• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.179-182
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• 2000

A Classifier System processes a discrete coded information from the environment. When the system codes the information to discontinuous data, it loses excessively the information of the environment. The Fuzzy Classifier System(FCS) makes the classifier system be able to carry out the mapping from continuous inputs to outputs. It is the FCS that applies this ability of the machine learning to the concept of fuzzy controller. It is that the antecedent and consequent of classifier is same as a fuzzy rule of the rule base. In this paper, the FCS is the Michigan style and fuzzifies the input values to create the messages. The system stores those messages in the message list and uses the implicit Bucket Brigade Algorithms. Also the FCS employs the Genetic Algorithms(GAs) to make new rules and modify rules when performance of the system needs to be improved. We will verify the effectiveness of the proposed FCS by applying it to AMR avoiding the obstacle.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2393-2398
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• 2003

In this paper, we proposes a new fuzzy inference system for modeling nonlinear systems given input and output data. In the suggested fuzzy inference system, the number of fuzzy rules and parameter values of membership functions are automatically decided by using the kernel-based method. The kernel-based method individually performs linear transformation and kernel mapping. Linear transformation projects input space into linearly transformed input space. Kernel mapping projects linearly transformed input space into high dimensional feature space. The structure of the proposed fuzzy inference system is equal to a Takagi-Sugeno fuzzy model whose input variables are weighted linear combinations of input variables. In addition, the number of fuzzy rules can be reduced under the condition of optimizing a given criterion by adjusting linear transformation matrix and parameter values of kernel functions using the gradient descent method. Once a structure is selected, coefficients in consequent part are determined by the least square method. Simulated result illustrates the effectiveness of the proposed technique.

• Journal of the Korean Operations Research and Management Science Society
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• v.12 no.2
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• pp.21-35
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• 1987

This paper presents a method for propagating fuzzy concepts through fuzzy IF-THEN-ELSE rules. A fuzzy IF-THEN-ELSE rule consists of a set of fuzzy condition and conclusion pairs. These pairs assumed to contain informations about a fuzzy mapping from fuzzy concepts of condition parts to the fuzzy concepts of conclusion parts. Conventionally, vectors are used to define fuzzy concepts and matrices are used to define a fuzzy mapping between fuzzy conditions and conclusions. This approach, however, does not satisfy the existing condition property, i.e., when a fuzzy input data exactly matches to a fuzzy condition, fuzzy output data should be mapped to a corresponding fuzzy conclusion. Alternatively, we propose a parameterized approach in which every fuzzy concept is described by a parameterized standard function, including fuzzy conditions and fuzzy conclusions. A fuzzy IF-THEN-ELSE rule takes the parameterized fuzzy concept as an input, and produces a standard function with new parameters as an output. New parameters are determined by a parameterwise interpolation. That is, each output parameters are determined by interpolating parameters of the same class contained in fuzzy conclusions. Obviously, the proposed scheme always satisfies the existing condition property.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.149-155
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• 2013

In these days, Entity-Relationship Model is the most popular modeling tool for designing databases, and XML is a de facto standard language for representing and exchanging data. But, because of many commercial products supporting Entity-Relationship Model use their's own representation formats, and thus it gives rise to difficulties the inter-operability between these products. In this paper, we propose an ERX, a generation tool of XML Schema from Entity-Relationship Model. ERX receives an Entity-Relationship Diagram as an input, transforms it based on transformation rules, and generates a XML Schema Definition as an output. Transformation rules contain entity set, relationship set, mapping cardinalities, and generalization.

• The KIPS Transactions:PartA
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• v.9A no.4
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• pp.479-484
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• 2002

This paper presents the model that integrates an SDL system and a CORBA system using mapping rules between method and signal for developing embedded systems connected with internet. In order to support communication between the two different systems (SDL and CORBA), it is essential to secure the conversion interface between SDL communication protocol and CORBA communication protocol. In this paper, IDL is adopted for the communication interface and the conversion of the communication protocol between the two systems, and the IDL compiler automatically generates the interface for protocol interoperability. The proposed model adopts middleware on the subpart of the SDL based legacy system, and hence, supports the service on the distributed system, regardless of the environment and location of the server system.

• IE interfaces
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• v.17 no.3
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• pp.330-337
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• 2004

Since XML (eXtensible Markup Language) was highlighted as an information interchange format, there is an increasing demand for incorporating XML with databases. Most of the approaches are focused on RDB (Relational Databases) because of legacy systems. But these approaches depend on the database system. Countless researches are being focused on DTD (Document Type Definition). However XML Schema is more comprehensive and efficient in many perspectives. We propose a meta-model for XML Schema that is independent of the database. There are three processes to build our meta-model: DOM (Document Object Model) tree analysis, object modeling and storing object into a fixed DB schema using model mapping approach. We propose four mapping rules for object modeling, which conform to the ODMG (Object Data Management Group) 3.0 standard. We expect that the model will be especially useful in building XML-based e-business applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.10
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• pp.1830-1840
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• 2011

CDISC (Clinical Data Interchanging Standards Consortium) standards are to support the acquisition, exchange, submission and archival of clinical trial and research data. SDTM (Study Data Tabulation Model) for Case Report Forms (CRFs) was recommended for U.S. Food and Drug Administration (FDA) regulatory submissions since 2004. Although the SDTM Implementation Guide gives a standardized and predefined collection of submission metadata 'domains' containing extensive variable collections, transforming CRFs to SDTM files for FDA submission is still a very hard and time-consuming task. For addressing this issue, we developed metadata based SDTM mapping rules. Using these mapping rules, we also developed a semi-automatic tool, named CDISC Transformer, for transforming clinical trial data to CDISC standard compliant data. The performance of CDISC Transformer with or without MDR support was evaluated using CDISC blank CRF as the 'gold standard'. Both MDR and user inquiry-supported transformation substantially improved the accuracy of our transformation rules. CDISC Transformer will greatly reduce the workloads and enhance standardized data entry and integration for clinical trial and research in various healthcare domains.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.16-21
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• 2008

Aero-engine, as one kind of rotating machinery with complex structure and high rotating speed, has complicated vibration faults. Therefore, condition monitoring and fault diagnosis system is very important for airplane security. In this paper, a vibration data acquisition and intelligent fault diagnosis system is introduced. First, the vibration data acquisition part is described in detail. This part consists of hardware acquisition modules and software analysis modules which can realize real-time data acquisition and analysis, off-line data analysis, trend analysis, fault simulation and graphical result display. The acquisition vibration data are prepared for the following intelligent fault diagnosis. Secondly, two advanced artificial intelligent(AI) methods, mapping-based and rule-based, are discussed. One is artificial neural network(ANN) which is an ideal tool for aero-engine fault diagnosis and has strong ability to learn complex nonlinear functions. The other is data mining, another AI method, has advantages of discovering knowledge from massive data and automatically extracting diagnostic rules. Thirdly, lots of historical data are used for training the ANN and extracting rules by data mining. Then, real-time data are input into the trained ANN for mapping-based fault diagnosis. At the same time, extracted rules are revised by expert experience and used for rule-based fault diagnosis. From the results of the experiments, the conclusion is obvious that both the two AI methods are effective on aero-engine vibration fault diagnosis, while each of them has its individual quality. The whole system can be developed in local vibration monitoring and real-time fault diagnosis for aero-engine.

• The KIPS Transactions:PartD
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• v.15D no.6
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• pp.803-814
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• 2008

Product line architectures include variable parts to be selected according to product specific requirements. In order to derive architectures that are valid for a particular product from product line architectures, variabilities of product line architectures must be systematically managed. In this paper, we adopt an approach to variability management of product line architectures through an explicit mapping between a feature model and product line architecture models. If this mapping is incorrect or there exists inconsistency among product line architectural elements, variabilities of product line architectures cannot be managed correctly. Therefore, this paper formally defines product line architectural models in terms of conceptual, process, deployment, and module views, and mapping relationships between the feature model and the architectural models. Consistency rules for correct variability management of product line architectures are defined in terms of consistency in each of product line architecture model, consistency between different architectural view models, and consistency between a feature model and product line architectural models. These consistency rules provide a theoretical foundation for deriving valid product architecture from product line architectures.

• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.887-896
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• 2002

Based on SPICE (Software Process Improvement and Capability dEtermination) evaluation model, SPIRE (Software Process Improvement in Regions of Europe) is developed and published as a process improvement model for small and medium size organizations. However, practical selection guidelines or mapping rules between business goals and software processes do not exist within SPIRE. This research aims to construct an objective reference mapping table between business goals and software processes, and to propose a process selection method using the mapping table. The mapping table is constructed by the convergence of domestic software process experts' opinions using Delphi techniques. In the suggested process selection method, target processes are selected using the intuition of project participants or project managers as well as the reference mapping table. The feasibility of the proposed selection method has been reviewed by applying to two small software companies. Using the reference mapping table, we could select key processes which were passed over by project managers.