INFORMATION FUSION STEADY-STATE WHITE NOISE DECONVOLUTION ESTIMATORS WITH TIME-DELAYED MEASUREMENTS AND COLORED MEASUREMENT NOISES

White noise deconvolution or input white noise estimation problem has important appli-cation backgrounds in oil seismic exploration,communication and signal processing.By the modern time series analysis method,based on the Auto-Regressive Moving Average(ARMA) innovation model,under the linear minimum variance optimal fusion rules,three optimal weighted fusion white noise deconvolution estimators are presented for the multisensor systems with time-delayed measurements and colored measurement noises.They can handle the input white noise fused filtering,prediction and smoothing problems.The accuracy of the fusers is higher than that of each local white noise estimator.In order to compute the optimal weights,the formula of computing the local estimation error cross-covariances is given.A Monte Carlo simulation example for the system with 3 sensors and the Bernoulli-Gaussian input white noise shows their effectiveness and performances.

Decoupled Wiener state fuser for descriptor systems

By the modem time series analysis method, based on the autoregressive moving average （ARMA） innovation models and white noise estimation theory, using the optimal fusion rule weighted by diagonal matrices, a distributed descriptor Wiener state fuser is presented by weighting the local Wiener state estimators for the linear discrete stochastic descriptor systems with multisensor. It realizes a decoupled fusion estimation for state components. In order to compute the optimal weights, the formulas of computing the cross-covariances among local estimation errors are presented based on cross-covariances among the local innovation processes, input white noise, and measurement white noises. It can handle the fused filtering, smoothing, and prediction problems in a unified framework. Its accuracy is higher than that of each local estimator. A Monte Carlo simulation example shows its effectiveness and correctness.

Detection of a high-confidence quasi-periodic oscillation in radio light curve of the high redshift FSRQ PKS J0805–0111

In this work,we have searched for quasi-periodic oscillations(QPOs)in the 15 GHz light curve of the FSRQ PKS J0805-0111 monitored by the Owens Valley Radio Observatory(OVRO)40 m telescope during the period from 2008 January 9 to 2019 May 9,using the weighted wavelet Z-transform(WWZ)and the Lomb-Scargle Periodogram(LSP)techniques.This is the first time to search for a periodic radio signal in the FSRQ PKS J0805-0111 by these two methods.Both methods consistently reveal a repeating signal with a periodicity of 3.38±0.8 yr(>99.7%confidence level).In order to determine the significance of the periods,the false alarm probability method was applied,and a large number of Monte Carlo simulations were performed.As possible explanations,we discuss a number of scenarios including the thermal instability of thin disks scenario,the spiral jet scenario and the binary supermassive black hole scenario.We expect that the binary black hole scenario,where the QPO is caused by the precession of binary black holes,is the most likely explanation.FSRQ PKS J0805-0111 thus could be a good binary black hole candidate.In the binary black hole scenario,the distance between the primary black hole and the secondary black hole is about 1.71×10^(16) cm.

A new information fusion white noise deconvolution estimator

The white noise deconvolution or input white noise estimation problem has important applications in oil seismic exploration, communication and signal processing. By the modern time series analysis method, based on the autoregressive moving average （ARMA） innovation model, a new information fusion white noise deconvolution estimator is presented for the general multisensor systems with different local dynamic models and correlated noises. It can handle the input white noise fused filtering, prediction and smoothing problems, and it is applicable to systems with colored measurement noises. It is locally optimal, and is globally suboptimal. The accuracy of the fuser is higher than that of each local white noise estimator. In order to compute the optimal weights, the formula computing the local estimation error cross-covariances is given. A Monte Carlo simulation example for the system with Bernoulli-Gaussian input white noise shows the effectiveness and performances.

Implications of clinical pathway reform on pharmaceutical costs at a hospital in Qingdao city

To study on the effect of clinical pathway （CP） on controlling pharmaceutical costs, we selected complex, chronic, non-communicable diseases, including cerebral infarction, cerebral hemorrhage, transient ischemic attack, and chronic obstructive pulmonary disease, as diseases to implement clinical pathways at a tertiary hospital in Qingdao. We then conducted intermittent time series analysis on pharmaceutical costs. After the implementation of clinical pathway, overall pharmaceutical costs of patients with transient ischemic attack reduced significantly. The effect was not significant for cerebral hemorrhage patients. The implementation of clinical pathway has a desirable outcome on controlling pharmaceutical costs.

Distributed fusion white noise deconvolution estimators

The white noise deconvolution or input white noise estimation problem has important applications in oil seismic exploration,communication and signal processing.By combining the Kalman filtering method with the modern time series analysis method,based on the autoregressive moving average(ARMA)innovation model,new distributed fusion white noise deconvolution estimators are presented by weighting local input white noise estimators for general multisensor systems with different local dynamic models and correlated noises.The new estimators can handle input white noise fused filtering,prediction and smoothing problems,and are applicable to systems with colored measurement noise.Their accuracy is higher than that of local white noise deconvolution estimators.To compute the optimal weights,the new formula for local estimation error cross-covariances is given.A Monte Carlo simulation for the system with Bernoulli-Gaussian input white noise shows their effectiveness and performance.