A hybrid precursor of titanium-containing polycarbosilane is prepared by blending hyperbranched polycarbosilane (HBPCS) and tetrabutyl titanate (TBT), and then crosslinking at 160 A degrees C, followed by pyrolyzing at high temperatures to afford SiC(Ti) ceramics. The crosslinking reaction of HBPCS-TBT hybrid precursor is investigated by FT-IR, solid state (29)Si MAS NMR, and GPC. The results indicate that the crosslinking reaction takes place via condensation between the Si-H bond of HBPCS and butoxy group in TBT leading to the formation of Si-O-Ti bonds. The thermal properties and structural evolution of crosslinked hybrid precursor and the crystallization behavior and composition of final ceramics are investigated by TGA, FT-IR, Raman spectroscopy, XRD and energy dispersive elemental analysis. The ceramic yield of hybrid precursor is significantly enhanced by introduction of TBT. The ceramic yield at 1,400 A degrees C is 83% for HBPCS-TBT-5 as measured by TGA. The Ti-content in the ceramic is controlled by varying the TBT content in the feed. The SiC(Ti) ceramic is amorphous at 900 A degrees C. The characteristic peaks of beta-SiC and TiC appear until 1,600 A degrees C. The growth of SiC crystals is inhibited by the formation of TiC.National Natural Science Foundation of China[50802079]; Aviation Science Foundation of China[2008ZH68005]; Natural Science Foundation of Fujian Province of China[2008J0165, 2010J01307
Purpose
To improve the efficiency, accuracy and adaptivity of the parameter inversion analysis method of a rockfill dam, this study aims to establish an adaptive model based on a harmony search algorithm (HS) and a mixed multi-output relevance vector machine (MMRVM).
Design/methodology/approach
By introducing the mixed kernel function, the MMRVM can accurately simulate the nonlinear relationship between the material parameters and dam settlement. Therefore, the finite element method with time consumption can be replaced by the MMRVM. Because of its excellent global search capability, the HS is used to optimize the kernel parameters of the MMRVM and the material parameters of a rockfill dam.
Findings
Because the parameters of the HS and the variation range of the MMRVM parameters are relatively fixed, the HS-MMRVM can imbue the inversion analysis with adaptivity; the number of observation points required and the robustness of the HS-MMRVM are analyzed. An application example involving a concrete-faced rockfill dam shows that the HS-MMRVM exhibits high accuracy and high speed in the parameter inversion analysis of static and creep constitutive models.
Practical implications
The applicability of the HS-MMRVM in hydraulic engineering is proved in this paper, which should further validate in inversion problems of other fields.
Originality/value
An adaptive inversion analysis model is established to avoid the parameters of traditional methods that need to be set by humans, which strongly affect the inversion analysis results. By introducing the mixed kernel function, the MMRVM can accurately simulate the nonlinear relationship between the material parameters and dam settlement. To reduce the data dimensions and verify the model’s robustness, the number of observation points required for inversion analysis and the acceptable degree of noise are determined. The confidence interval is built to monitor dam settlement and provide the foundation for dam monitoring and reservoir operation management.
Loudness describes the sound intensity sensations of the human hearing. A study about loudness calculation which is based on the Moore model shows us that starting from the mechanism of human`s ear, the basic principle and calculation skills of algorithm have been researched. By using Matlab to optimize the algorithm, the specific loudness curve and loudness level of any audio file can be calculated; equal-loudness curve by using Steffensen method is calculated compared with that from the acoustic standard, In order to validating the program. The loudness of the sewing machine under different conditions is calculated.
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