• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.369-372
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• 2004

유비쿼터스 환경에서는 프라이버시에 민감한 다양한 정보가 수집되고 이들이 통제되지 않은 채 배포될 수 있기 때문에 프라이버시 보호가 필수적이다. 유비쿼터스 환경에서 프라이버스 보안을 위해 사용되는 대표적인 방법론의 하나인 익명(anonymity) 기반 기법에서는, 사용자가 새로운 서비스 영역에 참여할 때 가명(pseudonym)을 사용할 수 있도록 하여, 사용자의 신분을 노출시키지 않도록 하는 방법이다. 이 방법은 사용자의 신분을 보호하는데는 효과적이지만, 친구 찾기 서비스, 위험지역경보, P2P통신 등 ID 기반의 서비스를 제공하기 어렵게 하는 단점이 있다. 이 논문에서는 익명기반의 프라이버스 보호 기법을 사용하는 유비쿼터스 환경에서 ID 기반의 서비스를 제공할 수 있도록 하는 시스템 구조를 제안한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.65-68
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• 2005

Privacy preservation is crucial in ubiquitous computing environment in which lots of privacy- sensitive information can be collected and distributed without appropriate control. The anonymity-based approach is a famous one used for privacy preservation communication, which allows users to use pseudonyms instead of real ID so as not to reveal their identities. This approach is effective in that it can hide the identity of users. However, it makes it difficult to provide ID-based services like buddy service, dangerous area alert, P2P communication in the ubiquitous computing. We proposes a system architecture which enables ID-based services in the ubiquitous computing environment employing anonymity - based privacy - preserving approach.

• Journal of Ocean Engineering and Technology
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• v.11 no.3
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• pp.200-211
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• 1997

Most recently, offshore Engineering & Construction field is concerned about integration management technology such as CIM(Computer Integrated Manufacturing), PDM(Product Data Management) and Enterprise Information Engineering in order to cope with the rapid change of engineering and manufacturer specification as per owner's requirement during construction stage of the project. System integration and integrated data modeling with relational database in integration management technology improve the quality of product and reduce the period of the construction project by reason of owing design information jointly. This paper represents the design methodology of system integration using Business Process Reengineering by the case study. The case study is about the offshore plant material information process from front end engineering design to detail engineering for the construction and the basis of monitoring system by integrating and sharing the design information between the 2D intelligent P&ID and 3D plant modeling using relational database. As a result of the integrated data modeling and system integration, it is possible to maintain the consistency of design process in point of view of the material balancing and reduce the design assumption/duration. Near future, this system will be expanded and connected with the MRP(Material Requirement Planing) and the POR (Purchase Order Requisition) system.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.2059-2063
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• 2004

Most power plants have operated many independent computerize systems for maintenance. Independence of systems have caused complexity of business process and inconvenience of computer system management. Because the equipment and material master data is not standardize and structurize, it is difficult to manage equipment maintenance history and material delivery. Especially equipment classification criterion is important for standardization of every maintenance information. It is necessary to integrate function of independent systems for business process simplification and rapid work flow. this paper provides equipment classification criterion design and system integration method with the case of live system development.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.1-17
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• 2015

In this paper, we report what we have observed with regards to a prediction model for the military based on enlisted men's internal(cumulative records) and external data(SNS data). This work is significant in the military's efforts to supervise them. In spite of their effort, many commanders have failed to prevent accidents by their subordinates. One of the important duties of officers' work is to take care of their subordinates in prevention unexpected accidents. However, it is hard to prevent accidents so we must attempt to determine a proper method. Our motivation for presenting this paper is to mate it possible to predict accidents using enlisted men's internal and external data. The biggest issue facing the military is the occurrence of accidents by enlisted men related to maladjustment and the relaxation of military discipline. The core method of preventing accidents by soldiers is to identify problems and manage them quickly. Commanders predict accidents by interviewing their soldiers and observing their surroundings. It requires considerable time and effort and results in a significant difference depending on the capabilities of the commanders. In this paper, we seek to predict accidents with objective data which can easily be obtained. Recently, records of enlisted men as well as SNS communication between commanders and soldiers, make it possible to predict and prevent accidents. This paper concerns the application of data mining to identify their interests, predict accidents and make use of internal and external data (SNS). We propose both a topic analysis and decision tree method. The study is conducted in two steps. First, topic analysis is conducted through the SNS of enlisted men. Second, the decision tree method is used to analyze the internal data with the results of the first analysis. The dependent variable for these analysis is the presence of any accidents. In order to analyze their SNS, we require tools such as text mining and topic analysis. We used SAS Enterprise Miner 12.1, which provides a text miner module. Our approach for finding their interests is composed of three main phases; collecting, topic analysis, and converting topic analysis results into points for using independent variables. In the first phase, we collect enlisted men's SNS data by commender's ID. After gathering unstructured SNS data, the topic analysis phase extracts issues from them. For simplicity, 5 topics(vacation, friends, stress, training, and sports) are extracted from 20,000 articles. In the third phase, using these 5 topics, we quantify them as personal points. After quantifying their topic, we include these results in independent variables which are composed of 15 internal data sets. Then, we make two decision trees. The first tree is composed of their internal data only. The second tree is composed of their external data(SNS) as well as their internal data. After that, we compare the results of misclassification from SAS E-miner. The first model's misclassification is 12.1%. On the other hand, second model's misclassification is 7.8%. This method predicts accidents with an accuracy of approximately 92%. The gap of the two models is 4.3%. Finally, we test if the difference between them is meaningful or not, using the McNemar test. The result of test is considered relevant.(p-value : 0.0003) This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of enlisted men's data. Additionally, various independent variables used in the decision tree model are used as categorical variables instead of continuous variables. So it suffers a loss of information. In spite of extensive efforts to provide prediction models for the military, commanders' predictions are accurate only when they have sufficient data about their subordinates. Our proposed methodology can provide support to decision-making in the military. This study is expected to contribute to the prevention of accidents in the military based on scientific analysis of enlisted men and proper management of them.