The paper presents a method to obtain effects similar to those obtained by real mechanical loading, actuator and measurement noises and parameter variation but without any mechanical parts supplementary to the electri...
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ISBN:
(纸本)9781424416653
The paper presents a method to obtain effects similar to those obtained by real mechanical loading, actuator and measurement noises and parameter variation but without any mechanical parts supplementary to the electric motor of the studied electric drive and without actually changing any electrical or mechanical parameter of the motor. The motor drive itself produces the load torque, noises and the parameter variations using a digital signal processing software. Therefore, the electric drive is virtually perturbed, while its behavior is similar to that of the really perturbed drive. The present method, which can be related to a class of Hardware-In-The-Loop Simulations, could be used to test the implementation of advanced control algorithms or at didactic purposes using motion control kits found on the market.
This paper proposes a control algorithm for a robotic swarm based on the center of gravity of the local swarm. In order to be compatible with maintaining a high stability of the whole swarm and advancing to the goal, ...
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ISBN:
(纸本)9781424416653
This paper proposes a control algorithm for a robotic swarm based on the center of gravity of the local swarm. In order to be compatible with maintaining a high stability of the whole swarm and advancing to the goal, virtual forces;local forces and an advancing force which are produced by the algorithm, are applied to multiple autonomous mobile robots. Local forces such as an attraction and a repulsion, are applied to each robot for increasing the stability of the local swarm. Overlapping each local swarm partially increases the stability of the whole swarm. The advancing force is applied to each robot for advancing to the goal while maintaining the stability of the local swarm. Since obstacles which prevent the robot advancing are considered as a disturbance from the viewpoint of the stability of the whole swarm, an effectiveness of the algorithm in obstacle space is evaluated using a dynamics simulation. As a result, it is found that the algorithm is able to maintain the high stability of the whole swarm advancing to the goal.
This paper presents a robust space vector-based hysteresis current controller (SVBHCC) which does not use the back-emf information directly to produce the optimal pulse width modulation (PWM) switching pattern. The co...
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ISBN:
(纸本)9781424416653
This paper presents a robust space vector-based hysteresis current controller (SVBHCC) which does not use the back-emf information directly to produce the optimal pulse width modulation (PWM) switching pattern. The controller operation is based on a generalized reference vector which tracks the fundamental harmonic of the load voltage. This vector is calculated iteratively by monitoring the PWM pattern generated by the current controller itself. The generalized reference vector is a direct replacement for the back-emf vector used by other SVBHCC methods previously published in the literature. The main advantages of this new current control method are simplicity and improved current control quality. Two versions of the new control algorithm are presented and test results are shown for an electrical drive system designed around this type of current controller.
In this paper, a novel dynamic matrix control algorithm based on RBF neural network for time varying nonlinear system is presented. RBFNN is used for system model identification, as well as DMC is adopted as optimized...
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ISBN:
(纸本)9781424441969
In this paper, a novel dynamic matrix control algorithm based on RBF neural network for time varying nonlinear system is presented. RBFNN is used for system model identification, as well as DMC is adopted as optimized controller. Besides, the predictive initiative value is solved by multi-steps prediction of RBF neural network,, and nonlinear dynamic matrix coefficients are derived correspondingly. Compared to regular DMC algorithm, the RBFNN-DMC algorithm not only effectively overcomes the large disturbance but also be very robustness. At last, the algorithm is applied in a time varying, high nonlinear Continuous Stirred Tank Reactor (CSTR) pH process model and presents a better control performance.
Data mining algorithms are increasingly being used to support the process of signal detection and evaluation in pharmacovigilance. Published data mining exercises formulated within a screening paradigm typically calcu...
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Data mining algorithms are increasingly being used to support the process of signal detection and evaluation in pharmacovigilance. Published data mining exercises formulated within a screening paradigm typically calculate classical performance indicators such as sensitivity, specificity, predictive value and receiver operator characteristic curves. Extrapolating signal detection performance from these isolated data mining exercises to performance in real-world pharmacovigilance scenarios is complicated by numerous factors and some published exercises may promote an inappropriate and exclusive focus on only one aspect of performance. In this article, we discuss a variation on positive predictive value that we call the 'number needed to detect' that provides a simple and intuitive screening metric that might usefully supplement the usual presentations of data mining performance. We use a series of figures to demonstrate the nature and application of this metric, and selected adaptive variations. Even with simple and intuitive metrics, precisely quantifying the performance of contemporary data mining algorithms in pharmacovigilance is complicated by the complexity of the phenomena under surveillance and the manner in which the data are recorded in spontaneous reporting systems.
This letter presents an improved cue integration approach to reliably separate coherent moving objects from their background scene in video sequences. The proposed method uses a probabilistic framework to unify bottom...
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This letter presents an improved cue integration approach to reliably separate coherent moving objects from their background scene in video sequences. The proposed method uses a probabilistic framework to unify bottom-up and top-down cues in a parallel, "democratic" fashion. The algorithm makes use of a modified Bayes rule where each pixel's posterior probabilities of figure or ground layer assignment are derived from likelihood models of three bottom-up cues and a prior model provided by a top-down cue. Each cue is treated as independent evidence for figure-ground separation. They compete with and complement each other dynamically by adjusting relative weights from frame to frame according to cue quality measured against the overall integration. At the same time, the likelihood or prior models of individual cues adapt toward the integrated result. These mechanisms enable the system to organize under the influence of visual scene structure without manual intervention. A novel contribution here is the incorporation of a top-down cue. It improves the system's robustness and accuracy and helps handle difficult and ambiguous situations, such as abrupt lighting changes or occlusion among multiple objects. Results on various video sequences are demonstrated and discussed. (Video demos are available at http://***:8376/similar to tangx/neco/***.).
A few distinct cortical operations have been postulated over the past few years, suggested by experimental data on nonlinear neural response across different areas in the cortex. Among these, the energy model proposes...
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A few distinct cortical operations have been postulated over the past few years, suggested by experimental data on nonlinear neural response across different areas in the cortex. Among these, the energy model proposes the summation of quadrature pairs following a squaring nonlinearity in order to explain phase invariance of complex VI cells. The divisive normalization model assumes a gain-controlling, divisive inhibition to explain sigmoid-like response profiles within a pool of neurons. A gaussian-like operation hypothesizes a bell-shaped response tuned to a specific, optimal pattern of activation of the presynaptic inputs. A max-like operation assumes the selection and transmission of the most active response among a set of neural inputs. We propose that these distinct neural operations can be computed by the same canonical circuitry, involving divisive normalization and polynomial nonlinearities, for different parameter values within the circuit. Hence, this canonical circuit may provide a unifying framework for several circuit models, such as the divisive normalization and the energy models. As a case in point, we consider a feedforward hierarchical model of the ventral pathway of the primate visual cortex, which is built on a combination of the gaussian-like and max-like operations. We show that when the two operations are approximated by the circuit proposed here, the model is capable of generating selective and invariant neural responses and performing object recognition, in good agreement with neurophysiological data.
Background: PSAIA (Protein Structure and Interaction Analyzer) was developed to compute geometric parameters for large sets of protein structures in order to predict and investigate protein-protein interaction sites. ...
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Background: PSAIA (Protein Structure and Interaction Analyzer) was developed to compute geometric parameters for large sets of protein structures in order to predict and investigate protein-protein interaction sites. Results: In addition to most relevant established algorithms, PSAIA offers a new method PIADA (Protein Interaction Atom Distance Algorithm) for the determination of residue interaction pairs. We found that PIADA produced more satisfactory results than comparable algorithms implemented in PSAIA. Particular advantages of PSAIA include its capacity to combine different methods to detect the locations and types of interactions between residues and its ability, without any further automation steps, to handle large numbers of protein structures and complexes. Generally, the integration of a variety of methods enables PSAIA to offer easier automation of analysis and greater reliability of results. PSAIA can be used either via a graphical user interface or from the command-line. Results are generated in either tabular or XML format. Conclusion: In a straightforward fashion and for large sets of protein structures, PSAIA enables the calculation of protein geometric parameters and the determination of location and type for protein-protein interaction sites. XML formatted output enables easy conversion of results to various formats suitable for statistic analysis. Results from smaller data sets demonstrated the influence of geometry on protein interaction sites. Comprehensive analysis of properties of large data sets lead to new information useful in the prediction of protein-protein interaction sites.
This book constitutes the joint refereed proceedings of the 11th International Workshop on Approximation algorithms for Combinatorial Optimization Problems, APPROX 2008 and the 12th International Workshop on Randomiza...
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ISBN:
(数字)9783540853633
ISBN:
(纸本)9783540853626
This book constitutes the joint refereed proceedings of the 11th International Workshop on Approximation algorithms for Combinatorial Optimization Problems, APPROX 2008 and the 12th International Workshop on Randomization and Computation, RANDOM 2008, held in Boston, MA, USA, in August 2008. The 20 revised full papers of the APPROX 2008 workshop were carefully reviewed and selected from 42 submissions and focus on algorithmic and complexity issues surrounding the development of efficient approximate solutions to computationally difficult problems. RANDOM 2008 is concerned with applications of randomness to computational and combinatorial problems and accounts for 27 revised full papers, also diligently reviewed and selected out of 52 workshop submissions.
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