scholarly journals Noise Factor Analysis for Health Monitoring, with Application to Brake Rotors

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Hamed Kazemi ◽  
Graeme Garner ◽  
Samba Drame ◽  
Xinyu Du ◽  
Hossein Sadjadi
Keyword(s):  
Factor Analysis ◽  
Health Monitoring ◽  
Noise Factor ◽  
Brake Rotors

scholarly journals Signal Deconvolution and Noise Factor Analysis Based on a Combination of Time–Frequency Analysis and Probabilistic Sparse Matrix Factorization

2020 ◽  
Vol 21 (8) ◽  
pp. 2978 ◽  
Author(s):  
Shunji Yamada ◽  
Atsushi Kurotani ◽  
Eisuke Chikayama ◽  
Jun Kikuchi
Keyword(s):  
Factor Analysis ◽  
Matrix Factorization ◽  
Sparse Matrix ◽  
Signal To Noise Ratio ◽  
Noise Factor ◽  
Data Cleansing ◽  
Time Frequency ◽  
Free Induction ◽  
Signal Deconvolution ◽  
Sparse Matrix Factorization

Nuclear magnetic resonance (NMR) spectroscopy is commonly used to characterize molecular complexity because it produces informative atomic-resolution data on the chemical structure and molecular mobility of samples non-invasively by means of various acquisition parameters and pulse programs. However, analyzing the accumulated NMR data of mixtures is challenging due to noise and signal overlap. Therefore, data-cleansing steps, such as quality checking, noise reduction, and signal deconvolution, are important processes before spectrum analysis. Here, we have developed an NMR measurement informatics tool for data cleansing that combines short-time Fourier transform (STFT; a time–frequency analytical method) and probabilistic sparse matrix factorization (PSMF) for signal deconvolution and noise factor analysis. Our tool can be applied to the original free induction decay (FID) signals of a one-dimensional NMR spectrum. We show that the signal deconvolution method reduces the noise of FID signals, increasing the signal-to-noise ratio (SNR) about tenfold, and its application to diffusion-edited spectra allows signals of macromolecules and unsuppressed small molecules to be separated by the length of the T2* relaxation time. Noise factor analysis of NMR datasets identified correlations between SNR and acquisition parameters, identifying major experimental factors that can lower SNR.

scholarly journals Feature Comparison in Structural Health Monitoring of a Vehicle Crane

2008 ◽  
Vol 15 (3-4) ◽  
pp. 207-215 ◽  
Author(s):  
J. Kullaa ◽  
T. Heine
Keyword(s):  
Factor Analysis ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Control Charts ◽  
Structural Health

Vibration-based structural health monitoring of a vehicle crane was studied. The performance of different features to detect damage was investigated after eliminating the normal operational variations using factor analysis. Using eight accelerometers, ten AR parameters from each record were identified for damage detection. Also transmissibilities between sensors were estimated. Damage was introduced with additional masses at different locations of the structure. All damage cases could be detected from either features using control charts, but transmissibilities proved to be more sensitive to damage than the AR coefficients.

Noise factor analysis for cDNA microarrays

2004 ◽  
Vol 9 (4) ◽  
pp. 663 ◽  
Author(s):  
Yoganand Balagurunathan ◽  
Naisyin Wang ◽  
Edward R. Dougherty ◽  
Danh Nguyen ◽  
Yidong Chen ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Cdna Microarrays ◽  
Noise Factor

Factor Analysis of Measures of Articulation, Language, Auditory Processing, Reading-Spelling, and Maxillofacial Structure

1977 ◽  
Vol 20 (2) ◽  
pp. 319-324
Author(s):  
Anita F. Johnson ◽  
Ralph L. Shelton ◽  
William B. Arndt ◽  
Montie L. Furr
Keyword(s):  
Factor Analysis ◽  
Clinical Judgment ◽  
Auditory Processing

This study was concerned with the correspondence between the classification of measures by clinical judgment and by factor analysis. Forty-six measures were selected to assess language, auditory processing, reading-spelling, maxillofacial structure, articulation, and other processes. These were applied to 98 misarticulating eight- and nine-year-old children. Factors derived from the analysis corresponded well with categories the measures were selected to represent.

Evidence-based subtypes in functional dyspepsia with confirmatory factor analysis: Psychosocial and physiopathological correlates

2001 ◽  
Vol 120 (5) ◽  
pp. A51-A52 ◽  
Author(s):  
B FISCHLER ◽  
J VANDENBERGHE ◽  
P PERSOONS ◽  
V GUCHT ◽  
D BROEKAERT ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Functional Dyspepsia ◽  
Evidence Based ◽  
Confirmatory Factor

The French Version of the Revised Child Anxiety and Depression Scale (RCADS) in a Nonclinical Sample

2015 ◽  
Vol 74 (3) ◽  
pp. 119-127 ◽  
Author(s):  
Martine Bouvard ◽  
Anne Denis ◽  
Jean-Luc Roulin
Keyword(s):  
Factor Analysis ◽  
Anxiety Disorder ◽  
Confirmatory Factor Analysis ◽  
Generalized Anxiety Disorder ◽  
Child Anxiety ◽  
Depression Scale ◽  
Anxiety And Depression ◽  
Generalized Anxiety ◽  
French Version ◽  
Confirmatory Factor

This article investigates the psychometric properties of the Revised Child Anxiety and Depression Scale (RCADS). A group of 704 adolescents completed the questionnaires in their classrooms. This study examines potential confirmatory factor analysis factor models of the RCADS as well as the relationships between the RCADS and the Screen for Child Anxiety Related Emotional Disorders-Revised (SCARED-R). A subsample of 595 adolescents also completed an anxiety questionnaire (Fear Survey Schedule for Children-Revised, FSSC-R) and a depression questionnaire (Center for Epidemiological Studies Depression Scale, CES-D). Confirmatory factor analysis of the RCADS suggests that the 6-factor model reasonably fits the data. All subscales were positively intercorrelated, with rs varying between .48 (generalized anxiety disorder-major depression disorder) and .65 (generalized anxiety disorder-social phobia/obsessive-compulsive disorder). The RCADS total score and all the RCADS scales were found to have good internal consistency (> .70). The correlations between the RCADS subscales and their SCARED-R counterparts are generally substantial. Convergent validity was found with the FSSC-R and the CES-D. The study included normal adolescents aged 10 to 19. Therefore, the findings cannot be extended to children under 10, nor to a clinical population. Altogether, the French version of the RCADS showed reasonable psychometric properties.

Measuring Impact of Stress in Sandwich Generation Caring for Demented Parents

GeroPsych ◽  
2014 ◽  
Vol 27 (4) ◽  
pp. 171-179 ◽  
Author(s):  
Laurence M. Solberg ◽  
Lauren B. Solberg ◽  
Emily N. Peterson
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Social Worker ◽  
Resource Availability ◽  
Well Being ◽  
Sandwich Generation ◽  
Item Questionnaire ◽  
Significant Difference ◽  
Adult Caregivers ◽  
The Impact

Stress in caregivers may affect the healthcare recipients receive. We examined the impact of stress experienced by 45 adult caregivers of their elderly demented parents. The participants completed a 32-item questionnaire about the impact of experienced stress. The questionnaire also asked about interventions that might help to reduce the impact of stress. After exploratory factor analysis, we reduced the 32-item questionnaire to 13 items. Results indicated that caregivers experienced stress, anxiety, and sadness. Also, emotional, but not financial or professional, well-being was significantly impacted. There was no significant difference between the impact of caregiver stress on members from the sandwich generation and those from the nonsandwich generation. Meeting with a social worker for resource availability was identified most frequently as a potentially helpful intervention for coping with the impact of stress.

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