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 ...
详细信息
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.
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...
详细信息
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.
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 feat...
详细信息
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 ...
详细信息
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.
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 s...
详细信息
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.
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 ...
详细信息
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.
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 m...
详细信息
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.
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).Addi...
详细信息
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.
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 unde...
详细信息
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.
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 est...
详细信息
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.
暂无评论