• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.1264-1272
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• 1993

The software for unmanned control of three row typed rice combine has been developed using fuzzy logic. Three fuzzy variables were used : operating status of combine, steering, and speed. Eleven fuzzy rules were constructed and the eleven linguistic variables were used for the fuzzy rules. Six sensors were use of to get input values and sensor input values were quantified into 11 levels. The fuzzy output was infered with fuzzy inferrence which uses the correlation product encoding , and it must have been defuzzified by the method of center of gravity to use it for the control. The result of performance test using graphic simulation showed that the intelligently unmanned control of a rice combine was possible using fuzzy logic control.

• The Journal of the Acoustical Society of Korea
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• v.19 no.7
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• pp.34-41
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• 2000

This paper proposes a new double-talk detection algorithm which is based on the fuzzy rules, in the adaptive echo canceller of telecommunication system. In this method, the two inputs of the fuzzy inference for detecting double-talk condition are used. One is the cross-correlation coefficient between the error signal and the primary signal which is the summation of the real echo signal and the near-end signal. The other one is the cross-correlation coefficient between the estimation error signal and the primary signal. The fuzzy controller makes a fuzzification for two inputs by the membership functions of trapezoid does the max-min composition using if-then rules. The composed result is defuzzificated by the center gravity method. And by defuzzificated values, the double-talt the echo path variance, and the echo path variance during the double-talk are detected. It is confirmed by computer simulation that this fuzzy double-talk detector is able to estimate the double talk and the echo path variation condition, and even track echo path variation more accurately than the conventional algorithm during the double-talk period.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.413-418
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• 2001

In this paper, we propose a new camera calibration method which is based on a fuzzy model instead of a physical camera model of the conventional method. The camera calibration is to determine the correlation between camera image coordinate and real world coordinate. The camera calibration method using a fuzzy model can not estimate camera physical parameters which can be obtained in the conventional methods. However, the proposed method is very simple and efficient because it can determine the correlation between camera image coordinate and real world coordinate without any restriction, which is the objective of camera calibration. With calibration points acquired out of experiments, 3-D real world coordinate and 2-D image coordinate are estimated using the fuzzy modeling method and the results of the experiments demonstrate the validity of the proposed method.

• Journal of Broadcast Engineering
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• v.5 no.1
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• pp.123-129
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• 2000

This paper addresses a new double detection algorithm which is based on the fuzzy control in the adaptive echo canceller of communication system. In this method, the two input of the fuzzy inference for detecting double talk condition are used. The one is the cross-correlation coefficient between the error signal and the primary signal which is the summed signal of the real echo signal and the near-end signal. The other is the cross-correlation coefficient between the estimation error signal and the primary signal. The fuzzy controller made a fuzzification for two inputs by the membership functions of trapezoid and them became the composition using inference rules. The composed result is defuzzificated by the center gravity method. The output is compared with two threshold values to detect double talk and echo path variation effectively. It is confirmed by computer simulation that this fuzzy double talk detector is able to track echo path variation accurately.

• Journal of the Korean Society of Safety
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• v.20 no.1 s.69
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• pp.126-132
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• 2005

The purpose of this study is to propose a method to evaluate task complexity using CIFs(Complexity Influencing Factors). We developed a method that CIFs can be used in the evaluation of task complexity using fuzzy linguistic approach. That is, a fuzzy linguistic multi-criteria method to assess task complexity in a specific task situation was proposed. The CIFs luting was assessed in linguistic terms, which are described by fuzzy numbers with triangular and trapezoidal membership function. A fuzzy weighted average algorithm, based on the extension principle, was employed to aggregate these fuzzy numbers. Finally, the method was validated by experimental approach. In the result, it was validated that TCIM(Tink Complexity Index Method) is an efficient method to evaluate task complexity because the correlation coefficient between task performance time and TCI(Task Complexity Index) was 0.699.

• Speech Sciences
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• v.7 no.3
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• pp.167-183
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• 2000

In this paper, we propose two kinds of chaos dimensions, the fuzzy correlation and fuzzy Lyapunov dimensions, for speaker recognition. The proposal is based on the point that chaos enables us to analyze the non-linear information contained in individual's speech signal and to obtain superior discrimination capability. We confirm that the proposed fuzzy chaos dimensions play an important role in enhancing speaker recognition ratio, by absorbing the variations of the reference and test pattern attractors. In order to evaluate the proposed fuzzy chaos dimensions, we suggest speaker recognition using the proposed dimensions. In other words, we investigate the validity of the speaker recognition parameters, by estimating the recognition error according to the discrimination error of an individual speaker from the reference pattern.

• Speech Sciences
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• v.10 no.4
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• pp.149-157
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• 2003

In this paper, we proposed the principal component analysis (PCA) fuzzy mixture model for speaker identification. A PCA fuzzy mixture model is derived from the combination of the PCA and the fuzzy version of mixture model with diagonal covariance matrices. In this method, the feature vectors are first transformed by each speaker's PCA transformation matrix to reduce the correlation among the elements. Then, the fuzzy mixture model for speaker is obtained from these transformed feature vectors with reduced dimensions. The orthogonal Gaussian Mixture Model (GMM) can be derived as a special case of PCA fuzzy mixture model. In our experiments, with having the number of mixtures equal, the proposed method requires less training time and less storage as well as shows better speaker identification rate compared to the conventional GMM. Also, the proposed one shows equal or better identification performance than the orthogonal GMM does.

• Journal of the Korean Society of Safety
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• v.30 no.4
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• pp.14-19
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• 2015

The purpose of this paper is to study the correlation application that electrical fire causes occurs for overcurrent and leakage current signals in the indoor wiring. In the order to purpose, the causes data of overcurrent, or leakage current of electrical fire are drawn out referring to past studies, consulting with experts and experimental data. The correlation application was then applied with fuzzy logic of intelligent technique. To check the reliability and performance of the correlation application, modified center of area(CoA) was adopted to calculate the possibility that electrical fire occurs, whose value was then compared to the results. The chance of electrical fire calculated is higher when two causes of fire are put into the CoA of the correlation application of this paper than that of when each cause is separately put into the CoA. The correlation application developed in this study enables better analysis on possible electrical fire due to overcurrent, or leakage current and provides managers with the possibility of electrical fire so that they can better manage at a time of overcurrent, or leakage current.

• Proceedings of the KIEE Conference
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• 1999.11b
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• pp.276-278
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• 1999

In this paper, we analyze customers' working electric energy (kWh) which is served pole-transformer in order to reduce peak load current error which is generated in application load correlation equation. The characteristic of electric load which customers are using is classified by customer's working electric energy (kWh) and ratio of cooling equipment possession. For the input data of fuzzy model, we used to kWh on April which represents basic load and kWh which is increased from April to August. The April kWh is used to classify into large, medium, small customer. Also, the increased kWh is used to know information of cooling equipment possession. For the output value of fuzzy model, we can determined peak load current limit in application load correlation equation.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.109.6-109
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• 2002

In this paper, the scheme of fuzzy system construction using GA(genetic algorithm) and FCM(Fuzzy c-means) clustering algorithm is proposed for TSK(Takagi-Sugeno-Kang) type fuzzy system. in the structure identification, input data is trans-formed by PCA(Principal Component Analysis) to reduce the correlation among input data components. And then, the number of fuzzy rule is obtained by a given performance criterion. In the parameter identification, the premise parameters are optimally searched by GA. On the other hand, the consequent parameters are estimated by RLSE(Recursive Least Square Estimate) to reduce the search space. From this, one can systematically obtain optimal parameter and the v..