Article
Refine
Year of publication
- 2024 (2)
- 2023 (8)
- 2022 (5)
- 2021 (10)
- 2020 (17)
- 2019 (12)
- 2018 (7)
- 2017 (9)
- 2016 (11)
- 2015 (6)
- 2014 (4)
- 2013 (5)
- 2012 (3)
- 2011 (9)
- 2010 (13)
- 2009 (9)
- 2008 (10)
- 2007 (15)
- 2006 (15)
- 2005 (28)
- 2004 (13)
- 2003 (13)
- 2002 (16)
- 2001 (10)
- 2000 (10)
- 1999 (10)
- 1998 (10)
- 1997 (4)
- 1996 (7)
- 1995 (7)
- 1994 (8)
- 1993 (5)
- 1992 (8)
- 1991 (8)
- 1990 (7)
- 1989 (4)
- 1988 (7)
- 1987 (5)
- 1986 (1)
- 1985 (7)
- 1984 (6)
Document Type
- Article (364) (remove)
Has Fulltext
- no (364) (remove)
Keywords
- avalanche (5)
- snow (3)
- Drinfeld modules (2)
- Transcendence (2)
- t-modules (2)
- 1P hub loads (1)
- Aeroelasticity (1)
- Antarctic Glaciology (1)
- Avalanche (1)
- CO2 emission reduction targets (1)
- Commercial Vehicle (1)
- Common Rail Injection System (1)
- Cost function (1)
- DLR-ESTEC GOSSAMER roadmap for solar sailing (1)
- Diesel Engine (1)
- Driving cycle recognition (1)
- Dry-low-NOx (DLN) combustion (1)
- ECMS (1)
- Energy management strategies (1)
- European Transient Cycle (1)
Institute
- Fachbereich Luft- und Raumfahrttechnik (364) (remove)
Wie sieht das unbemannte Flugzeug von Übermorgen aus? Dieser Frage stellen sich Forscher an der Fachhochschule Aachen. Die weltweit rasant fortschreitende Entwicklung des Marktes für unbemannte Fluggeräte (UAVs - „Unmanned Aerial Vehicles“) bietet großes Potenzial für Wachstum und Wertschöpfung. Unbemannte fliegende Systeme können – für bestimmte Anwendungsgebiete – wesentlich günstiger, kleiner und effizienter ausgelegt werden als bemannte Lösungen. Dabei sind sich viele Unternehmen über das mögliche Potential dieser Technologie noch gar nicht bewusst.
The Saturnian moon Enceladus with its extensive water bodies underneath a thick ice sheet cover is a potential candidate for extraterrestrial life. Direct exploration of such extraterrestrial aquatic ecosystems requires advanced access and sampling technologies with a high level of autonomy. A new technological approach has been developed as part of the collaborative research project Enceladus Explorer (EnEx). The concept is based upon a minimally invasive melting probe called the IceMole. The force-regulated, heater-controlled IceMole is able to travel along a curved trajectory as well as upwards. Hence, it allows maneuvers which may be necessary for obstacle avoidance or target selection. Maneuverability, however, necessitates a sophisticated on-board navigation system capable of autonomous operations. The development of such a navigational system has been the focal part of the EnEx project. The original IceMole has been further developed to include relative positioning based on in-ice attitude determination, acoustic positioning, ultrasonic obstacle and target detection integrated through a high-level sensor fusion. This paper describes the EnEx technology and discusses implications for an actual extraterrestrial mission concept.
Möglichkeiten der ottomotorischen Prozeßführung bei Verwendung des elektromechanischen Ventiltriebs
(1998)
Multiple Near-Earth Asteroid Rendezvous and Sample Return Using First Generation Solar Sailcraft
(2005)
This work presents the Multi-Bees-Tracker (MBT3D) algorithm, a Python framework implementing a deep association tracker for Tracking-By-Detection, to address the challenging task of tracking flight paths of bumblebees in a social group. While tracking algorithms for bumblebees exist, they often come with intensive restrictions, such as the need for sufficient lighting, high contrast between the animal and background, absence of occlusion, significant user input, etc. Tracking flight paths of bumblebees in a social group is challenging. They suddenly adjust movements and change their appearance during different wing beat states while exhibiting significant similarities in their individual appearance. The MBT3D tracker, developed in this research, is an adaptation of an existing ant tracking algorithm for bumblebee tracking. It incorporates an offline trained appearance descriptor along with a Kalman Filter for appearance and motion matching. Different detector architectures for upstream detections (You Only Look Once (YOLOv5), Faster Region Proposal Convolutional Neural Network (Faster R-CNN), and RetinaNet) are investigated in a comparative study to optimize performance. The detection models were trained on a dataset containing 11359 labeled bumblebee images. YOLOv5 reaches an Average Precision of AP = 53, 8%, Faster R-CNN achieves AP = 45, 3% and RetinaNet AP = 38, 4% on the bumblebee validation dataset, which consists of 1323 labeled bumblebee images. The tracker’s appearance model is trained on 144 samples. The tracker (with Faster R-CNN detections) reaches a Multiple Object Tracking Accuracy MOTA = 93, 5% and a Multiple Object Tracking Precision MOTP = 75, 6% on a validation dataset containing 2000 images, competing with state-of-the-art computer vision methods. The framework allows reliable tracking of different bumblebees in the same video stream with rarely occurring identity switches (IDS). MBT3D has much lower IDS than other commonly used algorithms, with one of the lowest false positive rates, competing with state-of-the-art animal tracking algorithms. The developed framework reconstructs the 3-dimensional (3D) flight paths of the bumblebees by triangulation. It also handles and compares two alternative stereo camera pairs if desired.