• Journal of Advanced Marine Engineering and Technology
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• v.34 no.1
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• pp.109-115
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• 2010

This paper deals with trajectory control of computer simulated mobile robot via fuzzy control. Mobile robot is controlled by Mamdani type fuzzy controller. Inputs of the fuzzy controller are angle between mobil robot and target, changed angle and output is the steering angle, which is control input. Fuzzy rules have seven rules and are selected by human experiential knowledge. Also we propose a scaling factors tuning scheme which is the another focus in designing fuzzy controller. In this paper, we adapt the RCGA which is well known in parameter optimization to adjust scaling factors. The simulation results show that the fuzzy control effectively realize trajectory stabilization of the mobile robot along a given reference target from various initial steering angles.

• Journal of Advanced Marine Engineering and Technology
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• v.34 no.1
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• pp.102-108
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• 2010

The T-S fuzzy model of a ship is made from the nonlinear extension of Nomoto's 2nd-order model as the previous step before designing of the fuzzy type autopilot to consider the design specifications and the economic efficiency. The T-S fuzzy model is considered as a design variable of the heading angular velocity of ship. The linear models will be combined as "IF-THEN" fuzzy rules after get in this one area of the linear model(sub-system) by change of the heading angular velocity of a ship. The dynamic characteristic of a ship with the parameters of linear models and fuzzy membership functions are estimated to match by using the model adjustment technic with input/output data and a RCGA.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.4
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• pp.48-57
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• 1997

This paper presents the automatic construction and parameter optimization technique for fuzzy logic controllers using genetic algorithm. In general. the design of fuzzy logic controllers has difficulties in the acq~lisition of expert's knowledge and relies to a great extent on empirical and heuristic knowledge which, in many cases, cannot be objectively justified. So, the performance of the controllers c:an be degraded in the case of plant parameter variations or unpredictable incident which a designer may have ignored, and the parameters of fuzzy logic controllers obtained by expert's control action may not be optirnal. Some of these problems can be resolved by the use of genetic algorithm. The proposed method can tune the parameters of fuzzy logic controllers including scaling factors and determine: the appropriate number of fuzzy rulcs systematically. Finally, we provides the second order dead time plant to evaluate the feasibility and generality of the proposed method. Comparison shows that the proposed method can produce fuzzy logic controllers with higher accuracy and a smaller number of fuzzy rules than manually tuned fuzzy logic controllers.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.85-90
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• 1998

The most important step image processing is stereo matching process. That is finding pixels of 3 dimensional pair in the left and right image. There are two matching methods. One is an area based approach and the other is a feature based approach. An area based approach needs much calculation time. In the other hand, we have the advantage of calculation time in the feature based approach, but can not obtain matched data for all pixels in the image. In recent years, fuzzy image processing methods are developed to manage vagueness and noise in image and ambiguous, inconsistent knowledge in recognition step. In this paper, we propose a fuzzy stereo matching algorithm. This method converts brightness data of image to fuzzy membership value and processes an area based approach method for stereo matching algorithm. We experiment with some stereo images to validate effectiveness of this algorithm.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.8
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• pp.1104-1110
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• 2012

The inverted pendulum system is a common, interesting control problem that involves many basic elements of control theory. In the early, controls of stabilization for the inverted pendulum system were used classical methods like PD, PID controller. In recently, however, control methods based on modern and intelligent control theory are widely applied. The fuzzy logic controller which is often used in nonlinear control is a little too hard to design due to increasing fuzzy rules rapidly if the given system like inverted pendulum has many state variables. Also, in case the state variables are divided into two parts, two fuzzy controllers are needed in the control system. In this paper, the authors propose FCSC(Fuzzy Controller based on State variables Combination) that reorganized into two new signals depending on the physical meaning of the four state variables of the inverted pendulum system. The proposed method is applied to the inverted pendulum system and simulations are accomplished to illustrate the control performance.

• Science of Emotion and Sensibility
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• v.7 no.3
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• pp.15-25
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• 2004

The regression analysis and Adaptive -Network based Fuzzy-inference system (ANFIS) were applied to the explanation on human's visual texture of cotton fabrics with 7 mechanical properties. The ANFIS uses the structure with fuzzy membership function and neural network. The results obtained by the statistical analysis through the coefficient of correlation and regression analysis showed that subjective texture had a linear relationship with mechanical properties. But It had a relatively low coefficient of determination and was difficult that the statistical analysis explained other relationship with the exception of a lineality and interaction among mechanical properties. Comparing the statistical analysis, the ANFIS was an effective tool to explain human's non-linear perceptions and their interactions. But to apply ANFIS to human's perceptions more effectively, it is necessary to discriminate effective input variables through controlling the properties of samples.

• The Transactions of the Korea Information Processing Society
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• v.3 no.7
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• pp.1880-1893
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• 1996

In this paper, we propose the fuzzy traffic control method in ABR service for the effective use of ATM link. This method, a modified version of EPRCA which is one of rate control methods in ABR service, controls the values of the transmission rates of source by using the fuzzy traffic inference based on switch buffer size and buffer variate rate. For this method, we developed a model and algorithm of the fuzzy traffic control method and a fuzzy traffic controller, after studying fuzzy and neural networks which applied to ATM traffic control and EPRCA. For the fuzzy traffic controller, we also designed a membership function, fuzzy control rules and a max-min inferencing method. We conducted a simulation and compared the link utilization of the fuzzy traffic control method with that of the EPRCA method. The results of the simulation indicated that, compared to EPRCA, the fuzzy traffic control method improves the link utilization by 2.3% in a normal distribution model and by 2.7% in the MMPP model of the source.

• Journal of Korea Multimedia Society
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• v.9 no.8
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• pp.1030-1036
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• 2006

The rapid proliferation of wireless networks and mobile computing applications has changed the landscape of network security. Anomaly detection is a pattern recognition task whose goal is to report the occurrence of abnormal or unknown behavior in a given system being monitored. This paper presents an efficient rough set based anomaly detection method that can effectively identify a group of especially harmful internal masqueraders in cellular mobile networks. Our scheme uses the trace data of wireless application layer by a user as feature value. Based on the feature values, the use pattern of a mobile's user can be captured by rough sets, and the abnormal behavior of the mobile can be also detected effectively by applying a roughness membership function considering weighted feature values. The performance of our scheme is evaluated by a simulation. Simulation results demonstrate that the anomalies are well detected by the method that assigns different weighted values to feature attributes depending on importance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.12
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• pp.2511-2520
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• 2011

This paper describes the parameter adjustment method of fuzzy membership function to improve the performance of multi-sensor fusion system using adaptive fuzzy-Kalman filter and cross-validation. The adaptive fuzzy-Kanlman filter has two input parameters, variation of accelerometer measurements and residual error of Kalman filter. The filter estimates system noise R and measurement noise Q, then changes the Kalman gain. To evaluate proposed adaptive fuzzy-Kalman filter, we make the two-axis AHRS(Attitude Heading Reference System) using fusion of an accelerometer and a gyro sensor. Then we verified its performance by comparing to NAV420CA-100 to be used in various fields of airborne, marine and land applications.

• Journal of Navigation and Port Research
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• v.31 no.8
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• pp.697-703
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• 2007

In this paper, we focuses on the development of Takagi-Sugeno (T-S) fuzzy modeling in a nonlinear container crane system. A T-S fuzzy model is characterized by fuzzy "if-then" rules which represent the locally input-output relationship whose consequence part is described by a state space equation as subsystem. The T-S fuzzy model in container cranes first obtains a few number of linear models according to operation conditions and blends these conditions using fuzzy membership functions. Parameters of the membership functions are adjusted by a RCGA to have same dynamic characteristics with nonlinear system of a container crane. Simulations are given to illustrate the performance of T-S fuzzy model.