• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.53-77
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• 2015

Korea has witnessed an increasing number of domestic patent applications, but a majority of them are not utilized to their maximum potential but end up becoming obsolete. According to the 2012 National Congress' Inspection of Administration, about 73% of patents possessed by universities and public-funded research institutions failed to lead to creating social values, but remain latent. One of the main problem of this issue is that patent creators such as individual researcher, university, or research institution lack abilities to commercialize their patents into viable businesses with those enterprises that are in need of them. Also, for enterprises side, it is hard to find the appropriate patents by searching keywords on all such occasions. This system proposes a patent recommendation system that can identify and recommend intellectual rights appropriate to users' interested fields among a rapidly accumulating number of patent assets in a more easy and efficient manner. The proposed system extracts core contents and technology sectors from the existing pool of patents, and combines it with secondary social knowledge, which derives from tags information created by users, in order to find the best patents recommended for users. That is to say, in an early stage where there is no accumulated tag information, the recommendation is done by utilizing content characteristics, which are identified through an analysis of key words contained in such parameters as 'Title of Invention' and 'Claim' among the various patent attributes. In order to do this, the suggested system extracts only nouns from patents and assigns a weight to each noun according to the importance of it in all patents by performing TF-IDF analysis. After that, it finds patents which have similar weights with preferred patents by a user. In this paper, this similarity is called a "Domain Similarity". Next, the suggested system extract technology sector's characteristics from patent document by analyzing the international technology classification code (International Patent Classification, IPC). Every patents have more than one IPC, and each user can attach more than one tag to the patents they like. Thus, each user has a set of IPC codes included in tagged patents. The suggested system manages this IPC set to analyze technology preference of each user and find the well-fitted patents for them. In order to do this, the suggeted system calcuates a 'Technology_Similarity' between a set of IPC codes and IPC codes contained in all other patents. After that, when the tag information of multiple users are accumulated, the system expands the recommendations in consideration of other users' social tag information relating to the patent that is tagged by a concerned user. The similarity between tag information of perferred 'patents by user and other patents are called a 'Social Simialrity' in this paper. Lastly, a 'Total Similarity' are calculated by adding these three differenent similarites and patents having the highest 'Total Similarity' are recommended to each user. The suggested system are applied to a total of 1,638 korean patents obtained from the Korea Industrial Property Rights Information Service (KIPRIS) run by the Korea Intellectual Property Office. However, since this original dataset does not include tag information, we create virtual tag information and utilized this to construct the semi-virtual dataset. The proposed recommendation algorithm was implemented with JAVA, a computer programming language, and a prototype graphic user interface was also designed for this study. As the proposed system did not have dependent variables and uses virtual data, it is impossible to verify the recommendation system with a statistical method. Therefore, the study uses a scenario test method to verify the operational feasibility and recommendation effectiveness of the system. The results of this study are expected to improve the possibility of matching promising patents with the best suitable businesses. It is assumed that users' experiential knowledge can be accumulated, managed, and utilized in the As-Is patent system, which currently only manages standardized patent information.

• 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.

• Archives of design research
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• v.18 no.1 s.59
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• pp.167-174
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• 2005

This study is to objectively support the emotional and intuitional decision making of the designer by means of developing the supporting models and tools of color coordination. Based on the color grouping system and representative vocabularies suggested in the precedent 'Study on the Grouping System of Fabric Color,' this study suggested the manufacture of the supporting model of color coordination that could be used practically through the design of coloring group. The results of this study can be summarized as below. Firstly, 687 colors in total have been collected from the four world famous collections, the street fashion of 2002 F/W 2003 S/S Season and the representative brands in each group for five years from 1999 to 2003 in order to single out the basic colors for the purpose of composing the color groups. Secondly, 687 collected colors have been grouped into 144 colors in total through the three-step process for the extraction of coloring groups. Thirdly, the final extracted colors have been divided into , , , group by the grouping system specified in the precedent study and the said four large groups have been again subdivided into 12 small groups. Fourthly, the suggested colors in each group have established a color coordination system by introducing the concept of the crossover coordination that could be matched with other groups as well as the coordination within the group. Fifthly, we have dyed 144 colors in total that have consisted of the coloring system of four representative groups (twelve subgroups) in each methodical tone as in the above in cotton yarn, one of the representative materials in fabric fashion design industry. Besides, we have specified the symbol of the Pantone Color Book and CMYK values in each color that has consisted of the system considering the industrial characteristics of fashion as a global business and the compatibility with the related design industry. Sixthly, we have packed the completed yam made of fabrics in the designed container for the easy use of cross-coordination and have completed a color coordination system that could be easily utilized for the fashion-related working-level staffs.

• Journal of Distribution Science
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• v.9 no.3
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• pp.5-14
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• 2011

One of the new roles of modern retail stores is to supply consumers with a memorable experience. In Korea, enhancing a store's environment so that customers remember a unique shopping experience is recognized as a sound strategy for strengthening the store's competitiveness. Motivated by this incentive, awareness of the experience-store concept is starting to increase in various categories of the retail industry. However, many experience stores, except in a few cases, have yet to derive a significant profit, explaining why Korean consumers are somewhat unfamiliar with, yet fascinated by, the experience stores that now exist in the country. Consumer satisfaction directly, and indirectly, affects a company's future profit and potential financial gain; customer satisfaction also affects loyalty. Therefore, knowing the significant factors that increase satisfaction and loyalty is essential for any company, in any field, to be able to effectively differentiate itself from the competition. Intrigued by increased competition opportunities, most Korean companies have adopted experience-store marketing strategies. When establishing the most effective processes for increasing sales and achieving a sustainable competitive advantage of a new concept, companies should consider certain factors that influence consumers' ability to accept new concepts and ideas. The Technology Acceptance Model (TAM) is a theory that models how people accept new concepts. TAM proposes the following two factors that influence a person's decisions about how, and when, he or she will use a new product: "perceived usefulness" and "perceived ease of use." Much of the existing research has suggested that a person's character also affects the process for accepting new ideas. Such personal character attributes as individual preferences, self-confidence, and a person's values, traits, and/or skills affect the process for willingly consenting to try something new. It will be meaningful to establish how the TAM theory's components, as well as personal character, affect individuals accepting the experience-store concept. To that end, as it pertains to an experience store, the first goal of the study is to examine the influence of innovative factors (perceived usefulness and perceived ease of use) on satisfaction and loyalty. The second objective is to define the moderate effect of consumers' personal characteristics on the model. The proposed model was tested on 149 respondents who were engaged in leisure sports activities and bought sports outdoor garments and equipment. According to the study's findings, the satisfaction and loyalty of an experience store can be explained by perceived usefulness and perceived ease of use, with the study's results demonstrating the stronger of the two factors being "perceived ease of use." The study failed to explain the effects of a person's character on the model. In conclusion, when the companies that operate the experience stores execute their marketing and promotion strategies, they should stress the stores' "ease of use" product components. Additionally, it can be extrapolated from the study data that since the experience-store idea is still relatively unfamiliar to Korean consumers, most customers are not yet able to evaluate, nor take a position regarding, their respective attitudes toward experience stores.

• Child Health Nursing Research
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• v.2 no.2
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• pp.45-57
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• 1996

A serious disease in a family influences the entire family member given the fact that the members closely interact with each other. Especially in terms of pediatric nursing, study on family gains importance as the need to care of families whose children with developmental disabilities and chronic disease This study was done based on The Resiliency Model of Family Adjustment and Adaptation(McCubbin, 1991) is intended to examine the stress of parents whose children suffer from cleft lip or /and cleft palate. It also helps them to cope with the stress and analyze the relationship between the stress and coping This study used Family Inventory of Life Events and Changes (FILE) and Coping Health Inventory for Parents(CHIP) for measuring family stress and coping. The two instruments are revised to fit the social and cultural environment of Korean culture. Data collection was done from April 18, 1996 to May 18, 1996 at 8 University medical centers located in Seoul. Those who answered questionnaires were 84 parents whose children have cleft lip or /and cleft palate. SPSS PC+ was used to analyze the data collotted. Programs used for data analysis were t-test, ANOVA, Pearson correlation coefficient. The study is summarized as follows .1. The average score of family stress is 10.46(percentage of the full score 24.90) and 'finance and business strains'(3.25), and 'intrafamily strains'(2.65) ranked the highest. The average score of family's coping is 1.93, which is close to the answer of' moderately helpful' and they are measured to put their utmost efforts to' intergration and cooperation of family and optimistic definition on the situation'. 2. There is no significant statistical correlation between the family stress and coping. 3. Mothers show more stress than fathers in the parts of 'illness and family care strains' and 'losses'(t〓-2.34, t〓-2.32, p<.05). 4. Fathers show more willingness to cope with the stress than mothers do in the parts of' seeking social support','self-esteem','emotional comfort' 5. Mothers are more stress than fathers in the parts of family stress and its coping with it by usual traits(t〓-2.78, p<.05). Parents with religion are measured to cope more willingly than those who are not 6. Income of a family shows positive correlationship with family coping (r〓.28, p<.05). The study shows that gender difference is significant variable in studying on family stress and coping. Mothers get more stress than fathers, which has much to do with the fact that they are in charge of raising children and keeping houseworks. Accordingly, managing family crisis and its survival can be induced by giving support for the mothers, studying fathers including the rest of the family members and giving nursing care and arbitration ; religious background is also considered to be one of the important factors in family stress , judging from the relationship between family income and family's coping, caring given to suffering children is needed on societal levels. The above considerations bring up the need to have a longitudinal study of children with congenital anomaly including cleft lip or /and cleft palate and their families about family stress and coping. Resiliency programs on family system and their effectiveness and the relationship between the enlarged families with social and cultural values reflecting Korean tradition are also needed to be studied.

• Korea Science and Art Forum
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• v.37 no.4
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• pp.149-159
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• 2019

This research was started to identify the color status of signboards at the target site and suggest the direction of improvement for the signboard project in Dongsang-dong, Jongno-gil, a central area of the old city center of Gimhae. The area under study forms a depressed street atmosphere with old facilities, and is a typical type of old city center sign that needs to be rebuilt. The purpose of this study is to investigate prior research related to signage and similar signboard improvement cases, and then to identify the current status of colors by sorting out the casting, auxiliary and highlighted colors through the survey of the color of the signboard in the target area, and to propose a desirable direction for the future sign business based on the basis of these findings. This paper divided the target sections by industry and conducted a color analysis of signboards. The results and contents of the research are as follows. First, cast-colored signboards in general businesses showed a variety of primary color distributions with high L* values, on average, with high intensity and high chromaticity. Second, the auxiliary colors were mostly white or black in color-free, making a contrast between the casting colors. Third, the highlight was that a* value showed a high distribution in positive water plus and was mainly distributed in obsolescence, such as red or yellow, and color was used to reflect the characteristics of each store. However, the stores in the entire section lack unity because they were using colors that were higher in color than middle colors, which was causing the street's aesthetic look to be undermined. Based on the results of these studies, it is thought that the future color scheme for Gimhae's signature improvement project will limit the number of colors to a certain extent and give a sense of security and visual comfort to the use of colorlessness and obscurity around the representative colors of Gimhae.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.552-560
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• 2018

Since 1960's, the large conglomerates of South Korea have grown due to the corporate-centered, fast-paced growth drive, while the small and medium-sized businesses supported the country's economy as the subordinate structure of these conglomerates. Due to the globalization of the business environments, the focus of competition shifted from competitions between individual companies to one between networks of companies. Therefore, more emphasis is now put on the capabilities of the cooperation networks between companies rather than the capabilities of individual companies. Therefore, in this study, the author examined the influence of the win-win growth strategy elements through cooperation with small and medium-sized businesses upon corporate performance. This study was conducted with the workers of small and medium-sized businesses that have previous cooperation experiences with South Korean conglomerates over the period from March 2 to May 17, 2018. For this, a total of 515 questionnaires were retrieves to obtain the data for analysis. The analysis was conducted using SPSS 22.0 and AMOS 18.0. The analytical processes that were taken included exploratory factor analysis, confirmatory factor analysis, confidence analysis, correlation analysis, and structural equation analysis model. The results of the analysis showed that, first of all, the win-win growth strategy factors that affected the strategic performance, which is a part of cooperate performance were, respectively, harmonization with the goals, production technical support, and quality system. Second, the win-win growth strategy factors that affected the financial performance, which is a part of corporate performance, turned out to be harmonization with the goals, quality system, and incentive. With the results of this study, it was shown that the elements such as harmonization with the goals, production technical support, quality systems, and incentives were key infrastructural factors that affected the corporate performance directly. On the other hand, its implication is that informative or knowledge-related factors, such as joint knowledge creation, do not have their own added values, while they are not too much likely to affect corporate performances for the moment.

• Journal of Service Research and Studies
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• v.11 no.1
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• pp.1-20
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• 2021

This study was conducted to derive a model of a sustainable economic system for humanity in the era of service economy that requires a paradigm shift. A new long-term sustainable development model has been built on the basis of thousands of years of economic operation experience. Currently, the world is operating the capitalism as the main economic system because there is no better alternative, and the changing economic and social environment such as the advent of the 4th Industrial Revolution is exacerbating the problems of the capitalism, such as job shortages and inequality. In this study, we analyzed the economic management system experienced by human society, and derived an economic system model that is ideal for the modern and future society and is sustainable in the long term. The conditions for a long-term sustainable economic system were presented first. It must be a model that can solve the problems of the current economic system. It must be a model that is faithful to the characteristics of the modern economic society and the nature of the economy itself. And since the new economic system is for humanity, it must be based on the common principles of human society. It should be a model that continuously guarantees core values such as equality and freedom required by human society. After analyzing the problems of the current economic system and analyzing the conditions required for the new system, the basic axioms that the new economic system should be based on were presented, and a desirable model was derived based on this. The structure of the derived model and the specific operation model were presented. In the future, research is needed to specify the operational model so that this model can be settled well in different environments for each country.

• Journal of Korean Society of Environmental Engineers
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• v.36 no.10
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• pp.719-723
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• 2014

For effective implementation of total maximum daily load (TMDL), this study presented the improving plans of non-point source pollution management including the classification of non-point source pollution, calculation of non-point source pollution load (generated, discharged), selection of non-point source pollution management regions and management of non-point source pollutant. First of all, the definition of point source pollution and non-point source pollution based on the legal and scientific viewpoint should be precisely classified and managed. Especially, the forest, grassland and river without occurrence of environmental damage by activity of business and human should be separately classified natural background pollutants. The unit for generated and discharged non-point source pollution should be preferentially changed according to actual condition of watershed. The calculation methods of generated and discharged non-point source pollution should be corrected consideration on the amount and duration of rainfall. While the TMDL is implemented, non-point source pollution management regions should be selected in the watersheds exceed the targeted water quality standards by the rainfall. The non-point source pollution management regions should be selected in the minimal regions where have high values of discharged non-point source pollution density in the urban area, farmland and site area except forest, grassland in the whole watershed. The non-point source pollutant treatment facilities, which take into consideration non-point source pollution load per unit area, duration of the excess concentration, realizable possibility of treatment, effectiveness of treatment cost versus point source pollutant, should be established in the regions with a large generated non-point source pollution load and a high concentration of water quality exceed the targeted water quality standards by the rainfall.