Multidimensional parallelepiped model-a new type of non-probabilistic convex model for structural uncertainty analysis

2015 ◽  
Vol 103 (1) ◽  
pp. 31-59 ◽  
Author(s):  
C. Jiang ◽  
Q. F. Zhang ◽  
X. Han ◽  
J. Liu ◽  
D. A. Hu
Keyword(s):  
Uncertainty Analysis ◽  
Structural Uncertainty ◽  
Convex Model ◽  
New Type ◽  
Multidimensional Parallelepiped Model

Uncertainty Analysis on Vehicle-Bridge System with Correlative Interval Variables Based on Multidimensional Parallelepiped Model

2018 ◽  
Vol 15 (05) ◽  
pp. 1850030 ◽  
Author(s):  
Van Huy Truong ◽  
Jie Liu ◽  
Xianghua Meng ◽  
Chao Jiang ◽  
Trong Tien Nguyen
Keyword(s):  
Dynamic Response ◽  
Uncertainty Analysis ◽  
Great Influence ◽  
Upper And Lower Bounds ◽  
Analysis Method ◽  
Partial Difference ◽  
Interval Analysis Method ◽  
Interval Variables ◽  
Multidimensional Parallelepiped Model ◽  
Bridge System

For vehicle-bridge system, structural uncertainties, especially the interval variables with correlation, have a great influence on dynamic response. Therefore, this paper proposes an effective uncertainty analysis method for vehicle-bridge system based on multidimensional parallelepiped (MP) model, which can reasonably deal with the correlation of interval variables. First, the vehicle-bridge system is simplified as a four degrees-of-freedom mass-spring vehicle model running on a simply supported beam. MP model is adopted to describe the uncertainties of all the interval variables. Second, via affine coordinate system transform, the interval variables with correlation are transformed as the independent variables, which is very convenient for uncertainty analysis. Finally, the uncertain dynamic response is approximated through the first-order Taylor interval expansion, and the upper and lower bounds can be calculated using the dynamic response at midpoints and the partial difference multiplied by interval radius. Because the correlation is sufficiently considered, the uncertainty analysis results on vehicle–bridge interaction system will be much more accurate than the traditional interval analysis method (IAM). Numerical example demonstrates the correctness and effectiveness of the proposed method.

A new type of multilevel system for image sparse recovery

2019 ◽  
Vol 17 (06) ◽  
pp. 1950049
Author(s):  
Guomin Sun ◽  
Jinsong Leng ◽  
Carlo Cattani
Keyword(s):  
Energy Distribution ◽  
Optimal Solution ◽  
Tight Frame ◽  
Sparse Recovery ◽  
Multilevel System ◽  
Convex Model ◽  
Generalized Gradient ◽  
Split Bregman ◽  
New Type ◽  
Specific Shape

This work focuses on image sparse recovery problem. First, we construct a new kind of pseudo-directional multilevel system, which forms a tight frame in [Formula: see text]. Different from the widely used directional multilevel transforms curvelts and shearlets, whose subbands are neat and nonsensitive to the energy distribution of signals in Fourier domain, the proposed multilevel system is designed to have subbands with specific shape, the shape is oriented by the energy distribution. Thus, we can obtain more sparse structure of signals and low computation for the multilevel transform. Moreover, to detect directional singularities of signals effectively, a local directional gradient operator is introduced to catch the signal variation along different directions, it can be seen as the generalized gradient. Then we proposed a simple but efficient method for image sparse recovery, the split Bregman algorithm is employed to solve the proposed convex model which guarantees the global optimal solution. Some contrast experiments suggest that the sparse recovery by the proposed method performs well in artifacts’ suppressing and details’ extraction.

scholarly journals From depth migration model building through to structural uncertainty analysis : an integrated workflow

2003 ◽  
Vol 2003 (2) ◽  
pp. 1-1
Author(s):  
Matthew G. Lamont ◽  
James Gunning ◽  
Michael E. Glinsky ◽  
James H. Robinson
Keyword(s):  
Uncertainty Analysis ◽  
Model Building ◽  
Structural Uncertainty ◽  
Migration Model ◽  
Depth Migration
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