Refine
Year of publication
- 2024 (127)
- 2023 (256)
- 2022 (301)
- 2021 (293)
- 2020 (241)
- 2019 (378)
- 2018 (259)
- 2017 (259)
- 2016 (277)
- 2015 (301)
- 2014 (301)
- 2013 (301)
- 2012 (332)
- 2011 (328)
- 2010 (342)
- 2009 (369)
- 2008 (311)
- 2007 (312)
- 2006 (330)
- 2005 (303)
- 2004 (323)
- 2003 (254)
- 2002 (250)
- 2001 (221)
- 2000 (245)
- 1999 (236)
- 1998 (242)
- 1997 (220)
- 1996 (202)
- 1995 (192)
- 1994 (174)
- 1993 (154)
- 1992 (144)
- 1991 (100)
- 1990 (108)
- 1989 (111)
- 1988 (104)
- 1987 (105)
- 1986 (81)
- 1985 (84)
- 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)
- 1925 (1)
Institute
- Fachbereich Medizintechnik und Technomathematik (2074)
- Fachbereich Elektrotechnik und Informationstechnik (1184)
- Fachbereich Wirtschaftswissenschaften (1158)
- Fachbereich Energietechnik (1115)
- Fachbereich Chemie und Biotechnologie (912)
- Fachbereich Maschinenbau und Mechatronik (868)
- Fachbereich Luft- und Raumfahrttechnik (798)
- Fachbereich Bauingenieurwesen (711)
- IfB - Institut für Bioengineering (690)
- INB - Institut für Nano- und Biotechnologien (615)
Language
- German (5114)
- English (4909)
- Russian (14)
- Portuguese (6)
- Multiple languages (5)
- Spanish (3)
- Dutch (2)
- Italian (1)
Document Type
- Article (5646)
- Conference Proceeding (1639)
- Book (1079)
- Part of a Book (559)
- Bachelor Thesis (327)
- Patent (174)
- Report (101)
- Doctoral Thesis (79)
- Other (76)
- Administrative publication (76)
Keywords
- Amtliche Mitteilung (71)
- Bachelor (33)
- Aachen University of Applied Sciences (31)
- Master (31)
- Prüfungsordnung (31)
- Bauingenieurwesen (30)
- Lesbare Fassung (28)
- Biosensor (25)
- Fachhochschule Aachen (23)
- Illustration (23)
Zugriffsart
- campus (2147)
- weltweit (1860)
- bezahl (766)
- fachbereichsweit (FB4) (34)
Die neue Generation wird mit einer Medienwelt konfrontiert, welche nicht auf unsere kleinen Nutzer:innen ausgelegt ist. Die Verwendung von digitalen Medien hatte nie einen größeren Einfluss auf unsere Gesellschaft wie im Hier und Jetzt. Somit ist es von großer Relevanz, diese Nutzung kindergerechter zu gestalten und einen sicheren Rahmen für sie zu schaffen. Das Bachelorprojekt „KLIKK“ legt einen Lösungsentwurf für den altersgerechten Einstieg für Kinder in diese digitale Welt vor, um den richtigen Umgang mit Smartwatches und darauf aufbauende Medien zu ermöglichen und gleichzeitig einen pädagogischen Mehrwert zu bieten. Das didaktische Produktkonzept, welches explizit für Kinder designed wurde, beinhaltet das Erlernen des Uhrlesens und befriedigt das aufkommende Bedürfnis nach Vernetzung in einem sicheren System. Zudem fördert es die Autonomie und ist an die kindliche Motorik angepasst. Mitinitiator und Namensgeber für diese Konzeption ist die deutsche Kinderuhrenmarke KWIO. KLIKK inkludiert Sicherheit, die Freude am Lernen und bietet eine Möglichkeit, die Kleinen ganz groß in unsere digitale Welt aufzunehmen.
Das Ausleben von Sexualität und Gender befindet sich im gesellschaftlichen Wandel. Vor allem junge Menschen lassen ihre Lebenswege nicht mehr von traditionellen Geschlechterrollen und -normen leiten. In einigen Bereichen ist das schon deutlich sichtbar, während andere Bereiche noch vor Herausforderungen stehen, wenn es um Inklusion, Akzeptanz und Repräsentation geht. Besonders betroffen ist dabei der Gamingbereich, der repräsentativ nach wie vor ein männlich-dominiertes Gebiet zu sein scheint und durch Vorurteile gegenüber Frauen und der LGBTQIA+ Community stigmatisiert ist. Die _FLUX Gaming Maus soll durch Gender-inklusives Design dazu beitragen, stereotype Rollenbilder in der Gaming Welt aufzubrechen und die Community nachhaltig wachsen zu lassen. Sie bietet dabei eine Alternative zu den „typisch männlich“ wirkenden Gaming Produkten auf dem Markt. Durch verschiedene Individualisierungsmöglichkeiten in Funktion und Gestalt kann _FLUX in einem Online-Konfigurator an den persönlichen Spielstil angepasst werden. So vermittelt _FLUX Zugehörigkeit und Repräsentation unabhängig von Geschlecht und bietet Spaß am Gaming für jede:n.
This paper addresses the pixel based recognition of 3D objects with bidirectional associative memories. Computational power and memory requirements for this approach are identified and compared to the performance of current computer architectures by benchmarking different processors. It is shown, that the performance of special purpose hardware, like neurocomputers, is between one and two orders of magnitude higher than the performance of mainstream hardware. On the other hand, the calculation of small neural networks is performed more efficiently on mainstream processors. Based on these results a novel concept is developed, which is tailored for the efficient calculation of bidirectional associative memories. The computational efficiency is further enhanced by the application of algorithms and storage techniques which are matched to characteristics of the application at hand.
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 study analyses the expected utilization of an urban distribution grid under high penetration of photovoltaic and e-mobility with charging infrastructure on a residential level. The grid utilization and the corresponding power flow are evaluated, while varying the control strategies and photovoltaic installed capacity in different scenarios. Four scenarios are used to analyze the impact of e-mobility. The individual mobility demand is modelled based on the largest German studies on mobility “Mobilität in Deutschland”, which is carried out every 5 years. To estimate the ramp-up of photovoltaic generation, a potential analysis of the roof surfaces in the supply area is carried out via an evaluation of an open solar potential study. The photovoltaic feed-in time series is derived individually for each installed system in a resolution of 15 min. The residential consumption is estimated using historical smart meter data, which are collected in London between 2012 and 2014. For a realistic charging demand, each residential household decides daily on the state of charge if their vehicle requires to be charged. The resulting charging time series depends on the underlying behavior scenario. Market prices and mobility demand are therefore used as scenario input parameters for a utility function based on the current state of charge to model individual behavior. The aggregated electricity demand is the starting point of the power flow calculation. The evaluation is carried out for an urban region with approximately 3100 residents. The analysis shows that increased penetration of photovoltaics combined with a flexible and adaptive charging strategy can maximize PV usage and reduce the need for congestion-related intervention by the grid operator by reducing the amount of kWh charged from the grid by 30% which reduces the average price of a charged kWh by 35% to 14 ct/kWh from 21.8 ct/kWh without PV optimization. The resulting grid congestions are managed by implementing an intelligent price or control signal. The analysis took place using data from a real German grid with 10 subgrids. The entire software can be adapted for the analysis of different distribution grids and is publicly available as an open-source software library on GitHub.
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.