Bibliographic record
Abstract
In this chapter, the mathematical modeling and simulation of the micro UAV with a payload is derived. When the load is slung underneath the UAVs by cable, the flight dynamics of the UAVs will be altered, which makes the stability of the UAVs disturbed. The unstable oscillation may occur to degrade the performance of the UAVs, and the accurate placement of the load will be affected. Unlike the external disturbance, the negative effects are related to the characteristics of the UAV and the payload. In order to make the UAVs have the ability to adopt the change of the system dynamics and reduce the effects caused by the swing of the load, one modeling method of micro UAVs with single slung payload is addressed. The slung payload is treated as a pendulum-like mass point, and the coupling factors between the UAVs and the payload are considered in the Lagrangian formulation. The conducted model can be used to estimate the Y. Feng ( ) • C.A. Rabbath • C.-Y. Su Department of Mechanical and Industrial Engineering, Concordia University, Montreal, QC, Canada e-mail: zhdfengying@gmail.com; Rabbath@bell.net; chun-yi.su@concordia.ca K.P. Valavanis, G.J. Vachtsevanos (eds.), Handbook of Unmanned Aerial Vehicles, DOI 10.1007/978-90-481-9707-1 108, © Springer Science+Business Media Dordrecht 2015 1257
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".