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Electroelastic Analysis of Two-Dimensional Piezoelectric Structures by the Localized Method of Fundamental Solutions

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Advances in Applied mathematics and Mechanics 2023年第4期15卷 880-900页

作者： Yan Gu Ji Lin Chia-Ming Fan Key Laboratory of Road Construction Technology&Equipment Chang’an UniversityXi’anShaanxi 710064China School of Mathematics and Statistics Qingdao UniversityQingdaoShandong 266071China Department of Engineering Mechanics College of Mechanics and MaterialsHohai UniversityNanjingJiangsuChina Department of Harbor and River Engineering&Computation and Simulation Center Taiwan Ocean UniversityKeelung 20224TaiwanChina

Accurate and efficient analysis of the coupled electroelastic behavior of piezoelectric structures is a challenging task in the community of computational *** the past few decades,the method of fundamental solutions(MFS)has emerged as a popular and well-established meshless boundary collocation method for the numerical solution of many engineering *** classical MFS formulation,however,leads to dense and non-symmetric coefficient matrices which will be computationally expensive for large-scale engineering *** this paper,a localized version of the MFS(LMFS)is devised for electroelastic analysis of twodimensional(2D)piezoelectric *** the LMFS,the entire computational domain is divided into a set of overlapping small sub-domains where the MFS-based approximation and the moving least square(MLS)technique are *** to the classical MFS,the LMFS will produce banded and sparse coefficient matrices which makes the method very attractive for large-scale *** numerical experiments illustrate that the present LMFM is very promising for coupled electroelastic analysis of piezoelectric materials.

关键词： Localized method of fundamental solutions meshless methods piezoelectric structures coupled electroelastic analysis fundamental solutions

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CaPS: collaborative and private synthetic data generation from distributed sources 24

CaPS: collaborative and private synthetic data generation fr...

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Proceedings of the 41st International Conference on Machine Learning

作者： Sikha Pentyala Mayana Pereira Martine De Cock School of Engineering and Technology University of Washington Tacoma Department of Electrical Engineering Universidade de Brasilia Brazil School of Engineering and Technology University of Washington Tacoma and Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium

Data is the lifeblood of the modern world, forming a fundamental part of AI, decision-making, and research advances. With increase in interest in data, governments have taken important steps towards a regulated data world, drastically impacting data sharing and data usability and resulting in massive amounts of data confined within the walls of organizations. While synthetic data generation (SDG) is an appealing solution to break down these walls and enable data sharing, the main drawback of existing solutions is the assumption of a trusted aggregator for generative model training. Given that many data holders may not want to, or be legally allowed to, entrust a central entity with their raw data, we propose a framework for collaborative and private generation of synthetic tabular data from distributed data holders. Our solution is general, applicable to any marginal-based SDG, and provides input privacy by replacing the trusted aggregator with secure multiparty computation (MPC) protocols and output privacy via differential privacy (DP). We demonstrate the applicability and scalability of our approach for the state-of-the-art select-measure-generate SDG algorithms MWEM+PGM and AIM.

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Dermatological Diagnostics: A Unified Deep Learning Framework for Skin Lesion and Cancer Classification 1

Dermatological Diagnostics: A Unified Deep Learning Framewor...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kumar, Vinod Jain, Isha Dongla, Durga Sritha Rai, Shivangi Rastogi, Vani Kaushik, Eva Kaushik, Rohit George Washington University School of Engineering and Applied Science WashingtonDC United States Dr. Akhilesh Das Gupta Institute of Technology and Management Computer Science and Engineering Department New Delhi India Gandhi Institute of Technology and Management Computer Science and Engineering Department Andhra Pradesh Visakhapatnam India Data Scientist DXC Technology Noida India Data Analytics Program University of Illinois CS and Mathematics Department Springfield United States

ISBN: (纸本)9798350356816

Skin pathologies encompass a spectrum of conditions, with malignancies such as melanoma representing a critical diagnostic urgency. This investigation delineates the deployment of Convolutional Neural Networks (CNNs) for the classification of dermatological anomalies, benchmarking CNN diagnostic fidelity against dermatological expert evaluations. The study underscores the efficacy of CNN classifiers in expediting the diagnostic workflow for various cutaneous disorders. Advocating for an automated diagnostic framework, the research introduces a CNN-based system aimed at reducing human diagnostic load, accelerating diagnostic timelines, and enhancing survival outcomes. Utilizing advanced image processing algorithms and deep learning architectures, the research presents an automated classification system for skin pathologies, addressing both benign and malignant presentations. The classification matrix includes nine dermatological conditions: actinic keratosis, basal cell carcinoma, benign keratosis, dermatofibroma, melanoma, nevus, seborrheic keratosis, squamous cell carcinoma, and vascular lesions. The objective is to engineer a CNN model with robust diagnostic performance across a diverse lesion dataset, ensuring accurate identification and categorization of dermatological conditions. © 2024 IEEE.

关键词： Convolutional neural networks

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Time-Varying Dispersion Integer-Valued GARCH Models

arXiv

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arXiv 2022年

作者： Barreto-Souza, Wagner Piancastelli, Luiza S.C. Fokianos, Konstantinos Ombao, Hernando School of Mathematics and Statistics University College Dublin Dublin 4 Ireland Department of Mathematics and Statistics University of Cyprus Nicosia Cyprus Statistics Program King Abdullah University of Science and Technology Thuwal Saudi Arabia

We propose a general class of INteger-valued Generalized AutoRegressive Conditionally Heteroscedastic (INGARCH) processes by allowing time-varying mean and dispersion parameters, which we call time-varying dispersion INGARCH (tv-DINGARCH) models. More specifically, we consider mixed Poisson INGARCH models and allow for dynamic modeling of the dispersion parameter (as well as the mean), similar to the spirit of the ordinary GARCH models. We derive conditions to obtain first and second-order stationarity, and ergodicity as well. Estimation of the parameters is addressed and their associated asymptotic properties are established as well. A restricted bootstrap procedure is proposed for testing constant dispersion against time-varying dispersion. Monte Carlo simulation studies are presented for checking point estimation, standard errors, and the performance of the restricted bootstrap approach. We apply the tv-DINGARCH process to model the weekly number of reported measles infections in North Rhine-Westphalia, Germany, from January 2001 to May 2013, and compare its performance to the ordinary INGARCH approach. Copyright © 2022, The Authors. All rights reserved.

关键词： Monte Carlo methods

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SHARP BOUNDS FOR TOADER-TYPE MEANS IN TERMS OF TWO-PARAMETER MEANS

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Acta Mathematica Scientia 2021年第3期41卷 719-728页

作者： Yueying YANG Weimao QIAN Hongwei ZHANG Yuming CHU School of Mechanical and Electrical Engineering Huzhou Vocational&Technical CollegeHuzhou 313000China School of Continuing Education Huzhou Vocational&Technical CollegeHuzhou 313000China School of Mathematics and Statistics Changsha University of Science&TechnologyChangsha 410014China Department of Mathematics Huzhou UniversityHuzhou 313000China

In the article,we prove that the double inequalities Gp[λ1a+(1-λ1)b,λ1 b+(1-λ1)a]A1-p(a,b)0 with a≠b if and only ifλ1≤1/2-(1-(2/π)2/p)1/2/2,μ1≥1/2-(2p)1/2/(4 p),λ2≤1/2+(2(3/(2 s)(E(21/2/2)/π)1/s)-1)1/2/2 andμ2≥1/2+s1/2/(4 s)ifλ1,μ1∈(0,1/2),λ2,μ2∈(1/2,1),p≥1 and s≥1/2,where G(a,b)=(ab)1/2,A(a,b)=(a+b)/2,T(a,b)=∫0π/2(a2 cos2 t+b2 sin2)1/2 tdt/π,Q(a,b)=((a2+b2)/2)1/2,C(a,b)=(a2+b2)/(a+b)and E(r)=∫0π/2(1-r^(2) sin^(2))1/2 tdt.

关键词： Geometric mean arithmetic mean Toader mean ontraharmonic mean complete elliptic integral

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UNSYNCHRONIZED DECENTRALIZED Q-LEARNING: TWO TIMESCALE ANALYSIS BY PERSISTENCE

arXiv

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arXiv 2023年

作者： Yongacoglu, Bora Arslan, Gürdal Yüksel, Serdar Department of Electrical and Computer Engineering University of Toronto Canada Department of Electrical Engineering University of Hawaii Manoa United States Department of Mathematics and Statistics Queen’s University Canada

Non-stationarity is a fundamental challenge in multi-agent reinforcement learning (MARL), where agents update their behaviour as they learn. Many theoretical advances in MARL avoid the challenge of non-stationarity by coordinating the policy updates of agents in various ways, including synchronizing times at which agents are allowed to revise their policies. Synchronization enables analysis of many MARL algorithms via multi-timescale methods, but such synchronization is infeasible in many decentralized applications. In this paper, we study an unsynchronized variant of the decentralized Q-learning algorithm, a recent MARL algorithm for stochastic games. We provide sufficient conditions under which the unsynchronized algorithm drives play to equilibrium with high probability. Our solution utilizes constant learning rates in the Q-factor update, which we show to be critical for relaxing the synchronization assumptions of earlier work. Our analysis also applies to unsynchronized generalizations of a number of other algorithms from the regret testing tradition, whose performance is analyzed by multi-timescale methods that study Markov chains obtained via policy update dynamics. This work extends the applicability of the decentralized Q-learning algorithm and its relatives to settings in which parameters are selected in an independent manner, and tames non-stationarity without imposing the coordination assumptions of prior work. © 2023, CC BY.

关键词： Reinforcement learning

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Widely accessible method for 3D microflow mapping at high spatial and temporal resolutions

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Microsystems & Nanoengineering 2022年第4期8卷 57-71页

作者： Evan Lammertse Nikhil Koditala Martin Sauzade Hongxiao Li Qiang Li Luc Anis Jun Kong Eric Brouzes Department of Biomedical Engineering Stony Brook UniversityStony BrookNY 11794USA Department of Mathematics and Statistics Department of Computer ScienceGeorgia State UniversityAtlantaGA 30302USA Laufer Center for Physical and Quantitative Biology Stony Brook UniversityStony BrookNY 11794USA Cancer Center Stony Brook School of MedicineStony BrookNY 11794USA Institute for Engineering Driven Medicine Stony Brook UniversityStony BrookNY 11794USA

Advances in microfluidic technologies rely on engineered 3D flow patterns to manipulate samples at the ***,current methods for mapping flows only provide limited 3D and temporal resolutions or require highly specialized optical ***,we present a simple defocusing approach based on brightfield microscopy and open-source software to map micro-flows in 3D at high spatial and temporal *** workflow is both integrated in ImageJ and *** track seed particles in 2D before classifying their Z-position using a reference *** compare the performance of a traditional cross-correlation method and a deep learning model in performing the classification *** validate our method on three highly relevant microfluidic examples:a channel step expansion and displacement structures as single-phase flow examples,and droplet microfluidics as a twophase flow ***,we elucidate how displacement structures efficiently shift large particles across ***,we reveal novel recirculation structures and folding patterns in the internal flow of microfluidic *** simple and widely accessible brightfield technique generates high-resolution flow maps and it will address the increasing demand for controlling fluids at the microscale by supporting the efficient design of novel microfluidic structures.

关键词： flow resolution method

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A Stochastic Approximate Expectation-Maximization for Structure Determination Directly from Cryo-EM Micrographs

arXiv

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arXiv 2023年

作者： Kreymer, Shay Singer, Amit Bendory, Tamir The School of Electrical Engineering Tel Aviv University Tel Aviv Israel The Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

A single-particle cryo-electron microscopy (cryoEM) measurement, called a micrograph, consists of multiple two-dimensional tomographic projections of a three-dimensional molecular structure at unknown locations, taken under unknown viewing directions. All existing cryo-EM algorithmic pipelines first locate and extract the projection images, and then reconstruct the structure from the extracted images. However, if the molecular structure is small, the signal-to-noise ratio (SNR) of the data is very low, and thus accurate detection of projection images within the micrograph is challenging. Consequently, all standard techniques fail in low-SNR regimes. To recover molecular structures from measurements of low SNR, and in particular small molecular structures, we devise a stochastic approximate expectation-maximization algorithm to estimate the three-dimensional structure directly from the micrograph, bypassing locating the projection images. We corroborate our computational scheme with numerical experiments, and present successful structure recoveries from simulated noisy measurements. © 2023, CC BY.

关键词： Molecular structure

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Wave Propagation Model in a Human Long Poroelastic Bone under Effect of Magnetic Field and Rotation

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Computers, Materials & Continua 2021年第8期68卷 1485-1504页

作者： A.M.Abd-Alla Hanaa Abu-Zinadah S.M.Abo-Dahab J.Bouslimi M.Omri Department of Mathematics Sohag UniversitySohagEgypt Department of Statistics University of JeddahCollege of ScienceJeddahSaudi Arabia Department of Mathematics South Valley UniversityQena83523Egypt Department of Engineering Physics and Instrumentation National Institute of Applied Sciences and TechnologyCarthage UniversityTunisia Department of Physics Taif UniversityTaifSaudi Arabia Deanship of Scientific Research King Abdulaziz UniversityJeddahSaudi Arabia

This article is aimed at describing the way rotation and magnetic field affect the propagation of waves in an infinite poroelastic cylindrical *** offers a solution with an exact closed *** authors got and examined numerically the general frequency equation for poroelastic ***,they calculated the frequencies of poroelastic bone for different values of the magnetic field and *** the results of previous studies,the authors noticed little frequency dispersion in the wet *** proposed model will be applicable to wide-range parametric projects of bone mechanical *** the vibration of surface waves in rotating cylindrical,long human bones under the magnetic field can have an *** findings of the study are offered in ***,a comparison with the results of the literature is conducted to show the effect of rotation and magnetic field on the wave propagation *** is worth noting that the results of the study highly match those of the literature.

关键词： Propagation of waves rotation magnetic field poroelastic wet bone natural frequency magnetic field

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Comparative Study of Classical Machine Learning Models in Credit Scoring 6

Comparative Study of Classical Machine Learning Models in Cr...

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6th International Conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2024

作者： Meng, Xiaoqi Jia, Lihan Chen, Shuo Zhou, Yu Liu, Chang School of Mathematics and Statistics Hainan Normal University Haikou571158 China McGill University MontrealH3A 0G4 Canada Miami University Oxford45056 United States College of Information Engineering Shanghai Maritime University Shanghai200135 China Department of Electrical and Computer Engineering University of Illinois at Urbana Champaign IL61801 United States

ISBN: (纸本)9781837242672

Credit scoring is a crucial aspect of financial risk management, enabling lenders to evaluate the creditworthiness of individuals and entities. This paper presents a comprehensive comparison of various classical machine learning models to determine the most effective one for predicting credit scores. The evaluated models include logistic regression, decision trees, random forests, support vector machines, gradient boosting, adaptive boosting (AdaBoost), k-nearest neighbors (KNN), Naive Bayes, eXtreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LightGBM), multi-layer perceptron (MLP) classifiers, deep neural networks (DNN), and convolutional neural networks (CNN). Each model underwent 10-fold cross-validation and was assessed based on metrics such as accuracy, precision, recall, F1 score, and receiver operating characteristic curve (ROC curve). Results indicate that XGBoost and LightGBM outperform other models across multiple metrics, demonstrating their suitability for credit score prediction. The paper concludes with recommendations for selecting models tailored to specific business requirements and data characteristics, highlighting the significant potential of advanced machine learning techniques in enhancing credit scoring accuracy and reliability. © The Institution of engineering & Technology 2024.

关键词： Convolutional neural networks

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