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Record W4392719439 · doi:10.1109/access.2024.3376407

Development and Testing of Novel Soft Sleeve Actuators

2024· article· en· W4392719439 on OpenAlex
Mohammed Abboodi, Marc Doumit

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

VenueIEEE Access · 2024
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsActuatorComputer scienceSystem testingArtificial intelligence

Abstract

fetched live from OpenAlex

The field of soft actuators and robotics has garnered considerable attention in recent years, driven by their distinct properties to adapt to diverse environments and enable secure and engaging interactions with humans. While current literature highlights a significant body of work on various soft actuators, it is noteworthy that the concept of soft sleeve actuation remains unexplored, as it has not yet been proposed. The concept of soft sleeve actuation represents a significant leap forward in the field of robotics, heralding tremendous potential for diverse applications, particularly for wearable robotics. This paper introduces a novel soft sleeve actuation mechanism, encompassing the development of two actuators capable of generating linear and bending motion. These actuators are lightweight and capable of generating considerable force and motion. Using Fused Deposition Modeling technology, a comprehensive fabrication framework was adopted to overcome manufacturing variability and fabricate high-quality airtight actuators. The mechanical performance of the proposed soft sleeve actuators (SLA) was investigated through a custom-built experimental testing setup. The impact of geometric parameters and material stiffness on the behavior of the developed actuators is studied and discussed.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.774
Threshold uncertainty score0.206

Codex and Gemma teacher scores by category

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 science0.0000.000
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 head0.061
GPT teacher head0.293
Teacher spread0.232 · 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