• Journal of KIISE:Software and Applications
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• v.34 no.8
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• pp.721-730
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• 2007

UML (Unified Modeling Language) is widely used to analyze and design target software. Developers also implement the target software based on the UML artifacts. However, it is difficult to validate whether the artifacts are generated to correspond to the modeling guidelines because the guidelines for UML modeling are described in natural language. This paper discusses rule based model checker focused on whether models are designed according to modeling methodology. We propose rules and their own checker, named GARDIAN, for UML model validation. The checkers are designed for COMET method for the real-time embedded system. We illustrate our checkers using Intelligent Robot system to validate our approach.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.8 s.197
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• pp.58-66
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• 2007

In order to improve the recyclability and to reduce the recycling cost and time, the disassembly technology should be systemized because the worn out products can be reused or recycled after disassembly processes. This paper attempts to propose the integrated CATIA-based DfD (Design for Disassembly) support system to promote the disassemblability of products. The system is composed of two modules; evaluation of disassemblability, generation of DfD alternatives. The disassemblability of current vehicle is evaluated to identify the weak point in terms of disassembly using the DELMIA and developed evaluation system. Furthermore a new expert system is developed to propose the optimal redesign rule and principle for generating the DfD alternatives. In order to generate the DfD alternatives, a CATIA-based design support system is implemented. The system can provide quick results and ensure consistency and completeness of the redesign alternatives.

• Journal of Korean Society for Atmospheric Environment
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• v.9 no.3
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• pp.207-215
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• 1993

In order to statistically predict $O_3$ levels in Seoul, the study used the TMS (telemeted air monitoring system) data from the Department of Environment, which have monitored at 20 sites in 1989 and 1990. Each data in each site was characterized by 6 major criteria pollutants ($SO_2, TSP, CO, NO_2, THC, and O_3$) and 2 meteorological parameters, such as wind speed and wind direction. To select proper variables and to determine each pollutant's behavior, univariate statistical analyses were extensively studied in the beginning, and then various applied statistical techniques like cluster analysis, regression analysis, and expert system have been intensively examined. For the initial study of high level $O_3$ prediction, the raw data set in each site was separated into 2 group based on 60 ppb $O_3$ level. A hierarchical cluster analysis was applied to classify the group based on 60 ppb $O_3$ into small calsses. Each class in each site has its own pattern. Next, multiple regression for each class was repeatedly applied to determine an $O_3$ prediction submodel and to determine outliers in each class based on a certain level of standardized redisual. Thus, a prediction submodel for each homogeneous class could be obtained. The study was extended to model $O_3$ prediction for both on-time basis and 1-hr after basis. Finally, an expect system was used to build a unified classification rule based on examples of the homogenous classes for all of sites. Thus, a concept of high level $O_3$ prediction model was developed for one of $O_3$ alert systems.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.28 no.2
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• pp.144-150
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• 1992

This paper describes an application of artificial neural network to diagnose the defects of rotating machiner. Induction motor was used to the object of defect diagnosis. For defect diagnosis, the frequency spectrum of vibration was utilized. Learning method of applied neural network was back propagation. Neural network has following advantage; Once it has been learned, inference time is very short and it can provide a reasonable conclusion regardless of insufficient input data. So, this defect diagnosis system can be used superiorly to rule based expert system as quality inspection of rotating machinery in the shop.

• Korean Journal of Computational Design and Engineering
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• v.10 no.4
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• pp.262-273
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• 2005

For CAD data users, few things are as frustrating as receiving CAD data that is unusable due to poor data quality. Users waste time trying to get better data, fixing the data, or even rebuilding the data from scratch from paper drawings or other sources. Most related works and commercial tools handle the boundary representation (B-Rep) shape of CAD models. However, we propose a design history?based approach for healing CAD model errors. Because the design history, which covers the features, the history tree, the parameterization data and constraints, reflects the design intent, CAD model errors can be healed by an interdependency analysis of the feature commands or of the parametric data of each feature command, and by the reconstruction of these feature commands through the rule-based reasoning of an expert system. Unlike other B Rep correction methods, our method automatically heals parametric feature models without translating them to a B-Rep shape, and it also preserves engineering information.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.1
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• pp.23-35
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• 1998

This article presents a network flow analysis to form flexible machine cells with minimum intercellular part moves and a neural network model to form part families. The operational sequences and production quantity of the part, and the number of cells and the cell size are taken into considerations for a 0-1 quadratic programming formulation and a network flow based solution procedure is developed. After designing the machine cells, a neural network approach for the integration of part families and the automatic assignment of new parts to the existing cells is proposed. A multi-layer backpropagation network with one hidden layer is used. Experimental results with varying number of neurons in hidden layer to evaluate the role of hidden neurons in the network learning performance are also presented. The comprehensive methodology developed in this article is appropriate for solving large-scale industrial applications without building the knowledge-based expert rule for the cellular manufacturing environment.

• Journal of the Korea Society for Simulation
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• v.7 no.2
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• pp.1-16
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• 1998

This paper presents an automated methodology for campus network design and performance analysis using the rule-based SES and DEVS modeling & simulation techniques. Proposed methodology for structural design and performance analysis can be utilized not only in the early stage of network design for selecting configurable candidate from all possible design alternatives, but also in simulation verification for generating performance data. Our approach supercedes conventional methodologies in that, first, it can support the configuration automation by utilizing the knowledge of design expert ; second, it can provide the simulation-based performance evaluation ; third, it is established on the basis of the well-formalized framework so that it can support a hierarchical and modular system design. Several simulation tests performed on a campus network example will illustrate our technique.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.9-12
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• 2004

Developing an efficient knowledge base construction mechanism as an input method for expert systems (ES) development is of extreme importance due to the fact that an input process takes a lot of time and cost in constructing an ES. Most ES require experts to explicit their tacit knowledge into a form of explicit knowledge base with a full sentence. In addition, the explicit knowledge bases were composed of strict grammar and keywords. To overcome these limitations, this paper proposes a knowledge conceptualization and construction mechanism for automated knowledge acquisition, allowing an efficient decision. To this purpose, we extended traditional knowledge map (KM) construction process to dynamic knowledge map (DKM) and combined this algorithm with relational database (RDB). In the experiment section, we used medical data to show the efficiency of our proposed mechanism. Each rule in the DKM was characterized by the name of disease, clinical attributes and their treatments. Experimental results with various disease show that the proposed system is superior in terms of understanding and convenience of use.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.6
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• pp.7-17
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• 2009

The precise analysis of exercise data for designing an effective rehabilitation system is very important as a feedback for planing the next exercising step. Many subjective and reliable research outcomes that were obtained by analysis and evaluation for the human motor ability by various methods of biomechanical experiments have been introduced. Most of them include quantitative analysis based on basic statistical methods, which are not practical enough for application to real clinical problems. In this situation, data mining technology can be a promising approach for clinical decision support system by discovering meaningful hidden rules and patterns from large volume of data obtained from the problem domain. In this research, in order to find relational rules between posture training type and muscle activation pattern, we investigated an application of the WAR(Weishted Association Rule) to the biomechanical data obtained mainly for evaluation of postural control ability. The discovered rules can be used as a quantitative prior knowledge for expert's decision making for rehabilitation plan. The discovered rules can be used as a more qualitative and useful priori knowledge for the rehabilitation and clinical expert's decision-making, and as a index for planning an optimal rehabilitation exercise model for a patient.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.6
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• pp.1058-1062
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• 2008

In clinical decision support system(CDSS), unlike rule-based expert method, appropriate data-driven machine learning method can easily provide the information of individual feature(clinical test) for disease classification. However, currently developed methods focus on the improvement of the classification accuracy for diagnosis. With the analysis of feature importance in classification, one may infer the novel clinical test sets which highly differentiate the specific diseases or disease states. In this background, we introduce a novel CDSS that integrate a classifier and feature selection module together. Random forest algorithm is applied for the classifier and the feature importance measure. The system selects the significant clinical tests discriminating the diseases by examining the classification error during backward elimination of the features. The superior performance of random forest algorithm in clinical classification was assessed against artificial neural network and decision tree algorithm by using breast cancer, diabetes and heart disease data in UCI Machine Learning Repository. The test with the same data sets shows that the proposed system can successfully select the significant clinical test set for each disease.