Wireless and autonomous safety-critical system utilizing feedback
Bibliographic record
Abstract
Nowadays, safety-critical systems are becoming more prominent with the increase in reliance on technology in households. With this dependency, reliability and reaction time needs to be improved on to maintain a high standard of service. However, current designs are stagnant and only implementing rote and obsolete open-loop designs as a means of easy manufacturing and for simplicity in design but does not fully provide safety to its users since open-loop designs rely on the user's interaction to initiate the safety process or mitigation. This is too variable and unreliable and delays the process which the user will incur more damages in the end degrading the effectiveness of a safety-critical system. This study aims to addresses these issues by designing a system that implements a closed-loop feedback using values collected from sensors to survey the condition of the surroundings and respond accordingly with different fog computing methodologies and utilizing a feedback loop. This alternative closed-loop implementation will be 40% more reliable than the open-loop version, 71% reduced latency, and have a faster overall response time compared to commercial systems based on the experimental results. All designs, workflows, and ideas discussed in this paper will be implemented all in an Arduino Environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".