Analysis on Nondispersive Infrared Device Characteristics Using Thermopile
Bibliographic record
Abstract
In the design process, analysis of important parts of the device will help to determine whether the upcoming device can function and which working aspects should be adjusted to meet the designing criteria. For nondispersive infrared device (NDIR) using thermopile, the first condition is damage threshold power at which the incident infrared power must not exceed to avoid damaging the thermopiles. The second condition is the maximum power, in which the incident infrared power should not be higher than a certain level to avoid degrading the sensitivity of the thermopiles. This paper shows a method to estimate the radiation from an IR source and the incident IR power to the thermopile. Signal-to-DC error, signal to noise ratio and other parameters were also analyzed. The real device was used to verify the theoretical values. The results prove that the analyzing method is useful in design, select components, modify, and optimize NDIR devices to detect gases and biological objects.
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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) | 0.023 | 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 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".