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Record W2053650061 · doi:10.1063/1.2403839

Feedforward control of a closed-loop piezoelectric translation stage for atomic force microscope

2007· article· en· W2053650061 on OpenAlexafffund
Yang Li, John Bechhoefer

Bibliographic record

VenueReview of Scientific Instruments · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFeed forwardControl theory (sociology)PiezoelectricityComputer scienceSIGNAL (programming language)Feedforward neural networkFilter (signal processing)Translation (biology)Feedback loopBandwidth (computing)AcousticsControl (management)Control engineeringPhysicsArtificial intelligenceComputer visionTelecommunicationsEngineeringChemistryArtificial neural network

Abstract

fetched live from OpenAlex

Simple feedforward ideas are shown to lead to a nearly tenfold increase in the effective bandwidth of a closed-loop piezoelectric positioning stage used in scanning probe microscopy. If the desired control signal is known in advance, the feedforward filter can be acausal: the information about the future can be used to make the output of the stage have almost no phase lag with respect to the input. This keeps in register the images assembled from right and left scans. We discuss the design constraints imposed by the need for the feedforward filter to work robustly under a variety of circumstances. Because the feedforward needs only to modify the input signal, it can be added to any piezoelectric stage, whether closed or open loop.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.304
Teacher spread0.291 · 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
GenreMethods

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

Citations60
Published2007
Admission routes2
Has abstractyes

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