Bibliographic record
Abstract
This thesis presents an advanced guidance and control system for unmanned aerial vehicles (UAV) with flexible wing.The control system is based on a stereo vision system and advanced fuzzy logic algorithms that can detect wing deflections and shapes.In general, this sensor has the capability, reliability, and performance of similar traditional control sensors, and provides reliable visual information that is useful for designing different control systems.The thesis proposes a novel Deflection-Detection-Vision-System (DDVS) to control a flexible wing of unmanned aerial vehicle (UAV).The technique measure the deflection of the flexible wing with a stereo camera and determines the three-dimensional (3D) coordinates to identify the wing shape.In addition, the fuzzy logic algorithm classifies the shapes and determines the flight parameters, such as the speed, angle of attack and roll angle.Three different algorithms (homography, Iterative Closest Point (ICP), and Horns Absolute Orientation) were used to determine the pose estimation based on the vision system.Those techniques were tested on the HD Life-web camera with a moving baseline before using a stereo camera to find a suitable baseline and distance.The relations between the optimal baseline and depth distance are discussed, and several experiments with different image noise levels are performed to determine noise levels influence on distance and pose estimate accuracy.Measurements and estimation errors are provided and compared using different methods.The Deflection-Detection-Vision-System (DDVS) consists Ph.D. program advisor, Professor Jurek Z. Sasiadek, for his continued support and guidance.He provided advice, opportunities, and assistance that greatly enhanced my research experience.Many thanks to my mother, my father, and my brothers for the patience and the support in all my choices, no matter what they were.In particular, I thank my lovely wife and my children "Fatima, Mohammed Ridha and Zainab" for their company and understanding during this thesis activity.Last but not certainly least, I would also like to
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".