In this study, the author discusses a Pareto strategy implemented via state and static output feedback for a class of weakly coupled large-scale discrete-time stochastic systems with state-and control-dependent noise....
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In this study, the author discusses a Pareto strategy implemented via state and static output feedback for a class of weakly coupled large-scale discrete-time stochastic systems with state-and control-dependent noise. The asymptotic structure along with the uniqueness and positive semi-definiteness of the solutions of cross-coupled non-linear matrix equations (CNMEs) is newly established via the implicit function theorem. The main contribution of this study is the proposal of a parameter-independent local state and static output feedback Pareto strategy. Moreover, a computational approach for solving the CNMEs is also considered if the information about the small parameter is available. Particularly, a new iterative algorithm based on the linear matrix inequality is established to design a Pareto strategy. Finally, in order to demonstrate the effectiveness of the proposed design method, a numerical example is provided for practical aircraft control problems.
In this paper we present an algorithm for segmenting or locating the endpoints of speech in a continuous signal stream. The proposed algorithm is based on non-linear likelihood-based projections derived from a Bayesia...
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In this paper we present an algorithm for segmenting or locating the endpoints of speech in a continuous signal stream. The proposed algorithm is based on non-linear likelihood-based projections derived from a Bayesian classifier. It-utilizes class distributions in a speech/non-speech classifier to project the signal into a 2-dimensional space where, in the ideal case, optimal classification can be performed with a simple linear discriminant. The projection results in the transformation of diffuse, nebulous classes in high-dimensional space into compact clusters in the low-dimensional space that can be easily separated by simple clustering mechanisms. In this space, decision boundaries for optimal classification can be more easily identified using simple clustering criteria. The segmentation algorithm proposed utilizes this property to determine and update optimal classification thresholds continuously for the signal being segmented. The performance of the proposed algorithm has been evaluated on data recorded under extremely diverse environmental noise conditions. The experiments show that the algorithm performs comparably to manual segmentations even under these diverse conditions. (C) 2002 Elsevier Science Ltd. All rights reserved.
The amount of energy needed to operate high-performance computing systems increases regularly since some years at a high pace, and the energy consumption has attracted a great deal of attention. Moreover, high energy ...
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The amount of energy needed to operate high-performance computing systems increases regularly since some years at a high pace, and the energy consumption has attracted a great deal of attention. Moreover, high energy consumption inevitably contains failures and reduces system reliability. However, there has been considerably less work of simultaneous management of system performance, reliability, and energy consumption on heterogeneous systems. In this paper, we first build the precedence-constrained parallel applications and energy consumption model. Then, we deduce the relation between reliability and processor frequencies and get their parameters approximation value by least squares curve fitting method. Thirdly, we establish a task execution reliability model and formulate this reliability and energy aware scheduling problem as a linear programming. Lastly, we propose a heuristic Reliability-Energy Aware Scheduling (REAS) algorithm to solve this problem, which can get good tradeoff among system performance, reliability, and energy consumption with lower complexity. Our extensive simulation performance evaluation study clearly demonstrates the tradeoff performance of our proposed heuristic algorithm.
Integrated power control algorithms with advanced receivers like adaptive antenna arrays and multiuser detectors have significantly better performance than conventional power control algorithms with matched filters. H...
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Integrated power control algorithms with advanced receivers like adaptive antenna arrays and multiuser detectors have significantly better performance than conventional power control algorithms with matched filters. However, these algorithms are very complex for practical implementation and their significant performance gains were only shown in idealised conditions. In this paper, we study the performance of integrated power control and advanced receivers algorithms for WCDMA uplink that are modified to be more suitable for practical implementation. We simultaneously take into account system imperfections, such as limited bandwidth for power control command, power control command errors, update rate of power control command, delay of power control command, fast fading effects, soft handover, limitation of mobile user minimum and maximum power, etc. Impact of mobile user velocities and errors in filter coefficients determination on performance of the algorithms are also shown. We show that also in this environment, power control algorithms integrated with advanced receivers significantly outperform conventional power control algorithm. Copyright (C) 2006 AEIT.
In this note, we present a robust version of the adaptive control algorithm established by Kreisselmeier [1]. We know that if the conventional dead-zone adaptive law is used in the adaptive system, it is troubleseome ...
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In this note, we present a robust version of the adaptive control algorithm established by Kreisselmeier [1]. We know that if the conventional dead-zone adaptive law is used in the adaptive system, it is troubleseome that the size of the unmodeled dynamics, denoted by epsilon, must be within the chosen dead-zone size for robustness. In this robust version, by introducing a positive design parameter epsilonBAR, the robust stability can be achieved without this constraint. It is also shown that the plant output and control input will converge asymptotically within a bound proportional to max {cepsilon, epsilonBAR} := epsilon(m), where c is a finite positive constant. Moreover, in the ideal case (i.e., epsilon = 0), the plant output and control input will converge to zero under some condition.
The combined use of modal and balanced truncations methods is proposed for model order reductions. To efficiently combine these methods, a stopping criterion based on spectral energy concepts is also proposed. This cr...
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The combined use of modal and balanced truncations methods is proposed for model order reductions. To efficiently combine these methods, a stopping criterion based on spectral energy concepts is also proposed. This criterion was implemented into the code of the widely known subspace accelerated dominant pole algorithm (SADPA), designed to compute a set of dominant poles and associated residues of transfer functions from large-scale, sparse, linear descriptor systems. The resulting enhanced SADPA code automatically stops once the computed set of dominant poles and associated residues is sufficient to build a modal reduced order model (ROM) whose energy content approaches that of the complete model within a specified tolerance and considering a frequency window of interest. The number of dominant poles in this set is much smaller than the number of poles of the full system model. Hence, their state-space realisation usually has a small enough dimension for the efficient application of the square root balanced truncation method. This new method, named hybrid modal-balanced truncation, produces ROMs whose order are much smaller than that of the modal ROMs and, most importantly, can also be applied to unstable models.
Accurate rapidly developing convection (RDC) detection is an essential part of a severe weather warning. A novel algorithm called object track and identification (OTI) is proposed for detecting RDC using infrared imag...
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Accurate rapidly developing convection (RDC) detection is an essential part of a severe weather warning. A novel algorithm called object track and identification (OTI) is proposed for detecting RDC using infrared image sequences from geostationary meteorology satellite. Convective cells are computed using extended maxima transform-based region growing algorithm. Firstly, a novel area overlap-based object tracking method is proposed to track convective cells in successive images. Secondly, the lowest 25% of overall brightness temperature of the same convective cloud is averaged in order to preserve the extremum information of evolution of cloud. Thirdly, a new identification criterion, which contains three subcriteria, is developed to detect RDC. Contingency table approach applied to various case studies over China shows that the OTI algorithm is efficient and accurate.
A hybrid approach, combining deterministic and Monte Carlo (MC) calculations, is proposed to compute the distribution of dose deposited during stereotactic synchrotron radiation therapy treatment. The proposed approac...
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A hybrid approach, combining deterministic and Monte Carlo (MC) calculations, is proposed to compute the distribution of dose deposited during stereotactic synchrotron radiation therapy treatment. The proposed approach divides the computation into two parts: (i) the dose deposited by primary radiation (coming directly from the incident x-ray beam) is calculated in a deterministic way using ray casting techniques and energy-absorption coefficient tables and (ii) the dose deposited by secondary radiation (Rayleigh and Compton scattering, fluorescence) is computed using a hybrid algorithm combining MC and deterministic calculations. In the MC part, a small number of particle histories are simulated. Every time a scattering or fluorescence event takes place, a splitting mechanism is applied, so that multiple secondary photons are generated with a reduced weight. The secondary events are further processed in a deterministic way, using ray casting techniques. The whole simulation, carried out within the framework of the Monte Carlo code Geant4, is shown to converge towards the same results as the full MC simulation. The speed of convergence is found to depend notably on the splitting multiplicity, which can easily be optimized. To assess the performance of the proposed algorithm, we compare it to state-of-the-art MC simulations, accelerated by the track length estimator technique (TLE), considering a clinically realistic test case. It is found that the hybrid approach is significantly faster than the MC/TLE method. The gain in speed in a test case was about 25 for a constant precision. Therefore, this method appears to be suitable for treatment planning applications.
A multisensor scheduling algorithm based on the hybrid task decomposition and modified binary particle swarm optimization (MBPSO) is proposed. Firstly, aiming at the complex relationship between sensor resources and t...
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A multisensor scheduling algorithm based on the hybrid task decomposition and modified binary particle swarm optimization (MBPSO) is proposed. Firstly, aiming at the complex relationship between sensor resources and tasks, a hybrid task decomposition method is presented, and the resource scheduling problem is decomposed into subtasks;then the sensor resource scheduling problem is changed into the match problem of sensors and subtasks. Secondly, the resource match optimization model based on the sensor resources and tasks is established, which considers several factors, such as the target priority, detecting benefit, handover times, and resource load. Finally, MBPSO algorithm is proposed to solve the match optimization model effectively, which is based on the improved updating means of particle's velocity and position through the doubt factor and modified Sigmoid function. The experimental results show that the proposed algorithm is better in terms of convergence velocity, searching capability, solution accuracy, and efficiency.
The high computational complexity of existing joint tracking and classification (JTC) algorithms hampers their application. After presenting a new description of the JTC problem-simultaneous tracking and classificatio...
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The high computational complexity of existing joint tracking and classification (JTC) algorithms hampers their application. After presenting a new description of the JTC problem-simultaneous tracking and classification (STC) instead of JTC, we derive two STC algorithms in both exact and approximate forms by applying Bayes' rule to the target state probability density function (pdf) and target class probability mass function (pmf) simultaneously under the assumption that the kinematic and attribute measurement processes are conditional independent. The mutual information exchange between tracker and classifier of the proposed STC algorithms is introduced by defining the simultaneous pdf-pmf of target state and class, the dependence of kinematic measurement on target class, the dependence of attribute measurement on target state and target model, class-dependent kinematic model sets, and class-dependent flight envelopes, etc. The proposed STC algorithms have four distinctive features. First, they have a modularized structure, i.e., they explicitly integrate a multiple-model filter and a Bayesian classifier. Second, the approximate versions, which follow easily from the proposed STC algorithms thanks to their modularized structure, have a closed form with a lower computational complexity and are more suitable for real-time applications. Third, the proposed exact STC algorithms are derived without the hidden approximation made in some existing multiple-model based JTC algorithms. Fourth, one of the proposed STC algorithms has the potential to further reduce the computational load since it has no redundant motion models. Simulation results suggest that the proposed STC algorithms provide a hopeful solution to a class of STC problems.
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