• Journal of the Korea Safety Management & Science
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• v.10 no.1
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• pp.67-73
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• 2008

National Education Information System (NEIS) is an ambitious reform project that can improve the competitiveness and performance of education field and to link administrational work of between schools and their senior administration offices via internet. NEIS is introduced to lighten the teachers' overburden, to standardize the work process and to bring better quality education to each classroom and make it possible for those involved in education to resolve any related educational problem on line. This paper aims to construct a hierarchy model consisting of key factors such as technological and administrative factors for the effective use of NEIS and to evaluate the relative importance among key factors using fuzzy AHP technique included fuzzy concepts. Eventually, the analysis results can be utilized to develop the future improvement strategy of NEIS and to satisfy the users.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.255-260
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• 2002

This paper describes a fuzzy-based system for analyzing the stress intensity factors (SIFs) of three-dimensional (3D) cracks. A geometry model, i.e. a solid containing one or several 3D cracks is defined. Several distributions of local node density are chosen, and then automatically superposed on one another over the geometry model by using the fuzzy knowledge processing. Nodes are generated by the bucketing method, and ten-coded quadratic tetrahedral solid elements are generated by the Delaunay triangulation techniques. The singular elements such that the mid-point nodes near crack front are shifted at the quarter-points, and these are automatically placed along the 3D crack front. The complete finite element(FE) model is generated, and a stress analysis is performed. The SIFs are calculated using the displacement extrapolation method. To demonstrate practical performances of the present system, semi-elliptical surface cracks in a inhomogeneous plate subjected to uniform tension are solved.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.119-124
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• 2001

In this pattern recognition on the large volumes of remote sensing satellite images, the inference time is much increased. In the case of the remote sensing data [5] having 4 wavebands, the 778 training patterns are learned. Each land cover pattern is classified by using 159, 900 patterns including the trained patterns. For the fuzzy classification, the 778 fuzzy rules are generated. Each fuzzy rule has 4 fuzzy variables in the condition part. Therefore, high performance parallel fuzzy inference system is needed. In this paper, we propose a novel parallel fuzzy inference system on T3E parallel computer. In this, fuzzy rules are distributed and executed simultaneously. The ONE_To_ALL algorithm is used to broadcast the fuzzy input to the all nodes. The results of the MIN/MAX operations are transferred to the output processor by the ALL_TO_ONE algorithm. By parallel processing of the fuzzy rules, the parallel fuzzy inference algorithm extracts match parallelism and achieves a good speed factor. This system can be used in a large expert system that ha many inference variables in the condition and the consequent part.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.287-297
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• 2018

To improve the performance of sensorless induction motor (IM) drives, a novel speed estimation method based on the real-time adaptive extended Kalman filter (RAEKF) is proposed in this paper. In this algorithm, the fuzzy factor is introduced to tune the measurement covariance matrix online by the degree of mismatch between the actual innovation and the theoretical. Simultaneously, the fuzzy factor can be continuously self-tuned tuned by the fuzzy logic reasoning system based on Takagi-Sugeno (T-S) model. Therefore, the proposed method improves the model adaptability to the actual systems and the environmental variations, and reduces the speed estimation error. Furthermore, a simple exponential function based on the fuzzy theory is used to reduce the computational burden, and the real-time performance of the system is improved. The correctness and the effectiveness of the proposed method are verified by the simulation and experimental results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.1820-1840
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• 2017

Aiming at the phenomenon of false information issued by service providers in cloud computing environment, a method for service evaluation based on fuzzy theory is put forward in this paper. According to the quality of services provided by cloud service providers and their behavior during interactions, a trust relationship between cloud service providers and cloud service consumers is established, which can be quantified by using fuzzy theory. The quality of services is evaluated by drawing on the trust relationship. In our method, the recommendation credibility of a cloud service consumer is determined through behavior similarity with evaluators and a praise factor. The introduction of the praise factor better suits the phenomenon of a high-quality service getting more repeat customers. The negative impact of dishonest customers is reduced, and the accuracy of trust and cloud service quality evaluation is improved by introducing a confidence factor that can be dynamically adjusted. The experimental results show that our method can effectively and accurately evaluate the trust value and service quality of providers, while weakening the influence of dishonest consumers, and quickly detect dishonest service providers. This is beneficial for consumers trying to find high quality service providers for similar services.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.4
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• pp.42-48
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• 2002

The case that cannot show the satisfactory control results with a modeling error and a shortage of related knowledge about a plant is if a fuzzy controller designed based on the plant model or the experience applies to an actual plant. We must adjust the scale factor which is a controller again in order to improve control performance in case of this and needs a lot of time and costs because this regulation process is carried out with a trial and error way We proposes the fuzzy controller that an automatic control adjust scale factors according to fuzzy logic and normalizer in this paper We confirmed that an automatic adjusted fuzzy controller displayed good performance than the fuzzy controller that scale factors was fixed through simulation. We implemented the controller using the DSP processor and applied in a hydraulic servo system. And then we observed an experimental results.

• Journal of Advanced Marine Engineering and Technology
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• v.19 no.4
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• pp.72-78
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• 1995

The diffusion of fuzzy logic techniques into real applications requires specific software supports which save development time and reduce the programming effort. But we has been lack of a tool devoted to support the design of fuzzy controllers. In this paper, on the basis of the general fuzzy set and .alpha.-cut set decomposition of fuzzy sets, a set of fuzzy reasoning tool(FRT) devoted to support the design of fuzzy dontroller for servo systems is developed. The major features of this tool are: 1) It supports users to analyze fuzzy ingerence status based on input deta and expected results by three-D graphic display. 2) It supports users to prepare input data and expected result. 3) It supports users to tuned scaling factor of membership functions, rules and fuzzy inference. The paper shows how the suggested design tools are suitable to give a consistent answer to the tuning of fuzzy control system. This FRT is expected to exert good performance and devoted to support which the design of fuzzy controller is illustrated in the servo systems.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.123-128
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• 1992

This paper presents an autotuning algorithm of scaling factor in order to improve system performance. We define the scaling factor of fuzzy controller as a function of error and error change. This function is tuned by the output of performance evaluation level utilizing the error of overshoot and rising time. Simulation results show that the proposed algorithm has good tuning performance for a system with parameter change.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.352-354
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• 2004

In this paper, a parallel fuzzy controller for one degree of freedom magnetic levitation is designed and its performance is compared with the performance of a PID controller. Input, output scaling factor of fuzzy controller and gain of PID controller were tuned using the GA algorithm. The designed controllers are validated by numerical simulations. So it's shown that parallel fuzzy controller can give the better performance for the plant than PID controller.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.1063-1067
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• 1993

Fuzzy logic is applied to a roll autopilot for missiles. Fuzzy rules are made so that the response duplicates that of the conventional control law for some flight condition. A scaling factor of the fuzzy controller is then scheduled by the missile velocity and altitude information to cope with the variation of the roll dynamics from that flight condition. By computer simulations and calculation of the stability margin, it is shown that the fuzzy control is robuster than the conventional one over the flight envelope even though two control laws work similarly for some flight conditions.