• Journal of the Korea Academia-Industrial cooperation Society
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• 제19권12호
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• pp.99-109
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• 2018

Tampering involves illegally removing technologies from a protected system through reverse engineering or developing a system without proper authorization. As tampering of a weapon system is a threat to national security, anti-tampering measures are required. Precedent studies on anti-tampering have discussed the necessity, related trends, application cases, and recent cybersecurity-based or other protection methods. In a domestic situation, the Defense Technology Protection Act focuses on how to prevent technology leakage occurring in related organizations through personnel, facilities and information systems. Anti-tampering design needs to determine which technologies are protected while considering the effects of development cost and schedule. The objective of our study is to develop methods of how to select target technologies and determine counter-measures to protect these technologies. Specifically, an evaluation matrix was derived based on the risk analysis concept to select the protection of target technologies. Also, based on the concept of risk mitigation, the classification of anti-tampering techniques was performed according to its applicability and determination of application levels. Results of the case study revealed that the methods proposed can be systematically applied for anti-tampering in weapon system development.

• Journal of the Korea Academia-Industrial cooperation Society
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• 제20권4호
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• pp.382-390
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• 2019

As there is severe competition in the global foldable display market, strategic technology planning is required. Patent analysis as a tool for technology planning has frequently been used due to data characteristics such as openness, formality, and detailed information. However, traditional patent analysis has various limitations such as quantitative approaches are limited in evaluating contents of patents and identifying core technologies of firms as they rely on number of patents, and qualitative approaches have time and cost problems as researchers have to investigate each patent on a case-by-case basis. In this research, we analyze core technologies of firms in the foldable display sector analyzing patent co-classification Network. Results show that the number of patent applications has rapidly increased since 2014, and 92% of these patents are held by two panel manufacturers, SDC and LGD, and two device manufacturers, SEC and LGE. Network analysis shows that the two panel manufacturers' core technologies are similar and two device manufacturers are notably different. This research provides implications to the sector. Moreover, this study provides unique results drawn from co-classification network analysis, and therefore, our research suggests patent co-classification analysis as an effective tool for technology planning.

• Journal of Intelligence and Information Systems
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• 제27권2호
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• pp.33-54
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• 2021

With the fourth industrial revolution and the arrival of the New Normal era due to Corona, the importance of Non-contact technologies such as artificial intelligence and big data research has been increasing. Convergent research is being conducted in earnest to keep up with these research trends, but not many studies have been conducted in the area of nuclear research using artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. This study was conducted to confirm the applicability of data science analysis techniques to the field of nuclear research. Furthermore, the study of identifying trends in nuclear spent fuel recognition is critical in terms of being able to determine directions to nuclear industry policies and respond in advance to changes in industrial policies. For those reasons, this study conducted a media trend analysis of pyroprocessing, a spent nuclear fuel treatment technology. We objectively analyze changes in media perception of spent nuclear fuel dry treatment techniques by applying text mining analysis techniques. Text data specializing in Naver's web news articles, including the keywords "Pyroprocessing" and "Sodium Cooled Reactor," were collected through Python code to identify changes in perception over time. The analysis period was set from 2007 to 2020, when the first article was published, and detailed and multi-layered analysis of text data was carried out through analysis methods such as word cloud writing based on frequency analysis, TF-IDF and degree centrality calculation. Analysis of the frequency of the keyword showed that there was a change in media perception of spent nuclear fuel dry treatment technology in the mid-2010s, which was influenced by the Gyeongju earthquake in 2016 and the implementation of the new government's energy conversion policy in 2017. Therefore, trend analysis was conducted based on the corresponding time period, and word frequency analysis, TF-IDF, degree centrality values, and semantic network graphs were derived. Studies show that before the 2010s, media perception of spent nuclear fuel dry treatment technology was diplomatic and positive. However, over time, the frequency of keywords such as "safety", "reexamination", "disposal", and "disassembly" has increased, indicating that the sustainability of spent nuclear fuel dry treatment technology is being seriously considered. It was confirmed that social awareness also changed as spent nuclear fuel dry treatment technology, which was recognized as a political and diplomatic technology, became ambiguous due to changes in domestic policy. This means that domestic policy changes such as nuclear power policy have a greater impact on media perceptions than issues of "spent nuclear fuel processing technology" itself. This seems to be because nuclear policy is a socially more discussed and public-friendly topic than spent nuclear fuel. Therefore, in order to improve social awareness of spent nuclear fuel processing technology, it would be necessary to provide sufficient information about this, and linking it to nuclear policy issues would also be a good idea. In addition, the study highlighted the importance of social science research in nuclear power. It is necessary to apply the social sciences sector widely to the nuclear engineering sector, and considering national policy changes, we could confirm that the nuclear industry would be sustainable. However, this study has limitations that it has applied big data analysis methods only to detailed research areas such as "Pyroprocessing," a spent nuclear fuel dry processing technology. Furthermore, there was no clear basis for the cause of the change in social perception, and only news articles were analyzed to determine social perception. Considering future comments, it is expected that more reliable results will be produced and efficiently used in the field of nuclear policy research if a media trend analysis study on nuclear power is conducted. Recently, the development of uncontact-related technologies such as artificial intelligence and big data research is accelerating in the wake of the recent arrival of the New Normal era caused by corona. Convergence research is being conducted in earnest in various research fields to follow these research trends, but not many studies have been conducted in the nuclear field with artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. The academic significance of this study is that it was possible to confirm the applicability of data science analysis technology in the field of nuclear research. Furthermore, due to the impact of current government energy policies such as nuclear power plant reductions, re-evaluation of spent fuel treatment technology research is undertaken, and key keyword analysis in the field can contribute to future research orientation. It is important to consider the views of others outside, not just the safety technology and engineering integrity of nuclear power, and further reconsider whether it is appropriate to discuss nuclear engineering technology internally. In addition, if multidisciplinary research on nuclear power is carried out, reasonable alternatives can be prepared to maintain the nuclear industry.

• Journal of Intelligence and Information Systems
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• 제24권3호
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• pp.67-88
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• 2018

Until recently, as we recognize the significance of sustainable growth and competitiveness of small-and-medium sized enterprises (SMEs), governmental support for tangible resources such as R&D, manpower, funds, etc. has been mainly provided. However, it is also true that the inefficiency of support systems such as underestimated or redundant support has been raised because there exist conflicting policies in terms of appropriateness, effectiveness and efficiency of business support. From the perspective of the government or a company, we believe that due to limited resources of SMEs technology development and capacity enhancement through collaboration with external sources is the basis for creating competitive advantage for companies, and also emphasize value creation activities for it. This is why value chain network analysis is necessary in order to analyze inter-company deal relationships from a series of value chains and visualize results through establishing knowledge ecosystems at the corporate level. There exist Technology Opportunity Discovery (TOD) system that provides information on relevant products or technology status of companies with patents through retrievals over patent, product, or company name, CRETOP and KISLINE which both allow to view company (financial) information and credit information, but there exists no online system that provides a list of similar (competitive) companies based on the analysis of value chain network or information on potential clients or demanders that can have business deals in future. Therefore, we focus on the "Value Chain Network System (VCNS)", a support partner for planning the corporate business strategy developed and managed by KISTI, and investigate the types of embedded network-based analysis modules, databases (D/Bs) to support them, and how to utilize the system efficiently. Further we explore the function of network visualization in intelligent value chain analysis system which becomes the core information to understand industrial structure ystem and to develop a company's new product development. In order for a company to have the competitive superiority over other companies, it is necessary to identify who are the competitors with patents or products currently being produced, and searching for similar companies or competitors by each type of industry is the key to securing competitiveness in the commercialization of the target company. In addition, transaction information, which becomes business activity between companies, plays an important role in providing information regarding potential customers when both parties enter similar fields together. Identifying a competitor at the enterprise or industry level by using a network map based on such inter-company sales information can be implemented as a core module of value chain analysis. The Value Chain Network System (VCNS) combines the concepts of value chain and industrial structure analysis with corporate information simply collected to date, so that it can grasp not only the market competition situation of individual companies but also the value chain relationship of a specific industry. Especially, it can be useful as an information analysis tool at the corporate level such as identification of industry structure, identification of competitor trends, analysis of competitors, locating suppliers (sellers) and demanders (buyers), industry trends by item, finding promising items, finding new entrants, finding core companies and items by value chain, and recognizing the patents with corresponding companies, etc. In addition, based on the objectivity and reliability of the analysis results from transaction deals information and financial data, it is expected that value chain network system will be utilized for various purposes such as information support for business evaluation, R&D decision support and mid-term or short-term demand forecasting, in particular to more than 15,000 member companies in Korea, employees in R&D service sectors government-funded research institutes and public organizations. In order to strengthen business competitiveness of companies, technology, patent and market information have been provided so far mainly by government agencies and private research-and-development service companies. This service has been presented in frames of patent analysis (mainly for rating, quantitative analysis) or market analysis (for market prediction and demand forecasting based on market reports). However, there was a limitation to solving the lack of information, which is one of the difficulties that firms in Korea often face in the stage of commercialization. In particular, it is much more difficult to obtain information about competitors and potential candidates. In this study, the real-time value chain analysis and visualization service module based on the proposed network map and the data in hands is compared with the expected market share, estimated sales volume, contact information (which implies potential suppliers for raw material / parts, and potential demanders for complete products / modules). In future research, we intend to carry out the in-depth research for further investigating the indices of competitive factors through participation of research subjects and newly developing competitive indices for competitors or substitute items, and to additively promoting with data mining techniques and algorithms for improving the performance of VCNS.

• KIPS Transactions on Software and Data Engineering
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• 제12권1호
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• pp.19-30
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• 2023

An electric scooter(e-scooter), one popularized micro-mobility vehicle has shown rapidly increasing use in many cities. In South Korea, the use of e-scooters has greatly increased, as some companies have launched e-scooter sharing services in a few large cities, starting with Seoul in 2018. However, the use of e-scooters is still controversial because of issues such as parking and safety. Since the perception toward the means of transportation affects the mode choice, it is necessary to track the trends for electric scooters to make the use of e-scooters more active. Hence, this study aimed to analyze the trends related to e-scooters. For this purpose, we analyzed news articles related to e-scooters published from 2014 to 2020 using dynamic topic modeling to extract issues and sentiment analysis to investigate how the degree of positive and negative opinions in news articles had changed. As a result of topic modeling, it was possible to extract three different topics related to micro-mobility technologies, shared e-scooter services, and regulations for micro-mobility, and the proportion of the topic for regulations for micro-mobility increased as shared e-scooter services increased in recent years. In addition, the top positive words included quick, enjoyable, and easy, whereas the top negative words included threat, complaint, and ilegal, which implies that people satisfied with the convenience of e-scooter or e-scooter sharing services, but safety and parking issues should be addressed for micro-mobility services to become more active. In conclusion, this study was able to understand how issues and social trends related to e-scooters have changed, and to determine the issues that need to be addressed. Moreover, it is expected that the research framework using dynamic topic modeling and sentiment analysis will be helpful in determining social trends on various areas.

• Management & Information Systems Review
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• 제36권1호
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• pp.273-290
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• 2017

Internet of Things (IoT) is considered to be the next wave of Information Technology transformation after the Internet has changed the process of doing business. Since the domain of IoT ranging from the sensor technology to service to the users is wide, the structure of the research domain is not delineated clearly. To do that we suggest to use the Technology Stack Model proposed by Porter et al.(2014) to measure the maturity level of IoT in organizations. Based on the Stack Model, for the general understandings of IoT, we do keyword analyses on the academic papers whose major research issue is IoT. It is found that the current status of IoT application from the perspectives of cloud and big data analytics is not active, meaning that the real value of IoT has not been realized. We also examine the cases which deal with the part of cloud process which is crucial for value accrual. Based on these findings, we suggest the future direction of IoT research. We also propose that IT is to value chain what IoT is to the Stack Model to derive value in organizations.

• The Journal of the Korea Contents Association
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• 제16권5호
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• pp.1-12
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• 2016

Internet of Things (IoTs) technologies have been computerized information generated from a variety of objects and humans, and have been applied to various fields by connecting via the Internet. In order to compensate for the marginal characteristics of IoT smart devices, such as low-power, limited processing and capacities, combining IoT and cloud computing technologies is now established itself as one of the paradigms. In this paper, we look at the definition, features and services of IoT and cloud computing technology, and we investigate and analyze the conversing needs of IoT and could computing, existing conversion paradigms, convergence cases, and platforms. In results, there are challenges to be solved, even though the cloud technologies complement a number of restrictions of IoT and offer various advantages such as scalability, interoperability, reliability, efficiency, availability, security, ease of access, ease of use, and reduced cost of deployment. We analyze the new research issues of convergence paradigm, and finally suggest a research challenges for convergence.

• Journal of the Korean Society of Propulsion Engineers
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• 제15권6호
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• pp.98-106
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• 2011

This paper reviews the state-of-the-art thermoacoustic(TA) modeling techniques and research trend to predict major parameters determining combustion instabilities in lean premixed gas turbine combustors. Linear TA modeling results give us an information on eigenfrequencies and initial growth rate of the instabilities. For the prediction, linear relation equation between acoustic waves and heat release oscillations should be derived in the determined system. Key information for this analysis is to determine the heat release fluctuations in the combustor, which is typically obtained by using n-${\tau}$ function from flame transfer function measurements and/or predictions. Great advancement in the linear TA modeling has been made over a couple of decades, and some successful prediction results have been reported in actual gas turbine combustors. However nonlinear TA model developments which are required to analyze nonlinear system behaviors such as limit cycle saturation and transition phenomena are still limited in a very simple system. In order to fully understand combustion instabilities in a complicated real system, nonlinear flame dynamics and acoustic wave interaction with nonlinear system boundary conditions should be explained from the nonlinear TA model developments.

• Korean Journal of Remote Sensing
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• 제35권6_1호
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• pp.1011-1030
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• 2019

Remote sensing sensors used in satellites or aircrafts measure electromagnetic waves passing through the earth's atmosphere, and thus the information on the surface of the earth is affected as it is absorbed or scattered by the earth's atmosphere. Although satellites have different wavelength ranges and resolutions depending on the purpose of onboard sensors, in general, atmospheric correction must be made to remove the influence of the atmosphere in order to accurately measure the spectral signal of an object on the earth's surface. The purpose of atmospheric correction is to remove the atmospheric effect from remote sensing images to determine surface reflectivity values and to derive physical parameters of the surface. Until recently, atmospheric correction algorithms have evolved from image-based empirical methods or indirect methods using in-situ observation data to direct methods that numerically interpret more complex radiative transfer processes. This study analyzes the research records of atmospheric correction algorithms developed over the past 40 years, systematically establishes the current state of atmospheric correction technology and the results of major atmospheric correction algorithms and presents the current status and research trends of related technologies.

• Journal of the Korean Data and Information Science Society
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• 제22권2호
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• pp.255-267
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• 2011

The aim of this study is to utilize system trading for making investment decisions and use technical analysis and Dynamic Time Warping (DTW) to determine similar patterns in the frequency of stock data and ascertain the optimal timing for trade. The study will examine some of the most common patterns in the futures market and use DTW in terms of their frequency (10, 30, 60 minutes, and daily) to discover similar patterns. The recognized similar patterns were verified by executing trade simulation after applying specific strategies to the technical indicators. The most profitable strategies among the set of strategies applied to common patterns were again applied to the similar patterns and the results from DTW pattern recognition were examined. The outcome produced useful information on determining the optimal timing for trade by using DTW pattern recognition through system trading, and by applying distinct strategies depending on data frequency.