Increasing the sensitivity of real-time method for diagnostic of autogenerators phase synchronization based on their non-stationary time series

Purpose of this work is to of the research – Increasing the sensitivity of a method for diagnosing phase synchronization of autogenerators based on their non-stationary time series in real time, and also a comparison of the statistical properties of the proposed modification of the method with the well-known method for diagnostics of loop synchronization, which has proven itself in the analysis of experimental data. Methods.The paper compares the probabilities of the appearance of an error of the second kind of the developed modified method for diagnostics of phase synchronization with the probabilities of occurrence of an error of the second kind of the known method at equal values of sensitivity. When comparing the methods, generated test time realizations with a priori known boundaries of the phase synchronization sections are used, which repeat the statistical properties of the experimental data. It also compares the computational complexity of the two methods. Results. A modification of the method for diagnosing phase synchronization of autonomic regulation circuits in real time is proposed. It is shown that the proposed modification provides similar values of sensitivity and probability of appearance of errors of the second kind as the previously proposed approach. The developed method has less computational complexity than the previously proposed method. The values of free parameters corresponding to different values of sensitivity and probability of appearance of errors of the second kind are obtained. Conclusion. The area of application of the developed method with modification is formulated. The low computational complexity of the proposed method, as well as the possibility of switching devices to integer computations in calculations, makes it possible to use it for wearable registrations performing calculations in real time, based on small-sized low-power processors that do not support floating-point arithmetic operations.

SACF of the Errors of Stationary Time Series Models in the Presence of a Large Additive Outlier

In this paper, the limiting behaviour of the Sample Autocorrelation Function(SACF) of the errors {et} of First-Order Autoregressive (AR(1)), First-Order Moving Average (MA(1)) and First Order Autoregressive First-Order Moving Average (ARMA(1,1)) stationary time series models in the presence of a large Additive Outlier(AO) is discussed. It is found that the errors which are supposed to be uncorrelated due to either white noise process or normally distributed process are not so in the presence of a large additive outlier. The SACF of the errors follows a particular pattern based on the time series model. In the case of AR(1) model, at lag 1, the contaminated errors {et} are correlated, whereas at higher lags, they are uncorrelated. But in the MA(1) and ARMA(1,1) models, the contaminated errors {et} are correlated at all the lags. Furthermore it is observed that the intensity of correlations depends on the parameters of the respective models.