In this letter, we propose a new method to detect faces in color images based on the characterized convex regional relationship. We detect skin and hair likeness regions using the derived skin and hair color models and the convex skin likeness and hair likeness regions are adopted as the characteristic convex regions. Finally, human faces can be detected via their intersection relationship. The proposed algorithm can accomplish face detection in an image including not only single face but also multi-faces and also detect deformed faces efficiently. To validity the effectiveness of the proposed method, we make experiments with various cases.

In this paper, we present an output feedback controller using a fuzzy controller and observer for nonlinear systems with unknown time-delay. Recently, Cao et al. proposed a stabilization method for the nonlinear time-delay systems using a fuzzy controller when the time-delay is known. In general, however, it is impossible to know or measure this time-varying delay. The proposed method requires only the upper bound of the derivative of the time-delay. We represent the nonlinear system with the unknown time-delay by Takagi-Sugeno (T-S) fuzzy model and design the fuzzy controller and observer for the systems using the parallel distributed compensation (PDC) scheme. In addition, we derive the sufficient condition for the asymptotic stability of the equilibrium point by applying Lyapunov-Krasovskii theorem to the closed-loop system and solve the condition in the formulation of LMI. Finally, computer simulations are included to demonstrate the effectiveness of the suggested method.

This paper proposes an analysis and design methodology for the robust control of affine-in-control nonlinear systems subject to actuator saturation in discrete-time formulation. The robust stability condition is derived for the closed-loop system by the introduction of the fuzzy Kronecker delta. Based on the newly acquired stability condition, a design method is proposed to guarantee the robust H∞ performance. In the design, LMI-based pole placement is employed to use the freedom allowed in the selection of the controller. The validity of the proposed method is asserted by the computer simulation.

This paper describes a vision-based and real-time system for detecting road signs from within a moving vehicle. The system architecture which is proposed in this paper consists of two parts, the learning and the detection part of road sign images. The proposed system has the standard architecture with adaboost algorithm. Adaboost is a popular algorithm which used to detect an object in real time. To improve the detection rate of adaboost algorithm, this paper proposes a new combination method of classifiers in every stage. In the case of detecting road signs in real environment, it can be ambiguous to decide to which class input images belong. To overcome this problem, we propose a method that applies fuzzy measure and fuzzy integral which use the importance and the evaluated values of classifiers within one stage. It is called fuzzy-boosting in this paper. Also, to improve the speed of a road sign detection algorithm using adaboost at the detection step, we propose a method which chooses several candidates by using MC generator. In this paper, as the sub-windows of chosen candidates pass classifiers which are made from fuzzy-boosting, we decide whether a road sign is detected or not. Using experiment result, we analyze and compare the detection speed and the classification error rate of the proposed algorithm applied to various environment and condition.

This paper presents a fuzzy model-based approach for synchronization of time-delay chaotic system with input saturation. Time-delay chaotic drive and response system is respectively represented by Takagi-Sugeno (T-S) fuzzy model. Specially, the response system contains input saturation. Using the unidirectional linear error feedback and the parallel distributed compensation (PDC) scheme, we design fuzzy chaotic synchronization system and analyze local stability for synchronization error dynamics. Since time-delay in the transmission channel always exists, we also take it into consideration. The sufficient condition for the local stability of the fuzzy synchronization system with input saturation and channel time-delay is derived by applying Lyapunov-Krasovskii theory and solving linear matrix inequalities (LMI's) problem. Numerical examples are given to demonstrate the validity of the proposed approach.

This paper presents an observer design methodology for a special class of MIMO nonlinear systems. First, we characterize the class of MIMO nonlinear systems that consists of the linear observable part and the nonlinear part with a block triangular structure. Also, the similarity transformation that plays an important role in proving the convergence of the proposed observer is generalized to MIMO systems. Since the gain of the proposed observer minimizes a nonlinear part of the system to suppress for the stability of the error dynamics, it improves the transient performance of the high gain observer. Moreover, by using the generalized similarity transformation, it is shown that under some observability and boundedness conditions, the proposed observer guarantees the global exponential convergence to zero of the estimation error. Finally, the simulation results for induction motor are included to illustrate the validity of our design scheme.

Nonlinear modeling of complex irregular systems constitutes the essential part of many control and decision-making systems and fuzzy logic is one of the most effective algorithms to build such a nonlinear model. In this paper, a new approach to fuzzy modeling is proposed. The model considered herein is the well-known Sugeno-type fuzzy system. The fuzzy modeling algorithm suggested in this paper is composed of two phases: coarse tuning and fine tuning. In the first phase (coarse tuning), a successive clustering algorithm with the fuzzy validity measure (SCFVM) is proposed to find the number of the fuzzy rules and an initial fuzzy model. In the second phase (fine tuning), a moving genetic algorithm with partial encoding (MGAPE) is developed and used for optimized tuning of membership functions of the fuzzy model. Two computer simulation examples are provided to evaluate the performance of the proposed modeling approach and compare it with other modeling approaches.

A new structure learning approach for Bayesian networks (BNs) based on dual genetic algorithm (DGA) is proposed in this paper. An individual of the population is represented as a dual chromosome composed of two chromosomes. The first chromosome represents the ordering among the BN nodes and the second represents the conditional dependencies among the ordered BN nodes. It is rigorously shown that there is no BN structure that cannot be encoded by the proposed dual genetic encoding and the proposed encoding explores the entire solution space of the BN structures. In contrast with existing GA-based structure learning methods, the proposed method learns not only the topology of the BN nodes, but also the ordering among the BN nodes, thereby, exploring the wider solution space of a given problem than the existing method. The dual genetic operators are closed in the set of the admissible individuals. The proposed method is applied to real-world and benchmark applications, while its effectiveness is demonstrated through computer simulation.

In this letter, a new output feedback tracking control using a fuzzy disturbance observer (FDO) is proposed and its application to control of a nonlinear system in the presence of the internal parameter perturbation and external disturbance is presented. An FDO using a filtered signal is developed and the high gain observer (HGO) is employed to implement the output feedback tracking control. It is shown in a rigorous manner that all the errors involved can be kept arbitrarily small. Finally, the effectiveness and the feasibility of the suggested method is demonstrated by computer simulation.