Recently, Unmanned Aerial Vehicles (UAVs) have emerged as relay platforms to maintain the connectivity of ground mobile ad hoc networks (MANETs). However, when deploying UAVs, existing methods have not consider one si...
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Recently, Unmanned Aerial Vehicles (UAVs) have emerged as relay platforms to maintain the connectivity of ground mobile ad hoc networks (MANETs). However, when deploying UAVs, existing methods have not consider one situation that there are already some UAVs deployed in the field. In this paper, we study a problem jointing the motion control of existing UAVs and the deployment of new UAVs so that the number of new deployed UAVs to maintain the connectivity of ground MANETs can be minimized. We firstly formulate the problem as a Minimum Steiner Tree problem with Existing Mobile Steiner points under Edge Length Bound constraints (MST-EMSELB) and prove the NP completeness of this problem. Then we propose three Existing UAVs Aware (EUA) approximate algorithms for the MST-EMSELB problem: Deploy-Before-Movement (DBM), Move-Before-Deployment (MBD), and Deploy-Across-Movement (DAM) algorithms. Both DBM and MBD algorithm decouple the joint problem and solve the deployment and movement problem one after another, while DAM algorithm optimizes the deployment and motion control problem crosswise and solves these two problems simultaneously. Simulation results demonstrate that all EUA algorithms have better performance than non-EUA algorithm. The DAM algorithm has better performance in all scenarios than MBD and DBM ones. Compared with DBM algorithm, the DAM algorithm can reduce at most 70% of the new UAVs number.
This paper is concerned with the robust state estimation problem for a class of jump Markov linear systems (JMLSs) with missing measurements. Two independent Markov chains are used to describe the behavior of the syst...
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This paper is concerned with the robust state estimation problem for a class of jump Markov linear systems (JMLSs) with missing measurements. Two independent Markov chains are used to describe the behavior of the system dynamics and the characteristic of missing measurements, respectively. A robust filtering algorithm is developed by applying the basic interacting multiple model (IMM) approach and the H-infinity technique, which is different from the traditional Kalman filtering with minimum estimation error variance criterion. A maneuvering target tracking example is provided to demonstrate the effectiveness of the proposed algorithm. (C) 2013 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
Online power system stability assessment during restoration has not been strongly addressed yet. The introduction of wide area measurement systems (WAMS), however, made it possible to monitor the stability online. Thi...
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Online power system stability assessment during restoration has not been strongly addressed yet. The introduction of wide area measurement systems (WAMS), however, made it possible to monitor the stability online. This study technically presents a detailed analysis of stability during restoration using WAMS. The power system build-up strategy is used as the restoration approach, based on which the early and the last stages of restoration are to divide the power system into islands and interconnect them, respectively. In fact, the practical use of WAMS at the early stages of restoration provides precise determination of generators loading steps. Moreover, unification of phase angle references at different islands and presentation of tie line energising priority list are significant benefits of WAMS at the last stages of restoration. The New England 39-bus power system is used to demonstrate the proposed algorithm and verify the results. The proposed method is also applied on the IEEE 118-bus system as a large-scale power system to prove its capability in practical systems.
This paper shows the analysis of the thin film flow of fourth-grade fluid on the outer side of a vertical cylinder. Solution of the governing nonlinear equation is obtained by Rational Homotopy Perturbation Method (RH...
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This paper shows the analysis of the thin film flow of fourth-grade fluid on the outer side of a vertical cylinder. Solution of the governing nonlinear equation is obtained by Rational Homotopy Perturbation Method (RHPM);comparison with exact solution reflects the reliability of the method. Analysis shows that this method is reliable for even high nonlinearity. Graphs and tables strengthen the idea.
Due to the fact that vastly different variables and constraints are simultaneously considered, truss layout optimization is a typical difficult constrained mixed-integer nonlinear program. Moreover, the computational ...
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Due to the fact that vastly different variables and constraints are simultaneously considered, truss layout optimization is a typical difficult constrained mixed-integer nonlinear program. Moreover, the computational cost of truss analysis is often quite expensive. In this paper, a novel fitness estimation based particle swarm optimization algorithm with an adaptive penalty function approach (FEPSO-AP) is proposed to handle this problem. FEPSO-AP adopts a special fitness estimate strategy to evaluate the similar particles in the current population, with the purpose to reduce the computational cost. Further more, a laconic adaptive penalty function is employed by FEPSO-AP, which can handle multiple constraints effectively by making good use of historical iteration information. Four benchmark examples with fixed topologies and up to 44 design dimensions were studied to verify the generality and efficiency of the proposed algorithm. Numerical results of the present work compared with results of other state-of-the-art hybrid algorithms shown in the literature demonstrate that the convergence rate and the solution quality of FEPSO-AP are essentially competitive.
In this paper, the problem of active sound cancellation in finite-length ducts is investigated. The closed-form solution of a one-dimensional wave equation is obtained as the plant model. The controllability, observab...
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In this paper, the problem of active sound cancellation in finite-length ducts is investigated. The closed-form solution of a one-dimensional wave equation is obtained as the plant model. The controllability, observability, and transmission zeros are discussed based on the transfer function model. For ducts with totally reflective boundaries, stabilization can be achieved by using a speaker (actuator) and a microphone (sensor). Cases of collocated and noncollocated sensors and actuators are presented. A repetitive control algorithm was developed to drive the actuator so that harmonic noises in a duct are attenuated. For a duct with partially reflective boundaries, the application of repetitive control prevents sound from leaking out of the duct at a chosen end. A simulation study demonstrating the effects of this feedback control scheme is also presented.
In this article, a comparison is made between the robustness and efficiency of the CLEAR algorithm and the SIMPLE algorithm on nonorthogonal curvilinear coordinates for compressible flows. Thirteen different high-orde...
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In this article, a comparison is made between the robustness and efficiency of the CLEAR algorithm and the SIMPLE algorithm on nonorthogonal curvilinear coordinates for compressible flows. Thirteen different high-order convection schemes are employed in the calculations. Subsonic flow, transsonic flow, and supersonic flow in a channel with a circular arc bump and compressible flow in a Laval nozzle are used as test cases. The CLEAR algorithm shows huge potential to compute the transsonic flow in the Laval nozzle and high-speed compressible flows. Results with the ADBQUICKEST scheme, the HLPA scheme, and the MUSCL scheme are stable for both the compressible SIMPLE and CLEAR algorithms for all the mentioned cases.
Recognizing objects in images is an active area of research in computer vision. In the last two decades, there has been much progress and there are already object recognition systems operating in commercial products. ...
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Recognizing objects in images is an active area of research in computer vision. In the last two decades, there has been much progress and there are already object recognition systems operating in commercial products. However, most of the algorithms for detecting objects perform an exhaustive search across all locations and scales in the image comparing local image regions with an object model. That approach ignores the semantic structure of scenes and tries to solve the recognition problem by brute force. In the real world, objects tend to covary with other objects, providing a rich collection of contextual associations. These contextual associations can be used to reduce the search space by looking only in places in which the object is expected to be;this also increases performance, by rejecting patterns that look like the target but appear in unlikely places. Most modeling attempts so far have defined the context of an object in terms of other previously recognized objects. The drawback of this approach is that inferring the context becomes as difficult as detecting each object. An alternative view of context relies on using the entire scene information holistically. This approach is algorithmically attractive since it dispenses with the need for a prior step of individual object recognition. In this paper, we use a probabilistic framework for encoding the relationships between context and object properties and we show how an integrated system provides improved performance. We view this as a significant step toward general purpose machine vision systems.
Inspired by bagging and boosting algorithms in classification, the non-weighing and weighing-based sampling approaches for clustering are proposed and studied in the paper. The effectiveness of non-weighing-based samp...
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Inspired by bagging and boosting algorithms in classification, the non-weighing and weighing-based sampling approaches for clustering are proposed and studied in the paper. The effectiveness of non-weighing-based sampling technique, comparing the efficacy of sampling with and without replacement, in conjunction with several consensus algorithms have been invested in this paper. Experimental results have shown improved stability and accuracy for clustering structures obtained via bootstrapping, subsampling, and boosting techniques. Subsamples of small size can reduce the computational cost and measurement complexity for many unsupervised data mining tasks with distributed sources of data. This empirical research study also compares the performance of boosting and bagging clustering ensembles using different consensus functions on a number of datasets. (C) 2013 Elsevier Ltd. All rights reserved.
We present a new modification of the global control improvement method based on a known Krotov's method for optimal control in quantum systems from a certain class. The algorithm is implemented for high-dimensiona...
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We present a new modification of the global control improvement method based on a known Krotov's method for optimal control in quantum systems from a certain class. The algorithm is implemented for high-dimensional systems as a parallel program. We give computations for the control in a quantum dynamical system that represents a well-known model of communicating the quantum state in spin chains.
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