Multi-Channel Blind System Identification for Noninvasive Cardiovascular Monitoring

2002 ◽  
Author(s):  
Yi Zhang ◽  
Andrew T. Reisner ◽  
H. Harry Asada
Keyword(s):  
System Identification ◽  
Cardiovascular System ◽  
Cardiovascular Monitoring ◽  
Identification Technique ◽  
Blind System Identification ◽  
Novel Method

A novel method for diagnostic monitoring of the cardiovascular system is presented, which combines peripheral measurements obtained continuously and non-invasively with a multi-channel blind system identification technique.

Active Non-Intrusive System Identification for Cardiovascular Monitoring: Part II — Development of System Identifiction Algorithm

2013 ◽  
Author(s):  
Nima Fazeli ◽  
Jin-Oh Hahn
Keyword(s):  
Blood Pressure ◽  
System Identification ◽  
Cardiovascular System ◽  
Pressure Waves ◽  
Identification Algorithm ◽  
Cardiovascular Monitoring ◽  
Arterial Tree ◽  
Aortic Blood ◽  
Aortic Blood Pressure ◽  
Central Aortic Blood Pressure

In this paper, we present an innovative active non-intrusive system identification approach to cardiovascular monitoring. The proposed approach is based on a dual collocated actuator-sensor system for cardiovascular system identification, in which the actuators actively excite the arterial tree to create rich and informative trans-mural pressure waves traveling in the arterial tree, which are then non-intrusively measured by the collocated sensors. In our previous work, we developed a mathematical model to reproduce the propagation of intra-vascular (arterial) and extra-vascular (artificial) pressure waves along the arterial tree. Then, we used a dual (radial-femoral) blood pressure cuff system as a prototype dual collocated actuator-sensor system to demonstrate the proposed methodological framework to create rich trans-mural pressure waves as well as to non-intrusively reconstruct them from sensor measurements. In this follow-up work, we propose a novel system identification algorithm to derive cardiovascular system dynamics and reconstruct central aortic blood pressure waveform from the trans-mural pressure waves observed at the peripheral locations. It was successfully demonstrated that the system identification algorithm was able to reconstruct the central aortic blood pressure accurately, and that its performance was superior to the passive non-intrusive approach.

A System Identification Method for Excitation-Frequency Dependent Rotordynamic Coefficients

2012 ◽  
Author(s):  
Timothy W. Dimond ◽  
Amir A. Younan ◽  
Paul Allaire
Keyword(s):  
System Identification ◽  
Measurement Uncertainty ◽  
Excitation Frequency ◽  
Frequency Dependent ◽  
Rotordynamic Coefficients ◽  
Adjoint Systems ◽  
Dynamic Coefficients ◽  
Backward Whirl ◽  
Identification Technique ◽  
Damped Systems

Experimental identification of rotordynamic systems presents unique challenges. Gyroscopics, generally damped systems, and non-self-adjoint systems due to fluid structure interaction forces mean that symmetry cannot be used to reduce the number of parameters to be identified. Rotordynamic system experimental measurements are often noisy, which complicates comparisons with theory. When linearized, the resulting dynamic coefficients are also often a function of excitation frequency, as distinct from operating speed. In this paper, a frequency domain system identification technique is presented that addresses these issues for rigid-rotor test rigs. The method employs power spectral density functions and forward and backward whirl orbits to obtain the excitation frequency dependent effective stiffness and damping. The method is highly suited for use with experiments that employ active magnetic exciters that can perturb the rotor in the forward and backward whirl directions. Simulation examples are provided for excitation-frequency reduced tilting pad bearing dynamic coefficients. In the simulations, 20 and 50 percent Gaussian output noise was considered. Based on ensemble averages of the coefficient estimates, the 95 percent confidence intervals due to noise effects were within 1.2% of the identified value. The method is suitable for identification of linear dynamic coefficients for rotordynamic system components referenced to shaft motion. The method can be used to reduce the effect of noise on measurement uncertainty. The statistical framework can also be used to make decisions about experimental run times and acceptable levels of measurement uncertainty. The data obtained from such an experimental design can be used to verify component models and give rotordynamicists greater confidence in the design of turbomachinery.

Multiparameter Real-World System Identification using Iterative Residual Tuning

2021 ◽  
pp. 1-12
Author(s):  
Adam Allevato ◽  
Mitch W Pryor ◽  
Andrea L. Thomaz
Keyword(s):  
System Identification ◽  
Robot Control ◽  
Tracking Error ◽  
Nonlinear System Identification ◽  
Visual Observation ◽  
Free System ◽  
Proposed Model ◽  
Derivative Free ◽  
Identification Technique ◽  
Using Data

Abstract In this work we consider the problem of nonlinear system identification, using data to learn multiple and often coupled parameters that allow a simulator to more accurately model a physical system or mechanism and close the so-called reality gap for more accurate robot control. Our approach uses iterative residual tuning (IRT), a recently-developed derivative-free system identification technique that utilizes neural networks and visual observation to estimate parameter differences between a proposed model and a target model. We develop several modifications to the basic IRT approach and apply it to the system identification of a 5-parameter model of a marble rolling in a robot-controlled labyrinth game mechanism. We validate our technique both in simulation—where we outperform two baselines—and on a real system, where we achieve marble tracking error of 4% after just 5 optimization iterations.

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