Ruin probability for correlated negative risk sums model with Erlang processes

This paper studies a Sparre Andersen negative risk sums model in which the distribution of ＂interclaim＂ time is that of a sum of n independent exponential random variables. Thus, the Erlang（n） model is a special case. On this basis the correlated negative risk sums process with the common Erlang process is considered. Integro-differential equations with boundary conditions for ψ（u） are given. For some special cases a closed-form expression for ψ（u） is derived.

Deformation tests and failure process analysis of an anchorage structure

In order to study the failure process of an anchorage structure and the evolution law of the body＇s defor- mation field, anchor push-out tests were carried out based on digital speckle correlation methods （DSCM）. The stress distribution of the anchorage interface was investigated using the particle flow numerical simulation method. The results indicate that there are three stages in the deformation and fail- ure process of an anchorage structure： elastic bonding stage, a de-bonding stage and a failure stage. The stress distribution in the interface controls the stability of the structure. In the elastic bonding stage, the shear stress peak point of the interface is close to the loading end, and the displacement field gradually develops into a ＂V＂ shape, in the de-bonding stage, there is a shear stress plateau in the center of the anchorage section, and shear strain localization begins to form in the deformation field. In the failure stage, the bonding of the interface fails rapidly and the shear stress peak point moves to the anchorage free end. The anchorage structure moves integrally along the macro-cracl~ The de-bonding stage is a research focus in the deformation and failure process of an anchorage structure, and plays an important guiding role in roadway support design and prediction of the stability of the surrounding rock.

Minimax Estimation Problem for Periodically Correlated Stochastic Processes

The problem of optimal linear estimation of the functional Aξ =10^∞a（t）ζ（（t）dt depending on the unknown values of periodically correlated stochastic process ζ（t） from observations of this process for t 〈 0 is considered. Formulas that determine the greatest value of mean square error and the minimax estimation for the functional are proposed for the given class of admissible processes. It is shown that one-sided moving average stationary sequence gives the greatest value of the mean square error.

Quality of correlation in quantum information

A strong and stable correlation in quantum information is of high quality for quantum information processing.We define two quantities,selective average correlation and ripple coefficient,to evaluate the quality of correlation in quantum information in a time interval.As a new communication channel,Heisenberg spin chains are widely investigated.We select a two-qubit Heisenberg XXZs pin chain with Dzyaloshinskii-Moriya interaction in an inhomogeneous magnetic field as an example,and use the two quantities to evaluate the qualities of the correlation in quantum information with different measures.The result shows that,if the time evolutions are similar,there needs only evaluating one of them to know when the correlation has high quality for quantum information processing.

Application of wavelet scale correlation filtering and its improved algorithm in signal processing with a spark sound source

It is seriously interfered by ship noise when analyzing and extracting broadband spark sound source signal. In the energy concentrated domain which is below 5 kHz, the traditional scale correlation filtering algorithm, which is based on adjacent-scale correlation, has limited anti-interference ability due to the low signal-to-noise ratio （SNR） and similar Lipschitz exponent characteristic of each other. However, because different frequency bands of the broadband electric spark signal have different noise interferences, the filtering algorithm based on adjacent-scale correlation is adapted to high SNR and small-scale high-frequency wavelet coefficients filtering; the filtering algorithm based on cross-scale correlation is adapted to low SNR and large-scale low-frequency wavelet coefficients filtering, and the threshold coefficient selection method had been corrected in the algorithm. It is shown that the filtering algorithm has a good filtering effect and extracts the broadband spark sound source signal effectively; it is applicable to broadband underwater acoustic signM processing in the presence of narrow-band strong interference background noise.

High-speed ground moving target detection research using triangular modulation FMCW

The frequency modulated continuous wave(FMCW)radar has the characteristics of low probability of interception,good hidden property and the ability to counter anti-radiation missiles.This paper proposes a new method for high-speed ground moving target detection(GMTD)using triangular modulation FMCW.According to the characteristic of the opposite range shift induced by the upslope and downslope modulation FMCW,the upslope and downslope are imaged,respectively.After compensation of continuous motion of the platform and time difference between upslope and downslope signals for imaging,the moving target can be detected through displaced phase center antenna(DPCA)technology.When the moving target is detected,the moving target image is extracted,and correlation processing is used to obtain the range shift,which can be used to estimate the target radial velocity,and further to find the real position of the target.The effectiveness of this method is verified by the result of computer simulation.

Correlated squared returns

Joint densities for a sequential pair of returns with weak autocorrelation and strong correlation in squared returns are formulated.The marginal return densities are either variance gamma or bilateral gamma.Two-dimensional matching of empirical characteristic functions to its theoretical counterpart is employed for dependency parameter estimation.Estimations are reported for 3920 daily return sequences of one thousand days.Path simulation is done using conditional distribution functions.The paths display levels of squared return correlation and decay rates for the squared return autocorrelation function that are comparable to these magnitudes in daily return data.Regressions of log characteristic functions at different time points are used to estimate time scaling coefficients.Regressions of these time scaling coefficients on squared return correlations support the view that autocorrelation in squared returns slows the rate of passage of economic time.An analysis of financial markets for 2020 in comparison with 2019 displays a post-COVID slowdown in financial markets.