scholarly journals Experimental dataset for measurements of freely vibrating structures equipped with impact dampers

Data in Brief ◽  
2021 ◽  
Vol 36 ◽  
pp. 107003
Author(s):  
Mohamed Gharib
Keyword(s):  
Experimental Dataset ◽  
Vibrating Structures

scholarly journals Modelling Weirs in Two-Dimensional Shallow Water Models

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2152
Author(s):  
Gonzalo García-Alén ◽  
Olalla García-Fonte ◽  
Luis Cea ◽  
Luís Pena ◽  
Jerónimo Puertas
Keyword(s):  
Experimental Data ◽  
Shallow Water ◽  
Shallow Water Equations ◽  
Water Model ◽  
Two Dimensional ◽  
Shallow Water Model ◽  
Experimental Dataset ◽  
River Hydraulics ◽  
Shallow Water Models ◽  
Water Models

2D models based on the shallow water equations are widely used in river hydraulics. However, these models can present deficiencies in those cases in which their intrinsic hypotheses are not fulfilled. One of these cases is in the presence of weirs. In this work we present an experimental dataset including 194 experiments in nine different weirs. The experimental data are compared to the numerical results obtained with a 2D shallow water model in order to quantify the discrepancies that exist due to the non-fulfillment of the hydrostatic pressure hypotheses. The experimental dataset presented can be used for the validation of other modelling approaches.

scholarly journals Statistical analysis of Stromboli VLP tremor in the band [0.1–0.5] Hz: some consequences for vibrating structures

2006 ◽  
Vol 13 (4) ◽  
pp. 393-400 ◽  
Author(s):  
E. De Lauro ◽  
S. De Martino ◽  
M. Falanga ◽  
M. Palo
Keyword(s):  
Frequency Band ◽  
High Frequency ◽  
Noise Source ◽  
Volcanic Tremor ◽  
Large Data ◽  
Musical Instruments ◽  
Data Set ◽  
Long Period ◽  
Vibrating Structures ◽  
Analyze Time Series

Abstract. We analyze time series of Strombolian volcanic tremor, focusing our attention on the frequency band [0.1–0.5] Hz (very long period (VLP) tremor). Although this frequency band is largely affected by noise, we evidence two significant components by using Independent Component Analysis with the frequencies, respectively, of ~0.2 and ~0.4 Hz. We show that these components display wavefield features similar to those of the high frequency Strombolian signals (>0.5 Hz). In fact, they are radially polarised and located within the crater area. This characterization is lost when an enhancement of energy appears. In this case, the presence of microseismic noise becomes relevant. Investigating the entire large data set available, we determine how microseismic noise influences the signals. We ascribe the microseismic noise source to Scirocco wind. Moreover, our analysis allows one to evidence that the Strombolian conduit vibrates like the asymmetric cavity associated with musical instruments generating self-sustained tones.

Analysis of randomly vibrating structures under hybrid uncertainty

2009 ◽  
Vol 31 (11) ◽  
pp. 2677-2686 ◽  
Author(s):  
Giuseppe Carlo Marano ◽  
Emiliano Morrone ◽  
Giuseppe Quaranta
Keyword(s):  
Vibrating Structures ◽  
Hybrid Uncertainty

Digital Tuning as an Exploratory Tool in the Modal Analysis of Randomly-Loaded Vibrating Structures

AIAA Journal ◽  
1973 ◽  
Vol 11 (11) ◽  
pp. 1539-1544
Author(s):  
D. R. CRUISE ◽  
R. G. CHRISTIANSEN
Keyword(s):  
Modal Analysis ◽  
Vibrating Structures ◽  
Digital Tuning

scholarly journals Total Acoustic Power Radiated by Planar Vibrating Structures.

1993 ◽  
Vol 59 (566) ◽  
pp. 3078-3085 ◽  
Author(s):  
Nobuo Tanaka ◽  
Scott Snyder D. ◽  
Yoshihiro Kikushima ◽  
Masaharu Kuroda
Keyword(s):  
Acoustic Power ◽  
Vibrating Structures

The Application of Large-Scale State Estimation to Vibrating Structures *

1982 ◽  
Vol 15 (4) ◽  
pp. 1289-1294
Author(s):  
D.L. Lager ◽  
S.G. Azevedo ◽  
J.V. Candy
Keyword(s):  
State Estimation ◽  
Large Scale ◽  
Vibrating Structures

scholarly journals Noise2Atom: Unsupervised Denoising for Scanning Transmission Electron Microscopy Images

2020 ◽  
Author(s):  
Feng Wang ◽  
Trond R Henninen ◽  
Debora Keller ◽  
Rolf Erni
Keyword(s):  
Electron Microscopy ◽  
Transmission Electron Microscopy ◽  
Scanning Transmission Electron Microscopy ◽  
Image Quality Assessment ◽  
Noise Model ◽  
Target Domain ◽  
Experimental Dataset ◽  
Scanning Transmission Electron ◽  
Transmission Electron ◽  
Scanning Transmission

Abstract We propose an effective deep learning model to denoise scanning transmission electron microscopy (STEM) image series, named Noise2Atom, to map images from a source domain S to a target domain C, where S is for our noisy experimental dataset, and C is for the desired clear atomic images. Noise2Atom uses two external networks to apply additional constraints from the domain knowledge. This model requires no signal prior, no noise model estimation, and no paired training images. The only assumption is that the inputs are acquired with identical experimental configurations. To evaluate the restoration performance of our model, as it is impossible to obtain ground truth for our experimental dataset, we propose consecutive structural similarity (CSS) for image quality assessment, based on the fact that the structures remain much the same as the previous frame(s) within small scan intervals. We demonstrate the superiority of our model by providing evaluation in terms of CSS and visual quality on different experimental datasets.

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