Intelligent Traction Control Model for Speed Sensor Vehicles in Computer-Based Transit System
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 paper, a real-time intelligent traction control model for speed sensor vehicles in computer-based transit systems is proposed. Using the Bayesian decision theory, the model analyzes speed sensor data to learn and classify the train traction conditions (i.e., spin/slip, normal, and slide) that are required for studying vehicle motion patterns. The patterns are applied on the sensor input in real-time format to classify train traction and reduce the error/risk of classification that may cause service interruptions and incidents. The model can enable us to manage a number of state natures (i.e., spin/slip, normal, and slide), features (i.e., delta speed and train speed), and prior knowledge traction conditions. This model engine can be implemented in any programming language in onboard or embedded computers. As a result, the impact of noisy sensors (inaccurate data) and its delays in such a hard real-time control system is mitigated. This conceptual model is applied to a case study with promising results for target and simulation systems.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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