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Record W2800184827 · doi:10.1139/tcsme-2007-0011

AN INDEPENDENT ACTIVE BALANCER FOR PLANAR MECHANISMS

2007· article· en· W2800184827 on OpenAlexvenueno aff
Ying Zhang, Yan‐an Yao, Jianzhong Cha

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2007
Typearticle
Languageen
FieldEngineering
TopicIterative Learning Control Systems
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsLinkage (software)PlanarControl theory (sociology)Mechanism (biology)Coupling (piping)Computer scienceVibrationJoint (building)Control engineeringControl (management)EngineeringMechanical engineeringStructural engineeringPhysics

Abstract

fetched live from OpenAlex

This paper proposed a novel concept of active balancer for dynamic balancing of planar mechanisms. Somewhat similar to a vibration absorber, the active balancer is designed as an independent device, which is placed outside of the mechanism to be balanced and can be installed easily. It consists of a two degree-of-freedom (DOF) linkage with two input shafts, one of which is connected to the output shaft of the mechanism to be balanced by a joint coupling, and the other one is driven by a controllable motor. Flexible dynamic balancing adapted to different working conditions can be achieved by varying speed trajectories of the control motor actively. A design method is developed for selecting suitable speed trajectories and link parameters of the two DOF linkage of the balancer to meet various design requirements and constraints. Numerical examples are given to demonstrate the design procedure and to verify the feasibility of the proposed concept.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.007
GPT teacher head0.211
Teacher spread0.204 · 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 designBench or experimental
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

Citations5
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicIterative Learning Control SystemsFrench-language works237,207