The Experts below are selected from a list of 41898 Experts worldwide ranked by ideXlab platform
Gregory Stephanopoulos - One of the best experts on this subject based on the ideXlab platform.
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an elementary metabolite unit emu based method of isotopically nonstationary Flux analysis
Biotechnology and Bioengineering, 2008Co-Authors: Jamey D Young, Jason Walther, Maciek R Antoniewicz, Gregory StephanopoulosAbstract:Nonstationary metabolic Flux analysis (NMFA) is at present a very computationally intensive exercise, especially for large reaction networks. We applied elemen- tary metabolite unit (EMU) theory to NMFA, dramatically reducing computational difficulty. We also introduced block decoupling, a new method that systematically and compre- hensively divides EMU systems of equations into smaller subproblems to further reduce computational difficulty. These improvements led to a 5000-fold reduction in simula- tion times, enabling an entirely new and more complicated set of problems to be analyzed with NMFA. We simulated a series of nonstationary and stationary GC/MS measure- ments for a large E. coli network that was then used to estimate parameters and their associated confidence inter- vals. We found that Fluxes could be successfully estimated using only nonstationary labeling data and External Flux measurements. Addition of near-stationary and stationary time points increased the precision of most parameters. Contrary to prior reports, the precision of nonstationary estimates proved to be comparable to the precision of estimates based solely on stationary data. Finally, we applied EMU-based NMFA to experimental nonstationary measure- ments taken from brown adipocytes and successfully esti- mated Fluxes and some metabolite concentrations. By using NFMA instead of traditional MFA, the experiment required only 6 h instead of 50 (the time necessary for most meta- bolite labeling to reach 99% of isotopic steady state). Biotechnol. Bioeng. 2008;99: 686-699.
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an elementary metabolite unit emu based method of isotopically nonstationary Flux analysis
Biotechnology and Bioengineering, 2008Co-Authors: Jamey D Young, Jason Walther, Maciek R Antoniewicz, Hyuntae Yoo, Gregory StephanopoulosAbstract:Nonstationary metabolic Flux analysis (NMFA) is at present a very computationally intensive exercise, especially for large reaction networks. We applied elementary metabolite unit (EMU) theory to NMFA, dramatically reducing computational difficulty. We also introduced block decoupling, a new method that systematically and comprehensively divides EMU systems of equations into smaller subproblems to further reduce computational difficulty. These improvements led to a 5000-fold reduction in simulation times, enabling an entirely new and more complicated set of problems to be analyzed with NMFA. We simulated a series of nonstationary and stationary GC/MS measurements for a large E. coli network that was then used to estimate parameters and their associated confidence intervals. We found that Fluxes could be successfully estimated using only nonstationary labeling data and External Flux measurements. Addition of near-stationary and stationary time points increased the precision of most parameters. Contrary to prior reports, the precision of nonstationary estimates proved to be comparable to the precision of estimates based solely on stationary data. Finally, we applied EMU-based NMFA to experimental nonstationary measurements taken from brown adipocytes and successfully estimated Fluxes and some metabolite concentrations. By using NFMA instead of traditional MFA, the experiment required only 6 h instead of 50 (the time necessary for most metabolite labeling to reach 99% of isotopic steady state).
Jamey D Young - One of the best experts on this subject based on the ideXlab platform.
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an elementary metabolite unit emu based method of isotopically nonstationary Flux analysis
Biotechnology and Bioengineering, 2008Co-Authors: Jamey D Young, Jason Walther, Maciek R Antoniewicz, Gregory StephanopoulosAbstract:Nonstationary metabolic Flux analysis (NMFA) is at present a very computationally intensive exercise, especially for large reaction networks. We applied elemen- tary metabolite unit (EMU) theory to NMFA, dramatically reducing computational difficulty. We also introduced block decoupling, a new method that systematically and compre- hensively divides EMU systems of equations into smaller subproblems to further reduce computational difficulty. These improvements led to a 5000-fold reduction in simula- tion times, enabling an entirely new and more complicated set of problems to be analyzed with NMFA. We simulated a series of nonstationary and stationary GC/MS measure- ments for a large E. coli network that was then used to estimate parameters and their associated confidence inter- vals. We found that Fluxes could be successfully estimated using only nonstationary labeling data and External Flux measurements. Addition of near-stationary and stationary time points increased the precision of most parameters. Contrary to prior reports, the precision of nonstationary estimates proved to be comparable to the precision of estimates based solely on stationary data. Finally, we applied EMU-based NMFA to experimental nonstationary measure- ments taken from brown adipocytes and successfully esti- mated Fluxes and some metabolite concentrations. By using NFMA instead of traditional MFA, the experiment required only 6 h instead of 50 (the time necessary for most meta- bolite labeling to reach 99% of isotopic steady state). Biotechnol. Bioeng. 2008;99: 686-699.
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an elementary metabolite unit emu based method of isotopically nonstationary Flux analysis
Biotechnology and Bioengineering, 2008Co-Authors: Jamey D Young, Jason Walther, Maciek R Antoniewicz, Hyuntae Yoo, Gregory StephanopoulosAbstract:Nonstationary metabolic Flux analysis (NMFA) is at present a very computationally intensive exercise, especially for large reaction networks. We applied elementary metabolite unit (EMU) theory to NMFA, dramatically reducing computational difficulty. We also introduced block decoupling, a new method that systematically and comprehensively divides EMU systems of equations into smaller subproblems to further reduce computational difficulty. These improvements led to a 5000-fold reduction in simulation times, enabling an entirely new and more complicated set of problems to be analyzed with NMFA. We simulated a series of nonstationary and stationary GC/MS measurements for a large E. coli network that was then used to estimate parameters and their associated confidence intervals. We found that Fluxes could be successfully estimated using only nonstationary labeling data and External Flux measurements. Addition of near-stationary and stationary time points increased the precision of most parameters. Contrary to prior reports, the precision of nonstationary estimates proved to be comparable to the precision of estimates based solely on stationary data. Finally, we applied EMU-based NMFA to experimental nonstationary measurements taken from brown adipocytes and successfully estimated Fluxes and some metabolite concentrations. By using NFMA instead of traditional MFA, the experiment required only 6 h instead of 50 (the time necessary for most metabolite labeling to reach 99% of isotopic steady state).
Raphaël Romary - One of the best experts on this subject based on the ideXlab platform.
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Detection of the Stator Winding Inter-Turn Faults in Asynchronous and Synchronous Machines Through the Correlation Between Harmonics of the Voltage of Two Magnetic Flux Sensors
IEEE Transactions on Industry Applications, 2019Co-Authors: Miftah Irhoumah, Rémus Pusca, David Mercier, Eric Lefevre, Raphaël RomaryAbstract:This paper presents a statistical methodology for detection of inter-turn short-circuit fault in asynchronous and synchronous machines. This methodology uses a correlation coefficient obtained from External magnetic field measured in the machine vicinity. It is a noninvasive method which follows up the signals of two External Flux sensors located symmetrically around the machine axis (180° spatially shifted). The principle is based on the calculation of the Pearson correlation coefficient between two signals delivered by two sensors S1 and S2 when the machine operates at different load conditions, which allows us to detect incipient faults in electrical induction and synchronous machines with a high probability of detection. Experimental tests are realized using two specific rewound machines to create inter-turn short-circuit faults with different severity levels.
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information fusion with belief functions for detection of interturn short circuit faults in electrical machines using External Flux sensors
IEEE Transactions on Industrial Electronics, 2018Co-Authors: Miftah Irhoumah, Rémus Pusca, Raphaël Romary, David Mercier, Eric Lefevre, Cristian DemianAbstract:This paper proposes a diagnosis method that exploits the information delivered by External Flux sensors placed in the vicinity of rotating electrical machines, in order to detect a stator interturn short circuit. This fault induces a dissymmetry in the External magnetic field that can be measured by the sensors. Sensitive harmonics are extracted from the signals delivered by a pair of sensors placed at 180° from each other around the machine, and data obtained for several sensor positions are analyzed by fusion techniques using the belief function theory. The diagnosis method is applied on induction and synchronous machines with artificial stator faults. It will be shown that one can obtain high probability to detect the fault using the proposed fusion technique: on various series of measurements, the proposed approach has obtained a 90% detection rate on a considered machine.
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information fusion of External Flux sensors for detection of inter turn short circuit faults in induction machines
Conference of the Industrial Electronics Society, 2017Co-Authors: Miftah Irhoumah, Rémus Pusca, David Mercier, Eric Lefevre, Raphaël RomaryAbstract:This paper presents a method based on fusion technique applied to signatures obtained from External stray Flux to detect an inter turn short circuits in induction machines. This technique uses the belief functions framework to represent and merge the information about short circuits obtained from sensors placed around the machine to be diagnosed. The influence of the sensors positions around the machine to detect faults is studied. This fusion technique leads to a new diagnosis method, which only uses the information captured from the stray magnetic field around the machine, having then the advantage of being non-invasive. Six External Flux sensors placed on a belt fixed around the machine provide information used for the diagnostic technique. These signatures are obtained by experimental tests using a rewound induction machine that allows one to create inter-turn short circuit faults with different severity levels.
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An improvement of a diagnosis procedure for AC machines using two External Flux sensors based on a fusion process with belief functions
IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society, 2012Co-Authors: Rémus Pusca, Cristian Demian, David Mercier, Eric Lefevre, Raphaël RomaryAbstract:In this paper, a method for diagnosis of AC machines using the spectrum of the near magnetic field is presented. The method is associated to a fusion process based on belief functions which analyze the measurements. In previous works, it has been shown that it is possible to detect the inter-turns short circuit in the stator windings of electrical machines using a noninvasive method. It is based on the analysis of the variation of sensitive harmonics when the load varies, and eliminates the main drawback presented by other diagnostic methods which use the comparison with a healthy state assumed known. Several measurements around the machine are necessary to increase the probability of the fault detection because the fault position relatively to the sensor can strongly influence the results. So in this paper it is proposed to exploit conjointly the whole measurements in order to obtain a more robust and reliable diagnostic and to increase the probability of detecting the fault. The merging of the different estimations being realized through the belief functions framework, this approach is tested on real measurements. Experimental tests are performed on a special rewound induction machine in order to validate the theoretical approach.
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an online universal diagnosis procedure using two External Flux sensors applied to the ac electrical rotating machines
Sensors, 2010Co-Authors: Rémus Pusca, Raphaël Romary, Andrian Ceban, Jeanfrancois BrudnyAbstract:This paper presents an original non-invasive procedure for the diagnosis of electromagnetic devices, as well as AC electrical rotating machines using two External Flux coil sensors that measure the External magnetic field in the machines’ vicinity. The diagnosis exploits the signal delivered by the two sensors placed in particular positions. Contrary to classical methods using only one sensor, the presented method does not require any knowledge of a presumed machine’s healthy former state. On the other hand, the loading operating is not a disturbing factor but it is used to the fault discrimination. In order to present this procedure, an internal stator inter-turn short-circuit fault is considered as well.
Jason Walther - One of the best experts on this subject based on the ideXlab platform.
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an elementary metabolite unit emu based method of isotopically nonstationary Flux analysis
Biotechnology and Bioengineering, 2008Co-Authors: Jamey D Young, Jason Walther, Maciek R Antoniewicz, Gregory StephanopoulosAbstract:Nonstationary metabolic Flux analysis (NMFA) is at present a very computationally intensive exercise, especially for large reaction networks. We applied elemen- tary metabolite unit (EMU) theory to NMFA, dramatically reducing computational difficulty. We also introduced block decoupling, a new method that systematically and compre- hensively divides EMU systems of equations into smaller subproblems to further reduce computational difficulty. These improvements led to a 5000-fold reduction in simula- tion times, enabling an entirely new and more complicated set of problems to be analyzed with NMFA. We simulated a series of nonstationary and stationary GC/MS measure- ments for a large E. coli network that was then used to estimate parameters and their associated confidence inter- vals. We found that Fluxes could be successfully estimated using only nonstationary labeling data and External Flux measurements. Addition of near-stationary and stationary time points increased the precision of most parameters. Contrary to prior reports, the precision of nonstationary estimates proved to be comparable to the precision of estimates based solely on stationary data. Finally, we applied EMU-based NMFA to experimental nonstationary measure- ments taken from brown adipocytes and successfully esti- mated Fluxes and some metabolite concentrations. By using NFMA instead of traditional MFA, the experiment required only 6 h instead of 50 (the time necessary for most meta- bolite labeling to reach 99% of isotopic steady state). Biotechnol. Bioeng. 2008;99: 686-699.
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an elementary metabolite unit emu based method of isotopically nonstationary Flux analysis
Biotechnology and Bioengineering, 2008Co-Authors: Jamey D Young, Jason Walther, Maciek R Antoniewicz, Hyuntae Yoo, Gregory StephanopoulosAbstract:Nonstationary metabolic Flux analysis (NMFA) is at present a very computationally intensive exercise, especially for large reaction networks. We applied elementary metabolite unit (EMU) theory to NMFA, dramatically reducing computational difficulty. We also introduced block decoupling, a new method that systematically and comprehensively divides EMU systems of equations into smaller subproblems to further reduce computational difficulty. These improvements led to a 5000-fold reduction in simulation times, enabling an entirely new and more complicated set of problems to be analyzed with NMFA. We simulated a series of nonstationary and stationary GC/MS measurements for a large E. coli network that was then used to estimate parameters and their associated confidence intervals. We found that Fluxes could be successfully estimated using only nonstationary labeling data and External Flux measurements. Addition of near-stationary and stationary time points increased the precision of most parameters. Contrary to prior reports, the precision of nonstationary estimates proved to be comparable to the precision of estimates based solely on stationary data. Finally, we applied EMU-based NMFA to experimental nonstationary measurements taken from brown adipocytes and successfully estimated Fluxes and some metabolite concentrations. By using NFMA instead of traditional MFA, the experiment required only 6 h instead of 50 (the time necessary for most metabolite labeling to reach 99% of isotopic steady state).
Maciek R Antoniewicz - One of the best experts on this subject based on the ideXlab platform.
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an elementary metabolite unit emu based method of isotopically nonstationary Flux analysis
Biotechnology and Bioengineering, 2008Co-Authors: Jamey D Young, Jason Walther, Maciek R Antoniewicz, Gregory StephanopoulosAbstract:Nonstationary metabolic Flux analysis (NMFA) is at present a very computationally intensive exercise, especially for large reaction networks. We applied elemen- tary metabolite unit (EMU) theory to NMFA, dramatically reducing computational difficulty. We also introduced block decoupling, a new method that systematically and compre- hensively divides EMU systems of equations into smaller subproblems to further reduce computational difficulty. These improvements led to a 5000-fold reduction in simula- tion times, enabling an entirely new and more complicated set of problems to be analyzed with NMFA. We simulated a series of nonstationary and stationary GC/MS measure- ments for a large E. coli network that was then used to estimate parameters and their associated confidence inter- vals. We found that Fluxes could be successfully estimated using only nonstationary labeling data and External Flux measurements. Addition of near-stationary and stationary time points increased the precision of most parameters. Contrary to prior reports, the precision of nonstationary estimates proved to be comparable to the precision of estimates based solely on stationary data. Finally, we applied EMU-based NMFA to experimental nonstationary measure- ments taken from brown adipocytes and successfully esti- mated Fluxes and some metabolite concentrations. By using NFMA instead of traditional MFA, the experiment required only 6 h instead of 50 (the time necessary for most meta- bolite labeling to reach 99% of isotopic steady state). Biotechnol. Bioeng. 2008;99: 686-699.
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an elementary metabolite unit emu based method of isotopically nonstationary Flux analysis
Biotechnology and Bioengineering, 2008Co-Authors: Jamey D Young, Jason Walther, Maciek R Antoniewicz, Hyuntae Yoo, Gregory StephanopoulosAbstract:Nonstationary metabolic Flux analysis (NMFA) is at present a very computationally intensive exercise, especially for large reaction networks. We applied elementary metabolite unit (EMU) theory to NMFA, dramatically reducing computational difficulty. We also introduced block decoupling, a new method that systematically and comprehensively divides EMU systems of equations into smaller subproblems to further reduce computational difficulty. These improvements led to a 5000-fold reduction in simulation times, enabling an entirely new and more complicated set of problems to be analyzed with NMFA. We simulated a series of nonstationary and stationary GC/MS measurements for a large E. coli network that was then used to estimate parameters and their associated confidence intervals. We found that Fluxes could be successfully estimated using only nonstationary labeling data and External Flux measurements. Addition of near-stationary and stationary time points increased the precision of most parameters. Contrary to prior reports, the precision of nonstationary estimates proved to be comparable to the precision of estimates based solely on stationary data. Finally, we applied EMU-based NMFA to experimental nonstationary measurements taken from brown adipocytes and successfully estimated Fluxes and some metabolite concentrations. By using NFMA instead of traditional MFA, the experiment required only 6 h instead of 50 (the time necessary for most metabolite labeling to reach 99% of isotopic steady state).
