Bauhaus University, Weimar

About: Bauhaus University, Weimar is a education organization based out in Weimar, Thüringen, Germany. It is known for research contribution in the topics: Finite element method & Isogeometric analysis. The organization has 1421 authors who have published 2998 publications receiving 104454 citations. The organization is also known as: Bauhaus-Universität Weimar & Hochschule für Architektur und Bauwesen.

Numerical and analytical calculations of the temperature and flow field in the upwind power plant

Abstract: The upwind power plant is an interesting system to generate electrical power from free solar energy. The authors have carried out an analysis to improve the description of the operation mode and efficiency. The pressure drop at the turbine and the mass flow rate have a decisive influence on the efficiency. This can be determined only by coupling of all parts of an upwind power plant. In this study the parts ground, collector, chimney and turbine are modelled together numerically. The basis for all sections is the numerical CFD programme FLUENT. This programme solves the basic equations of the thermal fluid dynamics. Model development and parameter studies particularly arise with this tool. Additional to the calculations using FLUENT a simple model is developed for comparison purposes and parameter studies. The numerical results with FLUENT compare well with the results given by the simple model, therefore, we can use the simple model for parameter studies. The basis for the geometry is the prototype Manzanares. Copyright © 2004 John Wiley & Sons, Ltd.

Immersive Group-to-Group Telepresence

Abstract: We present a novel immersive telepresence system that allows distributed groups of users to meet in a shared virtual 3D world. Our approach is based on two coupled projection-based multi-user setups, each providing multiple users with perspectively correct stereoscopic images. At each site the users and their local interaction space are continuously captured using a cluster of registered depth and color cameras. The captured 3D information is transferred to the respective other location, where the remote participants are virtually reconstructed. We explore the use of these virtual user representations in various interaction scenarios in which local and remote users are face-to-face, side-by-side or decoupled. Initial experiments with distributed user groups indicate the mutual understanding of pointing and tracing gestures independent of whether they were performed by local or remote participants. Our users were excited about the new possibilities of jointly exploring a virtual city, where they relied on a world-in-miniature metaphor for mutual awareness of their respective locations.

Embodied Presence in Virtual Environments

Abstract: Presence, the sense of being in a virtual environment (VE), is analysed in an embodied cognition framework. We propose that VEs are mentally represented as meshed sets of patterns of actions and that presence is experienced when these actions include the perceived possibility to navigate and move the own body in the VE. A factor analyses of survey data shows 3 different presence components: spatial presence, involvement, and judgement of realness. A path analysis shows that spatial presence is mostly determined by sources of meshed patterns of actions: interaction with the VE, understanding of dynamics, and perception of dramatic meaning.

A Survey of Deep Learning Techniques: Application in Wind and Solar Energy Resources

Abstract: Nowadays, learning-based modeling system is adopted to establish an accurate prediction model for renewable energy resources. Computational Intelligence (CI) methods have become significant tools in production and optimization of renewable energies. The complexity of this type of energy lies in its coverage of large volumes of data and variables which have to be analyzed carefully. The present study discusses different types of Deep Learning (DL) algorithms applied in the field of solar and wind energy resources and evaluates their performance through a novel taxonomy. It also presents a comprehensive state-of-the-art of the literature leading to an assessment and performance evaluation of DL techniques as well as a discussion about major challenges and opportunities for comprehensive research. Based on results, differences on accuracy, robustness, precision values as well as the generalization ability are the most common challenges for the employment of DL techniques. In case of big dataset, the performance of DL techniques is significantly higher than that for other CI techniques. However, using and developing hybrid DL techniques with other optimization techniques in order to improve and optimize the structure of the techniques is preferably emphasized. In all cases, hybrid networks have better performance compared with single networks, because hybrid techniques take the advantages of two or more methods for preparing an accurate prediction. It is recommended to use hybrid methods in DL techniques.