The Theory and Experiment Study on New Intelligent Electronically Controlled Pneumatic Brake for Freight Train
Bibliographic record
Abstract
The automatic air brake system is used for freight train in China for decades.Application and release propagation speed along the train can not surpass the velocity of sound 340 m·s~ -1,thus the brake action of front and rear car is not uniform along the long freight train and slack action is arose dramatically and the coupler is tended to break.All of these hinder the transportation of heavy haul freight train heavily.Nowadays,some developed countries,such as the United States of America,Canada and Australia,are involved with research on electronically controlled pneumatic brake system(ECPB).The microcomputer and network are used to co-ntrol the brake and release of car instead of automatic air-brake valves.The principles are quite different from the electronic-pneumatic brake of passenger train.All of these are the greatest advantages of ECPB.The system represents the highest standard of train brake all over the world.However,the control theory of the key part of this system,the car control device,is PID.The robust is very poor with the high non-linearity of all parame-ters of brake system,so the precision of control is roughness.The technique of ECPB is poor in China,but the brake techniques are continually developing and the experience is accumulated.The new intelligent ECPB for long heavy haul freight train is researched systematically in the fields of system design,the distribution of brake force,the definition of brake command and the software realization of the intelligent control theory in the pa-per.The relevant brake test rig is also designed.The indoor tests proved that this brake system is self-adjusted and strongly robust.All targets come up to the AAR specification S-4 300,especially the key target,the control precision of brake cylinder pressure,is limited to ±10 kPa which is superior to the ±20 kPa required by AAR standard.This system has wide application in the future.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".