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Record W2807431811 · doi:10.1109/tmech.2018.2843799

Realization of an Energy-Efficient Adjustable Mechatronic Spring

2018· article· en· W2807431811 on OpenAlexafffund
Seyed Hossein Kamali, Mehrdad Moallem, Siamak Arzanpour

Bibliographic record

VenueIEEE/ASME Transactions on Mechatronics · 2018
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRealization (probability)Spring (device)MechatronicsComputer scienceControl engineeringEngineeringMechanical engineeringMathematics

Abstract

fetched live from OpenAlex

Although semi-active variable mechanical dampers are well-known and highly developed concepts, creating an efficient variable stiffness has remained a challenge. In this paper, a brushless dc motor combined with a mechanical motion converter is utilized to synthesize the behavior of a mechanical spring. It is shown that synthesizing resistive and inductive elements in the motor shunt circuit can create damping and stiffness behaviors, respectively. The actuator shunt is digitally controlled by a bidirectional boost converter, which uses a sliding-mode algorithm. Using the proposed approach, the actuator can exhibit independent damping and stiffness effects in real-time. Analytic studies regarding the power requirements and criteria for controller convergence are presented and verified through experimental studies.

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 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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.917
Threshold uncertainty score0.864

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 head0.007
GPT teacher head0.209
Teacher spread0.202 · 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 teacher head, 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

Citations10
Published2018
Admission routes2
Has abstractyes

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