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Record W4399726855 · doi:10.32920/26052352.v1

Contact Pressure Based Passenger Following Seat Motion Control

2024· preprint· en· W4399726855 on OpenAlexaff
Dhvanit Desai

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsControl (management)Motion (physics)Automotive engineeringComputer scienceAeronauticsEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

The purpose of this thesis is to develop a seat control system that can provide constant support to the seated passenger's back. While providing support, it is important to ensure that the pressure exerted on the passenger's back is not excessive. In addition, as the backrest reclines, the static seat-pan would cause the passenger to slide, leading to uncomfortable sitting posture. To address these issues, three subsystems have been developed in this thesis research including: i) following control system, ii) pressure control system and iii) synchro-tilt control system. The following control system controls the backrest to follow the movement of the passenger. The pressure control system senses and controls the contact pressure between passenger's back and seat cushion. The synchro-tilt control system synchronizes the rotation of seat-pan with the reclination of the seat backrest to avoid passenger's sliding. Then the three subsystems are integrated into a combined control system. Two cases are studied. The first one is for passenger to lean forward. In this case, when the passenger leans forward, the backrest will rotate forward to follow the passenger's motion under the following control. When the passenger stops motion, the control system switches to the pressure control to provide comfortable support. In the course of seat motion, the synchro-tilt control system is always on to rotate the seat-pan.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.744
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.013
GPT teacher head0.297
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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