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Record W2579848557 · doi:10.1177/0954407016681683

Developing an active variable-wheelbase system for enhancing the vehicle dynamics

2017· article· en· W2579848557 on OpenAlex
Amir Soltani, Avesta Goodarzi, Mohammad Hassan Shojaiefard, Amir Khajepour

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsVehicle dynamicsVariable (mathematics)Moment (physics)EngineeringAutomobile handlingControl theory (sociology)Automotive engineeringYawControl (management)Fuzzy logicControl engineeringComputer sciencePhysicsMathematics

Abstract

fetched live from OpenAlex

In order to enhance the vehicle dynamics, this paper presents a novel and innovative yaw moment control system in which the longitudinal positions of the wheels are individually controlled. For this purpose, the proposed concept is analytically and numerically studied first, and it is shown that the method can considerably modify the inherent characteristics of a vehicle, such as the handling and the steerability. This system can generate a stabilizing yaw moment, without any considerable changes in the total lateral force and the total longitudinal force of the vehicle. Finally, a comprehensive vehicle model together with an intelligent fuzzy control strategy are developed to evaluate the effectiveness of the active variable-wheelbase system in different driving conditions. Based on the simulation results, this system has the potential to be considered as a new alternative technique for vehicle dynamics control in the future.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score0.707

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.211
Teacher spread0.203 · 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