A rough set-based method for aiming angle tuning of luminaires for outdoor sports lighting

When designing outdoor sports lighting it is a challenging task to achieve uniform illuminance over the whole field. Design simulations accomplish this objective by adjusting the aiming angles of the luminaires in horizontal and vertical planes. The lighting design software that is typically used for these calculations does not use any optimization technique. In this paper, rough set theory has been used to find an optimal design for a large scale outdoor sports lighting installation. This modified design process takes the aiming angles of determinant luminaires as the input for the optimization of a total of six lighting parameters. The initial results are very encouraging.

[1]  Irfanud Din,et al.  Joint blind and light control for lighting energy reduction while satisfying light level and anti-glare requirements , 2014 .

[2]  Jerzy W. Grzymala-Busse,et al.  Generalized probabilistic approximations of incomplete data , 2014, Int. J. Approx. Reason..

[3]  C. Elvidge,et al.  The relation of outdoor lighting characteristics to sky glow from distant cities , 2014 .

[4]  R. H. Simons,et al.  Lighting Engineering: Applied Calculations , 2001 .

[5]  Jerzy W. Grzymala-Busse,et al.  Generalized probabilistic approximations , 2013, 2013 6th International Conference on Human System Interactions (HSI).

[6]  Zhenmin Tang,et al.  On an optimization representation of decision-theoretic rough set model , 2014, Int. J. Approx. Reason..

[7]  Michael P. Royer,et al.  Illuminance Uniformity of Outdoor Sports Lighting , 2011 .

[8]  Kumar,et al.  Neural Networks a Classroom Approach , 2004 .

[9]  Zdzislaw Pawlak,et al.  Some Issues on Rough Sets , 2004, Trans. Rough Sets.

[10]  Yiyu Yao,et al.  Probabilistic rough set approximations , 2008, Int. J. Approx. Reason..

[11]  Amit Konar,et al.  Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of the Human Brain , 1999 .

[12]  Liang Gao,et al.  An expert system using rough sets theory for aided conceptual design of ship's engine room automation , 2009, Expert Syst. Appl..

[13]  I. Ignat,et al.  Reduct Equivalent Rule Induction Based On Rough Set Theory , 2007, 2007 IEEE International Conference on Intelligent Computer Communication and Processing.

[14]  B. Tudu,et al.  Optimization of Sensor Array in Electronic Nose: A Rough Set-Based Approach , 2011, IEEE Sensors Journal.

[15]  O. Rabaza,et al.  A new method of measuring and monitoring light pollution in the night sky , 2014 .

[16]  Bipan Tudu,et al.  Dealing With Redundant Features and Inconsistent Training Data in Electronic Nose: A Rough Set Based Approach , 2014, IEEE Sensors Journal.

[17]  Ping Yang,et al.  An Attribute Reduction Algorithm by Rough Set Based on Binary Discernibility Matrix , 2008, 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery.

[18]  Liang Gao,et al.  An expert system using rough sets theory and self-organizing maps to design space exploration of complex products , 2010, Expert Syst. Appl..

[19]  Zdzislaw Pawlak,et al.  Rough sets and intelligent data analysis , 2002, Inf. Sci..

[20]  Jyoti.  Rough Set Theory and Its Applications , 2013 .