Abstract
A design tool is proposed for lighting control system that reflects histories of residents’ past life using a genetic mechanism. There are many previous researches which show the preference of lighting design differs depending on people and their behaviors. And recently, due to the appearance of LED which can change light color easily, the number of lighting scenes have drastically increased. It is difficult for residents to grasp all patterns of lighting and understand what pattern of lighting design fits for their behaviors. So if we can extract lighting preferences and demands of each resident from histories of past life and reflect these information in next lighting control, it’s possible to make living space more comfortable. An evolutionally adaptation mechanism learnt from living organisms is proposed in this research to extract the information from lifelog, especially focusing on methylation and mutation. Methylation is one of the epigenetic algorithms making a difference in phenotype without changing DNA sequence. Mutation is one of the genetic algorithms making a difference in phenotype by changing DNA sequence. Those two mechanisms are applied in the system. First, the lifelog of residents and using hysteresis of lighting equipment are collected. Then the lifelog is converted into the genetic information and stored. When the lifelog is stored enough, the superior genes will be picked up from the stored genetic information to be reflected in lighting control in next generation. Simulations to verify the versatility of the system were conducted.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Fumika Kake and Akira Mita "Lifelog-based lighting design for biofied building", Proc. SPIE 9803, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2016, 98034O (20 April 2016); https://doi.org/10.1117/12.2218403
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Cited by 1 scholarly publication.
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KEYWORDS
Light sources and illumination

Genetics

Control systems

Control systems design

Data modeling

Computer simulations

Organisms

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