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Anisotropic adaptive Finite Elements for an Elliptic Problem with Strongly Varying Diffusion Coefficient

COMPUTATIONAL METHODS IN APPLIED MATHEMATICS 2022年第3期22卷 529-543页

作者： Dubuis, Samuel Passelli, Paride Picasso, Marco Ecole Polytech Fed Lausanne Inst Math CH-1015 Lausanne Switzerland

The elliptic problem - div(mu del u) = f is considered, where mu > 0 is smooth but strongly varying. Anisotropic a posteriori error estimates are derived, the effectivity index being bounded above and below by two constants independent of the data f, mu, the mesh size and aspect ratio, up to higher order terms. Numerical experiments on non-adapted and adapted anisotropic meshes confirm these predictions.

关键词： A Posteriori Error Estimates adaptive algorithm Anisotropic Finite Elements Elliptic Equation

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A Low-Cost Q-Learning-Based Approach to Handle Continuous Space Problems for Decentralized Multi-Agent Robot Navigation in Cluttered Environments

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IEEE ACCESS 2022年 10卷 35287-35301页

作者： Ajabshir, Vahid Babaei Guzel, Mehmet Serdar Bostanci, Erkan Ankara Univ Dept Comp Engn TR-06830 Ankara Turkey

This paper addresses the problem of navigating decentralized multi-agent systems in partially cluttered environments and proposes a new machine-learning-based approach to solve it. On the basis of this approach, a new robust and flexible Q-learning-based model is proposed to handle a continuous space problem. As in reinforcement learning (RL) algorithms, Q-learning does not require a model of the environment. Additionally, Q-Learning (QL) has the advantages of being fast and easy to design. However, one disadvantage of QL is that it needs a massive amount of memory, and it grows exponentially with each extra feature introduced to the state space. In this research, we introduce an agent-level decentralized collision avoidance low-cost model for solving a continuous space problem in partially cluttered environments, followed by introducing a method to merge non-overlapping QL features in order to reduce its size significantly by about 70% and make it possible to solve more complicated scenarios with the same memory size. Additionally, another method is proposed for minimizing the sensory data that is used by the controller. A combination of these methods is able to handle swarm navigation low memory cost with at least18 number of robots. These methods can also be adapted for deep q-learning architectures so as to increase their approximation performance and also decrease their learning time process. Experiments reveal that the proposed method also achieves a high degree of accuracy for multi-agent systems in complex scenarios.

关键词： Robot kinematics Navigation Q-learning Robot sensing systems Multi-agent systems Swarm robotics Task analysis adaptive algorithm continuous space problem multi-agent systems Q-learning

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Research on Building Energy-Saving Prediction Model of Building Air Conditioning Based on Deep Learning 2

Research on Building Energy-Saving Prediction Model of Build...

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2nd IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2024

作者： Qiu, Qingling Li, Min Wuhan Business University Wuhan China Wuhan Business University Institute of Environmental Pollution Control and Low Carbon Materials Wuhan China

ISBN: (纸本)9798350368208

Aiming at the problem of high energy consumption of building air conditioning system, this paper proposes an energy-saving prediction model construction system based on deep learning. The system uses the powerful feature extraction capability of deep learning algorithm and the running data of buildings to build a high-precision energy consumption prediction model. Through the analysis of many historical energy consumption data, the system can learn the complex relationship between building air conditioning energy consumption, environmental variables, and equipment status, and achieve accurate prediction of future energy consumption, to provide a scientific basis for the formulation of energy conservation strategies. In the algorithm design, this paper introduces the adaptive learning mechanism, so that the model can automatically adjust the prediction strategy according to the change of the environment, and improve the prediction accuracy and stability. In addition, the energy saving effect of the model under different working conditions is verified by energy consumption simulation experiments. The results show that the system can effectively reduce the energy consumption of the building air conditioning system while maintaining the indoor environment comfort. © 2024 IEEE.

关键词： adaptive algorithm air conditioning in buildings Deep learning algorithm energy consumption simulation energy saving prediction model

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Kernel-based ensemble gaussian mixture filtering for orbit determination with sparse data

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ADVANCES IN SPACE RESEARCH 2022年第12期69卷 4179-4197页

作者： Yun, Sehyun Zanetti, Renato Jones, Brandon A. Univ Texas Austin Cockrell Sch Engn Aerosp Engn & Engn Mech Dept Austin TX 78712 USA

In this paper, a modified kernel-based ensemble Gaussian mixture filtering (EnGMF) is introduced to produce fast and consistent orbit determination capabilities in a sparse measurement environment. The EnGMF is based on kernel density estimation (KDE) to combine particle filters and Gaussian sum filters. This work proposes using Silverman's rule of thumb to reduce the computational burden of KDE. Equinoctial orbital elements are used to improve the accuracy of the KDE bandwidth parameter in the modified EnGMF. A bi-fidelity approach to propagation and an adaptation algorithm for selecting the appropriate number of particles are also applied to the EnGMF to reduce the computational burden with an acceptable loss in accuracy for long time propagation. Through numerical simulation, the proposed implementation is compared to state-of-the-art approaches in terms of accuracy, consistency, and computational speed. (c) 2022 COSPAR. Published by Elsevier B.V. All rights reserved.

关键词： Low Earth orbit constellations Kernel density estimation Particle filter Gaussian mixture model Bi-fidelity propagation adaptive algorithm

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An Efficient Reference Free adaptive Learning Process for Speech Enhancement Applications

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Computers, Materials & Continua 2022年第2期70卷 3067-3080页

作者： Girika Jyoshna Md.Zia Ur Rahman L.Koteswararao Deptartment of Electronics and Communication Engineering Koneru Lakshmaiah Education FoundationKL UniversityVaddeswaramGunturAndhra PradeshIndia Department of Electronics and Communication Engineering Koneru Lakshmaiah Education FoundationKL UniversityHyderabadTelanganaIndia

In issues like hearing impairment,speech therapy and hearing aids play a major role in reducing the *** of noise signals from speech signals is a key task in hearing aids as well as in speech *** the transmission of speech signals,several noise components contaminate the actual speech *** paper addresses a new adaptive speech enhancement(ASE)method based on a modified version of singular spectrum analysis(MSSA).The MSSA generates a reference signal for ASE and makes the ASE is free from feeding reference *** MSSA adopts three key steps for generating the reference from the contaminated speech *** are decomposition,grouping and *** generated reference is taken as a reference for variable size adaptive learning *** this work two categories of adaptive learning algorithms are *** are step variable adaptive learning(SVAL)algorithm and time variable step size adaptive learning(TVAL).Further,sign regressor function is applied to adaptive learning algorithms to reduce the computational complexity of the proposed adaptive learning *** performance measures of the proposed schemes are calculated in terms of signal to noise ratio improvement(SNRI),excess mean square error(EMSE)and misadjustment(MSD).For cockpit noise these measures are found to be 29.2850,-27.6060 and 0.0758 dB respectively during the experiments using SVAL *** considering the reduced number of multiplications the sign regressor version of SVAL based ASE method is found to better then the counter parts.

关键词： adaptive algorithm speech enhancement singular spectrum analysis reference free noise canceller variable step size

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An adaptive hp-version stochastic Galerkin method for constrained optimal control problem governed by random reaction diffusion equations

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COMPUTATIONAL & APPLIED MATHEMATICS 2022年第3期41卷 1-30页

作者： Ge, Liang Sun, Tongjun Univ Jinan Sch Math Sci Jinan 250022 Shandong Peoples R China Shandong Univ Sch Math Jinan 250100 Shandong Peoples R China

Based on a posteriori error estimates, an adaptive stochastic Galerkin method is considered for constrained optimal control problem governed by random reaction diffusion equations. First, the optimality system of the model problem is derived, represented as a set of deterministic equations in high-dimensional parameter space by finite-dimensional noise assumption, and discretized by means of h x p-version stochastic Galerkin method. Second, computable a posteriori error estimators are derived for the state, co-state and control variables, which contain two contributions to the overall error: one error due to generalized polynomial chaos (gPC) discretization in the parameter space and the other error due to finite-element discretization in the physical space. Next, the adaptive refinement strategy is designed to steer the polynomial degree adaption in the parameter space, and the finite-element mesh refinement in the physical space. Using the L-2-projection operator on each dimensional parameter space, the error indicator for the parameter space is calculated and used to increase or decrease the polynomial degree on each dimensional parameter space. Finally, three numerical examples are presented to illustrate the derived theoretical results and the effectiveness of the proposed adaptive algorithm.

关键词： Random reaction diffusion equations Optimal control problem Stochastic Galerkin (SG) method A posteriori error estimates adaptive algorithm

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adaptive FEM FOR PARAMETER-ERRORS IN ELLIPTIC LINEAR-QUADRATIC PARAMETER ESTIMATION PROBLEMS*

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SIAM JOURNAL ON NUMERICAL ANALYSIS 2022年第3期60卷 1450-1471页

作者： Becker, Roland Innerberger, Michael Praetorius, Dirk Univ Pau & Pays Adour IPRA LMAP Ave Univ BP 1155 F-64013 Pau France TU Wien Inst Anal & Sci Comp Wiedner Hauptstr 8-10-E101-4 A-1040 Vienna Austria

We consider an elliptic linear-quadratic parameter estimation problem with a finite number of parameters. A novel a priori bound for the parameter error is proved and, based on this bound, an adaptive finite element method driven by an a posteriori error estimator is presented. Unlike prior results in the literature, our estimator, which is composed of standard energy error residual estimators for the state equation and suitable co-state problems, reflects the faster convergence of the parameter error compared to the (co-)state variables. We show optimal convergence rates of our method;in particular and unlike prior works, we prove that the estimator decreases with a rate that is the sum of the best approximation rates of the state and co-state variables. Experiments confirm that our method matches the convergence rate of the parameter error.

关键词： finite element method optimal control problem adaptive algorithm convergence optimal convergence rates

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Laser speckle contrast imaging for monitoring of acutepancreatitis at ischemia-reperfusion injury of thepancreas in rats

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Journal of Innovative Optical Health Sciences 2022年第1期15卷 90-102页

作者： P.A.Dyachenko(Timoshina) A.N.Bashkatov D.A.Alexandrov V.I.Kochubey V.V.Tuchin Saratov State University SaratovRussia Laboratories of Biophotonics and Laser Molecular Imaging and Machine Learning National Research Tomsk State University TomskRussia Saratov State Medical University SaratovRussia Institute of Precision Mechanics and Control Russian Academy of SciencesRussia A.N.Bach Institute of Biochemistry Research Center of Biotechnology of the Russian Academy of Sciences MoscowRussia

The influence of ischemia-reperfusion(I/R)action on pancreatic blood flow(PBF)and the development of acute pancreatitis(AP)in laboratory rats is evaluated in vivo by using the laser speckle contrast imaging(LSCI).Additionally,the optical properties in norm and under condition of AP in rats were assessed using a modied integrating sphere spectrometer and inverse Monte Carlo(IMC)*** results of the experimental study of microcirculation of the pancreas in 82 rats in the ischemic model are *** data obtained conrm the fact that local ischemia and changes in the blood°ow velocity of the main vessels cause and provoke acute pancreatitis.

关键词： Laser speckles contrast of speckle images adaptive algorithm microcirculation blood flow acute pancreatitis pancreas rats optical properties integrating sphere spectroscopy

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Robust Tensor Tracking With Missing Data and Outliers: Novel adaptive CP Decomposition and Convergence Analysis

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2022年 70卷 4305-4320页

作者： Le Trung Thanh Abed-Meraim, Karim Nguyen Linh Trung Hafiane, Adel Univ Orleans INSA CVL PRISME F-45100 Orleans France Vietnam Natl Univ VNU Univ Engn & Technol AVITECH Hanoi 100000 Vietnam

Canonical Polyadic (CP) decomposition is a powerful multilinear algebra tool for analyzing multiway (a.k.a. tensor) data and has been used for various signal processing and machine learning applications. When the underlying tensor is derived from data streams, adaptive CP decomposition is required. In this paper, we propose a novel method called robust adaptive CP decomposition (RACP) for dealing with high-order incomplete streaming tensors that are corrupted by outliers. At each time instant, RACP first performs online outlier rejection to accurately detect and remove sparse outliers, and then performs tensor factor tracking to efficiently update the tensor basis. A unified convergence analysis of RACP is also established in that the sequence of generated solutions converges asymptotically to a stationary point of the objective function. Extensive experiments were conducted on both synthetic and real data to demonstrate the effectiveness of RACP in comparison with state-of-the-art adaptive CP algorithms.

关键词： Tensors Convergence Signal processing algorithms Adaptation models Loading Linear programming Data models CANDECOMP PARAFAC (CP) decomposition adaptive algorithm streaming tensor missing data outlier

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The a Priori and a Posteriori Error Estimates of DG Method for the Steklov Eigenvalue Problem in Inverse Scattering

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JOURNAL OF SCIENTIFIC COMPUTING 2022年第1期91卷 1-32页

作者： Li, Yanjun Bi, Hai Yang, Yidu Guizhou Normal Univ Sch Math Sci Guiyang 550001 Peoples R China Guizhou Univ Finance & Econ Sch Big Data Applicat & Econ Guiyang 550001 Peoples R China

In this paper, we study the discontinuous Galerkin finite element method for the Steklov eigenvalue problem arising in inverse scattering. We present a complete error estimates including the a refined priori error estimate and the a posteriori error estimate, and prove the reliability and efficiency of the a posteriori error estimators for eigenfunctions up to higher order terms, and we also analyze the reliability of estimators for eigenvalues. Moreover, we carry out the numerical experiments in adaptive fashion which together with theoretical analysis show that our method reach the optimal convergence order.

关键词： Steklov eigenvalue Discontinuous Galerkin method A priori error estimate A posteriori error estimate adaptive algorithm

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