Titlebook: Data Science in Engineering Vol. 10; Proceedings of the 4 Thomas Matarazzo,Fran?ois Hemez,Austin Downey Conference proceedings 2025 The Soc

Lampoon

書目名稱Data Science in Engineering Vol. 10影響因子(影響力)




書目名稱Data Science in Engineering Vol. 10影響因子(影響力)學(xué)科排名




書目名稱Data Science in Engineering Vol. 10網(wǎng)絡(luò)公開度




書目名稱Data Science in Engineering Vol. 10網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Data Science in Engineering Vol. 10被引頻次




書目名稱Data Science in Engineering Vol. 10被引頻次學(xué)科排名




書目名稱Data Science in Engineering Vol. 10年度引用




書目名稱Data Science in Engineering Vol. 10年度引用學(xué)科排名




書目名稱Data Science in Engineering Vol. 10讀者反饋




書目名稱Data Science in Engineering Vol. 10讀者反饋學(xué)科排名

強(qiáng)制令

Statistical Evaluation of Machine Learning for Vibration Data,om large quantities of data collected under varying conditions to make predictions, such as detection, identification, and characterization. Current ML implementation consists of training a model for narrow, task-specific needs, such as training a neural network to detect the presence of structural

肉體

mortuary

模范

Optimal Modeling of Deep Groove Ball Bearings for Application in Multibody Dynamics Simulations,e normal contact force between the rolling elements and the two bearing races are established, including the effects of raceway surface roughness. Various contact force models with different hysteresis damping formulations are examined in order to select the best suited for the application. The bear

Matrimony

Utilization of Bridge Acceleration Response for Indirect Strain Sensing,onse under daily traffic loading. However, deploying and maintaining strain sensors is costly and labor-intensive compared to acceleration sensors. To address this issue, we propose a neural network architecture that can perform indirect sensing by estimating strain from measured acceleration respon

Matrimony

過份艷麗

Physics-Informed Machine Learning Part II: Applications in Structural Response Forecasting,interpretability of predictive models. By incorporating physical laws and constraints into the learning process, physics-informed machine learning enables more robust predictions and reduces the need for large amounts of training data. In part II of this two-part series, the authors present structur

使迷惑

diathermy

Understanding High-Frequency Modes in Electromechanical Impedance Measurement Using Noncontact Vibr measurements are recorded via a bonded piezoelectric transducer, at a high-frequency range, typically 30 kHz and above. Because EMI measurements are single input single output, the peaks in these measurements can be related to either mechanical, electrical, or coupled electromechanical modes, espec