An Artificial Intelligence Approach Towards Sensorial Materials (bibtex)
by Florian Pantke, Stefan Bosse, Michael Lawo, Dirk Lehmhus, Matthias Busse
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:
Florian Pantke, Stefan Bosse, Michael Lawo, Dirk Lehmhus, Matthias Busse, "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}
}
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