• Journal of Convergence for Information Technology
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• v.9 no.9
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• pp.13-19
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• 2019

This paper analyzed the SCMS and pilot policy, which is pursued by the U.S. government in connected vehicles. SCMS ensures authentication, integrity, privacy and interoperability. The SCMS Support Committee of U.S. government has established the National Unit SCMS and is responsible for system-wide control. Of course, it introduces security policy, procedures and training programs making. In this paper, the need for SCMS to be applied to C-ITS was discussed. The structure of the SCMS was analyzed and the U.S. government's filot policy for connected vehicles was discussed. The discussion of the need for SCMS highlighted the importance of the role and responsibilities of SCMS between vehicles and vehicles. The security certificate management system looked at the structure and analyzed the type of certificate used in the vehicle or road side unit (RSU). The functions and characteristics of the certificates were reviewed. In addition, the functions of basic safety messages were analyzed with consideration of the detection and warning functions of abnormal behavior in SCMS. Finally, the status of the pilot project for connected vehicles currently being pursued by the U.S. government was analyzed. In addition to the environment used for the test, the relevant messages were also discussed. We also looked at some of the issues that arise in the course of the pilot project.

• Journal of KIISE:Software and Applications
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• v.29 no.4
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• pp.211-224
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• 2002

As system become larger and more complex, it is important to correctly analyze and specify user's requirements. Use case modeling is widely used in Object-Oriented Analysis and Design(OOAD) and Component-Based Development(CBD). It is useful to mitigate the complexity of the requirements analysis. However, use cases are difficult to be structured, to explicitly represent non-functional requirements, and to analyze what is affected by changes of use cases. To alleviate these problems, we propose scenario-based goal-oriented approach for use case modeling. The approach is to apply goal-oriented analysis method to use case model. Since goal-oriented analysis method is not systematic and heuristics is considerably involved, we adopted scenarios as the basis for the goal extraction. The proposed method is applied to City Bus Information Subsystem(CBIS) in Intelligent Transport Systems(ITS) domain. The proposed approach helps software engineer to analyze what is affected by use case's changes and represent non-functional requirements.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.8
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• pp.171-180
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• 2020

Smart Factory consists of digital automation solutions throughout the production process, including design, development, manufacturing and distribution, and it is an intelligent factory that installs IoT in its internal facilities and machines to collect process data in real time and analyze them so that it can control itself. The smart factory's equipment works in a physical combination of numerous hardware, rather than a virtual character being driven by a single object, such as a game. In other words, for a specific common goal, multiple devices must perform individual actions simultaneously. By taking advantage of the smart factory, which can collect process data in real time, if reinforcement learning is used instead of general machine learning, behavior control can be performed without the required training data. However, in the real world, it is impossible to learn more than tens of millions of iterations due to physical wear and time. Thus, this paper uses simulators to develop grid sortation systems focusing on transport facilities, one of the complex environments in smart factory field, and design cooperative multi-agent-based reinforcement learning to demonstrate efficient behavior control.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.65-82
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• 2015

Recently, there has been a surge of interest in finding core issues and analyzing emerging trends for the future. This represents efforts to devise national strategies and policies based on the selection of promising areas that can create economic and social added value. The existing studies, including those dedicated to the discovery of future promising fields, have mostly been dependent on qualitative research methods such as literature review and expert judgement. Deriving results from large amounts of information under this approach is both costly and time consuming. Efforts have been made to make up for the weaknesses of the conventional qualitative analysis approach designed to select key promising areas through discovery of future core issues and emerging trend analysis in various areas of academic research. There needs to be a paradigm shift in toward implementing qualitative research methods along with quantitative research methods like text mining in a mutually complementary manner. The change is to ensure objective and practical emerging trend analysis results based on large amounts of data. However, even such studies have had shortcoming related to their dependence on simple keywords for analysis, which makes it difficult to derive meaning from data. Besides, no study has been carried out so far to develop core issues and analyze emerging trends in special domains like the aviation industry. The change used to implement recent studies is being witnessed in various areas such as the steel industry, the information and communications technology industry, the construction industry in architectural engineering and so on. This study focused on retrieving aviation-related core issues and emerging trends from overall research papers pertaining to aviation through text mining, which is one of the big data analysis techniques. In this manner, the promising future areas for the air transport industry are selected based on objective data from aviation-related research papers. In order to compensate for the difficulties in grasping the meaning of single words in emerging trend analysis at keyword levels, this study will adopt topic analysis, which is a technique used to find out general themes latent in text document sets. The analysis will lead to the extraction of topics, which represent keyword sets, thereby discovering core issues and conducting emerging trend analysis. Based on the issues, it identified aviation-related research trends and selected the promising areas for the future. Research on core issue retrieval and emerging trend analysis for the aviation industry based on big data analysis is still in its incipient stages. So, the analysis targets for this study are restricted to data from aviation-related research papers. However, it has significance in that it prepared a quantitative analysis model for continuously monitoring the derived core issues and presenting directions regarding the areas with good prospects for the future. In the future, the scope is slated to expand to cover relevant domestic or international news articles and bidding information as well, thus increasing the reliability of analysis results. On the basis of the topic analysis results, core issues for the aviation industry will be determined. Then, emerging trend analysis for the issues will be implemented by year in order to identify the changes they undergo in time series. Through these procedures, this study aims to prepare a system for developing key promising areas for the future aviation industry as well as for ensuring rapid response. Additionally, the promising areas selected based on the aforementioned results and the analysis of pertinent policy research reports will be compared with the areas in which the actual government investments are made. The results from this comparative analysis are expected to make useful reference materials for future policy development and budget establishment.