• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.1
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• pp.65-71
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• 2013

In this paper, we address fault detection observer design problem of T-S fuzzy systems with sensor fault. To detect fault, T-S fuzzy model-based observer is used. By introducing $\mathfrak{H}$_ performance index, an observer is designed as sensitive to fault as possible. The fault is then detected by a fault decision logic. The design conditions are derived in terms of linear matrix inequalities. An illustrative example is provided to verify the effectiveness of the proposed fault detection technique.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3128-3149
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• 2018

A number of effective methods for multiple-attribute group decision making (MAGDM) with interval-valued intuitionistic fuzzy numbers (IVIFNs) have been proposed in recent years. However, the different methods frequently yield different, even sometimes contradictory, results for the same problem. In this paper a novel criterion to determine the advantages and disadvantages of different methods is proposed. First, the decision-making process is divided into three parts: translation of experts' preferences, aggregation of experts' opinions, and comparison of the alternatives. Experts' preferences aggregation is considered the core step, and the quality of the collective matrix is considered the most important evaluation index for the aggregation methods. Then, methods to calculate the similarity measure, correlation, correlation coefficient, and energy of the intuitionistic fuzzy matrices are proposed, which are employed to evaluate the collective matrix. Thus, the optimal method can be selected by comparing the collective matrices when all the methods yield different results. Finally, a novel approach for aggregating experts' preferences with IVIFN is presented. In this approach, experts' preferences are mapped as points into two-dimensional planes, with the plant growth simulation algorithm (PGSA) being employed to calculate the optimal rally points, which are inversely mapped to IVIFNs to establish the collective matrix. In the study, four different methods are used to address one example problem to illustrate the feasibility and effectiveness of the proposed approach.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.6
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• pp.1070-1079
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• 2016

In this paper, the design of recursive radial basis function neural networks based on incremental fuzzy c-means is introduced for processing the big data. Radial basis function neural networks consist of condition, conclusion and inference phase. Gaussian function is generally used as the activation function of the condition phase, but in this study, incremental fuzzy clustering is considered for the activation function of radial basis function neural networks, which could effectively do big data processing. In the conclusion phase, the connection weights of networks are given as the linear function. And then the connection weights are calculated by recursive least square estimation. In the inference phase, a final output is obtained by fuzzy inference method. Machine Learning datasets are employed to demonstrate the superiority of the proposed classifier, and their results are described from the viewpoint of the algorithm complexity and performance index.

• Journal of Korea Multimedia Society
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• v.18 no.2
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• pp.207-217
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• 2015

A multi-cup electric cupping system (MECS) was proposed, based on the ancient cupping method. MECS consisted of several cups that could be used simultaneously to treat 85 lumbago patients. Each cup was equipped with its own pump and pressure-monitoring system. The vacuum pressure of the cups was controlled using fuzzy logic. Through automated control of the vacuum pressure, long-term relief of muscle tightness was achieved. To develop a scientific foundation for this alternative treatment, we compared the Oswestry Disability Index (ODI) scores from conventional basic cupping to the ODI scores for our proposed MECS. The ODI scores using MECS decreased from $11.71{\pm}1.61$ before treatment to $4.81{\pm}1.48$ and $1.87{\pm}1.61$ after three and five treatments, respectively. The improvement rate in the ODI scores using MECS after three treatments was higher than that achieved by basic cupping. These results, combined with the convenience offered by enhanced information technology and fuzzy logic capabilities, should increase the efficiency of this device, and facilitate the opportunity to further explore the potential of Oriental medical practices.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.6
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• pp.767-781
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• 2013

In this paper, we proposes the AC input voltage and current sensorless control scheme to control the input power factor and DC output voltage of the three-phase Z-source PWM rectifier. For DC-link voltage control which is sensitive to the system parameters of the PWM rectifier, fuzzy-PI controller is used. Because the AC input voltage and current are estimated using only the DC-link voltage and current, AC input voltage and current sensors are not required. In addition, the unity input power factor and DC output voltage can be controlled. The phase-angle of the detected AC input voltage and estimated voltage, the response characteristics of the DC output voltage according to the DC voltage references, the FFT results of the estimated voltage and current, efficiency, and the response characteristics of the conventional PI controller and fuzzy-PI controller are verified by PSIM simulation.

• Journal of Electrical Engineering and Technology
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• v.4 no.1
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• pp.1-12
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• 2009

In recent years, integration of new distributed generation (DG) technology in distribution networks has become one of the major management concerns for professional engineers. This paper presents a dynamic methodology of optimal allocation and sizing of DG units for a given practical distribution network, so that the cost of active power can be minimized. The approach proposed is based on a combined Genetic/Fuzzy Rules. The genetic algorithm generates and optimizes combinations of distributed power generation for integration into the network in order to minimize power losses, and in second step simple fuzzy rules designs based upon practical expertise rules to control the reactive power of a multi dynamic shunt FACTS Compensator (SVC, STATCOM) in order to improve the system loadability. This proposed approach is implemented with the Matlab program and is applied to small case studies, IEEE 25-Bus and IEEE 30-Bus. The results obtained confirm the effectiveness in sizing and integration of an assigned number of DG units.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.39-44
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• 2006

Ship's collision avoidance is a skill that masters of merchant marine vessels have acquired through years of experience and that makes them feel at ease to guide their ship out from danger quickly compared to inexperienced officers. Case based reasoning(CBR) uses the same technique in solving tasks that needs reference from variety of situations. CBR can render decision-making easier by retrieving past solutions from situations that are similar to the one at hand and make necessary adjustments in order to adapt them. In this paper, we propose to utilize the advantages of CBR in a support system for ship's collision avoidance while using fuzzy algorithm for its retrieval of similar navigational situations, stored in the casebase, thus avoiding the cumbersome tasks of creating a new solution each time a new situation is encountered. There will be two levels within the Fuzzy-CBR. The first level will identify the dangerous ships and index the new case. The second level will retrieve cases from casebase and adapt the solution to solve for the output. While CBR's accuracy depends on the efficient retrieval of possible solutions to be adapted from stored cases, fuzzy algorithm will improve the effectiveness of solving the similarity to a new case at hand.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.8
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• pp.527-534
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• 2013

The software is changed according to the changing businesses and the user requirement, it involves increasing complexity and cost. Considering the repetitive changes required for the software, replacement is more efficient than maintenance at some point. In this study, the replacement time was predicted using the service dissatisfaction index and replacement point assessment index by the software group for each task. First, fuzzy inference was used to develop the method and indicator for the user's service level dissatisfaction. Second, the replacement point assessment method was established considering the quality, costs, and new technology of the software. Third, a replacement time prediction technique that used the gap between the user service measurement and replacement point assessment values was proposed. The results of the case study with the business solutions of three organizations, which was conducted to verify the validity of the proposed prediction technique in this study, showed that the service dissatisfaction index decreased by approximately 16% and the replacement point assessment index increased by approximately 9%.

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.108.1-108.1
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• 2005

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.83-89
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• 1998

본 논문에서는, 퍼지 클러스터의 수가 증가함에 따라 나타나는 퍼지 클러스터링 타당성 평가 기준의 단조 감소 현상을 억제하는 새로운 퍼지 클러스터링 타당성 평가 기준을 제시한다. 또한, 제시된 평가 기준의 성질을 조사하고 기존의 퍼지 클러스터링 타당성 평가 기준과의 차이점에 대하여 논한다. 마지막으로, 퍼지 크러스터링에 자주 인용되는 몇 가지 전형적인 자료에 대한 모의 실험을 통하여 제시된 평가 기준의 효용성을 보인다.