ALLFlight - Helicopter Flight Trials under DVE conditions with an AI-130 mmW radar system \n
Bibliographic record
Abstract
One of the biggest challenges for any kind of technology used for DVE landings and takeoffs is to provide an intuitive display and keeping the workload low while providing all the necessary cues to perform the tasks safely and efficiently. Mounting different complementary types of sensors (TV, Infrared (EVS-1000, Max-Viz, USA), mmW (AI-130, ICx Radar Systems, Canada) and Ladar (HELLAS-W, EADS, Germany)) with different characteristics onto DLR’s research helicopter FHS (flying helicopter simulator) is the first step to gather information of the surrounding world. The data processing is designed and realized by a high performance sensor co-computer (SCC) cluster architecture, which is installed into the helicopter’s experimental electronic cargo bay. The aim of generating a single comprehensive description of the current outside situation shall be achieved by a sophisticated data fusion concept. Data from the different sensors are collected in parallel and finally fed into that “scene description”, which grows over time. The idea of displaying this information on a helmet mounted display is followed by the Institute of Flight Guidance in the scope of the internal DLR project ALLFlight (Assisted Low Level Flight and Landing on Unprepared Landing Sites). The output of the project will result in a broader mission potential of the helicopter compared to the present situation, where it is in common that a mission cannot be performed or has to be canceled due to bad visual conditions. \n \nAs a preparation of ALLFlight’s flight trials in 2011, a scientist of our institute spent three months during 2010 at the National Research Council (NRC) in Canada to conduct some Bell 205 flight trials with the same mmW radar sensor (AI-130, ICx Radar Systems, Canada) which is also used within ALLFlight’s sensor suite. A challenge during his habitation was the adaptation of the data interfaces to use the software suite at NRC and DLR. Furthermore, a software application for controlling the radar’s parameters manually and automatically as well as for monitoring the radar has been developed. \n \nAfter a short introduction regarding the motivation of the ALLFlight project, the paper describes data evaluations of mmW radar sensor data recorded within the flight trials at NRC. Advantages and disadvantages of the sensor’s technical characteristics will be pointed out. \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 | 0.000 | 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 | 0.001 | 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) | 0.006 | 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".