• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.101-107
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• 2018

Recently, Korea's defense industry has advanced highly, and defense R&D budget is gradually increasing in defense budget. However, without objective analysis of defense industry technology, effective defense R&D activities are limited and defense budgets can be used inefficiently. Therefore, in addition to analyzing the defense industry technology quantitatively reflecting the opinions of the experts, this paper aims to analyze the defense industry technology objectively by quantitative methods, and to make efficient use of the defense budget. In addition, we propose a patent analysis method to grasp the characteristics of the defense industry technology and the vacant technology objectively and systematically by applying the big data analysis method, which is one of the keywords of the 4th industrial revolution, to the defense industry technology. The proposed method is applied to the technology of the firepower industry among several defense industrial technologies and the case analysis is conducted. In the process, the patents of 10 domestic companies related to firepower were collected through the Kipris in the defense industry companies' classification of the Korea Defense Industry Association(KDIA), and the data matrix was preprocessed to utilize IPC codes among them. And then, we Implemented association rule mining which can grasp the relation between each item in data mining technique using R program. The results of this study are suggested through interpretation of support, confidence lift index which were resulted from suggested approach. Therefore, this paper suggests that it can help the efficient use of massive national defense budget and enhance the competitiveness of defense industry technology.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.6
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• pp.2021-2029
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• 2010

This paper suggested the method to design and analyze FMC robot's dispatching rule using the Simulation and Sequential Patterns. To do this, first of all, we built FMC using simulation and then, extracted signals that facilities call a robot, saved it as the log type. Secondly, we built robot's optimal path using the Sequential Pattern Mining with the results of analyzing the log and relationship between machine and robot actions. Lastly, we adapted it to the A corp.'s manufacturing line for verifying its performance. As a result of applying the new dispatching rule in FMC, total throughput and total flow time decrease because of decreasing material loss time and increasing robot utility. Furthermore, because this method can be applied for every manufacturing plant using simulation, it can contribute to advance total FMC efficiency as well.

• The Journal of Society for e-Business Studies
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• v.23 no.4
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• pp.63-85
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• 2018

The "About Us" page on the website of a corporation provides information regarding the organization's vision, philosophy, and values. We examine the association between institutional information provided on corporate websites (i.e., the "About Us" section) with present and future financial performance. Utilizing a text mining technique, we analyze the institutional information of S&P500 firms in the year 2016. We conduct a factor analysis including words that are intentionally repeated in the introductory text of corporate websites. The results of the analysis reveal that keywords from this institutional information can be grouped into six factors. We then carry out an ordinary least squares regression analysis to determine the associations between these six factors and present financial performance. The results show that keywords in Factor 2 (those related to Purchasing experience) are positively associated with ROE, a variable representing present financial performance, while keywords in Factor 1 (those related to Note to customers) show a negative relationship with ROE. On the other hand, keywords in Factor 1 have a positive relationship with Tobin's Q, a variable representing future financial performance. These results indicate that there is some relationship between the words used in the institutional information in this section of corporate websites and firms' financial performance. Hence, the institutional information on a website may be a useful indicator of current firm performance and future firm value.

• Journal of the Korea Society for Simulation
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• v.31 no.3
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• pp.35-44
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• 2022

In this study, we analyze and forecast quantum computer technology trends. Previous research has been mainly focused on application fields centered on technology for quantum computer technology trends analysis. Therefore, this paper analyzes important quantum computer technologies and performs future signal detection and prediction, for a more market driven technical analysis and prediction. As analyzing words used in news articles to identify rapidly changing market changes and public interest. This paper extends conference presentation of Cha & Chang (2022). The research is conducted by collecting domestic news articles from 2019 to 2021. First, we organize the main keywords through text mining. Next, we explore future quantum computer technologies through analysis of Term Frequency - Inverse Document Frequency(TF-IDF), Key Issue Map(KIM), and Key Emergence Map (KEM). Finally, the relationship between future technologies and supply and demand is identified through random forests, decision trees, and correlation analysis. As results of the study, the interest in artificial intelligence was the highest in frequency analysis, keyword diffusion and visibility analysis. In terms of cyber-security, the rate of mention in news articles is getting overwhelmingly higher than that of other technologies. Quantum communication, resistant cryptography, and augmented reality also showed a high rate of increase in interest. These results show that the expectation is high for applying trend technology in the market. The results of this study can be applied to identifying areas of interest in the quantum computer market and establishing a response system related to technology investment.

• Journal of Internet Computing and Services
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• v.13 no.1
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• pp.65-74
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• 2012

Cloud services are used for improving business. Moreover, customer relationship management(CRM) approaches use social networking as tools to enhance services to customers. However, most cloud systems do not support the semantic structures, and because of this, vital information from social network sites is still hard to process and use for business strategy. This paper proposes a collaboration framework based on social semantic web for cloud system. The proposed framework consists of components to support social semantic web to provide an efficient collaboration system for cloud consumers and service providers. The knowledge acquisition module extracts rules from data gathered by social agents and these rules are used for collaboration and business strategy. This paper showed the implementations of processing of social network site data in the proposed semantic model and pattern extraction which was used for the virtual grouping of cloud service providers for efficient collaboration.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.4
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• pp.767-780
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• 2015

In recent years, as smart phone penetration rate is growing explosively, new forms of cyber crime data is poured out beyond the limits of management system for cyber crime investigation. These new forms of data are collected and stored in police station but, some of data are not systematically managed. As a result, investigators sometimes miss the hidden data which can be critical for a case. Crime data is usually generated by computer which produces complex and huge data and records many logs automatically, so it is necessary to simplify a collected data and cluster by crime pattern. In this paper, we categorize all kinds of cyber crime and simplify crime database and extract critical clues relative to other cases. Through data mining and network-visualization, we found there is correlation between clues of a case. From this result, we conclude cyber crime data mining helps crime prevention, early blocking and increasing the efficiency of the investigation.

• The Journal of the Korea Contents Association
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• v.20 no.8
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• pp.1-9
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• 2020

As the aging problem of the regional industry ecosystem has gradually become serious, research to measure and regenerate the regional industry ecosystem decline has been actively conducted. However, little research has been done on regional industry ecosystem crises. Crisis emerges radically over a short period of time, and it is often impossible to respond by post-response, so you must respond before the crisis occurs. In other words, it is more necessary and required when looking at the crisis early and taking a proactive response from a long-term perspective. Therefore, it is necessary to develop a predictive model that can proactively recognize and respond to the crisis in the regional industry ecosystem. Therefore, this study checked the possibility of predicting the risk of regional industry and market according to the emotional score of the news by using large-scale news data. News sentiment analysis was performed using the Google sentiment analysis API, and this was organized by month to check the correlation between actual events.

• The Journal of the Korea Contents Association
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• v.18 no.10
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• pp.443-452
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• 2018

With the explosively increasing intellectual property rights, securing technological competitiveness of companies is more and more important. In particular, since patents include core technologies and element technologies, patent analysis researches are actively conducted to measure the technological value of companies. Various patent analysis studies have been conducted by the International Patent Classification(IPC), which does not include the latest technical classification, and the technical classification accuracy is low. In order to overcome this problem, the Cooperative Patent Classification(CPC), which includes the latest technology classification and detailed technical classification, has been developed. In this paper, we propose a model to analyze the classification of the technologies included in the patent by using the detailed classification system of CPC. It is possible to analyze the inventor's patents in consideration of the relation, importance, and efficiency between the detailed classification schemes of the CPCs to extract the core technology fields and to analyze the details more accurately than the existing IPC-based methods. Also, we perform the comparative evaluation with the existing IPC based patent analysis method and confirm that the proposed model shows better performance in analyzing the inventor's core technology classification.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.129-130
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• 2018

Big data analysis is a technique for effectively analyzing unstructured data such as the Internet, social network services, web documents generated in the mobile environment, e-mail, and social data, as well as well formed structured data in a database. The most big data analysis techniques are data mining, machine learning, natural language processing, and pattern recognition, which were used in existing statistics and computer science. Global research institutes have identified analysis of big data as the most noteworthy new technology since 2011. Therefore, companies in most industries are making efforts to create new value through the application of big data. In this study, we analyzed using the Social Matrics which a big data analysis tool of Daum communications. We analyzed public perceptions of "Artificial Intelligence" keyword, one month as of May 19, 2018. The results of the big data analysis are as follows. First, the 1st related search keyword of the keyword of the "Artificial Intelligence" has been found to be technology (4,122). This study suggests theoretical implications based on the results.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.21-26
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• 2018

In this study, HRD-NET (http://hrd.go.kr), a vocational and training integrated computer network operated by the Ministry of Employment and Labor, is used to confirm whether job training information required by job seekers is being provided smoothly The question bulletin board was extracted using 'R' program which is optimized for big data technique. Therefore, the effectiveness, appropriateness, visualization, frequency analysis and association analysis of the vocational training system were conducted through this, The results of the study are as follows. First, the issue of vocational training card, video viewing, certificate issue, registration error, Second, management and processing procedures of learning cards for tomorrow 's learning cards are complicated and difficult. In addition, it was analyzed that the training cost system and the refund structure differentiated according to the training occupation, the process, and the training institution in the course of the training. Based on this paper, we will study not only the training system of the Ministry of Employment and Labor but also the improvement of the various training computer system of the government department through the analysis of big data.