• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.246-249
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• 2001

In this study we propose a technology selection model, which captures technology seeds for new business area by a fuzzy structural modeling method and then, design a model, which evaluates the validity of New Business Development plans for the selected technology seeds with regard to the properties of manufacturing, product, market, and economy as well. Finally, a fuzzy inference system is designed in order to decide the degree of success of New Business Development plans based on the preceding validity evaluation.

• Industrial Engineering and Management Systems
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• v.13 no.1
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• pp.15-28
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• 2014

Evaluation of the implementation and performance of a firm's green supply chain management (GSCM) is an ongoing process. Balanced scorecard is a multi-criteria evaluation concept that highlights implementation and performance measures. The literature on the framework is abundant literature but scarce on how to build a hierarchical framework under uncertainty with dependence relations. Hence, this study proposes a hybrid approach, which includes applied interpretive structural modeling to build a hierarchical structure and uses the analytic network process to analyze the dependence relations. Additionally, this study applies the fuzzy set theory to determine linguistic preferences. Twenty dependence criteria are evaluated for a GSCM implemented firm in Taiwan. The result shows that the financial aspect and life cycle assessment are the most important performance and weighted criteria.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.720-724
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• 2009

This thesis analyze structure of consciousness of success factors of company project by applying FSM(Fuzzy Structural Modeling). FSM is a theory that implied fuzzy theory to ISM(Interpretive Structural Modeling) and is known to be more valid in recognizing a complex pluralistic value system and it is also designed to choose structure model that fits reality with when it is changed by parameter p and $\lambda$. It is desirable to conduct conformity assessment to complement even though selected structure model is considered as conformed because structure model is chosen without objective evaluation for conformity. Therefore, this paper present more objective structure model through conformity evaluation using structural equation modeling on success factors to achieve company project obtained by FSM and analyze the consciousness structure according to that structure.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.877-884
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• 2002

This paper deals with a multi-stage TSK fuzzy modeling method by using Genetic Programming (GP). Based on the time sequence of sampling data the best structural change points of complex systems are detemined by using GP, and also the moving window is simultaneously introduced to overcome the excessive amount of calculation during the generating procedure of GP tree. Therefore, a multi-stage TSK fuzzy model that attempts to represent a complex problem by decomposing it into multi-stage sub-problems is addressed and its learning algorithm is proposed based on the Radial Basis Function (RBF) network. This approach allows us to determine the model structure and parameters by stages so that the problems ran be simplified.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.322-324
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• 2005

In this paper, we introduce the advanced Self-Organizing Fuzzy Polynomial Neural Network based on optimized FPN by evolutionary algorithm and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed model gives rise to a structurally and parametrically optimized network through an optimal parameters design available within Fuzzy Polynomial Neuron(FPN) by means of GA. Through the consecutive process of such structural and parametric optimization, an optimized and flexible the proposed model is generated in a dynamic fashion. The performance of the proposed model is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.

• Journal of Navigation and Port Research
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• v.27 no.5
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• pp.569-576
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• 2003

Although the Free Trade Zone(FTZ) are actually competing with various strategies, the definition and structural understanding of activation strategies are not known very much Therefore this study has launched from this fact, and has the objective of obtaining the structural model for activation strategies in Gunsan FTZ, and understanding the components of activation in these region The process began by abstracting the components that composed the success factors in FTZ through recent research, and grouping it by the most core components. Also, by using the FSM(Fuzzy Structural Modeling) method to understand the structure of the grouped components, and the structural model for activation of FTZ was able to obtain as the result. When analyzing the obtained structural model, expansion of tax reduction, flexibility of law systems and good business environment came out to be the most important component groups, and especially flexibility of law systems and good business environment were the most effective component that effected all the other components overall.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.519-521
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• 1998

Fuzzy inference systems have found many applications in recent years. The fuzzy inference system design procedure is related to an expert or a skilled human operator in many fields. Various attempts have been made in optimizing its structure using genetic algorithm automated designs. This paper presents a new approach to structurally optimized designs of FNN models. The messy genetic algorithm is used to obtain structurally optimized fuzzy neural network models. Structural optimization is regarded important before neural network based learning is switched into. We have applied the method to the problem of a time series estimation.

• Journal of the Korea Concrete Institute
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• v.17 no.6 s.90
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• pp.1045-1051
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• 2005

For critical structures and application, where a given reliability must be met, it is necessary to account for uncertainties and variability in material properties, structural parameters affecting the corrosion process, in addition to the statistical and decision uncertainties. This paper presents an approach to the fuzzy arithmetic based modeling of the chloride-induced corrosion of reinforcement in concrete structures that takes into account the uncertainties in the physical models of chloride penetration into concrete and corrosion of steel reinforcement, as well as the uncertainties in the governing parameters, including concrete diffusivity, concrete cover depth, surface chloride concentration and critical chloride level for corrosion initiation. The parameters of the models are regarded as fuzzy numbers with proper membership function adapted to statistical data of the governing parameters and the fuzziness of the corrosion time is determined by the fuzzy arithmetic of interval arithmetic and extension principle

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.297-300
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• 2005

In this study, we proposed genetically optimized self-organizing fuzzy polynomial neural network based on information granulation and evolutionary algorithm (gdSOFPNN), develop a comprehensive design methodology involving mechanisms of genetic optimization. The proposed gdSOFPNN gives rise to a structural Iy and parametrically optimized network through an optimal parameters design available within FPN (viz. the number of input variables, the order of the polynomial, input variables, the number of membership functions, and the apexes of membership function). Here, with the aid of the information granulation, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. The performance of the proposed gdSOFPNN is quantified through experimentation that exploits standard data already used in fuzzy modeling.

• Advances in aircraft and spacecraft science
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• v.2 no.3
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• pp.263-273
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• 2015

Carbon nanotube strain sensors, or so called "fuzzy fiber" sensors have not yet been studied sufficiently. These sensors are composed of a bundle of fiberglass fibers coated with CNT through a thermal chemical vapor deposition process. The characteristics of these fuzzy fiber sensors differ from a conventional nanocomposite in that the CNTs are anchored to a substrate fiber and the CNTs have a preferential orientation due to this bonding to the substrate fiber. A numerical model was constructed to predict the strain response of a composite with embedded fuzzy fiber sensors in order to compare result with the experimental results obtained in an earlier study. A comparison of the numerical and experimental responses was conducted based on this work. The longitudinal sensor output from the model matches nearly perfectly with the experimental results. The transverse and off-axis tests follow the correct trends; however the magnitude of the output does not match well with the experimental data. An explanation of the disparity is proposed based on microstructural interactions between individual nanotubes within the sensor.