• Journal of Applied Reliability
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• v.1 no.2
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• pp.175-181
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• 2001

Recently, the quality of software has become a major issue. The statistical models used in making predictions about the quality of software are termed software reliability growth models (SRGM). However, the existing SRGMs have not been satisfactory in predicting software reliability behavior (Keiller and Miller(1991), Keiller and Littlewood(1984), Musa(1987)). In this paper, we present a fuzzy-based interval estimation of software errors (failures).

• Journal of Electrical Engineering and Technology
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• v.5 no.1
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• pp.8-15
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• 2010

A new technique using a search method which is based on fuzzy multi-criteria function is proposed for GMS(generator maintenance scheduling) in order to consider multi-objective function. Not only minimization of probabilistic production cost but also maximization of system reliability level are considered for fuzzy multi-criteria function. To obtain an optimal solution for generator maintenance scheduling under fuzzy environment, fuzzy multi-criteria relaxation method(fuzzy search method) is used. The practicality and effectiveness of the proposed approach are demonstrated by simulation studies for a real size power system model in Korea in 2010.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.486-490
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• 2003

Software qualify models can predict the risk of faults in the software early enough for cost-effective prevention of problems. This paper introduces a least squares support vector machine (LS-SVM) as a fuzzy regression method for predicting fault ranges in the software under development. This LS-SVM deals with the fuzzy data with crisp inputs and fuzzy output. Predicting the exact number of bugs in software is often not necessary. This LS-SVM can predict the interval that the number of faults of the program at each session falls into with a certain possibility. A case study on software reliability problem is used to illustrate the usefulness of this LS -SVM.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1406-1409
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• 1993

This paper describes a model of the subjective reliability analysis, which uses a fuzzy set, natural language expressions and parameterized operations of fuzzy sets, and reflects analysts' subjectivity. The model has the problem of many different analysis results being obtained since the results depend on their subjectivity. As one of the solutions two kinds of mutual agreements based on the analysis results are considered. One is the intersection and the union of the fuzzy sets obtained by the analysis. The other is the weighted average of the fuzzy sets. This paper gives these interpretations from the viewpoint of system reliability analysis. This paper also shows examples of these considerations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.7
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• pp.1314-1323
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• 2011

The maintenance of generating units is implicitly related with power system reliability and has a tremendous bearing on the operation of the power system. A technique using a fuzzy search method which is based on fuzzy multi-criteria function has been proposed for GMS (generator maintenance scheduling) in order to consider multi-objective function. In this study, a new technique using combined fuzzy set theory and genetic algorithm(GA) is proposed for generator maintenance scheduling. The genetic algorithm(GA) is expected to make up for that fuzzy search method might search the local solution. The effectiveness of the proposed approach is demonstrated by the simulation results on a practical size test systems.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.157.2-157
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• 2001

This paper describes the synergism of Artificial Neural Network and Fuzzy Logic based approach to improve the reliability of transformer differential protection, the conventional transformer differential protection commonly used a harmonic restraint principle to prevent a tripping from inrush current during initial transformer´s energization but such a principle can not performs the best optimization on tripping time. Furthermore, in some cases there may be false operation such as during CT saturation, high DC offset or harmonic containing in the line. Therefore an artificial neural network and fuzzy logic has been proposed to improve reliability of the transformer protection relay. By using EMTP-ATP the power transformer is modeled, all currents flowing ...

• International Journal of Reliability and Applications
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• v.2 no.2
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• pp.137-146
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• 2001

In general, analysis of the decision problems in nuclear system management involves a simultaneous consideration of various criteria and decision alternatives. Sometimes, it is a complex, unstructured, ill-defined process incorporating the multi-criteria and the data of impreciseness. To cope with this analysis, a fuzzy hierarchical analysis methodology is proposed and demonstrated with a simple example.

• Journal of the Korean Institute of Intelligent Systems
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• v.2 no.2
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• pp.34-41
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• 1992

A robust and reliable learning and reasoning mechanism is addressed based upon fuzzy set theory and fuzzy associative memories. The mechanism stores a priori an initial knowledge base via approximate learning and utilizes this information for decision-making systems via fuzzy inferencing. We called this fuzzy computer architecture a 'fuzzy hypercube' processing all the rules in one clock period in parallel. Fuzzy hypercubes can be applied to control of a class of complex and highly nonlinear systems which suffer from vagueness uncertainty. Moreover, evidential aspects of a fuzzy hypercube are treated to assess the degree of certainty or reliability together with parameter sensitivity.

• Journal of KIISE:Software and Applications
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• v.27 no.4
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• pp.393-401
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• 2000

This paper explores neuro-fuzzy system in order to improve the software reliability predictability from failure data. We perform numerical simulations for actual 10 failure count and 4 failure time data sets from different software projects with the various number of rules. Comparative results for next-step prediction problem is presented to show the prediction ability of the neuro-fuzzy system. Experimental results show that neuro-fuzzy system is adapt well across different software projects. Also, performance of neuro-fuzzy system is favorably with the other well-known neural networks and statistical SRGMs.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.9
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• pp.1055-1063
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• 1999

This paper present a conceptual possibilistic approach using fuzzy set theory to manage the uncertainties in the given reliability input date of the practical power system. In this paper, an algorithm is introduced to calculate the possibilstic reliability indices according to the degree of uncertainty in the given data. The probability distribution function can be transformed into an appropriate possibilstic representation using the probability-Possibility Consistency principle(PPCP) algorithm. In this the algorithm, the transformation is performation by making a compromise between the transformation consistency and the human updating experience. Fuzzy classifcation theory is applied to reduced the number of load data. The fuzzy classification method determines the closeness of load data points by assigning them to various clusters and then determening the distance between the clusters. The IEEE-RTS with 32-generating units is used to demonstrate the capability of the proposed algorithm.