Conference Proceeding
Refine
Year of publication
- 2024 (14)
- 2023 (36)
- 2022 (48)
- 2021 (52)
- 2020 (55)
- 2019 (91)
- 2018 (74)
- 2017 (86)
- 2016 (72)
- 2015 (78)
- 2014 (55)
- 2013 (74)
- 2012 (81)
- 2011 (70)
- 2010 (67)
- 2009 (64)
- 2008 (48)
- 2007 (44)
- 2006 (53)
- 2005 (29)
- 2004 (25)
- 2003 (22)
- 2002 (25)
- 2001 (18)
- 2000 (14)
- 1999 (15)
- 1998 (21)
- 1997 (10)
- 1996 (7)
- 1995 (6)
- 1994 (4)
- 1993 (11)
- 1992 (6)
- 1991 (3)
- 1990 (2)
- 1989 (5)
- 1988 (3)
- 1987 (1)
- 1986 (2)
- 1985 (4)
- 1984 (3)
- 1983 (3)
- 1981 (2)
- 1980 (1)
- 1979 (1)
- 1978 (3)
- 1977 (2)
- 1975 (3)
- 1974 (1)
- 1973 (3)
Institute
- Fachbereich Elektrotechnik und Informationstechnik (286)
- Fachbereich Energietechnik (226)
- Fachbereich Luft- und Raumfahrttechnik (198)
- Fachbereich Maschinenbau und Mechatronik (188)
- Solar-Institut Jülich (164)
- Fachbereich Medizintechnik und Technomathematik (152)
- IfB - Institut für Bioengineering (114)
- Fachbereich Bauingenieurwesen (103)
- ECSM European Center for Sustainable Mobility (59)
- Fachbereich Wirtschaftswissenschaften (55)
Has Fulltext
- no (1417) (remove)
Document Type
- Conference Proceeding (1417) (remove)
Keywords
- Enterprise Architecture (5)
- Gamification (5)
- Energy storage (4)
- Natural language processing (4)
- Power plants (4)
- hydrogen (4)
- solar sail (4)
- Associated liquids (3)
- Concentrated solar power (3)
- Hybrid energy system (3)
Entscheidungsunterstützung bei der Gestaltung der betrieblichen Energieversorgung und -nutzung
(1998)
Smart pixel : photonic mixer device (PMD) ; new system concept of a 3D-imaging camera-on-a-chip
(1998)
Harte Echtzeit für Windows
(1999)
Aim of the AXON2 project (Adaptive Expert System for Object Recogniton using Neuml Networks) is the development of an object recognition system (ORS) capable of recognizing isolated 3d objects from arbitrary views. Commonly, classification is based on a single feature extracted from the original image. Here we present an architecture adapted from the Mixtures of Eaqerts algorithm which uses multiple neuml networks to integmte different features. During tmining each neural network specializes in a subset of objects or object views appropriate to the properties of the corresponding feature space. In recognition mode the system dynamically chooses the most relevant features and combines them with maximum eficiency. The remaining less relevant features arz not computed and do therefore not decelerate the-recognition process. Thus, the algorithm is well suited for ml-time applications.
10 Regeln für das Desaster
(2001)