A Variable Data Fusion Approach for Electromechanical Impedance-Based Damage Detection

There is continuing research in the area of structural health monitoring (SHM) as it may allow a reduction in maintenance costs as well as lifetime extension. The search for a low-cost health monitoring system that is able to detect small levels of damage is still on-going. The present study is one more step in this direction. This paper describes a data fusion technique by combining the information for robust damage detection using the electromechanical impedance (EMI) method. The EMI method is commonly used for damage detection due to its sensitivity to low levels of damage. In this paper, the information of resistance (R) and conductance (G) is studied in a selected frequency band and a novel data fusion approach is proposed. A novel fused parameter (F) is developed by combining the information from G and R. The difference in the new metric under different damage conditions is then quantified using established indices such as the root mean square deviation (RMSD) index, mean absolute percentage deviation (MAPD), and root mean square deviation using k-th state as the reference (RMSDk). The paper presents an application of the new metric for detection of damage in three structures, namely, a thin aluminum (Al) plate with increasing damage severity (simulated with a drilled hole of increasing size), a glass fiber reinforced polymer (GFRP) composite beam with increasing delamination and another GFRP plate with impact-induced damage scenarios. Based on the experimental results, it is apparent that the variable F increases the robustness of the damage detection as compared to the quantities R and G.

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A comparative study of two indirect methods to monitor surface integrity of ground components

Structural Health Monitoring ◽

10.1177/1475921720903442 ◽

2020 ◽

Vol 19 (6) ◽

pp. 1856-1870 ◽

Cited By ~ 1

Author(s):

Henrique Butzlaff Hübner ◽

Rosemar Batista da Silva ◽

Marcus Antônio Viana Duarte ◽

Marcio Bacci da Silva ◽

Fabio Isaac Ferreira ◽

...

Keyword(s):

Acoustic Emission ◽

Root Mean Square ◽

Root Mean Square Deviation ◽

Surface Integrity ◽

Mean Square ◽

Mean Square Deviation ◽

Time Frequency ◽

Electromechanical Impedance ◽

Deviation Index ◽

Radial Depth

One of the major challenges in grinding is to conciliate the material removal rate with components free from damages. Due to the poor thermal conductivity of conventional grinding wheels, most of the heat generated during grinding is transferred to the workpiece surface and subsurface, which can cause thermal damages and impair the performance in service of machined components. Hence, it is very important to monitor the grinding process to ensure the quality of the machined parts. Thus, this work presents an innovative study comparing two indirect monitoring methods in monitoring surface integrity of steel during grinding: the acoustic emission technique and the electromechanical impedance technique. Worktable speed and radial depth of cut were used as input parameters. Visual inspection and scanning electron microscopy images of ground surfaces as well as microhardness were the output variables used to assess surface integrity and to establish a relationship with the acoustic emission and electromechanical impedance techniques. Since the acoustic emission signals are non-stationary in nature, these signals were analyzed in the time–frequency domain by applying the short-time Fourier transform and the continuous wavelet transform. The root mean square deviation index was extracted as feature from the acoustic emission and electromechanical impedance signals. Results showed that both techniques presented similar results. The root mean square deviation index showed a good correlation with alterations in surface integrity under the conditions investigated.

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GNINA 1.0: molecular docking with deep learning

Journal of Cheminformatics ◽

10.1186/s13321-021-00522-2 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Andrew T. McNutt ◽

Paul Francoeur ◽

Rishal Aggarwal ◽

Tomohide Masuda ◽

Rocco Meli ◽

...

Keyword(s):

Molecular Docking ◽

Root Mean Square ◽

Root Mean Square Deviation ◽

Computational Cost ◽

Scoring Function ◽

Binding Pocket ◽

Protein Docking ◽

Mean Square ◽

Mean Square Deviation ◽

Autodock Vina

AbstractMolecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and evaluate the 1.0 release of the Gnina docking software, which utilizes an ensemble of convolutional neural networks (CNNs) as a scoring function. We also explore an array of parameter values for Gnina 1.0 to optimize docking performance and computational cost. Docking performance, as evaluated by the percentage of targets where the top pose is better than 2Å root mean square deviation (Top1), is compared to AutoDock Vina scoring when utilizing explicitly defined binding pockets or whole protein docking. Gnina, utilizing a CNN scoring function to rescore the output poses, outperforms AutoDock Vina scoring on redocking and cross-docking tasks when the binding pocket is defined (Top1 increases from 58% to 73% and from 27% to 37%, respectively) and when the whole protein defines the binding pocket (Top1 increases from 31% to 38% and from 12% to 16%, respectively). The derived ensemble of CNNs generalizes to unseen proteins and ligands and produces scores that correlate well with the root mean square deviation to the known binding pose. We provide the 1.0 version of Gnina under an open source license for use as a molecular docking tool at https://github.com/gnina/gnina.

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G-RMSD: Root Mean Square Deviation Based Method for Three-dimensional Molecular Similarity Determination

Bulletin of the Chemical Society of Japan ◽

10.1246/bcsj.20200258 ◽

2020 ◽

Author(s):

Tomonori Fukutani ◽

Kohei Miyazawa ◽

Satoru Iwata ◽

Hiroko Satoh

Keyword(s):

Root Mean Square ◽

Root Mean Square Deviation ◽

Molecular Similarity ◽

Three Dimensional ◽

Mean Square ◽

Mean Square Deviation

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root-mean-square deviation

10.1007/springerreference_23871 ◽

2011 ◽

Keyword(s):

Root Mean Square ◽

Root Mean Square Deviation ◽

Mean Square ◽

Mean Square Deviation

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Fast collocation for Moho estimation from GOCE gravity data: the Iran case study

Geophysical Journal International ◽

10.1093/gji/ggaa026 ◽

2020 ◽

Vol 221 (1) ◽

pp. 651-664

Author(s):

H Heydarizadeh Shali ◽

D Sampietro ◽

A Safari ◽

M Capponi ◽

A Bahroudi

Keyword(s):

Root Mean Square ◽

Root Mean Square Deviation ◽

Seismic Data ◽

Active Faults ◽

Earth’S Crust ◽

Arabian Plate ◽

Moho Depth ◽

Mean Square ◽

Mean Square Deviation ◽

Earth's Crust

SUMMARY The study of the discontinuity between crust and mantle beneath Iran is still an open issue in the geophysical community due to its various tectonic features created by the collision between the Iranian and Arabian Plate. For instance in regions such as Zagros, Alborz or Makran, despite the number of studies performed, both by exploiting gravity or seismic data, the depth of the Moho and also interior structure is still highly uncertain. This is due to the complexity of the crust and to the presence of large short wavelength signals in the Moho depth. GOCE observations are capable and useful products to describe the Earth’s crust structure either at the regional or global scale. Furthermore, it is plausible to retrieve important information regarding the structure of the Earth’s crust by combining the GOCE observations with seismic data and considering additional information. In the current study, we used as observation a grid of second radial derivative of the anomalous gravitational potential computed at an altitude of 221 km by means of the space-wise approach, to study the depth of the Moho. The observations have been reduced for the gravitational effects of topography, bathymetry and sediments. The residual gravity has been inverted accordingly to a simple two-layer model. In particular, this guarantees the uniqueness of the solution of the inverse problem which has been regularized by means of a collocation approach in the frequency domain. Although results of this study show a general good agreement with seismically derived depths with a root mean square deviation of 6 km, there are some discrepancies under the Alborz zone and also Oman sea with a root mean square deviation up 10 km for the former and an average difference of 3 km for the latter. Further comparisons with the natural feature of the study area, for instance, active faults, show that the resulting Moho features can be directly associated with geophysical and tectonic blocks.

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