Title

Fastener Failure Detection Using A Surface Acoustic Wave Strain Sensor

Abstract

Stochastic models are often used to study the behavior of biochemical systems and biomedical devices. While the structure of such models is often readily available from first principles, several quantitative features of the model are not easily determined. These quantitative features are often incorporated into the model as parameters. The algorithmic discovery of parameter values from experimentally observed facts (including extreme-scale data) remains a challenge for the computational systems biology community. In this paper, we present a new parameter discovery algorithm based on Wald's sequential probability ratio test (SPRT). Our algorithm uses a combination of simulated annealing and sequential hypothesis testing to reduce the number of samples required for parameter discovery of stochastic models. We use probabilistic bounded linear temporal logic (PBLTL) to express the desired behavioral specification of a model. We also present theoretical results on the correctness of our algorithm, and demonstrate the effectiveness of our algorithm by studying a detailed model of glucose and insulin metabolism. © 2012 IEEE.

Publication Date

5-8-2012

Publication Title

IEEE Sensors Journal

Volume

12

Number of Pages

1993-2000

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/JSEN.2011.2181160

Socpus ID

84860539976 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84860539976

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