A multivariate nonlinear regression model for the resistance power of a light rail vehicle
Bibliographic record
Abstract
In Turkey, 20% of energy use caused by transportation. Light rail transportation has developing nowadays. However, it is very important to issue having information about the energy consumption of the light rail vehicle according to different both vehicle and railway situations. In Turkey, approximately 20% of the energy expended is spent on transportation. Light rail transportation technology is still in an evolving process. In this development process, it is crucial to have information about the energy consumption of the light rail vehicle according to the different situations of both the vehicle and the railway. It is necessary to predict the power losses that will occur under different driving conditions sensitively to ensure energy efficiency in light rail systems. The most important of these power losses is the resistance loss caused by contact with the route. Resistance loss is dependent multiple environmental conditions. The most important of these conditions can be listed as the weight of the light rail vehicle, the instantaneous speed of the vehicle, the curve of the route, the ramp slope of the route and the friction force arising from these conditions. Resistance loss is proportional and linearly dependent to some of these variables while others show reverse or nonlinear dependence. Due to these different types of dependencies, it is seen that a single multivariate nonlinear model is needed to explain the loss of resistance in all different conditions. In this study, a new and accurate model for resistance losses has been developed by fitting numerical values obtained from different scenarios to multivariate nonlinear regression model.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".