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A PEM-based topology optimization for structures subjected to stationary random excitations

ENGINEERING STRUCTURES 2021年 229卷 111613-111613页

作者： Zhao, Xuqi Wu, Baisheng Lai, Siu-Kai Li, Zhengguang Zhong, Huixiang Guangdong Univ Technol Sch Electromech Engn Guangzhou 510006 Peoples R China Hong Kong Polytech Univ Dept Civil & Environm Engn Hong Kong Peoples R China Hong Kong Polytech Univ Hong Kong Branch Natl Rail Transit Elect & Automat Engn Technol Re Hong Kong Peoples R China Jilin Univ Sch Math Changchun 130012 Peoples R China

This paper focuses on the topological optimization of structures subjected to stationary random excitations. A new topology optimization scheme based on the pseudo excitation method (PEM) for calculating structural random responses in a frequency domain is proposed. In this method, the Sturm sequence is applied to adaptively determine the number of lower-order modes used for mode superposition analysis. The contribution of unknown higher-order modes is approximated by the partial sum of a constructed convergent series. Since the method can offer an approximate expression of structural response solutions, not only it can enhance the flexibility of implementation and also improve the computational effort and accuracy. In addition, derivatives of the objective function are derived by means of the adjoint method. They can be achieved by solving an adjoint problem that is similar to the original governing equation of the system. Two illustrative examples are presented to affirm the proposed scheme in terms of computational accuracy and efficiency.

关键词： Topology optimization Stationary random excitation Pseudo excitation method adaptive algorithm

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Inversion Based adaptive Feedforward Control for Multivariable Systems 21st

Inversion Based Adaptive Feedforward Control for Multivariab...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Rouhani, Shahin Rai, Sandeep Tsao, Tsu-Chin Univ Calif Los Angeles Los Angeles CA 90095 USA

This paper presents a novel adaptive feedforward control (AFC) method for rejecting sinusoidal disturbances with known frequencies acting on multi-input-multi-output (MIMO) discrete time linear systems based on the H-infinity synthesis. First, the gradient AFC (GAFC) for MIMO systems is reviewed, and the linear time invariant (LTI) equivalent form of the GAFC is approximated for stability analysis. For single-input-single-output (SISO) systems, this paper shows small adaptation gains guarantee the stability of GAFC for any disturbance frequency. Then inspired by the stability of SISO GAFC, the inversion based AFC (IAFC) is proposed for MIMO systems. In this method, the GAFC is compensated by an H-infinity model matching filter, which renders nearly decoupled systems with fixed time delays. The LTI analysis, simulation study and experimental results from an open-loop unstable MIMO Active Magnetic Bearing Spindle (AMBS) are presented to demonstrate the stability and effectiveness of the proposed IAFC in rejecting narrow-band disturbances. Copyright (C) 2020 The Authors.

关键词： adaptive algorithm multivariable system optimization active magnetic bearing high speed machining

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Online Robust Reduced-Rank Regression 11

Online Robust Reduced-Rank Regression

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IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)

作者： Yang, Yangzhuoran Fin Zhao, Ziping Monash Univ Dept Econometr & Business Stat Melbourne Vic Australia ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9781728119465

The reduced-rank regression (RRR) model is widely used in data analytics where the response variables are believed to depend on a few linear combinations of the predictor variables, or when such linear combinations are of special interest. In this paper, we will address the RRR model estimation problem by considering two targets which are popular especially in big data applications: i) the estimation should be robust to heavy-tailed data distribution or outliers;ii) the estimation should be amenable to large-scale data sets or data streams. In this paper, we address the robustness via the robust maximum likelihood estimation procedure based on Cauchy distribution and a stochastic estimation procedure is further adopted to deal with the large-scale data sets. An efficient algorithm leveraging on the stochastic majorization minimization method is proposed for problem-solving. The proposed model and algorithm is validated numerically by comparing with the state-of-the-art methods.

关键词： Multivariate regression low-rank heavy-tails outliers stochastic optimization majorization minimization large-scale optimization adaptive algorithm

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Research on digital predistortion technology based on improved NLMS algorithm

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INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING 2021年第7期31卷 e22661-e22661页

作者： Wang, Xin Li, Simin Ye, Jincai Fan, Xiangsuo Qin, Mimi Jia, Xiaobo Wang, Guofu Liu, Qingzheng Guangxi Univ Sci & Technol Sch Elect & Informat Engn Liuzhou Peoples R China Guilin Univ Elect Technol Sch Informat Engn Guilin Peoples R China

In modern communication systems, digital predistortion (DPD) improve the linearity of PA and reduce the influence of nonlinear behavior on signal transmission. When identifying the inverse model of PA with traditional variable step size algorithm, the rate of convergence is slower, easy to be effected by noise and convergence instability exists at the optimal solution, which affects the optimization degree of the whole predistortion system. In this article, a digital predistortion technique based on improved NLMS algorithm is proposed to improve the accuracy of inverse model identification, convergence rate and anti-noise performance of the predistorter by changing the iteration function and introducing the autocorrelation matrix of the error of adjacent moments. The instability of the system at the optimal solution is reduced by introducing a new weight coefficient to update the correlation term. The simulation results show that the anti-noise performance, convergence rate and stability of the predistortion system based on New variable step size NLMS algorithm(NVNLMS) are obviously better than NLMS, and the out-of-band suppression of predistortion is optimized by 10 dB compared with the original system EVM is improved by 0.0031, ACPR is improved 9.1 dB.

关键词： adaptive algorithm autocorrelation matrix digital predistortion NVNLMS pattern recognition

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Optimal resilient distributed data collection in mobile edge environments

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COMPUTERS & ELECTRICAL ENGINEERING 2021年第PartB期96卷 107580-107580页

作者： Audrito, Giorgio Casadei, Roberto Damiani, Ferruccio Pianini, Danilo Viroli, Mirko Univ Torino Turin Italy Univ Bologna Alma Mater Studiorum Bologna Italy

A key goal of edge computing is to achieve "distributed sensing'' out of data continuously generated from a multitude of interconnected physical devices. The traditional approach is to gather information into sparse collector devices by relying on hop-by-hop accumulation, but issues of reactivity and fragility naturally arise in scenarios with high mobility. We propose novel algorithms for dynamic data summarisation across space, supporting high reactivity and resilience by specific techniques maximising the speed at which information propagates towards collectors. Such algorithms support idempotent and arithmetic aggregation operators and, under reasonable network assumptions, are proved to achieve optimal reactivity. We provide evaluation via simulation: first in multiple scenarios showing improvement over the state of art, and then by a case study in edge data mining, which conveys the practical impact in higher-level distributed sensing patterns.

关键词： Data aggregation adaptive algorithm Aggregate programming Computational field

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A POSTERIORI ERROR ESTIMATES FOR adaptive QM/MM COUPLING METHODS

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SIAM JOURNAL ON SCIENTIFIC COMPUTING 2021年第4期43卷 A2785-A2808页

作者： Wang, Yangshuai Chen, Huajie Liao, Mingjie Ortner, Christoph Wang, Hao Zhang, Lei Shanghai Jiao Tong Univ Inst Nat Sci Sch Math Sci Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ MOE LSC Shanghai 200240 Peoples R China Beijing Normal Univ Sch Math Sci Beijing 100875 Peoples R China Univ British Columbia Vancouver BC V6T 1Z2 Canada Sichuan Univ Sch Math Chengdu 610065 Sichuan Peoples R China

Hybrid quantum/molecular mechanics models (QM/MM methods) are widely used in material and molecular simulations when MM models do not provide sufficient accuracy but pure QM models are computationally prohibitive. adaptive QM/MM coupling methods feature on-the-fly classification of atoms during the simulation, allowing the QM and MM subsystems to be updated as needed. In this work, we propose such an adaptive QM/MM method for material defect simulations based on a new residual stemming from an a posteriori error estimator, which provides both lower and upper bounds for the true error. We validate the analysis and illustrate the effectiveness of the new scheme on numerical simulations for material defects.

关键词： QM/MM coupling a posteriori error estimate adaptive algorithm crystal defects

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ONLINE GRAPH TOPOLOGY INFERENCE WITH KERNELS FOR BRAIN CONNECTIVITY ESTIMATION

ONLINE GRAPH TOPOLOGY INFERENCE WITH KERNELS FOR BRAIN CONNE...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Moscu, Mircea Borsoi, Ricardo Richard, Cedric Univ Cote dAzur OCA CNRS Nice France Univ Fed Santa Catarina Florianopolis SC Brazil

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066315

In graph signal processing, there are often settings where the graph topology is not known beforehand and has to be estimated from data. Moreover, some graphs can be dynamic, such as brain activity supported by neurons or brain regions. This paper focuses on estimating in an online and adaptive manner a network structure capturing the non-linear dependencies among streaming graph signals in the form of a possibly directed, adjacency matrix. By projecting data into a higher- or infinite-dimension space, we focus on capturing nonlinear relationships between agents. In order to mitigate the increasing number of data points, we employ kernel dictionaries. Finally, we run a series of tests in order to experimentally illustrate the usefulness of our kernel-based approach on biomedical data, on which we obtain results comparable to state-of-the-art methods.

关键词： Topology inference reproducing kernel graph signal processing adaptive algorithm brain connectivity estimation

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adaptive, predictive machine condition assessment for resilient digital solutions

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Procedia CIRP 2021年 104卷 821-826页

作者： Manja Mai-Ly Pfaff Felix Dörrer Uwe Friess Michael Praedicow Matthias Putz Fraunhofer IWU Reichenhainer Str. 88 09112 Chemnitz Germany

Current developments illustrate the need for resilient value creation. However, the former persistent trend of constantly changing production environments also requires adaptive and self-learning tools. Although digitization solutions already exist, these are often specialized methods for a specific problem based on a large amount of data. Instead, the novel approach explained here enables the direct and immediate interpretation of characteristic values for predictive condition assessment with a small amount of data. An adaptive algorithm was developed and tested in a real environment, which performs a dynamic limit value formation using an adaptive characteristic value segmentation.

关键词： adaptive algorithm machine learning predictive maintenance structure based segmentation probabilistic causalities small amount of data

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Application of adaptive wavelet transform based multiple analytical mode decomposition for damage progression identification of Cable-Stayed bridge via shake table test

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2021年 149卷 107055-107055页

作者： Qu, Hongya Li, Tiantian Wang, Ruilong Li, Jianzhong Guan, Zhongguo Chen, Genda Tongji Univ Dept Bridge Engn 1239 Siping Rd Shanghai 200092 Peoples R China Shanghai Typhoon Inst China Meteorol Adm 166 Puxi Rd Shanghai 200030 Peoples R China Shanghai Municipal Engn Design Inst Grp Co Ltd 901 North Zhongshan Rd 2nd Shanghai 200092 Peoples R China Missouri Univ Sci & Technol Dept Civil Architectural & Environm Engn Rolla MO 65401 USA

Extracting useful information (damage existence, location, identification, and quantifica-tion) from measured signals for damage identification is critical in structural health monitoring, while time-varying nature of most signals often require huge efforts. In this paper, adaptive wavelet analysis AWT is first introduced as a preprocessing approach of clearer, smoother and more accurate time-frequency representation. Optimized analytical mode decomposition (AMD) is then utilized for signal component extraction, with the help of AWT for bisecting frequency determination. Examples of time-varying signals of sinusoidal function and Duffing systems are used to illustrate the advantages of the algorithm, which proves to be successful in signal decomposition. Multiple AMD (MAMD) with the optimized algorithm is then utilized together with AWT for signal decomposition and system identification of the shake table test of a 1/20-scale cable-stayed bridge model. The extracted stiffness and damping coefficients retain a preliminary indication of the damage progression during the earthquake input. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Signal processing adaptive algorithm Wavelet transform Hilbert transform System identification Shake table test Cable-stayed bridge

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Fault Estimation for A Class of Descriptor Systems - An adaptive Robust Extended Kalman Filter Approach 5

Fault Estimation for A Class of Descriptor Systems - An Adap...

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5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Li Kexin Liang Tiantian Dalian Jiaotong Univ Sch Elect & Informat Engn Dalian Peoples R China

ISBN: (纸本)9781665423144

This paper proposes an adaptive Robust Extended Klaman filter for a class of non-linear descriptor systems with unknown system noise. Firstly, a robust bound is given to decrease the influence of the linearization error on the estimation accuracy;an adaptive algorithm is introduced to implement an unbiased estimation of the noise, then;an numeral example is given to show the effectiveness of the method at last.

关键词： descriptor system Kalman filter fault estimation non-linear adaptive algorithm

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