scholarly journals Displacement reconstruction and strain refinement of clustering-based homogenization

2021 ◽  
pp. 100285
Author(s):  
Lei Zhang ◽  
Shaoqiang Tang
Keyword(s):  
Displacement Reconstruction

Real-time displacement reconstruction by orthogonalFabry-Perot interferometer

2021 ◽  
Author(s):  
Hongwei Liang ◽  
Shiwei Liu ◽  
SIHAN CHEN ◽  
Zhen Huang ◽  
Chunlei Jiang ◽  
...  
Keyword(s):  
Real Time ◽  
Displacement Reconstruction

scholarly journals Reconstruction with velocities

2020 ◽  
Vol 494 (3) ◽  
pp. 4244-4254 ◽  
Author(s):  
Hong-Ming Zhu ◽  
Martin White ◽  
Simone Ferraro ◽  
Emmanuel Schaan
Keyword(s):  
Boundary Effect ◽  
Large Fraction ◽  
Acoustic Oscillation ◽  
Reconstruction Algorithms ◽  
Microwave Background ◽  
Level Measurement ◽  
Redshift Surveys ◽  
Cent Level ◽  
Velocity Information ◽  
Displacement Reconstruction

ABSTRACT Reconstruction is becoming a crucial procedure of galaxy clustering analysis for future spectroscopic redshift surveys to obtain subper cent level measurement of the baryon acoustic oscillation scale. Most reconstruction algorithms rely on an estimation of the displacement field from the observed galaxy distribution. However, the displacement reconstruction degrades near the survey boundary due to incomplete data and the boundary effects extend to ${\sim}100\, \mathrm{Mpc}/h$ within the interior of the survey volume. We study the possibility of using radial velocities measured from the cosmic microwave background observation through the kinematic Sunyaev–Zeldovich effect to improve performance near the boundary. We find that the boundary effect can be reduced to ${\sim}30-40\, \mathrm{Mpc}/h$ with the velocity information from Simons Observatory. This is especially helpful for dense low redshift surveys where the volume is relatively small and a large fraction of total volume is affected by the boundary.

scholarly journals Micro-displacement reconstruction using a laser self-mixing grating interferometer with multiple-diffraction

2017 ◽  
Vol 25 (25) ◽  
pp. 31394 ◽  
Author(s):  
Dongmei Guo ◽  
Liheng Shi ◽  
Yanguang Yu ◽  
Wei Xia ◽  
Ming Wang
Keyword(s):  
Multiple Diffraction ◽  
Grating Interferometer ◽  
Displacement Reconstruction

Spline surface interpolation for calculating 3-D ventricular strains from MRI tissue tagging

1996 ◽  
Vol 270 (1) ◽  
pp. H281-H297 ◽  
Author(s):  
M. J. Moulton ◽  
L. L. Creswell ◽  
S. W. Downing ◽  
R. L. Actis ◽  
B. A. Szabo ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Reference Image ◽  
Line Detection ◽  
Elasticity Solution ◽  
Surface Interpolation ◽  
Spline Surface ◽  
Tissue Tagging ◽  
Measurement Analysis ◽  
Displacement Reconstruction ◽  
Rms Errors

A method is developed and validated for approximating continuous smooth distributions of finite strains in the ventricles from the deformations of magnetic resonance imaging (MRI) tissue tagging "tag lines" or "tag surfaces." Tag lines and intersections of orthogonal tag lines are determined using a semiautomated algorithm. Three-dimensional (3-D) reconstruction of the displacement field on tag surfaces is performed using two orthogonal sets of MRI images and employing spline surface interpolation. The 3-D regional ventricular wall strains are computed from an initial reference image to a deformed image in diastole or systole by defining a mapping or transformation of space between the two states. The resultant mapping is termed the measurement analysis solution and is defined by determining a set of coefficients for the approximating functions that best fit the measured tag surface displacements. Validation of the method is performed by simulating tag line or surface deformations with a finite element (FE) elasticity solution of the heart and incorporating the measured root-mean-square (rms) errors of tag line detection into the simulations. The FE-computed strains are compared with strains calculated by the proposed procedure. The average difference between two-dimensional (2-D) FE-computed strains and strains calculated by the measurement analysis was 0.022 +/- 0.009 or 14.2 +/- 3.6% of the average FE elasticity strain solution. The 3-D displacement reconstruction errors averaged 0.087 +/- 0.002 mm or 2.4 +/- 0.1% of the average FE solution, and 3-D strain fitting errors averaged 0.024 +/- 0.011 or 15.9 +/- 2.8% of the average 3-D FE elasticity solution. When the rms errors in tag line detection were included in the 2-D simulations, the agreement between FE solution and fitted solution was 24.7% for the 2-D simulations and 19.2% for the 3-D simulations. We conclude that the 3-D displacements of MRI tag lines may be reconstructed accurately; however, the strain solution magnifies the small errors in locating tag lines and reconstructing 3-D displacements.

scholarly journals Bridge Health Monitoring via Displacement Reconstruction-Based NB-IoT Technology

2020 ◽  
Vol 10 (24) ◽  
pp. 8878
Author(s):  
John Thedy ◽  
Kuo-Wei Liao ◽  
Chun-Chieh Tseng ◽  
Chia-Ming Liu
Keyword(s):  
Health Monitoring ◽  
Micro Electro Mechanical System ◽  
Small Scale ◽  
Bridge Health Monitoring ◽  
Bridge Model ◽  
Narrow Bandwidth ◽  
Acceleration Sensors ◽  
Bridge Damage ◽  
The Difference ◽  
Displacement Reconstruction

An aged bridge’s performance is affected by degradation and becomes one of the major concerns in maintenance. A preliminary, simple and workable procedure of bridge damage detection is required to minimize maintenance costs. In the past, frequency is one of the most common indicators to detect damage occurrence. Recent research found that using frequency as a health indicator still has room to improve. Alternatively, dynamic displacement is used as an indicator in the current study. These dynamic displacements are reconstructed based on measured acceleration records from micro electro mechanical system (MEMS) sensors. The Newmark-beta method with Windows is proposed to acquire the reconstructed displacements of considered bridges. To demonstrate the accuracy and applicability of the proposed approach, three different experiments are carried out; (i) A small scale bridge with the implementation of MEMS acceleration sensors; (ii) a numerical complex finite element method (FEM) bridge model; (iii) an actual bridge with the implementation of MEMS acceleration sensors and narrow bandwidth Internet of things (NB-IoT) technology. The first experiment shows that the proposed method can successfully identify the difference between damaged/undamaged bridges and determine damage location. The second experiment indicates that the proposed method is able to identify the difference between stiffened/unstiffened bridges. The last experiment shows the applicability of the proposed method on an actual bridge health monitoring project.

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