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Record W3209614597

Design and Optimization of Automatic Glass Window Cleaning Robot

2021· article· en· W3209614597 on OpenAlexvenueno aff
Shruti Jha, Madhukar B. Sorte, Yogita Chaudhari, Priyanka Kushwaha

Bibliographic record

VenueInternational Journal of Robotics and Automation · 2021
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsnot available
Fundersnot available
KeywordsWindow (computing)ClimbsortComputer scienceRobotWindow of opportunitySpace (punctuation)Task (project management)SimulationArtificial intelligenceEngineeringReal-time computingOperating systemAerospace engineering
DOInot available

Abstract

fetched live from OpenAlex

The idea of getting a compact or autonomous workplace or house window cleansing golem is sort of straightforward and extremely enticing. This little glass window cleansing golem ought to be able to move autonomously on an outdoor surface high-rise building workplace window with a comparatively giant space and meanwhile climb and wash it. Being manually connected to the skin surface of the space window the golem can execute and attain the task of the window cleansing mechanically in a very predefined pattern. This report includes background and objectives of this analysis, prototyped mechanical systems, moving system, experimental results of basic traveling management and window wiping motion by comparison to with or while not of occurrence system, some discussion in every experiment and a conclusion.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.019
GPT teacher head0.259
Teacher spread0.240 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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