Modeling and control of color tunable lighting systems

Afshari, Sina; Mishra, Sandipan; Julius, A. Agung; Lizarralde, Fernando; Wason, John; Wen, John T.

doi:10.1016/j.enbuild.2013.08.036

Cited by 63 publications

(55 citation statements)

References 9 publications

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“…Such initiatives have already been described [26][27][28][29][30], but now they can be studied with greater depth and can focus on particular problems. Table 3 shows the average Mean Squared Error (MSE) and standard deviation of the implemented classifiers.…”

Section: Solution a Acquisitionmentioning

confidence: 99%

Classification of artificial light sources and estimation of Color Rendering Index using RGB sensors, K Nearest Neighbor and Radial Basis Function

2015

International Journal on Smart Sensing and Intelligent Systems

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Section: Solution a Acquisitionmentioning

confidence: 99%

Classification of artificial light sources and estimation of Color Rendering Index using RGB sensors, K Nearest Neighbor and Radial Basis Function

2015

International Journal on Smart Sensing and Intelligent Systems

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“…We use the Robot Raconteur software [32] for communication: The software connects to the color sensors with Wi-Fi, and sends input signals to the fixtures via Bluetooth. This same testbed has been used for a number of other investigations, including lighting control algorithms [33][34][35][36] and visual tracking systems [37].…”

Section: Testbed Setupmentioning

confidence: 99%

“…Then other methods such as recursive least squares (RLS) [35,42], low rank approximation, or sparse approximation [43] can be used. In our problem, we can always make enough measurements to ensure n > m 1 and use the simple pseudo-inverse method.…”

Section: Light Transport In Fixture-sensor Systemsmentioning

confidence: 99%

Occupancy distribution estimation for smart light delivery with perturbation-modulated light sensing

2014

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The advent of modern light-emitting diode (LED) techniques enables us to develop novel lighting systems with numerous previously unavailable features. Specifically, by using the fixtures for both illumination and to interrogate the space, source-to-sensor communication becomes possible at very low cost. In this paper, we present a novel framework to estimate the occupancy distribution in an indoor space using color-controllable LED fixtures (the same fixtures providing the illumination, simultaneously) and sparsely distributed non-imaging color sensors. By modulating randomly generated perturbation patterns onto the drive signals of the LED fixtures and measuring the changes in the color sensor responses, we are able to recover a light transport model for the room. Two approaches are proposed to estimate the spatial distribution of the occupancy, based on a light blockage model and a light reflection model, respectively. These two approaches, which can be combined, can faithfully reveal the occupancy scenario of the indoor space, while preserving the privacy of its occupants. An occupancy-sensitive lighting system can be designed based on this technique.

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“…Therefore, any gradient update law may be used to ensure convergence to the optimum. Similar to the method discussed in [5], we will use gradient update with projection for solving (5), which results in the control law…”

Section: Light Field Control Problemmentioning

confidence: 99%

“…These approaches have been used to achieve different objectives such as generation of high-quality lighting in terms of color rendering index (CRI) [4] or other metrics [5][6][7], incorporation of daylight in illumination to achieve energy saving [5,6,8,9], resolution of conflict between occupants [6], and achieving plug-and-play functionality and uniform lighting across the floor by using dis-tributed and decentralized control schemes [10,11]. In these approaches, the control problem is typically posed as an optimization problem and is solved based on point sensor measurements in the space using a variety of optimization methods.…”

Section: Introductionmentioning

confidence: 99%

Light Field Estimation and Control Using a Graphical Rendering Engine

Afshari

Jia

Radke

et al. 2014

Volume 1: Active Control of Aerospace Structure; Motion Control; Aerospace Control; Assistive Robotic Systems; Bio-Inspired Sys

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State-of-the-Art feedback control of lighting depends on point sensor measurements for light field generation. However, since the occupant's perception depends on the entire light field in the room instead of the illumination at a limited set of points, the performance of these lighting control systems may be unsatisfactory. Therefore, it is critical to reconstruct the light field in the room from point sensor measurements and use it for feedback control of lights. This paper presents a framework for using graphical rendering tools along with point sensor measurements for the estimation of a light field and using these estimates for feedback control. Computer graphics software is used to efficiently and accurately model building spaces, while a game engine is used to render different lighting conditions for the space on the fly. These real-time renderings are then used together with sensor measurements to estimate and control the light field in the room using an optimization-based feedback control approach. We present a set of estimation algorithms for this purpose and analyze their convergence and performance limitations. Finally, we demonstrate closed loop lighting control systems that use these estimation algorithms and compare their relative performance, highlighting their benefits and disadvantages.

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Modeling and control of color tunable lighting systems

Cited by 63 publications

References 9 publications

Classification of artificial light sources and estimation of Color Rendering Index using RGB sensors, K Nearest Neighbor and Radial Basis Function

Classification of artificial light sources and estimation of Color Rendering Index using RGB sensors, K Nearest Neighbor and Radial Basis Function

Occupancy distribution estimation for smart light delivery with perturbation-modulated light sensing

Light Field Estimation and Control Using a Graphical Rendering Engine

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