In this paper, we propose the development of Artificial Hippocampus Algorithm(AHA) which remodels a principle of brain of hippocampus. Hippocampus takes charge auto-associative memory and controlling functions of long-term or short-term memory strengthening. We organize auto-associative memory based 4 steps system (EC, DG CA3, and CA1) and improve speed of teaming by addition of modulator to long-term memory teaming. In hippocampus system, according to the 3 steps order, information applies statistical deviation on Dentate Gyrus region and is labeled to responsive pattern by adjustment of a good impression. In CA3 region, pattern is reorganized by auto-associative memory. In CA1 region, convergence of connection weight which is used long-term memory is learned fast a by neural network which is applied modulator. To measure performance of Artificial Hippocampus Algorithm, PCA(Principal Component Analysis) and LDA(Linear Discriminants Analysis) are applied to face images which are classified by pose, expression and picture quality. Next, we calculate feature vectors and learn by AHA. Finally, we confirm cognitive rate. The results of experiments, we can compare a proposed method of other methods, and we can confirm that the proposed method is superior to the existing method.

This paper deals with the controller for a stable walking of a humanoid robot using genetic algorithm. A humanoid robot has instability during walking because it isn't fixed on the ground, and its nonlinearities of the joints increase its instability. If controller isn't robust, the robot may fall down at the ground during walking because of its nonlinearities. To solve this problem, robust controller is required to reduce the effect of nonlinearities and to gain the good tracking performance. In this paper, motion controller that is based on fuzzy-sliding mode controller is proposed. This controller can remove the effect of the saturation by limitation of the input voltage. It also includes compensator for reducing the effect of the nonlinearity by backlash and PI controller improving the tracking performance. In here, genetic algorithm is used for searching the optimal gains of the controller. From the given controller, a humanoid robot can moved more preciously. All the processes are investigated through simulations and are verified experimentally in a real joint system for a humanoid robot.

This paper proposes a self-recurrent wavelet neural network(SRWNN) based adaptive backstepping control technique for the robust steering control of autonomous underwater vehicles(AUVs) with unknown model uncertainties and external disturbance. The SRWNN, which has the properties such as fast convergence and simple structure, is used as the uncertainty observer of the steering model of AUV. The adaptation laws for the weights of SRWNN and reconstruction error compensator are induced from the Lyapunov stability theorem, which are used for the on-line control of AUV. Finally, simulation results for steering control of an AUV with unknown model uncertainties and external disturbance are included to illustrate the effectiveness of the proposed method.

This paper presents a engine/brake integrated VDC(Vehicle Dynamic Control) system using neural network algorithm methods for wheel slip and yaw rate control. For stable performance of vehicle, not only is the lateral motion control(wheel slip control) important but the yaw motion control of the vehicle is crucial. The proposed NNPI(Neural Network Proportional-Integral) controller operates at throttle angle to improve the performance of wheel slip. Also, the suggested NNPID controller performs at brake system to improve steering performance. The proposed controller consists of multi-hidden layer neural network structure and PID control strategy for self-learning of gain scheduling. Computer Simulation have been performed to verify the proposed neural network based control scheme of 17 dof vehicle dynamic model which is implemented in MATLAB Simulink.

This paper presents a neural network based adaptive control approach to a reconfigurable flight control law that keeps handling qualities in the presence of faults or failures to the control surfaces of an aircraft. This approach removes the need for system identification for control reallocation after a failure and the need for an accurate aerodynamic database for flight control design, thereby reducing the cost and time required to develope a reconfigurable flight controller. Neural networks address the problem caused by uncertainties in modeling an aircraft and pseudo control hedging deals with the nonlinearity in actuators and the reconfiguration of a flight controller. The effect of the reconfigurable flight control law is illustrated in results of a nonlinear simulation of an unmanned aerial vehicle Durumi-II.

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and ststor resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

Presently, the exploration of an unknown environment is an important task for thee new generation of mobile service robots and mobile robots are navigated by means of a number of methods, using navigating systems such as the sonar-sensing system or the visual-sensing system. To fully utilize the strengths of both the sonar and visual sensing systems. This paper presents a technique for localization of a mobile robot using fusion data of multi-ultrasonic sensors and vision system. The mobile robot is designed for operating in a well-structured environment that can be represented by planes, edges, comers and cylinders in the view of structural features. In the case of ultrasonic sensors, these features have the range information in the form of the arc of a circle that is generally named as RCD(Region of Constant Depth). Localization is the continual provision of a knowledge of position which is deduced from it's a priori position estimation. The environment of a robot is modeled into a two dimensional grid map. we defines a vision-based environment recognition, phisically-based sonar sensor model and employs an extended Kalman filter to estimate position of the robot. The performance and simplicity of the approach is demonstrated with the results produced by sets of experiments using a mobile robot.

In this paper, we propose an improved reduction model and a reduction model-based hybrid smith-predictor fuzzy controller. The transient and steady-state responsed of the reduction model was evaluated. In tuning the controller, the parameters of PID and the factors fuzzy controllers were obtained from the reduced model and by using genetic algorithms, respectively. Simulation examples demonstrated a better performance of the proposed controller than conventional ones.

Adaptive anti-sway and trajectory tracking control of overhead crane is presented, which utilizes Fuzzy Uncertainty Observer(FUO) and Fuzzy based Variable Structure Control(FVSC). We consider an overhead crane system which can be decoupled into the actuated and unactuated subsystems with its own lumped uncertainty such as parameter uncertainties and external disturbance. First, a new method for anti-sway control using FVSC is proposed to improve the conventional method based on Lyapunov direct method, while a conventional trajectory tracking control law using feedback linearization is directly adopted. Second, FUO is designed to estimate one of the two lumped uncertainties which can compensate both of them, based on the fact that two lumped uncertainties are coupled with each other. Then, an adaptive anti-sway control is proposed by incorporating the proposed FVSC and FUO. Under the condition that the observation error is Uniformly Ultimately Bounded(UUB) within an arbitrarily shrinkable region, the overall closed-loop system is shown to be Globally Uniformly Ultimately Bounded(GUUB). In addition, the Global Asymptotic Stability(GAS) of it is shown under the vanishing disturbance assumption. Finally, the effectiveness of the proposed scheme has been confirmed by numerical simulations.

This paper proposes an automatic bandwidth control method for the performance improvement of Binary Amplitude Shift Keying (BASK) system for Giga-Bit Modem in millimeter band. In order to improve the performance of the BASK system with a fixed bandwidth, the proposed method is to adjust a bandwidth of low pass filter in receiver using the fuzzy system. The BASK system consists of a high speed shutter of the transmitter and a counter and a repeater of receiver. The repeater consists of four stage converters, and a converter is constructed with a low pass filter and a limiter. The inputs to the fuzzy system are the reminder and integral of remainder of counter, and output is a bandwidth. We used a Viterbi algorithm to find the optimum detection from the output of the counter. Simulation results showed that the proposed system improves the performance compared to the fixed bandwidth.

In this paper, we present a position-based robot hand tracking scheme where a pan-tilt camera is controlled such that a robot hand is always shown in the center of an image frame. We calculate the rotation angles of a pan-tilt camera by transforming the coordinate systems. In order to identify the model parameters, we applied two optimization techniques: a nonlinear least square optimizer and a particle swarm optimizer. From the simulation results, it is shown that the considered parameter identification problem is characterized by a highly multimodal landscape; thus, a global optimization technique such as a particle swarm optimization could be a promising tool to identify the model parameters of a robot hand tracking system, whereas the nonlinear least square optimizer often failed to find an optimal solution even when the initial candidate solutions were selected close to the true optimum.

This paper presents an application of intelligent modeling method to manual control system with human operator. Human operator as a part of controller is difficult to be modeled because of changes in individual characteristics and operation environment. So in these situation, a fuzzy model developed relying on the expert's experiences or trial and error may not be acceptable. To supplement the fuzzy model block, a neural network based modeling error compensator is incorporated. The feasibility of the present fuzzy-neural modeling scheme has been investigated for the real human based target tracking system.

Wireless Sensor Network(WSNs) consists of small sensor nodes with sensing, computation, and wireless communication capabilities. The large number of sensor nodes in a WSN means that there will often be some nodes which give erroneous sensor data owing to several reasons such as power shortage and transmission error. Generally, these sensor data are gathered by a sink node to monitor and diagnose the current environment. Therefore, this can make it difficult to get an effective monitoring and diagnosis. In this paper, to overcome the aforementioned problems, intelligent sensor data validation method based on PCA(Principle Component Analysis) is utilized. Furthermore, a practical implementation using embedded system is given to show the feasibility of the proposed scheme.

This paper presents the development of digital robust fuzzy controller for uncertain nonlinear systems. The proposed approach is based on the intelligent digital redesign(IDR) method with considering the relaxed stability condition of fuzzy control system. The term IDR in the concerned system is to convert an existing analog robust control into an equivalent digital counterpart in the sense of the state-matching. We shows that the IDR problem can be reduced to find the digital fuzzy gains minimizing the norm distance between the closed-loop states of the analog and digital robust control systems. Its constructive conditions are expressed as the linear matrix inequalities(LMIs) and thereby easily tractable by the convex optimization techniques. Based on the nonquadratic Lyapunov function, the robust stabilization conditions are given for the sampled-data fuzzy system, and hence less conservative. A numerical example, chaotic Lorentz system, is demonstrated to visualize the feasibility of the proposed methodology.

In this paper, a 2-dimensional accurate edge-based matching algorithm using subpixel edges is proposed that combines the Generalized Hough Transform(GHT) and the Chamfer matching to complement the weakness of either method. First, the GHT is used to find the approximate object positions and orientations, and then these positions and orientations are used as starting parameter values to find more accurate position and orientation using the Chamfer matching with distance interpolation. Finally, matching accuracy is further refined by using a subpixel algorithm. Testing results demonstrate that greater matching accuracy is achieved using subpixel edges rather than edge pixels.

The tiny camera module used in a modern cellular phone requires precise assembly processes. To meet the requirement of high resolution and functionality, the number of parts used in a camera module becomes larger and larger. As the market grows rapidly, an automatic camera phone assembly process is required. However, diverse production line and short life cycle make it difficult to build an affordable assembly line. To attack this problem, a flexible and expandable lens assembly system is proposed. To save the manufacturing line set-up time, modular concept is adopted. Also, each module is designed to have intelligence to simplify the set-up process. The assembly system is built up on the standard flat-form that includes a vibration free base, air and electric supplies, and electronic controllers, etc. Furthermore, the assembly cell has the capability of handling tiny, thin, or transparent parts which are very difficult to identify without machine vision.

This paper presents a tracking filter with ship's motion compensation for a ship-borne radar tracking system. The ship's maneuver is described by displacement and rotational motions in the ship-centered east-north frame. The first order Taylor series approximation of the measurement error covariance of the converted measurement is derived in the ship-centered east-north frame. The ship's maneuver is compensated by incorporating the measurement error covariance of the converted measurement and displacement of the position state in the tracking filter. The simulation results via 500 Monte-Carlo runs show that the proposed method follows the target successfully and provides consistent tracking performance during ship's maneuvers while the conventional tracking filter without ship motion compensation fails to track during such periods.