• Journal of the Korean Statistical Society
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• v.28 no.1
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• pp.21-34
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• 1999

In this paper, conditional expectation of a fuzzy random variable is introduced and its properties are investigated. Using this, we introduce the concept of fuzzy martingales and prove some convergence theorems which generalize te corresponding results for the classical martingales.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.487-487
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• 2000

The scheduling of real-time tasks needs both correctness and timeliness. But it is not easy to schedule real-time tasks having different characteristics in a single system. In this paper we solve the problem through an approach using the performance evaluation of real-time tasks through fuzzy-random variables. Using the performance evaluation through fuzzy-random variable, we can achieve flexible and efficient scheduling for real-time systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.9-15
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• 1998

In this paper, we introduce new results of law large numbers for mutually T-related fuzzy numbers, when T is Archimedean t-norm and spreads are random variables, and generalize earlier result of Fullr[FSS 45(1992) 299-303].

• Korean Journal of Mathematics
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• v.1
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• pp.47-53
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• 1993

• Kyungpook Mathematical Journal
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• v.32 no.3_spc
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• pp.533-546
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• 1992

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.147-155
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• 2012

We propose some properties for a fuzzy correlation test by reduced-spread fuzzy variance for sample fuzzy data. First, we define the condition of fuzzy data for repeatedly observed data or that which includes error term data. By using the average of spreads for fuzzy numbers, we reduce the spread of fuzzy variance and define the agreement index for the degree of acceptance and rejection. Given a non-normal random fuzzy sample, we have bivariate normal distribution by apply Box-Cox power fuzzy transformation and test the fuzzy correlation for independence between the variables provided by the agreement index.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.512-516
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• 1998

Fuzzy mathematical programming (FMP) can be treated an uncertainty condition using fuzzy concept. Further, it can be extended to the multiple objective (or goal) programming problem, naturally. But we feel that FMP have some shortcomings such as the fuzzy number in FMP is the one dimesional possibility set, so it can not be represented the relationship between them, and, in spite of FMP includes some (uncertainty) fuzzy paramenters, many alogrithms are only obtained a crisp solution.In this study, we propose a method of FMS. Our method use the scenario approach (or fuzzy random variables) to represent the relationship between fuzzy numbers, and can obtain the fuzzy solution.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.3
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• pp.97-110
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• 2017

This study investigates the efficiencies of machine learning models, including artificial neural network (ANN), generalized regression neural network (GRNN), adaptive neuro-fuzzy inference system (ANFIS) and random forest (RF), for reservoir water level forecasting in the Chungju Dam, South Korea. The models' efficiencies are assessed based on model efficiency indices and graphical comparison. The forecasting results of the models are dependent on lead times and the combination of input variables. For lead time t = 1 day, ANFIS1 and ANN6 models yield superior forecasting results to RF6 and GRNN6 models. For lead time t = 5 days, ANN1 and RF6 models produce better forecasting results than ANFIS1 and GRNN3 models. For lead time t = 10 days, ANN3 and RF1 models perform better than ANFIS3 and GRNN3 models. It is found that ANN model yields the best performance for all lead times, in terms of model efficiency and graphical comparison. These results indicate that the optimal combination of input variables and forecasting models depending on lead times should be applied in reservoir water level forecasting, instead of the single combination of input variables and forecasting models for all lead times.

• Journal of Welding and Joining
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• v.28 no.6
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• pp.76-83
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• 2010

The purpose of this study is to propose a method to decide near-optimal settings of the constant current fuzzy control parameters using a controlled random search. This method tries to find the near-optimal settings of the constant current fuzzy control parameters through experiments. It has an advantage of being able to carry out searches in the search domain which includes some irregular points. The method suggested in this study was used to determine the fuzzy control parameters by which the desired welding current were formed during inverter DC resistance spot welding. The output variable was the ITAE (integral of time multiplied by the absolute error). This output variable was determined according to the input variables, which are the GE, GDE, and GDU. This study described how to obtained near-optimal welding current condition over a wide search space conducting a relatively small number of experiments.

• Structural Engineering and Mechanics
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• v.22 no.6
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• pp.665-683
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• 2006

The strength behaviors of Fiber Reinforced Plastics (FRP) Composites can be greatly influenced by the properties of constitutive materials, the laminate structures, and load conditions etc, accompanied by many uncertainty factors. So the reliability study on FRP is an important subject of research. Many achievements have been made in reliability studies based on the probability theory, but little has been done on the roles played by fuzzy variables. In this paper, a fuzzy reliability model for FRP laminates is established first, in which the loads are considered as random variables and the strengths as fuzzy variables. Then a numerical model is developed to assess the fuzzy reliability. The Monte Carlo simulation method is utilized to compute the reliability of laminas under the maximum stress criterion. In the second part of this paper, a generalized fuzzy reliability model (GFRM) is proposed. By virtue of the fact that there may exist a series of states between the failure state and the function state, a fuzzy assumption for the structure state together with the probabilistic assumption for strength parameters is adopted to construct the GFRM of composite materials. By defining a generalized limit state function, the problem is converted to the conventional reliability formula that enables the first-order reliability method (FORM) applicable in calculating the reliability index. Several examples are worked out to show the validity of the models and the efficiency of the methods proposed in this paper. The parameter sensitivity analysis shows that some of the mean values of the strength parameters have great influence on the laminated composites' reliability. The differences resulting from the application of different failure criteria and different fuzzy assumptions are also discussed. It is concluded that the GFRM is feasible to use, and can provide an effective and synthetic method to evaluate the reliability of a system with different types of uncertainty factors.