• Honam Mathematical Journal
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• v.30 no.1
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• pp.171-183
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• 2008

In a previous work [18], the authors investigated autocontinuity, converse-autocontinuity, uniformly autocontinuity, uniformly converse-autocontinuity, and fuzzy multiplicativity of monotone set function defined by Choquet integral([3,4,13,14,15]) instead of fuzzy integral([16,17]). We consider nonnegative monotone interval-valued set functions and nonnegative measurable interval-valued functions. Then the interval-valued Choquet integral determines a new nonnegative monotone interval-valued set function which is a generalized concept of monotone set function defined by Choquet integral in [18]. These integrals, which can be regarded as interval-valued aggregation operators, have been used in [10,11,12,19,20]. In this paper, we investigate some characterizations of monotone interval-valued set functions defined by the interval-valued Choquet integral such as autocontinuity, converse-autocontinuity, uniform autocontinuity, uniform converse-autocontinuity, and fuzzy multiplicativity.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.76-81
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• 2016

In this paper, we present the entropy and similarity measure for generalized hesitant fuzzy information, and discuss their desirable properties. Some measure formulas are developed, and the relationships among them are investigated. We show that the similarity measure and entropy for generalized hesitant fuzzy information can be transformed by each other based on their axiomatic definitions. Furthermore, an approach of multiple attribute decision making problems where attribute weights are unknown and the evaluation values of attributes for each alternative are given in the form of GHFEs is investigated.

• Journal of Electrical Engineering and Technology
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• v.7 no.6
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• pp.884-890
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• 2012

Hysteretic behavior model of soft magnetic material usually used in electrical machines and electronic devices is necessary for numerical solution of Maxwell equation. In this study, a new dynamic hysteresis model is presented, based on the nonlinear dynamic system identification from measured data capabilities of fuzzy clustering algorithm. The developed model is based on a Gustafson-Kessel (GK) fuzzy approach used on a normalized gathered data from measured dynamic cycles on a C core transformer made of 0.33mm laminations of cold rolled SiFe. The number of fuzzy rules is optimized by some cluster validity measures like 'partition coefficient' and 'classification entropy'. The clustering results from the GK approach show that it is not only very accurate but also provides its effectiveness and potential for dynamic magnetic hysteresis modeling.

• Journal of the Korea Society of Computer and Information
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• v.6 no.4
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• pp.138-145
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• 2001

In this paper, we propose fuzzy closed BCMP queueing network model for the performance evaluation of distributed processing system. Which has the ambiguous service requirements of job to servers and service rates of server according to network environments. This model can derive the measures for system Performances using fuzzy mean value analysis which can process the fuzzy factors. Computer simulation has been performed for verifying the effectiveness of derived equations of performance evaluation according to service requirements of job and the numbers of clients.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1248-1253
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• 2009

Although there have been many efforts to reduce accidents on construction sites, such accidents continue to occur. New technologies have recently been developed to improve safety and their performance needs to be evaluated to determine their suitability prior to the application. The assessment for safety performance mainly is conducted depending on qualitative and subjective judgment of supervisors. However, there are rarely proper approaches to assess such qualitative measures. Therefore, we propose a fuzzy-based approach to assessing the performance of a new technology. The assessment of a new technology, called a mobile detector (MD), was carried out as a case study. The output is compared with those by a numerical simulation. As a result, the fuzzy-based performance assessment is shown to be appropriate for this evaluation.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.179-192
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• 2023

The widespread use of Cloud Computing, Internet of Things (IoT), and social media in the Information Communication Technology (ICT) field has resulted in continuous and unavoidable cyber-attacks on users and critical infrastructures worldwide. Traditional security measures such as firewalls and encryption systems are not effective in countering these sophisticated cyber-attacks. Therefore, Intrusion Detection and Prevention Systems (IDPS) are necessary to reduce the risk to an absolute minimum. Although IDPSs can detect various types of cyber-attacks with high accuracy, their performance is limited by a high false alarm rate. This study proposes a new technique called Fuzzy Logic - Objective Risk Analysis (FLORA) that can significantly reduce false positive alarm rates and maintain a high level of security against serious cyber-attacks. The FLORA model has a high fuzzy accuracy rate of 90.11% and can predict vulnerabilities with a high level of certainty. It also has a mechanism for monitoring and recording digital forensic evidence which can be used in legal prosecution proceedings in different jurisdictions.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.174-178
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• 2015

This paper presents a new method for forecasting construction project cost and time at completion or at any intermediate time horizon of the project duration. The method is designed to overcome identified limitations of current applications of earned value method in forecasting project cost and time. The proposed method usesfuzzy set theory to model uncertainties associated with project performance and it integrates the earned value technique and the contractors' judgement. The fuzzy set theory is applied as an alternative approach to deterministic and probabilistic methods. Using fuzzy set theory allows contractors to: (1) perform risk analysis for different scenarios of project performance indices, and (2) perform different scenarios expressing vagueness and imprecision of forecasted project cost and time using a set of measures and indices. Unlike the current applications of Earned Value Method(EVM), The proposed method has a numberof interesting features: (1) integrating contractors' judgement in forecasting project performance; (2) enabling contractors to evaluate the risk associated with cost overrun in much simpler method comparing with that of simulation, and (3) accounting for uncertainties involved in the forecasting project cost.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.350-354
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• 2009

Y. R$\acute{e}$ball$\acute{e}$[Fuzzy Sets and Systems, vol.157, pp.3025-2039, 2006] discussed the representation of necessity measure through the Choquet integral criterian. He also considered a decision maker who ranks necessity measures related with Choquet integral representation. Our motivation of this paper is that a decision maker have an "ambiguity" necessity measure to present preferences. In this paper, we discuss the representation of interval-valued necessity measures through the Choquet integral criterian.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2004.06a
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• pp.188-188
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• 2004

In the present study, a multicriteria decision-making (MCDM) problem of ranking of important radionuclide release scenarios in a low and intermediate radioactive waste repository is to treat on the basis of non-additive fuzzy measures and fuzzy integral theory. Ranking of important scenarios can lead to the provision of more effective safety measure in a design stage of the repository. The ranking is determined by a relative degree of appropriateness of scenario alternatives.(omitted)

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.335-338
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• 2004

본 논문에서는 기존의 정보량(Entropy) 기반 클러스터링 기법을 향상시키기 위한 방법으로서 퍼지 정보량을 이용하였다 가우시안 혼합 모델을 이용하면, 프로토타입의 목적 함수를 이용하는 클러스터링 기법보다 향상된 결과를 얻을 수 있고, Parameter의 조정이 요구되지 않는다. 그러나, 가우시안 혼합 모델의 사용은 주어진 패턴 집합을 클러스터링하는데 계산량의 증가를 초래하게 된다. 본 논문에서는 가우시안 혼합 모델의 정형화에 요구되는 계산량을 감소시키는 방법을 제시한다 또한 퍼지정보량(Fuzzy Entropy)을 적용하여 기존의 정보량 기반의 클러스터링 결과와 비교 분석하였다.