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Record W1592424079 · doi:10.3233/jae-121624

Robotic cell injection force control based on static PVDF sensor and Fuzzy-PID control method

2013· article· en· W1592424079 on OpenAlex
Zhiyong Sun, Lina Hao, Wenlin Chen, Zhi Li

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.

Bibliographic record

VenueInternational Journal of Applied Electromagnetics and Mechanics · 2013
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsConcordia University
Fundersnot available
KeywordsPID controllerControl theory (sociology)Fuzzy logicControl engineeringComputer scienceControl (management)EngineeringArtificial intelligenceTemperature control

Abstract

fetched live from OpenAlex

Cell injection procedure is very essential in the field of molecular biology, and the injection force affects the success rates very much. However, conventional methods of manipulating individual biological cell failed to make use of the injection force information. This article is intended to design a static micro-force sensor with a simple structure which employs the piezoelectric material PVDF (polyvinylidene fluoride) film as its sensing element to detect the micro-force during cells injection and to develop a close-loop control method to regulate the whole fore-tracking system. A Fuzzy-PID and an ordinary PD feedback control method are employed separately in this article to regulate the micro-force tracking system which is used to carry out automatic living-cell injection tests. Experimental results with different control methods are achieved. And the Fuzzy-PID and PVDF sensor based force control method is validated.

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 score0.934
Threshold uncertainty score0.643

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 head0.003
GPT teacher head0.202
Teacher spread0.199 · 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