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Titlebook: Data Science in Engineering, Volume 9; Proceedings of the 4 Ramin Madarshahian,Francois Hemez Conference proceedings 2022 The Society for E

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發(fā)表于 2025-3-21 18:25:01 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Data Science in Engineering, Volume 9
副標(biāo)題Proceedings of the 4
編輯Ramin Madarshahian,Francois Hemez
視頻videohttp://file.papertrans.cn/264/263128/263128.mp4
叢書名稱Conference Proceedings of the Society for Experimental Mechanics Series
圖書封面Titlebook: Data Science in Engineering, Volume 9; Proceedings of the 4 Ramin Madarshahian,Francois Hemez Conference proceedings 2022 The Society for E
描述.Data Science in Engineering, Volume 9:??Proceedings of the 40.th.?IMAC,.?.A Conference and Exposition on Structural Dynamics, 2022,?.the nineth volume of nine from the Conference brings together contributions to this important area of research and engineering.??The collection presents early findings and case studies on fundamental and applied aspects of Data Science in Engineering, including papers on:.Novel Data-driven Analysis Methods.Deep Learning Gaussian Process Analysis.Real-time Video-based Analysis.Applications to Nonlinear Dynamics and Damage Detection.High-rate Structural Monitoring and Prognostics.
出版日期Conference proceedings 2022
關(guān)鍵詞data science; Structural Dynamics; Dynamic Substructures; Structural Engineering; Conference Proceedings
版次1
doihttps://doi.org/10.1007/978-3-031-04122-8
isbn_softcover978-3-031-04124-2
isbn_ebook978-3-031-04122-8Series ISSN 2191-5644 Series E-ISSN 2191-5652
issn_series 2191-5644
copyrightThe Society for Experimental Mechanics, Inc. 2022
The information of publication is updating

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Estimation of Structural Vibration Modal Properties Using a Spike-Based Computing Paradigm, combine spike-based computing and machine-learning-based neural networks that emulate the operation of the human brain. Spiking neural networks have the ability to be easily integrated into neuromorphic hardware, such as Intel’s . chip. The advantages of neuromorphic hardware are its high-speed com
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Transmittance Anomalies for Model-Based Damage Detection with Finite Element-Generated Data and Deee on damage detection and identification tasks. The main advantage of finite element (FE)-generated data is the substitution of costly and sometimes impossible experiments to acquire data for different healthy and damaged states. On the other hand, numerically generated data is strongly limited on t
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Machine Learning-Based Condition Monitoring with Multibody Dynamics Models for Gear Transmission Faata is used to train a convolutional neural network (CNN) which performs damage identification on two experimental damaged states. The multibody dynamics (MBD) model of a two-stage helical gear transmission is first developed and used to model the healthy and the damaged state of the problem. Data i
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Structural Damage Detection Framework Using Metaheuristic Algorithms and Optimal Finite Element Moda structure. The recent trends show that there is an increasing interest in the use of machine learning (ML) for SHM systems that rely on the experimentally measured data or artificially collected data to properly train the ML model for classification. The proposed method however is taking another a
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Data-Driven Structural Identification for Turbomachinery Blisks,ece of material with uniform sector-to-sector material properties and geometry. However, due to manufacturing tolerances, blisks contain sector-to-sector perturbations in material properties and geometry known as mistuning, which can result in increased response amplitudes due to energy localization
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