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Record W1623448149 · doi:10.1109/wac.2002.1049456

Computer aided stability and safety analysis of forklifts

2003· article· en· W1623448149 on OpenAlexaff
Surinder Singh Cheema, Nariman Sepehri

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSoftwareAutomotive engineeringEnhanced Data Rates for GSM EvolutionComputer scienceStability (learning theory)Measure (data warehouse)Automobile handlingSimulationEngineeringArtificial intelligenceData miningOperating system

Abstract

fetched live from OpenAlex

Forklift is a common type of material handling equipment. As a result of carrying various loads at varying speed and in different types of terrains, a forklift is susceptible to tipping over. This paper presents the development of a software to study the effects of loading, wheelbase size, vehicle speed, top-heaviness and inclination on the stability of forklifts. The goal is to bring more insight into the stability of forklifts, and to help improving their future design. The approach taken is to employ the concept of energy stability as a measure to indicate the potential overturning of the vehicle over each edge. A graphic interface is also developed to interactively demonstrate the results. The software package is applied to the stability analysis of a Caterpillar DP-90 forklift. The software is capable of indicating the bounds of many variables/parameters within which the forklift operates safely.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.019
GPT teacher head0.244
Teacher spread0.225 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations8
Published2003
Admission routes1
Has abstractyes

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