• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.809-813
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• 2009

We want to show that the construct of best fuzzy tests for certain fuzzy situations of Poisson distribution. Due to Neyman and Pearson theorem, if we have ${\theta}_0$ and ${\theta}_1$ be distinct fuzzy values of ${\Omega}=\{{\theta}\;:\;{\theta}\;=\;{\theta}_0,\;{\theta}_1\}$ such that $L({\theta}_0\;:\;X)/L({\theta}_1\;:\;X)$ < k, then k is a fuzzy number. For each fuzzy random samples point $X\;{\subset}\;C$, we have most power test for fuzzy critical region C by agreement index.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2666-2668
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• 2001

Fuzzy system which are based on membership functions and rules, can control nonlinear, uncertian, complex system well. However, Fuzzy controller has problems: It is difficult to design a stable for amateur. To update the then-part membership functions of the fuzzy controller can be designed using the Optimal fuzzy controller. Then we could be optimized the system choosing a good performance index. The proposed fuzzy controller based on Optimal fuzzy control is an Truck-Backer for demonstration of the robustness of proposed methodology.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2669-2671
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• 2001

In this paper, we propose design a Multi-Fuzzy Inference model structure. In order to determine structure of the proposed Multi-Fuzzy Inference model, HCM clustering method is used. The parameters of membership function of the Multi-Fuzzy are identified by genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. We use simplified inference and linear inference as inference method of the proposed Multi-Fuzzy model and the standard least square method for estimating consequence parameters of the Multi-Fuzzy. Finally, we use some of numerical data to evaluate the proposed Multi-Fuzzy model and discuss about the usefulness.

• Structural Engineering and Mechanics
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• v.11 no.3
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• pp.287-299
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• 2001

Columns in multi-storey frames are presently categorised as either braced or unbraced, usually by means of the stability index criterion, for estimating their effective length ratios by design aids such as 'alignment charts'. This procedure, however, ignores the transition in buckling behaviour between the braced condition and the unbraced one. Hence, this results in either an overestimation or an underestimation of effective length estimates of columns in frames that are in fact 'partially braced'. It is shown in this paper that the transitional behaviour is gradual, and can be approximately modelled by means of a 'fuzzy logic' based technique. The proposed technique is simple and intuitively agreeable. It fills the existing gap between the braced and unbraced conditions in present codal provisions.

• 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.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.149-155
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• 2020

Air pollution is a problem of environmental health risk in big cities. Recently, researchers have proposed using various artificial intelligence technologies to predict air pollution. The proposed model is Cooperative of Artificial Neural Network (ANN) and Fuzzy Inference System (FIS), to predict air pollution of Korean cities using Python. Data air pollutant variables were collected and the Air Korean Web site air quality index was downloaded. This paper's aim was to predict on the health risks and the very unhealthy values of air pollution. We have predicted the air pollution of the environment based on the air quality index. According to the results of the experiment, our model was able to predict a very unhealthy value.

• Journal of the Ergonomics Society of Korea
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• v.17 no.2
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• pp.33-48
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• 1998

This paper illustrates the application of Fuzzy Theory and Analytic Hierarchy Process(AHP) for developing Occupational Stress Index(OSI). The purpose of the OSI development is for future prediction and problem solving of prevailing occupational stress. In developing OSI, the concept of fuzzy set theory was introduced to determine the existence and level of perceived occupational stress instead of actually measuring the strain parameters. The AHP is adopted to collect different weighting factors, since there exist various perceptions and responses to the occupational stress by different individuals. The validation study revealed that the OSI is a reliable predictor of work-related accident and illness and the physiological health of employees. Creating preventive measures, such as early detection of stress, proper placement and promotion of employees, and job enlargement will be possible by using this OSI effectively.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.54 no.2
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• pp.67-72
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• 2005

This paper presents a method to select contingency ranking considering voltage security problems in power systems. Direct method which needs not the detailed knowledge of the post contingency voltage at each bus is used. Based on system operator's experience and knowledge, the membership functions for the MVAR mismatch and allowable voltage violation are justified describing linguistic representation with heuristic rules. Rule base is used for the computation of severity index for each contingency by fuzzy inference. Contingency ranking harmful to the system is formed by the index for security evaluation. Compared with 1P-1Q iteration, this algorithm using direct method and fuzzy inference shows higher computation speed and almost the same accuracy. The proposed method is applied to model system and KEPCO pratical system which consists of 311 buses and 609 lines to show its effectiveness.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1205-1223
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• 2018

Image enhancement is an emerging method for analyzing the images clearer for interpretation and analysis in the spatial domain. The goal of image enhancement is to serve an input image so that the resultant image is more suited to the particular application. In this paper, a novel method is proposed based on Mamdani fuzzy inference system (FIS) using multiple fuzzy membership functions. It is observed that the shape of membership function while converting the input image into the fuzzy domain is the essential important selection. Then, a set of fuzzy If-Then rule base in fuzzy domain gives the best result in image contrast enhancement. Based on a different combination of membership function shapes, a best predictive solution can be determined which can be suitable for different types of the input image as per application requirements. Our result analysis shows that the quality attributes such as PSNR, Index of Fuzziness (IOF) parameters give different performances with a selection of numbers and different sized membership function in the fuzzy domain. To get more insight, an optimization algorithm is proposed to identify the best combination of the fuzzy membership function for best image contrast enhancement.

• The Journal of Economics, Marketing and Management
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• v.10 no.5
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• pp.1-6
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• 2022

Purpose: To overcome the question that depends too much on expert's subjective judgment in traditional risk identification, this paper structure the multilevel generalized fuzzy comprehensive evaluation mathematics model of the risk identification of project, to research the risk identification of the project. Research design, data and methodology: This paper constructs the multilevel generalized fuzzy comprehensive evaluation mathematics model. Through iterative algorithm of AHP analysis, make sure the important degree of the sub project in risk analysis, then combine expert's subjective judgment with objective quantitative analysis, and distinguish the risk through identification models. Meanwhile, the concrete method of multilevel generalized fuzzy comprehensive evaluation is probed. Using the index weights to analyse project risks is discussed in detail. Results: The improved fuzzy comprehensive evaluation algorithm is proposed in the paper, at first the method of fuzzy sets core is used to optimize the fuzzy relation matrix. It improves the capability of the algorithm. Then, the method of entropy weight is used to establish weight vectors. This makes the computation process fair and open. And thereby, the uncertainty of the evaluation result brought by the subjectivity can be avoided effectively and the evaluation result becomes more objective and more reasonable. Conclusions: In this paper, we use an improved fuzzy comprehensive evaluation method to evaluate a railroad engineering project risk. It can give a more reliable result for a reference of decision making.