Implementation of an adaptive intelligent controller for benchmark thermal system
Classification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this work, a neural network (NN) based adaptive controller is developed and implemented for precise temperature control of a benchmark thermal system in cold climate. The newly devised NN controller is capable of overcoming the limitations of model dependent conventional fixed gain temperature controllers. The proposed NN controller is designed using the combination of off-line and on-line trainings of the feed-forward neural network. The transient and steady-state behaviors of the proposed NN-based thermal control system for central heating are improved by incorporating a unique feature of adaptive learning which aids the on-line robust temperature control over a wide operating range. The stability of the proposed NN-based thermal system has been ensured by a combination of off-line and on-line trainings of the NN. As an integral part of this work, efforts have been directed for the real-time implementation of the NN-based thermal system using a digital signal processor (DSP) controller board ds1102. A series of tests have been carried out in order to evaluate the performances of the NN-based benchmark thermal system for central heating. The laboratory test results validate the efficiency of the NN controller as an adaptive controller in the high performance benchmark thermal systems.
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How this classification was reachedexpand
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) | 0.000 | 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