Software-based Monitoring for Calibration of Measurement Units in Real-time Systems
Bibliographic record
Abstract
In real-time systems, every task is characterized by its deadline where each task is expected to perform a function producing a correct result within a specified amount of time. A hard real-time system can lead to catastrophic failure if any task misses delivering the correct value at the right time. Although it is very important, most research works in real-time systems avoid discussion on the correctness of values at different points in time. Measurement units or instruments can be integrated with real-time systems to perform sensitive measurements where the measurement accuracy of a device is an essential factor for the precise result. Periodic inspections and calibrations of the measurement units validate the consistent measurement accuracy to ensure the safety of a system. In this paper, we present a software-based monitoring approach for the auto-calibration process that compares sporadically the accuracy of measurement units with the set of determined measurement standards such as National Institute of Standards and Technology (NIST) to ensure the correctness of the measurement instruments. This approach will automatically guide us to correct the measurement errors if the electronic devices are unable to perform with expected accuracy. To explain the applicability of our proposed strategy, we define different techniques considering the availability of the calibration standards and finally show an experiment of anomaly detection in a resistive voltage divider as a case study.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".