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This Paper Proposes a Scheme for Filtering of Stochastic Distributed Parameter Systems. it is Assumed that a Real-Time Environment Consists of m Groups of Sensors, each of which Provides Necessarily State Spatially Measurements from Sensing Devices. Base on Lyapunov Stability Theorem and Itô formula, a Class of Distributed Adaptive Filters with Penalty Terms Result in the State Errors Forming a Stable Evolution System and Asymptotically Converge to Stochastic Distributed Parameter Systems, and then the Preferable State Estimation is Derived. Numerical Simulation Demonstrates the Effectiveness of the Proposed Method.

To evaluate the utility of ADC values and Gd-DTPA equilibrium phase MR imaging in staging hepatic fibrosis in rats. 48 rats were allocated into experimental and control groups. Experimental rats were subcutaneously injected with a mixture of CCl4. From 4th-12th weeks, MR images were obtained, which include pre-enhanced phase imaging, DWI and equilibrium phase imaging. Then the rat groups were subdivided according to the stages of fibrosis (S0, S1, S2, S3 and S4) after histopathological analysis. The original MRI data were forwarded to the workstation to obtain apparent diffusion coefficient (ADC) maps at b value of 500 s/mm2. Pre-enhanced phase and equilibrium phase signal intensities and relative contrast enhancement index (RCEI) were measured as well. Lastly, the ADC values and RCEI of the experimental group were compared with each other and with the control group. All statistical analyses were carried out with SPSS, where P < 0.05 is considered to represent a significant difference. Hepatic ADC values are significantly different between the experimental and control groups (P = 0). There is a statistically significant difference between the experimental and control groups on RCEI (P = 0). Comparing the S1, S2, S3 and S4 groups, there is a statistically significant difference between the mild group (S1 and S2) and the severe group (S4) in terms of ADC values and RCEI (all P < 0.05). A statistically significant difference is also found between the moderate group (S3) and the severe group in ADC values. As the degree of fibrosis increases, there are a reduction in ADC values and an increase in RCEI. Comparing the groups with ADC values and enhancement index, there are statistically significant differences in sensitivity and specificity on diagnosis of hepatic fibrosis. The ADC values have the best sensitivity (93.1%) and specificity (83.3%). Quantitative ADC values and RCEI may be helpful to the staging of rat fibrosis, but their application in human is controversial.

This paper proposes three different adaptive schemes corresponding to three different kinds of time-delayed chaotic systems with unknown parameters or unknown structures. All of the adaptive control laws guarantee that the master and slave systems are synchronization. Finally, two examples are demonstrated to verify the effectiveness and feasibility of the proposed synchronization methods.

This paper aims to improve the performance of a class of distributed parameter systems for the optimal switching of actuators and controllers based on event-driven control. It is assumed that in the available multiple actuators, only one actuator can receive the control signal and be activated over an unfixed time interval, and the other actuators keep dormant. After incorporating a state observer into the event generator, the event-driven control loop and the minimum inter-event time are ultimately bounded. Based on the event-driven state feedback control, the time intervals of unfixed length can be obtained. The optimal switching policy is based on finite horizon linear quadratic optimal control at the beginning of each time subinterval. A simulation example demonstrate the effectiveness of the proposed policy.