Intelligence-based safety decision models for train traction control systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it