Scaled Acoustics Experiments and Vibration Prediction Based Structural Health Monitoring

2008 ◽  
Author(s):  
Nesrin Sarigul-Klijn ◽  
Israel Lopez ◽  
Seung-Il Baek
Keyword(s):  
Health Monitoring ◽  
Dimensional Reduction ◽  
Structural Damage ◽  
Numerical Study ◽  
Damage Index ◽  
Time Frequency ◽  
Structural Health ◽  
Reduction Techniques ◽  
Vibration Prediction ◽  
Dimensional Reduction Techniques

Vibration and acoustic-based health monitoring techniques are presented to monitor structural health under dynamic environment. In order to extract damage sensitive features, linear and nonlinear dimensional reduction techniques are applied and compared. First, a vibration numerical study based on the damage index method is used to provide both location and severity of impact damage. Next, controlled scaled experimental measurements are taken to investigate the aeroacoustic properties of sub-scale wings under known damage conditions. The aeroacoustic nature of the flow field in and around generic aircraft wing damage is determined to characterize the physical mechanism of noise generated by the damage and its applicability to battle damage detection. Simulated battle damage is investigated using a baseline, and two damage models introduced; namely, (1) an undamaged wing as baseline, (2) chordwise-spanwise-partial-penetration (SCPP), and (3) spanwise-chordwise-full-penetration (SCFP). Dimensional reduction techniques are employed to extract time-frequency domain features, which can be used to detect the presence of structural damage. Results are given to illustrate effectiveness of this approach.

Effects of Dimensional Reduction Techniques on Structural Damage Assessment Under Uncertainty

2011 ◽  
Vol 133 (6) ◽  
Author(s):  
Israel Lopez ◽  
Nesrin Sarigul-Klijn
Keyword(s):  
Dimensional Reduction ◽  
Damage Assessment ◽  
Structural Damage ◽  
Principal Component ◽  
Index Method ◽  
Reduction Techniques ◽  
Structural Deterioration ◽  
Curvature Index ◽  
And Performance ◽  
Dimensional Reduction Techniques

In this paper, we present a study of dimensional reduction techniques for structural damage assessment of time-varying structures under uncertainty. Discrete tracking of the frequency response and the mode shape curvature index method is employed to perform damage assessment. Assessment of spontaneous damage in deteriorating structures is important as it can have potential benefits in improving their safety and performance. Most of the available damage assessment techniques incorporate the usage of system identification and classification techniques for detecting damage, location, and/or severity; however, much work is needed in the area of dimensional reduction in order to compress the ever-increasing data and facilitate decision-making in damage assessment classification. A comparison of dimensional reduction techniques is presented and evaluated in terms of separating damaged from undamaged data sets under two types of uncertainty, structural deterioration and environmental uncertainties. The use of a recursive principal component analysis for detecting and tracking structural deterioration and spontaneous damage is evaluated via computational simulations. The results of this study reveal that dimensional reduction techniques can greatly enhance structural damage assessment under uncertainties. This paper compares multiple dimensional reduction techniques by identifying their weaknesses and strengths.

scholarly journals PieParty: Visualizing cells from scRNA-seq data as pie charts

2020 ◽  
Author(s):  
Stefan Kurtenbach ◽  
James J. Dollar ◽  
Anthony M. Cruz ◽  
Michael A. Durante ◽  
J. William Harbour
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Cell Types ◽  
Three Dimensions ◽  
Research Fields ◽  
Reduction Techniques ◽  
Single Cell Rna Sequencing ◽  
Pie Chart ◽  
Simultaneous Visualization ◽  
Dimensional Reduction Techniques

AbstractSingle cell RNA sequencing (scRNA-seq) has been a transformative technology in many research fields. Dimensional reduction techniques such as UMAP and tSNE are used to visualize scRNA-seq data in two or three dimensions in order for cells to be clustered in biologically meaningful ways. Subsequently, gene expression is frequently mapped onto these plots to show the distribution of gene expression across the plots, for instance to distinguish cell types. However, plotting each cell with only one color leads to repetitive and unintuitive representations. Here, we present Pie Party, which allows scRNA-seq data to be plotted such that every cell is represented as a pie chart, and every slice in the pie charts corresponds to the gene expression of individual genes. This allows for the simultaneous visualization of the expression of multiple genes and gene networks. The resulting figures are information dense, space efficient and highly intuitive. PieParty is publicly available on GitHub at https://github.com/harbourlab/PieParty.

scholarly journals Evaluación de la regresión logística como clasificador de espectros Raman en el diagnóstico automático de cáncer de mama

2020 ◽  
pp. 1-12
Author(s):  
Israel De La Parra-González ◽  
Francisco Javier Luna-Rosas ◽  
Laura Cecilia Rodríguez-Martínez ◽  
Claudio Frausto-Reyes
Keyword(s):  
Logistic Regression ◽  
Raman Spectra ◽  
Dimensional Reduction ◽  
Classification Accuracy ◽  
Principal Component ◽  
Computational Time ◽  
Cáncer De Mama ◽  
Histopathological Analysis ◽  
K Nearest Neighbors ◽  
Reduction Techniques

We evaluated logistic regression as a classifier in the diagnosis of breast cancer based on Raman spectra. Common studies published in the subject use dimensional reduction techniques to generate the classifier. Instead, we proposed to observe the effect of using all intensity values recorded in the spectra as input variables to the algorithm. We used leaving one out cross-validation measuring classification accuracy, sensitivity and specificity. We used Raman spectra taken from breast tissue previously diagnosed by histopathological analysis, some from healthy tissue and some from tissue with cancer. Each spectrum is formed by 605 intensity values in the range of 687 to 1781 cm-1. Logistic regression classifier exhibited 100% classification accuracy. To establish comparative references, we evaluated in the same way: 1) a logistic model preceded by dimensional reduction with Principal Component Analysis (PCA+LR), 2) two classifiers obtained with weighted K nearest neighbors algorithm, and 3) a classifier using the naive Bayes (NB) algorithm. We found that PCA+LR and NB showed the same performance of 100% in classification accuracy. Nevertheless, PCA+LR requires more processing computational time.

Catalogue reduction techniques

1978 ◽  
Vol 48 ◽  
pp. 389-390 ◽  
Author(s):  
Chr. de Vegt
Keyword(s):  
Adjustment Process ◽  
Proper Motions ◽  
Measuring Process ◽  
Aerial Photogrammetry ◽  
Reduction Techniques ◽  
Observational Technique ◽  
The Common ◽  
Astrometric Data

AbstractReduction techniques as applied to astrometric data material tend to split up traditionally into at least two different classes according to the observational technique used, namely transit circle observations and photographic observations. Although it is not realized fully in practice at present, the application of a blockadjustment technique for all kind of catalogue reductions is suggested. The term blockadjustment shall denote in this context the common adjustment of the principal unknowns which are the positions, proper motions and certain reduction parameters modelling the systematic properties of the observational process. Especially for old epoch catalogue data we frequently meet the situation that no independent detailed information on the telescope properties and other instrumental parameters, describing for example the measuring process, is available from special calibration observations or measurements; therefore the adjustment process should be highly self-calibrating, that means: all necessary information has to be extracted from the catalogue data themselves. Successful applications of this concept have been made already in the field of aerial photogrammetry.

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