Stabilization of the output signal of thermopile sensors in the thermal environment of automotive applications
Bibliographic record
Abstract
Thermopile pyrometer modules are the state of the art for contactless temperature measurements in automotive applications. In such an application, the thermopile has to operate precisely in a challenging thermal environment. While the compensation of the steady state ambient temperature is a well known technique when using thermopiles for temperature measurments, transient thermal effects are still an issue. The change of the ambient temperature as well as temperature flow through the sensor can lead to substantial errors due to unwanted thermal gradients within the device. In the thermopile chip they lead to an error signal since the measurement principle is based on quantifying thermal gradients of the chip that result from the detected IR-radiation. Thermal gradients in the cap and between the cap and the thermopile chip will lead to an exchange of heat radiation between the thermopile chip and the cap, which also leads to measurement errors. Different methods were developed that separately or in combination allow for a significant improvement of the accuracy and signal stability. The methods are based on the reduction of thermal gradients within the thermopile chip and the entire sensor device (isothermal, high thermal mass cap), reduction of radiation exchange between the sensor chip and the housing (low emissive inner cap surface) and prediction and software compensation of the error signal.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".