Modeling of the Cloud Interconnected Human Friendly Multi-Agent Based Sustainable Power Controller

Abstract

The paper presents model of the cloud interconnected multi-agent human friendly sustainable power controller (Controller). The Controller is based on the human ambient comfort affect reward index (ACAR index). The ACAR index depends on human physiological parameters: the temperature, the ECG- electrocardiogram and the EDA-electro-dermal activity. These physiological parameters are used for sustainable power control by multi-agent system developed as the cloud interconnected, instrumented, and intelligent environment. The Environment Sense Agent, the Environment Adaptation Agent, the Raw Data Processing and EDA Parameters Extraction Agent, and the Neural Network Training Agent are proposed and implemented into the multi-agent based emotion recognition and environment control system. The modeling results show that proposed system can find such the environmental state characteristics that may improve comfort for people affected by this environment

Keywords

• -

References

1. A. Haag, S. Goronzy, P. Schaich, J. Williams, “Emotion Recognition Using Bio-sensors: First Steps towards an Automatic System”, Affective Dialogue Systems Lecture Notes in Computer Science, Vol. 3068, 2004, pp.36-48.
2. P. Rainville, A. Bechara, N. Naqvi, A.R. Damasio, “Basic emotions are associated with distinct patterns of cardiorespiratory activity“, International Journal of Psychophysiology, Vol.61, No.1, July 2006, pp.5-18.
3. A.A. Abdullah, U.H. Hassan, “Design and development of an emotional stress indicator (ESI) kit”, Sustainable Utilization and Development in Engineering and Technology (STUDENT), 2012 IEEE Conference, 6-9 October 2012, pp.253-257.
4. P. Mika, Anu S. Tarvainen, Koistinen, Minna Valkonen-Korhonen, Juhani Partanen and Pasi A. Karjalainen, “Analysis of Galvanic Skin Responses With Principal Components and Clustering Techniques“, IEEE Transactions on Biomedical Engineering, Vol.48, No.10, October 2001, pp.1071-1079.
5. Philippot, P., Rimé, B., The perception of bodily sensations during emotion: A cross-cultural perspective”, Polish Psychological Bulletin, 1997, 28, pp.175-188.
6. G. Zheng, Y. Jing, H. Huang, P. Ma,“Thermal Comfort and Indoor Air Quality of Task Ambient Air Conditioning in Modern Office Buildings,” International Conference on Information Management, Innovation Management and Industrial Engineering, ICIII '09 Proc. 2009, Vol.2, pp.533-536.
7. S.M. Zanoli, D. Barchiesi, “Thermal and Lighting Control System with Energy Saving and Users Comfort Features“, Proc. 2012 IEEE 20th Mediterranean Conference on Control & Automation (MED) Barcelona, pp.1322-1327.
8. DIRECTIVE 2006/25/EC of the European Parliament and of the Council of 5 April 2006 on the minimum health and safety requirements regarding the exposure of workers to risks arising from physical agents (artificial optical radiation), 19th individual Directive within the meaning of Article 16(1) of Directive 89/391/EEC.

9. Quarto, A., Di Lecce, V., Dario, R., Uva, J., “Personal dosimeter for the measurement of artificial optical radiation (AOR) exposure”, Proc. IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA),2011, pp.1-6.
10. A.A. Bielskis, A. Andziulis, O. Ramašauskas, E. Guseinovienė, D. Dzemydienė, G. Gricius, “Multi-Agent Based E-Social Care Support System for Inhabitancies of a Smart Eco-Social Apartment”, Electronics and Electrical Engineering, 2011, Vol.1-(107), pp.11–14.
11. R.S. Sutton, A.G. Barto, Reinforcement learning: An Introduction. Cambridge, MA: MIT Press, 2011. Environmental Control“, Reinforcement Learning: Theory and Applications, Book edited by Cornelius Weber, Mark Elshaw and Norbert Michael Mayer ISBN 978-3-902613-14-1, I-Tech Education and Publishing, Vienna, Austria, January 2008, pp.424.
12. ICNIRP Guidelines on limits of exposure to broad-band incoherent optical radiation (0.38 to 3 microM), ICNRP Statement @, 2011