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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書目名稱Data Science in Engineering, Volume 9影響因子(影響力)




書目名稱Data Science in Engineering, Volume 9影響因子(影響力)學科排名




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書目名稱Data Science in Engineering, Volume 9網(wǎng)絡(luò)公開度學科排名




書目名稱Data Science in Engineering, Volume 9被引頻次




書目名稱Data Science in Engineering, Volume 9被引頻次學科排名




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書目名稱Data Science in Engineering, Volume 9年度引用學科排名




書目名稱Data Science in Engineering, Volume 9讀者反饋




書目名稱Data Science in Engineering, Volume 9讀者反饋學科排名

LANCE

Malcontent

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

BROW

傾聽

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

做作

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

做作

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

媒介

Robust

種族被根除

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