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Optimal multilevel adaptive FEM for the Argyris element

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2022年 399卷 1页

作者： Graessle, Benedikt Humboldt Univ Inst Math D-10117 Berlin Germany

The main drawback for the application of the conforming Argyris FEM is the labourious implementation on the one hand and the low convergence rates on the other. If no appropriate adaptive meshes are utilised, only the convergence rate caused by corner singularities (Blum and Rannacher, 1980), far below the approximation order for smooth functions, can be achieved. The fine approximation with the Argyris FEM produces high-dimensional linear systems and for a long time an optimal preconditioned scheme was not available for unstructured grids. This paper presents numerical benchmarks to confirm that the adaptive multilevel solver for the hierarchical Argyris FEM from Carstensen and Hu (2021) is in fact highly efficient and of linear time complexity. Moreover, the very first display of optimal convergence rates in practically relevant benchmarks with corner singularities and general boundary conditions leads to the rehabilitation of the Argyris finite element from the computational perspective. (c) 2022 Elsevier B.V. All rights reserved.

关键词： A posteriori adaptive algorithm Argyris element Optimal convergence Multilevel preconditioning Linear time complexity

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AN adaptive PLANEWAVE METHOD FOR ELECTRONIC STRUCTURE CALCULATIONS

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MULTISCALE MODELING & SIMULATION 2022年第1期20卷 524-550页

作者： Liu, Beilei Chen, Huajie Dusson, Genevieve Fang, Jun Gao, Xingyu Beijing Normal Univ Sch Math Sci 19 Xinjiekouwai St Beijing 100875 Peoples R China Univ Bourgogne Franche Comte UMR CNRS 6623 Lab Math Besancon 16 Route Gray F-25030 Besancon France Inst Appl Phys & Computat Math Lab Computat Phys Beijing 100088 Peoples R China

We propose an adaptive planewave method for eigenvalue problems in electronic structure calculations. The method combines a priori convergence rates and accurate a posteriori error estimates into an effective way of updating the energy cut-off for planewave discretizations, for both linear and nonlinear eigenvalue problems. The method is error controllable for linear eigenvalue problems in the sense that for a given required accuracy, an energy cut-off for which the solution matches the target accuracy can be reached efficiently. Further, the method is particularly promising for nonlinear eigenvalue problems in electronic structure calculations as it shall reduce the cost of early iterations in self-consistent algorithms. We present some numerical experiments for both linear and nonlinear eigenvalue problems. In particular, we provide electronic structure calculations for some insulator and metallic systems simulated with the Kohn--Sham density functional theory and the projector augmented wave method, illustrating the efficiency and potential of the algorithm.

关键词： electronic structure calculation planewave method energy cut-off a posteriori error estimate adaptive algorithm projector augmented wave method

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A Literature Review of the adaptive algorithms Adopted in adaptive Learning Systems 4

A Literature Review of the Adaptive Algorithms Adopted in Ad...

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4th IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Ge, Zigang Xi, Miaoyin Li, Yanfei Beijing Univ Posts & Telecommun Sch Network Educ Beijing Peoples R China

ISBN: (纸本)9781728136608

The present study screened out 16 papers from SSCI-indexed and SCI-indexed journals for a literature review on the topic of adaptive algorithms applied in various adaptive learning systems. It is found that most of the reviewed articles directly adopted some long-standing algorithms, although a few of them employed game engines or developed their own algorithms for the construction of the adaptive learning systems. The literature review also shows that the majority of the studies have testified the effectiveness of the adopted algorithms, although many of them cannot achieve the ideal dynamic adaptivity.

关键词： adaptive algorithm adaptive learning system literature review adaptivity

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Achieving adaptive Visual Multi-Object Tracking with Unscented Kalman Filter

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SENSORS 2022年第23期22卷 9106-9106页

作者： Zhang, Guowei Yin, Jiyao Deng, Peng Sun, Yanlong Zhou, Lin Zhang, Kuiyuan China Univ Min & Technol Sch Safety Engn Xuzhou 221116 Peoples R China Shenzhen Urban Publ Safety & Technol Inst Shenzhen 518046 Peoples R China Minist Emergency Management Key Lab Urban Safety Risk Monitoring & Early Warni Shenzhen 518046 Peoples R China China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Peoples R China Greater China Int Exchange Sq 10-F Fuhua 1st Rd Shenzhen 518030 Peoples R China

As an essential part of intelligent monitoring, behavior recognition, automatic driving, and others, the challenge of multi-object tracking is still to ensure tracking accuracy and robustness, especially in complex occlusion environments. Aiming at the issues of the occlusion, background noise, and motion state violent change for multi-object in a complex scene, an improved DeepSORT algorithm based on YOLOv5 is proposed for multi-object tracking to enhance the speed and accuracy of tracking. Firstly, a general object motion model is devised, which is similar to the variable acceleration motion model, and a multi-object tracking framework with the general motion model is established. Then, the latest YOLOv5 algorithm, which has satisfactory detection accuracy, is utilized to obtain the object information as the input of multi-object tracking. An unscented Kalman filter (UKF) is proposed to estimate the motion state of multi-object to solve nonlinear errors. In addition, the adaptive factor is introduced to evaluate observation noise and detect abnormal observations so as to adaptively adjust the innovation covariance matrix. Finally, an improved DeepSORT algorithm for multi-object tracking is formed to promote robustness and accuracy. Extensive experiments are carried out on the MOT16 data set, and we compare the proposed algorithm with the DeepSORT algorithm. The results indicate that the speed and precision of the improved DeepSORT are increased by 4.75% and 2.30%, respectively. Especially in the MOT16 of the dynamic camera, the improved DeepSORT shows better performance.

关键词： multi-object tracking YOLOv5 object detection improved DeepSORT unscented Kalman filter adaptive algorithm

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A hybrid radial basis functions collocation technique to numerically solve fractional advection-diffusion models

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NUMERICAL METHODS FOR PARTIAL DIFFERENTIAL EQUATIONS 2020年第6期36卷 1254-1279页

作者： Hussain, Manzoor Haq, Sirajul Natl Univ Sci & Technol PAF Acad Risalpur Dept Humanities & Sci Coll Aeronaut Engn Risalpur Pakistan GIK Inst Fac Engn Sci Topi Pakistan

In this work, we propose a hybrid radial basis functions (RBFs) collocation technique for the numerical solution of fractional advection-diffusion models. In the formulation of hybrid RBFs (HRBFs), there exist shape parameter (c*) and weight parameter (epsilon) that control numerical accuracy and stability. For these parameters, an adaptive algorithm is developed and validated. The proposed HRBFs method is tested for numerical solutions of some fractional Black-Sholes and diffusion models. Numerical simulations performed for several benchmark problems verified the proposed method accuracy and efficiency. The quantitative analysis is made in terms of L-infinity, L-2, L-rms, and L-rel error norms as well as number of nodes N over space domain and time-step delta t. Numerical convergence in space and time is also studied for the proposed method. The unconditional stability of the proposed HRBFs scheme is obtained using the von Neumann methodology. It is observed that the HRBFs method circumvented the ill-conditioning problem greatly, a major issue in the Kansa method.

关键词： adaptive algorithm advection-diffusion model Black-Scholes model Caputo fractional derivative hybrid RBFs method optimum parameters

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An adaptive finite element method for semilinear parabolic interface problems with nonzero flux jump

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APPLIED NUMERICAL MATHEMATICS 2020年 153卷 381-398页

作者： Ray, Tanushree Sinha, Rajen Kumar Indian Inst Technol Guwahati Dept Math Gauhati 781039 India

We present and analyze an adaptive finite element method for a semilinear parabolic interface problem subject to nonzero flux jump in a two-dimensional bounded convex polygonal domain. The residual-based a posteriori error estimates are derived using energy argument. Our strategy is to avoid solving the nonlinear system by considering a linearized fully discrete scheme. An adaptive algorithm is constructed using the derived error estimators. A global upper bound for the error is derived which is bounded by the element residual and interior jump residual, whereas a local lower bound in terms of the space error indicator is established. The theory presented is complemented by numerical experiments to illustrate the proposed algorithm. (C) 2020 IMACS. Published by Elsevier B.V. All rights reserved.

关键词： Parabolic interface problem Semilinear A posteriori error estimates adaptive algorithm

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Temperature distribution estimation via data-driven model and adaptive Kalman filter in modular data centers

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2020年第7期234卷 809-819页

作者： Jiang, Kai Shi, Shizhu Moazanigoodarzi, Hosein Hu, Chuan Pal, Souvik Yan, Fengjun McMaster Univ Dept Mech Engn Hamilton ON L8S 4L7 Canada McMaster Univ Comp Infrastruct Res Ctr Hamilton ON Canada Univ Texas Austin Dept Mech Engn Austin TX 78712 USA

With the rapid development of information and communications technology, increasing number of data centers is required to support the cloud computing, and critical web-based services that run our daily lives. The conventional cloud data centers usually adopt computer room air conditioner or inRow units as the cooling sytem, while the rack mountable cooling unit is a more promising equipment due to the economy, exact controllability, flexibility, and scalability. To ensure the efficiency of control system in rack mountable cooling unit and the security of servers in the data centers, the information of temperature distribution is very essential. Basically, the temperature distribution could be obtained through physical sensors easily. However, considering the cost of whole system and the burden of fault diagnosis in sensor networks, the number of temperature sensors should be kept down to a bare minimum. Therefore, it is necessary to develop an effective and real-time observer to estimate the temperature distribution in the system. Besides, due to the complex air flow and heat transfer in the container, it is quite difficult to construct a physics model. To this end, a novel observer embracing data-driven model and adaptive Kalman filter is proposed in this work. Auto regression exogenous model is adopted as the framework of data-driven model, and the model is identified through a algorithm of partial least square. Moreover, to represent the nonlinear behaviors in the system, fuzzy c-means is applied for data classification and getting multiple local linear models. Finally, adaptive Kalman filter is utilized to estimate the temperature distribution on the basis of proposed data-driven model. The estimation results based on experimental data indicate the performance of proposed approach is remarkable.

关键词： Data center temperature distribution data-driven model adaptive algorithm Kalman filter

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Optimization Model and algorithm Design for Rural Leisure Tourism Passenger Flow Scheduling

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IEEE ACCESS 2020年 8卷 125295-125305页

作者： Su, Fang Duan, Chengrui Wang, Ruopeng Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China

A constrained optimization model and an iterative optimization algorithm based on PSO are designed for rural leisure tourism passenger flow scheduling. Compared with the traditional tourist dispatching scheme, this model maximizes the overall tourist experience and operation profit of the whole region on the base of protection of tourists' travel experience and the interests of operators in the dispatching spots. Simulations and comparisons are taken to evaluate the feasibility and effectiveness of the model and the optimization. The simulation results show that compared with the shortest-distance-based traffic scheduling scheme and the gravity-model-based scheme, the new model and optimization could meet the requirements of the rural leisure tourists dispatching and bring better tourist experience and tourism profit.

关键词： adaptive algorithm optimization model particle swarm optimization rural leisure tourism tourist scheduling

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Generation of broadcasting for fractal adaptive Internet of things reconfiguration under the swarm intelligence paradigm

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2020年第6期16卷

作者： Moreno, Jaime Morales, Oswaldo Tejeida, Ricardo Quintana, Hugo Sidorov, Grigori Inst Politecn Nacl Unidad Zacatenco Escuela Super Ingn Mecan & Elect Mexico City DF Mexico Inst Politecn Nacl Escuela Super Turismo Mexico City DF Mexico Inst Politecn Nacl Ctr Invest Computac Mexico City DF Mexico

Recently, a wide range of small devices, such as Wi-Fi Internet of things development boards, which are a kind of the microcontroller units in a general purpose board, are interrelated throughout the planet. In addition, certain microcontroller units interact inside our homes when turning lights on or detecting movements, measuring various parameters, such as gas concentrations, CO2, humidity, and the temperature inside a room, or adjusting the intensity of the lights inside and outside of the house. Likewise, there is a great diversity of microcontroller units, ranging from smart cellular telephones or reduced general purpose devices, ESP8266 or RaspberryPi3 to any type of Internet of things devices. Therefore, the general way of connecting the microcontroller units to the Internet is through hub nodes, so that the information can be propagated and shared among them. The main purpose of this article is to yield an adaptive reconfiguration algorithm to link all the sensor nodes (microcontroller units) of a network based on the fractal topology, avoiding the use of hub nodes, in order for the microcontroller units to share all the parameters established in the Internet of things network only through two adjacent sensor nodes as long as any sensor node in the network knows all the parameters of the other ones, even if the sensor nodes are not adjacent. To achieve the above, in this work, an Internet of things network was built based on the Hilbert fractal for being a filling-space curve yielded from the L-systems paradigm, so this fractal Hilbert topology allows access to the entire Internet of things network in a dynamic way, and it is possible to reconfigure the network topology when a new sensor node is attached by applying artificial intelligence to intelligent and dynamic environments.

关键词： Wireless sensor network Internet of things network Hilbert fractal topology L-system adaptive algorithm machine learning intelligent systems

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Pre- and post-earthquake regional loss assessment using deep learning

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EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS 2020年第7期49卷 657-678页

作者： Kim, Taeyong Song, Junho Kwon, Oh-Sung Seoul Natl Univ Dept Civil & Environm Engn Seoul South Korea Univ Toronto Dept Civil & Mineral Engn Toronto ON Canada

As urban systems become more highly sophisticated and interdependent, their vulnerability to earthquake events exhibits a significant level of uncertainties. Thus, community-level seismic risk assessments are indispensable to facilitate decision making for effective hazard mitigation and disaster responses. To this end, new frameworks for pre- and post-earthquake regional loss assessments are proposed using deep learning methods. First, to improve the accuracy of the response prediction of individual structures during the pre-earthquake loss assessment, a widely used nonlinear static procedure is replaced by the recently developed probabilistic deep neural network model. The variabilities of the nonlinear responses of a structural system given the seismic intensity can be quantified during the loss assessment process. Second, to facilitate near-real-time post-earthquake loss assessments, an adaptive algorithm, which identifies the optimal number and locations of sensors in a given urban area, is proposed. Using a deep neural network that estimates area-wide structural damage given the spatial distribution of the seismic intensity levels as a surrogate model, the algorithm adaptively places additional sensors at property lots at which errors from surrogate estimations of the structural damage are the greatest. Note that the surrogate model is constructed before earthquake events using simulated datasets. To test and demonstrate the proposed frameworks, the paper introduces thorough numerical investigations of two hypothetical urban communities. The proposed frameworks using the deep learning methods are expected to make critical advances in pre- and post-earthquake regional loss assessments.

关键词： adaptive algorithm deep learning earthquake optimal sensor placement probabilistic seismic risk assessment surrogate model

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