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Record W2057854532 · doi:10.3109/13645700903516742

A catheter side wall tactile sensor: Design, modeling and experiments

2010· article· en· W2057854532 on OpenAlexaff
Huanran Wang, Peter Liu, Shuxiang Guo, Xiufen Ye

Bibliographic record

VenueMinimally Invasive Therapy & Allied Technologies · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsCarleton University
Fundersnot available
KeywordsPolyvinylidene fluorideHaptic technologyTactile sensorCatheterAcousticsPressure sensorPiezoelectricitySurgical instrumentBiomedical engineeringEngineeringSimulationComputer scienceSurgeryMechanical engineeringMaterials scienceMedicineArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Intravascular neurosurgery is a new and important technique of minimally invasive surgery. The current surgical device, however, does not provide realistic tactile feedback, which makes the operation a very difficult task, and the surgeon must exert extreme caution in order to avoid medical accidents. In this paper, a novel tactile sensor, which is based on polyvinylidene fluoride, is developed to measure the pressure on the side wall of the catheter for intravascular neurosurgery. The relationship between the input force and the output charge signals is identified based on the composite laminate theory, shell theory and linear piezoelectric theory. The design, mathematical model, interface circuit and calibrating experiment of the tactile sensor are presented in detail. With this sensor, surgeons will be able to "feel" the contact force between the side wall of the catheter and the blood vessel. Experimental results show that the tactile sensor measures the pressure well when it contacts the side wall of blood.

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)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.243
Teacher spread0.208 · 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

Citations4
Published2010
Admission routes1
Has abstractyes

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