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IEEE Annual Conference on Decision and Control

作者： Robert Schmid Thang Nguyen Department of Electrical and Electronic Engineering University of Melbourne Australia Department of Engineering University of Exeter UK

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.

关键词： Delay systems pole assignment State feedback eigenvalues time delay control control input optimization algorithms information matrix

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Distributed multi-hop reactive power compensation in smart micro-grids subject to saturation constraints

Distributed multi-hop reactive power compensation in smart m...

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IEEE Annual Conference on Decision and Control

作者： Saverio Bolognani Andrea Carron Alberto Di Vittorio Diego Romeres Luca Schenato Sandro Zampieri Dept. of Information Engineering University of Padova

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.

关键词： MICRO-GRID reactive compensation power compensation Spread spectrum communication optimization algorithms Converge global optimum Communication Research Reactive power Distribute

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COHERENCE REGULARIZED DICTIONARY LEARNING

COHERENCE REGULARIZED DICTIONARY LEARNING

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Mansour Nejati Shadrokh Samavi S. M. Reza Soroushmehr Kayvan Najarian ECE Department Isfahan University of Technology Iran Department of Emergency Medicine University of Michigan Ann Arbor USA

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.

关键词： Dictionary learning mutual coherence sparse representation optimization sparse coding Dictionaries as Topic Sparse coding sparse representation coherence optimization algorithms

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Factory Planning Model of Aircraft Engine Transmission Parts based on the Genetic Annealing Algorithm

Factory Planning Model of Aircraft Engine Transmission Parts...

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International Conference on Computer Science and Network Technology

作者： Jie Jia Pei Qin China Aviation Planning and Design Institute(Group) Co. Ltd. Beijing 100120 China Aviation Planning and Design Institue (Group) Co. Ltd. Beijing 100120

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.

关键词： Aircraft engine transmission parts factory planning model simulated annealing algorithm improved genetic algorithm Aircraft engines simulated annealing algorithm Genetic Algorithm (GA) Industrial Plants optimization algorithms

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NUCLEAR NORM MINIMIZATION AND TENSOR COMPLETION IN EXPLORATION SEISMOLOGY

NUCLEAR NORM MINIMIZATION AND TENSOR COMPLETION IN EXPLORATI...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Nadia Kreimer Mauricio D. Sacchi Department of Physics University of Alberta Edmonton AB T6G 2E1 Canada

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.

关键词： seismic data signal reconstruction tensor completion nuclear norm rank reduction seismic data Signal reconstruction Tensor seismology Atomic nuclei optimization algorithms Exploration noise attenuation signal to noise ratios

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COMPLEXITY AND NUMERICAL EXPERIMENTS OF A NEW ADAPTIVE GENERIC PROXIMAL BUNDLE METHOD

arXiv

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arXiv 2024年

作者： Guigues, Vincent Monteiro, Renato D.C. Tran, Benoit School of Applied Mathematics FGV Praia de Botafogo Rio de Janeiro Brazil Georgia Institute of Technology AtlantaGA30332-0205 United States

This paper develops an adaptive generic proximal bundle method, shows its complexity, and presents numerical experiments comparing this method with two bundle methods on a set of optimization problems. Copyright ©... 详细信息

关键词： optimization algorithms

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Discrete MRF Inference of Marginal Densities for Non-uniformly Discretized Variable Space

Discrete MRF Inference of Marginal Densities for Non-uniform...

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Masaki Saito Takayuki Okatani Koichiro Deguchi Tohoku University Japan

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.

关键词： problems efficiency accuracy discrete transforms Traffic estimation optimization algorithms Computational efficiency

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Optimal and least restrictive supervisory control: safety verification methods for human-driven vehicles at traffic intersections

Optimal and least restrictive supervisory control: safety ve...

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IEEE Annual Conference on Decision and Control

作者： Gabriel Rodrigues de Campos Fabio Della Rossa Alessandro Colombo DEIB at Politecnico di Milano Italy

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.

关键词： vehicle performance indicators Scheduling Theory Safety Verification Preorder Optimal Solution Set Supervisory control Conflict Resolution Supervisors Human Traffic optimal solution optimization algorithms

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Ontologies for Models and algorithms in Applied Mathematics and Related Disciplines

arXiv

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arXiv 2023年

作者： Schembera, Björn Wübbeling, Frank Kleikamp, Hendrik Biedinger, Christine Fiedler, Jochen Reidelbach, Marco Shehu, Aurela Schmidt, Burkhard Koprucki, Thomas Iglezakis, Dorothea Göddeke, Dominik Institute of Applied Analysis and Numerical Simulation University of Stuttgart Germany Institute of Applied Mathematics: Analysis and Numerics University of Münster Germany Fraunhofer Institute for Industrial Mathematics Kaiserslautern Germany Zuse Institute Berlin Germany Weierstrass Institute for Applied Analysis and Stochastics Berlin Germany University Library University of Stuttgart Germany University of Stuttgart Germany

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 mathematical research data, the Mathematical Research Data Initiative has developed, merged and implemented ontologies and knowledge graphs. This contributes to making mathematical research data FAIR by introducing semantic technology and documenting the mathematical foundations accordingly. Using the concrete example of microfracture analysis of porous media, it is shown how the knowledge of the underlying mathematical model and the corresponding numerical algorithms for its solution can be represented by the ontologies. © 2023, CC BY.

关键词： optimization algorithms

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High-Dimensional Sparse Data Low-rank Representation via Accelerated Asynchronous Parallel Stochastic Gradient Descent

arXiv

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arXiv 2024年

作者： Hu, Qicong Wu, Hao College of Computer and Information Science Southwest University Chongqing China

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 spaces and infer node interactions via modeling data latent associations. Unfortunately, existing optimization algorithms for LR models are computationally inefficient and slowly convergent on large-scale datasets. To address this issue, this paper proposes an Accelerated Asynchronous Parallel Stochastic Gradient Descent (A2PSGD) for High-Dimensional Sparse Data Low-rank Representation with three fold-ideas: a) establishing a lock-free scheduler to simultaneously respond to scheduling requests from multiple threads;b) introducing a greedy algorithm-based load balancing strategy for balancing the computational load among threads;c) incorporating Nesterov's accelerated gradient into the learning scheme to accelerate model convergence. Empirical studies show that A2PSGD outperforms existing optimization algorithms for HDS data LR in both accuracy and training time. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

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