Visual Similarity to Aid Alternative-Use Concept Generation for Retired Wind-Turbine Blades
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".