Using SVD to Detect Damage in Structures With Different Operational Conditions

Ruotolo, Romualdo; Surace, Cecilia

doi:10.1006/jsvi.1999.2305

Cited by 72 publications

(34 citation statements)

References 5 publications

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“…It is essentially an orthogonal transformation. If a matrix has a linearly correlated row or column, it can be changed into a diagonal matrix by multiplying the orthogonal matrix on the left with that on the right [21]. After the change, we can obtain some singular values whose number reflects the independent row (column) vector number of the original matrix.…”

Section: Svd Integrated Into Visual Attentionmentioning

confidence: 99%

SAR image target detection in complex environments based on improved visual attention algorithm

Liu

Cao

2014

J Wireless Com Network

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A novel target detection algorithm for synthetic aperture radar (SAR) images based on an improved visual attention method is proposed in this paper. With the development of SAR technology, target detection algorithms are confronted with many difficulties such as a complicated environment and scarcity of target information. Visual attention of the human visual system can make humans easily focus on key points in a complex picture, and the visual attention algorithm has been used in many fields. However, existing algorithms based on visual attention models cannot obtain satisfactory results for SAR image target detection under complex environmental conditions. After analysing the existing visual attention models, we combine the pyramid model of visual attention with singular value decomposition to simulate the human retina, which can make the visual attention model more suitable to the characteristics of SAR images. We introduce variance weighted information entropy into the model to optimize the detection results. The results obtained by the existing visual attention algorithm for target detection in SAR images yield a large number of false alarms and misses. However, the proposed algorithm can improve both the efficiency and accuracy of target detection in a complicated environment and under weak-target conditions. The experimental results validate the performance of our method.

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Section: Svd Integrated Into Visual Attentionmentioning

confidence: 99%

SAR image target detection in complex environments based on improved visual attention algorithm

Liu

Cao

2014

J Wireless Com Network

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“…Conventional approaches include regression analysis [17], novelty detection [12], missing data analysis [8,9], singular value decomposition [19,22], and a support vector machine [15]. Another approach evaluating the environmental effects statistically with measured signals only without using an analytical model includes principal component analysis(PCA) [25], factor analysis [5,7] and neural network(NN) [6].…”

Section: Elimination Of Environmental Effectsmentioning

confidence: 99%

Bridge Health Monitoring with Consideration of Environmental Effects

Kim¹,

Kim²,

Shin

et al. 2012

Journal of the Korean Society for Nondestructive Testing

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Reliable response measurements are extremely important for proper bridge health monitoring but incomplete and unreliable data may be acquired due to sensor problems and environmental effects. In the case of a sensor malfunction, parts of the measured data can be missing so that the structural health condition cannot be monitored reliably. This means that the dynamic characteristics of natural frequencies can change as if the structure is damaged due to environmental effects, such as temperature variations. To overcome these problems, this paper proposes a systematic procedure of data analysis to recover missing data and eliminate the environmental effects from the measured data. It also proposes a health index calculated statistically using revised data to evaluate the health condition of a bridge. The proposed method was examined using numerically simulated data with a truss structure and then applied to a set of field data measured from a cable-stayed bridge.

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“…In general, POD has been used to obtain approximate, low-dimensional descriptions of turbulent fluid flow (Holmes et al, 1996), structural vibrations (Cusumano et al, 1994;Feeny and Kappagantu, 1998), and damage detection (Ruotolo and Surace, 1999), and is a very useful technique in signal processing, pattern recognition, and image compression (Chatterjee, 2000). Generally speaking, SVD is used to decompose a set of data, like a matrix, into orthonormal basis functions whereby an approximation of that data set can be reconstructed using a finite number of these basis functions.…”

Section: Singular Value Decompositionmentioning

confidence: 99%

Modeling Cumulative Damage to Flow Surfaces and Assessing its Impact on Wall Turbulence

Licari

Christensen

2010

40th Fluid Dynamics Conference and Exhibit

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The present effort investigated the influence of cumulative damage of an initially smooth surface on the statistical characteristics of wall turbulence. Singular value decomposition was employed to decompose a highly-irregular surface topography replicated from a turbine blade damaged by spallation into topographical modes embodying successively smaller roughness length scales. These modes were then used to develop multiple reduced-order models of the original roughness that, at least in spirit, successively captured the evolution of the surface roughness from initial smaller-scale defects that would likely form just after deployment to the eventual cumulative formation of largerscale roughness features. Particle-image velocimetry measurements of flow at a friction Reynolds number around 1825 over various topographical models were gathered in turbulent channel flow.The cumulative impact of these reduced-order models on wall turbulence was then assessed by comparing mean velocities, Reynolds normal stresses, and Reynolds shear stresses. It was observed that the turbulence statistics of the flow increase in magnitude as larger-scale features are introduced into the surface, leading up to the full, original surface. In addition, even weak surface damage that a practical flow surface might endure within the first fraction of its deployment lifetime can significantly enhance turbulence and therefore progressively degrade system performance. Thus, proper modeling of flow-system performance must account for the dynamic nature of flow surfaces, particularly those exposed to severe operating conditions.ii

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Using SVD to Detect Damage in Structures With Different Operational Conditions

Cited by 72 publications

References 5 publications

SAR image target detection in complex environments based on improved visual attention algorithm

SAR image target detection in complex environments based on improved visual attention algorithm

Bridge Health Monitoring with Consideration of Environmental Effects

Modeling Cumulative Damage to Flow Surfaces and Assessing its Impact on Wall Turbulence

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