The Experts below are selected from a list of 156 Experts worldwide ranked by ideXlab platform
Rick C. Morgans - One of the best experts on this subject based on the ideXlab platform.
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Active control analysis of mining vehicle cabin noise using finite element modelling
Journal of Sound and Vibration, 2020Co-Authors: D.a. Stanef, Colin H. Hansen, Rick C. MorgansAbstract:Numerical simulation has been used to predict the reduction of acoustic potential energy in a mobile mining vehicle cabin as a result of active noise control (ANC). Resonance frequencies and mode shapes of both the structural and cavity modes were calculated using a finite element (FE) model. Modal coupling analysis was used to determine the coupled response of the model to an interior acoustic source, and the results were compared to measurements taken inside the cabin. Correlation between the FE model and physical measurements was improved to the extent that the model could be used to predict the effect of ANC in the cabin for different configurations of control sources and error Sensors. As expected from previous work, it was found that the acoustic potential energy inside the cabin could be significantly reduced if a control source is placed in close proximity to the primary volume velocity source. However, increasing the number of Sensors and/or increasing the number of control sources located remotely from the primary source had little impact on the achievable reduction in the overall acoustic potential energy in the cabin. This supported results obtained in off-line experiments using control source to error Sensor Transfer Function measurements and quadratic optimization theory, where it was found that good reduction at the error Sensors was possible inside the mining vehicle cabin but that global control was not feasible using sources remotely located from the primary source.D. A. Stanef, C. H. Hansen and R. C. Morganshttp://www.elsevier.com/wps/find/journaldescription.cws_home/622899/description#descriptio
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Active control analysis of mining vehicle cabin noise using finite element modelling
Journal of Sound and Vibration, 2004Co-Authors: D.a. Stanef, Colin H. Hansen, Rick C. MorgansAbstract:Numerical simulation has been used to predict the reduction of acoustic potential energy in a mobile mining vehicle cabin as a result of active noise control (ANC). Resonance frequencies and mode shapes of both the structural and cavity modes were calculated using a finite element (FE) model. Modal coupling analysis was used to determine the coupled response of the model to an interior acoustic source, and the results were compared to measurements taken inside the cabin. Correlation between the FE model and physical measurements was improved to the extent that the model could be used to predict the effect of ANC in the cabin for different configurations of control sources and error Sensors. As expected from previous work, it was found that the acoustic potential energy inside the cabin could be significantly reduced if a control source is placed in close proximity to the primary volume velocity source. However, increasing the number of Sensors and/or increasing the number of control sources located remotely from the primary source had little impact on the achievable reduction in the overall acoustic potential energy in the cabin. This supported results obtained in off-line experiments using control source to error Sensor Transfer Function measurements and quadratic optimization theory, where it was found that good reduction at the error Sensors was possible inside the mining vehicle cabin but that global control was not feasible using sources remotely located from the primary source.
Yide Wang - One of the best experts on this subject based on the ideXlab platform.
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Robust Blind Deconvolution Process for Vehicle Reidentification by an Inductive Loop Detector
IEEE Sensors Journal, 2014Co-Authors: David Guilbert, Sio-song Ieng, Cédric Le Bastard, Yide WangAbstract:International audienceA robust blind deconvolution algorithm is proposed to cancel the Sensor averaging effect caused by its wide detection area. The purpose herein is to retrieve features of the "real" signal that have been distorted by the averaging effect. The algorithm is applied to the case of an Inductive Loop Detector. To perform the proposed algorithm, speed estimation is required. Vehicle reidentification rate from both raw signals and estimated "real" signals is compared. The Sensor Transfer Function is calculated once from a learning phase; the estimated "real" signal is then computed in real time for the re-identification of each vehicle
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Robust Blind Deconvolution Process for Vehicle Reidentification by an Inductive Loop Detector
IEEE Sensors Journal, 2014Co-Authors: David Guilbert, Sio-song Ieng, Cédric Le Bastard, Yide WangAbstract:A robust blind deconvolution algorithm is proposed to cancel the Sensor averaging effect caused by its wide detection area. The purpose herein is to retrieve features of the real signal that have been distorted by the averaging effect. The algorithm is applied to the case of an inductive loop detector. To perform the proposed algorithm, speed estimation is required. Vehicle reidentification rate from both raw signals and estimated real signals is compared. The Sensor Transfer Function is calculated once from a learning phase; the estimated real signal is then computed in real time for the reidentification of each vehicle.
D.a. Stanef - One of the best experts on this subject based on the ideXlab platform.
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Active control analysis of mining vehicle cabin noise using finite element modelling
Journal of Sound and Vibration, 2020Co-Authors: D.a. Stanef, Colin H. Hansen, Rick C. MorgansAbstract:Numerical simulation has been used to predict the reduction of acoustic potential energy in a mobile mining vehicle cabin as a result of active noise control (ANC). Resonance frequencies and mode shapes of both the structural and cavity modes were calculated using a finite element (FE) model. Modal coupling analysis was used to determine the coupled response of the model to an interior acoustic source, and the results were compared to measurements taken inside the cabin. Correlation between the FE model and physical measurements was improved to the extent that the model could be used to predict the effect of ANC in the cabin for different configurations of control sources and error Sensors. As expected from previous work, it was found that the acoustic potential energy inside the cabin could be significantly reduced if a control source is placed in close proximity to the primary volume velocity source. However, increasing the number of Sensors and/or increasing the number of control sources located remotely from the primary source had little impact on the achievable reduction in the overall acoustic potential energy in the cabin. This supported results obtained in off-line experiments using control source to error Sensor Transfer Function measurements and quadratic optimization theory, where it was found that good reduction at the error Sensors was possible inside the mining vehicle cabin but that global control was not feasible using sources remotely located from the primary source.D. A. Stanef, C. H. Hansen and R. C. Morganshttp://www.elsevier.com/wps/find/journaldescription.cws_home/622899/description#descriptio
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Active control analysis of mining vehicle cabin noise using finite element modelling
Journal of Sound and Vibration, 2004Co-Authors: D.a. Stanef, Colin H. Hansen, Rick C. MorgansAbstract:Numerical simulation has been used to predict the reduction of acoustic potential energy in a mobile mining vehicle cabin as a result of active noise control (ANC). Resonance frequencies and mode shapes of both the structural and cavity modes were calculated using a finite element (FE) model. Modal coupling analysis was used to determine the coupled response of the model to an interior acoustic source, and the results were compared to measurements taken inside the cabin. Correlation between the FE model and physical measurements was improved to the extent that the model could be used to predict the effect of ANC in the cabin for different configurations of control sources and error Sensors. As expected from previous work, it was found that the acoustic potential energy inside the cabin could be significantly reduced if a control source is placed in close proximity to the primary volume velocity source. However, increasing the number of Sensors and/or increasing the number of control sources located remotely from the primary source had little impact on the achievable reduction in the overall acoustic potential energy in the cabin. This supported results obtained in off-line experiments using control source to error Sensor Transfer Function measurements and quadratic optimization theory, where it was found that good reduction at the error Sensors was possible inside the mining vehicle cabin but that global control was not feasible using sources remotely located from the primary source.
A. Witulski - One of the best experts on this subject based on the ideXlab platform.
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Range-Finding Sensor Degradation in Gamma Radiation Environments
IEEE Sensors Journal, 2015Co-Authors: Z. J. Diggins, N. Mahadevan, D. Herbison, E. Barth, Gabor Karsai, Robert A. Reed, Ron D. Schrimpf, Robert A. Weller, Michael L. Alles, A. WitulskiAbstract:The effects of gamma radiation on common Sensors used in robots intended for nuclear remediation scenarios are examined. Commercial rangefinders are chosen as an exemplar of the impact of gamma radiation on Sensors and systems. This paper illustrates Sensor radiation degradation not only in operational failure, but also in changes in the Sensor Transfer Function. Three types of commercial range-finding Sensors are considered [infrared (IR) triangulation using a position sensitive detector, sonar using time of flight, and laser rangefinder using triangulation and a CMOS camera]. Experimental results show significant changes in the IR Sensor's static sensitivity with dose, abrupt failure of the laser range finder at low dose, and degradation and abrupt failure for the sonar detector. The input-output relationship of the IR Sensor showed further variation after a period of room-temperature annealing. Significant part-to-part variation in radiation response is shown for both the sonar and IR Sensor. System level impacts due to Sensor input-output relationship degradation and a technique to diagnose the degradation extendable to more complex Sensor assemblies are presented.
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Impact of gamma radiation on range finding Sensor performance
SENSORS 2013 IEEE, 2013Co-Authors: Z. J. Diggins, N. Mahadevan, D. Herbison, E. Barth, A. WitulskiAbstract:The effect of gamma radiation on common Sensors in robots intended for nuclear remediation scenarios is examined. Rangefinders are chosen as an exemplar of the impact of gamma radiation on Sensors and systems. This work extends previous work by calculating not just Sensor failure point but changes in the Sensor Transfer Function of three different types of commercial range-finding Sensors (infrared (IR) triangulation using a position sensitive detector, sonar using time of flight, and laser range finder using triangulation and a CMOS camera) in response to gamma total radiation dose. Experimental results show significant changes in the IR Sensor's static sensitivity with dose, abrupt failure of the laser range finder at low dose, and degradation and abrupt failure for the sonar detector. The input-output relationship of the IR Sensor showed further variation after a period of room-temperature annealing. Significant part-to-part variation in radiation response is shown for both the sonar and IR Sensor.
David Guilbert - One of the best experts on this subject based on the ideXlab platform.
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Robust Blind Deconvolution Process for Vehicle Reidentification by an Inductive Loop Detector
IEEE Sensors Journal, 2014Co-Authors: David Guilbert, Sio-song Ieng, Cédric Le Bastard, Yide WangAbstract:International audienceA robust blind deconvolution algorithm is proposed to cancel the Sensor averaging effect caused by its wide detection area. The purpose herein is to retrieve features of the "real" signal that have been distorted by the averaging effect. The algorithm is applied to the case of an Inductive Loop Detector. To perform the proposed algorithm, speed estimation is required. Vehicle reidentification rate from both raw signals and estimated "real" signals is compared. The Sensor Transfer Function is calculated once from a learning phase; the estimated "real" signal is then computed in real time for the re-identification of each vehicle
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Robust Blind Deconvolution Process for Vehicle Reidentification by an Inductive Loop Detector
IEEE Sensors Journal, 2014Co-Authors: David Guilbert, Sio-song Ieng, Cédric Le Bastard, Yide WangAbstract:A robust blind deconvolution algorithm is proposed to cancel the Sensor averaging effect caused by its wide detection area. The purpose herein is to retrieve features of the real signal that have been distorted by the averaging effect. The algorithm is applied to the case of an inductive loop detector. To perform the proposed algorithm, speed estimation is required. Vehicle reidentification rate from both raw signals and estimated real signals is compared. The Sensor Transfer Function is calculated once from a learning phase; the estimated real signal is then computed in real time for the reidentification of each vehicle.
