• International Journal of Safety
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• 제10권1호
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• pp.22-31
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• 2011

Seismic response control method of the bridge structures with semi-active control device, i.e., magneto-rheological (MR) damper, is studied in this paper. Design of various kinds of clipped optimal controller and fuzzy controller are suggested as a semi-active control algorithm. For determining the control force of MR damper, clipped optimal control method adopts bi-state approach, but the fuzzy control method continuously quantifies input currents through fuzzy inference mechanism to finely modulate the damper force. To investigate the performances of the suggested control techniques, numerical simulations of a multi-span continuous bridge system subjected to various earthquakes are performed, and their performances are compared with each other. From the comparison of results, it is shown that the fuzzy control system can provide well-balanced control force between girder and pier in the view point of structural safety and stability and be quite effective in reducing both girder and pier displacements over the existing control method.

• Journal of Electrical Engineering and Technology
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• 제13권5호
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• pp.2086-2098
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• 2018

Security plays a vital role and is the key challenge in Mobile Ad-hoc Networks (MANET). Infrastructure-less nature of MANET makes it arduous to envisage the genre of topology. Due to its inexhaustible access, information disseminated by roaming nodes to other nodes is susceptible to many hazardous attacks. Intrusion Detection and Prevention System (IDPS) is undoubtedly a defense structure to address threats in MANET. Many IDPS methods have been developed to ascertain the exceptional behavior in these networks. Key issue in such IDPS is lack of fast self-organized learning engine that facilitates comprehensive situation awareness for optimum decision making. Proposed "Intelligent Behavioral Hybridized Intrusion Detection and Prevention System (IBH_IDPS)" is built with computational intelligence to detect complex multistage attacks making the system robust and reliable. The System comprises of an Intelligent Client Agent and a Smart Server empowered with fuzzy inference rule-based service engine to ensure confidentiality and integrity of network. Distributed Intelligent Client Agents incorporated with centralized Smart Server makes it capable of analyzing and categorizing unethical incidents appropriately through unsupervised learning mechanism. Experimental analysis proves the proposed model is highly attack resistant, reliable and secure on devices and shows promising gains with assured delivery ratio, low end-to-end delay compared to existing approach.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.201-203
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• 2007

The paper concerns the successive optimization for structure and parameters of fuzzy inference systems that is based on parallel Genetic Algorithms (PGA) and information data granulation (IG). PGA is multi, population based genetic algorithms, and it is used tu optimize structure and parameters of fuzzy model simultaneously, The granulation is realized with the aid of the C-means clustering. The concept of information granulation was applied to the fuzzy model in order to enhance the abilities of structural optimization. By doing that, we divide the input space to form the premise part of the fuzzy rules and the consequence part of each fuzzy rule is newly' organized based on center points of data group extracted by the C-Means clustering, It concerns the fuzzy model related parameters such as the number of input variables to be used in fuzzy model. a collection of specific subset of input variables, the number of membership functions according to used variables, and the polynomial type of the consequence part of fuzzy rules, The simultaneous optimization mechanism is explored. It can find optimal values related to structure and parameter of fuzzy model via PGA, the C-means clustering and standard least square method at once. A comparative analysis demonstrates that the Dnmosed algorithm is superior to the conventional methods.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 B
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• pp.394-396
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• 1997

This paper describes RBF network controller based on fuzzy control rules for intelligent control of nonlinear systems. The proposed scheme is derived from the functional equivalence between RBF networks and fuzzy inference systems. The design procedure of the proposed scheme is realized by first transforming the fuzzy control rules into the parameters of RBF networks. The optimized RBF network controller is then performed through the gradient descent learning mechanism to an error function. The proposed method is rigorously tested using a nonlinear and unstable nonlinear system. Simulation is performed to demonstrate the feasibility and effectiveness of the proposed scheme.

• 한국지능시스템학회논문지
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• 제14권3호
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• pp.342-348
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• 2004

As the advent of the Internet, B2B negotiation process on the Internet has been given attention from both researchers and practitioners. However, literature still shows that only structured conditions have been explicitly considered, despite the fact that unstructured conditions should be rendered as well. In this sense, this paper proposes a new negotiation support mechanism to incorporate causal relationships between structured and unstructured conditions in the process of B2B negotiation. Fuzzy cognitive map was used as a main source of causal knowledge as well causal inference engine. A prototype named CAKES-NEGO was developed to perform experiments with an illustrative example. Results revealed the robustness of our proposed negotiation support mechanism.

• 한국안전학회지
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• 제26권4호
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• pp.69-79
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• 2011

This paper proposes a GA-based optimal fuzzy control technique for the vibration control of earthquakeexcited adjacent structures interconnected with semi-active magneto-rheological(MR) dampers. Rule-based fuzzy logic controllers are designed first by implementing heuristic knowledge and the genetic algorithm(GA) is then introduced to optimally tune the fuzzy controllers for enhancing the seismic performance of semi-active control system. For practical implementation, the fuzzy controller simply uses locally measured responses of the dampers involved and directly returns the input voltage to the magneto-rheological dampers in real time through the fuzzy inference mechanism. The local measurement based fuzzy controller provides optimal damping force in a decentralized manner so that it does not require a primary central controller unlike the conventional semi-active control techniques. As a result, it can avoid the unbridgeable discrepancy between the desired control force and the actual damper force that may occur in the conventional control approaches. The validity and effectiveness of the proposed control method are shown numerically on two 20-story earthquake-excited buildings interconnected with MR dampers.

• 제어로봇시스템학회논문지
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• 제11권2호
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• pp.127-136
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• 2005

In this paper, a new architecture and comprehensive design methodology of genetically optimized Rule-based Fuzzy Polynomial Neural Networks(gRFPNN) are introduced and a series of numeric experiments are carried out. The architecture of the resulting gRFPNN results from asynergistic usage of the hybrid system generated by combining rule-based Fuzzy Neural Networks(FNN) with polynomial neural networks (PNN). FNN contributes to the formation of the premise part of the overall rule-based structure of the gRFPNN. The consequence part of the gRFPNN is designed using PNNs. At the premise part of the gRFPNN, FNN exploits fuzzy set based approach designed by using space partitioning in terms of individual variables and comes in two fuzzy inference forms: simplified and linear. As the consequence part of the gRFPNN, the development of the genetically optimized PNN dwells on two general optimization mechanism: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gRFPNN, the models are experimented with the use of several representative numerical examples. A comparative analysis shows that the proposed gRFPNN are models with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• 산업기술연구
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• 제17권
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• pp.323-330
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• 1997

In this paper, a new approach to modeling of nonlinear systems using fuzzy theory is presented. To express the various and complex behavior of nonlinear system, we combine multiple model method with hierachical prioritized structure, and the mountain clustering technique is used in partitioning of system. TSK rule structure is adopted to form the fuzzy rules, and Back propagation algorithm is used for learning parameters in consequent parts of the rules. Also we soften the paradigm of Mamdani's inference mechanism by using Yager's S-OWA operators. Computer simulations are performed to verify the effectiveness of the proposed method.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1994년도 추계학술대회 논문집
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• pp.664-669
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• 1994

This paper focuses on development and implementation of a performance management algorithm for IEEE802.4 token bus networks to serve large-scale integrated manufacturing systems. Such factory automation networks have to satisgy delay constraints imposed on time-critical messages while maintaining as much network capacity as possible for non-time-critical messages. This paper presents a network perfomance manager that adjusts queue apacity as well as timers by using a set of fuzzy rules and fuzzy inference mechanism. The efficacy of the performance management has been demonstrated by a series of simulation experiments.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 B
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• pp.425-427
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• 1997

In this paper, a new approach to modeling and control of nonlinear systems using fuzzy theory is presented. To express the various and complex behavior of nonlinear system, we combine multiple model method with hierachical prioritized structure. The mountain clustering technique is used in partitioning of system, and TSK rule structure is adopted to form the fuzzy rules. Also we soften the paradigm of Mamdani's inference mechanism by using Yager's S-OWA operators.