The Experts below are selected from a list of 32487 Experts worldwide ranked by ideXlab platform
Yoshikata Koga - One of the best experts on this subject based on the ideXlab platform.
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to curve fit or not to curve fit comments on water structure enhancement in water rich binary solvent mixtures part ii the excess partial molar heat capacity of the water by yizhak marcus in this journal
Journal of Molecular Liquids, 2012Co-Authors: Yoshikata KogaAbstract:Abstract Comments are given to show that for calculating the composition derivative of the Mother thermodynamic quantity in obtaining the partial molar quantity, the curve-fitting of the Mother Function followed by arithmetic differentiation of the resulting Function is often dangerous. An example is provided.
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partial molar quantity of an intensive Mother Function
Journal of Chemical Physics, 2012Co-Authors: Yoshikata KogaAbstract:A new formal definition is given to the partial molar quantity of a component i for an intensive Mother Function. We perturb the entire system by increasing the amount of the target component by δni keeping others constant and measure the response of the system in terms of an intensive Mother Function, Φ, δΦ. We then define its partial molar quantity of the ith component, ϕi, as ϕi = [δΦ/{δni/(N + δni)]] in the limit of δni → 0. Thus, the physical meaning of ϕi is the effect of the ith component (only) on Φ of the system, just as the partial molar quantity for an extensive Mother Function. This new formal definition could serve as a starting point for statistical mechanics development of a microscopic connection to the third derivatives of G. We show a number of examples such as an enthalpic inter-solute interaction, a partial molar S-V cross fluctuation density of solute, their analogues, and an excess partial molar absorptivity of solute. These examples were used for studying the nature of aqueous solut...
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partial molar quantity of an intensive Mother Function
Journal of Chemical Physics, 2012Co-Authors: Yoshikata KogaAbstract:A new formal definition is given to the partial molar quantity of a component i for an intensive Mother Function. We perturb the entire system by increasing the amount of the target component by δni keeping others constant and measure the response of the system in terms of an intensive Mother Function, Φ, δΦ. We then define its partial molar quantity of the ith component, ϕi, as ϕi = [δΦ/{δni/(N + δni)]] in the limit of δni → 0. Thus, the physical meaning of ϕi is the effect of the ith component (only) on Φ of the system, just as the partial molar quantity for an extensive Mother Function. This new formal definition could serve as a starting point for statistical mechanics development of a microscopic connection to the third derivatives of G. We show a number of examples such as an enthalpic inter-solute interaction, a partial molar S-V cross fluctuation density of solute, their analogues, and an excess partial molar absorptivity of solute. These examples were used for studying the nature of aqueous solutions without realizing their formal definition and were instrumental in advancing our understandings.
N Speciale - One of the best experts on this subject based on the ideXlab platform.
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acoustic emission localization in plates with dispersion and reverberations using sparse pzt sensors in passive mode
Smart Materials and Structures, 2012Co-Authors: Alessandro Perelli, Luca De Marchi, Alessandro Marzani, N SpecialeAbstract:A strategy for the localization of acoustic emissions (AE) in plates with dispersion and reverberation is proposed. The procedure exploits signals received in passive mode by sparse conventional piezoelectric transducers and a three-step processing framework. The first step consists in a signal dispersion compensation procedure, which is achieved by means of the warped frequency transform. The second step concerns the estimation of the differences in arrival time (TDOA) of the acoustic emission at the sensors. Complexities related to reflections and plate resonances are overcome via a wavelet decomposition of cross-correlating signals where the Mother Function is designed by a synthetic warped cross-signal. The magnitude of the wavelet coefficients in the warped distance?frequency domain, in fact, precisely reveals the TDOA of an acoustic emission at two sensors. Finally, in the last step the TDOA data are exploited to locate the acoustic emission source through hyperbolic positioning. The proposed procedure is tested with a passive network of three/four piezo-sensors located symmetrically and asymmetrically with respect to the plate edges. The experimentally estimated AE locations are close to those theoretically predicted by the Cram?r?Rao lower bound.
Mostafa Sadeghi - One of the best experts on this subject based on the ideXlab platform.
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a novel technique for selecting Mother wavelet Function using an intelli gent fault diagnosis system
Expert Systems With Applications, 2009Co-Authors: J Rafiee, A Harifi, Mostafa SadeghiAbstract:This paper presents an optimized gear fault identification system using genetic algorithm (GA) to investigate the type of gear failures of a complex gearbox system using artificial neural networks (ANNs) with a well-designed structure suited for practical implementations due to its short training duration and high accuracy. For this purpose, slight-worn, medium-worn, and broken-tooth of a spur gear of the gearbox system were selected as the faults. In fault simulating, two very similar models of worn gear have been considered with partial difference for evaluating the preciseness of the proposed algorithm. Moreover, the processing of vibration signals has become much more difficult because a full-of-oil complex gearbox system has been considered to record raw vibration signals. Raw vibration signals were segmented into the signals recorded during one complete revolution of the input shaft using tachometer information and then synchronized using piecewise cubic hermite interpolation to construct the sample signals with the same length. Next, standard deviation of wavelet packet coefficients of the vibration signals considered as the feature vector for training purposes of the ANN. To ameliorate the algorithm, GA was exploited to optimize the algorithm so as to determine the best values for ''Mother wavelet Function'', ''decomposition level of the signals by means of wavelet analysis'', and ''number of neurons in hidden layer'' resulted in a high-speed, meticulous two-layer ANN with a small-sized structure. This technique has been eliminated the drawbacks of the type of Mother Function for fault classification purpose not only in machine condition monitoring, but also in other related areas. The small-sized proposed network has improved the stability and reliability of the system for practical purposes.
Alessandro Perelli - One of the best experts on this subject based on the ideXlab platform.
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acoustic emission localization in plates with dispersion and reverberations using sparse pzt sensors in passive mode
Smart Materials and Structures, 2012Co-Authors: Alessandro Perelli, Luca De Marchi, Alessandro Marzani, N SpecialeAbstract:A strategy for the localization of acoustic emissions (AE) in plates with dispersion and reverberation is proposed. The procedure exploits signals received in passive mode by sparse conventional piezoelectric transducers and a three-step processing framework. The first step consists in a signal dispersion compensation procedure, which is achieved by means of the warped frequency transform. The second step concerns the estimation of the differences in arrival time (TDOA) of the acoustic emission at the sensors. Complexities related to reflections and plate resonances are overcome via a wavelet decomposition of cross-correlating signals where the Mother Function is designed by a synthetic warped cross-signal. The magnitude of the wavelet coefficients in the warped distance?frequency domain, in fact, precisely reveals the TDOA of an acoustic emission at two sensors. Finally, in the last step the TDOA data are exploited to locate the acoustic emission source through hyperbolic positioning. The proposed procedure is tested with a passive network of three/four piezo-sensors located symmetrically and asymmetrically with respect to the plate edges. The experimentally estimated AE locations are close to those theoretically predicted by the Cram?r?Rao lower bound.
J Rafiee - One of the best experts on this subject based on the ideXlab platform.
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a novel technique for selecting Mother wavelet Function using an intelli gent fault diagnosis system
Expert Systems With Applications, 2009Co-Authors: J Rafiee, A Harifi, Mostafa SadeghiAbstract:This paper presents an optimized gear fault identification system using genetic algorithm (GA) to investigate the type of gear failures of a complex gearbox system using artificial neural networks (ANNs) with a well-designed structure suited for practical implementations due to its short training duration and high accuracy. For this purpose, slight-worn, medium-worn, and broken-tooth of a spur gear of the gearbox system were selected as the faults. In fault simulating, two very similar models of worn gear have been considered with partial difference for evaluating the preciseness of the proposed algorithm. Moreover, the processing of vibration signals has become much more difficult because a full-of-oil complex gearbox system has been considered to record raw vibration signals. Raw vibration signals were segmented into the signals recorded during one complete revolution of the input shaft using tachometer information and then synchronized using piecewise cubic hermite interpolation to construct the sample signals with the same length. Next, standard deviation of wavelet packet coefficients of the vibration signals considered as the feature vector for training purposes of the ANN. To ameliorate the algorithm, GA was exploited to optimize the algorithm so as to determine the best values for ''Mother wavelet Function'', ''decomposition level of the signals by means of wavelet analysis'', and ''number of neurons in hidden layer'' resulted in a high-speed, meticulous two-layer ANN with a small-sized structure. This technique has been eliminated the drawbacks of the type of Mother Function for fault classification purpose not only in machine condition monitoring, but also in other related areas. The small-sized proposed network has improved the stability and reliability of the system for practical purposes.