Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Capacitive Imaging (CI) sensors are capable of non-destructively detecting both surface and hidden defects in dielectric materials and characterizing conducting surfaces through a relatively thick insulation layer. However, the complex Measurement Sensitivity Distribution (MSD) of CI sensors render the sensor capacitance variation with lift-off highly non-linear, which may lead to misinterpretation of defect indications. This work systematically studied the lift-off effect using both Finite Element (FE) analysis and experimental approaches. Sensor MSD was used as a tool to predict the imaging performance. Normalized Variation Ratio () was introduced and used to characterise sensor responses due to defects for a CI sensor. Both the FE analysis and experiments suggest that the lift-off effect for a CI sensor is specimen type and condition dependent. For a given defect, the may vary non-monotonically with increased lift-offs. A case study on a glass-fibre composite/aluminium hybrid structure with multiple artificial defects demonstrated the feasibility of defects discrimination using multiple CI scans with increased lift-offs.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6308521 | PMC |
http://dx.doi.org/10.3390/s18124286 | DOI Listing |
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!