Energy Correlated Damage Indices in Fatigue Crack Extent Quantification

2013 ◽  
Vol 569-570 ◽  
pp. 1186-1193 ◽  
Author(s):  
Krzysztof Dragan ◽  
Michal Dziendzikowski ◽  
Slawomir Klimaszewski ◽  
Sylwester Klysz ◽  
Artur Kurnyta
Keyword(s):  
Fatigue Crack ◽  
Wave Interaction ◽  
Principal Component ◽  
Guided Wave ◽  
Linear Discriminant ◽  
Reduction Techniques ◽  
Damage Indices ◽  
Significant Difference ◽  
Detection Capabilities ◽  
Transmission And Reflection

Signals received by piezoelectric transducers (PZT) network can be influenced by many factors. Apart from environmental conditions, whose variability should be compensated, significant difference in a signal can be also caused by relative geometry changes of a designed sensors node, e.g. the damage localization and its orientation with respect to sensors location in the node. In the adopted approach a set of damage indices (DIs), carrying marginal signal information content and correlated with the total energy received by a given sensor are proposed. These are sensitive to the two main modes of guided wave interaction with a fatigue crack, i.e. its transmission and reflection from a damage. Detailed description of DIs detection capabilities are delivered in the paper. Two dimensional reduction techniques: Principal Component Analysis and Fishers Linear Discriminant are compared. The results of the data collected from specimen fatigue test are used to compare several classification models based on the emerged effective damage indices.

scholarly journals Can Smoking Cause Differences in Urine Microbiome in Male Patients With Bladder Cancer? A Retrospective Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Wenchao Ma ◽  
Wentao Zhang ◽  
Liliang Shen ◽  
Ji Liu ◽  
Fuhang Yang ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Urinary Tract ◽  
Tobacco Smoking ◽  
Principal Component ◽  
Alpha Diversity ◽  
Vital Role ◽  
Pca Analysis ◽  
Microbial Composition ◽  
Linear Discriminant ◽  
Significant Difference

BackgroundTobacco smoking is a carcinogen for many cancers including bladder cancer. The microbiota is involved in the occurrence, development, and treatment of tumors. We explored the composition of male urinary microbiome and the correlation between tobacco smoking and microbiome in this study.MethodsAlpha diversity, principal component analysis (PCA) and Adonis analysis, linear discriminant analysis (LDA) coupled with effect size measurement, and PICRUSt function predictive analysis were used to compare different microbiome between smokers and non-smokers in men.ResultsThere were 26 qualified samples included in the study. Eleven of them are healthy controls, and the others are from men with bladder cancer. Simpson index and the result of PCA analysis between smokers and non-smokers were not different (P > 0.05) in healthy men. However, the abundance of Bacteroidaceae, Erysipelotrichales, Lachnospiraceae, Bacteroides, and so on in the urinary tract of smokers is much higher than that of non-smokers. Compared to non-smokers, the alpha diversity in smokers was elevated in patients with bladder cancer (P < 0.05). PCA analysis showed a significant difference between smokers and non-smokers (P < 0.001), indicating that tobacco smoking plays a vital role in urinary tract microbial composition.ConclusionThe composition of microbiome in the urinary tract is closely related to tobacco smoking. This phenomenon is more significant in patients with bladder cancer. This indicates tobacco smoking may promote the occurrence and development of bladder cancer by changing urinary tract microbiome.

scholarly journals Comparative Analysis of Milk Microbiomes and Their Association with Bovine Mastitis in Two Farms in Central Russia

Animals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1401
Author(s):  
Sergei Sokolov ◽  
Ksenia Fursova ◽  
Irina Shulcheva ◽  
Daria Nikanova ◽  
Olga Artyemieva ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Bovine Mastitis ◽  
Principal Component ◽  
16S Rrna Genes ◽  
Rrna Genes ◽  
Linear Discriminant ◽  
Milk Samples ◽  
Central Russia ◽  
Animal Populations ◽  
Significant Difference

Bovine mastitis is a widespread infectious disease. In addition to the economic damages associated with reduced milk yield due to mastitis, the problem of food contamination by microorganism metabolites, in particular toxins, is also a concern. Horizontal transfer of microorganisms from animal populations to humans can also be complicated by antibiotic resistance. Therefore, bovine mastitis is relevant to the study of microbiology and veterinary medicine. In this study, we investigated the microbiome of milk samples from healthy cows and cows with different forms of mastitis from individual quarters of the udder of cows during first and second lactation. Total DNA was extracted from milk samples. The V3–V4 regions of the bacterial 16S rRNA genes from each sample were amplified to generate a library via high-throughput sequencing. We revealed significant dominance of several operational taxonomic units (OTUs) corresponding mostly to groups of Staphylococcus aureus, Aerococcus spp., and Streptococcus spp. In addition, we unexpectedly identified Streptococcus thermophilus in samples with high SCC quantities. We found some infectious agents that characterized summer mastitis. We demonstrated that in Central Russia, mastitis is associated with a wide variety of causal organisms. We observed some differences in the diversity of the two investigated farms. However, we did not find any significant difference among healthy, mastitis and subclinical samples according to their SCC status from either farms by principal component analysis. Linear discriminant analysis effect size (LEfSe) confirmed the presence of several indicator genera in farms from Moscow and the Tula Region. These results confirm the complex bacterial etiology of bovine mastitis.

Lamb Wave Based Monitoring of Fatigue Crack Growth Using Principal Component Analysis

2013 ◽  
Vol 558 ◽  
pp. 260-267
Author(s):  
Ye Lu ◽  
Ming Yu Lu ◽  
Lin Ye ◽  
Dong Wang ◽  
Li Min Zhou ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Fatigue Crack ◽  
Fatigue Crack Growth ◽  
Crack Growth ◽  
Lamb Waves ◽  
Principal Component ◽  
Component Analysis ◽  
Statistical Parameters ◽  
Data Set ◽  
Damage Indices

Fatigue crack growth in metallic plates was monitored using Lamb waves which were generated and captured by surface-mounted piezoelectric wafers in a pitch-catch configuration. Instead of directly pinpointing signal segments to quantify wave scattering caused by the existence of crack damage and related severity, principal component analysis (PCA), as an efficient approach for information compression and classification, was undertaken to distinguish different structural conditions due to fatigue crack growth. For this purpose, a variety of statistical parameters in the time domain as damage indices were extracted from the wave signals. A series of contaminated counterparts with different signal-to-noise ratios were also simulated to increase the statistical size of the data set. It was concluded that PCA is capable of reducing the dimensions of a complex set of original data, whose information can be represented and highlighted by the first few principal components. With the assistance of PCA, the different structural conditions attributable to crack growth can be classified.

Detection and localization of fatigue-induced transverse crack in a rotor shaft using principal component analysis

2020 ◽  
pp. 147592172093358
Author(s):  
Sagi Rathna Prasad ◽  
AS Sekhar
Keyword(s):  
Principal Component Analysis ◽  
Fatigue Crack ◽  
Transverse Crack ◽  
Principal Component ◽  
Component Analysis ◽  
Statistical Features ◽  
Rotor Shaft ◽  
Rotor Faults ◽  
Damage Indices ◽  
Detection And Localization

Rotor shafts subjected to severe operating stresses are prone to develop transverse fatigue cracks at the localized stress raisers. Therefore, the ability to identify and locate the incipient fatigue crack is imperative in order to avoid catastrophic failure. The literature on rotor crack detection discussed the importance of monitoring the steady-state 1X, 2X and 3X harmonic response components of rotors. However, the other rotor faults such as misalignment and unbalance, exhibit similar symptoms. Thus, the main aim is to develop new independent fault-related features which measure the driving principle governing the behaviour of various rotor faults. In this article, the application of principal component analysis–based statistical pattern analysis, as a tool for early detection and localization of fatigue-induced transverse crack in a rotor shaft is investigated. To perform this study, accelerated fatigue experiments are conducted on a customized setup. This developed test rig is novel and unique by itself that facilitates generating a fatigue crack in a shaft, under conditions that mimic a real in-service loading environment of industrial rotors. Unlike conventional methods, noise in the acquired vibration and strain data is denoised via classical principal component analysis method. Time- and frequency-domain statistical features extracted from different vibration and strain sensor signals are used for this study. Damage indices such as Hotelling’s T2-statistic and Q-index are used to detect the presence of the crack. It is observed that irrespective of the sensor location, damage index such as Q-statistic of all the sensors is very effective to detect the presence and time of incipient crack. Partial decomposition contributions method is found to be very effective in identifying the location of the crack. This article provides the most significant vibration-based statistical features, which are sensitive to shaft transverse cracks, for different sensor types and their mounting location. Finally, a new fused health indicator which is highly sensitive to the presence of rotor shaft crack is defined and is found successful when applied to a new experimental data.

scholarly journals Handwritten Digit Recognition Using Different Dimensionality Reduction Techniques

2019 ◽  
Vol 8 (2) ◽  
pp. 999-1002
Keyword(s):  
Principal Component ◽  
Training Data ◽  
Clear Understanding ◽  
Data Set ◽  
Linear Discriminant ◽  
Digit Recognition ◽  
Reduction Techniques ◽  
Maximum Variance ◽  
Dimensionality Reduction Techniques ◽  
Handwritten Digit

The objective of this paper is to introduce to Technologies of linear dimension reduction popularly known as Principal Component Analysis and Linear Discriminant Analysis. PCA reduces the size of data and conserve maximum variance in the form of new variable called principal components where LDA works with minimum class distance and maximizing difference between the classes. Axis of maximum variance is found by PCA while axis of class separability is found by LDA. This method is experimented over and MNIST handwritten digit data set. Our conclusion explains PCA can outperform LDA when training data set a small and recalls values with lesser computational complexity. The present in linear techniques in this paper presents clear understanding and methods in comparative manner

scholarly journals Investigation of Preeclampsia Using Raman Spectroscopy

2012 ◽  
Vol 27 ◽  
pp. 239-252 ◽  
Author(s):  
Günay Başar ◽  
Uğur Parlatan ◽  
Şeyma Şeninak ◽  
Tuba Günel ◽  
Ali Benian ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Control Group ◽  
Serum Samples ◽  
Linear Discriminant ◽  
Significant Difference ◽  
And Control

Preeclampsia is associated with increased perinatal morbidity and mortality. There have been numerous efforts to determine preeclampsia biomarkers by means of biophysical, biochemical, and spectroscopic methods. In this study, the preeclampsia and control groups were compared via band component analysis and multivariate analysis using Raman spectroscopy as an alternative technique. The Raman spectra of serum samples were taken from nine preeclamptic, ten healthy pregnant women. The Band component analysis and principal component analysis-linear discriminant analysis were applied to all spectra after a sensitive preprocess step. Using linear discriminant analysis, it was found that Raman spectroscopy has a sensitivity of 78% and a specificity of 90% for the diagnosis of preeclampsia. Via the band component analysis, a significant difference in the spectra of preeclamptic patients was observed when compared to the control group. 19 Raman bands exhibited significant differences in intensity, while 11 of them decreased and eight of them increased. This difference seen in vibrational bands may be used in further studies to clarify the pathophysiology of preeclampsia.

scholarly journals Evaluating the Effect of a Brewery By-Product as Feed Supplementation on the Quality of Eggs by Means of a Human Panel and E-Tongue and E-Nose Analysis

Chemosensors ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 213
Author(s):  
Juan Pablo Aguinaga Bósquez ◽  
Zoltan Kovacs ◽  
Zoltán Gillay ◽  
György Bázár ◽  
Csaba Palkó ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Egg Quality ◽  
Principal Component ◽  
Substantial Effect ◽  
Linear Discriminant ◽  
Organic Zinc ◽  
Organoleptic Characteristics ◽  
Significant Difference ◽  
Clear Change

The objective of our research was to evaluate the possible alteration of the organoleptic properties of eggs produced by hens (Lohmann Brown-Classic) fed with diets containing different doses of an industrial by-product enriched with organic zinc (Zincoppyeast, ZP): Control 0%, ZP 2.5%, and ZP 5.0%. Eggs were collected after 30 days (batch 1) and 60 days (batch 2) of feeding with the experimental diets and subjected to chemical, microbiological, human sensory, e-nose, and e-tongue analyses. There was no significant difference among the microbiological status of eggs of the three groups, but there were significant differences (p < 0.05) in the fat (9.5% vs. 9.3%) and protein contents (12.7% vs. 13.4%) of the Control and ZP 5.0% groups, respectively. Human sensory analysis showed no clear change in the organoleptic characteristics of the eggs. Using linear discriminant analysis (LDA), the e-tongue could recognize the three groups of eggs in batch 1 and batch 2 with 95.9% and 100% accuracy and had a prediction accuracy of 64.8% and 56.2%, respectively. When the eggs were incubating at 50 °C or 80 °C before the e-nose analysis, the groups of eggs could be recognized with 98.0% and 82.7% accuracy, and predicted with 68.5% and 62.2% accuracy, respectively, using principal component analysis-based discriminant analysis (PCA–DA). The aroma compounds and respective sensory descriptors showing changes among the different groups of eggs (batch, storage, and feeding) were identified based on the e-nose analysis. The supplementation of laying hens’ feed with the investigated industrial by-product can be applied without any substantial effect on egg quality, which can, however, be detected with advanced analytical methods.

scholarly journals Real Time Breath Analysis Using Portable Gas Chromatography for Adult Asthma Phenotypes

Metabolites ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 265
Author(s):  
Ruchi Sharma ◽  
Wenzhe Zang ◽  
Menglian Zhou ◽  
Nicole Schafer ◽  
Lesa A. Begley ◽  
...  
Keyword(s):  
Gas Chromatography ◽  
Real Time ◽  
Rapid Assessment ◽  
Principal Component ◽  
Corticosteroid Treatment ◽  
Breath Analysis ◽  
Blood Eosinophil ◽  
Exhaled Breath ◽  
Respiratory Illnesses ◽  
Linear Discriminant

Asthma is heterogeneous but accessible biomarkers to distinguish relevant phenotypes remain lacking, particularly in non-Type 2 (T2)-high asthma. Moreover, common clinical characteristics in both T2-high and T2-low asthma (e.g., atopy, obesity, inhaled steroid use) may confound interpretation of putative biomarkers and of underlying biology. This study aimed to identify volatile organic compounds (VOCs) in exhaled breath that distinguish not only asthmatic and non-asthmatic subjects, but also atopic non-asthmatic controls and also by variables that reflect clinical differences among asthmatic adults. A total of 73 participants (30 asthma, eight atopic non-asthma, and 35 non-asthma/non-atopic subjects) were recruited for this pilot study. A total of 79 breath samples were analyzed in real-time using an automated portable gas chromatography (GC) device developed in-house. GC-mass spectrometry was also used to identify the VOCs in breath. Machine learning, linear discriminant analysis, and principal component analysis were used to identify the biomarkers. Our results show that the portable GC was able to complete breath analysis in 30 min. A set of nine biomarkers distinguished asthma and non-asthma/non-atopic subjects, while sets of two and of four biomarkers, respectively, further distinguished asthmatic from atopic controls, and between atopic and non-atopic controls. Additional unique biomarkers were identified that discriminate subjects by blood eosinophil levels, obese status, inhaled corticosteroid treatment, and also acute upper respiratory illnesses within asthmatic groups. Our work demonstrates that breath VOC profiling can be a clinically accessible tool for asthma diagnosis and phenotyping. A portable GC system is a viable option for rapid assessment in asthma.

Guided wave‐based cable damage detection using wave energy transmission and reflection

2021 ◽  
Author(s):  
Zhi‐Feng Tang ◽  
Xiao‐Dong Sui ◽  
Yuan‐Feng Duan ◽  
Peng‐fei Zhang ◽  
Chung Bang Yun
Keyword(s):  
Damage Detection ◽  
Wave Energy ◽  
Guided Wave ◽  
Energy Transmission ◽  
Cable Damage ◽  
Transmission And Reflection

scholarly journals A model for spectroscopic food sample analysis using data sonification

2021 ◽  
Author(s):  
Hsein Kew
Keyword(s):  
Dimensionality Reduction ◽  
Classification Accuracy ◽  
Reduction Method ◽  
Real Life ◽  
Principal Component ◽  
Relevant Information ◽  
Analysis Model ◽  
Linear Discriminant ◽  
Audio Output ◽  
Dimensionality Reduction Method

AbstractIn this paper, we propose a method to generate an audio output based on spectroscopy data in order to discriminate two classes of data, based on the features of our spectral dataset. To do this, we first perform spectral pre-processing, and then extract features, followed by machine learning, for dimensionality reduction. The features are then mapped to the parameters of a sound synthesiser, as part of the audio processing, so as to generate audio samples in order to compute statistical results and identify important descriptors for the classification of the dataset. To optimise the process, we compare Amplitude Modulation (AM) and Frequency Modulation (FM) synthesis, as applied to two real-life datasets to evaluate the performance of sonification as a method for discriminating data. FM synthesis provides a higher subjective classification accuracy as compared with to AM synthesis. We then further compare the dimensionality reduction method of Principal Component Analysis (PCA) and Linear Discriminant Analysis in order to optimise our sonification algorithm. The results of classification accuracy using FM synthesis as the sound synthesiser and PCA as the dimensionality reduction method yields a mean classification accuracies of 93.81% and 88.57% for the coffee dataset and the fruit puree dataset respectively, and indicate that this spectroscopic analysis model is able to provide relevant information on the spectral data, and most importantly, is able to discriminate accurately between the two spectra and thus provides a complementary tool to supplement current methods.

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