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.

Wideband Direction-of-Arrival Estimation Based on Deep Learning

The performance of traditional high-resolution direction-of-arrival(DOA)estimation methods is sensitive to the inaccurate knowledge on prior information,including the position of ar-ray elements,array gain and phase,and the mutual coupling between the array elements.Learning-based methods are data-driven and are expected to perform better than their model-based counter-parts,since they are insensitive to the array imperfections.This paper presents a learning-based method for DOA estimation of multiple wideband far-field sources.The processing procedure mainly includes two steps.First,a beamspace preprocessing structure which has the property of fre-quency invariant is applied to the array outputs to perform focusing over a wide bandwidth.In the second step,a hierarchical deep neural network is employed to achieve classification.Different from neural networks which are trained through a huge data set containing different angle combinations,our deep neural network can achieve DOA estimation of multiple sources with a small data set,since the classifiers can be trained in different small subregions.Simulation results demonstrate that the proposed method performs well both in generalization and imperfections adaptation.

Multiple Debye Spherical Layers and Universe —Gravitation Originates from Electric Force

Electric field superposition principle and Gauss’s law are the basis of electrostatics. By extended analysis on the electric field lines of a charge, it is shown that electric field superposition principle and Gauss’s law are not tenable in some states, involving the electric field of ion atmosphere that is a key concept in Debye-Hückel theory of electrolyte solution and plasma. Unveiling Debye shield, ion atmosphere (Debye spherical layer 1) actually is equivalent to continue to transmit the electric field originated from the central ion, just changing the direction of the electric field. Debye spherical layer 2 and multiple Debye spherical layers generate in the transmission. Due to the effect of the multiple Debye spherical layers of charged particles in the universe, gravitation originates from electric force.

EXISTENCE OF PERIODIC SOLUTIONS TO A LINARD EQUATION WITH MULTIPLE DELAYS

In this paper,by a coincidence degree theory,the existence of periodic solutions to a Linard equation with multiple delays is established,which substantially extends some results in the previous literatures.