Intelligent Spectrum Controlled Supplemental Lighting for Daylight Harvesting
Why this work is in the frame
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Bibliographic record
Abstract
This article presents a neural network-based control method for daylight harvesting in a proof-of-concept greenhouse consisting of emulated sunlight and dimmable light emitting diode light fixtures. The objective of this multi-input-multi-output lighting system is to deliver desired levels of light, within a specific spectrum range, to locations of interest in a grow tent. To this end, a learning neural network controller with online adaptive weights is presented which can achieve stability with small errors in the presence of disturbances and modeling uncertainties. A stability analysis of the closed-loop system is presented along with a selection method for obtaining the control parameters. The neural controller is enhanced with an antiwindup mechanism to account for the nonlinear effect of actuator saturation. Experimental results are presented to verify the proposed daylighting control strategy which confirm analytic and simulation studies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it