Estimation and control of temperature profile over a sheet in thermoforming process using non-equidistant temperature sensor
Bibliographic record
Abstract
Fourier transform is a very popular technique for the estimation and reconstruction of signal and image. This technique can be used for the estimation of the temperature profile over the sheet. But one of the drawbacks of this method is in order to use this method for the estimation of the temperature profile of the sheet; the sensors have to be placed at equidistant position over the sheet. This placement may not be optimum position for the prediction of the temperature profile of the sheet. The temperature sensors usually placed at the optimum position of the sheet for the best estimation of surface temperature of a sheet with least number of sensors. The proposed method for the estimation of temperature is can be used for the arrangement of sensor with non-equidistant. The proposed method is developed in such a way that it can do a high-quality estimation of the temperature profile over the entire sheet through its harmonics and the spatial harmonic controller can control the harmonics to obtain the desired temperature profile. Computational cost is also considered during the development of the proposed method. The computational cost of computing Non-Uniform Fast Fourier Transform (NUFFT) to estimate the temperature profile is reduced to make the technique more computational efficient. The performance of the proposed estimator and controller is tested in simulation at different operating conditions to compare with the conventional method of the estimation of temperature profile which is based on the weighted average of the temperature of the sensors surrounding the point. The superiority of the proposed method for the estimation of temperature and hence the accuracy of the developed controller and its prospective real-time application is evidenced by the results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".