Research on the Influence of Multiple Interference Factors on Infrared Temperature Measurement
Bibliographic record
Abstract
Infrared thermal imager is an important means of temperature measurement. However, the measurement results of infrared thermal imagers are easily affected by interference factors, resulting in non-negligible temperature measurement errors, and the infrared thermal imager may be affected by multiple factors at the same time. To research the influence of multiple interference factors on infrared temperature measurement, this paper takes two common interference factors in actual temperature measurement processes, dust and measuring distance, as examples. Firstly, this paper analyzed the influence of dust and measuring distance and carried out the temperature measurement experiments under multi-factor interference and the experiments under single-factor interference respectively. Then, based on nonlinear polynomial regression, a compensation method is proposed to compensate for the measurement errors caused by dust and measuring distance. Results demonstrate that the proposed compensation method can significantly reduce the measurement errors caused by multiple interference factors, which is essential to promote the application of infrared thermal imagers in complex scenes with multiple interferences.
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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 | 0.001 | 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 | 0.001 |
| 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 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".