• Journal of the Society of Cosmetic Scientists of Korea
• /
• v.29 no.2 s.43
• /
• pp.233-250
• /
• 2003

We often use the KCID(Korean Cosmetic Ingredient Dictionary) and ICID(International Cosmetic Ingredient Dictionary) within cosmetics research and within their export and import. so far, we do not have a database of a cosmetics' raw materials. Because of this, we consume a lot of time to find the raw material data that is needed. This study constructs a cosmetics' raw material database and develops the program to retrieve it. We used a Linux machine as the equipment for this study and we used Apache web server, MySQL database server and PHP as the tools of this study. 11,817 kinds of raw materials data were registered as ICID, 866 kinds of raw materials data were registered as KCID and 28,008 kinds of raw materials data with registered trade name into the database. Also, The database was composed of the database of the association form. The database of the online form could ultimately reduce the task time as soon as it did its purpose. The product of this study can become a good basis of data to reconfigure. In the future, it can become a good database in relation with different databases.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.11 no.4
• /
• pp.51-63
• /
• 2008

It is important to develop standard metadata to give more plentiful information about the forest cover type map and to promote distribution by National Geographic Information Clearinghouse. In this study metadata for the forest cover type map was designed based on TTAS.IS-19115 and it consisted of 10 packages and 50 elements. Also metadata editor was developed to implement metadata with standard schema and metadata viewer to service more user friendly interface. This work was about the first standard metadata for forest GIS data. So it would be a useful reference to develop metadata for other digital map concerning forest.

• Journal of the Korea Society of Computer and Information
• /
• v.13 no.3
• /
• pp.15-25
• /
• 2008

Recently, with the growing use of e-learning, instructors develop new online courses using a variety of contents and then store the results on their computers. These contents should be updated with new information as time goes on, and a new content also can be produced by reusing these ones. However, a lot of time will be needed for instructors to search, edit, and manage various contents stored from place to place on their computers. Currently, the development of the e-learning content management tool. which performs efficiently these functions on the PC environment, leaves much to be desired. Therefore, in this paper, we proposed an e-learning content creation and management system which can manage efficiently a variety of contents stored from different locations on an instructor's computer and can develop easily new online courses. The proposed system can be used widely to develop contents for instructors based on the PC environment. For performance evaluation, this paper compared the proposed system with the previous system according to the retrieval time of content keyword, and the experiment showed that our system is much better than the previous one.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.9
• /
• pp.13-25
• /
• 2021

In the age of digital innovation based on the Internet, Information and Communication and Artificial Intelligence technologies, huge amounts of datasets are being generated, collected, accumulated, and opened on the web by various public institutions providing useful and public information. In order to analyse, gain useful insights and information from data, Formal Concept Analysis(FCA) has been successfully used for analyzing, classifying, clustering and visualizing data based on the binary relation between objects and attributes in the dataset. In this paper, we present an approach for enhancing the analysis of relational attributes of data within the extended framework of FCA, which is designed to classify, conceptualize and visualize sets of objects described not only by attributes but also by relations between these objects. By using the proposed tool, RCA wizard, several experiments carried out on some public open datasets demonstrate the validity and usability of our approach on generating and visualizing conceptual hierarchies for extracting more useful knowledge from datasets. The proposed approach can be used as an useful tool for effective data analysis, classifying, clustering, visualization and exploration.

• Journal of Global Scholars of Marketing Science
• /
• v.8
• /
• pp.61-87
• /
• 2001

Internet, which has been developed as a new exchange revolution, forms a huge virtual exchange market, and the innovative electronic commerce has completely broken off the way of existing goods distribution. This study begins with an awareness of the importance of customer retention to keep winning over the competition in internet shopping mall. In order to explain of the customer retention between individual and internet shopping mall, the study introduces first a satisfaction on shopping followed by an awareness of the importance of customer retention, and looks into a formation process of trust, satisfaction, and relationship orientation occurred by the offer of valuable convenience to customers. The study also explores the influence on shopping value by the characteristics with which internet shopping mall can bear, unfold by a cause and effect relationship the degree of shopping satisfaction, trust, and relationship orientation, and inquires a question to find out how to fuse the characteristics for internet retention. Therefore, this study has the following purposes: After examining prior research for the characteristics of internet shopping mall, it presents a possibility to connect shopping value with customer retention in light of theoretical system on characteristic elements derived from emotional and utilitarian perspectives. In order to achieve the purposes, the characteristics of internet retailing shop included site design, virtual reality, web awareness, customer concern, merchandise search, information supply, product value, and transaction system. Hypotheses were set up for the relationship with these characteristics and substantially analyzed. To prove this research, we analyzed collected data in which customers had experienced in shopping at internet shopping mall and discussed strategic current issues about its analytic results.

• Journal of The Korean Association For Science Education
• /
• v.37 no.2
• /
• pp.253-262
• /
• 2017

This study aims at investigating the perceptions of science teachers about the role of visual representations in the teaching of electromagnetism, and finding out how science teachers use visual representations in their teaching of electromagnetism and the difficulties they experience in dealing with those representations. A total of 121 science teachers responded to the online survey. The results showed that most of the teachers agreed to the significance of using visual representations in the classroom but regarded their role as means of simply delivering science knowledge rather than constructing or generating knowledge. For the three visual representations widely used in teaching of electromagnetism in secondary schools (electrostatic induction on electroscope, magnetic field around current carrying wire, structure and principle of electric motor), the teachers preferred teacher-centered use of visual representations rather than student-centered and teacher's construction of representations were the most frequent among four types of use; interpretation, construction, application, and evaluation. The difficulties of teaching with these three visual representations were categorized into several factors; teachers, students, the characteristics of the representations, and lack of resources and classroom environment. Teachers' limited perceptions about the role of visual representations were associated with the ways of using visual representations in their teaching. Implications for the effective use of visual representations for science learning and teaching were discussed.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.1-19
• /
• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.