This paper shows how to integrate CDMA's pilot signals and GPS pseudoranges in order to improve the location accuracy, coverage and robustness to various errors in the position-determination. Since two radio posit...
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This paper shows how to integrate CDMA's pilot signals and GPS pseudoranges in order to improve the location accuracy, coverage and robustness to various errors in the position-determination. Since two radio positioning systems are complementary in urban canyons and open areas, the hybrid integration of them gives better performance, especially of wider coverage. Due to signal propagation, ionospheric error, multipath and NLOS(none-line-of-sight) errors cause the measured pseudoranges to contain too severe bias error to be ignored. Hence, this paper presents a 3-stage constrained optimization filter that takes the measurement bias into account. Comparison with simple ILS(iterated least square), extended Kalman filter shows that the presented filter reduces both bias error and linearization error effectively.
A new concept genetic algorithm has been implemented and tested for the use in the particle tracking velocimetry. The algorithm is applicable to relatively large numbers of particles with relatively high degrees of di...
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A new concept genetic algorithm has been implemented and tested for the use in the particle tracking velocimetry. The algorithm is applicable to relatively large numbers of particles with relatively high degrees of distribution density. This is mainly due to a new fitness function as well as unique genetic operations devised especially for the purpose of a particle pairing problem. The new fitness function is based on the relaxation of movement of a group of particles and is particularly suited for an increased density of particle images. The new genetic operations give rise to concentration of 'good' genes in a limited part of the gene strings and prevent them from being destroyed in the crossover and mutation processes. Another merit of the new algorithm is the genetic encoding scheme which can deal with all the possible concerns of the particle pairing problem, typical of which is the existence of unpaired particles.
The majority of current Virtual Prototyping (VP) systems are capable of providing high fidelity visual feedback only, leaving users in a more or less passive role. This paper discusses an upgrading of the visual feedb...
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The majority of current Virtual Prototyping (VP) systems are capable of providing high fidelity visual feedback only, leaving users in a more or less passive role. This paper discusses an upgrading of the visual feedback by haptic feedback. By allowing the user to interact with objects in a Virtual Environment (VE) with his/her hands and fingers such an advanced VP system permits them to play an active role. For the purposes of upgrading a wrist/finger kinesthetic display as well as a vibrotactile and temperature globe were developed. The combination of both devices leads to a novel haptic human system interface. This paper describes the interface hardware and the corresponding haptic rendering algorithms for generating a high fidelity haptic feedback from a VP environment. Moreover, experimental results are reported concerning the performance of the various haptic displays as well as the quality of the generated haptic sensations. An outlook to future multimodal VP techniques is given.
The problem of simultaneous state and system parameter estimation for aquatic ecosystems is considered in this paper. This problem has been solved through the identification of an innovation dynamical model representa...
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In general, characteristics of classical control theory and properties of real-time scheduling algorithms may cause unexpected control system responses in the implementation of real-time computer-controlled systems. R...
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We consider the iterative learning control problem from a 2D systems/adaptive control viewpoint. In particular, it is shown how some fundamental results from nonlinear adaptive control can be successfully applied in t...
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Power systems for hybrid electric vehicles, like most autonomous power systems, must fully and effectively utilize internal power resources to ensure the desired performance on one hand and longevity of system compone...
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Power systems for hybrid electric vehicles, like most autonomous power systems, must fully and effectively utilize internal power resources to ensure the desired performance on one hand and longevity of system components on the other. The optimum solution involves tradeoffs between two often contradictory things. Achieving the optimum depends on the capabilities of design tools used. The Synergetic Approach used in the work described here opens new opportunities to solve this problem more effectively.
This paper presents a feasible open-canal water-distribution control system based on the dynamic regulation principle and the theory of Kalman optimal controller. The existing nonlinear phenomena, as well as the trans...
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This paper presents a feasible open-canal water-distribution control system based on the dynamic regulation principle and the theory of Kalman optimal controller. The existing nonlinear phenomena, as well as the transport delay phenomenon are accounted for. The obtained controller is simulated using the full Saint-Venant partial differential equations
In this paper, the Hopfield neural network with delay (HNND) is studied from the standpoint of regarding it as an optimizing computational model. Two general updating rules for networks with delay (GURD) are given bas...
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In this paper, the Hopfield neural network with delay (HNND) is studied from the standpoint of regarding it as an optimizing computational model. Two general updating rules for networks with delay (GURD) are given based on Hopfield-type neural networks with delay for optimization problems and characterized by dynamic thresholds. It is proved that in any sequence of updating rule modes, the GURD monotonously converges to a stable state of the network. The diagonal elements of the connection matrix are shown to have an important influence on the convergence process, and they represent the relationship of the local maximum value of the energy function to the stable states of the networks. All the ordinary discrete Hopfield neural network (DHNN) algorithms are instances of the GURD. It can be shown that the convergence conditions of the GURD may be relaxed in the context of applications, for instance, the condition of nonnegative diagonal elements of the connection matrix can be removed from the original convergence theorem. A new updating rule mode and restrictive conditions can guarantee the network to achieve a local maximum of the energy function with a step-by-step algorithm. The convergence rate improves evidently when compared with other methods. For a delay item considered as a noise disturbance item, the step-by-step algorithm demonstrates its efficiency and a high convergence rate. Experimental results support our proposed algorithm.
Recent years has seen much progress in the theory and application of iterative learning control schemes for both linear and (classes of) nonlinear dynamics. In the case of the former, many algorithms based on minimizi...
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Recent years has seen much progress in the theory and application of iterative learning control schemes for both linear and (classes of) nonlinear dynamics. In the case of the former, many algorithms based on minimizing a suitable cost function have been reported. Here the interest is in the so-called norm optimal approach where the basic philosophy is to compute the control input on the current trial such that the tracking error is reduced in an optimal way without too much deviation from the control input used on the previous trial. This paper compares the performance of a range of controllers arising from use of the norm optimal approach - both stand alone and against alternatives.
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