Back to results
US 2026/0239503 A1Published application
Luminaire with multicolor neural network control
AssigneeSuntracker Technologies Ltd.
InventorIan Edward Ashdown
Life of this patent
Estimating the term…
Abstract
The chromaticity and luminous flux output of a multicolor luminaire with multiple independent wavelength outputs is controlled by a controller that has fewer input channels than there are output channels. A trained neural network is used such that the multi-color wavelength settings of the luminaire produce the specified chromaticity and luminous flux output while optimizing luminous efficacy or color rendering capabilities, or satisfying other constraints applied to the luminaire.
Claims20
An illumination system comprising:
- n light-emitting elements (LEEs) each having a different peak wavelength;
- a dimming control with 3 input channels, wherein:
- n>3, where n is a whole number;
- each input channel provides a control output;
- and
- the 3 control outputs together represent a chromaticity and a luminous flux;
- and
- a neural network having n output channels that each correspond to a different one of the peak wavelengths, the neural network connected to the dimming control to receive the 3 control outputs and trained to provide a driver signal on each output channel such that the n driver signals together control the LEEs to emit light with the chromaticity and the luminous flux while maximizing:
- a ratio between a melanopic flux in the light and the luminous flux;
- or
- another ratio, of a first wavelength flux in the light to a second wavelength flux in the light;
- or
- an efficacy of the illumination system.
10 more elements
A method for producing light comprising:
- receiving, by a neural network, 3 control outputs from a dimming control in an illumination system, the 3 control outputs together representing a chromaticity of the light and a luminous flux of the light;
- receiving, by the neural network, a constraint to maximize:
- a ratio between a melanopic flux in the light and said luminous flux;
- or
- another ratio, of a first wavelength flux in the light to a second wavelength flux in the light;
- or
- an efficacy of the illumination system;
- converting, by the neural network, the 3 control outputs to n driver signals, wherein:
- n>3, where n is a whole number;
- each driver signal is for a different one of n light-emitting elements (LEEs) in the illumination system, each LEE having a different peak wavelength;
- and
- the n driver signals together correspond to said chromaticity and said luminous flux and satisfy the constraint;
- and
- sending the n driver signals to drivers for the n LEEs;
- wherein, when the n LEEs are driven by the drivers, the n LEEs produce the light with said chromaticity and said luminous flux while satisfying the constraint.
12 more elements
A method for training a neural network comprising:
- (a) specifying, with 3 parameters, a target chromaticity and a target luminous flux for an illumination system;
- (b) using a genetic algorithm to determine multiple solutions for the target chromaticity and the target luminous flux, each solution comprising a set of at least n driver signals, wherein n>3, n is a whole number and each driver signal is for a different one of at least n different peak wavelengths;
- (c) calculating, for each solution, a ratio of a melanopic flux to a luminous flux, used to maximize a ratio of a melanopic flux of the illumination system to a luminous flux of the illumination system;
- or
- calculating, for each solution, a ratio of the luminous flux to the melanopic flux, used to maximize a ratio of the luminous flux of the illumination system to the melanopic flux of the illumination system;
- or
- calculating, for each solution, a ratio of a first wavelength flux to a second wavelength flux, used to maximize a ratio of a first wavelength flux of the illumination system to a second wavelength flux of the illumination system;
- or
- calculating, for each solution, an efficacy, used to maximize an efficacy of the illumination system;
- (d) selecting, from the multiple solutions, the solutions that are within a predetermined tolerance of the target chromaticity and the target luminous flux;
- (e) repeating (a) to (d) multiple times, each time for a different target chromaticity and a different target luminous flux;
- (f) using some of the selected solutions to train the neural network;
- and
- (g) using others of the selected solutions to validate the neural network.
11 more elements
Dependencies are inferred from the claim text.