Coverage optimization is an important task which directly affects the performance of cellular networks. The signal-to-interference and noise ratio (SINR) is a key metrics for evaluating coverage effect and its ability...
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ISBN:
(纸本)9781479903085
Coverage optimization is an important task which directly affects the performance of cellular networks. The signal-to-interference and noise ratio (SINR) is a key metrics for evaluating coverage effect and its ability of resisting radio propagation condition variation. It is difficult to improve the ability of the pilot coverage of multiple sectors to resist environment variation simultaneously by using the existing methods. In this paper, a novel coverage optimization method based on multi-sector joint beamforming is proposed to maximize the minimum SINR of the sectors. An iterative algorithm is then developed to obtain antenna array excitation weights. Simulation results show that the performance of our algorithm is superior to that of the existing algorithm.
Railway traffic controllers face the problem that trains are often not operated as planned in timetables due to perturbations such as unexpected, degraded operations, and technical failures. This paper deals with the ...
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Railway traffic controllers face the problem that trains are often not operated as planned in timetables due to perturbations such as unexpected, degraded operations, and technical failures. This paper deals with the problem of train rerouting and rescheduling faced by the controllers of different regional railway control centers that must coordinate their decisions to minimize the impact of perturbations over the whole network. We split this real-time railway traffic management problem into two decision levels. At the lower level, the local area controllers, the dispatchers, manage train schedules and routes in their control areas from a microscopic perspective. At the higher level, a coordinator ensures the compatibility of dispatchers' decisions over two or more areas from a macroscopic perspective. To solve our problem, we propose an iterative optimization algorithm. The preliminary experimental results show that the proposed algorithm can reduce the delay propagation.
The order of the projection in the algebraic reconstruction technique(ART)method has great influence on the rate of the *** many scholars have studied the order of the projection,few theoretical proofs are *** Strohme...
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The order of the projection in the algebraic reconstruction technique(ART)method has great influence on the rate of the *** many scholars have studied the order of the projection,few theoretical proofs are *** Strohmer and Roman Vershynin introduced a randomized version of the Kaczmarz method for consistent,and over-determined linear systems and proved whose rate does not depend on the number of equations in the systems in *** this paper,we apply this method to computed tomography(CT)image reconstruction and compared images generated by the sequential Kaczmarz method and the randomized Kaczmarz *** demonstrates the feasibility of the randomized Kaczmarz algorithm in CT image reconstruction and its exponential curve convergence.
We use a simple iterative algorithm for two quasi-nonexpansive mappings to approximate their common fixed point through Delta-convergence and strong convergence of the algorithm. Our results are new in the literature ...
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We use a simple iterative algorithm for two quasi-nonexpansive mappings to approximate their common fixed point through Delta-convergence and strong convergence of the algorithm. Our results are new in the literature of metrical fixed point theory and are also valid in CAT(0) spaces.
A framework to extend Luenberger observer to nonlinear systems is proposed. The theory of invariant manifolds plays a central role in the framework. It is shown that the invariant manifolds for observer design are oft...
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A framework to extend Luenberger observer to nonlinear systems is proposed. The theory of invariant manifolds plays a central role in the framework. It is shown that the invariant manifolds for observer design are often non-standard ones and this makes their computations challenging. The proposed theory successfully removes the condition imposed on the system to be observed in the previous research in nonlinear Luenberger observer. Numerical examples show that the design methods proposed produce more effective observers compared with linear observers.
In this paper, an iterative algorithm to solve a special class of Hamilton-Jacobi-Bellman-Isaacs (HJBI) equations is proposed. By constructing two series of nonnegative functions, we replace the problem of solving an ...
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In this paper, an iterative algorithm to solve a special class of Hamilton-Jacobi-Bellman-Isaacs (HJBI) equations is proposed. By constructing two series of nonnegative functions, we replace the problem of solving an HJBI equation by the problem of solving a sequence of Hamilton-Jacobi-Bellman (HJB) equations whose solutions can be approximated recursively by existing methods. The local convergence of the algorithm is guaranteed. A numerical example is provided to demonstrate the accuracy of the proposed algorithm.
New requirements of autonomous mobile vehicles necessitate hierarchical motion-planning techniques that not only find a plan to satisfy high-level specifications, but also guarantee that this plan is suitable for exec...
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ISBN:
(纸本)9783952426937
New requirements of autonomous mobile vehicles necessitate hierarchical motion-planning techniques that not only find a plan to satisfy high-level specifications, but also guarantee that this plan is suitable for execution under vehicle dynamical constraints. In this context, the H-cost motion planning technique has been reported in the recent literature. We propose an incremental motion-planning algorithm based on this technique. The proposed algorithm retains the benefits of the original technique, while significantly reducing the associated computational time. In particular, the proposed iterative algorithm presents during intermediate iterations feasible solutions, with the guarantee that the algorithm eventually converges to an optimal solution. The costs of solutions at intermediate iterations are almost always nonincreasing. Therefore, the proposed algorithm is suitable for real-time implementations, where hard bounds on the available computation time are imposed, and where the original H-cost optimization algorithm may not have sufficient time to converge to a solution at all. We illustrate the proposed algorithm with numerical simulation examples.
This paper suggests a simple iterative and robust maximum likelihood estimation method for the parameters of a high-dimensional Student t-distribution that is substantially faster than direct maximum likelihood approa...
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This paper suggests a simple iterative and robust maximum likelihood estimation method for the parameters of a high-dimensional Student t-distribution that is substantially faster than direct maximum likelihood approaches. (C) 2019 Elsevier B.V. All rights reserved.
Multiple sequence alignment (MSA) is an essential cornerstone in bioinformatics, which can reveal the potential information in biological sequences, such as function, evolution and structure. MSA is widely used in man...
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Multiple sequence alignment (MSA) is an essential cornerstone in bioinformatics, which can reveal the potential information in biological sequences, such as function, evolution and structure. MSA is widely used in many bioinformatics scenarios, such as phylogenetic analysis, protein analysis and genomic analysis. However, MSA faces new challenges with the gradual increase in sequence scale and the increasing demand for alignment accuracy. Therefore, developing an efficient and accurate strategy for MSA has become one of the research hotspots in bioinformatics. In this work, we mainly summarize the algorithms for MSA and its applications in bioinformatics. To provide a structured and clear perspective, we systematically introduce MSA's knowledge, including background, database, metric and benchmark. Besides, we list the most common applications of MSA in the field of bioinformatics, including database searching, phylogenetic analysis, genomic analysis, metagenomic analysis and protein analysis. Furthermore, we categorize and analyze classical and state-of-the-art algorithms, divided into progressive alignment, iterative algorithm, heuristics, machine learning and divide-and-conquer. Moreover, we also discuss the challenges and opportunities of MSA in bioinformatics. Our work provides a comprehensive survey of MSA applications and their relevant algorithms. It could bring valuable insights for researchers to contribute their knowledge to MSA and relevant studies.
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