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Record W4220849341 · doi:10.1109/sii52469.2022.9708835

All-weather autonomous inspection robot for electrical substations

2022· article· en· W4220849341 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venue2022 IEEE/SICE International Symposium on System Integration (SII) · 2022
Typearticle
Languageen
FieldEngineering
TopicPower Line Inspection Robots
Canadian institutionsUniversité de SherbrookeHydro-Québec
Fundersnot available
KeywordsRobotTransformerCircuit breakerComputer scienceElectric power systemElectric power transmissionReliability engineeringSoftwareEngineeringElectric powerSystems engineeringReal-time computingControl engineeringPower (physics)Electrical engineeringArtificial intelligenceVoltage

Abstract

fetched live from OpenAlex

Electric utilities that operate power transmission networks must periodically inspect their numerous substations which contain diversified types of equipment (power transformers, circuit breakers, etc.). Completing all the inspection tasks is challenging for a geographically distributed network like the one operated by Hydro-Quebec. Using remote robot systems´ to accomplish those tasks is beneficent in terms of personnel safety, operational efficiency and asset management. The robot system must, however, be capable of operating reliably in harsh winter conditions. This paper presents a new robot system developed at Hydro-Quebec capable of addressing these specific´ inspection needs. It provides an overview of the system and its main components followed by a more detailed description of the software and algorithms required for autonomous operation of such an inspection robot, particularly in winter conditions.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.942
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.250
Teacher spread0.237 · 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