Authors:
Philippe Thomas
;
William Derigent
and
Marie-Christine Suhner
Affiliation:
Université de Lorraine, France
Keyword(s):
indoor air quality, neural networks, relearning, control charts
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer-Supported Education
;
Domain Applications and Case Studies
;
Fuzzy Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Industrial, Financial and Medical Applications
;
Learning Paradigms and Algorithms
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
Indoor air quality is a major determinant of personal exposure to pollutants in today’s world since people spend much of their time in numerous different indoor environments. The Anaximen company develops a smart and connected object named Alima, which can measure every minute several physical parameters: temperature, humidity, concentrations of COV, CO2, formaldehyde and particulate matter (pm). Beyond the measurement aspect, Alima presents some data analysis feature named ‘predictive analytics’, whose primary aim is to predict the evolution of indoor pollutants in time. In this article, the neural network (NN) model,embedded in this object and designed for pollutant prediction, is presented. In addition with this NN model, this article also details an approach where batch learning is performed periodically when a too important drift between the model and the system is detected. This approach is based on control charts.