“Light’em: A Multiplexed Lighting System”
Conference:
Experience Type(s):
Title:
- Light'em: A Multiplexed Lighting System
Organizer(s)/Presenter(s):
Description:
“Light’em” realizes multiplexing of the lighting environments using an active shutter system. The effect of visual stimuli on indoor environment desirability varies for each individual. However, different desired environments collide between individuals in one space. We propose a lighting environment multiplexing system simultaneously making independently controllable multiple different lighting environments.
References:
[1]
Yukio Akashi, Sueko Kanaya, and Akihiro Yagi. 1996. Relationship between Concentration of Subjects and the Illuminance Ratio between Ambient Lighting and Task Lighting. J. Illum. Engng. Jpn. 80, 8A (1996), 27–36.
[2]
Yousef Al Horr 2016. Occupant productivity and office indoor environment quality: A review of the literature. Building and Environment 105 (2016), 369–389.
[3]
Zinping Bao 2015. Seat Optimization-Based Intelligent Lighting Control. Ricoh Technical Report40(2015), 64–72.
[4]
K. Dijkstra, M. E. Pieterse, and A. Th H. Pruyn. 2008. Individual differences in reactions towards color in simulated healthcare environments: The role of stimulus screening ability. J. Env. Psychol. 28, 3 (2008), 268–277.
[5]
Naoya Koizumi 2012. Stop Motion Goggle: Augmented visual perception by subtraction method using high speed liquid crystal. ACM Int. Conf. Proc.(2012).
[6]
Mineo Mori 1998. Effect of the Brightness on the Suprathreshold Flicker Light Field. J. Inst. Image Inform. TV. Engnr. 52, 4 (1998), 612–615.
[7]
Ross T Smith and Wayne Piekarski. 2008. Public and Private Workspaces on Tabletop Displays. Proceedings of the ninth conference on Australasian user interface 1 (2008), 51–54.
[8]
Shingo Tanaka 2009. An evolutional optimization algorithm to provide individual illuminance in workplaces. Conf. Proc. IEEE Int. Conf. Syst., Man and Cybern.October (2009), 941–947.
[9]
Zizhen Wang and Yen Kheng Tan. 2013. Illumination control of LED systems based on neural network model and energy optimization algorithm. Energ. Buildings 62(2013), 514–521.
