The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
Shanben Chen - One of the best experts on this subject based on the ideXlab platform.
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arc Spectral processing technique with its application to wire feed monitoring in al mg alloy pulsed gas tungsten arc welding
Journal of Materials Processing Technology, 2013Co-Authors: Huanwei Yu, Yanling Xu, Na Lv, Huabin Chen, Shanben ChenAbstract:Abstract The principal component analysis (PCA) is applied for three purposes: Spectral line identification, redundancy removal and Spectral Characteristic signals extraction. The Spectral information is classified as the first and the second principal components, associated with Ar I lines and metal lines, respectively. With the mean value method, pulse interference resulted from the pulse current is eliminated from the Spectral signals. The relationships among these extracted signals and the defects resulted from wire feed are discussed and the results show that the second principal component is closely related to these defects while the first principal component has relationship with the arc states. To test validity of the extracted signals, a back-propagation neural network is designed and appropriately trained with “Early Stopping” technique to detect these defects automatically.
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arc Spectral processing technique with its application to wire feed monitoring in al mg alloy pulsed gas tungsten arc welding
Journal of Materials Processing Technology, 2013Co-Authors: Huabin Chen, Shanben ChenAbstract:Abstract The principal component analysis (PCA) is applied for three purposes: Spectral line identification, redundancy removal and Spectral Characteristic signals extraction. The Spectral information is classified as the first and the second principal components, associated with Ar I lines and metal lines, respectively. With the mean value method, pulse interference resulted from the pulse current is eliminated from the Spectral signals. The relationships among these extracted signals and the defects resulted from wire feed are discussed and the results show that the second principal component is closely related to these defects while the first principal component has relationship with the arc states. To test validity of the extracted signals, a back-propagation neural network is designed and appropriately trained with “Early Stopping” technique to detect these defects automatically.
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application of arc plasma Spectral information in the monitor of al mg alloy pulsed gtaw penetration status based on fuzzy logic system
The International Journal of Advanced Manufacturing Technology, 2013Co-Authors: Huanwei Yu, Zhen Ye, Shanben ChenAbstract:In this paper, an intelligent fuzzy system based on arc Spectral information was proposed to recognize the weld penetration status. A spectrometer and a visual sensor were used simultaneously to collect the arc Spectral and visual information about the Al–Mg alloy pulsed GTAW process, respectively. Principal component analysis was utilized for Spectral line identification, redundancy removal and Spectral Characteristic signals extraction; wavelet packet denoising technique was used to filter these signals. The relationship between the penetration status under different welding conditions and the corresponding Spectral signals was discussed and clarified. Finally, a fuzzy system based on the Spectral signals was developed and successfully used to estimate the percentage index of the weld penetration status. With a binary decision of penetration index, the average recognition rate for the penetration status about the specific welding conditions was no less than 82.1 %.
E Radziemska - One of the best experts on this subject based on the ideXlab platform.
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the effect of temperature on the power drop in crystalline silicon solar cells
Renewable Energy, 2003Co-Authors: E RadziemskaAbstract:The influence of temperature and wavelength on electrical parameters of crystalline silicon solar cell and a solar module are presented. At the experimental stand a thick copper plate protected the solar cell from overheating, the plate working as a radiation heat sink, or also as the cell temperature stabilizer during heating it up to 80°C. A decrease of the output power (−0.65%/K), of the fill-factor (−0.2%/K) and of the conversion efficiency (−0.08%/K) of the PV module with the temperature increase has been observed. The Spectral Characteristic of the open-circuit voltage of the single-crystalline silicon solar cell is also presented. It is shown that the radiation-rate coefficient of the short-circuit current-limit of the solar cell at 28°C is 1.2%/(mW/cm2).
Jianzhong Chen - One of the best experts on this subject based on the ideXlab platform.
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crystal growth nonlinear frequency doubling and Spectral Characteristic of nd ca9la vo4 7 crystal
Journal of Alloys and Compounds, 2014Co-Authors: Naifeng Zhuang, Xiaofeng Liu, Xin Chen, Bin Zhao, Jianzhong ChenAbstract:Abstract The pure Ca9La(VO4)7 and the Nd:Ca9La(VO4)7 crystals with various Nd3+-doped concentrations were grown by Czochraski method. The effective segregation coefficient of Nd3+ ion in Ca9La(VO4)7 crystal was measured as 1.07, which is favorable to the even distribution of Nd3+ ions. The frequency-doubling experiment and the crystal structure analyses show that the presence of La3+ ion in crystal can improve the SHG effect of Ca9La(VO4)7 crystal. The intensity of SHG effect, measured by the Kurtz powder method, is found to be about 4 times as large as that of KH2PO4 (KDP) crystal. The polarized absorption spectra and the polarized fluorescence spectra are analyzed based on Judd–Ofelt theory, which show that the Nd:Ca9La(VO4)7 crystal is hopeful to generate laser light at the wavelengths of about 921, 1069 and 1349 nm. The luminescence decay kinetics are analyzed based on the migration model, and the results indicate that Nd3+ ion in Ca9La(VO4)7 crystal has a high quantum efficiency, about 98.2% in 3.2 at.% Nd:Ca9La(VO4)7 crystal, and a weak concentration quenching effect. Therefore, Nd:Ca9La(VO4)7 crystal may be a high efficiency laser material and a candidate of self-frequency-doubling (SFD) laser crystal.
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crystal growth nonlinear frequency doubling and Spectral Characteristic of nd ca9la vo4 7 crystal
ChemInform, 2014Co-Authors: Naifeng Zhuang, Xiaofeng Liu, Xin Chen, Bin Zhao, Jianzhong ChenAbstract:Pure and Nd-doped Ca9La(VO4)7 crystals with various Nd3+ levels (3.2, 18.9, 31.4, and 32 at.%) are grown by the Czochralski method at 1430 °C from powders prepared by solid state reaction of a stoichiometric mixture of CaCO3, V2O5, La2O3, and Nd2O3 (alumina crucible, 1000 °C, 8 h).
Huabin Chen - One of the best experts on this subject based on the ideXlab platform.
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arc Spectral processing technique with its application to wire feed monitoring in al mg alloy pulsed gas tungsten arc welding
Journal of Materials Processing Technology, 2013Co-Authors: Huanwei Yu, Yanling Xu, Na Lv, Huabin Chen, Shanben ChenAbstract:Abstract The principal component analysis (PCA) is applied for three purposes: Spectral line identification, redundancy removal and Spectral Characteristic signals extraction. The Spectral information is classified as the first and the second principal components, associated with Ar I lines and metal lines, respectively. With the mean value method, pulse interference resulted from the pulse current is eliminated from the Spectral signals. The relationships among these extracted signals and the defects resulted from wire feed are discussed and the results show that the second principal component is closely related to these defects while the first principal component has relationship with the arc states. To test validity of the extracted signals, a back-propagation neural network is designed and appropriately trained with “Early Stopping” technique to detect these defects automatically.
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arc Spectral processing technique with its application to wire feed monitoring in al mg alloy pulsed gas tungsten arc welding
Journal of Materials Processing Technology, 2013Co-Authors: Huabin Chen, Shanben ChenAbstract:Abstract The principal component analysis (PCA) is applied for three purposes: Spectral line identification, redundancy removal and Spectral Characteristic signals extraction. The Spectral information is classified as the first and the second principal components, associated with Ar I lines and metal lines, respectively. With the mean value method, pulse interference resulted from the pulse current is eliminated from the Spectral signals. The relationships among these extracted signals and the defects resulted from wire feed are discussed and the results show that the second principal component is closely related to these defects while the first principal component has relationship with the arc states. To test validity of the extracted signals, a back-propagation neural network is designed and appropriately trained with “Early Stopping” technique to detect these defects automatically.
Bela Szilagyi - One of the best experts on this subject based on the ideXlab platform.
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Spectral Characteristic evolution a new algorithm for gravitational wave propagation
Classical and Quantum Gravity, 2015Co-Authors: Casey J Handmer, Bela SzilagyiAbstract:We present a Spectral algorithm for solving the full nonlinear vacuum Einstein field equations in the Bondi framework. Developed within the Spectral Einstein Code, we demonstrate Spectral Characteristic evolution as a technical precursor to Cauchy Characteristic extraction, a rigorous method for obtaining gauge-invariant gravitational waveforms from existing and future astrophysical simulations. We demonstrate the new algorithm's stability, convergence, and agreement with existing evolution methods. We explain how an innovative Spectral approach enables a two orders of magnitude improvement in computational efficiency.
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Spectral Characteristic evolution a new algorithm for gravitational wave propagation
arXiv: General Relativity and Quantum Cosmology, 2014Co-Authors: Casey J Handmer, Bela SzilagyiAbstract:We present a Spectral algorithm for solving the full nonlinear vacuum Einstein field equations in the Bondi framework. Developed within the Spectral Einstein Code (SpEC), we demonstrate Spectral Characteristic evolution as a technical precursor to Cauchy Characteristic Extraction (CCE), a rigorous method for obtaining gauge-invariant gravitational waveforms from existing and future astrophysical simulations. We demonstrate the new algorithm's stability, convergence, and agreement with existing evolution methods. We explain how an innovative Spectral approach enables a two orders of magnitude improvement in computational efficiency.
