G
Gian Marco Revel
Researcher at Marche Polytechnic University
Publications - 171
Citations - 2052
Gian Marco Revel is an academic researcher from Marche Polytechnic University. The author has contributed to research in topics: Laser Doppler vibrometer & Thermal comfort. The author has an hindex of 23, co-authored 148 publications receiving 1539 citations.
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Proceedings ArticleDOI
Continuous scanning laser Doppler vibrometry and wavelet processing for diagnostics: A time domain approach
TL;DR: In this paper, a wavelet processing of vibration data collected by Continuous Scanning Laser Doppler Vibrometry (CSLDV) is used to identify damages in structures.
Proceedings ArticleDOI
Dynamic characterization of teeth by laser vibrometry
TL;DR: In this paper, a method for the investigation of human teeth dynamic response, in terms of natural frequencies and modal shapes has been proposed, in which very short laser pulses have been used to excite teeth vibrations and a scanning laser doppler vibrometer was used to measure the dynamic response.
Journal ArticleDOI
XDEM for Tuning Lumped Models of Thermochemical Processes Involving Materials in the Powder State
Edoardo Copertaro,Paolo Chiariotti,Alvaro Antonio Estupinan Donoso,Nicola Paone,Bernhard Peters,Gian Marco Revel +5 more
TL;DR: In this article, the use of the XDEM framework was used for fine tuning a lumped representation of the non-isothermal decarbonation of a CaCO3 sample in powder state.
Proceedings ArticleDOI
Validation and accuracy estimation of a novel measurement system based on a mobile robot for human detection in indoor environment
TL;DR: This paper validates an indoor measurement system for human detection using a RGB camera installed on a mobile robot at three different robot’s head configuration and in two suboptimal collected scenarios and indicates that in two user configurations YOLO-v3 fails.
Book ChapterDOI
Spatial Noise Component Identification Based on Different Vibro-Acoustic Data Sets
TL;DR: A Time Domain Correlation method based on simultaneously collected acoustic and vibration data is exploited for separating the acoustic contribution coming from the different components of a three epicyclical gear electric motor.