We consider the problem of robust pole assignment for a linear time invariant plant with state feedback subject to time delay in the control input. For systems with a known time delay, we offer a parametric formula fo...
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ISBN:
(纸本)9781467360890
We consider the problem of robust pole assignment for a linear time invariant plant with state feedback subject to time delay in the control input. For systems with a known time delay, we offer a parametric formula for the feedback gain matrix that will assign a desired set of closed-loop eigenvalues to the time-delay system. Secondly we consider systems subject to small input time delays and introduce an unconstrained optimisation algorithm for the computation of a state feedback matrix to deliver the desired pole placement in a manner that minimises the sensitivity of the eigenvalues to input time delays. The performance of the algorithm is compared against an alternative robust pole placement method from the recent literature and found to give significantly reduced time delay sensitivity.
In this paper we address the problem of exploiting the distributed energy resources (DER) available in a smart micro-grid to minimize the power distribution losses via optimal reactive power compensation. Due to their...
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ISBN:
(纸本)9781467320658
In this paper we address the problem of exploiting the distributed energy resources (DER) available in a smart micro-grid to minimize the power distribution losses via optimal reactive power compensation. Due to their typically small size, the amount of reactive power provided by each micro-generator is subject to tight saturation constraints. As a consequence, it might be impossible to achieve convergence to the global optimum based on algorithms that rely on short-range, gossip-type communication. We therefore propose a randomized multi-hop protocol that guarantees convergence of the distributed optimization algorithm also when only short-range communications are possible, at the expense of some additional communication overhead.
Sparse representations over redundant learned dictionaries have shown to produce high quality results in various image processing tasks. An important characteristic of a learned dictionary is the mutual coherence of d...
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ISBN:
(纸本)9781479999897
Sparse representations over redundant learned dictionaries have shown to produce high quality results in various image processing tasks. An important characteristic of a learned dictionary is the mutual coherence of dictionary that affects its generalization performance and the optimality of sparse codes generated from it. In this paper, we present a dictionary learning model equipped with coherence regularization. For this model, two novel dictionary optimization algorithms based on group-wise minimization of inter- and intra-coherence penalties are proposed. Experimental results demonstrate that the proposed algorithms improve the generalization properties and sparse approximation performance of the trained dictionary compared to several incoherent dictionary learning methods.
According to the premature convergence and low searching efficiency of the standard simulated annealing algorithm in workshop planning applications, this paper proposes a factory planning model of aircraft engine tran...
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ISBN:
(纸本)9781467381741
According to the premature convergence and low searching efficiency of the standard simulated annealing algorithm in workshop planning applications, this paper proposes a factory planning model of aircraft engine transmission parts based on improved genetic algorithm optimized simulated annealing. First, the mechanism floatingpoint coding is referenced to arithmetic crossover operation for crossover operator of genetic algorithm, and then adaptive mutation operators are adopted to keep the diversity of population, and the global optimization of the algorithm is improved. Finally, transmission parts factory programming model is constructed according to the characteristic of aviation engine. The results showed that compared with simulated annealing algorithm, the genetic simulated annealing algorithm has good convergence in application to factory planning.
We consider the problem of multidimensional seismic data signal recovery and noise attenuation. These data are multidimensional signals that can be described via a low-rank fourth-order tensor in the frequency - space...
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ISBN:
(纸本)9781479903573
We consider the problem of multidimensional seismic data signal recovery and noise attenuation. These data are multidimensional signals that can be described via a low-rank fourth-order tensor in the frequency - space domain. Tensor completion strategies can be used to recover unrecorded observations and to improve the signal-to-noise ratio of seismic data volumes. Tensor completion is posed as an inverse problem and solved via a convex optimization algorithm where a misfit function is minimized in conjunction with the nuclear norm of the tensor. This formulation offers automatic rank determination. We illustrate the performance of the algorithm with a synthetic example and with a real data set obtained by an onshore seismic survey.
This paper is concerned with the inference of marginal densities based on MRF models. The optimization algorithms for continuous variables are only applicable to a limited number of problems, whereas those for discret...
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ISBN:
(纸本)9781467364102
This paper is concerned with the inference of marginal densities based on MRF models. The optimization algorithms for continuous variables are only applicable to a limited number of problems, whereas those for discrete variables are versatile. Thus, it is quite common to convert the continuous variables into discrete ones for the problems that ideally should be solved in the continuous domain, such as stereo matching and optical flow estimation. In this paper, we show a novel formulation for this continuous-discrete conversion. The key idea is to estimate the marginal densities in the continuous domain by approximating them with mixtures of rectangular densities. Based on this formulation, we derive a mean field (MF) algorithm and a belief propagation (BP) algorithm. These algorithms can correctly handle the case where the variable space is discretized in a non-uniform manner. By intentionally using such a non-uniform discretization, a higher balance between computational efficiency and accuracy of marginal density estimates could be achieved. We present a method for actually doing this, which dynamically discretizes the variable space in a coarse-to-fine manner in the course of the computation. Experimental results show the effectiveness of our approach.
We consider a cooperative conflict resolution problem at traffic intersections. Our goal is to design a least restrictive supervisor able to identify the optimal corrections to a human-decided input with respect to a ...
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ISBN:
(纸本)9781479978878
We consider a cooperative conflict resolution problem at traffic intersections. Our goal is to design a least restrictive supervisor able to identify the optimal corrections to a human-decided input with respect to a given performance index, while keeping the system safe. Here, safety is formulated in terms of a maximal safe controlled invariant set. Leveraging results from scheduling theory, we characterize the preorder of the optimal solution set and propose an efficient optimization algorithm providing Pareto optimal solutions. We illustrate the application of the proposed algorithm through simulations in which vehicles crossing an intersection are optimally overridden by the supervisor only when necessary to maintain safety.
In applied mathematics and related disciplines, the modeling-simulation-optimization workflow is a prominent scheme, with mathematical models and numerical algorithms playing a crucial role. For these types of mathema...
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Data characterized by high dimensionality and sparsity are commonly used to describe real-world node interactions. Low-rank representation (LR) can map high-dimensional sparse (HDS) data to low-dimensional feature spa...
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