• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.432-434
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• 2003

The main objective of this research is to utilize the parallel Fuzzy arithmetic for constructing ocean bathymetry from polarized remote sensing data such as TOPSAR image. In doing so, the parallel library for Fuzzy arithmetic has been developed. Three- dimensional surface modeling consisted of Volettra model, non-linear model which construct a global topological structure between the data points, used to support an approximation of real surface. The output of the parallel library was a digital terrain model for bathymetry along the coastal waters of Kuala Terengganu Malaysia. This paper describes the principles behind the Fuzzy algorithm, indicates for what type of application it might be useful, notes on the accuracy and gives an example of an application.

• Journal of Korea Port Economic Association
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• v.20 no.1
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• pp.141-151
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• 2004

Although the small and medium ports are actually competing with various strategies, the definition and structural understanding of small and medium ports are not known very much. Therefore this study has launched from this fact, and has the objective of obtaining the structural model for increasing shippers' attraction of small and medium ports. The process began by abstracting the components that composed the success factors through recent research, and grouping it by FA(Factor Analysis) method. Also, by using the FSM(Fuzzy Structural Modeling) method to understand the structure of the grouped components, and the structural model for increasing shippers' attraction of small and medium ports was able to obtain as the result. When analyzing the obtained structural model, easiness of shipment, connection to hubport and efficiency of hinterland network came out to be the most important component groups.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.113-116
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• 1994

In this paper. we suggest new fuzzy modeling algorithm, which can be easily implemented, by combining HPC-MEANS Algorithm and Genetic Algorithm. HPC-MEANS used to cluster the sample data in input-output space will hyper planes and to make structure identification roughly and Genetic Algorithm is used to nine the premise and consequent parameters. For the validity of suggested methods we model the system with I/O data from known system. and then compare two systems.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 1999.11a
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• pp.279-284
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• 1999

Although the switched reluctance motor (SRM) has a several advantages such as simple magnetic structure, robustness, wide range of speed characteristics and simple driving, it has a considerable inherent torque ripple and speed variation duet to the driving characteristics of pulse current waveform and the nonlinear inductance profile. The high torque ripple and speed variation inhibits wide application. The minimization of the torque ripple is very important in high performance servo drive applications, which require smooth operation with minimum torque pulsations. This paper presents the new SRM torque modeling technique for the control of instantaneous torque. The SRM is modeled by the database of torque profiles for every small variation in currents and rotor angles, which is inferred from the several measured data by the adaptive neuro-fuzzy inference technique. Simulation results demonstrating the effectiveness of proposed torque modeling technique are presented.

• Journal of Navigation and Port Research
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• v.37 no.5
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• pp.493-498
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• 2013

This paper is aimed to priority evaluation of preliminary cases for IMO -IMS(International Maritime Organization- Information Management System) using fuzzy TOPSIS(Technique for Order Performance by Similarity to Ideal Solution) and AHP(Analytic Hierarchy Process). To this solve, therefore, this paper extract 24 preliminary cases and select 4 major preliminary alternative cases after analysing the structure of its alternative cases using FSM(Fuzzy Structure Modeling). Also, the weights of evaluation factors determine using AHP which able to keep the consistency when decision-makers assess. In AHP method, but, the numbers of paired comparison incerase as much as the numbers of the comparison items increase and because this evaluation have the many of vagueness, the decision of final ranking is used to fuzzy TOPSIS method which is included TOPSIS and Fuzzy Set Theory. The result are developed as order as Management of IMO Convention Information, Delivery of IMO Convention Information, Total IMO Database, Knowledge Hub of IMO Convention Information in IMO-IMS.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.2
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• pp.132-137
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• 2007

Experimental software data capturing the essence of software projects (expressed e.g., in terms of their complexity and development time) have been a subject of intensive modeling. In this study, we introduce a new category of Hybrid Fuzzy Neural Networks (gHFNN) and discuss their comprehensive design methodology. The gHFNN architecture results from highly synergistic linkages between Fuzzy Neural Networks (FNN) and Polynomial Neural Networks (PNN). We develop a rule-based model consisting of a number of "if-then" statements whose antecedents are formed in the input space and linked with the consequents (conclusion pats) formed in the output space. In this framework, FNNs contribute to the formation of the premise part of the overall network structure of the gHFNN. The consequences of the rules are designed with the aid of genetically endowed PNNs. The experiments reported in this study deal with well-known software data such as the NASA dataset. In comparison with the previously discussed approaches, the proposed self-organizing networks are more accurate and yield significant generalization abilities.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.2
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• pp.97-105
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• 1996

In this paper, an adaptive model predictive controller for nodinear processes using fuzzy model is proposed. Adaptive structure is implemented by recursive fuzzy modeling. The model and control law can be obtained the same as GPC, because the consequent parts of the fuzzy model comprise linear equations of input and output variables. The proposed Adaptive fuzzy model predictive controller (AFMPC) controls nonlinear process well due to the intrinsic nonlinearity of the fuzzy model. When AFMPC's output is variation in the process control input, it maintains zero steady-state offset for a constant reference input and has superior performance. The properties and performance of the proposed control scheme were examined with nonlinear plant by simulation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.4
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• pp.730-737
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• 2001

In this paper, an stabilization control method using adaptive neuro-fuzzy controller(ANFC) is proposed for modeling of nonlinear complex systems. The proposed adaptive neuro-fuzzy controller implements system structure and parameter identification using the intelligent schemes together with optimization theory, linguistic fuzzy implication rules, and neural networks from input and output data of processes. The results show that the proposed method can produce the intelligence model with higher accuracy than other works achieved previously.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.6
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• pp.415-424
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• 2004

In this paper, we introduce and investigate a new category of rule-based fuzzy inference system based on Information Granulation(IG). The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of “If..., then...” statements, and exploits the theory of system optimization and fuzzy implication rules. The form of the fuzzy rules comes with three types of fuzzy inferences: a simplified one that involves conclusions that are fixed numeric values, a linear one where the conclusion part is viewed as a linear function of inputs, and a regression polynomial one as the extended type of the linear one. By the nature of the rule-based fuzzy systems, these fuzzy models are geared toward capturing relationships between information granules. The form of the information granules themselves becomes an important design features of the fuzzy model. Information granulation with the aid of HCM(Hard C-Means) clustering algorithm hell)s determine the initial parameters of rule-based fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial function being used in the Premise and consequence Part of the fuzzy rules. And then the initial Parameters are tuned (adjusted) effectively with the aid of the improved complex method(ICM) and the standard least square method(LSM). In the sequel, the ICM and LSM lead to fine-tuning of the parameters of premise membership functions and consequent polynomial functions in the rules of fuzzy model. An aggregate objective function with a weighting factor is proposed in order to achieve a balance between performance of the fuzzy model. Numerical examples are included to evaluate the performance of the proposed model. They are also contrasted with the performance of the fuzzy models existing in the literature.