Bridge mode shape identification using moving vehicles at traffic speeds through non‐parametric sparse matrix completion

2021 ◽  
Author(s):  
Qipei Mei ◽  
Nima Shirzad‐Ghaleroudkhani ◽  
Mustafa Gül ◽  
S. Farid Ghahari ◽  
Ertugrul Taciroglu
Keyword(s):  
Mode Shape ◽  
Sparse Matrix ◽  
Matrix Completion ◽  
Shape Identification ◽  
Moving Vehicles ◽  
Non Parametric

High Resolution Bridge Mode Shape Identification via Matrix Completion Approach

2019 ◽  
Author(s):  
SOHEIL SADEGHI ESHKEVARI ◽  
MARTIN TAKÁC ◽  
SHAMIM N. PAKZAD ◽  
SOHEILA SADEGHI ESHKEVARI
Keyword(s):  
High Resolution ◽  
Mode Shape ◽  
Matrix Completion ◽  
Shape Identification

scholarly journals A Gradient System for Low Rank Matrix Completion

Axioms ◽  
2018 ◽  
Vol 7 (3) ◽  
pp. 51 ◽  
Author(s):  
Carmela Scalone ◽  
Nicola Guglielmi
Keyword(s):  
Sparse Matrix ◽  
Matrix Completion ◽  
Low Rank ◽  
Frobenius Norm ◽  
Gradient System ◽  
Large Matrix ◽  
Rank Matrix ◽  
Matrix Differential Equations ◽  
Low Rank Matrix Completion ◽  
Matrix Differential

In this article we present and discuss a two step methodology to find the closest low rank completion of a sparse large matrix. Given a large sparse matrix M, the method consists of fixing the rank to r and then looking for the closest rank-r matrix X to M, where the distance is measured in the Frobenius norm. A key element in the solution of this matrix nearness problem consists of the use of a constrained gradient system of matrix differential equations. The obtained results, compared to those obtained by different approaches show that the method has a correct behaviour and is competitive with the ones available in the literature.

Mode shape identification and orthogonalization

AIAA Journal ◽  
1990 ◽  
Vol 28 (4) ◽  
pp. 711-716 ◽  
Author(s):  
Alvar M. Kabe
Keyword(s):  
Mode Shape ◽  
Shape Identification

scholarly journals The Geometric Sparse Matrix Completion Model for Predicting Drug Side effects

2019 ◽  
Author(s):  
Diego Galeano ◽  
Alberto Paccanaro
Keyword(s):  
Clinical Trials ◽  
Side Effects ◽  
Side Effect ◽  
Large Scale ◽  
Learning Algorithm ◽  
Sparse Matrix ◽  
Matrix Completion ◽  
Optimal Solution ◽  
Drug Side Effects ◽  
Chemical Structures

AbstractPair-input associations for drug-side effects are obtained through expensive placebo-controlled experiments in human clinical trials. An important challenge in computational pharmacology is to predict missing associations given a few entries in the drug-side effect matrix, as these predictions can be used to direct further clinical trials. Here we introduce the Geometric Sparse Matrix Completion (GSMC) model for predicting drug side effects. Our high-rank matrix completion model learns non-negative sparse matrices of coefficients for drugs and side effects by imposing smoothness priors that exploit a set of pharmacological side information graphs, including information about drug chemical structures, drug interactions, molecular targets, and disease indications. Our learning algorithm is based on the diagonally rescaled gradient descend principle of non-negative matrix factorization. We prove that it converges to a globally optimal solution with a first-order rate of convergence. Experiments on large-scale side effect data from human clinical trials show that our method achieves better prediction performance than six state-of-the-art methods for side effect prediction while offering biological interpretability and favouring explainable predictions.

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