Open AccessMulti-Sensor Signal based Situation Recognition with Bayesian Networks
Author(s) -
Jin-Pyung Kim,
Gyu-Jin Jang,
Jason J. Jung,
Moon-Hyun Kim
Publication year - 2014
Publication title -
journal of electrical engineering and technology/journal of electrical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.226
H-Index - 27
eISSN - 2093-7423
pISSN - 1975-0102
DOI - 10.5370/jeet.2014.9.3.1051
Subject(s) - computer science , bayesian network , wireless sensor network , artificial intelligence , pattern recognition (psychology) , set (abstract data type) , artificial neural network , bayesian probability , data mining , signal (programming language) , window (computing) , soft sensor , machine learning , data set , computer network , process (computing) , programming language , operating system
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.