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2022 Saudi Arabia Smart Grid Conference, SASG 2022

作者： Kamarianakis, Yiannis Pantazis, Yannis Kalligiannaki, Evangelia Katsaounis, Theodoros D. Kotsovos, Konstantinos Gereige, Issam Abdullah, Marwan Jamal, Aqil Tzavaras, Athanasios Foundation for Research and Technology-Hellas Institute of Applied and Computational Mathematics Data Science Group Greece University of Crete Dept. of Mathematics and Applied Mathematics Greece Saudi Aramco RandD Center Carbon Management Division Dhahran Saudi Arabia Computer Electrical and Mathematical Sciences and Engineering Division Thuwal Saudi Arabia

ISBN: (纸本)9781665475891

Energy production from solar photovoltaic (PV) plants is unpredictable, mainly due to the stochastic formation and movement of clouds or aerosol - dust particles which scatter or disperse solar radiation. Accurate forecasts of PV output are essential to Distribution and Transportation System Operators as they assist efficient solar energy trading and management of electricity grids. This work evaluates an autoregressive, computationally-light KNN-regression scheme (TSFKNN) for hourly, day-ahead forecasts of solar irradiance and energy yield of various PV technologies. The model is being tested and validated using data measured in Thuwal, Saudi Arabia. The available measured records span a 60-month period. The developed forecasting models are designed for online systems and provide increased levels of accuracy and low computational cost. Several parametric and nonparametric specifications, coupled with conventional versus outlier-robust estimation procedures are tested, in order to derive an optimal month-specific daily profile (MDP). Current results demonstrate that including intraday variability to the monthly-based irradiance models achieve improved predictive accuracy between 10% and 25% on average. © 2022 IEEE.

关键词： Forecasting

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Multi-Frequency Progressive Refinement for Learned Inverse Scattering

arXiv

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arXiv 2024年

作者： Melia, Owen Tsang, Olivia Charisopoulos, Vasileios Khoo, Yuehaw Hoskins, Jeremy Willett, Rebecca Department of Computer Science University of Chicago United States Data Science Institute University of Chicago United States Computational and Applied Mathematics Department of Statistics University of Chicago United States

Interpreting scattered acoustic and electromagnetic wave patterns is a computational task that enables remote imaging in a number of important applications, including medical imaging, geophysical exploration, sonar and radar detection, and nondestructive testing of materials. However, accurately and stably recovering an inhomogeneous medium from far-field scattered wave measurements is a computationally difficult problem, due to the nonlinear and non-local nature of the forward scattering process. We design a neural network, called Multi-Frequency Inverse Scattering Network (MFISNet), and a training method to approximate the inverse map from far-field scattered wave measurements at multiple frequencies. We consider three variants of MFISNet, with the strongest performing variant inspired by the recursive linearization method - a commonly used technique for stably inverting scattered wavefield data - that progressively refines the estimate with higher frequency content. MFISNet outperforms past methods in regimes with high-contrast, heterogeneous large objects, and inhomogeneous unknown backgrounds. Copyright © 2024, The Authors. All rights reserved.

关键词： Forward scattering

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Evaluating the spike in the symptomatic proportion of SARS-CoV-2 in China in 2022 with variolation effects:a modeling analysis

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Infectious Disease Modelling 2024年第2期9卷 601-617页

作者： Salihu S.Musa Shi Zhao Ismail Abdulrashid Sania Qureshi Andrés Colubri Daihai He Department of Genomics and Computational Biology University of Massachusetts Chan Medical SchoolWorcesterMA01605USA Department of Applied Mathematics Hong Kong Polytechnic UniversityHong Kong SARChina Department of Mathematics Aliko Dangote University of Science and TechnologyKanoNigeria School of Public Health Tianjin Medical UniversityTianjin300070China School of Finance and Operations Management The University of Tulsa800 South Tucker Dr.TulsaOK74104USA Department of Basic Sciences and Related Studies Mehran University of Engineering and Tech.JamshoroPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon

Despite most COVID-19 infections being asymptomatic,China's Mainland had a high increase in symptomatic cases at the end of *** this study,we examine China's sudden COVID-19 symptomatic surge using a conceptual SIR-based *** model considers the epidemiological characteristics of SARS-CoV-2,particularly variolation,from non-pharmaceutical intervention(facial masking and social distance),demography,and disease mortality in China's *** increase in symptomatic proportions in China may be attributable to(1)higher sensitivity and vulnerability during winter and(2)enhanced viral inhalation due to spikes in SARS-CoV-2 infections(high transmissibility).These two reasons could explain China's high symptomatic proportion of COVID-19 in December *** study,therefore,can serve as a decision-support tool to enhance SARS-CoV-2 prevention and control ***,we highlight that facemask-induced variolation could potentially reduces transmissibility rather than severity in infected ***,further investigation is required to understand the variolation effect on disease severity.

关键词： SARS-CoV-2 Epidemic Epidemiological modeling Reproduction number Variolation

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Effect of radiofrequency coil and primary magnetic field on radiolucent composite plates

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Noise and Vibration Worldwide 2023年第1期54卷 44-58页

作者： Gawande, Atul S Mukka Ramachanra, Shreyas Kamyab, Hesam Trung, Nguyen-Thoi Kattimani, Subhaschandra Department of Mechanical Engineering National Institute of Technology Karnataka Surathkal India Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia Laboratory for Applied and Industrial Mathematics Institute of Computational Science and Artificial Intelligence Ho Chi Minh City Viet Nam Faculty of Mechanical-Electrical and Computer Engineering Van Lang University Ho Chi Minh City Viet Nam

Magnetic Resonance Imaging (MRI) systems need a material compatible with the imaging technique with lesser attenuation and provide accurate images without distortion. Carbon fibers are the best-suited materials for x-ray applications because of their radiolucent properties and reduced attenuation characteristics. However, carbon fiber produces images with distortion in the MRI system by absorbing electromagnetic energy because of its conductive nature. In the present study, five different fiber-reinforced radiolucent composite plates are analyzed to predict their suitability for a radio frequency coil and the effect of a primary magnetic field of 1.5 T and 3.0 T on mechanical responses. Simulation models are built to explore the impact of electromagnetic waves and birdcage coil configurations on composite material and quantify the temperature changes caused due to energy absorption. A multiphysics coupling simulation is being used to understand the effect of stacking sequence, ply orientation, and boundary conditions on the response of composite plates under an electro-magneto-mechanical environment. © The Author(s) 2022.

关键词： Magnetic resonance imaging

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Developing, Analyzing, and Evaluating Vehicular Lane Keeping Algorithms Under Dynamic Lighting and Weather Conditions Using Electric Vehicles

arXiv

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arXiv 2024年

作者： Khalfin, Michael Volgren, Jack Jones, Matthew LeGoullon, Luke Siegel, Joshua Chung, Chan-Jin Department of Computational Applied Mathematics Rice University United States Department of Computer Science and Engineering Pennsylvania State University United States Department of Mathematics and Computer Science Willamette University United States Division of Computer Science and Engineering Louisiana State University United States Department of Computer Science and Engineering Michigan State University United States Department of Mathematics and Computer Science Lawrence Technological University United States

Self-driving vehicles have the potential to reduce accidents and fatalities on the road. Many production vehicles already come equipped with basic self-driving capabilities, but have trouble following lanes in adverse lighting and weather conditions. Therefore, we develop, analyze, and evaluate two vehicular lane-keeping algorithms under dynamic weather conditions using a combined deep learning- and hand-crafted approach and an end-to-end deep learning approach. We use image segmentation- and linear-regression based deep learning to drive the vehicle toward the center of the lane, measuring the amount of laps completed, average speed, and average steering error per lap. Our hybrid model completes more laps than our end-to-end deep learning model. In the future, we are interested in combining our algorithms to form one cohesive approach to lane-following. © 2024, CC BY.

关键词： computer vision

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Multirate Time-Integration Based on Dynamic Ode Partitioning Through Adaptively Refined Meshes for Compressible Fluid Dynamics

SSRN

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SSRN 2024年

作者： Doehring, Daniel Schlottke-Lakemper, Michael Gassner, Gregor J. Torrilhon, Manuel Applied and Computational Mathematics RWTH Aachen University Schinkelstrasse 2 North Rhine-Westphalia Aachen52062 Germany University of Stuttgart Germany Department of Mathematics and Computer Science Center for Data and Simulation Science University of Cologne Germany

In this paper, we apply the Paired-Explicit Runge-Kutta (P-ERK) schemes by Vermeire et. al. (2019, 2022) to dynamically partitioned systems arising from adaptive mesh refinement. The P-ERK schemes enable multirate time-integration with no changes in the spatial discretization methodology, making them readily implementable in existing codes that employ a method-of- lines *** show that speedup compared to a range of state of the art Runge-Kutta methods can be realized, despite additional overhead due to the dynamic re-assignment of flagging variables and restricting nonlinear stability properties. The effectiveness of the approach is demonstrated for a range of simulation setups for viscous and inviscid convection-dominated compressible flows for which we provide a reproducibility *** addition, we perform a thorough investigation of the nonlinear stability properties of the Paired-Explicit Runge-Kutta schemes regarding limitations due to the violation of monotonicity properties of the underlying spatial discretization. Furthermore, we present a novel approach for estimating the relevant eigenvalues of large Jacobians required for the optimization of stability polynomials . © 2024, The Authors. All rights reserved.

关键词： Runge Kutta methods

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Ergodicity of Two Class Priority Queues with Preemptive Priority

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Journal of Mathematical sciences (United States) 2022年第2期267卷 255-259页

作者： Mistryukov, A.V. Ushakov, V.G. Faculty of Computational Mathematics and Cybernetics Lomonosov Moscow State University Moscow 119991 Russian Federation Moscow Center for Fundamental and Applied Mathematics Moscow Russian Federation Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences Moscow 119333 Russian Federation

Well-known results on the ergodicity of queues with preemptive priority were obtained under the assumption that jobs arrive according to the Poisson process. This assumption, however, does not always hold true in practice. In our work we find sufficient ergodicity conditions for queues with two priority classes with a single server, where interarrival times of high-priority jobs have either Erlang or hyperexponential distribution and interarrival times of low-priority jobs and service times of jobs of both classes have arbitrary continuous distributions. We present results in disciplines with loss of an interrupted job and with retrial of an interrupted job. © 2022, Springer science+Business Media, LLC, part of Springer Nature.

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MC-ELMM: Multi-Chip Endurance-Limited Memory Management 23

MC-ELMM: Multi-Chip Endurance-Limited Memory Management

引用

Proceedings of the International Symposium on Memory Systems

作者： Andrew M. Bartolo Mohamed M. Sabry Aly George Michelogiannakis Subhasish Mitra Computer Science Stanford University USA Computer Science and Engineering Nanyang Technological University Singapore Electrical Engineering Stanford University USA and Applied Mathematics and Computational Research Lawrence Berkeley National Laboratory USA Electrical Engineering Computer Science Stanford University USA

ISBN: (纸本)9798400716447

Non-volatile memories (NVMs) have become a staple of architectural research. NVMs naturally enable techniques such as crash consistency; persistent snapshots, logs, and heaps; security-enhanced memories; and even near- or in-memory processing. However, all current NVM technologies suffer from limited write endurance. Monolithic 3D integration (M3D), an NVM-enabled, near-memory technique, drastically increases compute-to-memory connectivity, improving the energy-delay product (EDP), especially for data-intensive workloads. However, M3D systems have another constraint: scaling M3D memory capacity adds multiple compute-plus-memory chips in a NUMA arrangement. In response, we first develop a lifetime extension mechanism for endurance-limited memories (ELMs) that extends chip lifetime from mere minutes to several years. Our page-based scheme minimizes execution disruption. Second, we extend our single-chip scheme to multiple M3D chips. We show that NUMA policies for DRAM systems are ill-suited for M3D because they either incur too many costly off-chip accesses or sacrifice lifetime. Our technique preserves NUMA locality benefits while significantly improving overall system lifetime. For homogeneous multi-threaded workloads running across multiple NUMA nodes (chips), our multi-chip scheme increases lifetime over our single-chip scheme by a geometric mean of 48%. For a heterogeneous mixture of workloads running on a multi-monolithic-chip cluster, we increase cluster lifetime by a factor of 4.7 ×, bounded by a 6% energy and 6% runtime overhead (commonly with no runtime overhead at all).

关键词： endurance endurance-limited memories multi-chip multi-node non-volatile memories phase-change memory resistive RAM

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Quadratic Finite Volume Element Schemes over Triangular Meshes for a Nonlinear Time-Fractional Rayleigh-Stokes Problem

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computer Modeling in Engineering & sciences 2021年第5期127卷 487-514页

作者： Yanlong Zhang Yanhui Zhou Jiming Wu Graduate School of China Academy of Engineering Physics Beijing100088China School of Data and Computer Science Sun Yat-Sen UniversityGuangzhou510275China Institute of Applied Physics and Computational Mathematics Beijing100088China

In this article,we study a 2D nonlinear time-fractional Rayleigh-Stokes problem,which has an anomalous subdiffusion term,on triangular meshes by quadratic finite volume element ***-fractional derivative,defined by Caputo fractional derivative,is discretized through L2−1σformula,and a two step scheme is used to approximate the time first-order derivative at time tn−α/2,where the nonlinear term is approximated by using a matching linearized difference scheme.A family of quadratic finite volume element schemes with two parameters are proposed for the spatial discretization,where the range of values for two parameters areβ1∈(0,1/2),β2∈(0,2/3).For testing the precision of numerical algorithms,we calculate some numerical examples which have known exact solution or unknown exact solution by several kinds of quadratic finite volume element schemes,and contrast with the results of an existing quadratic finite element scheme by drawing diversified comparison plots and showing the detailed data of L2 error results and convergence *** results indicate that,L2 error estimate of one scheme with parameters β_(1)=(3−√3)/6,β2=(6+√3−√21+6√3)/9 is O(h^(3)+△t^(2)),and L^(2) error estimates of other schemes are O(h^(2)+△t^(2)),where h and △t denote the spatial and temporal discretization parameters,respectively.

关键词： Quadratic finite volume element schemes anomalous sub-diffusion term L2 error estimate quadratic finite element scheme

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Max-Compound Cox Processes. III

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Journal of Mathematical sciences (United States) 2022年第2期267卷 273-288页

作者： Korolev, V. Yu. Sokolov, I.A. Gorshenin, A.K. Faculty of Computational Mathematics and Cybernetics Lomonosov Moscow State University Moscow Russian Federation Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences Moscow Russian Federation Hangzhou Dianzi University Hangzhou China Moscow Center for Fundamental and Applied Mathematics Moscow Russian Federation

Extreme values are considered in samples with random size that have a mixed Poisson distribution being generated by a doubly stochastic Poisson process. We prove some inequalities providing bounds on the rate of convergence in limit theorems for the distributions of max-compound Cox processes. © 2022, Springer science+Business Media, LLC, part of Springer Nature.

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