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

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.93-107
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• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.97-121
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• 2013

With the growing importance of knowledge for sustainable competitive advantages and innovation in a volatile environment, many researches on knowledge sharing have been conducted. However, previous researches have mostly relied on the questionnaire survey which has inherent perceptive errors of respondents. The current research has drawn the relationship among primary participant behaviors towards the participation level in knowledge sharing, basically from online user behaviors on Wikipedia, a representative community for online knowledge collaboration. Without users' participation in knowledge sharing, knowledge collaboration for creating knowledge cannot be successful. By the way, the editing patterns of Wikipedia users are diverse, resulting in different revisiting periods for the same number of edits, and thus varying results of shared knowledge. Therefore, we illuminated the participation level of knowledge sharing from two different angles of number of edits and revisiting period. The behavioral dimensions affecting the level of participation in knowledge sharing includes the article talk for public discussion and user talk for private messaging, and community registration, which are observable on Wiki platform. Public discussion is being progressed on article talk pages arranged for exchanging ideas about each article topic. An article talk page is often divided into several sections which mainly address specific type of issues raised during the article development procedure. From the diverse opinions about the relatively trivial things such as what text, link, or images should be added or removed and how they should be restructured to the profound professional insights are shared, negotiated, and improved over the course of discussion. Wikipedia also provides personal user talk pages as a private messaging tool. On these pages, diverse personal messages such as casual greetings, stories about activities on Wikipedia, and ordinary affairs of life are exchanged. If anyone wants to communicate with another person, he or she visits the person's user talk page and leaves a message. Wikipedia articles are assessed according to seven quality grades, of which the featured article level is the highest. The dataset includes participants' behavioral data related with 2,978 articles, which have reached the featured article level, with editing histories of articles, their article talk histories, and user talk histories extracted from user talk pages for each article. The time period for analysis is from the initiation of articles until their promotion to the featured article level. The number of edits represents the total number of participation in the editing of an article, and the revisiting period is the time difference between the first and last edits. At first, the participation levels of each user category classified according to behavioral dimensions have been analyzed and compared. And then, robust regressions have been conducted on the relationships among independent variables reflecting the degree of behavioral characteristics and the dependent variable representing the participation level. Especially, through adopting a motivational theory adequate for online environment in setting up research hypotheses, this work suggests a theoretical framework for the participation level of online knowledge sharing. Consequently, this work reached the following practical behavioral results besides some theoretical implications. First, both public discussion and private messaging positively affect the participation level in knowledge sharing. Second, public discussion exerts greater influence than private messaging on the participation level. Third, a synergy effect of public discussion and private messaging on the number of edits was found, whereas a pretty weak negative interaction effect of them on the revisiting period was observed. Fourth, community registration has a significant impact on the revisiting period, whereas being insignificant on the number of edits. Fifth, when it comes to the relation generated from private messaging, the frequency or depth of relation is shown to be more critical than the scope of relation for the participation level.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.193-215
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• 2016

In recent years, the proliferation of diverse social networking services has led users to use many mediums simultaneously depending on their individual purpose and taste. Besides, while collecting information about particular themes, they usually employ various mediums such as social networking services, Internet news, and blogs. However, in terms of management, each document circulated through diverse mediums is placed in different categories on the basis of each source's policy and standards, hindering any attempt to conduct research on a specific category across different kinds of sources. For example, documents containing content on "Application for a foreign travel" can be classified into "Information Technology," "Travel," or "Life and Culture" according to the peculiar standard of each source. Likewise, with different viewpoints of definition and levels of specification for each source, similar categories can be named and structured differently in accordance with each source. To overcome these limitations, this study proposes a plan for conducting category mapping between different sources with various mediums while maintaining the existing category system of the medium as it is. Specifically, by re-classifying individual documents from the viewpoint of diverse sources and storing the result of such a classification as extra attributes, this study proposes a logical layer by which users can search for a specific document from multiple heterogeneous sources with different category names as if they belong to the same source. Besides, by collecting 6,000 articles of news from two Internet news portals, experiments were conducted to compare accuracy among sources, supervised learning and semi-supervised learning, and homogeneous and heterogeneous learning data. It is particularly interesting that in some categories, classifying accuracy of semi-supervised learning using heterogeneous learning data proved to be higher than that of supervised learning and semi-supervised learning, which used homogeneous learning data. This study has the following significances. First, it proposes a logical plan for establishing a system to integrate and manage all the heterogeneous mediums in different classifying systems while maintaining the existing physical classifying system as it is. This study's results particularly exhibit very different classifying accuracies in accordance with the heterogeneity of learning data; this is expected to spur further studies for enhancing the performance of the proposed methodology through the analysis of characteristics by category. In addition, with an increasing demand for search, collection, and analysis of documents from diverse mediums, the scope of the Internet search is not restricted to one medium. However, since each medium has a different categorical structure and name, it is actually very difficult to search for a specific category insofar as encompassing heterogeneous mediums. The proposed methodology is also significant for presenting a plan that enquires into all the documents regarding the standards of the relevant sites' categorical classification when the users select the desired site, while maintaining the existing site's characteristics and structure as it is. This study's proposed methodology needs to be further complemented in the following aspects. First, though only an indirect comparison and evaluation was made on the performance of this proposed methodology, future studies would need to conduct more direct tests on its accuracy. That is, after re-classifying documents of the object source on the basis of the categorical system of the existing source, the extent to which the classification was accurate needs to be verified through evaluation by actual users. In addition, the accuracy in classification needs to be increased by making the methodology more sophisticated. Furthermore, an understanding is required that the characteristics of some categories that showed a rather higher classifying accuracy of heterogeneous semi-supervised learning than that of supervised learning might assist in obtaining heterogeneous documents from diverse mediums and seeking plans that enhance the accuracy of document classification through its usage.

• Journal of the Korean Institute of Landscape Architecture
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• v.39 no.2
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• pp.103-112
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• 2011

The purpose of this study is to specify the prior eco-preserve zone by establishing the eco-landscape unit on the stream corridor and evaluating the stream ecosystem in the dam basin. The fundamental ecological data was surveyed and collected through "the ecosystem project on Yongdam multipurpose dam watershed" from 2008 to 2009. The Yongdam Dam Watershed has several streams, Jujacheon, Jeongjacheon and Guryangcheon, of which the area is $930km^2$, stretching to Jinangun, Jangsugun and Mujugun Jellabukdo. In spite of being used for drinking purpose, the dam water quality and ecosystem is threatened by in-watershed pollution produced by development, golf course grounds and sports complex, etc. The landscape unit of stream corridor was zoned across by 250m, 500m, and 750m from the vicinity line of stream, which was decided to the accuracy of mapping and surveying. Types of evaluation are the Stream Corridor Evaluation(SCE) and the Vegetated Area Evaluation(VAE). In the process of SCE, several indices were analysed, fish species diversity, species peculiarity, and stream naturality. Indices for VAE were forest stand map, vegetation protection grade, species diversity and peculiarity for wild bird and mammal life. The importance of the ecological items is categorized into three levels and overlapped for specifying the prior preserve zone. The area at which legally protecting species appeared is categorized as absolute preserve area. This study might be meaningful for proposing the evaluation process of a stream corridor ecosystem, which can synthesize a lot of individual ecological surveys. We hope further research will be actively performed about the ecotope mapping which is based on a individual wildlife territory and habitats and also their relationships.

• Journal of the Korean Institute of Landscape Architecture
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• v.38 no.3
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• pp.62-74
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• 2010

This study aims to examine what factors citizens value in urban parks and green spaces in terms of usage and aesthetic value and to find ways to deal with the changing patterns of user satisfaction for these various green elements. To achieve this, the study developed a dynamic model employing a transactional approach to evaluate environmental quality for 1999 and 2007 in Anyang City as well as a conceptual model of parks and greens satisfaction. This study relied on an empirical study method including the 1999 and 2007 green conditional survey and citizen questionnaires totaling 573 in the year 1999 and 982 in the year 2007. As a result, first, the factor 'urban parks' is the most important factor and 'cityscape' is the second most important factor in parks and greens satisfaction(PGS). Second, PGS in turn causes environmental quality satisfaction(EQS), which is related to two items--'urban livability' and 'aesthetic quality'--in the model. This means that PGS is the intervening variable of urban livability. Third, the factor analysis resulted in six factors: cityscape, urban green, linear facilities, urban parks, riverside green, and urban forest. 'Riverside green' emerged as a factor in 2007 as a result of public participation in the 'Anyang River Revitalization Project'. Fourth, through a transactional view, the environmental changes result in either a change in or stability of public attitude. The levels of satisfaction were elevated but patterns of satisfied-unsatisfied items remained unchanged for most factors. The perception of riverside a greenway and linear surface facilities(pedestrian walkways, biking and jogging trails, etc.) have changed positively. PGS changed significantly in 2007, as a result of urban events and development, including parks, rivers and greenways which were built through the joint effort of the local government and civic participation.

• Korean Chemical Engineering Research
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• v.43 no.2
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• pp.206-214
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• 2005

Heterogeneously-catalyzed oxidation of aqueous phase trichloroethylene (TCE) over supported metal oxides has been conducted to establish an approach to eliminate ppm levels of organic compounds in water. A continuous flow reactor system was designed to effect predominant reaction parameters in determining catalytic activity of the catalysts for wet TCE decomposition as a model reaction. 5 wt.% $CoO_x/TiO_2$ catalyst exhibited a transient period in activity vs. on-stream time behavior, suggesting that the surface structure of the $CoO_x$ might be altered with on-stream hours; regardless, it is probable to be the most promising catalyst. Not only could the bare support be inactive for the wet decomposition reaction at $36^{\circ}C$, but no TCE removal also occurred by the process of adsorption on $TiO_2$ surface. The catalytic activity was independent of all particle sizes used, thereby representing no mass transfer limitation in intraparticle diffusion. Very low TCE conversion appeared for $TiO_2$-supported $NiO_x$ and $CrO_x$ catalysts. Wet oxidation performance of supported Cu and Fe catalysts, obtained through an incipient wetness and ion exchange technique, was dependent primarily on the kinds of the metal oxides, in addition to the acidic solid supports and the preparation routes. 5 wt.% $FeO_x/TiO_2$ catalyst gave no activity in the oxidation reaction at $36^{\circ}C$, while 1.2 wt.% Fe-MFI was active for the wet decomposition depending on time on-stream. The noticeable difference in activity of the both catalysts suggests that the Fe oxidation states involved to catalytic redox cycle during the course of reaction play a significant role in catalyzing the wet decomposition as well as in maintaining the time on-stream activity. Based on the results of different $CoO_x$ loadings and reaction temperatures for the decomposition reaction at $36^{\circ}C$ with $CoO_x/TiO_2$, the catalyst possessed an optimal $CoO_x$ amount at which higher reaction temperatures facilitated the catalytic TCE conversion. Small amounts of the active ingredient could be dissolved by acidic leaching but such a process gave no appreciable activity loss of the $CoO_x$ catalyst.

• Journal of Korean Society of Forest Science
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• v.104 no.1
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• pp.26-34
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• 2015

This study was carried out to evaluate the growth characteristics, carbon and nitrogen content of containerized 1-0 Chamaecyparis obtusa seedlings at various levels of fertilizer (2 g/L, 1 g/L, control) and three container volumes (500 mL, 400 mL, 320 mL). The growth of root collar diameter was highest in the 2 g/L (3.14 mm), followed by the 1 g/L (2.75 mm) and control (2.41 mm) treatments, while the height of seedling was significantly higher in the 1 g/L (21.88 cm) than other treatments (2 g/L: 20.92 cm; control: 19.06 cm). The growth of root collar diameter by container volume was better in the 500 mL than in the 320 mL. Dry weight of seedling was the highest in the 1 g/L ($4.41g\;seedling^{-1}$), followed by the control ($3.67g\;seedling^{-1}$) and the 2 g/L ($2.92g\;seedling^{-1}$) treatments. The dry weight of seedlings by container volume was significantly higher in the 500 mL than in the 320 mL. Nitrogen concentration in foliage was ranged from 1.51% in the control to 2.45-2.60% in the fertilizer treatments. However, carbon concentration of seedlings was not affected by the fertilizer or the container volume treatments. The growth of seedlings following planting in mountain area was better in the fertilized seedlings compared with in the unfertilized seedlings. The results indicate that the 1 g/L fertilization was an optimum rate for growth following planting of Chamaecyparis obtusa seedlings.

• Journal of the Korean Chemical Society
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• v.59 no.2
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• pp.164-178
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• 2015

The purpose of this study was to design a teaching and learning method conductive to the development of creative thinking skills and investigate its effects. It has been developed integrated mind map with feature of visualizing the divergent thinking to the aspects of Science (S), Technology (T) & Engineering (E), Arts (A), Mathematics (M). Integrated mind map can be divided into four types of STEAM type, STEA type, STEM type, STE type depending on the category of key words in the first branch. And Integrated mind map can be divided into three levels of guided, intermediate, open depending on the teacher's guide degree. And also integrated mind map activities were carried out in the form of group, class share as well as individual. This study was implemented during a semester and students in experiment group experienced individual-integrated mind map activity 10 times, group-integrated mind map activity 10 times, class share-integrated mind map activity 3 times. The results indicated that the experimental group presented statistically meaningful improvement in creative thinking skills (p<.05). And there was a statistically meaningful improvement in fluency, flexibility, originality as a sub-category of creative thinking skills(p <.05). Also creative thinking skills are not affected by the level of cognitive, academic performance, gender (p<.05). In conclusion, it was found that 'integrated mind map activity' improved student's creative thinking skills. There was no interaction effect about creative thinking skills between the teaching strategy and cognitive level, achivement, gender of those students.

• Journal of the Korean Chemical Society
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• v.54 no.5
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• pp.556-566
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• 2010

A new kinetic spectrophotometric method is developed for the measurement of Mn(II) in natural water samples. The method is based on the catalytic effect of Mn(II) with the oxidation of Gallocyanin by $KIO_4$ using nitrilotriacetic acid (NTA) as an activation reagent at 620 nm. The optimum conditions obtained are $4.00{\times}1^{-5}\;M$ Gallocyanin, $KIO_4$, $1.00{\times}10^{-4}\;M$ NTA, 0.1 M HAc/NaAc buffer of pH = 3.50, the reaction time of 5 min and the temperature of $30^{\circ}C$. Under the optimum conditions, the proposed method allows the measurement of Mn(II) in a range of $0.1\;-\;4.0\;ng\;mL^{-1}$ and with a detection limit of down to $0.025\;ng\;mL^{-1}$. The recovery efficiency in measuring the standard Mn(II) solution is in a range of 98.5 - 102%, and the RSD is in a range of 0.76 - 1.25%. The newly developed kinetic method has been successfully applied to the measurement of Mn(II) in both some environmental water samples and certified standard reference river water sample, JAC-0031 with satisfying results. Moreover, few cations and anions interfere with the measurement of Mn(II). Compared with the other catalytic-kinetic methods and instrumental methods, the proposed kinetic method shows fairly good selectivity and sensitivity, low cost, cheapness, low detection limit and rapidity. It can easily and successfully be applied to the real water samples with relatively low salt content and complex matrices such as bottled drinking water, cold and hot spring waters, lake water, river water samples.