Conference Proceeding
Refine
Year of publication
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 (150)
- IfB - Institut für Bioengineering (114)
- Fachbereich Bauingenieurwesen (103)
- ECSM European Center for Sustainable Mobility (59)
- Fachbereich Wirtschaftswissenschaften (54)
Has Fulltext
- no (1413) (remove)
Document Type
- Conference Proceeding (1413) (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)
A research framework for human aspects in the internet of production: an intra-company perspective
(2020)
Digitalization in the production sector aims at transferring concepts and methods from the Internet of Things (IoT) to the industry and is, as a result, currently reshaping the production area. Besides technological progress, changes in work processes and organization are relevant for a successful implementation of the “Internet of Production” (IoP). Focusing on the labor organization and organizational procedures emphasizes to consider intra-company factors such as (user) acceptance, ethical issues, and ergonomics in the context of IoP approaches. In the scope of this paper, a research approach is presented that considers these aspects from an intra-company perspective by conducting studies on the shop floor, control level and management level of companies in the production area. Focused on four central dimensions—governance, organization, capabilities, and interfaces—this contribution presents a research framework that is focused on a systematic integration and consideration of human aspects in the realization of the IoP.
A platform technology for the automated reaction control in magnetizable micro-fluidic droplets
(2014)
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.