• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Tuberculosis and Respiratory Diseases
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• v.44 no.5
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• pp.1019-1029
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• 1997

Background : Elevation of resting energy expenditure(REE) in patients with lung cancer has been described in earlier studies and may contribute to cancer cachexia, but limited information is available regarding the prevalence and determinants of the increased REE. The aim of this study was to assess the prevalence and contributing factors of a hypermetabolic state in newly detected patients with lung cancer and to assess the energy balance in order to improve our knowledge about weight loss in patients with lung cancer. Method : Thirty one consecutive, newly detected patients with lung cancer and 20 control patients with benign lung diseases were included in this study. Resting energy expenditure(REE) was measured by indirect calorimetry using ventilated hood system and predicted REE was calculated by the Harris-Benedict formular. Results : The energy balance in newly detected lung cancer patients was disturbed in a high proportion of patients, and hypermetabolic state occurred in 61% of the patients. Tumor volume, cancer type, location, stage, the presence of atelectasis or infiltration, pulmonary function, or smoking behavior were not associated with increase in REE. But patients with distant metastasis had significantly higher REE comparing with patients without metastasis. Thirty nine percents of the patients with lung cancer had substantial loss of more than 10% of their pre-illness weight. Weight losing patients with lung cancer were not accompanied by an increase in REE. Conclusion : We concluded that the REE was elevated in a higher proportion of patients with lung cancer and distant metastasis was found to be contributing factor to the elevated REE.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.191-210
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• 2014

As mobile devices that can be connected to the Internet have spread and networking has become possible whenever/wherever, the Internet has become central in the dissemination and consumption of news. Accordingly, the ways news is gathered, disseminated, and consumed have changed greatly. In the traditional news media such as magazines and newspapers, expert editors determined what events were worthy of deploying their staffs or freelancers to cover and what stories from newswires or other sources would be printed. Furthermore, they determined how these stories would be displayed in their publications in terms of page placement, space allocation, type sizes, photographs, and other graphic elements. In turn, readers-news consumers-judged the importance of news not only by its subject and content, but also through subsidiary information such as its location and how it was displayed. Their judgments reflected their acceptance of an assumption that these expert editors had the knowledge and ability not only to serve as gatekeepers in determining what news was valuable and important but also how to rank its value and importance. As such, news assembled, dispensed, and consumed in this manner can be said to be expert-based recommended news. However, in the era of Internet news, the role of expert editors as gatekeepers has been greatly diminished. Many Internet news sites offer a huge volume of news on diverse topics from many media companies, thereby eliminating in many cases the gatekeeper role of expert editors. One result has been to turn news users from passive receptacles into activists who search for news that reflects their interests or tastes. To solve the problem of an overload of information and enhance the efficiency of news users' searches, Internet news sites have introduced numerous recommendation techniques. Recommendations based on popularity constitute one of the most frequently used of these techniques. This popularity-based approach shows a list of those news items that have been read and shared by many people, based on users' behavior such as clicks, evaluations, and sharing. "most-viewed list," "most-replied list," and "real-time issue" found on news sites belong to this system. Given that collective intelligence serves as the premise of these popularity-based recommendations, popularity-based news recommendations would be considered highly important because stories that have been read and shared by many people are presumably more likely to be better than those preferred by only a few people. However, these recommendations may reflect a popularity bias because stories judged likely to be more popular have been placed where they will be most noticeable. As a result, such stories are more likely to be continuously exposed and included in popularity-based recommended news lists. Popular news stories cannot be said to be necessarily those that are most important to readers. Given that many people use popularity-based recommended news and that the popularity-based recommendation approach greatly affects patterns of news use, a review of whether popularity-based news recommendations actually reflect important news can be said to be an indispensable procedure. Therefore, in this study, popularity-based news recommendations of an Internet news portal was compared with top placements of news in printed newspapers, and news users' judgments of which stories were personally and socially important were analyzed. The study was conducted in two stages. In the first stage, content analyses were used to compare the content of the popularity-based news recommendations of an Internet news site with those of the expert-based news recommendations of printed newspapers. Five days of news stories were collected. "most-viewed list" of the Naver portal site were used as the popularity-based recommendations; the expert-based recommendations were represented by the top pieces of news from five major daily newspapers-the Chosun Ilbo, the JoongAng Ilbo, the Dong-A Daily News, the Hankyoreh Shinmun, and the Kyunghyang Shinmun. In the second stage, along with the news stories collected in the first stage, some Internet news stories and some news stories from printed newspapers that the Internet and the newspapers did not have in common were randomly extracted and used in online questionnaire surveys that asked the importance of these selected news stories. According to our analysis, only 10.81% of the popularity-based news recommendations were similar in content with the expert-based news judgments. Therefore, the content of popularity-based news recommendations appears to be quite different from the content of expert-based recommendations. The differences in importance between these two groups of news stories were analyzed, and the results indicated that whereas the two groups did not differ significantly in their recommendations of stories of personal importance, the expert-based recommendations ranked higher in social importance. This study has importance for theory in its examination of popularity-based news recommendations from the two theoretical viewpoints of collective intelligence and popularity bias and by its use of both qualitative (content analysis) and quantitative methods (questionnaires). It also sheds light on the differences in the role of media channels that fulfill an agenda-setting function and Internet news sites that treat news from the viewpoint of markets.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.1
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• pp.105-117
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• 2018

In this study, we identify the obstacles that occur through the relationship between I-U cooperation and look for factors that can overcome them in the 'university administration's efforts' and 'Trust between I-U'. In the study of existing I-U cooperation, the relationship between industry and university has accumulated experiences and various channels of bilateral cooperation by sustaining interactions and absorbing capacity of knowledge by path dependence. However, as cooperation increases, 'I-U cooperation barrier' are inevitable, which is explained by two perspectives: 'Difference in mutual recognition' and 'Institutional barriers'. In order to induce the achievement of effective I-U cooperation, it is necessary to overcome these obstacles stemming from mutual relations, and it will be possible to maintain the relationship of continuous I-U cooperation. The researchers conducted research on companies participating in the I-U cooperation technology development project of the 'Ministry of Small and Medium Venture Business', which is a representative I-U cooperation program in Korea. This project will be promoted in the 'Small & Medium Business I-U cooperation Center', an administration-dedicated organization of the university. The researchers measure 'University administration's efforts' and 'Trust between I-U'to overcome'I-U cooperation barrier' In order to clarify the data of the research sample, a questionnaire survey of organizational units was conducted for all companies participating in the 'I-U cooperation technology development projects' of the SMEs and Startups between 2011 and 2015, and the responses of 356 organizations were drawn. The results showed that the higher the level of 'University administration's efforts' and Trust between I-U', the lower 'Difference in mutual recognition' and 'Institutional barriers'. Particularly, it showed higher explanatory power to overcome 'Institutional barriers' among obstacles. Therefore, it should be accompanied by the interest, implementation and institutional support of I-U-R subjects to raise the level of these two factors that can overcome 'I-U cooperation barrier'.

• Archives of design research
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• v.19 no.5 s.67
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• pp.203-212
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• 2006

With interests and participation in the costumeplay that mimics characters appeared on carton or animation in recent days, the costumeplay becomes one of cultural phenomena. Using a qualitative research method, this study identified costumeplayers' costume consumption pattern and explored its meanings from the perspective of consumption culture. Indeed, this study intended to help for understanding costumeplayer group as a consumer, and to provide basic knowledge about new market analysis related to fashion design and marketing. The results from the analyzing participant observation and in-depth interviews data are as follows: first, costumeplayers usually begin costumeplay by friends' invitations or by themselves and then continue on participating. Through the costumeplay, participants have benefits such as fun, departure from the daily life, and social interaction. Second, participants acquire costumes through purchase, rent, producing or combination of daily wear, but both purchase and rent account high. Third, the meanings of consumption culture in costumeplay include consumption behavior repeating possession and disposal. Also, costumeplayers concerns efficiency when purchasing or renting the costumes, and internet is a place where information search, comparison, and actual purchasing are occurred. Based on the results, fashion design and marketing implication, limitation of this study and further research ideas were suggested.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.12
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• pp.1337-1346
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• 2006

Soil humin is the insoluble fraction of humic materials and play an important roles in the irreversible sorption of hydrophobic organic contaminants onto soil particles. However, there have been limited knowledge about the sorption and chemical properties of humin due to the difficulties in its separation from the inorganic matrix(mainly clays and oxides). In this study, de-ashed soil humins($Hu_1-Hu_6$) were isolated from a soil residues(Crude Hu) after removing alkali-soluble organic fractions followed by consecutive dissolution of the mineral matrix with 2%-HF for 2 hr. The humin samples were characterized by elemental analysis and $^{13}C$ NMR spectroscopic method and their sorption-desorption behavior for 1-naphthol were investigated from aqueous solution. The results were compared one another and that with peat humin. $^{13}C$ NMR spectra features indicate that the soil humin molecules are mainly made up of aliphatic carbons(>80% in total carbon) including carbohydrate, methylene chain. Freundlich sorption parameter, n was increased from 0.538 to 0.697 and organic carbon-normalized sorption coefficient(log $K_{OC}$) values also increased from 2.43 to 2.74 as inorganic matrix of the soil humin removed by HF de-ashing. The results suggest that inorganic phase in humin plays an important, indirect role in 1-naphthol sorption and the effects on the sorption non-linearity and intensity are analyzed by comparison between the results of soil humin and peat humin. Sorption-desorption hysteresis were also observed in all the humin samples and hysteresis index(HI) at low solute concentration($C_e$=0.1 mg/L) are in order of Peat humin(2.67)>De-ashed humin(0.74)>Crude Hu(0.59).

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.3
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• pp.322-331
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• 2003

As the complexity of a 3D game is increased by various factors of the game scenario, it has a problem for controlling the interrelation of the game objects. Therefore, a game system has a necessity of the coordination of the responses of the game objects. Also, it is necessary to control the behaviors of animations of the game objects in terms of the game scenario. To produce realistic game simulations, a system has to include a structure for designing the interactions among the game objects. This paper presents a method that designs the dynamic control mechanism for the interaction of the game objects in the game scenario. For the method, we suggest a game agent system as a framework that is based on intelligent agents who can make decisions using specific rules. Game agent systems are used in order to manage environment data, to simulate the game objects, to control interactions among game objects, and to support visual authoring interface that ran define a various interrelations of the game objects. These techniques can process the autonomy level of the game objects and the associated collision avoidance method, etc. Also, it is possible to make the coherent decision-making ability of the game objects about a change of the scene. In this paper, the rule-based behavior control was designed to guide the simulation of the game objects. The rules are pre-defined by the user using visual interface for designing their interaction. The Agent State Decision Network, which is composed of the visual elements, is able to pass the information and infers the current state of the game objects. All of such methods can monitor and check a variation of motion state between game objects in real time. Finally, we present a validation of the control method together with a simple case-study example. In this paper, we design and implement the supervised classification systems for high resolution satellite images. The systems support various interfaces and statistical data of training samples so that we can select the most effective training data. In addition, the efficient extension of new classification algorithms and satellite image formats are applied easily through the modularized systems. The classifiers are considered the characteristics of spectral bands from the selected training data. They provide various supervised classification algorithms which include Parallelepiped, Minimum distance, Mahalanobis distance, Maximum likelihood and Fuzzy theory. We used IKONOS images for the input and verified the systems for the classification of high resolution satellite images.

• Journal of Distribution Science
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• v.15 no.5
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• pp.75-83
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• 2017

Purpose - With the rise of the fourth industrial revolution, it has converged with the existing industrial revolution to give shape to increased accessibility of knowledge and information. As a result, it has become easier for scholars to actively persue and compile research in various fields. This current study aims to focus and assess the current standing of KODISA: the Journal of Distribution Science (JDS), International Journal of Industrial Distribution & Business(IJIDB), the East Asian Journal of Business Management (EAJBM), the Journal of Asian Finance, Economics and Business (JAFEB) in a rapidly evolving era. Novel strategies for creating the future vision of KODISA 2020 will also be examined. Research design, data, and methodology - The current research will analyze published journals of KODISA in order to offer a vision for the KODISA 2020 future. In part 1, this paper will observe the current address of the KODISA journal and its overview of past achievements. Next, part 2 will discuss the activities that will be needed for journals of KODISA, JDS, IJIDB, EAJBM, JAFEB to branch out internationally and significant journals will be statistically analyzed in part 3. The last part 4 will offer strategies for the continued growth of KODISA and visions for KODISA 2020. Results - Among the KODISA publications, IJIDB was second, JDS was 23rd (in economic publications of 54 journals), and EAJBM was 22nd (out of 79 publications in management field journals). This shows the high quality of the KODISA publication journals. According to 2016 publication analysis, JDS, IJIDB, etc. each had 157 publications, 15 publications, 16 publications, and 28 publications. In the case of JDS, it showed an increase of 14% compared to last year. Additionally, JAFEB showed a significant increase of 68%. This shows that compared to other journals, it had a higher rate of paper submission. IJIDB and EAJBM did not show any significant increases. In JDS, it showed many studies related to the distribution, management of distribution, and consumer behavior. In order to increase the status of the KODISA journal to a SCI status, many more international conferences will open to increase its international recognition levels. Second, the systematic functions of the journal will be developed further to increase its stability. Third, future graduate schools will open to foster future potential leaders in this field and build a platform for innovators and leaders. Conclusions - In KODISA, JDS was first published in 1999, and has been registered in SCOPUS February 2017. Other sister publications within the KODISA are preparing for SCOPUS registration as well. KODISA journals will prepare to be an innovative journal for 2020 and the future beyond.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.227-240
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• 2011

New concepts and ideas often result from extensive recombination of existing concepts or ideas. Both researchers and developers build on existing concepts and ideas in published papers or registered patents to develop new theories and technologies that in turn serve as a basis for further development. As the importance of patent increases, so does that of patent analysis. Patent analysis is largely divided into network-based and keyword-based analyses. The former lacks its ability to analyze information technology in details while the letter is unable to identify the relationship between such technologies. In order to overcome the limitations of network-based and keyword-based analyses, this study, which blends those two methods, suggests the keyword network based analysis methodology. In this study, we collected significant technology information in each patent that is related to Light Emitting Diode (LED) through text mining, built a keyword network, and then executed a community network analysis on the collected data. The results of analysis are as the following. First, the patent keyword network indicated very low density and exceptionally high clustering coefficient. Technically, density is obtained by dividing the number of ties in a network by the number of all possible ties. The value ranges between 0 and 1, with higher values indicating denser networks and lower values indicating sparser networks. In real-world networks, the density varies depending on the size of a network; increasing the size of a network generally leads to a decrease in the density. The clustering coefficient is a network-level measure that illustrates the tendency of nodes to cluster in densely interconnected modules. This measure is to show the small-world property in which a network can be highly clustered even though it has a small average distance between nodes in spite of the large number of nodes. Therefore, high density in patent keyword network means that nodes in the patent keyword network are connected sporadically, and high clustering coefficient shows that nodes in the network are closely connected one another. Second, the cumulative degree distribution of the patent keyword network, as any other knowledge network like citation network or collaboration network, followed a clear power-law distribution. A well-known mechanism of this pattern is the preferential attachment mechanism, whereby a node with more links is likely to attain further new links in the evolution of the corresponding network. Unlike general normal distributions, the power-law distribution does not have a representative scale. This means that one cannot pick a representative or an average because there is always a considerable probability of finding much larger values. Networks with power-law distributions are therefore often referred to as scale-free networks. The presence of heavy-tailed scale-free distribution represents the fundamental signature of an emergent collective behavior of the actors who contribute to forming the network. In our context, the more frequently a patent keyword is used, the more often it is selected by researchers and is associated with other keywords or concepts to constitute and convey new patents or technologies. The evidence of power-law distribution implies that the preferential attachment mechanism suggests the origin of heavy-tailed distributions in a wide range of growing patent keyword network. Third, we found that among keywords that flew into a particular field, the vast majority of keywords with new links join existing keywords in the associated community in forming the concept of a new patent. This finding resulted in the same outcomes for both the short-term period (4-year) and long-term period (10-year) analyses. Furthermore, using the keyword combination information that was derived from the methodology suggested by our study enables one to forecast which concepts combine to form a new patent dimension and refer to those concepts when developing a new patent.

• Korean Journal of Social Welfare
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• v.67 no.2
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• pp.85-112
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• 2015

This study examined the relationship structure among personal and social cognitive factors contributing to professional help-seeking decisions to relieve distress of those in low-income, then suggested an appropriate model to inform knowledge for better social work practice. Using data of a purposive sampling from 331 low-income people, covariance structural analyses were conducted in two stages of model exploration, one for TPB model and another for its extended model including the level of distress, family support, and willingness. As results, in the path analyses with the observed variables of the basic components of the TPB, subjective norm showed the strongest effect on the intention, following by attitudes towards help-seeking, then behavioral control the least; in turn both the intention, positively, and behavioral control, negatively, contributed to help-seeking decisions. In the second stage of the path analyses with the extended model of the TPB, each of distress and family support demonstrated direct positive effect on each of attitudes, subjective norm, and behavioral control; each of the attitudes, subjective norm, and behavioral control showed positive effect on both intention and willingness; in turn, while intention showed strong positive effect on help-seeking decisions, willingness had no significant effect and behavioral control had negative effect on decisions. There were significant indirect effects of behavioral control on intention through willingness and of willingness on decisions through intention. These results suggested that the TPB model is useful for modeling help-seeking decisions through personal and social cognitions, especially the significance of subjective norm implied the importance of social cognition for the people in low-income with distress. Further, it was implied that the extended model needs to address particularity of those people in low-income and the mechanism shown by behavioral control and willingness implied the importance of practicing respect for the client's autonomy and will for self-support in social work practice.