Several variants of a special approach aiming at the development of a new branch of soft computing (SC) for the adaptive control of approximately and partially known mechanical systems are under investigation since 19...
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Several variants of a special approach aiming at the development of a new branch of soft computing (SC) for the adaptive control of approximately and partially known mechanical systems are under investigation since 1995. Like "traditional" SC it uses "uniform structures" for modeling, but these structures are obtained from the symplectic group (SG) as a mathematical means describing the inner symmetry of conservative mechanical systems. It considerably reduces the number of free parameters in the model in comparison with that of the neural networks or ample sets of fuzzy rules. It also replaces the process of parameter tuning with simple, lucid, and explicit algebraic operations of limited steps. Further considerations reveals that on the basis of quite formal mathematical observations SG can either be replaced by several Lie groups not representing any symmetry of the physical systems under consideration, or it can be used in a different way not based on the symmetry principle. This approach eliminates certain measurability problems, and the existence of certain singularities related to the symmetry-based application can be evaded using it by building in dummy parameters into the control. In this paper the robustness of this new formal approach is demonstrated in the adaptive control of a SCARA-type robot arm with one introduced dummy parameter.
An algorithm derived from the mixture probability algorithm has been constructed by using the autoregressive model(AR) with bias component to analyze the cross-sectional mean void fraction signals. However in the algo...
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An algorithm derived from the mixture probability algorithm has been constructed by using the autoregressive model(AR) with bias component to analyze the cross-sectional mean void fraction signals. However in the algorithm with a maximum likelihood estimates, equations for parameter estimation were very complicated. So in this paper, we modified the mixture probability algorithm and proposed new algorithm based on a stochastic Newton method. The effectiveness of our method is confirmed by applying ones to classify the crosssectional mean void fraction signals of gas-liquid two-phase flow.
A new methodology is proposed for the design of path following systems for autonomous marine craft in the presence of constant but unknown currents. Convergence to reference paths is achieved with a nonlinear control ...
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A new methodology is proposed for the design of path following systems for autonomous marine craft in the presence of constant but unknown currents. Convergence to reference paths is achieved with a nonlinear control strategy that takes explicitly into account the dynamics of the vehicle as well as those of a current estimator. Formal convergence proofs are indicated. Simulation results with the model of a prototype marine craft are presented to illustrate the performance of the path following system derived.
A new methodology is proposed for the design of path following systems for autonomous marine vehicles in the presence of constant but unknown currents. Convergence to reference paths is achieved with a nonlinear contr...
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A new methodology is proposed for the design of path following systems for autonomous marine vehicles in the presence of constant but unknown currents. Convergence to reference paths is achieved with a nonlinear control strategy that takes explicitly into account the dynamics of the vehicle as well as those of a current estimator. Formal convergence proofs are indicated. Simulation results with the model of a prototype marine craft are presented to illustrate the performance of the path following system derived.
This paper proposes a factorization method that reconstructs camera motion and scene shape based on the matching of multiple images under the condition that the camera captures a perspective view. Starting from the af...
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This paper proposes a factorization method that reconstructs camera motion and scene shape based on the matching of multiple images under the condition that the camera captures a perspective view. Starting from the affine projection camera model, the projection depth is iteratively estimated until the measurement matrix has rank 4. Then, the obtained measurement matrix is factorized to restore the three-dimensional information of the scene in the projection space. This approach eliminates noise sensitive processes, such as the calculation of the fundamental matrix, that are required in the factorization for the conventional perspective projection image, and a stable reconstruction is realized. Furthermore, the metric constraint in the conventional affine model is extended, and the metric constraint in the perspective projection condition is derived. It is shown that the reconstruction in Euclidean space is realized if the internal parameters of the camera are given.
This paper present a new effective algorithm for the optimal realization of state estimate feedback controller structures for discrete time systems subject to finite-word-leng.h constraints. The problem is formulated ...
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Robotic soccer is an interesting test bench for the field of self-organizing and cooperating multi-agent systems. This paper deals with learning of two basic low-level behaviors that will enable the robotic player to ...
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Robotic soccer is an interesting test bench for the field of self-organizing and cooperating multi-agent systems. This paper deals with learning of two basic low-level behaviors that will enable the robotic player to participate further in higher-level collaborative and adversarial learning situations. First, a ball interception and obstacle avoidance behavior is learned. Then the acquired skills are incorporated into a next higher-level multi-agent learning scenario, namely the shooting ball behavior. The proposed control scheme for these behaviors consists of a trajectory generator with a layered structure, which supplies data to a trajectory-tracking controller.
Neural networks are shown to be effective in being able to distinguish incomplete penetration-like weld defects by directly analyzing the plasma which is generated on each impingement of the laser on the materials. Th...
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Various alternative aircraft inspection methods are first discussed and advantages of using robots are analysed. ANDI (Automated NonDestructive Inspector) and CIMP (Crown Inspection Mobile Platform) are then described...
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Various alternative aircraft inspection methods are first discussed and advantages of using robots are analysed. ANDI (Automated NonDestructive Inspector) and CIMP (Crown Inspection Mobile Platform) are then described. Remote 3D stereoscopic visual inspection is outlined and algorithms developed for crack detection and surface and subsurface corrosion detection are described. Future development trends are also outlined.
For applications involving large data sets yielding variablecost computations, achieving both efficient I/O and load balancing may become particularly challeng.ng though performance-critical tasks. In this work, we in...
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ISBN:
(纸本)3540631380
For applications involving large data sets yielding variablecost computations, achieving both efficient I/O and load balancing may become particularly challeng.ng though performance-critical tasks. In this work, we introduce a data scheduling approach that integrates several optimizing techniques, including dynamic allocation, prefetching, and asynchronous I/O and communications. We show that good scalability is obtained by both hiding the I/O latency and appropriately balancing the workloads. We use a statistical metric for data skewness to further improve the performance by adequately selecting among data-scheduling. We test our approach on sparse benchmark matrices for matrix-vector computations and show experimentally that our method can accurately predict the relative performance of different input/output schemes for a given data set and choose the best technique accordingly.
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