• Journal of The Geomorphological Association of Korea
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• v.24 no.3
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• pp.105-118
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• 2017

The debris slope landforms which are existent in Korean mountains is generally on the steep slopes and mostly covered by vegetation, it is difficult to investigate the landform. Therefore a scientific method is required to come up with an effective field investigation plan. For this purpose, the use of Remote Sensing and GIS technologies for a spatial analysis is essential. This study has extracted the potential area of debrisslope landform formation using Fuzzy set and Bayesian Predictive Discriminate Model as mathematical data integration methods. The first step was to obtain information about debris locations and their related factors. This information was verified through field investigation and then used to build a database. In the second step, the map that zoning the study area based on the degree of debris formation possibility was generated using two modeling methods, and then cross validation technique was applied. In order to quantitatively analyze the accuracy of two modeling methods, the calculated potential rate of debrisformation within the study area was evaluated by plotting SRC(Success Rate Curve) and calculating AUC(Area Under the Curve). As a result, the prediction accuracy of Fuzzy set model wes 83.1% and Bayesian Predictive Discriminate Model wes 84.9%. It showed that two models are accurate and reliable and can contribute to efficient field investigation and debris landform management.

• Journal of The Geomorphological Association of Korea
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• v.24 no.1
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• pp.77-91
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• 2017

Many debris landforms in the mountains of Korea have formed in the periglacial environment during the last glacial stage when the generation of sediments was active. Because these landforms are generally located on steep slopes and mostly covered by vegetation, however, it is difficult to observe and access them through field investigation. A scientific method is required to reduce the survey range before performing field investigation and to save time and cost. For this purpose, the use of remote sensing and GIS technologies is essential. This study has extracted the potential area of debris landform formation using a fuzzy set model as a mathematical data integration method. The first step was to obtain information about the location of debris landforms and their related factors. This information was verified through field observation and then used to build a database. In the second step, we conducted the fuzzy set modeling to generate a map, which classified the study area based on the possibility of debris formation. We then applied a cross-validation technique in order to evaluate the map. For a quantitative analysis, the calculated potential rate of debris formation was evaluated by plotting SRC(Success Rate Curve) and calculating AUC(Area Under the Curve). The prediction accuracy of the model was found to be 83.1%. We posit that the model is accurate and reliable enough to contribute to efficient field investigation and debris landform management.

• International Journal of Advanced Culture Technology
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• v.4 no.2
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• pp.47-56
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• 2016

In transformer cooling systems, oil temperature is controlled through the use of a blower and oil pump. For this paper, set-point algorithms, a reset algorithm and control algorithms of the cooling system were developed by neural networks and fuzzy logics. The oil inlet temperature was set by a $2{\times}2{\times}1$ neural network, and the oil temperature difference was set by a $2{\times}3{\times}1$ neural network. Inputs used for these neural networks were the transformer operating ratio and the air inlet temperature. The inlet set temperature was reset by a fuzzy logic based on the transformer operating ratio and the oil outlet temperature. A blower was used to control the inlet oil temperature while the oil pump was used to control the oil temperature difference by fuzzy logics. In order to analysis the performance of these algorithms, the initial start-up test and the step change test were performed by using the dynamic model of a transformer cooling system. Test results showed that algorithms developed for this study were effective in controlling the oil temperature of a transformer cooling system.

• Journal of the Korean Institute of Intelligent Systems
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• v.2 no.2
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• pp.3-33
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• 1992

The successful application of fuzzy reasoning models to fuzzy control systems depends on a number of parameters, such as fuzzy membership functions, that are usually decided upon subjectively. It is shown ill this paper that the performance of fuzzy control systems call be improved if the fuzzy reasoning model is supplemented by a genetic-based learning mechanism. The genetic algorithm enables us to generate all optimal set of parameters for the fuzzy reasoning model based either on their initial subjective selection or on a random selection. It is shown that if knowledge of the domain is available, it is exploited by the genetic algorithm leading to an even better performance of the fuzzy controller.

• Journal of Korea Multimedia Society
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• v.7 no.6
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• pp.841-850
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• 2004

This paper proposes a GA and GDM-based method for removing unnecessary rules and generating relevant rules from the fuzzy rules corresponding to several fuzzy partitions. The aim of proposed method is to find a minimum set of fuzzy rules that can correctly classify all the training patterns. When the fine fuzzy partition is used with conventional methods, the number of fuzzy rules has been enormous and the performance of fuzzy inference system became low. This paper presents the application of GA as a means of finding optimal solutions over fuzzy partitions. In each rule, the antecedent part is made up the membership functions of a fuzzy set, and the consequent part is made up of a real number. The membership functions and the number of fuzzy inference rules are tuned by means of the GA, while the real numbers in the consequent parts of the rules are tuned by means of the gradient descent method. It is shown that the proposed method has improved than the performance of conventional method in formulating and solving a combinatorial optimization problem that has two objectives: to maximize the number of correctly classified patterns and to minimize the number of fuzzy rules.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.461-464
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• 2007

In this paper, we propose a fuzzy Kalman filtering to deal with a estimation error covariance. The T-S fuzzy model structure is further rearranged to give a set of linear model using standard Kalman filter theory. And then, to minimize the estimation error covariance, which is inferred using the fuzzy system. It can be used to find the exact Kalman gain. We utilize the genetic algorithm for optimizing fuzzy system. The proposed state estimator is demonstrated on a truck-trailer.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.254-259
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• 1998

A new design methology is proposed to identify the structure and parameters of fuzzy model using PNN and a fuzzy inference method. The PNN is the extended structure of the GMDH(Group Method of Data Handling), and uses several types of polynomials such as linear, quadratic and cubic besides the biquadratic polynomial used in the GMDH. The FPNN(Fuzzy Polynomial Neural Networks) algorithm uses PNN(Polynomial Neural networks) structure and a fuzzy inference method. In the fuzzy inference method, the simplified and regression polynomial inference methods are used. Here a regression polynomial inference is based on consequence of fuzzy rules with a polynomial equations such as linear, quadratic and cubic equation. Each node of the FPNN is defined as fuzzy rules and its structure is a kind of neuro-fuzzy architecture. In this paper, we will consider a model that combines the advantage of both FPNN and PNN. Also we use the training and testing data set to obtain a balance between the approximation and generalization of process model. Several numerical examples are used to evaluate the performance of the our proposed model.

• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.5
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• pp.196-206
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• 2000

These days, the fuzzy logic or the fuzzy set theory has received attention from a number of researchers in the area of industrial application. Moreover, the fuzzy logic control has been successfully applied to a large numbers of control problems where the conventional control methods had failed. Using this control theory we designed a climate controller for an automotive climate control system whose mathematical model is difficult. This paper describes an automotive climate control where the fuzzy control has been used to stabilize parameter uncertainties and disturbance effects. To show the validity and effectiveness of the proposed control method, the fuzzy logic controller was implemented with a philips 80C552 microcomputer chip and tested in an actual vehicle. From the experimental results, it could be conduced that the proposed controller is superior to conventional controllers in both control performance and thermal comfort. The climate control system in cars is difficult to model mathematically so we tested a fuzzy logic control system which promised better results.

• Transactions of the Korean Society of Automotive Engineers
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• v.2 no.5
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• pp.23-29
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• 1994

The design procedure for fuzzy logic controller depends on the expert's knowledge or trial and error. Moreover, it is very difficult to guarantee the stability and robustness of the system due to the linguistic expression of fuzzy control. However, fuzzy logic control has succeeded in many control problems that the conventional control theory has difficulties to deal with. As a result, this control theory is applied to the engine control system which a mathematical model is difficult. In this study, the fuzzy logic is applied to obtain the gain of PI control at idle speed control system, and a simple engine model is developed in order to perform simulation. Experimental results show that the response to reach the target engine speed at idle speed control system is improved by adopting the gain obtained with fuzzy logic.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.166-169
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• 2002

The accuracy in maneuvering target tracking using multiple models is caused by the suitability of each target motion model to be used. The interacting multiple model (IMM) algorithm and the adaptive IMM algorithm require the predefined sub-models and the predetermined acceleration intervals, respectively, in consideration of the properties of maneuvers to construct multiple models. In this paper, to solve these problems intelligently, a genetic algorithm (GA) based-IMM method using fuzzy logic is proposed. In the proposed method, a sub-model is represented as a set of fuzzy rules to model the time-varying variances of the process noises of a new piecewise constant white acceleration model, and the GA is applied to identify this fuzzy model. The proposed method is compared with the AIMM algorithm in simulations.