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Record W2039019440 · doi:10.1109/robio.2014.7090601

Design, simulation and manufacturing of a tracked robot for nuclear accidents

2014· article· en· W2039019440 on OpenAlexaff
Jie Ma, Jun Luo, Huayan Pu, Yan Peng, Shaorong Xie, Jason Gu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Structural Analysis Methods
Canadian institutionsDalhousie University
Fundersnot available
KeywordsRobotMobile robotDomain (mathematical analysis)Computer scienceSimulationWork (physics)Rescue robotEngineeringArtificial intelligenceMechanical engineering

Abstract

fetched live from OpenAlex

Many mobile robots especially tracked robots have been developed and used in disaster sites, however, few robots can really work in a nuclear accident. This paper presents a procedure of making a robot for nuclear accidents. Concepts and calculations before designing the robot are introduced, then Autodesk Inventor is used for designing the 3D model, Recurdyn is used for the dynamic simulation. After that the manufactured prototype is introduced, and then several tests on the prototype are presented. This robot provides with one method for researchers in the domain of Search and Rescue robots.

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)0.000
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.268
Teacher spread0.246 · 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

Citations14
Published2014
Admission routes1
Has abstractyes

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