• Korean Journal of Environment and Ecology
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• v.32 no.2
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• pp.165-175
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• 2018

The purpose of this study is to provide the basic data such as distribution status, growth characteristics, and the structural characteristics of forest stands for the systematic conservation and management of relict forest trees (stem girth of 300cm or larger) established naturally in Gangwon Province, Korea. The survey showed that 434 individuals of 19 species (conifers: 228 individuals of 4 species, broad-leaved trees: 206 individuals of 15 species) were distributed in Gangwon Province, and Taxus cuspidata was the most abundant among them with 203 individuals or about 46.7 % of the total. The stem girth was average of 404cm (conifers: 373cm, broad-leaves: 421cm), and Tilia amurensis with multi-stemmed growing on Sorak mountain range had the largest stem girth at 1,113cm. The average height and the crown width of relict forest trees were 15.4m and 10.0m, respectively. Although the environments of relict forest trees showed a slight difference by species, the relative appearance frequencies of most trees were high in the environments where the altitude was higher than 1,000 m, slope degree was greater than $25^{\circ}$, the slope faced north, and microtopography was at the upper of slopes. Regarding the stand characteristics of relict forest trees per unit area ($/100m^2$), the average total coverage was 294% (max. 475%), the total average number of species was 36 species (max. 60 species), the average species diversity index (H') was 2.560 (max. 3.593), the average canopy closure was 84.8% (max. 94.6%), and the average basal area (/ha) was $52.7m^2$ (max. $116.4m^2$, relict trees $30.0m^2$, and other trees $22.7m^2$). The analysis of the dynamics of the forest stands where relict forest trees were growing showed four types of the maintenance mechanisms of relict forest trees depending on the supply pattern of succeeding trees: "Low-density but persistent type (Quercus mongolica, Abies holophylla, Tilia amurensis, and Pyrus ussuriensis)," "Long ago stopped type (Pinus densiflora)," "Recently stopped type (Abies nephrolepis, Quercus variabilis, and Betula schmidtii)," and "Periodically repeated types of supply and stop (Salix caprea and Quercus serrata).".

• Journal of Sasang Constitutional Medicine
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• v.8 no.1
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• pp.349-376
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• 1996

In orther to find the characteristics of each constitution, the examinee of Kyung Hee medical center was diagnosed constitution, and resulted body measures and diagnosis. That was considered and the results are as follows 1. The Diagnosis result of Sa-sang Constitutional Medicine is that there are 110 persons of Taeum-In(56. 1%), 58 persons of Soum-In(29.6%), 28 persons of Soyang-In(14. 3%). 2. The distribution of occupation, there are many of Taeum-In who are engaged in business, administeration, and management and many of Soeum-In who are engaged in reserch. 3. QSCC(I) has a tendency that other constitutions diagnose to Taeyang-In, the quastionare 1 has the accuracy of 76. 4% to diagnose Soeum-In. 4. Taeum-In sweats easily but Soeum-In doesn't sweat easily, Taeum-In has a good appetite and likes cold food and digests well, but Soeum-In has a poor appetite and like hot food and digest poorly. 5. The degree of obesity is the highest in Taeum-In. 6. The systoric blood pressure and diastolic blood pressure is high in Taeum-In and the high blood pressure are frequent in Taeum-In. 7. Triglyceride is the highest in Taeum-In and the Hyperlipidemia is the most frequent in Taeum-In, but Total cholesterol has no difference among constitutions. 8. GPT GGT is higher in Taeum-In than Soyang-In, but GOT has no difference among constitutions. 9. The frequency of fatty liver is the highest in Taeum-In.

• Journal of Distribution Science
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• v.10 no.4
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• pp.21-28
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• 2012

While recent economic stagnation has left consumers dispirited, rapid growth has been seen in the domestic coffee industry recently. With the growth in coffee consumption, a tremendous increase in the number of specialty coffee shops has been seen in the domestic coffee market. The expectation that markets specializing in coffee will continue to grow for a long time will cause existing enterprises to expand their shops and increase the rate of entry of new shops. Intense competition in the domestic coffee market will force companies to create a competitive advantage through differentiated marketing strategies. This paper focuses on how the shopping value and repatronage intention of customers using coffee shops is affected by service quality. Moreover, this paper intends to examine the service quality that is critical for the successful management of relationships and the values that are important to consumers. For these purposes, the discriminative effect of service quality on shopping value was analyzed and the effect of utilitarian and hedonic value on repatronage intention was reviewed. The results of this study are detailed below. First, interaction and outcome quality can positively affect the hedonic value, whereas environment quality is not meaningful for utilitarian value. Considering the relative effect on utilitarian value outcome, the effect of outcome quality is greater than that of interaction. This result shows that the role of outcome quality is most important for improving utilitarian value. Second, outcome and environment quality positively affect hedonic value; however, interaction quality does not meaningfully increase hedonic value. These results indicate that customers recognize hedonic value on the basis of their evaluation of the service outcomes and the background to delivery service. In particular, it was revealed that the relative effect of outcome quality on hedonic value is greater than that of environment quality. Third, both utilitarian value and hedonic value had a positive effect on repatronage intention. The relative influence of the hedonic value is that the shopping value affects the repatronage intention more than the utilitarian value. These results mean that customers recognize coffee shops as spaces for satisfying utilitarian and hedonic values, and they place more importance on the benefits of the emotional experience than functional needs. Finally, this study suggests that output quality is more important than other service factors, and the results of this paper give some implications to the coffee shop industry that customers seek utilitarian needs based on economic value and place more weight on hedonic value, such as that offered by relationship media.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.34 no.6
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• pp.643-651
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• 2001

Paddlewheel-driven circulation in a culture pond has been simulated based on the depth integrated 2 dimensional hydrodynamic model. Acceleration by paddlewheel is expressed as shaft force divided by water mass discharged by paddlewheel blades. The model has been calibrated and applied to culture ponds as following steps:- i) The model predicted velocities at every 10 m along longitudinal direction from the paddlewheel. The model was calibrated comparing the results with the measured values at mass correction factor $\alpha$ and dimensionless eddy viscosity constant $\gamma$, respectively, in a range $15\~20$ and 6. ii) Wind shear stress was simulated under conditions of direction $0^{\circ}C,\;90^{\circ}C\;and\;180^{\circ}C$ and speed 0.0, 2.5, 5.0 and 7.5 m/s. Change rate of current speed was <$1\%$ at wind in parallel or opposite direction to the paddlewheel-driven jet flow, while $4\%$ at orthogonal angle. iii) The model was then applied to 2 culture ponds located at the Western coast of Korea. The measured and predicted currents for the ponds were compared using the regression analysis. Analysis of flow direction and speed showed correlation coefficients 0.8928 and 0.6782 in pond A, 0.8539 and 0.7071 in pond B, respectively. Hence, the model is concluded to accurately predict circulation driven by paddlewheel such that it can be a useful tool to provide pond management strategy relating to paddlewheel operation and water quality.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.159-172
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• 2010

The recommender system is one of the possible solutions to assist customers in finding the items they would like to purchase. To date, a variety of recommendation techniques have been developed. One of the most successful recommendation techniques is Collaborative Filtering (CF) that has been used in a number of different applications such as recommending Web pages, movies, music, articles and products. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. Broadly, there are memory-based CF algorithms, model-based CF algorithms, and hybrid CF algorithms which combine CF with content-based techniques or other recommender systems. While many researchers have focused their efforts in improving CF performance, the theoretical justification of CF algorithms is lacking. That is, we do not know many things about how CF is done. Furthermore, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting the performances of CF algorithms in advance is practically important and needed. In this study, we propose an efficient approach to predict the performance of CF. Social Network Analysis (SNA) and Artificial Neural Network (ANN) are applied to develop our prediction model. CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. SNA facilitates an exploration of the topological properties of the network structure that are implicit in data for CF recommendations. An ANN model is developed through an analysis of network topology, such as network density, inclusiveness, clustering coefficient, network centralization, and Krackhardt's efficiency. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Inclusiveness refers to the number of nodes which are included within the various connected parts of the social network. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. Krackhardt's efficiency characterizes how dense the social network is beyond that barely needed to keep the social group even indirectly connected to one another. We use these social network measures as input variables of the ANN model. As an output variable, we use the recommendation accuracy measured by F1-measure. In order to evaluate the effectiveness of the ANN model, sales transaction data from H department store, one of the well-known department stores in Korea, was used. Total 396 experimental samples were gathered, and we used 40%, 40%, and 20% of them, for training, test, and validation, respectively. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. The input variable measuring process consists of following three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used Net Miner 3 and UCINET 6.0 for SNA, and Clementine 11.1 for ANN modeling. The experiments reported that the ANN model has 92.61% estimated accuracy and 0.0049 RMSE. Thus, we can know that our prediction model helps decide whether CF is useful for a given application with certain data characteristics.

• 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.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.

• Korean Security Journal
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• no.42
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• pp.7-36
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• 2015

This study aims at analyzing difference of social Security network, Community unity and local government trust according to socio-demographical features, exploring the relation of social Security network, Community unity and local government trust according to socio-demographical features, presenting results between each variable as a model and verifying the property of mutual ones. This study sampled general citizens in Gwangju for about 15 days Aug. 15 through Aug. 30, 2014, distributed total 450 copies using cluster random sampling, gathered 438 persons, 412 persons of whom were used for analysis. This study verified the validity and credibility of the questionnaire through an experts' meeting, preliminary test, factor analysis and credibility analysis. The credibility of questionnaire was ${\alpha}=.809{\sim}{\alpha}=.890$. The inout data were analyzed by study purpose using SPSSWIN 18.0, as statistical techniques, factor analysis, credibility analysis, correlation analysis, independent sample t verification, ANOVA, multi-regression analysis, path analysis etc. were used. the findings obtained through the above study methods are as follows. First, building a social Security network has an effect on Community institution. That is, the more activated a, the higher awareness on institution. the more activated street CCTV facilities, anti-crime design, local government Security education, the higher the stability. Second, building a social Security network has an effect on trust of local government. That is, the activated local autonomous anti-crime activity, anti-crime design. local government's Security education, police public oder service, the more increased trust of policy, service management, busines performance. Third, Community unity has an effect on trust of local government. That is, the better Community institution is achieved, the higher trust of policy. Also the stabler Community institution, the higher trust of business performance. Fourth, building a social Security network has a direct or indirect effect on Community unity and local government trust. That is, social Security network has a direct effect on trust of local government, but it has a higher effect through Community unity of parameter. Such results showed that Community unity in Gwangju Region is an important factor, which means it is an important variable mediating building a social Security network and trust of local government. To win trust of local residents, we need to prepare for various cultural events and active communication space and build a social Security network for uniting them.

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

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

• Journal of Distribution Science
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• v.9 no.3
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• pp.73-82
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• 2011

This study presents an effective and distinctive marketing strategy through the implementation of the aesthetic art marketing communication technique of storytelling. The reason applying art to marketing is effective is that it gives "class" and aesthetic beauty to the brand's image, which will lead to an increase in revenue and loyalty of consumers. The story stands in for the brand's subject of "desire." Luxury brand customers not only consume high-quality products, require the utmost in service, and value of the brand, they also appreciate the story the brand is telling. The story, combined with art, is called art marketing communication; it makes the brand more unique through its enhanced visual elements. The study discusses art collaboration, art exhibition, a transforming architecture project, art advertisement, a flagship store, and a human resource training center. Based on the "desire," I adopted the element and principle of storytelling. By visualizing the brand with a symbol, the company is able to relate to consumers' sentimentality. Through storytelling art marketing communication, and the strategy using relevance of brand and artist's popularity, the research shows efficient art marketing influences to the brand's image. The results of the research indicate that by using adequate art marketing communication that best reflects the identity and story of the luxury brand, it produces great results; the research also demonstrated, in various ways, that art marketing will succeed. The case showed the following outcomes. First, consumers have a tendency to choose a brand that is associated with an empathizing story. World renowned brands see through the market's "desires" for unique stories, and they also provide the ability to amuse consumers. The story in a product will become an important competitive element in future markets. Second, the art marketing communication applying a story rendered a brand with distinction. The most effective art marketing communications are art collaboration, art exhibition, locomotive architecture project, and others that are adopted as various means. Third, the brand's products were considered as an art piece, which led to not only strengthening the loyalty of consumers but also an increase in sales. In addition, the company could sustain a premium price for the goods sold. By adapting art to a brand's tradition, an innovative and creative new product provides consumer satisfaction, and producing goods in limited editions creates enthusiastic collectors. Fourth, this study suggests an abridged report, implication, limitation of the study, and directions for further research. Referring to the case for the adaptation of luxury brands, efficient art marketing strategies considering Korean company brand and efficiently study preceding Korean company brand art marketing strategy could be proposed.