• Journal of Institute of Control, Robotics and Systems
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• v.6 no.11
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• pp.963-967
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• 2000

This paper deals with a sliding mode control with uncertainty adaptation for the robust stabilization of input-delay systems with unknown uncertainties. A sliding surface including a state predictor is employed to compensate for the effect of the input delay. The proposed method does not need a priori knowledge of upper bounds on the norm of uncertainties, but estimates those upper bounds by adaptation laws based on the sliding surface. Then, a robust control law with the uncertainty adaptation is derived to ensure the existence of the sliding mode. A numerical example is given to illustrate the design procedure.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.1
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• pp.50-63
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• 1995

In this paper, we propose a face recognition method using a neuro-fuzzy algorithm. In the preprocessing step, we extract the face part from the background image by tracking face boundaries. Then based on the a priori knowledge of human faces we extract the features such as widths of eyes and mouth, and distances from eye to nose and nose to mouth. In the recognition step. We use a neuro-fuzzy algorithm that employs a fuzzy membership function and modified error backpropagation algorithm. The former absorbs the variation of feature values and the latter shows good learning efficiency. Computer simulation results with 20 persons show that the proposed method gives higher recognition rate than the conventional ones.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.7
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• pp.51-60
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• 1997

Prototype based methods are commonly used in cluster analysis and the results may be highly dependent on the prototype used. In this paper, we propose a fuzzy clustering method that involves adaptively expanding convex polytopes. Thus, the dependency on the use of prototypes can be eliminated. The proposed method makes it possible to effectively represent an arbitrarily distributed data set without a priori knowledge of the number of clusters in the data set. Specifically, nonlinear membership functions are utilized to determine whether a new cluster is created or which vertex of the cluster should be expanded. For this, the membership function of a new vertex is assigned according to not only a distance measure between an incoming pattern vector and a current vertex, but also the amount how much the current vertex has been modified. Therefore, cluster expansion can be only allowed for one cluster per incoming pattern. Several experimental results are given to show the validity of our mehtod.

• Journal of Communications and Networks
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• v.12 no.3
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• pp.253-265
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• 2010

The problem of pricing for a telecommunication network is investigated with respect to the users' sensitivity to the pricing structure. A functional optimization problem is formulated, in order to compute price reallocations as functions of data collected in real time during the network evolution. No a-priori knowledge about the users' utility functions and the traffic demands is required, since adaptive reactions to the network conditions are sought in real time. To this aim, a neural approximation technique is studied to exploit an optimal pricing control law, able to counteract traffic changes with a small on-line computational effort. Owing to the generality of the mathematical framework under investigation, our control methodology can be generalized for other decision variables and cost functionals.

• Industrial Engineering and Management Systems
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• v.1 no.1
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• pp.79-86
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• 2002

This paper proposes a new algorithm for estimating ARMA model parameters. In estimating ARMA model parameters, several methods such as generalized least square method, instrumental variable method have been developed. Among these methods, the utilization of a bootstrap type algorithm is known as one of the effective approach for the estimation, but there are cases that it does not converge. Hence, in this paper, making use of a cross correlation function and utilizing the relation of structural a priori knowledge, a new bootstrap algorithm is developed. By introducing theoretical relations, it became possible to remove terms, which is liable to include much noise. Therefore, this leads to robust parameter estimation. It is shown by numerical examples that using this algorithm, all simulation cases converge while only half cases succeeded with the previous one. As for the calculation time, judging from the fact that we got converged solutions, our proposed method is said to be superior as a whole.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1589-1592
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• 1997

Among the prerequisite abilities (perception of environment, path planning and position estimation) of an autonomous mobile robot, position estimation has been seldom studied by mobile robot researchers. In most cases, conventional positioin estimation has been performed by placing landmarks or giving the entrire environmental information in advance. Unlikely to the conventional ones, the study addresses a new method that the robot itself can select distinctive features in the environment and save them as landmarks without any a priori knowledge, which can maximize the autonomous behavior of the robot. First, an orjentaion probaility model is applied to construct a lcoal map of robot's surrounding. The feature of the object in the map is then extracted and the map is saved as landmark. Also, presented is the position estimation method that utilizes the correspondence between landmarks and current local map. In dong this, the uncertainty of the robot's current positioin is estimated in order to select the corresponding landmark stored in the previous steps. The usefulness of all these approaches are illustrated with the results porduced by a real robot equipped with ultrasonic sensors.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.177-182
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• 1994

In this paper, an output feedback adaptive variable structure control scheme is presented for stabilization of large scale power systems. An additional input signal which is called a power system stabilizer(PSS) is needed to improve the stability of a power system and to maintain the synchronization of generators. The proposed PSS scheme does not require a priori knowledge of uncertainty bounds. It is guaranteed that the closed-loop system is globally uniformly ultimately bounded by the Lyapunov stability theory. Simulation results for a multimachine power system are given to show the feasibility of the proposed scheme and the superiority of the proposed PSS in comparison with the conventional lead-lag PSS of PID-type.

• The Journal of the Acoustical Society of Korea
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• v.8 no.4
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• pp.39-43
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• 1989

In this paper, the concept of ultrasonic transmission imaging system with crossed -arrays is Introduced. The crossed-array system is simulated by angular spectrum method In the operating frequency of 12MHz. A theoritical development of a system transfer function matrix 1M is presented. Using this matrix, a priori knowledge on the physical properties of the system is understood. It proves to be a block Toeplitz matrix with Toeplitz entries. Using the Inversion procedure, the spatial degradations of the measured image can be removed.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.12
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• pp.1670-1679
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• 1995

In this paper, an optimal variable structure controller with a multilayer neural inverse identifier is proposed. A multilayer neural network with error back propagation learning algorithm is used for construction the neural inverse identifier which is an observer of the external disturbances and the parameter variations of the system. The variable structure controller with the multilayer neural inverse identifier not only needs a small part of a priori knowledge of the bounds of external disturbances and parameter variations but also alleviates the chattering magnitude of the control input. Also, an optimal sliding line is designed by the optimal linear regulator technique and an integrator is introduced for solving the reaching phase problem. Computer simulation results show that the proposed approach gives the effective control results by reducing the chattering magnitude of control input.

• International Journal of Control, Automation, and Systems
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• v.6 no.4
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• pp.607-612
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• 2008

This paper proposes a robot visual servo approach based on image appearance and a wavelet function neural network. The inputs of the wavelet neural network are changes of image features or the elements of image appearance vector, and the outputs are changes of robot joint angles. Image appearance vector is calculated by using eigen subspace transform algorithm. The proposed approach does not need a priori knowledge of the robot kinematics, hand-eye geometry and camera models. The experiment results on a real robot system show that the proposed method is practical and simple.