• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.3
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• pp.179-182
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• 2005

A model of an assessment module has been created, using intuitionistic fuzzy estimations, which render account on the knowledge of the trained objects. The final mark is determined on the basis of a set of evaluation units. An opportunity is offered no only fur tracing the changes of the parameters of the trainer object, but there is also an opportunity of tracing the status of the already comprehended knowledge, as well as evaluating and changing the training themes and evaluation criteria.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.13 no.5
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• pp.123-134
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• 1993

This study was deviced to apply fuzzy concepts to pavement serviceability evaluation. An evaluation model was developed based on workmanship of pavement during construction, external load on pavement and current distress level. Five rating fuzzy sets, three weight fuzzy sets were developed based on the concept that the most appropriate balance was achieved in Gd which was established for grading the fuzzy overall rating results. Evaluation criteria and corresponding fuzzy rating scale were suggested. A computer program for evaluating serviceability based on the criteria was developed. The program was operated by simply typing in input data on each question and producing output as Gd on the screen. lt was possible to estimate the pavement serviceability level well using this fuzzy-set-based approach.

• Nuclear Engineering and Technology
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• v.39 no.4
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• pp.337-348
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• 2007

A fuzzy neural network model is presented to predict residual stress for dissimilar metal welding under various welding conditions. The fuzzy neural network model, which consists of a fuzzy inference system and a neuronal training system, is optimized by a hybrid learning method that combines a genetic algorithm to optimize the membership function parameters and a least squares method to solve the consequent parameters. The data of finite element analysis are divided into four data groups, which are split according to two end-section constraints and two prediction paths. Four fuzzy neural network models were therefore applied to the numerical data obtained from the finite element analysis for the two end-section constraints and the two prediction paths. The fuzzy neural network models were trained with the aid of a data set prepared for training (training data), optimized by means of an optimization data set and verified by means of a test data set that was different (independent) from the training data and the optimization data. The accuracy of fuzzy neural network models is known to be sufficiently accurate for use in an integrity evaluation by predicting the residual stress of dissimilar metal welding zones.

• Journal of Electrical Engineering and Technology
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• v.11 no.1
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• pp.47-54
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• 2016

Microgrids offer several reliability benefits, such as the improvement of load-point reliability and the opportunity for reliability-differentiated services. The primary goal of this work is to investigate the impacts of operating condition on the reliability index for microgrid system. It relies on a component failure rate model which quantifies the relationship between component failure rate and state variables. Some parameters involved are characterized by subjective uncertainty. Thus, fuzzy numbers are introduced to represent such parameters, and an optimization model based on Fuzzy Chance Constrained Programming (FCCP) is established for reliability index calculation. In addition, we present a hybrid algorithm which combines scenario enumeration and fuzzy simulation as a solution tool. The simulations in a microgrid test system show that reliability indices without considering operating condition can often prove to be optimistic. We also investigate two groups of situations, which include the different penetration levels of microsource and different confidence levels. The results support the necessity of considering operating condition for achieving accurate reliability evaluation.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.662-671
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• 2007

Since variety of construction projects with their individual specifications could be handled through different procurement systems, selection of the most appropriate project delivery system is a vital step towards more efficient project execution. The appropriate selection of project delivery system may also ensure more competent management of the project. Its impacts are not only limited to the first stages of the project, as it could also influence pre-construction, construction and operational phases of the project. Among different approaches exerted for this purpose, none has taken uncertainty into account, despite the fact that during first stages of the project most of the selection factors are still uncertain and not clearly defined. This paper, hence, aims to provide a fuzzy insight into the project delivery system selection. Through this approach more tangible model of the evaluation process may be presented. Proposed fuzzy method is indeed a multi criteria decision making model, based on the group of criteria, assigned for the evaluation procedure. A case study is also conducted, based on the opinion of an invented group of the experts.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.828-833
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• 2009

WWW which is an applicable massive set of document on the Web is a thesaurus of various information for users. However, Search engines spend a lot of time to retrieve necessary information and to filter out unnecessary information for user. In this paper, we propose the EFAM(the Extended Fuzzy AHP Method) model to manage the Web resource efficiently, and to make a decision in the problem of specific domain definitely. The EFAM model is concerned with the emotion analysis based on the domain corpus information, and it composed with systematic common concept grids by the knowledge of multiple experts. Therefore, The proposed the EFAM model can extract the documents by considering on the emotion criteria in the semantic context that is extracted concept from the corpus of specific domain and confirms that our model provides more efficient decision-making through an experiment than the conventional methods such as AHP and Fuzzy AHP which describe as a hierarchical structure elements about decision-making based on the alternatives, evaluation criteria, subjective attribute weight and fuzzy relation between concept and object.

• Architectural research
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• v.12 no.2
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• pp.85-92
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• 2010

In recent years, construction projects have been forced to cope with lack of skilled labor and increasing hazard circumstance of human operations. A construction robotic system has been frequently accomplished as one alterative for overcoming these difficulties in increasing construction quality, enhancing productivity, and improving safety. However, while the complexity of such a system increases, there are few ways to carry out an assessment of the system. This paper introduces a knowledge-based multi-criteria decision-making process to assist decision makers in systematically evaluating an automated system for a given project and quantifying its system performance index. The model employs linguistic terms and fuzzy numbers in attempts to deal with the vagueness inherent in experts' or decision makers' subjective opinions, considering the contribution resulted from their knowledge on a decision problem. As an illustrative case, the system, called Robotic-based Construction Automation, for constructing steel erection of high-rise buildings was applied into this model. The results show the model's capacities and imply the application to other extended types of construction robotic systems.

• Journal of the Korea Society of Computer and Information
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• v.12 no.5
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• pp.49-56
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• 2007

This paper deals with grid job allocation and grid resource scheduling to provide a stable and quicker job processing service to grid users. In this paper, we proposed a fuzzy logic-based grid job scheduling model for an effective job scheduling in computational grid environment. The fuzzy logic-based grid job scheduling model measures resource efficiency of all grid resources by a fuzzy logic system based on diverse input parameters like CPU speed and network latency and divides resources into several groups by resource efficiency. And, the model allocates jobs to resources of a group with the highest resource efficiency. For performance evaluation, we implemented the fuzzy logic-based grid job scheduling model on the DEVS modeling and simulation environment and measured reduction rates of turnaround time, job loss, and communication messages in comparison with existing job scheduling models such as the random scheduling model and the MCT(Minimum Completion time) model. Experiment results that the proposed model is useful to improve the QoS of the grid job processing service.

• Journal of Korean Society of Transportation
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• v.23 no.6 s.84
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• pp.31-40
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• 2005

Main airports in the Intra-Asian market have faced competition not only to attract China-bound transshipment cargoes but also to be hub airport in same region. In spite of such a importance, the previous research has been short of evaluation of airport competitiveness. Implication of the previous research has mainly been focused on evaluation of airport critical factor service qualify and efficiency. The aim of this paper is to present critical points that affect airport competitiveness using an algorithm based on reversed fuzzy evaluation and senario method. The results of senario analysis and reversed fuzzy evaluation shows that Incheon international airport needs to enhance service level of 7% as a result of senario analysis and service level of 5% and brand equity level of 10% at the same time as a result of reversed fuzzy evaluation analysis, to ensure competitiveness in same region.

• Journal of Internet Computing and Services
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• v.13 no.5
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• pp.21-31
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• 2012

Threat Evaluation(TE) which has air intelligence attained by identifying friend or foe evaluates the target's threat degree, so it provides information to Weapon Assignment(WA) step. Most of TE data are passed by sensor measured values, but existing techniques(fuzzy, bayesian network, and so on) have many weaknesses that erroneous linkages and missing data may fall into confusion in decision making. Therefore we need to efficient Threat Evaluation system that can refine various sensor data's linkages and calculate reliable threat values under unpredictable war situations. In this paper, we suggest new threat evaluation system based on information fusion JDL model, and it is principle that combine fuzzy which is favorable to refine ambiguous relationships with bayesian network useful to inference battled situation having insufficient evidence and to use learning algorithm. Finally, the system's performance by getting threat evaluation on an air defense scenario is presented.