The Experts below are selected from a list of 301353 Experts worldwide ranked by ideXlab platform
J M Lopezhiguera - One of the best experts on this subject based on the ideXlab platform.
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spectral Processing Technique based on feature selection and artificial neural networks for arc welding quality monitoring
Ndt & E International, 2009Co-Authors: Pilar Beatriz Garciaallende, Olga M. Conde, J Mirapeix, A Cobo, J M LopezhigueraAbstract:The validity of a Processing Technique, based on feature selection and artificial neural networks, when applied to arc-welding on-line quality monitoring is analyzed. An optical fiber embedded within the welding torch captures the plasma radiation, which is delivered to a CCD spectrometer. In the proposed solution the spectral analysis is performed in two stages: dimensionality reduction is accomplished using a feature selection algorithm and, after that, an artificial neural network carries out the spectral identification task. The validity of the Technique has been successfully demonstrated by means of several experimental welding tests.
Ian Higginbottom - One of the best experts on this subject based on the ideXlab platform.
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a post Processing Technique to estimate the signal to noise ratio and remove echosounder background noise
Ices Journal of Marine Science, 2007Co-Authors: Alex De Robertis, Ian HigginbottomAbstract:A simple and effective post-Processing Technique to estimate echosounder background-noise levels and signal-to-noise ratios (SNRs) during active pinging is developed. Similar to other methods of noise estimation during active pinging, this method assumes that some portion of the sampled acoustic signal is dominated by background noise, with a negligible contribution from the backscattered transmit signal. If this assumption is met, the method will provide robust and accurate estimates of background noise equivalent to that measured by the receiver if the transmitter were disabled. It provides repeated noise estimates over short intervals of time without user intervention, which is beneficial in cases where background noise changes over time. In situations where background noise is dominant in a portion of the recorded signal, it is straightforward to make first-order corrections for the effects of noise and to estimate the SNR to evaluate the effects of background noise on acoustic measurements. Noise correction and signal-to-noise-based thresholds have the potential to improve inferences from acoustic measurements in lower signal-to-noise situations, such as when surveying from noisy vessels, using multifrequency Techniques, surveying at longer ranges, and when working with weak acoustic targets such as invertebrates and fish lacking swimbladders.
Neil Hopkinson - One of the best experts on this subject based on the ideXlab platform.
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Investigating a semi-solid Processing Technique using metal powder bed Additive Manufacturing processes
24th International SFF Symposium - An Additive Manufacturing Conference, SFF 2013, 2013Co-Authors: Parvez Vora, Kamran Mumtaz, Fatos Derguti, Iain Todd, Neil HopkinsonAbstract:The work reported investigates in-situ alloying using a semi-solid Processing Technique with metal powder bed Additive Manufacturing (AM); in this instance Selective Laser Melting (SLM) and Electron Beam Melting (EBM) were employed. This Technique utilised customised powder blends that were processed at elevated temperatures. The selection of Processing temperature considered specific alloy solidification ranges. As a result, parts with reduced residual stresses can be produced. In addition, the use of customised powder blends explored the feasibility of developing alloys specific to the process/application, thus increasing available material ranges for AM metal powder bed processes.
Pilar Beatriz Garciaallende - One of the best experts on this subject based on the ideXlab platform.
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spectral Processing Technique based on feature selection and artificial neural networks for arc welding quality monitoring
Ndt & E International, 2009Co-Authors: Pilar Beatriz Garciaallende, Olga M. Conde, J Mirapeix, A Cobo, J M LopezhigueraAbstract:The validity of a Processing Technique, based on feature selection and artificial neural networks, when applied to arc-welding on-line quality monitoring is analyzed. An optical fiber embedded within the welding torch captures the plasma radiation, which is delivered to a CCD spectrometer. In the proposed solution the spectral analysis is performed in two stages: dimensionality reduction is accomplished using a feature selection algorithm and, after that, an artificial neural network carries out the spectral identification task. The validity of the Technique has been successfully demonstrated by means of several experimental welding tests.
Alex De Robertis - One of the best experts on this subject based on the ideXlab platform.
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a post Processing Technique to estimate the signal to noise ratio and remove echosounder background noise
Ices Journal of Marine Science, 2007Co-Authors: Alex De Robertis, Ian HigginbottomAbstract:A simple and effective post-Processing Technique to estimate echosounder background-noise levels and signal-to-noise ratios (SNRs) during active pinging is developed. Similar to other methods of noise estimation during active pinging, this method assumes that some portion of the sampled acoustic signal is dominated by background noise, with a negligible contribution from the backscattered transmit signal. If this assumption is met, the method will provide robust and accurate estimates of background noise equivalent to that measured by the receiver if the transmitter were disabled. It provides repeated noise estimates over short intervals of time without user intervention, which is beneficial in cases where background noise changes over time. In situations where background noise is dominant in a portion of the recorded signal, it is straightforward to make first-order corrections for the effects of noise and to estimate the SNR to evaluate the effects of background noise on acoustic measurements. Noise correction and signal-to-noise-based thresholds have the potential to improve inferences from acoustic measurements in lower signal-to-noise situations, such as when surveying from noisy vessels, using multifrequency Techniques, surveying at longer ranges, and when working with weak acoustic targets such as invertebrates and fish lacking swimbladders.
