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arXiv 2024年

作者： Klug, Simon Homrighausen, Jonas Glösekötter, Peter Schell, Andreas W. Gregor, Markus Department of Physics Engineering FH Muenster - University of Applied Sciences Stegerwaldstrasse 39 SteinfurtD-48565 Germany Department of Electrical Engineering and Computer Science FH Muenster - University of Applied Sciences Stegerwaldstrasse 39 SteinfurtD-48565 Germany Institute of Semiconductor and Solid State Physics Johannes Kepler University Linz Altenberger Str 69 Linz4040 Austria

This work presents a cost-effective method for collecting single photons emitted from single nitrogen-vacancy centers in nanodiamonds. Conventional components of a confocal laser-scanning microscope, such as microscope objectives and the piezo translation stages, are replaced by two affordable Blu-ray optical pickup units. A Hanbury Brown and Twiss setup is used to identify single photon emission. The proposed approach is inexpensive and simple and lowers the entry-level to single photon research for quantum technologies. This enables student lab experiments or demonstration experiments at schools and shows that efficient sources of quantum light can be made from standard components compatible with established industry processes. Copyright © 2024, The Authors. All rights reserved.

关键词： Nanocrystals

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Theoretical and Numerical Investigation of Acoustic Cavitation Bubble Based on the Impact of Ultrasound Frequency

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Bulletin of the Russian Academy of Sciences: Physics 2023年第Suppl 3期87卷 S341-S347页

作者： Ahmed K. Abu-Nab Abu-Bakr, Ali F. Zain Abu Shaeer Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom32511 Egypt Phystech School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology Moscow oblast Dolgoprudny141700 Russia Department of Basic Science Higher Institute of Engineering and Technology Kafr El Sheikh33511 Egypt

Abstract: Acoustic cavitation is the expansion and contraction of existing microbubbles in liquids brought on by an ultrasonic field. The dynamics of oscillations at higher pressures and temperatures when the cavitation bubbles collapse can be used to characterize cavitation dynamical behavior. This work deals with the theoretical and numerical investigation of acoustic cavitation in viscoelastic media considering the influence of ultrasound frequency. An acoustic pressure equation combined with a modified Rayleigh–Plesset equation forms the theoretical framework for ultrasound cavitation dynamics. The numerical solutions of the given cavitation bubble model were obtained taking into account the effect of acoustic pressure, using the exponential B-Spline collection method. The dynamics of the cavitation bubbles’ radius and the internal pressure of acoustic cavitation were examined at different values of viscosity and the impact of physical parameters on the monatomic, diatomic, and adiabatic gas-filled spherical bubble’s collapse dynamics for (k = 7/5, 5/3, 1) respectively. © Pleiades Publishing, Ltd. 2023. ISSN 1062-8738, Bulletin of the Russian Academy of Sciences: Physics, 2023, Vol. 87, Suppl. 3, pp. S341–S347. Pleiades Publishing, Ltd., 2023. ISSN 1062-8738, Bulletin of the Russian Academy of Sciences: Physics, 2023. Pleiades Publishing, Ltd., 2023.

关键词： Dynamics

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APPROXIMATE NEWTON POLICY GRADIENT ALGORITHMS

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SIAM Journal on Scientific Computing 2023年第5期45卷 A2585-A2609页

作者： Li, Haoya Gupta, Samarth Yu, Hsiangfu Ying, Lexing Dhillon, Inderjit Department of Mathematics Stanford University StanfordCA94305 United States Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA15213 United States Amazon Search Palo AltoCA94303 United States Department of Mathematics Stanford University StanfordCA94305-2125 United States Department of Computer Sciences University of Texas at Austin AustinTX78712-1188 United States

Policy gradient algorithms have been widely applied to Markov decision processes and reinforcement learning problems in recent years. Regularization with various entropy functions is often used to encourage exploration and improve stability. This paper proposes an approximate Newton method for the policy gradient algorithm with entropy regularization. In the case of Shannon entropy, the resulting algorithm reproduces the natural policy gradient algorithm. For other entropy functions, this method results in brand-new policy gradient algorithms. We prove that all these algorithms enjoy Newton-type quadratic convergence and that the corresponding gradient flow converges globally to the optimal solution. We use synthetic and industrial-scale examples to demonstrate that the proposed approximate Newton method typically converges in single-digit iterations, often orders of magnitude faster than other state-of-the-art algorithms. © 2023 Society for Industrial and applied mathematics.

关键词： Reinforcement learning

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Enhancing Object Tracking with Adaptive Spatial-Temporal Regularization Correlation Filter Models

SSRN

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SSRN 2024年

作者： Aryanfar, Ehsan Aliyari-Shoorehdeli, Mahdi Seydi, Vahid The Department of Computer Engineering Artificial Intelligence South Tehran Branch Islamic Azad University Tehran Iran The Department of Electrical Engineering Khajeh Nasirodin Toosi University of Technology Tehran Iran The Centre for Applied Marine Sciences School of Ocean Sciences Bangor University Menai Bridge United Kingdom

Despite the rise of deep learning-based tracking models, Correlation Filter models remain favored for their balance of accuracy and efficiency in real-time tracking. However, they struggle with challenges like occlusion, boundary effects, and scale variations. While existing trackers use basic regularization and multi-scale approaches to address these, they often lack adaptability to diverse target appearances and dynamic scale *** study proposes an adaptive spatial-temporal regularization correlation filter model that responds to changing target appearances. We create a spatial regularization map based on a reliability criterion by integrating results from local correlation filters across overlapping parts of varying sizes and positions. Additionally, we leverage spatial relationships to estimate scale variations and propose an adaptive temporal regularization coefficient. Experiments on TC128, OTB2013, and OTB2015 datasets show significant gains in accuracy and success rates with minimal speed compromise. © 2024, The Authors. All rights reserved.

关键词： Target tracking

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Robust Voltage Regulation for DC Microgrids via Passivity-Based Sliding Mode Control

Robust Voltage Regulation for DC Microgrids via Passivity-Ba...

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International Workshop on Variable Structure Systems, VSS

作者： Edoardo Vacchini Michele Cucuzzella Pablo Borja Antonella Ferrara Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Jan C. Willems Center for Systems and Control Engineering and Technology Institute Groningen (ENTEG) Faculty of Science and Engineering University of Groningen Groningen The Netherlands School of Engineering Computing and Mathematics University of Plymouth Plymouth U.K.

ISBN: (数字)9798350353686

ISBN: (纸本)9798350353693

This paper proposes a decentralized sliding mode control approach to the voltage regulation problem in a DC microgrid consisting of distributed generation units interconnected with each other through resistive-inductive power lines and supplying unknown nonlinear loads. In particular, the port-Hamiltonian structure of the system suggests the design of a suitable sliding manifold such that the system on this manifold exhibits desired passivity properties. This approach simplifies the control design and relaxes some restrictive assumptions required by other controllers proposed in the literature, while offering satisfactory performance.

关键词： Manifolds Variable structure systems Power transmission lines Microgrids Power system stability Stability analysis Robustness Reduced order systems Voltage control Sliding mode control

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Parametric Amplification of an Optomechanical Quantum Interconnect

arXiv

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arXiv 2022年

作者： Chen, Huo Vives, Marti Metcalf, Mekena Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Connecting superconducting qubits to optical fiber necessitates the conversion of microwave photons to optical photons. Modern experimental demonstrations exhibit strong coupling between a microwave resonator and an optical cavity mediated through phononic modes in a mechanical oscillator. This paradigmatic transduction experiment is bounded by a theoretical efficiency with constant driving amplitudes on the electromagnetic resonators. By adding a parametric drive to the microwave resonator and optical cavity we discover the converted signal through the quantum transducer is amplified, while maintaining a lower level of the added noise. We propose a theoretical framework for time-dependent control of the driving lasers based on the input-output formalism of quantum optics, and solve analytically the transduction efficiency and added noise when the control signals parametrically drive the system. Our results show better transduction efficiency and lower added noise in varying parameter regimes relevant to current transduction experiments. Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum optics

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GROMOV-WASSERSTEIN DISTANCES: ENTROPIC REGULARIZATION, DUALITY, AND SAMPLE COMPLEXITY

arXiv

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arXiv 2022年

作者： Zhang, Zhengxin Goldfeld, Ziv Mroueh, Youssef Sriperumbudur, Bharath K. Center for Applied Mathematics Cornell University United States School of Electrical and Computer Engineering Cornell University United States IBM Research AI Department of Statistics Pennsylvania State University United States

The Gromov-Wasserstein (GW) distance, rooted in optimal transport (OT) theory, quantifies dissimilarity between metric measure spaces and provides a framework for aligning heterogeneous datasets. While computational aspects of the GW problem have been widely studied, a duality theory and fundamental statistical questions concerning empirical convergence rates remained obscure. This work closes these gaps for the quadratic GW distance over Euclidean spaces of different dimensions dx and dy. We treat both the standard and the entropically regularized GW distance, and derive dual forms that represent them in terms of the well-understood OT and entropic OT (EOT) problems, respectively. This enables employing proof techniques from statistical OT based on regularity analysis of dual potentials and empirical process theory, using which we establish the first GW empirical convergence rates. The derived two-sample rates are n−2/max{min{dx,dy},4} (up to a log factor when min{dx,dy} = 4) for standard GW and n−1/2 for EGW, which matches the corresponding rates for standard and entropic OT. The parametric rate for EGW is evidently optimal, while for standard GW we provide matching lower bounds, which establish sharpness of the derived rates. We also study stability of EGW in the entropic regularization parameter and prove approximation and continuity results for the cost and optimal couplings. Lastly, the duality is leveraged to shed new light on the open problem of the one-dimensional GW distance between uniform distributions on n points, illuminating why the identity and anti-identity permutations may not be optimal. Our results serve as a first step towards a comprehensive statistical theory as well as computational advancements for GW distances, based on the discovered dual formulations. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Implicit Neural Representation for Mesh-Free Inverse Obstacle Scattering

Implicit Neural Representation for Mesh-Free Inverse Obstacl...

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Asilomar Conference on Signals, Systems & computers

作者： Tin Vlašić Hieu Nguyen AmirEhsan Khorashadizadeh Ivan Dokmanić Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Department of Mathematics and Computer Science University of Basel Basel Switzerland

Implicit representation of shapes as level sets of multilayer perceptrons has recently flourished in different shape analysis, compression, and reconstruction tasks. In this paper, we introduce an implicit neural representation-based framework for solving the inverse obstacle scattering problem in a mesh-free fashion. We express the obstacle shape as the zero-level set of a signed distance function which is implicitly determined by network parameters. To solve the direct scattering problem, we implement the implicit boundary integral method. It uses projections of the grid points in the tubular neighborhood onto the boundary to compute the PDE solution directly in the level-set framework. The proposed implicit representation conveniently handles the shape perturbation in the optimization process. To update the shape, we use PyTorch's automatic differentiation to backpropagate the loss function w.r.t. the network parameters, allowing us to avoid complex and error-prone manual derivation of the shape derivative. Additionally, we propose a deep generative model of implicit neural shape representations that can fit into the framework. The deep generative model effectively regularizes the inverse obstacle scattering problem, making it more tractable and robust, while yielding high-quality reconstruction results even in noise-corrupted setups.

关键词： Shape Perturbation methods Integral equations Scattering Manuals Programming Multilayer perceptrons

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Implicit Neural Representation for Mesh-Free Inverse Obstacle Scattering

arXiv

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arXiv 2022年

作者： Vlašić, Tin Nguyen, Hieu Khorashadizadeh, AmirEhsan Dokmanić, Ivan Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Department of Mathematics and Computer Science University of Basel Basel Switzerland

Implicit representation of shapes as level sets of multilayer perceptrons has recently flourished in different shape analysis, compression, and reconstruction tasks. In this paper, we introduce an implicit neural representation-based framework for solving the inverse obstacle scattering problem in a mesh-free fashion. We express the obstacle shape as the zero-level set of a signed distance function which is implicitly determined by network parameters. To solve the direct scattering problem, we implement the implicit boundary integral method. It uses projections of the grid points in the tubular neighborhood onto the boundary to compute the PDE solution directly in the level-set framework. The proposed implicit representation conveniently handles the shape perturbation in the optimization process. To update the shape, we use PyTorch’s automatic differentiation to backpropagate the loss function w.r.t. the network parameters, allowing us to avoid complex and error-prone manual derivation of the shape derivative. Additionally, we propose a deep generative model of implicit neural shape representations that can fit into the framework. The deep generative model effectively regularizes the inverse obstacle scattering problem, making it more tractable and robust, while yielding high-quality reconstruction results even in noise-corrupted setups. Copyright © 2022, The Authors. All rights reserved.

关键词： Mesh generation

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Robust H∞ switched control for uncertain linear systems via chattering free static output feedback*

Robust H∞ switched control for uncertain linear systems via...

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IEEE Conference on Decision and Control

作者： Flávio A. Faria Leandro J. Elias Rodrigo Cardim Dante J. S. Oncoy Vilma A. Oliveira Department of Mathematics School of Engineering São Paulo State University (UNESP) Ilha Solteira Brazil Education Department Instituto Federal de Educação Ciência e Tecnologia de São Paulo (IFSP) Araraquara Brazil Department of Electrical Engineering School of Engineering São Paulo State University (UNESP) Ilha Solteira Brazil Department of Electrical and Computer Engineering Universidade de São Paulo (USP) São Carlos Brazil

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This work proposes a switched control strategy to attenuate the effect of ${\mathcal{L}}_{2}$ type disturbances on the output performance of uncertain linear systems. The switched controller improves the control performance and reduces the conservatism of existing solutions. Stabilization conditions are given in terms of LMIs which are designed such that the proposed solution guarantees an ${\mathcal{H}}_{\infty}$ cost and be free of chattering. The efficiency of the stabilizing conditions are evaluated by numerical examples.

关键词： Linear systems Costs Control design Switches Convex functions Output feedback

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