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arXiv 2025年

作者： Salas, Ike Griss Ebers, Megan R. Stevens-Haas, Jake Kutz, J. Nathan Department of Applied Mathematics University of Washington SeattleWA98195 United States Department of Electrical and Computer Engineering University of Washington SeattleWA98195 United States

Monitoring the atmospheric dispersion of pollutants is increasingly critical for environmental impact assessments. High-fidelity computational models are often employed to simulate plume dynamics, guiding decision-making and prioritizing resource deployment. However, such models can be prohibitively expensive to simulate, as they require resolving turbulent flows at fine spatial and temporal resolutions. Moreover, there are at least two distinct dynamical regimes of interest in the plume: (i) the initial ejection of the plume where turbulent mixing is generated by the shear-driven Kelvin-Helmholtz instability, and (ii) the ensuing turbulent diffusion and advection which is often modeled by the Gaussian plume model. We address the challenge of modeling the initial plume generation. Specifically, we propose a data-driven framework that identifies a reduced-order analytical model for plume dynamics – directly from video data. We extract a time series of plume center and edge points from video snapshots and evaluate different regressions based to their extrapolation performance to generate a time series of coefficients that characterize the plume’s overall direction and spread. We regress to a sinusoidal model inspired by the Kelvin-Helmholtz instability for the edge points in order to identify the plume’s dispersion and vorticity. Overall, this reduced-order modeling framework provides a data-driven and lightweight approach to capture the dominant features of the initial nonlinear point-source plume dynamics, agnostic to plume type and starting only from video. The resulting model is a pre-cursor to standard models such as the Gaussian plume model and has the potential to enable rapid assessment and evaluation of critical environmental hazards, such as methane leaks, chemical spills, and pollutant dispersal from smokestacks. Copyright © 2025, The Authors. All rights reserved.

关键词： Turbulent flow

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Feature Fusion Based Deep Transfer Learning Based Human Gait Classification Model

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Intelligent Automation & Soft Computing 2023年第8期37卷 1453-1468页

作者： C.S.S.Anupama Rafina Zakieva Afanasiy Sergin E.Laxmi Lydia Seifedine Kadry Chomyong Kim Yunyoung Nam Department of Electronics and Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Candidate of Pedagogical Sciences Department of Industrial Electronics and Lighting EngineeringKazan State Power Engineering UniversityKazan420066Russia Department of Theories and Principles of Physical Education and Life Safety North-Eastern Federal University Named After M.K.AmmosovYakutsk677000Russia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea

Gait is a biological typical that defines the method by that people *** is the most significant performance which keeps our day-to-day life and physical *** electromyography(sEMG)is a weak bioelectric signal that portrays the functional state between the human muscles and nervous system to any *** classifiers dependent upon sEMG signals are extremely utilized in analysing muscle diseases and as a guide path for recovery *** approaches are established in the works for gait recognition utilizing conventional and deep learning(DL)*** study designs an Enhanced Artificial Algae Algorithm with Hybrid Deep Learning based Human Gait Classification(EAAA-HDLGR)technique on sEMG *** EAAA-HDLGR technique extracts the time domain(TD)and frequency domain(FD)features from the sEMG signals and is *** addition,the EAAA-HDLGR technique exploits the hybrid deep learning(HDL)model for gait *** last,an EAAA-based hyperparameter optimizer is applied for the HDL model,which is mainly derived from the quasi-oppositional based learning(QOBL)concept,showing the novelty of the work.A brief classifier outcome of the EAAA-HDLGR technique is examined under diverse aspects,and the results indicate improving the EAAA-HDLGR *** results imply that the EAAA-HDLGR technique accomplishes improved results with the inclusion of EAAA on gait recognition.

关键词： Feature fusion human gait recognition deep learning electromyography signals artificial algae algorithm

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Overview of Modelling of the Meteorological Effects on Environmental Noise in Urban Street Canyons 24

Overview of Modelling of the Meteorological Effects on Envir...

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24th International Congress on Acoustics, ICA 2022

作者： Feriani, Sophia Julia Jeong, Cheol-Ho Engsig-Karup, Allan Peter Pind, Finnur Acoustic Technology Department of Electrical and Photonics Engineering Technical University of Denmark Denmark Scientific Computing Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Treble Technologies Iceland

Urban noise pollution is a harmful environmental stressor for city inhabitants and its impact has been proven to be harmful for short- and long-term health. Having accurate models to predict and analyze the noise in ever-growing cities is therefore crucial. If we focus on the scale of city streets, several wave-based numerical methods have been developed and optimized to predict the sound propagation in urban street canyons. Some methods are more suited than others to incorporate relevant meteorological effects such as wind profile, temperature gradient and air absorption into their model. However, integrating those phenomena comes at a certain computational cost. Assessing in advance the relevant meteorological effects worth taking into account and only considering those effects in the method would possibly reduce the computational burden of the modelling. This paper aims to summarize previous modelling efforts, key effects and relate these to established computational methods for accounting meteorological conditions inside an urban street canyon. This overview seeks to identify current limitations in computational modelling with the overall aim to improve next-generation simulations using advanced wave-based computing methods. © ICA *** rights reserved

关键词： DGFEM Environmental noise Meteorological effects Urban Street canyon

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Free vibration analysis of functionally graded porous curved nanobeams on elastic foundation in hygro-thermo-magnetic environment

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Frontiers of Structural and Civil engineering 2023年第4期17卷 584-605页

作者： Quoc-Hoa PHAM Parviz MALEKZADEH Van Ke TRAN Trung NGUYEN-THOI Faculty of Engineering and Technology Nguyen Tat Thanh UniversityHo Chi Minh City 700000Vietnam Department of Mechanical Engineering Persian Gulf UniversityBushehr ***Iran Faculty of Mechanical Engineering Le Quy Don Technical UniversityHanoi 100000Vietnam Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 700000Vietnam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 700000Vietnam Bualuang ASEAN Chair Professor Thammasat School of EngineeringThammasat UniversityPathumtani 12120Thailand

Herein,a two-node beam element enriched based on the Lagrange and Hermite interpolation function is proposed to solve the governing equation of a functionally graded porous(FGP)curved nanobeam on an elastic foundation in a hygro–thermo–magnetic *** material properties of curved nanobeams change continuously along the thickness via a power-law distribution,and the porosity distributions are described by an uneven porosity *** effects of magnetic fields,temperature,and moisture on the curved nanobeam are assumed to result in axial loads and not affect the mechanical properties of the *** equilibrium equations of the curved nanobeam are derived using Hamilton’s principle based on various beam theories,including the classical theory,first-order shear deformation theory,and higher-order shear deformation theory,and the nonlocal elasticity *** accuracy of the proposed method is verified by comparing the results obtained with those of previous reliable ***,the effects of different parameters on the free vibration behavior of the FGP curved nanobeams are investigated comprehensively.

关键词： functionally graded porous material curved nanobeam hygro-thermo-magnetic enriched finite element method

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On First-Order Meta-Reinforcement Learning with Moreau Envelopes

On First-Order Meta-Reinforcement Learning with Moreau Envel...

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IEEE Conference on Decision and Control

作者： Mohammad Taha Toghani Sebastian Perez-Salazar César A. Uribe Department of Electrical and Computer Engineering Rice University Houston TX USA Computational Applied Mathematics and Operations Research Rice University Houston TX USA

Meta-Reinforcement Learning (MRL) is a promising framework for training agents that can quickly adapt to new environments and tasks. In this work, we study the MRL problem under the policy gradient formulation, where we propose a novel algorithm that uses Moreau envelope surrogate regularizers to jointly learn a meta-policy that is adjustable to the environment of each individual task. Our algorithm, called Moreau Envelope Meta-Reinforcement Learning (MEMRL), learns a meta-policy that can adapt to a distribution of tasks by efficiently updating the policy parameters using a combination of gradient-based optimization and Moreau Envelope regularization. Moreau Envelopes provide a smooth approximation of the policy optimization problem, which enables us to apply standard optimization techniques and converge to an appropriate stationary point. We provide a detailed analysis of the MEMRL algorithm, where we show a sublinear convergence rate to a first-order stationary point for non-convex policy gradient optimization. We finally show the effectiveness of MEMRL on a multi-task 2D-navigation problem.

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A Pseudo-Spectral Scheme for Systems of Two-Point Boundary Value Problems with Left and Right Sided Fractional Derivatives and Related Integral Equations

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computer Modeling in engineering & Sciences 2021年第7期128卷 21-41页

作者： I.G.Ameen N.A.Elkot M.A.Zaky A.S.Hendy E.H.Doha Department of Mathematics Faculty of ScienceAl-Azhar UniversityCairoEgypt Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt Department of Applied Mathematics Physics DivisionNational Research CentreDokkiCairo12622Egypt Department of Computational Mathematics and Computer Science Institute of Natural Sciences and MathematicsUral Federal UniversityYekaterinburg620002Russia Department of Mathematics Faculty of ScienceBenha UniversityBenha13511Egypt

We target here to solve numerically a class of nonlinear fractional two-point boundary value problems involving left-and right-sided fractional *** main ingredient of the proposed method is to recast the problem into an equivalent system of weakly singular integral ***,a Legendre-based spectral collocation method is developed for solving the transformed ***,we can make good use of the advantages of the Gauss quadrature *** present the construction and analysis of the collocation *** results can be indirectly applied to solve fractional optimal control problems by considering the corresponding Euler–Lagrange *** numerical examples are given to confirm the convergence analysis and robustness of the scheme.

关键词： Spectral collocation method weakly singular integral equations two-point boundary value problems convergence analysis

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Recognition of Complex Human Activities for Wellness Management from Smartwatch using Deep Residual Neural Network 7

Recognition of Complex Human Activities for Wellness Managem...

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7th International Conference on Digital Arts, Media and Technology, DAMT 2022 and 5th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, NCON 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (纸本)9781665495103

Activity-based wellbeing administration integrating health apps on intelligent wearables is becoming increasingly prominent. The accessibility of multimodal sensors in everyday wearable devices makes it feasible to measure human behavior and provide wellness services that are aware of the user's environment. In smartphones, smartwatches, and many other wearables, integrated sensors provide a wealth of information that can be utilized to recognize human movement. This research shows how effective deep learning algorithms can extract abstract information for human movement categorization in daily life. We came up with an approach to deal with this issue is what we name one-dimensional deep residual neural network (1D-ResNet). With an overall accuracy of 94.15%, we observed that the proposed 1D-ResNet outperforms standard deep learning models in categorizing complicated human activities such as reading, writing, eating, and other hand-oriented tasks. © 2022 IEEE.

关键词： Deep neural networks

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A hysteresis type strategy for server reservation in a multi-server queueing system with priority and retrials 35

A hysteresis type strategy for server reservation in a multi...

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35th European Modeling and Simulation Symposium, EMSS 2023

作者： D’Apice, Ciro Dudin, Alexander Manzo, Rosanna Rarità, Luigi Dipartimento di Scienze Aziendali Management & Innovation Systems University of Salerno Via Giovanni Paolo II 132 SA Fisciano84084 Italy Department of Applied Mathematics and Computer Science Belarusian State University 4 Nezavisimosti Ave. Minsk220030 Belarus Department of Information and Electrical Engineering and Applied Mathematics University of Salerno Via Giovanni Paolo II 132 SA Fisciano84084 Italy

ISBN: (纸本)9788885741881

This work deals with a possible model of a cell of cognitive radio architectures via a multi-server queueing system with two different types of requests and preemptive priority of one type of requests on the other. Service demands arrive according to Markovian Arrival Processes in order to capture traffic correlation. Cognitive engine tries to find a solution or optimize a performance goal based on the received inputs that define the radio’s current internal state and operating environment. Hence, beside priority mechanisms, a possible deactivation of information to transmit is considered via a hysteresis type strategy for the access to the service, with the aim of smoothing the effects of service interruptions for low priority requests. Fixed values for the thresholds of the strategy are assumed. The system is statistically described by a level-dependent multi-dimensional Markov chain, which allows the computation of stationary probabilities and robust performance indices. Numerical results are presented to compare the system performances in the case of uncorrelated flows and flows with different correlation levels. © 2020 The Authors.

关键词： Cognitive radio

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A Machine Learning and Ratemaking Evaluation of Four Auto Insurance Pure Premium Modeling Algorithms 28

A Machine Learning and Ratemaking Evaluation of Four Auto In...

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28th International computer Science and engineering Conference, ICSEC 2024

作者： Kumar, Rakesh Rakhra, Manik Prashar, Deepak Upadhyay, Saurabh Mrsic, Leo Khan, Arafat Ahmad Lovely Professional University Department of Mathematics and Statistics Punjab Phagwara India Lovely Professional University School of Computer Science and Engineering Punjab Phagwara India Algebra University Vice Rector for Science and Research Zagreb10000 Croatia Khon Kaen Thailand

ISBN: (纸本)9798350366860

Actuarial science is increasingly using machine learning. Machine learning has improved actuarial pricing prediction by forecasting future claims. Insurers are testing these algorithms, but model explainability and implementation cost are issues. The current literature barely touches upon this broad field of inquiry. Fujita et al. published a 2020 study on predictive frequency models using AGLM, GLM, GAM, and GBM. König and Loser (2020) compared GLMs, neural networks, and XGBoost for frequency prediction. We want to add to existing research by forecasting pure premium rather than frequency or severity, comparing four methodologies, and analyzing and presenting a wide range of quantitative and qualitative performance evaluation metrics. This paper compares four approaches for predicting pure premium: GLM, AGLM, XGBoost, and neural networks. On a test set, the study compares each model's pros and cons in P&C insurance pricing, as well as its quantitative and qualitative performance. Despite these obstacles, machine learning in actuarial pricing has clear benefits. By may improve risk profiles, pricing accuracy, profitability, and market competitiveness by using massive volumes of data and advanced analytics. However, getting these results needs a holistic approach that balances machine learning's technical sophistication with insurance's regulatory and operational limits. Further research should construct interpretable machine learning models customized to insurance pricing needs and limits while addressing the ethical, regulatory, and implementation obstacles to their adoption. © 2024 IEEE.

关键词： Adversarial machine learning

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Integration of aggregated data in causally interpretable meta-analysis by inverse weighting

arXiv

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arXiv 2025年

作者： Vo, Tat Thang Le, Tran Trong Khoi Afach, Sivem Vansteelandt, Stijn Research Group EPIDERME Faculty of Medicine University Paris Est Creteil France Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium

Obtaining causally interpretable meta-analysis results is challenging when there are differences in the distribution of effect modifiers between eligible trials. To overcome this, recent work on transportability methods has considered standardizing results of individual studies over the case-mix of a target population, prior to pooling them as in a classical random-effect meta-analysis. One practical challenge, however, is that case-mix standardization often requires individual participant data (IPD) on outcome, treatments and case-mix characteristics to be fully accessible in every eligible study, along with IPD case-mix characteristics for a random sample from the target population. In this paper, we aim to develop novel strategies to integrate aggregated-level data from eligible trials with non-accessible IPD into a causal meta-analysis, by extending moment-based methods frequently used for population-adjusted indirect comparison in health technology assessment. Since valid inference for these moment-based methods by M-estimation theory requires additional aggregated data that are often unavailable in practice, computational methods to address this concern are also developed. We assess the finite-sample performance of the proposed approaches by simulated data, and then apply these on real-world clinical data to investigate the effectiveness of risankizumab versus ustekinumab among patients with moderate to severe psoriasis. © 2025, CC BY.

关键词： Inverse problems

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