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Multi-Sensor Signal based Situation Recognition with Bayesian Networks
- Source :
- Journal of Electrical Engineering and Technology. 9:1051-1059
- Publication Year :
- 2014
- Publisher :
- The Korean Institute of Electrical Engineers, 2014.
-
Abstract
- In this paper, we propose an intelligent situation recognition model by collecting and analyzing multiple sensor signals. Multiple sensor signals are collected for fixed time window. A training set of collected sensor data for each situation is provided to K2-learning algorithm to generate Bayesian networks representing causal relationship between sensors for the situation. Statistical characteristics of sensor values and topological characteristics of generated graphs are learned for each situation. A neural network is designed to classify the current situation based on the extracted features from collected multiple sensor values. The proposed method is implemented and tested with UCI machine learning repository data.
- Subjects :
- Training set
Artificial neural network
Computer science
business.industry
SIGNAL (programming language)
Window (computing)
Bayesian network
Pattern recognition
computer.software_genre
Multiple sensors
Multi sensor
Fixed time
Artificial intelligence
Data mining
Electrical and Electronic Engineering
business
computer
Subjects
Details
- ISSN :
- 19750102
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- Journal of Electrical Engineering and Technology
- Accession number :
- edsair.doi...........cf609f3d6ba0afc0387c3df7fe8772f6