Increasing the Safety of People Activity in Aggressive Potential Locations, Analyzed through the Probability Theory, Modeling/Simulation and Application in Underground Coal Mining
Bibliographic record
Abstract
This paper deals with the increasing safety of working in aggressive potential locations, having SCADA system and WSN sensors, using a “probabilistic strategy” in comparison with a “deterministic” one, modeling/simulation and application in underground coal mining. In general, three conditions can be considered: 1) an unfriendly environment that facilitates the risk of accidents, 2) aggressive equipments that can compete to cause accidents and 3) the work security breaches that can cause accidents. These conditions define the triangle of accidents and are customized for an underground coal mining where the methane gas is released with the exploitation of the massive coal. In this case, the first two conditions create an explosive potential atmosphere. To allow people to work in a safe location it needs: first, a continuing monitoring through SCADA system of the explosive potential atmosphere and second, the use of antiexplosive equipment. This method, named “deterministic strategy”, increases the safety of working, but the explosions have not been completely eliminated. In order to increase the safety of working, the paper continues with the presentation of a new method based on hazard laws, named “probabilistic strategy”. This strategy was validated through modeling/simulation using CupCarbon software platform, and application of WSN networks implemented on Arduino equipments. At the end of the paper the interesting conclusions are emphases which are applicable to both strategies.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".