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Record W2120168363 · doi:10.1109/ccece.2007.307

Design and Development of a Smart Vehicle for Inspection of In-Service Water Mains

2007· article· en· W2120168363 on OpenAlexaff
Saeed Poozesh, Mehran Mehrandezh, Homayoun Najjaran, Raman Paranjape

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsUniversity of British ColumbiaUniversity of Regina
Fundersnot available
KeywordsRobotPipeline transportPipeline (software)EngineeringTestbedService robotMains electricityService (business)SimulationComputer scienceMarine engineeringReal-time computingEmbedded systemMechanical engineeringArtificial intelligenceElectrical engineeringAerospace engineering

Abstract

fetched live from OpenAlex

This paper describes the design of an underwater robotic vehicle which is based on the principle of screw-type motion. The robot is targeted for inspecting in-service water mains using its onboard sensors. The proposed robot can freely move along the basic configuration of pipelines such as horizontal or vertical. Moreover it can travel along reducers, and elbows. Various robotic pipeline inspection vehicles and different material specified non-destructive testing (NDT) techniques have been reviewed in this paper. While other in-pipe inspection robots only work in pipes with constant diameter the proposed robot can negotiate pipes whose diameter varies widely during the robot's course of motion. The main features of the proposed system are illustrated and some details about the mechanical structure and its dynamics are provided. A Hardware-in-loop (HIL) testbed with virtual reality capabilities is under investigation in order to prepare the final integration of the in-pipe robotic inspection system.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.125

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 head0.035
GPT teacher head0.232
Teacher spread0.197 · 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 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

Citations6
Published2007
Admission routes1
Has abstractyes

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