• Journal of Korean Institute of Industrial Engineers
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• v.38 no.2
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• pp.98-106
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• 2012

The current market preassure of least environmental effects of products needs companies to consider whole life cycle of their products during their design phase. To support the integrated and collaborative development of the products, this paper proposed product data model for extended Product Data Managemen (PDM) that can support integrated design of product, manufacturing process, and customer services, based on the consistent and comprehensive PDM databases. The product data model enables design, manufacturing, and service engineers to express their products and services efficiently, with sharing consistent product data, engineering changes, and both economical and environmental evaluations on their design alternatives. The product data model was implemented with a prototype PDM system, and validated through an example product. The result shows that the PDM based on the proposed product data model can support the integrated design for products, manufacturing process, and customer services, and provide an environment of collaborative product development for design, manufacturing and service engineers.

• The Journal of Fisheries Business Administration
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• v.44 no.1
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• pp.59-70
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• 2013

The purpose of this paper is to integrate various models of price formation and let the data choose the most proper model. After the data choose the proper model, one can analyze the price formation process and demand structures for fishery resources under the restriction of Korean fisheries regulations. This study suggests the integrated model including quasi-linear price formation model, Translog price formation model, AIDS price formation model and Lewbel price formation model as level variables. It also suggests another integrated model including AIDS price formation model, Rotterdam price formation model, Latinen-Theil price formation model and Neves price formation model as difference variables. The empirical results show that the AIDS price formation model is the most preferred in both level and difference variables of fishery resources. The estimated parameters show that all sample species have (-) sign of price flexibilities, thus following the law of demand. The scale flexibilities of all species are estimated as (-) sign, thus being adapted to the theory. The contribution and results are summarized as follows. First, the integrated model of fishery market demand has been developed and the data can choose the proper model without arbitrary choice of the researcher. Second, the fishery market demand structure could be analyzed in a way different from the ordinary demand analysis, which is based upon price flexibility and scale flexibility. Third, the integrated model for fishery resources can be used easily when catching restrictions are imposed by policies.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.785-790
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• 2006

In RFID-based SCM, The traceability and product information is the important target data. In this paper, efficient items traceability model and the integrated model of the product between RFID network and GDS(Global Data Synchronization) network are studied. Information consists of the dynamic data generated from RFID network and static data generated from GDS Network. The integrated model will provide the interoperability between 2 kinds of networks.

• Journal of Korean Institute of Industrial Engineers
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• v.21 no.3
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• pp.441-461
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• 1995

This research proposes an Integrated Product Information Model (IPIM) using STEP (Standard for the Exchange of product model data) for Computer Integrated Manufacturing (CIM) of Concurrent Engineering (CE). IPIM is based on Geometry and Topology (STEP Part 42), Form Feature (STEP Part 48), and Tolerance (STEP Part 48) for representing the integrated information of mechanical parts. For the IPIM, 1) new entities are developed for integration of existing entities, and 2) the existing entities are restructured and modified for a special application protocol. In CIM or CE, the advantages of using IPIM having integrated form of geometry, feature and tolerance are 1) integration of product design, process design and manufacturing sequentially or concurrently. 2) keep the product data consistency, modified by different domain, and 3) automatic data exchange between different application software and different hardware. The prototype system is composed of CAD, Data Probe, DBMS and SDAI (Standard Data Access Interface), and the generated STEP data is stored in a step file of DBMS for other applications.

• Journal of Applied Reliability
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• v.17 no.4
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• pp.296-304
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• 2017

Purpose: Recent weapon systems in defense have increased the complexity and importance of software when developing multifunctional equipment. In this study, we analyze the accuracy of the proposed software reliability model when applied to weapon systems. Methods: Determine the similarity between software reliability analysis results (prediction/estimation) utilizing data from developing weapon systems and system failures data during operation of weapon systems. Results: In case of a software reliability prediction model, the predicted failure rate was higher than the actual failure rate, and the estimation model was consistent with actual failure history data. Conclusion: The software prediction model needs to adjust the variables that are appropriate for the domestic weapon system environment. As the reliability of software is increasingly important in the defense industry, continuous efforts are needed to ensure accurate reliability analysis in the development of weapon systems.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1999.10a
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• pp.444-451
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• 1999

The purpose of this study is to Propose data models for central database in integrated system for structural design building. In order to efficiently express data related to structure, I analyzed the structure design process and classified data considering design step. 1 used an object-oriented modeling methodology for logical data model and relational modeling for physical data model. Based on this model, we will develop an integration system with several applications for structure design. Each application will communicate through the central database.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.825-836
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• 2003

Based on customer information and financing processes in capital market, we derived individual models by applying multi-layered perceptrons, MDA, and decision tree. Further, the results from the existing single models were compared with the results from the integrated model that was developed using genetic algorithm. This study contributes not only to verifying the existing individual models and but also to overcoming the limitations of the existing approaches. We have depended upon the approaches that compare individual models and search for the best-fit model. However, this study presents a methodology to build an integrated data mining model using genetic algorithm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.18 no.2
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• pp.143-155
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• 2000

With the increasing needs for the integrated use of topographic and cadastral data in order to build an efficient geo-spatial information system. it is urgently necessary to research into its solution. The intention of this study is to detect error types of data and to propose adjustment methods for solving the problems caused by integrating topographic and cadastral data. For this purpose a primary integrated data model is created to link attribute data(land management system) and graphic data within cadastral information in the first step. In next, a secondary integrated data model based on the improved method is formed to coincide the graphic data of cadastral map with that of topographic map. At the first, because a numerous error types md sources caused by separate management of graphic and attribute data are easily checked, it is possible to suggest an improved method to correct these errors using the primary integrated data model. In addition, the accuracy in position and area with coordinate transformation method based on multi-block adjustment is more efficient than rubber-sheeting method. As a result, the secondary integrated data model could be built by harmonizing cadastral map with topographic map using the improved solution.

• Korean Management Science Review
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• v.24 no.2
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• pp.177-196
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• 2007

For better predictions and classifications in customer recommendation, this study proposes an integrative model that efficiently combines the currently-in-use statistical and artificial intelligence models. In particular, by integrating the models such as Association Rule, Frequency Matrix, and Rule Induction, this study suggests an integrative prediction model. Integrated models consist of four models: ASFM model which combines Association Rule(A) and Frequency Matrix(B), ASRI model which combines Association Rule(A) and Rule Induction(C), FMRI model which combines Frequency Matrix(B) and Rule Induction(C), and ASFMRI model which combines Association Rule(A), Frequency Matrix(B), and Rule Induction(C). The data set for the tests is collected from a convenience store G, which is the number one in its brand in S. Korea. This data set contains sales information on customer transactions from September 1, 2005 to December 7, 2005. About 1,000 transactions are selected for a specific item. Using this data set. it suggests an integrated model predicting whether a customer buys or not buys a specific product for target marketing strategy. The performance of integrated model is compared with that of other models. The results from the experiments show that the performance of integrated model is superior to that of all other models such as Association Rule, Frequency Matrix, and Rule Induction.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.57-72
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• 2003

This article provides integrated neural network models for the interest rate forecasting using change-point detection. The model is composed of three phases. The first phase is to detect successive structural changes in interest rate dataset. The second phase is to forecast change-point group with data mining classifiers. The final phase is to forecast the interest rate with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the predictability of integrated neural network models to represent the structural change.