Refine
Year of publication
- 2024 (101)
- 2023 (190)
- 2022 (237)
- 2021 (229)
- 2020 (236)
- 2019 (309)
- 2018 (254)
- 2017 (258)
- 2016 (267)
- 2015 (284)
- 2014 (283)
- 2013 (286)
- 2012 (314)
- 2011 (311)
- 2010 (332)
- 2009 (341)
- 2008 (291)
- 2007 (271)
- 2006 (276)
- 2005 (263)
- 2004 (286)
- 2003 (218)
- 2002 (232)
- 2001 (210)
- 2000 (234)
- 1999 (232)
- 1998 (236)
- 1997 (214)
- 1996 (200)
- 1995 (192)
- 1994 (175)
- 1993 (155)
- 1992 (144)
- 1991 (100)
- 1990 (108)
- 1989 (110)
- 1988 (103)
- 1987 (105)
- 1986 (81)
- 1985 (83)
- 1984 (75)
- 1983 (70)
- 1982 (57)
- 1981 (54)
- 1980 (61)
- 1979 (58)
- 1978 (52)
- 1977 (32)
- 1976 (30)
- 1975 (28)
- 1974 (17)
- 1973 (12)
- 1972 (17)
- 1971 (11)
- 1970 (2)
- 1969 (2)
- 1968 (2)
- 1967 (1)
- 1963 (1)
Institute
- Fachbereich Medizintechnik und Technomathematik (1936)
- Fachbereich Elektrotechnik und Informationstechnik (1150)
- Fachbereich Wirtschaftswissenschaften (1121)
- Fachbereich Energietechnik (1067)
- Fachbereich Chemie und Biotechnologie (897)
- Fachbereich Maschinenbau und Mechatronik (813)
- Fachbereich Luft- und Raumfahrttechnik (769)
- Fachbereich Bauingenieurwesen (664)
- IfB - Institut für Bioengineering (629)
- INB - Institut für Nano- und Biotechnologien (586)
Has Fulltext
- no (9333) (remove)
Language
Document Type
- Article (5534)
- Conference Proceeding (1421)
- Book (1062)
- Part of a Book (567)
- Patent (177)
- Bachelor Thesis (169)
- Report (83)
- Doctoral Thesis (82)
- Conference: Meeting Abstract (76)
- Other (67)
Keywords
- Illustration (10)
- Nachhaltigkeit (10)
- Corporate Design (9)
- Erscheinungsbild (8)
- Gamification (8)
- Redesign (7)
- Animation (6)
- Datenschutz (6)
- Deutschland (6)
- Digitalisierung (6)
This paper addresses the pixel based classification of three dimensional objects from arbitrary views. To perform this task a coding strategy, inspired by the biological model of human vision, for pixel data is described. The coding strategy ensures that the input data is invariant against shift, scale and rotation of the object in the input domain. The image data is used as input to a class of self organizing neural networks, the Kohonen-maps or self-organizing feature maps (SOFM). To verify this approach two test sets have been generated: the first set, consisting of artificially generated images, is used to examine the classification properties of the SOFMs; the second test set examines the clustering capabilities of the SOFM when real world image data is applied to the network after it has been preprocessed to be invariant against shift, scale and rotation. It is shown that the clustering capability of the SOFM is strongly dependant on the invariance coding of the images.
This paper describes the realization of a novel neurocomputer which is based on the concepts of a coprocessor. In contrast to existing neurocomputers the main interest was the realization of a scalable, flexible system, which is capable of computing neural networks of arbitrary topology and scale, with full independence of special hardware from the software's point of view. On the other hand, computational power should be added, whenever needed and flexibly adapted to the requirements of the application. Hardware independence is achieved by a run time system which is capable of using all available computing power, including multiple host CPUs and an arbitrary number of neural coprocessors autonomously. The realization of arbitrary neural topologies is provided through the implementation of the elementary operations which can be found in most neural topologies.
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.
Environmental emissions, global warming, and energy-related concerns have accelerated the advancements in conventional vehicles that primarily use internal combustion engines. Among the existing technologies, hydrogen fuel cell electric vehicles and fuel cell hybrid electric vehicles may have minimal contributions to greenhouse gas emissions and thus are the prime choices for environmental concerns. However, energy management in fuel cell electric vehicles and fuel cell hybrid electric vehicles is a major challenge. Appropriate control strategies should be used for effective energy management in these vehicles. On the other hand, there has been significant progress in artificial intelligence, machine learning, and designing data-driven intelligent controllers. These techniques have found much attention within the community, and state-of-the-art energy management technologies have been developed based on them. This manuscript reviews the application of machine learning and intelligent controllers for prediction, control, energy management, and vehicle to everything (V2X) in hydrogen fuel cell vehicles. The effectiveness of data-driven control and optimization systems are investigated to evolve, classify, and compare, and future trends and directions for sustainability are discussed.
In der Vergangenheit basierten große Systemintegrationsprojekte in der Regel auf Individualentwicklungen für einzelne Kunden. Getrieben durch Kostendruck steigt aber der Bedarf nach standardisierten Lösungen, die gleichzeitig die individuellen Anforderungen des jeweiligen Umfelds berücksichtigen. T-Systems GEI GmbH wird beiden Anforderungen mit Produktkerneln gerecht. Neben den technischen Aspekten der Kernelentwicklung spielen besonders organisatorische Aspekte eine Rolle, um Kernel effizient und qualitativ hochwertig zu entwickeln, ohne deren Funktionalitäten ins Uferlose wachsen zu lassen. Umgesetzt hat T-Systems dieses Konzept für Flughafeninformationssysteme. Damit kann dem wachsenden Bedarf der Flughafenbetreiber nach einer effizienten und kostengünstigen Softwarelösung zur Unterstützung Ihrer Geschäftsprozesse entsprochen werden.
Der Erfolg eines Softwarenentwicklungsprojektes insbesondere eines Systemintegrationsprojektes wird mit der Erfüllung des „Teufelsdreiecks“, „In-Time“, „In-Budget“, „In-Quality“ gemessen. Hierzu ist die Kenntnis der Software- und Prozessqualität essenziell, um die Einhaltung der Qualitätskriterien festzustellen, aber auch, um eine Vorhersage hinsichtlich Termin- und Budgettreue zu treffen. Zu diesem Zweck wurde in der T-Systems Systems Integration ein System aus verschiedenen Key Performance Indikatoren entworfen und in der Organisation implementiert, das genau das leistet und die Kriterien für CMMI Level 3 erfüllt.
In this paper we report on CO2 Meter, a do-it-yourself carbon dioxide measuring device for the classroom. Part of the current measures for dealing with the SARS-CoV-2 pandemic is proper ventilation in indoor settings. This is especially important in schools with students coming back to the classroom even with high incidents rates. Static ventilation patterns do not consider the individual situation for a particular class. Influencing factors like the type of activity, the physical structure or the room occupancy are not incorporated. Also, existing devices are rather expensive and often provide only limited information and only locally without any networking. This leaves the potential of analysing the situation across different settings untapped. Carbon dioxide level can be used as an indicator of air quality, in general, and of aerosol load in particular. Since, according to the latest findings, SARS-CoV-2 can be transmitted primarily in the form of aerosols, carbon dioxide may be used as a proxy for the risk of a virus infection. Hence, schools could improve the indoor air quality and potentially reduce the infection risk if they actually had measuring devices available in the classroom. Our device supports schools in ventilation and it allows for collecting data over the Internet to enable a detailed data analysis and model generation. First deployments in schools at different levels were received very positively. A pilot installation with a larger data collection and analysis is underway.