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Record W1989569605 · doi:10.1109/robio.2011.6181376

On network simulation for performance evaluation of real-time Internet-based teleoperation

2011· article· en· W1989569605 on OpenAlexaff
Stephen Shaoyi Liao, Wai-keung Fung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsTeleoperationEmulationComputer scienceSimulationNetwork simulationOperator (biology)The InternetComputationTeleroboticsFocus (optics)Real-time computingDistributed computingRobotMobile robotArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper we discuss various approaches to simulating real-time Internet-based teleoperation systems. Focus is on simulation of the communication link, which is a critical component in teleoperation systems. Some of the objectives of simulations are to compare the performance of operators in fixed network conditions and to compare the performance of an operator over varying network conditions. To accomplish these goals, NS-2 is used with modified classes to model teleoperation systems. Two methods for simulation are co-simulation, where the system dynamics and the network are simulated at the same time, and sequential simulation, where the network and system dynamics are simulated in separate steps. The differences in accuracy and implementation are compared by an illustrative experiment. This paper also investigates methods for interactive simulations, which receive command inputs from an external master device controlled by a human operator. The real-time scheduler and network emulation are NS-2 addons that enable interactive simulations. Since computation time is an important limitation for simulations, a sequential simulation approach can be used for large-scale networks.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.754

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)0.0010.000

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 head0.050
GPT teacher head0.262
Teacher spread0.212 · 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

Citations3
Published2011
Admission routes1
Has abstractyes

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