Optimal zero-crossing group selection method of the absolute gravimeter based on improved auto-regressive moving average model

An absolute gravimeter is a precision instrument for measuring gravitational acceleration, which plays an important role in earthquake monitoring, crustal deformation, national defense construction, etc. The frequency of laser interference fringes of an absolute gravimeter gradually increases with the fall time. Data are sparse in the early stage and dense in the late stage. The fitting accuracy of gravitational acceleration will be affected by least-squares fitting according to the fixed number of zero-crossing groups. In response to this problem, a method based on Fourier series fitting is proposed in this paper to calculate the zero-crossing point. The whole falling process is divided into five frequency bands using the Hilbert transformation. The multiplicative auto-regressive moving average model is then trained according to the number of optimal zero-crossing groups obtained by the honey badger algorithm. Through this model, the number of optimal zero-crossing groups determined in each segment is predicted by the least-squares fitting. The mean value of gravitational acceleration in each segment is then obtained. The method can improve the accuracy of gravitational measurement by more than 25% compared to the fixed zero-crossing groups method. It provides a new way to improve the measuring accuracy of an absolute gravimeter.

Tea Plantation Frost Damage Early Warning Using a Two-Fold Method for Temperature Prediction

As the source and main producing area of tea in the world, China has formed unique tea culture, and achievedremarkable economic benefits. However, frequent meteorological disasters, particularly low temperature frostdamage in late spring has seriously threatened the growth status of tea trees and caused quality and yield reduction of tea industry. Thus, timely and accurate early warning of frost damage occurrence in specific tea garden isvery important for tea plantation management and economic values. Aiming at the problems existing in currentmeteorological disaster forecasting methods, such as difficulty in obtaining massive meteorological data, largeamount of calculation for predicted models and incomplete information on frost damage occurrence, this paperproposed a two-fold algorithm for short-term and real-time prediction of temperature using field environmentaldata, and temperature trend results from a nearest local weather station for accurate frost damage occurrence leveldetermination, so as to achieve a specific tea garden frost damage occurrence prediction in a microclimate. Timeseries meteorological data collected from a small weather station was used for testing and parameterization of atwo-fold method, and another dataset acquired from Tea Experimental Base of Zhejiang University was furtherused to validate the capability of a two-fold model for frost damage forecasting. Results showed that comparedwith the results of autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR),the proposed two-fold method using a second order Furrier fitting model and a K-Nearest Neighbor model(K = 3) with three days historical temperature data exhibited excellent accuracy for frost damage occurrence prediction on consideration of both model accuracy and computation (98.46% forecasted duration of frost damage,and 95.38% for forecasted temperature at the onset time). For field test in a tea garden, the proposed methodaccurately predicted three times frost damage occurrences, including onset time, duration and occurrence level.These results suggested the newly-proposed two-fold method was suitable for tea plantation frost damage occurrence forecasting.

Preliminary study on characteristics of earthquake precursor of earth resistivity anisotropy degree in East China

Introduction The degree of earth-resistivity anisotropy was described (MAO, et al, 1995, 1998) as follows: S=|1\nn∑I=1(ρSN\ρEW)I|-1|×103 n=6(1) whereρNS and ρEW are monthly mean values of earth resistivity in the direction of NS and EW, respectively, S is the half-year value. Equation (1) shows that if ρNS=ρEW, then S =0 and the electrical property of medium is isotropic; if ρNS≠ρEW, then S≠0 and the electrical property of medium is anisotropic. When S increases, the anisotropy of electrical property of medium is enhanced. Because the rightside of equation (1) is absolute value and the possibility ofρNS＞ρEW and ρNS＜ρEW can be different at different stations, the increase or decrease of S cannot reflect the relative changes between ρNS and ρEW. FENG, et al (2000) defined S as follows:……

P-FFT and FG-FFT with real coefficients algorithm for the EFIE

In order to reduce the storage amount for the sparse coefficient matrix in pre-corrected fast Fourier transform （P-FFT） or fitting the Green function fast Fourier transform （FG-FFT）, the real coefficients are solved by improving the solution method of the coefficient equations. The novel method in both P-FFT and FG-FFT for the electric field integral equation （EFIE） is employed. With the proposed method, the storage amount for the sparse coefficient matrix can be reduced to the same level as that in the adaptive integral method （AIM） or the integral equation fast Fourier transform （IE-FFT）. Meanwhile, the new algorithms do not increase the number of the FFTs used in a matrix-vector product, and maintain almost the same level of accuracy as the original versions. Besides, in respect of the time cost in each iteration, the new algorithms have also the same level as AIM （or IE- FFF）. The numerical examples demonstrate the advantages of the proposed method.