rating categories(A1, A2, A3, B, C) according to credit levels. Second, in the progress of model building and analysis we deduct the similarity matrix following binary logic and fuzzy composition to measure the similarity between cases containing symbolic data. In this process, the used types of fuzzy composition are max-min, max-product, max-average. And then, the analysis is carried out by case-based reasoning approach with the deducted similarity matrix. Third, in the progress of validation analysis we verify the validation of model through McNemar test based on hit ratio. Finally, we draw a conclusion from the study. As a result, the similarity measuring method using fuzzy relation and composition shows good forecasting performance compared to the similarity measuring method using binary logic for similarity measurement between two symbolic data. But the results of the analysis are not statistically significant in forecasting performance among the types of fuzzy composition. The contributions of this study are as follows. We propose another methodology that fuzzy relation and fuzzy composition could be applied for the similarity measurement between two symbolic data. That is the most important factor to build case-based reasoning model.