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Record W2001500196 · doi:10.1115/1.1904049

Automated Type Synthesis of Planar Mechanisms Using Numeric Optimization With Genetic Algorithms

2004· article· en· W2001500196 on OpenAlex
Yi Liu, John McPhee

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Mechanical Design · 2004
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaMassachusetts Institute of Technology
KeywordsConcatenation (mathematics)Topology optimizationAlgorithmComputer scienceString (physics)Genetic algorithmAdjacency matrixTopology (electrical circuits)Network topologyOptimization problemAdjacency listBinary relationMathematical optimizationTheoretical computer scienceMathematicsEngineering

Abstract

fetched live from OpenAlex

This paper presents a novel method for the automated type synthesis of planar mechanisms and multibody systems. The method explicitly includes topology as a design variable in an optimization framework based on a genetic algorithm (GA). Each binary string genome of the GA represents the concatenation of the upper-right triangular portion of the link adjacency matrix of a mechanism. Different topologies can be explored by the GA by applying genetic operators to the genomes. The evolutionary process is not dependent on the results obtained from enumeration. Two examples of topology-based optimization show the applicability of this method to mechanism type synthesis problems. This method is distinct from others in the literature in that it represents the first fully automated algorithm for solving a general type synthesis problem with the help of a numeric optimizer.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.106
Threshold uncertainty score0.586

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 head0.018
GPT teacher head0.220
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