The Konkuk/DLR MoU within the International Rotorcraft R&D Hub at Konkuk University
Bibliographic record
Abstract
The Korean Utility Helicopter (KUH - SURION) program is devoted to develop (in cooperation with Eurocopter) a 7-8 ton utility helicopter inside South Korea. In parallel, the scientific aspects of rotorcraft technology were established by means of the generation of the International Rotorcraft R&D (= Research and Development) Hub (IRH) at Konkuk University, which acts as an interface between Korean and foreign establishments. Cooperation agreements in form of Memorandum of Understanding (MoU) exist between Konkuk University and renowned research institutions worldwide, namely Georgia Institute of Technology (GIT, USA), Japan Aerospace Exploration Agency (JAXA), Ryerson University (Canada), Uzbekistan TSAI University, and German Aerospace Center (DLR) since 2005. In 2006, the French Aerospace Lab (Onera) and US Army AFDD joined, the latter with a Data Exchange Agreement (DEA). The presentation will outline the history of cooperative development between Konkuk University and DLR as well as the activities performed within the MoU under the umbrella of the IRH. The active tasks encompass high resolution rotor simulation with up-to-date CFD (Computational Fluid Dynamics) coupled with CSD (Computational Structural Dynamics) technologies, dynamic stall and active blade twist control investigations. \n
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.015 |
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".