• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.4
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• pp.67-76
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• 1995

In recent research on the fuzzy controller, the crisp type fuzzy controller model, in which the consequent part of the fuzzy control rules are crisp real numbers instead of fuzzy sets, due to its simplicity in calculation, has been widely used in various applications. In this paper we try to analyze the dynamical behavior of the crisp type fuzzy controller with both inference methods of min-max compositional rule and product-sum inference. The analysis reveals that a crisp type fuzzy controller behaves approximately like a PD controller.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1149-1151
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• 2003

We performed a validation study of the MODIS active fire detection algorithm using high resolution SPOT image as the reference data set. Fire with visible smoke plumes are detected in the SPOT scenes, while the hotspots in MODIS data are detected using NASA's new version 4 fire detection algorithm. The detection performance is characterized by the commission error rate (false alarms) and the omission error rate (undetected fires). In the Sumatra and Kalimantan study area, the commission rate and the omission rate are 27% and 34% respectively. False alarms are probably due to recently burnt areas with warm surfaces. False negative detection occur where there are long smoke plumes and where fires occur in densely vegetated areas.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.1
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• pp.14-18
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• 2002

The perceptron algorithm, which is one of a class of gradient descent techniques, has been widely used in pattern recognition to determine linear decision boundaries. However, it may not give desirable results when pattern sets are nonlinerly separable. A fuzzy version was developed to male up for the weaknesses in the crisp perceptron algorithm. This was achieved by assigning memberships to the pattern sets. However, still another drawback exists in that the pattern memberships do not consider class typicality of the patterns. Therefore, we propose a possibilistic approach to the crisp perceptron algorithm. This algorithm combines the linearly separable property of the crisp version and the convergence property of the fuzzy version. Several examples are given to show the validity of the method.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.301-314
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• 2007

The aim of this paper is to deal with a method to construct a separate fuzzy regression model with crisp input and fuzzy output data using a best response function for the center and the width of the predicted output. Also we introduce the crisp mean and variance of the predicted fuzzy value and also give some examples to compare a performance of the proposed fuzzy model with various other fuzzy regression model.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.507-513
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• 2004

Least-squares support vector machine (LS-SVM) has been very successful in pattern recognition and function estimation problems for crisp data. In this paper, we propose LS-SVM approach to evaluating fuzzy regression model with multiple crisp inputs and a Gaussian fuzzy output. The proposed algorithm here is model-free method in the sense that we do not need assume the underlying model function. Experimental result is then presented which indicate the performance of this algorithm.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.449-455
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• 2004

Support vector machine (SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate interval regression models for crisp input-output data. The proposed method is based on quadratic loss SVM, which implements quadratic programming approach giving more diverse spread coefficients than a linear programming one. The proposed algorithm here is model-free method in the sense that we do not have to assume the underlying model function. Experimental result is then presented which indicate the performance of this algorithm.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.172-174
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• 2003

Using MODIS data and several different compositing algorithms utilizing the average cloud free days in a compositing period, maximum ndvi, or dual maximum NDVI/minimum blue, multi resolution composites (250m, 500m, 1km) have been produced for Southeast Asia, with spectral bands ranging from the visible to short-wave infrared with a single band in the thermal (for land and sea surface temperature). A total of nine composites have been produced for the months of May and August in 2003, including blue, green, red, NIR, three in the SWIR, and several to specifically monitor vegetation health.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.32 no.1
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• pp.86-91
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• 1987

Changes in the contents of sugars and soluble solids and flavor rate of cooked kernels of three sweet com hybrids (Great Bell, Danok 1, and Golden Cross Bantam) and a super sweet corn hybrid (Crisp Super Sweet 720) were observed from 15 days after silking (DAS) to 27 or 33 DAS in 1985 and 1986 to determine the optimum harvest time. Sucrose content in all hybrids and fructose and glucose contents of Crisp Super Sweet 720 increased from 15 DAS to 21 or 24 DAS and then decreased. However, in the three sweet corn hybrids both fructose and glucose contents were highest at 15 DAS and then continuously decreased with maturity. At harvest time the major sugar component was sucrose and the highest total sugar content of Crisp Super Sweet 720 was 2-3 times higher than that of three sweet corn hybrids. Soluble solids were continuously increased with maturity in three sweet com varieties, but that of Crisp Super Sweet 720 increased up to 24 DAS, maintained the level through 30 DAS and then decreased. At harvest time soluble solids of three sweet com varieties were much higher than that of Crisp Super Sweet 720. The optimum harvest time seems to be about 27 DAS considering flavor rate and marketing conditions although the total sugar content was lower than those of earlier harvests. Soluble solids but not total sugars were positively correlated with the flavor of cooke6 com harvested at different growth stages in the same variety. Total sugars were not correlated with soluble solids or negatively correlated depending on hybrids.

• Journal of Biosystems Engineering
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• v.23 no.4
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• pp.365-372
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• 1998

This study was carried out to develop a nutrient-solution mixing-and-supplying system, which used a low-cost metering device instead of expensive metering pumps and a fuzzy logic controller. A low cost and precise overflow-type metering device was developed and evaluated by testing the flow discharge for the automatic nutrient-solution mixing-and-supplying system for snail-scale hydroponic sewers. The fuzzy logic controllers, which could predict and meet the desired values of EC and supply rate of nutrient solution were developed and verified by simulation and experiment. this fuzzy logic controller, whose algorithm consists of four crisp inputs, two crisp outputs and nine rules, was developed to predict the desired value of EC and supply rate of nutrient solution and two crisp inputs, one crisp output and nine rules used to control EC to the desired values. The nutrient-solution mixing-and-supplying system showed satisfactory EC control performance with the maximum overshooting of 0.035 mS/cm and the maximum settling time of 15 minutes in case of increasing 0.7 mS/cm. also, the accuracy of the overflow-type metering device in terms of the full-scale error was 2.29% when using solenoid valve only and 0.2% when using solenoid valve and flow control valve together.

• Communications for Statistical Applications and Methods
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• v.17 no.2
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• pp.141-151
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• 2010

Hybrid fuzzy regression analysis is used for integrating randomness and fuzziness into a regression model. Least squares support vector machine(LS-SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate hybrid fuzzy linear and nonlinear regression models with crisp inputs and fuzzy output using weighted fuzzy arithmetic(WFA) and LS-SVM. LS-SVM allows us to perform fuzzy nonlinear regression analysis by constructing a fuzzy linear regression function in a high dimensional feature space. The proposed method is not computationally expensive since its solution is obtained from a simple linear equation system. In particular, this method is a very attractive approach to modeling nonlinear data, and is nonparametric method in the sense that we do not have to assume the underlying model function for fuzzy nonlinear regression model with crisp inputs and fuzzy output. Experimental results are then presented which indicate the performance of this method.