orient 发表于 2025-3-30 09:27:34
Optimal Modeling of Deep Groove Ball Bearings for Application in Multibody Dynamics Simulations, bearing model. The system’s response is examined by means of signal analysis as well as by using deep learning methods in order to characterize the health state of the system, thus proving the applicability of the present bearing modeling method for condition monitoring applications.NATTY 发表于 2025-3-30 15:38:51
Utilization of Bridge Acceleration Response for Indirect Strain Sensing,h our novel approach, we can estimate strain with high accuracy from acceleration data and reconstruct rainflow cycle counting diagrams that can subsequently be used for bridge condition and life cycle assessment.法律 发表于 2025-3-30 16:46:22
http://reply.papertrans.cn/29/2845/284452/284452_53.png极大的痛苦 发表于 2025-3-30 22:40:57
http://reply.papertrans.cn/29/2845/284452/284452_54.pngarbovirus 发表于 2025-3-31 02:47:18
http://reply.papertrans.cn/29/2845/284452/284452_55.pngMaximizer 发表于 2025-3-31 08:40:02
On the Use of Symbolic Regression for Population-Based Modelling of Structures,that of symbolic regression and the transfer is attempted between an extensively monitored structure and a data-poor structure for a regression application. The methodology is applied in a prognosis problem of crack growth in metal plates, and the results reveal the potential of symbolic regressionProsaic 发表于 2025-3-31 13:00:28
Identification of Bird Species in Large Multi-channel Data Streams Using Distributed Acoustic Sensi benefit that DAS does not suffer from time synchronization errors and remote power issues like traditional microphone arrays. This work investigates the performance of DAS when used to detect bird calls, with particular focus on the Great Horned Owl (GHO), an indicator species for prey vulnerabilitCardiac-Output 发表于 2025-3-31 13:55:37
http://reply.papertrans.cn/29/2845/284452/284452_58.pngNEX 发表于 2025-3-31 19:06:48
Adaptive Radio Frequency Target Localization,blem as a Partially Observable Markov Decision Process (POMDP) and was solved through the use of particle filtering and reinforcement learning. The purpose of this work is to build upon this prior study by training a deep neural network in a simulated environment and applying inference in the real w慢慢啃 发表于 2025-3-31 23:22:30
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