• Membrane and Water Treatment
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• v.9 no.6
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• pp.421-434
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• 2018

Decision-makers have different and sometimes conflicting goals with utilities in operating dam reservoirs. As repeated interactions exist between decision-makers in the long-term, and the utility of each decision-making organization is affected not only by its selected strategy, but also by other rivals' strategies; selecting and prioritizing optimum strategies from a decision maker's point of view are of great importance while interacting with others. In this paper, a model based on a fuzzy set theory, for determining the priority of decision-makers' strategies in optimal qualitative-quantitative operation management of dam reservoir is presented. The fuzzy priority matrix is developed via defining membership functions of a fuzzy set for each decision maker's strategies, so that all uncertainties are taken into account. This matrix includes priorities assigned to possible combination for other decision makers' strategies in bargaining with each player's viewpoint. Here, the 15-Khordad Dam located in the central part of Iran, suffering from low water quality, was studied in order to evaluate the effectiveness of the model. Then, the range of quality of water withdrawal agreed by all decision-makers was determined using the prioritization matrix based on fuzzy logic. The results showed that the model proposed in the study had high effectiveness model.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2222-2229
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• 2023

Radon and radon progeny being natural radioactive pollutants, seriously affect the health of uranium miners. Radon reduction by ventilation is an essential means to improve the working environment. Firstly, the relational model is built between the radon exhalation rate of the loose body and the ventilation parameters in the stope with radon percolation-diffusion migration dynamics. Secondly, the model parameters of radon exhalation dynamics are uncertain and described by triangular membership functions. The objective functions of the left and right equations of the radon exhalation model are constructed according to different possibility levels, and their extreme value intervals are obtained by the immune particle swarm optimization algorithm (IPSO). The fuzzy target and fuzzy constraint models of radon exhalation are constructed, respectively. Lastly, the fuzzy aggregation function is reconstructed according to the importance of the fuzzy target and fuzzy constraint models. The optimal control decision with different possibility levels and importance can be obtained using the swarm intelligence algorithm. The case study indicates that the fuzzy aggregation function of radon exhalation has an upward trend with the increase of the cut set, and fuzzy optimization provides the optimal decision-making database of radon treatment and prevention under different decision-making criteria.

• Journal of the Ergonomics Society of Korea
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• v.17 no.1
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• pp.37-46
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• 1998

A human sensibility ergonomics design supporting system was applied to the product development for the customer's satisfaction based on ergonomics technology. The system is composed of three major subsystems such as customer's sensibility analysis, inference mechanism, and presentation technology. The main approaches of the system are to analyze customer's sensibilities and to translate them into product design elements. The purpose of this paper is to develop a design supporting system in which the relationship between customer's sensibility and product design elements is reasoned by a MADM(Multi-Attribute Decision Making) fuzzy model. In this model, three variables such as multiple correlation coefficients, partial correlation coefficients, and category scores were used in reasoning process. The weighted value of the words were also considered in fuzzy decision process. As a case study, the design supporting system with the MADM fuzzy model was applied to the personnel computer design.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.886-889
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• 2017

An active Fault Tolerant Model Predictive Control (FTMPC) using Fuzzy scheduler is developed. Fault tolerant Control (FTC) system stages are broadly classified into two namely Fault Detection and Isolation (FDI) and fault accommodation. Basically, the faults are identified by means of state estimation techniques. Then using the decision based approach it is isolated. This is usually performed using soft computing techniques. Fuzzy Decision Making (FDM) system classifies the faults. After identification and classification of the faults, the model is selected by using the information obtained from FDI. Then this model is fed into FTC in the form of MPC scheme by Takagi-Sugeno Fuzzy scheduler. The Fault tolerance is performed by switching the appropriate model for each identified faults. Thus by incorporating the fuzzy scheduled based FTC it becomes more efficient. The system will be thereafter able to detect the faults, isolate it and also able to accommodate the faults in the sensors and actuators of the Continuous Stirred Tank Reactor (CSTR) process while the conventional MPC does not have the ability to perform it.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.17-20
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• 2002

This paper proposes a new 75 fuzzy model approximation method which reduces error in nonlinear fuzzy model approximation over the V-type decision rules. Employing rational Bezier curves used in computer graphics to represent curves or surfaces, the proposed method approximates the decision rule by constructing a tractable linear equation in the highly non-linear fuzzy rule interval. This algorithm is applied to the self-adjusting air cushion for spinal cord injury patients to automatically distribute the patient's weight evenly and balanced to prevent decubitus. The simulation results indicate that the performance of the proposed method is bettor than that of the conventional TS Fuzzy model in terms of error and stability.

• Journal of applied mathematics & informatics
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• v.20 no.1_2
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• pp.549-556
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• 2006

In this paper, we consider interval-valued Choquet integrals and fuzzy measures. Using these properties, we discuss some applications of them in multicriteria decision aid. In particular, we show how these interval-valued Choquet integrals can model behavioral analysis of aggregation in ulticriteria decision aid.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.111-120
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• 1999

Although the concept of viewing knowledge as a critical resource has been widely accepted in prior studies, it is not fully understood how to acquire available knowledge in order to improve organizational effectiveness. However, it si sure that organizational knowledge management should pursuit the achievement of the business goal by delivering relevant and useful information to the right person at the right time. Group Support System (GSS) can play an important role to transfer scatter information into meaningful business knowledge for supporting strategic corporate decision-making. This study proposes a fuzzy GSS framework for acquiring workgroup knowledge from individual memory and aggregating workgroup knowledge to organizational knowledge. This study also proposes an architecture to support the fuzzy GSS framework. The architecture consists of user agents, information management agents, and a fuzzy model manager. To illustrate how the fuzzy GSS framework can be used to support the whole process of organization knowledge acquisition, an Internet-based GSS was developed and applied in a marketing decision process. It showed that the framework was effective for acquiring organizational knowledge.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2925-2948
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• 2019

In order to improve the utilization of irrigation water resources of greenhouse tomatoes, a water-saving irrigation decision-making model based on genetic optimization T-S fuzzy neural network is proposed in this paper. The main work are as follows: Firstly, the traditional genetic algorithm is optimized by introducing the constraint operator and update operator of the Krill herd (KH) algorithm. Secondly, the weights and thresholds of T-S fuzzy neural network are optimized by using the improved genetic algorithm. Finally, on the basis of the real data set, the genetic optimization T-S fuzzy neural network is used to simulate and predict the irrigation volume for greenhouse tomatoes. The performance of the genetic algorithm improved T-S fuzzy neural network (GA-TSFNN), the traditional T-S fuzzy neural network algorithm (TSFNN), BP neural network algorithm(BPNN) and the genetic algorithm improved BP neural network algorithm (GA-BPNN) is compared by simulation. The simulation experiment results show that compared with the TSFNN, BPNN and the GA-BPNN, the error of the GA-TSFNN between the predicted value and the actual value of the irrigation volume is smaller, and the proposed method has a better prediction effect. This paper provides new ideas for the water-saving irrigation decision in greenhouse tomatoes.

• The Transactions of the Korea Information Processing Society
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• v.4 no.12
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• pp.3045-3051
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• 1997

Generally, multi-criteria decisions are made by group of people because of their complexity. In the existing fuzzy aggregation method, the operators using minimum, maximum and average are used to aggregate the viewpoints of many staffs. These methods have problems in that they do not reflect the decision situation in the decision process. In order to solve these problems we propose a new fuzzy multi-criteria decision support systems model that aids the decision maker to aggregate the viewpoints of many staffs according to the decision situation. Moreover, we design the algorithms which can be used in the fuzzy multi-criteria decision support systems and develop its prototying system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.284-310
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• 2024

Identifying clinical pathways for disease diagnosis and treatment process recommendations are seriously decision-intensive tasks for health care practitioners. It requires them to rely on their expertise and experience to analyze various categories of health parameters from a health record to arrive at a decision in order to provide an accurate diagnosis and treatment recommendations to the end user (patient). Technological adaptation in the area of medical diagnosis using AI is dispensable; using expert systems to assist health care practitioners in decision-making is becoming increasingly popular. Our work architects a novel knowledge-based recommender system model, an expert system that can bring adaptability and transparency in usage, provide in-depth analysis of a patient's medical record, and prescribe diagnostic results and treatment process recommendations to them. The proposed system uses a set of parallel discrete fuzzy rule-based classifier systems, with each of them providing recommended sub-outcomes of discrete medical conditions. A novel knowledge-based combiner unit extracts significant relationships between the sub-outcomes of discrete fuzzy rule-based classifier systems to provide holistic outcomes and solutions for clinical decision support. The work establishes a model to address disease diagnosis and treatment recommendations for primary lung disease issues. In this paper, we provide some samples to demonstrate the usage of the system, and the results from the system show excellent correlation with expert assessments.