Bibliographic record
Abstract
Tilting is a worldwide accepted technology concept in railway transportation. Theparticular benefit from tilting trains use is reduction in journey times due to speedincrease on track corners (while maintaining acceptable passenger comfort), a pointthat facilitates improved customer service. An additional benefit is cost effectivenessdue to the train running on existing rail tracks. Many countries opted to using tiltingtrains as means of fast public transportation (UK, USA, Canada, Sweden, Norway,Switzerland, Germany, Japan).The industrial norm of tilting high speed trains is that of precedence tilt wherebypreview tilt enabling signals are used to provide the required information to thevehicles (it can also use a combination of track database information or GPS but theconcept is the same). Precedence tilt tends to be complex (mainly due to the signalinterconnections between vehicles and the advanced signal processing required formonitoring). Research studies of earlier than precedence schemes,i.e. the so-callednulling-type schemes whereby local-per-vehicle signals are used to provide tilt (adisturbance rejection-scheme although tends to suffer from inherent delays in thecontrol feedback), are still an important research aim due to the simple natureand most importantly due to the more straightforward fault detection compared toprecedence. Use of nulling-type tilt has been supported by recent studies in thiscontext.The research presented in this thesis highly contributes to simplified single-inputsingle-output robust tilt control using the simplest rail vehicle tilt structure, i.e. anActive Anti-Roll Bar. Proposed are both robust conventional (integer-type) controlapproaches and non-conventional (non-integer) schemes with a rigorous investigation of the difficult to achieve deterministic/stochastic tilt trade-off. Optimizationhas been used extensively for the designs. A by-product of the work is the insightprovided into the relevant tilting train model Non Minimum Phase characteristicsand its link to uncertainty for control design. Work has been undertaken usingMatlab, including proper assessment of tilt ride quality considerations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".