This paper is proposed a fuzzy neural network controller based on the vector controlled induction motor drive system. The hybrid combination of fuzzy control and neural network will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed estimation and control of speed of induction motor using ANN Controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed SOFPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional SOFPNNs. The design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial of the consequent part of fuzzy rules, and a collection of the specific subset of input variables) and addresses specific aspects of parametric optimization. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized SOFPNN, the model is experimented with using two time series data(gas furnace and chaotic time series), A comparative analysis reveals that the proposed SOFPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literatures.

In this paper, new architectures and comprehensive design methodologies of Genetic Algorithms(GAs) based Genetically optimized Neurofuzzy Networks(GoNFN) are introduced, and a series of numeric experiments are carried out. The proposed GoNFN is based on the rule-based Neurofuzzy Networks(NFN) with the extended structure of the premise and the consequence parts of fuzzy rules being formed within the networks. The premise part of the fuzzy rules are designed by using space partitioning in terms of fuzzy sets defined in individual variables. In the consequence part of the fuzzy rules, three different forms of the regression polynomials such as constant, linear and quadratic are taken into consideration. The structure and parameters of the proposed GoNFN are optimized by GAs. GAs being a global optimization technique determines optimal parameters in a vast search space. But it cannot effectively avoid a large amount of time-consuming iteration because GAs finds optimal parameters by using a given space. To alleviate the problems, the dynamic search-based GAs is introduced to lead to rapidly optimal convergence over a limited region or a boundary condition. In a nutshell, the objective of this study is to develop a general design methodology o GAs-based GoNFN modeling, come up a logic-based structure of such model and propose a comprehensive evolutionary development environment in which the optimization of the model can be efficiently carried out both at the structural as well as parametric level for overall optimization by utilizing the separate or consecutive tuning technology. To evaluate the performance of the proposed GoNFN, the models are experimented with the use of several representative numerical examples.

In this paper, we describe the structure, function and the design factor of common module for KOMPSAT-2 OBC, which will be launched in 2005. By analysing OBC's performance, we can know the throughput and how much improve performance than KOMPSAT-l. it is used in the satellite mission design by system engineer. We verify the usefulness of common module for KOMPSAT-2 OBC through environment test.

The CSVM(Current Support Vector Machine) that is a digital architecture performing all phases of recognition process including kernel computing, learning, and recall of SVM(Support Vector Machine) on a chip is proposed. Concurrent operation by parallel architecture of elements generates high speed and throughput. The classification problems of bio data having high dimension are solved fast and easily using the CSVM. Quadratic programming in original SVM learning algorithm is not suitable for hardware implementation, due to its complexity and large memory consumption. Hardware-friendly SVM learning algorithms, kernel adatron and kernel perceptron, are embedded on a chip. Experiments on fixed-point algorithm having quantization error are performed and their results are compared with floating-point algorithm. CSVM implemented on FPGA chip generates fast and accurate results on high dimensional cancer data.

Fault-tolerant gait planning in legged locomotion is to design gaits with which legged robots can maintain static stability and motion continuity against a failure in a leg. For planning a robust and deadlock-free fault-tolerant gait, kinematic constraints caused by a failed leg should be closely examined with respect to remaining mobility of the leg. In this paper, based on the authors's previous results, deadlock avoidance scheme for fault-tolerant gait planning is proposed for a hexapod robot walking over even terrain. The considered fault is a locked joint failure, which prevents a joint of a leg from moving and makes it locked in a known position. It is shown that for guaranteeing the existence of the previously proposed fault-tolerant tripod gait of a hexapod robot, the configuration of the failed leg must be within a range of kinematic constraints. Then, for coping with failure situations where the existence condition is not satisfied, the previous fault-tolerant tripod gait is improved by including the adjustment of the foot trajectory. The foot trajectory adjustment procedure is analytically derived to show that it can help the fault-tolerant gait avoid deadlock resulting from the kinematic constraint and does not make any harmful effect on gait mobility. The post-failure walking problem of a hexapod robot with the normal tripod gait is addressed as a case study to show the effectiveness of the proposed scheme.

We analyze checkpoint strategy for multiple real-time periodic tasks with hard deadlines. Real-time tasks usually have deadlines associated with them. For multiple real-time tasks, checkpoint strategy considering deadlines of all tasks is very difficult to derive. We analyze the problem of checkpoint placement for such multiple periodic tasks. In our strategy, the interval between checkpoints is determined for each task considering its deadline. An approximated failure probability over a specified interval is derived. Then the number of checkpoints for each task is selected to minimize the approximated failure probability. To show the usefulness of our strategy, error bound between the exact and the approximated failure probability is estimated, which is revealed to be quite small.

A novel motion vector re-estimation technique for transcoding into lower spatial resolution is proposed. This technique is based on the fact that the block matching error is proportional to the complexity of the reference block with Taylor series expansion. It is shown that the motion vectors re-estimated by the proposed method are closer to optimal ones and offer better quality than those of previous techniques.

A noble caption text detection and recognition algorithm using the temporal nature of video is proposed in this paper. A text registration technique is used to locate the temporal and spatial positions of captions in video from the accumulated frame difference information. Experimental results show that the proposed method is effective and robust. Also, a high processing speed is achieved since no time consuming operation is included.

In this paper, we propose an adaptive blocking artifact reduction algorithm using characteristics of the block boundaries. Blocking artifacts contain the high frequency components near the block boundaries, so the lowpass filtering can remove them. But a simple lowpass filtering results into blurring by remove important features such as edges. To overcome this problem, we determine the modes depending on the characteristics around boundaries then proper filter is applied to each area. Simulation results show that the proposed method improves deblocking performance compared to that of MPEG-4.