Intelligence-based safety decision models for train traction control systems
Bibliographic record
Abstract
In this thesis, two intelligence-based safety decision models for train traction control systems are proposed. These models are to prove the effectiveness of a modern method for speed sensor vehicles in a communication-based train control system (CBTC). Fuzzy theory and Bayesian decision theory have been modeled to learn and to classify the vehicle traction conditions using a pattern recognition concept. The proposed models are original and formulated for such integrated and complex systems like automatic train protection (ATP) and automatic train operation (ATO). In the intelligent format, the train traction’s patterns are extracted and applied on speed sensors’ input to classify the train traction. The error and risk of traction misclassification is also calculated to reduce the impact and exposure of safety and hazards. The proposed safety models are suitable for such a decision system due to processing the manageable number of state of nature (i.e., slip/spin, normal and slide), features (speed and acceleration) and having the prior knowledge of the vehicle’s behaviour which can be collected either from field tests or lab simulations. Both models involve a mathematical problem which can be solved in any programming language and to be used in the on-board or embedded computers. The conceptual models are applied to a hypothetical case study with promising results.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".