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Record W4286489406 · doi:10.1115/1.4055059

Actuation Arrangement of Rigid Foldable Waterbomb Origami

2022· article· en· W4286489406 on OpenAlexaff
Sen Wang, Dongbo Liang, Hailin Huang, Bing Li, Rongqiang Liu, Fengfeng Xi

Bibliographic record

VenueJournal of Mechanisms and Robotics · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsToronto Metropolitan University
FundersNational Natural Science Foundation of China
KeywordsDNA origamiComputer scienceDegrees of freedom (physics and chemistry)CorrectnessTopology (electrical circuits)EngineeringPhysicsAlgorithm

Abstract

fetched live from OpenAlex

Abstract A novel approach is proposed to arrange the actuations of rigid foldable waterbomb origami with multiple facet loops such that the number of actuations equaled the degrees-of-freedom (DOF) of the origami. In this approach, the rigid waterbomb origami was regarded as a combination of three types of six-crease origami units, which is equivalent to spherical 6R mechanisms with three DOF. Then, clear, target, and arrangement parts were created to define the facets of the origami pattern in the proposed extrapolation method. The actuation arrangement for a waterbomb origami pattern, which extended outwards circumferentially from a six-crease origami unit, was completed, and adams software was used to verify the correctness of the arrangement. Finally, an intuitive mathematical method was used to arrange the actuations for this type of waterbomb origami. The proposed approach provided DOF for the rigid foldable waterbomb origami and facilitated an actuation design such that the origami exhibits unique motion and can be normally actuated.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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

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 head0.007
GPT teacher head0.187
Teacher spread0.180 · 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
Published2022
Admission routes1
Has abstractyes

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