A method to calculate hardware in the loop applications representativeness
Bibliographic record
Abstract
Nowadays, technology advances have been growing exponentially, creating many branches of possibilities for new products and ways to improve the ones that are already on the market. These new possibilities have been used in the automotive industry to increase efficiency, quality, robustness of their products and also to introduce several functionalities that were not possible before. As result, project complexity is also exponentially increasing. On this context, several tools, techniques and processes have been developed and used to guarantee the quality and robustness of their products, while minimizing development time in order to stay competitive at the market. Considering this context, the use of Hardware In The Loop systems (HIL) is increasing, due to its flexibility, reliability and representativeness that have been verified over time for verification and validation activities using Electronic Control Modules. However, usually the HIL setup quality evaluation is measured indirectly (for example model accuracy) or even just qualitatively. It is hard to define how representative is the HIL setup when compared with the final application. This representativeness depends on several aspects of the HIL, such as model accuracy, precision, signal reliability, technical specification of the elements that composes HIL equipment, as well as the nature of the tests that needs to be done. Thus, this paper presents some main concepts related to the representativeness and use them as a base to show a methodology to estimate the representativeness of a HIL application setup for validation and verification purposes. To illustrate this methodology, some test examples were used on powertrain HIL system.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".