by Pantke, Florian, Bosse, Stefan, Lawo, Michael, Lehmhus, Dirk and Busse, Matthias
Abstract:
Sensorization aims at equipping technical structures with an analog of a nervous system by providing a network of sensors and communication facilities that link them. The objective is that, instead of having been designed to loads and tested to conditions, a structure can experience and report design constraint violations by means of real-time self-monitoring. Specialized electronic components and computational algorithms are needed to derive meaning from the combined signals of integrated sensors. For this task, artificial intelligence approaches constantly gain importance; the more so as the trend of ever increasing sensor network size and density suggests that sensor and structure may soon become one, forming a sensorial material. Current simulation techniques capture many aspects of sensor networks and structures. For decision making and communication, the intelligent agent paradigm is an accepted approach, as is finite element analysis for structural behavior. To gain knowledge how sensorial structures can most effectively be built, an artificial intelligence based process for the design of such structures was developed that uses machine learning methods for fast load inference. It is presented in this paper, along with evaluation results obtained in experiments using a finite element model of a strain gauge equipped plate, which demonstrate the general practicability.
Reference:
Pantke, Florian, Bosse, Stefan, Lawo, Michael, Lehmhus, Dirk and Busse, Matthias, "An Artificial Intelligence Approach Towards Sensorial Materials", In FutureComputing2011, IARIA, pp. 62–68, 2011.
Bibtex Entry:
@INPROCEEDINGS{Pantke2011a,
author = {Pantke, Florian and Bosse, Stefan and Lawo, Michael and Lehmhus,
Dirk and Busse, Matthias},
title = {An Artificial Intelligence Approach Towards Sensorial Materials},
booktitle = {FutureComputing2011},
year = {2011},
pages = {62--68},
month = {September25--30},
publisher = {IARIA},
abstract = {Sensorization aims at equipping technical structures with an analog
of a nervous system by providing a network of sensors and communication
facilities that link them. The objective is that, instead of having
been designed to loads and tested to conditions, a structure can
experience and report design constraint violations by means of real-time
self-monitoring. Specialized electronic components and computational
algorithms are needed to derive meaning from the combined signals
of integrated sensors. For this task, artificial intelligence approaches
constantly gain importance; the more so as the trend of ever increasing
sensor network size and density suggests that sensor and structure
may soon become one, forming a sensorial material. Current simulation
techniques capture many aspects of sensor networks and structures.
For decision making and communication, the intelligent agent paradigm
is an accepted approach, as is finite element analysis for structural
behavior. To gain knowledge how sensorial structures can most effectively
be built, an artificial intelligence based process for the design
of such structures was developed that uses machine learning methods
for fast load inference. It is presented in this paper, along with
evaluation results obtained in experiments using a finite element
model of a strain gauge equipped plate, which demonstrate the general
practicability.},
isbn = {978-1-61208-154-0},
keywords = {Multi-Agent Systems,machine learning,ISIS,sensorial material,finite
element method,sensor network},
owner = {pmania},
timestamp = {2012.11.06},
url = {http://www.thinkmind.org/index.php?view=article{\&}articleid=future{\_}computing{\_}2011{\_}3{\_}20{\_}30061}
}