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Angklung Smart Band: An IoT-Based System to Support Music Activities for Elderly in Thailand

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Angklung Smart Band: An IoT-Based System to Support Music Ac...

2022 IEEE European Technology and Engineering Management Summit, E-TEMS 2022

作者： Hiranpanthaporn, Saranya Phoasavadi, Pornprapit Cooharojananone, Nagul Technopreneurship and Innovation Management Program Graduate School Chulalongkorn University Bangkok Bangkok Thailand Emili Sagol Creative Arts Research and Innovation for Well-being Center Chulalongkorn University Faculty of Fine and Applied Arts Department of Thai Music Bangkok Bangkok Thailand Chulalongkorn University Faculty of Science Department of Mathematics and Computer Science Bangkok Bangkok Thailand

ISBN: (数字)9781665411004

ISBN: (纸本)9781665411004

Organizing music activities for the elderly is another way that supports their well-being. Angklung, an Indonesian musical instrument, has been used for music activities for the elderly in Thailand with the hand signs method. Unfortunately, music activity is not widely used because the experts with the hand sign skills are few and not enough for demanding in the aging healthcare. At the same time, technologies of remote systems using the Internet of Things (IoT) have been developed to assist the elderly in supporting and improving their quality of life. This paper will introduce 'Angklung Smart Band,' an IoT-based system to support Angklung music activities for the elderly in Thailand. This work aims at setting up Angklung music activities for the elderly by using technology instead of hand signs. The system is composed of a web application, IoT devices, and Angklung musical instruments connected together via a wireless network. We did a preliminary study with the experts to improve experiences and system performance. After that, we asked the music experts to use the smart band to conduct the music activities experiment with the elderly for three days and give us feedback. The results found that the smart band could assist the Angklung music experts and help with the shortage of music expertise in aged care facilities or nursing homes. © 2022 IEEE.

关键词： Music

Parallel Approximations for High-Dimensional Multivariate Normal Probability Computation in Confidence Region Detection Applications

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Parallel Approximations for High-Dimensional Multivariate No...

International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Xiran Zhang Sameh Abdulah Jian Cao Hatem Ltaief Ying Sun Marc G. Genton David E. Keyes Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Statistics Program King Abdullah University of Science and Technology Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Mathematics The University of Houston Houston Texas USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Addressing the statistical challenge of computing the multivariate normal (MVN) probability in high dimensions holds significant potential for enhancing various applications. For example, the critical task of detecting confidence regions where a process probability surpasses a specific threshold is essential in diverse applications, such as pinpointing tumor locations in magnetic resonance imaging (MRI) scan images, determining hydraulic parameters in groundwater flow issues, and forecasting regional wind power to optimize wind turbine placement, among numerous others. One common way to compute high-dimensional MVN probabilities is the Separation-of-Variables (SOV) algorithm. This algorithm is known for its high computational complexity of O(n 3 ) and space complexity of O(n 2 ), mainly due to a Cholesky factorization operation for an n×n covariance matrix, where n represents the dimensionality of the MVN problem. This work proposes a high-performance computing framework that allows scaling the SOV algorithm and, subsequently, the confidence region detection algorithm. The framework leverages parallel linear algebra algorithms with a task-based programming model to achieve performance scalability in computing process probabilities, especially on large-scale systems. In addition, we enhance our implementation by incorporating Tile Low-Rank (TLR) approximation techniques to reduce algorithmic complexity without compromising the necessary accuracy. To evaluate the performance and accuracy of our framework, we conduct assessments using simulated data and a wind speed dataset. Our proposed implementation effectively handles high-dimensional multivariate normal (MVN) probability computations on shared and distributed-memory systems using finite precision arithmetics and TLR approximation computation. Performance results show a significant speedup of up to 20X in solving the MVN problem using TLR approximation compared to the reference dense solution without sacrificin

关键词： Accuracy Tensors Wind speed Magnetic resonance imaging Linear algebra Wind power generation Approximation algorithms

A Cautionary Note on Poli's Stability Condition for Particle Swarm Optimization

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A Cautionary Note on Poli's Stability Condition for Particle...

IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Daniel H. Von Eschwege Andries P. Engelbrecht Department of Industrial Engineering Stellenbosch University Stellenbosch South Africa Department of Industrial Engineering and Computer Science Division Stellenbosch University Stellenbosch South Africa Center for Applied Mathematics and Bioinformatics Gulf University for Science and Technology Kuwait Kuwait

Particle swarm optimization (PSO) is a swarm intelligence algorithm that finds candidate solutions by iteratively updating the positions of particles in a swarm. PSO performance depends on the use of a suitable control parameter (CP) configuration, which governs the trade-off between exploration and exploitation in the swarm. Various methods of adapting or tuning CPs exist, but many result in exploding particle velocities and an unstable search process. Poli's stability condition ensures convergence in the mathematical limit, and is often used to inform CP configuration. However, this study shows that since it does not place any practical convergence constraints, it cannot be used to guarantee a stable search process. Velocity explosion occurs nonetheless and can lead to floating-point overflow and numerical instability. The investigation into various CP configurations across diverse functions and measurements of particle velocities provides empirical evidence of velocity explosion, and cautions against the assumption that enforcing Poli's criterion guarantees stability. The findings underline the need for comprehensive understanding of CP tuning and stability conditions in PSO, as well as the crucial role of empirical evidence in evaluating the real-world performance of swarm intelligence algorithms.

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Statistical Data Mining with Slime Mould Optimization for Intelligent Rainfall Classification

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computer Systems science & Engineering 2023年第10期47卷 919-935页

作者： Ramya Nemani G.Jose Moses Fayadh Alenezi K.Vijaya Kumar Seifedine Kadry Jungeun Kim Keejun Han Department of Mathematics Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering GITAM School of TechnologyVishakhapatnam CampusGITAM(Deemed to be a University)VishakhapatnamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so *** data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the *** varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other ***,this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)*** the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation *** rainfall *** neural network with long short-term memory(CNN-LSTM)technique is *** last,this study involves the pelican optimization algorithm(POA)as a hyperparameter *** experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive *** comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.

关键词： Statistical data mining predictive models deep learning rainfall prediction parameter tuning

State-space models are accurate and efficient neural operators for dynamical systems

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arXiv 2024年

作者： Hu, Zheyuan Daryakenari, Nazanin Ahmadi Shen, Qianli Kawaguchi, Kenji Em Karniadakis, George Department of Computer Science National University of Singapore Singapore119077 Singapore Center for Biomedical Engineering School of Engineering Brown University ProvidenceRI02912 United States Division of Applied Mathematics Brown University ProvidenceRI02912 United States Advanced Computing Mathematics and Data Division Pacific Northwest National Laboratory RichlandWA United States

Physics-informed machine learning (PIML) has emerged as a promising alternative to classical methods for predicting dynamical systems, offering faster and more generalizable solutions. However, existing models, including recurrent neural networks (RNNs), transformers, and neural operators, face challenges such as long-time integration, long-range dependencies, chaotic dynamics, and extrapolation, to name a few. To this end, this paper introduces state-space models implemented in Mamba for accurate and efficient dynamical system operator learning. Mamba addresses the limitations of existing architectures by dynamically capturing long-range dependencies and enhancing computational efficiency through reparameterization techniques. To extensively test Mamba and compare against another 11 baselines, we introduce several strict extrapolation testbeds that go beyond the standard interpolation benchmarks. We demonstrate Mamba’s superior performance in both interpolation and challenging extrapolation tasks. Mamba consistently ranks among the top models while maintaining the lowest computational cost and exceptional extrapolation capabilities. Moreover, we demonstrate the good performance of Mamba for a real-world application in quantitative systems pharmacology for assessing the efficacy of drugs in tumor growth under limited data scenarios. Taken together, our findings highlight Mamba’s potential as a powerful tool for advancing scientific machine learning in dynamical systems modeling. (The code will be available at https://***/zheyuanhu01/State_ Space_Model_Neural_Operator upon acceptance.) © 2024, CC BY.

关键词： Recurrent neural networks

A SPECTRAL LEVENBERG-MARQUARDT-DEFLATION METHOD FOR MULTIPLE SOLUTIONS OF SEMILINEAR ELLIPTIC SYSTEMS

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arXiv 2025年

作者： Li, Lin Zhou, Yuheng Xie, Pengcheng Li, Huiyuan School of Mathematics and Physics University of South China Hengyang China Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States State Key Laboratory of Computer Science Laboratory of Parallel Computing Institute of Software Chinese Academy of Sciences Beijing100190 China

Many nonlinear differential equations arising from practical problems may permit nontrivial multiple solutions relevant to applications, and these multiple solutions are helpful to deeply understand these practical problems and to improve some applications. Developing an efficient numerical method for finding multiple solutions is very necessary due to the nonlinearity and multiple solutions of these equations. Moreover, providing an efficient iteration plays an important role in successfully obtaining multiple solutions with fast and stable convergence. In the current paper, an efficient algorithm for finding multiple solutions of semilinear elliptic systems is proposed, where the trust region Levenberg-Marquardt method is firstly used to iterate the resulted nonlinear algebraic system. When the nonlinear term in these equations has only the first derivative, our algorithm can efficiently find multiple solutions as well. Several numerical experiments are tested to show the efficiency of our algorithm, and some solutions which have not been shown in the literature are also found and *** Codes 65N35, 65N22, 65F05, 65L10 Copyright © 2025, The Authors. All rights reserved.

关键词： Nonlinear equations

Multi-index Sequential Monte Carlo ratio estimators for Bayesian Inverse problems

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arXiv 2022年

作者： Law, Kody J.H. Walton, Neil Yang, Shangda Jasra, Ajay School of Mathematics University of Manchester ManchesterM13 9PL United Kingdom Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

We consider the problem of estimating expectations with respect to a target distribution with an unknown normalizing constant, and where even the unnormalized target needs to be approximated at finite resolution. This setting is ubiquitous across science and engineering applications, for example in the context of Bayesian inference where a physics-based model governed by an intractable partial differential equation (PDE) appears in the likelihood. A multi-index Sequential Monte Carlo (MISMC) method is used to construct ratio estimators which provably enjoy the complexity improvements of multi-index Monte Carlo (MIMC) as well as the efficiency of Sequential Monte Carlo (SMC) for inference. In particular, the proposed method provably achieves the canonical complexity of MSE−1, while single level methods require MSE−ξ for ξ > 1. This is illustrated on examples of Bayesian inverse problems with an elliptic PDE forward model in 1 and 2 spatial dimensions, where ξ = 5/4 and ξ = 3/2, respectively. It is also illustrated on more challenging log Gaussian process models, where single level complexity is approximately ξ = 9/4 and multilevel Monte Carlo (or MIMC with an inappropriate index set) gives ξ = 5/4 + ω, for any ω > 0, whereas our method is again canonical. We also provide novel theoretical verification of the product-form convergence results which MIMC requires for Gaussian processes built in spaces of mixed regularity defined in the spectral domain, which facilitates acceleration with fast Fourier transform methods via a cumulant embedding strategy, and may be of independent interest in the context of spatial statistics and machine learning. Copyright © 2022, The Authors. All rights reserved.

关键词： Inverse problems

Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker-Planck Equations

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arXiv 2024年

作者： Hu, Zheyuan Zhang, Zhongqiang Em Karniadakis, George Kawaguchi, Kenji Department of Computer Science National University of Singapore Singapore119077 Singapore Department of Mathematical Sciences Worcester Polytechnic Institute WorcesterMA01609 United States Division of Applied Mathematics Brown University USA ProvidenceRI02912 United States Advanced Computing Mathematics and Data Division Pacific Northwest National Laboratory RichlandWA United States

The Fokker-Planck (FP) equation is a foundational partial differential equation (PDE) in stochastic processes involving Brownian motions. However, the curse of dimensionality (CoD) poses a formidable challenge when dealing with high-dimensional FP equations. Although Monte Carlo simulation and (vanilla) Physics-Informed Neural Networks (PINNs) have shown the potential to tackle CoD, both methods exhibit significant numerical errors in high dimensions when dealing with the probability density function (PDF) associated with Brownian motion. The point-wise PDF values tend to decrease exponentially as dimensionality increases, surpassing the precision of numerical simulations and resulting in substantial errors. Moreover, due to its massive sampling, Monte Carlo fails to offer fast sampling. Modeling the logarithm likelihood (LL) via vanilla PINNs transforms the FP equation into a notoriously difficult Hamilton-Jacobi-Bellman (HJB) equation, which is impractical for PINN learning, whose error grows rapidly with dimension. To this end, we propose a novel approach utilizing a score-based solver to fit the score function in stochastic differential equations (SDEs). The score function, defined as the gradient of the LL, plays a fundamental role in inferring LL and PDF and enables fast SDE sampling, offering an effective means to overcome the CoD. Three fitting methods, Score Matching (SM), Sliced Score Matching (SSM), and Score-PINN, are introduced, each contributing unique advantages in computational complexity, accuracy, and generality. The proposed score-based SDE solver operates in two stages: first, employing score matching or Score-PINN to acquire the score function;and second, solving the LL via an ordinary differential equation (ODE) using the obtained score function. Comparative evaluations across these methods showcase varying trade-offs. The proposed methodology is evaluated across diverse SDEs, including anisotropic Ornstein-Uhlenbeck processes, geometric Browni

关键词： Fokker Planck equation

Multiphysics simulations of microstructure influence on hysteresis and eddy current losses of electrical steel

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arXiv 2025年

作者： Kühn, Patrick Yang, Yangyiwei Chen, Guanyu Foster, Shanelle N. Egger, Herbert Xu, Bai-Xiang Mechanics of Functional Materials Division Institute of Materials Science Technische Universität Darmstadt Darmstadt64287 Germany Department of Electrical and Computer Engineering Michigan State University MI48824 United States Johann Radon Institute for Computational and Applied Mathematics Linz Austria Institute for Numerical Mathematics Johannes Kepler University Linz Austria

Improving efficiency of electrical machines requires fundamental knowledge on the mechanisms behind magnetic and eddy current losses of the magnetic core materials, with Fe-Si alloy as a prototype. These losses are intrinsically influenced by the microstructure of the materials. This necessitates physics-based, microstructure-informed multiscale simulations. In the present paper, we utilised micromagnetic simulations and computational homogenization methods to calculate the effective hysteresis and effective conductivities of Fe-Si electrical steels. To demonstrate the methodology, binder-jet printed electrical steel material samples with different microstructure were investigated. The microstructure samples were digitized based on both the descriptor-based synthetic reconstruction and SEM-image-based digitization. More samples were generated with varying microstructure features such as grain size and grain boundary phases. The micromagnetic simulations were then performed to investigate the magnetic hysteresis and hysteresis loss. The eddy current loss was also evaluated by using the effective conductivity through computational homogenization. By performing parameter research on a series of synthetic microstructures, effects of average grain size and grain boundary (GB) phase thickness on the hysteresis loss and eddy current loss were unveiled. An average grain size around 120 µm has the lowest hysteresis loss, although the eddy current loss increases with the grain size. Increasing GB-phase thickness helps reduce both losses. Results indicate the potential to decrease loss of magnetic core materials by microstructure optimization. © 2025, CC BY.

关键词： Silicon alloys