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Nonlocal and Gradient Theories of Piezoelectric Nanoplates

Procedia Engineering 2017年 190卷 178-185页

作者： Jan Sladek Vladimir Sladek Jozef Kasala Ernian Pan Institute of Construction and Architecture Slovak Academy of Sciences 84503 Bratislava Slovakia Faculty of Special Technology Alexander Dubcek University of Trencin 91150 Trencin Slovakia Computer Modeling and Simulation Group Department of Civil Engineering University of Akron Akron OH 44325-3905 USA

Classical continuum models are unable to describe the size effect in nano/micro structures, even though this effect is observed experimentally. Therefore, modified continuum models are frequently applied for the investigation of nanomechanics due to their computational efficiency and capability of providing accurate results which are comparable to the atomistic models. In this paper, the Mindlin plate is extended to the piezoelectric nanoplate with nonlocal and gradient theories for size effect. The governing equations for bending moments, normal and shear stresses are derived via the Hamilton‘s principle. Differences between the two theories are described.

关键词： Nonlocal gradient elasticity Mindlin plate Hamilton‘s principle size effect

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FACS-CHARM: A Hybrid Agent-Based and Discrete-Event simulation Approach for Covid-19 Management at Regional Level

FACS-CHARM: A Hybrid Agent-Based and Discrete-Event Simulati...

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simulation Winter Conference

作者： Anastasia Anagnostou Derek Groen Simon J.E. Taylor Diana Suleimenova Nura Abubakar Arindam Saha Kate Mintram Maziar Ghorbani Habiba Daroge Tasin Islam Yani Xue Edward Okine Nana Anokye Modeling and Simulation Group Department of Computer Science Brunel University London Kingston Lane Uxbridge UK Global Public Health Department of Health Sciences Brunel University London Kingston Lane Uxbridge UK

ISBN: (纸本)9781665476621

Pandemics have huge impact on all aspect of people's lives. As we have experienced during the Coronavirus pandemic, healthcare, education and the economy have been put under extreme strain. It is important therefore to be able to respond to such events fast in order to limit the damage to the society. Decision-makers typically are advised by experts in order to inform their response strategies. One of the tools that is widely used to support evidence-based decisions is modeling and simulation. In this paper, we present a hybrid agent-based and discrete-event simulation for the Coronavirus pandemic management at regional level. Our model considers disease dynamics, population interactions and dynamic ICU bed capacity management and predicts the impact of various public health preventive measures on the population and the healthcare service.

关键词： COVID-19 Pandemics Hospitals Sociology Education Predictive models Statistics

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Weighting construction by bag-of-words with similarity-learning and supervised training for classification models in court text documents[Formula presented]

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Applied Soft Computing 2022年 124卷

作者： Castro, Antonio P. Wainer, Gabriel A. Calixto, Wesley P. Electrical Mechanical & Computer Engineering School Federal University of Goias Goias Goiania Brazil Experimental & Technological Research and Study Group Federal Institute of Goias GoianiaGoias Brazil Visualization Simulation and Modeling Carleton University Ottawa Canada Court of Justice Goias Brazil

Traditional models of bag-of-words for text classification are unable to identify weights for the co-occurrence of terms, and, mainly, for this reason, they are being replaced by models of word embedding. This article proposes a method to enhance traditional bag-of-words models in two aspects: (a) build features on the co-occurrence of terms and (b) smooth the non-linearity or make the terms linear for different corpus categories. The datasets used are characterized by the non-linearity of the terms, having four different categories of documents. Two computational representations of the datasets are generated: binary and frequency, being used for supervised training of nine classification technologies: random forest, multilayer perceptron neural networks, adaptive boosting, gradient boosting, Gaussian process, support vector machine, Naive Bayes, k-nn and decision trees, its results are compared with nine other algorithms used in other research work. The combinations of each obtained result are compared and assessed using the accuracy, f-measure, precision, and recall metrics. The research and studies generated resulted in the construction of an API that will integrate the Department of Justice software that controls the judicial proceedings. The results of the evaluation metrics and the comparisons with other studies demonstrate that the proposed methodology is feasible to be applied in meeting the needs of the court, allowing to speed up the judgment of lawsuits. © 2022 Elsevier B.V.

关键词： Knowledge management

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AETA 2022—Recent Advances in Electrical Engineering and Related Sciences: Theory and Application 1

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丛书名： Lecture Notes in Electrical Engineering

1000年

作者： Tran Trong Dao Vo Hoang Duy Chau Si Thien Dong Phuong T. Tran Ivan Zelinka

ISBN: (数字)9789819987030

ISBN: (纸本)9789819987023;9789819987054

This proceedings book features selected papers on 12 themes, including wireless communications, power systems, signal processing, robotics, control systems, sustainable energy, power electronics, intelligent networks and more. Covering topics such as performance of reconfigurable intelligent surfaces for 6G communication networks, improved neural network algorithms for optimizing the power flow of renewable energy sources, anomaly detection using stationary and non-stationary signal analysis in data sciences, novel object identification and distance estimation algorithms for self-driving vehicles, the book presents interesting ideas and state-of-the-art overviews.

关键词： Computational Intelligence Control and Systems Theory Power Electronics, Electrical Machines and Networks Communications Engineering, Networks

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Physical clearing mechanisms in power industry

Physical clearing mechanisms in power industry

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IEEE PES Power Systems Conference and Exposition, PSCE

作者： K. Atkins C.M. Homan A. Marathe Basic and Applied Simulation Science Group Los Alamos National Laboratory Los Alamos NM USA Department of Computer Science Rochester Institute of Technology Rochester NY USA Modeling Algorithms and Informatics Group Los Alamos National Laboratory Los Alamos NM USA

This paper studies the performance of several different physical clearing mechanisms in the electricity market. The study is done using the simulation tool, Marketecture, developed at the Los Alamos National Laboratory. Two cities, Portland and Chicago, are used for simulating the physical clearing algorithms. The algorithms were chosen according to their ability to serve as a proxy for some of the well known policies. Our results show that, based on the economic measures chosen, both cities perform very similarly under the different physical clearing mechanisms.

关键词： Power industry Contracts Laboratories Computational modeling Power generation economics ISO Cities and towns Energy consumption Computational complexity computer industry

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Knee Detection in Bayesian Multi-Objective Optimization Using Thompson Sampling

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IEEE Transactions on Evolutionary Computation 2024年

作者： Heidari, Arash Qing, Jixiang Gonzalez, Sebastian Rojas Branke, Juergen Dhaene, Tom Couckuyt, Ivo Computer Science at IDLab Ghent University Ghent Belgium Imperial College London United Kingdom Department of Computational Mathematics Data Science Institute Belgium Warwick Business School University of Warwick Coventry United Kingdom Computer Modeling and Simulation Research Group University of Antwerp Antwerp Belgium Cluster in IDLab Ghent University Belgium

Real-world problems often consist of multiple conflicting objectives to be optimized simultaneously, featuring a set of Pareto-optimal solutions. Estimating the entire Pareto front can be computationally expensive, and is not always necessary, as decision makers will likely be interested only in specific regions of the Pareto front. In the absence of knowledge about the decision maker preferences, the so-called knees in the Pareto front are considered to be particularly attractive. In this paper, we propose using Thompson Sampling in the Bayesian optimization framework to estimate the location of the knee regions in a data-efficient manner. Our experimental results show that the proposed methods accurately locate the knee regions after a very small number of evaluations, providing a computationally efficient approach to single-and multi-knee detection in multi-objective optimization. © 1997-2012 IEEE.

关键词： Multiobjective optimization

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Neuro-symbolic Explainable Artificial Intelligence Twin for Zero-touch IoE in Wireless Network

arXiv

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arXiv 2022年

作者： Munir, Shirajum Kim, Ki Tae Adhikary, Apurba Saad, Walid Shetty, Sachin Park, Seong-Bae Hong, Choong Seon Virginia Modeling Analysis and Simulation Center Department of Computational Modeling and Simulation Engineering Old Dominion University SuffolkVA23435 United States Department of Computer Science and Engineering Kyung Hee University Yongin-si17104 Korea Republic of Department of Computer Science and Engineering School of Computing Kyung Hee University Kyung Hee University Yongin-si17104 Korea Republic of Wireless@VT Group Bradley Department of Electrical and Computer Engineering Virginia Tech ArlingtonVA22203 United States

Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) for sixth-generation (6G) wireless networks. A reliable XAI twin system for ZSM requires two composites: an extreme analytical ability for discretizing the physical behavior of the Internet of Everything (IoE) and rigorous methods for characterizing the reasoning of such behavior. In this paper, a novel neuro-symbolic explainable artificial intelligence twin framework is proposed to enable trustworthy ZSM for a wireless IoE. The physical space of the XAI twin executes a neural-network driven multivariate regression to capture the time-dependent wireless IoE environment while determining unconscious decisions of IoE service aggregation. Subsequently, the virtual space of the XAI twin constructs a directed acyclic graph (DAG)-based Bayesian network that can infer a symbolic reasoning score over unconscious decisions through a first-order probabilistic language model. Furthermore, a Bayesian multi-arm bandits-based learning problem is proposed for reducing the gap between the expected explained score and current obtained score of the proposed neuro-symbolic XAI twin. To address the challenges of extensible, modular, and stateless management functions in ZSM, the proposed neuro-symbolic XAI twin framework consists of two learning systems: 1) implicit learner that acts as an unconscious learner in physical space, and 2) explicit leaner that can exploit symbolic reasoning based on implicit learner decisions and prior evidence. Experimental results show that the proposed neuro-symbolic XAI twin can achieve around 96.26% accuracy while guaranteeing from 18% to 44% more trust score in terms of reasoning and closed-loop automation. © 2022, CC BY-NC-ND.

关键词： Internet of Everything

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Tracking nanostructural evolution in alloys: Large-scale analysis of atom probe tomography data on Blue Gene/L

Tracking nanostructural evolution in alloys: Large-scale ana...

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37th International Conference on Parallel Processing, ICPP 2008

作者： Seal, Sudip Moody, Michael Ceguerra, Anna Ringer, Simon Rajan, Krishna Aluru, Srinivas Modeling and Simulation Group Oak Ridge National Laboratory Oak Ridge TN 37831 United States Australian Key Center for Microscopy and Microanalysis Madsen Building F09 University of Sydney NSW 2006 Australia Dept. of Material Sciences and Engineering Iowa Suite University Ames IA 50011 United States Dept. of Electrical and Computer Engineering Iowa Suite University Ames IA 50011 United States

ISBN: (纸本)9780769533742

The advent of Local Electrode Atom Probe (LEAP) tomography is revolutionizing materials science by enabling near atomic scale imaging of materials. Analysis of threedimensional atom probe tomography (APT) data holds the promise of relating combinatorial arrangement of atoms to material properties and enable better design and synthesis of complex materials. Existing techniques, which are serial and require O(n2) work for n atoms, do not scale to the hundred million large data sets produced by current generation atom probe microscopes. In this paper, we present an O(n) work autocorrelation based technique that reveals clustering of constituent atoms and spatial associations between them. We present an efficient parallelization of this method and show scaling on a 1,024 node Blue Gene/L. To our knowledge, this is the first parallel algorithm for the analysis of APT data, and together with our linear work autocorrelation technique, is demonstrated to easily scale to billion atom data sets expected in the very near future. © 2008 IEEE.

关键词： Atoms

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Two-Dimensional Thin Layer Convective Flow and Heat Transfer of a Dusty Fluid on a Nonlinear Stretching Sheet

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Multiscale Science and Engineering 2022年第3期4卷 111-118页

作者： Islam, Md. Robiul Ferdows, M. Misra, J. C. Murtaza, M. G. Research Group of Fluid Flow Modeling and Simulation Department of Applied Mathematics University of Dhaka Dhaka Bangladesh Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Centre for Healthcare Science and Technology Indian Institute of Engineering Science and Technology Howrah India Department of Mathematics Comilla University Cumilla Bangladesh

In this study, steady two-dimensional boundary layer flow and heat transfer of a dusty fluid over a nonlinearly stretching sheet with variable thickness, $${u}_{w} (x)={U}_{0}{(x+b)}^{m}$$ has been considered. A variable magnetic field is applied transversely to the direction of the flow. The general problem has been reduced to a system of coupled non-linear ordinary differential equations of two-dimensional boundary layer. By means of similarity transformation, the basic governing equations are transformed into a set of nonlinear ordinary differential equation and then are solved numerically by dsolve routine in MAPLE. Results of the present study have been duly validated by adopting an appropriate procedure. The influence of various parameters such as velocity power index, wall thickness parameter, magnetic parameter fluid-particle interaction parameter for velocity, fluid-particle interaction parameter for temperature, mass concentration of dust particle, Prandtl number, Eckert number has been investigated. The effects are then presented graphically both for dust phase and fluid phase. It is clear that flow field and other quantities of physical interest are significantly influenced by these parameters. Also the effects of the parameters on the skin-friction coefficients and rate of heat transfer in terms of the Nusselt number, which are of physical interest, are shown graphically/in tabular form. The study reveals that the impact of the parameters for the dusty fluid on Nusselt number is considerably higher than that for a clean fluid.

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Oscillation Detection and Diagnosis in Process Industries by Pattern Recognition Technique

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IFAC-PapersOnLine 2019年第1期52卷 299-304页

作者： Jônathan W.V. Dambros Marcelo Farenzena Jorge O. Trierweiler Group of Intensification Modeling Simulation Control and Optimization of Processes (GIMSCOP) Chemical Engineering Department Federal University of Rio Grande do Sul (UFRGS) R. Eng. Luiz Englert s/n Campus Central Porto Alegre RS Brazil Department of Computer Science University of Kaiserslautern 67663 Kaiserslautern Deutschland

Oscillation in control loops is a frequent problem faced in process industries. It deviates the process variables from their desired condition, affecting negatively plant productivity. To guarantee profitability, oscillation must be detected, diagnosed, and, finally, eliminated. Dozens of automatic detection and diagnosis techniques have been proposed over the last years. However, the application to real industrial data reveals low efficiencies, which indicates that the techniques require improvement. This work presents a new method for oscillation detection and diagnosis. The technique classifies the loops based on the shape of the PV(OP) diagram by a pattern recognition approach, where the model is trained with simulated examples that cover a large range of processes and different conditions found in industrial data, such as noise and disturbance presence. The performance of the proposed technique is compared to well-established oscillation detection and diagnosis methods, returning better results.

关键词： Oscillation detection diagnosis process control fault detection diagnosis pattern recognition process monitoring

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