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Record W2799635521 · doi:10.1139/tcsme-2015-0015

ADAPTIVE NEURAL NETWORK CONTROL OF A HUMAN SWING LEG AS A DOUBLE-PENDULUM CONSIDERING SELF-IMPACT JOINT CONSTRAINT

2015· article· en· W2799635521 on OpenAlex

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsnot available
Fundersnot available
KeywordsSwingControl theory (sociology)Double pendulumConstraint (computer-aided design)PendulumComputer scienceArtificial neural networkNonlinear systemGaitInverted pendulumMathematicsEngineeringControl (management)Physical medicine and rehabilitationArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

For human walking, the swing leg is usually modeled as a double pendulum. Considering a joint self-impact constraint at the knee joint of the double pendulum model is the main difference in this study. The primary objective of this research is to propose a nonlinear Adaptive Neural Network (ANN) for this system. By using Gaussian RBF networks, asymptotically stable tracking is attained. We will use the available data of normal human walking for the desired trajectories of the hip and knee joints. By simulation of the system, we perceive that the swing leg tracks the normal human gait with a negligible and tolerable error.

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: Empirical · Consensus signal: none
Teacher disagreement score0.957
Threshold uncertainty score0.900

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.026
GPT teacher head0.222
Teacher spread0.196 · 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