• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 한국소음진동공학회 1997년도 춘계학술대회논문집; 경주코오롱호텔; 22-23 May 1997
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• pp.428-433
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• 1997

This paper presents a new control strategy for the position and force control of flexible manipulators. The governing equation of motion of a two-link flexible manipulator which features piezoceramic actuators bonded on each flexible beam is derived via Hamilton's principle. The control torque of the motor to command desired position and force is determined by a sliding mode controller on the basis of the rigid-mode dynamics. In the controller formulation, the sliding mode controller with perturbation estimation(SMCPE) is adopted to determine appropriate control gains. The SMCPE is then incorporated with the fuzzy technique to mitigate inherent chattering problem while maintaining the stability of the system. A set of fuzzy parameters and control rules are obtained from a relation between estimated perturbation and actual perturbation. During the commanded motion, undesirable oscillation is actively suppressed by applying feedback control voltages to the piezoceramic actuators. These feedback voltages are also determined by the SMCPE. Consequently, accurate force and position control of a two-link flexible manipulator are achieved. Computer simulations are undertaken in order to demonstrate the effectiveness of the proposed control methodology.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 한국퍼지및지능시스템학회 2000년도 춘계학술대회 학술발표 논문집
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• pp.47-50
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• 2000

In this paper, an optimal identification method using Multi-FNN(Fuzzy-Neural Network) is proposed for model ins of nonlinear complex system. In order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM clustering algorithm which carry out the input-output data preprocessing function and Genetic Algorithm which carry out optimization of model. The proposed Multi-FNN is based on Yamakawa's FNN and it uses simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rules. HCM clustering method which carry out the data preprocessing function for system modeling, is utilized to determine the structure of Multi-FNN by means of the divisions of input-output space. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. Also, a performance index with a weighting factor is presented to achieve a sound balance between approximation and generalization abilities of the model, To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• 제15권4호
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• pp.37-42
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• 2001

This paper resents a biological reaction model of describing processing features in treating wastewater via activated sludge A proposed model is designed by combining fuzzy rules investigating several elements which have influence on variables to be supervised BOD and SS are suggested as common variables in input and output variables, and O$_2$quantity is closed as input variable. We chose triangular type membership functions for input variables and determined the grades in each membership function based upon process data According to simulation result to show the validity of proposed model, fuzzy model's outputs give almost similar data to process output under same input conditions.

• Journal of Institute of Control, Robotics and Systems
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• 제7권12호
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• pp.973-979
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• 2001

Sliding mode control guarantees robustness in the presence of modeling uncertainties and external disturbances. However, this can be obtained at the cost of high control activity that may lead to chattering As one way to alleviate this problem a boundary layer around sliding surface is typically used. In this case the selection of controller gain, control ban width and boundary layer thickness is a crucial problem for the trade-off between tracking error and chattering. The parameter tuning is usually done by trail-and-error in practice causing significant effort and time. An auto tuning method based on fuzzy rules is proposed in the paper in this method tracking error and chattering are monitored by performance indices and the controller tunes the design parameters intelligently in order to compromise both indices. To demonstrate the efficiency of the propose method a mass-spring translation system and a roboic control system are simulated and tested It is shown that the proposed algorithm is effective to facilitae the parameter tuning for sliding mode controllers.

• KIEE International Transactions on Power Engineering
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• 제5A권3호
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• pp.260-268
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• 2005

This paper proposes an improved investment pnonty decision method of facilities considering the reliability of distribution networks. The proposed method decides an investment order of the facilities combining, by fuzzy rules, the investment priority decision by KEPCO and that by reliability evaluation indices. The reliability evaluation indices are SAIFI (System Average Interruption Frequency Index) and SAIDI (System Average Interruption Duration Index). The reliability analysis method of distribution networks applied in this paper utilizes the analytic method, where the used reliability data is the historical data of KEPCO. Particularly, we assumed that the failure rate increases as the equipment ages. To verify the performance of the proposed method, we applied it with the planned projects to reinforce the weak electrical facilities in KEPCO in 2004. The evaluation result showed that, under a limited budget, the reliability of KEPCO in the Busan region using the proposed method could be enhanced if used rather than the conventional method typically in place. Therefore, the results verify that the proposed method can be efficiently used in the actual priorities method for investing in the electrical facilities.

• Proceedings of the KIEE Conference
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.1377-1379
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• 1996

In this paper, each parameter was identified by the gradient descent method to overcome difficulty deciding fuzzy rules of FLC for the unknown process and the type of membership Junctions. Usually PID or optimal control theories have been mostly usee in control field so far. However, optimal control requires much time for calculation because of adaptation for disturbance and nonlinearity. And intricate technique such as MRAS which can be realized only by an expert are limited to be used in the systems requiring rapid and precise response because of comparatively longer calculating time and complicateness. Gradient descent method is a method to find Z minimizing a function about a certain vector Z. And required output of FLC is gained using gradient approaching method in order to adapt control rule parameters of FLC. Simulation proved validation of this algorithm.

• Journal of Information Processing Systems
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• 제11권3호
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• pp.421-437
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• 2015

This paper aims to present a supervised classification algorithm based on data fusion for the segmentation of the textured images. The feature extraction method we used is based on discrete wavelet transform (DWT). In the segmentation stage, the estimated feature vector of each pixel is sent to the support vector machine (SVM) classifier for initial labeling. To obtain a more accurate segmentation result, two strategies based on information fusion were used. We first integrated decision-level fusion strategies by combining decisions made by the SVM classifier within a sliding window. In the second strategy, the fuzzy set theory and rules based on probability theory were used to combine the scores obtained by SVM over a sliding window. Finally, the performance of the proposed segmentation algorithm was demonstrated on a variety of synthetic and real images and showed that the proposed data fusion method improved the classification accuracy compared to applying a SVM classifier. The results revealed that the overall accuracies of SVM classification of textured images is 88%, while our fusion methodology obtained an accuracy of up to 96%, depending on the size of the data base.

• Proceedings of the KIEE Conference
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.1964-1966
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• 2001

Active Noise control(ANC) is rapidly becoming the most effective way to reduce noises that can otherwise be very difficult and expensive to control. This research presents ANFIS (Adaptive Network Fuzzy Inference System) controller for adaptively noise cancelling in a duct. ANC system generates secondary control sound pressure with same amplitude and with opposite phase as noise to be eliminated. ANFIS controller is trained to optimize its parameters for adaptively cancelling noise. That is ANFIS train its parameters by gradient descent and LSE method so called hybrid method. This paper present ANFIS in active noise control which provides an improvement convergence speed and limitation of linearity condition. It can model nonlinear functions of arbitrary complexity and ANFIS can construct an input-ouput mapping based on both human knowledge in the form of Takagi and Sugeno's fuzzy if-then rules and stipulated input-output data pairs. This paper also shows that the proposed ANFIS active noise control system successfully cancelled noise.

• Journal of Korean Association for Spatial Structures
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• 제21권3호
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• pp.43-50
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• 2021

A tilted tall building is actively constructed as landmark structures around world to date. Because lateral displacement responses of a tilted tall building occurs even by its self-weight, reduction of seismic responses is very important to ensure structural safety. In this study, a smart tuned mass damper (STMD) was applied to the example tilted tall building and its seismic response control performance was investigated. The STMD was composed of magnetorheological (MR) damper and it was installed on the top floor of the example building. Control performance of the STMD mainly depends on the control algorithn. Fuzzy logic controller (FLC) was selected as a control algorithm for the STMD. Because composing fuzzy rules and tuning membership functions of FLC are difficult task, evolutionary optimization algorithm (EOA) was used to develop the FLC. After numerical simulations, it has been seen that the STMD controlled by the EOA-optimized FLC can effectively reduce seismic responses fo the tilted tall building.

• Journal of the Korean Institute of Intelligent Systems
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• 제24권2호
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• pp.155-160
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• 2014

There are many image processing based algorithms and applications that fail when illumination change occurs. Therefore, the illumination change has to be detected then the illumination change occurred images need to be enhanced in order to keep the appropriate algorithm processing in a reality. In this paper, a new method for detecting illumination changes efficiently in a real time by using local region information and fuzzy logic is introduced. The effective way for detecting illumination changes in lighting area and the edge of the area was selected to analyze the mean and variance of the histogram of each area and to reflect the changing trends on previous frame's mean and variance for each area of the histogram. The ways are used as an input. The changes of mean and variance make different patterns w hen illumination change occurs. Fuzzy rules were defined based on the patterns of the input for detecting illumination changes. Proposed method was tested with different dataset through the evaluation metrics; in particular, the specificity, recall and precision showed high rates. An automatic parameter selection method was proposed for contrast limited adaptive histogram equalization method by using entropy of image through adaptive neural fuzzy inference system. The results showed that the contrast of images could be enhanced. The proposed algorithm is robust to detect global illumination change, and it is also computationally efficient in real applications.