Pattern Recognition

Vito, Saverio De; Falasconi, M.; Pardo, Matteo

doi:10.1002/9781118768495.ch5

Cited by 2 publications

(2 citation statements)

References 121 publications

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“…Selected features try to highlight both the dynamic and steady state behavior of each sensor’s response in the SNIFFI e-nose array as prompted by relevant literature ([ 25 , 26 ]).…”

Section: Pattern Analysis and Resultsmentioning

confidence: 99%

Electronic Noses for Composites Surface Contamination Detection in Aerospace Industry

Vito

Miglietta

Massera

et al. 2017

Sensors

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The full exploitation of Composite Fiber Reinforced Polymers (CFRP) in so-called green aircrafts design is still limited by the lack of adequate quality assurance procedures for checking the adhesive bonding assembly, especially in load-critical primary structures. In this respect, contamination of the CFRP panel surface is of significant concern since it may severely affect the bonding and the mechanical properties of the joint. During the last years, the authors have developed and tested an electronic nose as a non-destructive tool for pre-bonding surface inspection for contaminants detection, identification and quantification. Several sensors and sampling architectures have been screened in view of the high Technology Readiness Level (TRL) scenarios requirements. Ad-hoc pattern recognition systems have also been devised to ensure a fast and reliable assessment of the contamination status, by combining real time classifiers and the implementation of a suitable rejection option. Results show that e-noses could be used as first line low cost Non Destructive Test (NDT) tool in aerospace CFRP assembly and maintenance scenarios.

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“…Selected features try to highlight both the dynamic and steady state behavior of each sensor’s response in the SNIFFI e-nose array as prompted by relevant literature ([ 25 , 26 ]).…”

Section: Pattern Analysis and Resultsmentioning

confidence: 99%

Electronic Noses for Composites Surface Contamination Detection in Aerospace Industry

Vito

Miglietta

Massera

et al. 2017

Sensors

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“…The systems also implements supervised classification tools, under development, by different PARC algorithms such as Support Vector Machines (SVM) and Artificial Neural Networks (ANN). A recent and comprehensive overview of PARC techniques for machine olfaction has been provided by De Vito et al [ 102 ].…”

Section: Electronic Nose Applicationsmentioning

confidence: 99%

Metal Oxide Gas Sensors, a Survey of Selectivity Issues Addressed at the SENSOR Lab, Brescia (Italy)

Ponzoni

Baratto

Cattabiani

et al. 2017

Sensors

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140

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This work reports the recent results achieved at the SENSOR Lab, Brescia (Italy) to address the selectivity of metal oxide based gas sensors. In particular, two main strategies are being developed for this purpose: (i) investigating different sensing mechanisms featuring different response spectra that may be potentially integrated in a single device; (ii) exploiting the electronic nose (EN) approach. The former has been addressed only recently and activities are mainly focused on determining the most suitable configuration and measurements to exploit the novel mechanism. Devices suitable to exploit optical (photoluminescence), magnetic (magneto-optical Kerr effect) and surface ionization in addition to the traditional chemiresistor device are here discussed together with the sensing performance measured so far. The electronic nose is a much more consolidated technology, and results are shown concerning its suitability to respond to industrial and societal needs in the fields of food quality control and detection of microbial activity in human sweat.

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Pattern Recognition

Cited by 2 publications

References 121 publications

Electronic Noses for Composites Surface Contamination Detection in Aerospace Industry

Electronic Noses for Composites Surface Contamination Detection in Aerospace Industry

Metal Oxide Gas Sensors, a Survey of Selectivity Issues Addressed at the SENSOR Lab, Brescia (Italy)

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