Homeostasis Lighting Control System Using a Sensor Agent Robot

Tatsuya Akiba, Akira Mita

Department of System Design Engineering, Keio University, Yokohama, Japan.

DOI: 10.4236/ica.2013.42019                

Abstract

In this study, “homeostasis”, the function by which living things keep their constancy, was emulated as a lighting control for a building space. The algorithm we developed mimics the mechanisms of the endocrine and immune systems. The endocrine system transmits information entirely, whereas the immune system transmits information with a concentration gradient. A lighting control system using the proposed algorithm was evaluated in a simulation and experiment using a sensor agent robot. In this algorithm, a robot recognizes a person’s behavior and uses it to decide his or her preference as to the illuminance. The results indicate that the algorithm can be used to realize a comfortable lighting control in several situations.

Keywords

Homeostasis; Lighting Control; Sensor Agent Robot; Human Tracking

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	J. A. Kientz, et al., “The Georgia Tech Aware Home,” Proceedings of CHI 2008, Florence, 5-10 April 2008, pp. 3675-3680.
[2]	D. H. Stefanov, Z. Bien and W.-C. Bang, “The Smart House for Older Persons and Persons with Physical Disabilities: Structure, Technology Arrangements, and Perspectives,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 12, No. 2, 2004, pp. 228-250. doi:10.1109/TNSRE.2004.828423
[3]	H. Sako and H. Takai, “Example of Natural Lighting Control in Office Building,” Journal of the Illuminating Engineering Institute of Japan, Vol. 82, No. 10, 1988, pp. 821-824.
[4]	J. Werner, “System Properties, Feedback Control and Effector Coordination of Human Temperature Regulation,” European Journal of Applied Physiology, Vol. 109, No. 1, 2010, pp. 13-25. doi:10.1007/s00421-009-1216-1
[5]	K. Baba, T. Enohara and K. Nagata, “Solution Applying Recognition Technology for Safe, Secure, and EnergySaving Buildings,” Toshiba Review, Vol. 65, No. 5, 2010, pp. 15-18.
[6]	I.-H. Yang, M.-S. Yeo and K.-W. Kim, “Application of Artificial Neural Network to Predict the Optimal Start Time for Heating System in Building,” Energy Conversion and Management, Vol. 44, No. 17, 2003, pp. 27912809. doi:10.1016/S0196-8904(03)00044-X
[7]	S. W. Wang and X. Q. Jin, “Model-Based Optimal Control of VAV Air-Conditioning System,” Building and Environment, Vol. 35, No. 6, 2000, pp. 471-487. doi:10.1016/S0360-1323(99)00032-3
[8]	Y. Tabuchi, K. Matsushima and H. Nakamura, “Preferred Illuminances on Surrounding Surfaces in Relation to Task Illuminance in Office Room Using Task-Ambient Lighting,” Journal of Light & Visual Environment, Vol. 19, No. 1, 1995, pp. 28-39. doi:10.2150/jlve.19.1_28
[9]	N. Bellotto and H. S. Hu, “Multisensor-Based Human Detection and Tracking for Mobile Service Robots,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 39, No. 1, 2009, pp. 167-181. doi:10.1109/TSMCB.2008.2004050
[10]	T. Akiba and A. Mita, “Human Tracking Using Sensor Agent Robot for Biofied Building,” AIJ Journal of Technology and Design, Vol. 18, No. 39, 2012, pp. 775-778. doi:10.3130/aijt.18.775
[11]	Y.-W. Bai and Y.-T. Ku, “Automatic Room Light Intensity Detection and Control Using a Microprocessor and Light Sensors,” IEEE Transactions on Consumer Electronics, Vol. 54, No. 3, 2008, pp. 1173-1176. doi:10.1109/TCE.2008.4637603