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Record W4249356701 · doi:10.1115/detc2018-85670

Visual Similarity to Aid Alternative-Use Concept Generation for Retired Wind-Turbine Blades

2018· article· en· W4249356701 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMultimodal Machine Learning Applications
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsReuseSimilarity (geometry)TurbineComputer scienceScale (ratio)Turbine bladeWork (physics)Wind powerArtificial intelligenceIndustrial engineeringHuman–computer interactionEngineeringMechanical engineeringImage (mathematics)

Abstract

fetched live from OpenAlex

This work is motivated by finding alternative uses for retired wind-turbine blades, which have limited disposal options. Two reuse concept-generation activities (CGA) conducted in German universities revealed difficulties with the parts’ large scale and seeing beyond their original use. Existing methods, e.g., using functional analogy, are less applicable, since for safety reasons, these parts should not be reused in the same function. Therefore, this work explores the use of visual similarity to support reuse-concept generation. A method was developed that 1) finds visually similar images (VSI) for wind-turbine-blade photos; and 2) derives potential-reuse concepts based on objects that are visually similar to wind-turbine blades in these images. Comparing reuse concepts generated from the two methods, VSI produced fewer smaller-than-scale concepts than CGA. While other qualities like feasibility depend on the specific photo selected, this work provides a new framework to exploit visual similarity to find alternative uses. As demonstrated for wind-turbine blades, this method aids in generating alternative-use concepts, especially for large-scale objects.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.904
Threshold uncertainty score0.516

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 science0.0010.000
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 head0.052
GPT teacher head0.351
Teacher spread0.299 · 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

Quick stats

Citations1
Published2018
Admission routes2
Has abstractyes

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