MétaCan
Menu
Back to cohort
Record W4286501741 · doi:10.1038/s41598-022-16655-9

Haptic virtual surgery simulation system under field programmable analogue array-based hybrid control

2022· article· en· W4286501741 on OpenAlexaff
Sun Ru, Ting Yang, Liang Zhang, Lin Wang, Yili Fu, Mahdi Tavakoli

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsUniversity of Alberta
FundersNatural Science Research of Jiangsu Higher Education Institutions of ChinaGovernment of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsHaptic technologyController (irrigation)Computer scienceStability (learning theory)Task (project management)Virtual realityField (mathematics)Operator (biology)SimulationMechatronicsControl theory (sociology)Artificial intelligenceControl (management)EngineeringMathematics

Abstract

fetched live from OpenAlex

In this paper, a bilateral haptic virtual surgery simulation system under a hybrid controller was studied. An analogue controller realized by a field programmable analogue array (FPAA) was paralleled in the operator robot side, which reduced the impact of controller discretisation on the system. A system stability conditions under hybrid control with multiple-operators were deduced. The stability analysis indicates that the addition of analogue derivative term widens the range of haptic controls gains that satisfy the multiple-users' stability conditions. Finally, the human's performance of a stiffness discrimination task was studied in an independently developed minimally invasive surgical (MIS) platform. The experiment results show that, human operators under the hybrid controller achieve the highest task success rates.

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)0.000
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.015
GPT teacher head0.213
Teacher spread0.198 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueScientific ReportsSame topicTeleoperation and Haptic SystemsFrench-language works237,207