• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.37-52
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• 2015

The recent global trends display expansion and growing solidity in both cooperative collaboration between industry, education, and research and R&D network systems. A greater support for the network and cooperative research sector would open greater possibilities for the evolution of new scholar and industrial fields and the development of new theories evoked from synergized educational research. Similarly, the national need for a strategy that can most efficiently and effectively support R&D network that are established through the government's R&D project research is on the rise. Despite the growing urgency, due to the habitual dependency on simple individual personal information data regarding R&D industry participants and generalized statistical data references, the policies concerning network system are disappointing and inadequate. Accordingly, analyses of the relationships involved for each subject who is participating in the R&D industry was conducted and on the foundation of an educational-industrial-research network system, possible changes within and of the network that may arise were predicted. To predict the R&D network transitions, Common Neighbor and Jaccard's Coefficient models were designated as the basic foundational models, upon which a new prediction model was proposed to address the limitations of the two aforementioned former models and to increase the accuracy of Link Prediction, with which a comparative analysis was made between the two models. Through the effective predictions regarding R&D network changes and transitions, such study result serves as a stepping-stone for an establishment of a prospective strategy that supports a desirable educational-industrial-research network and proposes a measure to promote the national policy to one that can effectively and efficiently sponsor integrated R&D industries. Though both weighted applications of Common Neighbor and Jaccard's Coefficient models provided positive outcomes, improved accuracy was comparatively more prevalent in the weighted Common Neighbor. An un-weighted Common Neighbor model predicted 650 out of 4,136 whereas a weighted Common Neighbor model predicted 50 more results at a total of 700 predictions. While the Jaccard's model demonstrated slight performance improvements in numeric terms, the differences were found to be insignificant.

• 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.18 no.1
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• pp.109-123
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• 2012

Many kinds of the education systems are provided to students. Many kinds of the contents like School subjects, license, job training education and so on are provided through many kinds of the media like text, image, video and so on. Students will apply the knowledge they learnt and will use it when they learn other things. In the existing education system, there have been many situations that the education system isn't really helpful to the students because too hard contents are transferred to them or because too easy contents are transferred to them and they learn the contents they already know again. To solve this phenomenon, a method that transfers the most proper lecture contents to the students is suggested in the thesis. Because the difficulty is relative, the contents A can be easier than the contents B to a group of the students and the contents B can be easier than the contents A to another group of the students. Therefore, it is not easy to measure the difficulty of the lecture contents. A method considering this phenomenon to transfer the proper lecture contents is suggested in the thesis. The whole lecture contents are divided into many lecture modules. The students solve the pattern recognition questions, a kind of the prior test questions, before studying the lecture contents and the system selects and provides the most proper lecture module among many lecture modules to the students according to the score about the questions. When the system selects the lecture module and transfer it to the student, the students' answer and the difficulty of the lecture modules are considered. In the existing education system, 1 kind of the content is transferred to various students. If the same lecture contents is transferred to various students, the contents will not be transferred efficiently. The system selects the proper contents using the students' pattern recognition answers. The pattern recognition question is a kind of the prior test question that is developed on the basis of the lecture module and used to recognize whether the student knows the contents of the lecture module. Because the difficulty of the lecture module reflects the all scores of the students' answers, whenever a student submits the answer, the difficulty is changed. The suggested system measures the relative knowledge of the students using the answers and designates the difficulty. The improvement of the suggested method is only applied when the order of the lecture contents has nothing to do with the progress of the lecture. If the contents of the unit 1 should be studied before studying the contents of the unit 2, the suggested method is not applied. The suggested method is introduced on the basis of the subject "English grammar", subjects that the order is not important, in the thesis. If the suggested method is applied properly to the education environment, the students who don't know enough basic knowledge will learn the basic contents well and prepare the basis to learn the harder lecture contents. The students who already know the lecture contents will not study those again and save more time to learn more various lecture contents. Many improvement effects like these and so on will be provided to the education environment. If the suggested method that is introduced on the basis of the subject "English grammar" is applied to the various education systems like primary education, secondary education, job education and so on, more improvement effects will be provided. The direction to realize these things is suggested in the thesis. The suggested method is realized with the MySQL database and Java, JSP program. It will be very good if the suggested method is researched developmentally and become helpful to the development of the Korea education.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.7
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• pp.3062-3071
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• 2011

This study proposed to compare the performance differences between a manufacturing company and a construction company in accordance with the mutual relations and ownership structures with the management performance based on the increase or decrease of the large shareholders' share-holding ratio (insider ownership, foreign share-holding, institutional investors' share-holding) of a KOSPI listed company in Korea during 10 years(1998-2007). To sum up the research work, first, the increase of foreign share-holding supported the results of previous studies which foreign share-holding has a positive effect on the long term performance by having a positive(+) effect on MTB, and the increase of an insider ownership supported the management entrenchment hypothesis of previous studies by having a negative(-) effect on MTB. However, relations between institutional investors's share-holding and MTB could not find out linkages in spite of the results of previous studies where dealt with the active monitoring hypothesis. Also, to examine the linkages of ROA and the ownership structure, though the increases of foreign share-holding and insider ownership had a positive(+) effect on ROA, the increases of institutional investors' share-holding had a negative(-) effect on it. It showed different analysis results from the active monitoring hypothesis of institutional investors. As a result of verifying whether there is "any difference in the management performances between the construction industry and the manufacturing industry according to the equity structure" which is the second hypothesis, nothing of the insider ownership and whether or not there is the construction industry, foreign share-holding and whether or not there is the construction, and the institutional ownership and whether or not there is the construction industry gave a statistical difference to MTB and ROA. Accordingly, it was possible to find out there is no difference in the management performance between the construction industry and the manufacturing industry based on the ownership structure in spite of different characteristics from the manufacturing industry such as the revenue recognition in ordering, production and accounting.

• Restorative Dentistry and Endodontics
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• v.34 no.3
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• pp.177-183
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• 2009

The aim of this study was to evaluate the marginal and internal gaps in CEREC3 CAD/CAM inlays of three different preparation designs. CEREC3 Inlays of three different preparation designs (n=10) were fabricated according to Group I-conventional functional cusp capping/shoulder preparation, Group II-horizontal reduction of cusps and Group III-complete reduction of cusps/shoulder preparation. After cementation of inlays. the bucco-lingual cross section was performed through the center of tooth. Cross section images of 20 magnifications were obtained through the stereomicroscope. The gaps were measured using the Leica application suite software at each reference point. Statistical analysis was performed using one-way ANOVA and Tukey's test (${\alpha}<0.05$). The marginal gaps ranged from 80.0 to $97.8{\mu}m$ for Group I, 42.0 to $194.8{\mu}m$ for Group II, 51.0 to $80.2{\mu}m$ for Group III. The internal gaps ranged from 90.5 to $304.1{\mu}m$ for Group I, 80.0 to $274.8{\mu}m$ for Group II, 79.7 to $296.7{\mu}m$ for Group III. The gaps of each group were the smallest on the margin and the largest on the horizontal wall. For the CEREC3 CAD/CAM inlays, the simplified designs (groups II and III) did not demonstrate superior results compared to the traditional cusp capping design (group I).

• Restorative Dentistry and Endodontics
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• v.35 no.2
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• pp.80-87
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• 2010

The purpose of this study was to compare the different canal irrigation methods to prevent the formation of precipitate between sodium hypochlorite (NaOCl) and chlorhexidine (CHX). Extracted 50 human single-rooted teeth were used. The root canals were instrumented using NiTi rotary file (Profile .04/#40) with 2.5% NaOCl and 17% EDTA as irrigants. Teeth were randomly divided into four experimental groups and one control group as follows; Control group: 2.5% NaOCl only, Group 1: 2.5% NaOCl + 2% CHX, Group 2: 2.5% NaOCl + paper points + 2% CHX, Group 3: 2.5% NaOCl + preparation with one large sized-file + 2% CHX, Group 4: 2.5% NaOCl +95% alcohol+ 2% CHX. The teeth were split in bucco-lingual aspect and the specimens were observed using Field Emission Scanning Electron Microscope. The percentages of remaining debris and patent dentinal tubules were determined. Statistical analysis was performed with one-way analysis of variance (ANOVA). Energy Dispersive x-ray Spectroscopy was used for analyzing the occluded materials in dentinal tubule for elementary analysis. There were no significant differences in percentage of remaining debris and patent tubules between all experimental groups at all levels (p > .05). In elementary analysis, the most occluded materials in dentinal tubule were dentin debris. NaOCl/CHX precipitate was detected in one tooth specimen of Group 1. In conclusion, there were no significant precipitate on root canal, but suspected material was detected on Group 1. The irrigation system used in this study could be prevent the precipitate formation.

• Restorative Dentistry and Endodontics
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• v.32 no.4
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• pp.385-396
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• 2007

The aims of this study were to compare the shaping effect and safety between single length technique recommended by manufacturer and crown-down technique using Mtwo rotary file and to present a modified method in use of Mtwo file. Sixty simulated root canal resin blocks were used. The canals were divided into three groups according to instrument and the manner of using methods. Each group had 20 specimens. Group MT was instrumented with single length technique of Mtwo, group MC was instrumented with crown-down technique of Mtwo and group PT was instrumented with crown-down technique of ProTaper. All of the rotary files used in this study were operated by an electric motor. The scanned canal images of before and after preparation were superimposed. These superimposed images were evaluated at apical 1 to 8 mm levels Angle changes were calculated. The preparation time, weight loss, instrument failure and binding, canal aberrations, and centering ratio were measured. Statistical analysis of the three experimental groups was performed with ANOVA and Duncan's multiple range tests for post-hoc comparison and Fisher's exact test was done for the frequency comparison. In total preparation time, group MT and group MC were less than group PT. In the aberrations, group MT had more elbows than those of group MC and group PT. The binding of group MC was least and group MT was less than group PT (P < 0.05). Under the condition of this study, crown-down technique using Mtwo rotary file is better and safer method than single length technique recommended by the manufacturer.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1117-1132
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• 2019

Forest covers 30% of the Earth's land area and plays an important role in global carbon flux through its ability to store much greater amounts of carbon than other terrestrial ecosystems. The Gross Primary Production (GPP) represents the productivity of forest ecosystems according to climate change and its effect on the phenology, health, and carbon cycle. In this study, we estimated the daily GPP for a forest ecosystem using remote-sensed data from Moderate Resolution Imaging Spectroradiometer (MODIS) and machine learning algorithms Support Vector Machine (SVM). MODIS products were employed to train the SVM model from 75% to 80% data of the total study period and validated using eddy covariance measurement (EC) data at the six flux tower sites. We also compare the GPP derived from EC and MODIS (MYD17). The MODIS products made use of two data sets: one for Processed MODIS that included calculated by combined products (e.g., Vapor Pressure Deficit), another one for Unprocessed MODIS that used MODIS products without any combined calculation. Statistical analyses, including Pearson correlation coefficient (R), mean squared error (MSE), and root mean square error (RMSE) were used to evaluate the outcomes of the model. In general, the SVM model trained by the Unprocessed MODIS (R = 0.77 - 0.94, p < 0.001) derived from the multi-sites outperformed those trained at a single-site (R = 0.75 - 0.95, p < 0.001). These results show better performance trained by the data including various events and suggest the possibility of using remote-sensed data without complex processes to estimate GPP such as non-stationary ecological processes.

• Korean Security Journal
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• no.29
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• pp.139-162
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• 2011

The purpose of this study is to compare with the general and behavioral characteristics between simple and serial arsonists using the data derived from Scientific Crime Analysis System, Criminal Filing Search System, and Crime Information Management System. The analysis and findings reported here are derived from data extracted from 160 arsonists arrested by police officer. The independent variables included such socio-economic characteristic as arsonists' gender, age, occupation, education level, and previous criminal records of arsonists, and finally the general characteristics of the scene of fire settings. The dependent variable is whether or not serial fire setter. To achieve the purpose, the analysis of frequencies and cross-tab were conducted. According to frequence and cross-tab analysis, there are great differences of the general and behavior characteristics between two groups. In the comparison of simple and serial arsonists, serial arsonists are more likely to have previous criminal records, low socio-economic status, unmarried and no cohabitants than simple arsonists. furthermore, serial arsonists are more likely to use garbage papers for fire setting in the scene of the crime, to have mental or psychological problems, and to get involved in fire setting for the psychological pleasure than simple arsonists do. The present research has some obvious limitations. First, the analysis is based only on arsonists arrested by police officers. These may be considerable differences in arsonists arrested by police officers and fire setters not arrested by them. Additional research is needed to assess the extent to which these findings would apply to fire setters not arrested by police officer in Korea. Secondly, the data in this study are cross-sectional and simple cross-tab analysis are used. Potential limitation of cross-sectional data concerns the inability to specify the changes in measures as arsonists behavioral characteristics. Therefore, further studies need to use longitudinal data and more complicate statistical techniques such as correlation analysis, multiple regression analysis, or LISREL models to specify the casual relationships between dependent and independent variables for fire settings. Even if this study has some limitations, it is meaningful in which it first investigated the comparison of simple and serial arsonists focusing on the general and behavioral characteristics between two groups in Korea.