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Record W2071107719 · doi:10.1115/fpmc2013-4454

Wireless Control of a Teleoperated Hydraulic Manipulator With Application Towards Live-Line Maintenance

2013· article· en· W2071107719 on OpenAlexaff
Yaser Maddahi, Nariman Sepehri, Stephen Shaoyi Liao, Wai-keung Fung, Ekram Hossain

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsTeleoperationMaster/slaveChannel (broadcasting)Hydraulic machineryComputer scienceWirelessWireless networkReal-time computingRouterSimulationEngineeringComputer networkRobotTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents the procedure of establishing performance charts for effective utilization of a teleoperated hydraulic manipulator working under wireless communication channels. A teleoperated system, comprising a master haptic device and an industrial hydraulic manipulator, is constructed. The master and slave communicate through a communication channel emulated using the NS2 simulator. Two sets of experiments are designed to construct performance charts that guide us to select appropriate parameters of wireless network setup by which a particular value of position error appears at the slave hydraulic manipulator end-effector. The network parameters are: configuration of environment obstruction, transmission power of the router, and distance between the master and slave sites. The first set of experiments is conducted to define three regions of tracking quality, and to construct the performance charts. The second set of experiments confirms satisfactory performance, when the teleoperated system is located within the recommended regions in the established charts. One application of this study is live-line maintenance using remotely-operated hydraulic manipulators.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.787
Threshold uncertainty score0.381

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 head0.007
GPT teacher head0.187
Teacher spread0.180 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations7
Published2013
Admission routes1
Has abstractyes

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