Determination of Coastal Sea Level Heights around Taiwan by improved GNSS Reflectometry

<p>Rapid sea level rise, a severe consequence of global warming, could significantly damage the lives and properties of numerous human beings living in low-lying coastal areas. Therefore, realizing and monitoring coastal sea level variations are of great importance for human society. Conventionally, sea level heights are measured by using tide gauges; however, the records are contaminated by vertical land motions which are difficult to be separated. Recently, Global Navigation Satellite System Reflectometry (GNSS-R) technology has been proved to effectively monitor the coastal sea level changes from GNSS signal-to-noise ratio (SNR) data. However, the generation of detrended SNR ( SNR) depending on different satellite elevation angle intervals via a quadratic fitting, considerably influences the accuracy of sea level retrievals. Moreover, the quadratic fitting cannot perfectly describe the trend of SNR data. Therefore, we proposed a method combining ensemble empirical mode decomposition (EEMD) and ocean tide model to compute SLHs. EEMD can decompose the original SNR data into several intrinsic mode functions (IMFs) corresponding to specific frequencies. Then, Lomb-Scargle Periodogram (LSP) is applied to calculate the dominant frequency of the IMF with maximum spectral power. EEMD is not only suitable for dealing with nonlinear and nonstationary data but also eliminates the mode mixing problem of empirical mode decomposition (EMD) by adding white noises. In addition, we set an empirical SLH interval from ocean tide model as a quality control. In this study, the existing GNSS stations at the coasts of Taiwan are used to examine the proposed approach and then compare the results with those from the traditional quadratic fitting. Finally, the measurements from co-located or nearby traditional tide gauges are served as ground truth to evaluate the accuracy and stability of the mentioned methods.</p>

Grey prediction models for the standard limit of vehicle noise

With an increasing number of vehicles and increasing environmental protection requirements, countries have accelerated the rate of revision of automobile noise standards and legislation. Scientific prediction of the limiting values in future noise standards is helpful to promote the development of automobile noise reduction technology and measurement analysis technology. The development of noise standard limits has its own objective laws and is restricted to the current and future developments in automotive technology. The amplitude of noise will be reduced increasingly less in the future. Grey prediction theory can explore the variation rules by processing a few effective data. In this paper, grey theory is used to deal with the limited original data in the vehicle noise standard. Non-equal-interval quadratic fitting of the grey Verhulst direct model to predict the future noise standard limits is selected on the basis of calculation and comparison of different models. The Verhulst model is employed to describe the system development by using the characteristics of saturation. By means of quadratic fitting, the accuracy of the Verhulst model can be further improved. The simulation results show the validity and the accuracy of the model. The prediction result is useful for standards and regulations makers and for car manufacturers.

Investigation on Zener-Hollomon Parameter in the Hot Compressive Deformation Behavior of SWRCH 35K

The hot compressive deformation behavior of SWRCH 35K was studied with uniaxial hot compression simulation tests at 923 ~ 1223 K and strain rate of 0.01 ~ 20 /s. The results show that the hot compressive deformation activation energy was 408 kJ/mol and the rang of deformation stored energy was 10 ~ 50 J/mol. The quadratic fitting expression between deformation stored energy and Zener-Hollomon parameter (Z) was established and the deformation stored energy was considered to increased with increasing Z or with lower deformation temperature and increasing deformation rate.

The Model Reconstruction of Carbon Black/Rubber Composites’ Heat Generation Ratio Based on Levenberg-Marquardt BP Algorithm

Different temperature, different frequency, different recipe, then heat generation ratio of carbon black/rubber composites will be different. An artificial neural network model has been developed based on the analysis of the relation between carbon black/rubber composites‘ heat generation ratio and temperature,frequency.The neural network model is compared with the quadratic fitting formulae and relative errors is analyzed. The simulation results indicated the neural network model’s precision is greater than the quadratic fitting formulae’s, and artificial neural network theory can be used in the research field of carbon black/rubber composites’ thermophysical properties.

QSPR MODELING OF ENTHALPIES OF FORMATION FROM ELEMENTS OF COORDINATION COMPOUNDS BY CORRELATION WEIGHTING OF NEAREST NEIGHBORING CODES

We have obtained the enthalpies of formation from elements of a representative set of 158 coordination compounds resorting to the QSPR theory. The basic variables to determine the regression equations are the correlation weights of the nearest neighboring codes. Both linear and quadratic fitting relationships were determined as well as an optimal exponent method. Statistical parameters are quite satisfactory and they show the suitability of this sort of methodology to predict molecular enthalpies of formation.