Title
Recursive Subspace Identification for Online Thermal Management of Implantable Devices
Abstract
This paper focuses on application of subspace identification methods to predict the thermal dynamics of bioimplants, e.g. UEA. Recursive subspace identification method implemented in this paper predicts the temperature readings of heat sensors in an online fashion within a finite time window and updates the system parameters iteratively to improve the performance of the algorithm. Algorithm validation is realized using COMSOL software simulations as well as using an in vitro experimental system. Both simulation and experimental results indicate that the proposed method can accurately predict the thermal dynamics of the system. The experimental results show online prediction of the thermal effect with a mean squared error of 1.569x10(-2)degrees C for randomly generated Gaussian inputs and 3.46x10(-3)degrees C for square wave inputs after adaptive filters converge.
Year
DOI
Venue
2019
10.1109/ALLERTON.2019.8919656
2019 57TH ANNUAL ALLERTON CONFERENCE ON COMMUNICATION, CONTROL, AND COMPUTING (ALLERTON)
Keywords
Field
DocType
subspace identification,predictive modeling,implantable medical device,thermal effect
Mathematical optimization,Experimental system,Subspace topology,Computer science,Algorithm,Mean squared error,Gaussian,Software,Square wave,Adaptive filter,Recursion
Conference
ISSN
Citations 
PageRank 
2474-0195
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Ayca Ermis101.01
Yen-Pang Lai241.46
Xinhai Pan300.34
ruizhi chai401.35
Ying Zhang53811.41