• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.238-238
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• 2000

In this paper, to increase the utilization of uranium resources contained in the spent fuel, the spent fuel is reused. for this, the spent fuel is dismantled or spent fuel rod is extracted from the spent fuel assembly. Therefore, to achieve the performance of compacting the spent fuel assembly, we proposed the controller consisting of adaptive and fuzzy with teaming algorithm. In order to show the performance of proposed algorithm compares, we compared the controller with conventional controller in plant.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2683-2702
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• 2015

A Content-based Image Retrieval (CBIR) system employs visual features rather than manual annotation of images. The selection of optimal features used in classification of images plays a key role in its performance. Category proliferation problem has a huge impact on performance of systems using Fuzzy Artmap (FAM) classifier. The proposed CBIR system uses a modified version of FAM called Non-Proliferation Fuzzy Artmap (NPFAM). This is developed by introducing significant changes in the learning process and the modified algorithm is evaluated by extensive experiments. Results have proved that NPFAM classifier generates a more compact rule set and performs better than FAM classifier. Accordingly, the CBIR system with NPFAM classifier yields good retrieval.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.6
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• pp.616-628
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• 2000

In this paper, we present the modeling of the ozone prediction system using Neuro-Fuzzy approaches. The mechanism of ozone concentration is highly complex, nonlinear, and nonstationary, the modeling of ozone prediction system has many problems and the results of prediction is not a good performance so far. The Dynamic Polynomial Neural Network(DPNN) which employs a typical algorithm of GMDH(Group Method of Data Handling) is a useful method for data analysis, identification of nonlinear complex system, and prediction of a dynamical system. The structure of the final model is compact and the computation speed to produce an output is faster than other modeling methods. In addition to DPNN, this paper also includes a Fuzzy Logic Method for modeling of ozone prediction system. The results of each modeling method and the performance of ozone prediction are presented. The proposed method shows that the prediction to the ozone concentration based upon Neuro-Fuzzy approaches gives us a good performance for ozone prediction in high and low ozone concentration with the ability of superior data approximation and self organization.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.1
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• pp.184-192
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• 2011

In this study, we introduce a identification methodology for FCM-based fuzzy model. The two underlying design mechanisms of such networks involve Fuzzy C-Means (FCM) clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on FCM clustering method for efficient processing of data and the optimization of model was carried out using PSO. The premise part of fuzzy rules does not construct as any fixed membership functions such as triangular, gaussian, ellipsoidal because we build up the premise part of fuzzy rules using FCM. As a result, the proposed model can lead to the compact architecture of network. In this study, as the consequence part of fuzzy rules, we are able to use four types of polynomials such as simplified, linear, quadratic, modified quadratic. In addition, a Weighted Least Square Estimation to estimate the coefficients of polynomials, which are the consequent parts of fuzzy model, can decouple each fuzzy rule from the other fuzzy rules. Therefore, a local learning capability and an interpretability of the proposed fuzzy model are improved. Also, the parameters of the proposed fuzzy model such as a fuzzification coefficient of FCM clustering, the number of clusters of FCM clustering, and the polynomial type of the consequent part of fuzzy rules are adjusted using PSO. The proposed model is illustrated with the use of Automobile Miles per Gallon(MPG) and Boston housing called Machine Learning dataset. A comparative analysis reveals that the proposed FCM-based fuzzy model exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• Honam Mathematical Journal
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• v.30 no.3
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• pp.557-566
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• 2008

In this paper, we introduce the concept of IVF weakly continuity and investigate some characterizations for IVF weakly continuous mappings on the interval-valued fuzzy topological spaces. We introduce and study the concepts of almost IVF compactness and nearly IVF compactness.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1944-1949
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• 1991

In coming years fuzzy control techniques will be increasingly applied to plants in such fields as industry and public works. For this reason, it is extremely important that a high-quality support tool for systems configuration be developed. In this paper we will describe a comprehensive, user-friendly design tool for configuration of fuzzy control systems, called FZtool, that runs on a compact and cost-effective laptop computer.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.123-126
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• 2006

We propose a new-focusing method for test handlers of compact camera module (CCM), The MMD (max-min difference) method is applied to calculate the focus value quickly considering the noisy output of CCM. Also, the fuzzy search method is applied to find the maximum focus value effectively. We design a fuzzy processor to control the lens position by focus values and brightness values, which improves the focusing performance in the sense of speed and processor memory. The proposed method is implemented by program and installed at the CCM test handler machines. The simulation results are presented to verify the usefulness of the proposed method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.315-320
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• 1997

Rule minimization technique adapted from rough set theory was applied to remove redundant knowledge which is not necessary to make a knowledge base. New algorithm to diagnose fault using Improved Fuzzy Cognitive Maps(I-FCMs), and Fuzzy Associative Memory(FAM) is proposed. I-FCM[22] is superior to gathering knowledge from many experts and descries dynamic behaviors of systems very well. I-FCM is not only a knowledge base, but also a inference engine. FAM has learning capability like neural network[12]. Rule minimization and composition of I-FCM and FAM make it possible to construct compact knowledge base and breaks the border between inference engine and knowledge base.

• ETRI Journal
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• v.32 no.4
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• pp.530-539
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• 2010

As the conventional silicon metal-oxide-semiconductor field-effect transistor (MOSFET) approaches its scaling limits, quantum mechanical effects are expected to become more and more important. Accurate quantum transport simulators are required to explore the essential device physics as a design aid. However, because of the complexity of the analysis, it has been necessary to simulate the quantum mechanical model with high speed and accuracy. In this paper, the modeling of double gate MOSFET based on an adaptive neuro-fuzzy inference system (ANFIS) is presented. The ANFIS model reduces the computational time while keeping the accuracy of physics-based models, like non-equilibrium Green's function formalism. Finally, we import the ANFIS model into the circuit simulator software as a subcircuit. The results show that the compact model based on ANFIS is an efficient tool for the simulation of nanoscale circuits.

• Nuclear Engineering and Technology
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• v.25 no.3
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• pp.371-380
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• 1993

A model free controller utilizing a set of linguistic fuzzy logic of the human operator's experience is developed to control the steam generator water level in a pressurized water reactor. Only 9 rules for control action are generated from the inputs of water level error and mass flow error implicitly representing the time variation of the collapsed water level. The bell type membership functions of the premise side and the result side are tuned by the sensitivity study. This compact fuzzy logic controller shows a robust control during transient and no offset error and oscillation during steady state operation. For a multi-ramp power increase from start-up to full power, the proposed controller shows good performance for the entire range.