58 research outputs found
Improving Teaching in Engineering Mechanics with Augmented Reality: Concept and Implementation of an E-Learning Application
No full textEngineering mechanics is a basic subject of many engineering study programmes. It teaches the systemic approach to describe and calculate a mechanical system in an abstract fashion. However, the process of abstraction itself plays a rather subordinate role. For a signifi cant number of students, the systemic way of thinking in engineering mechanics is diffi cult to grasp. This paper is trying to address this issue by conceptualising an augmented reality e-learning application for smartphones and then putting it to practise via the game engine Unity and the framework ARFoundation. The application off ers students a low-threshold and interactive entry into the subject fi elds ‘trusses’ and ‘stress resultants’. Initially, the user creates a two-dimensional static model from given components. Afterwards, there are two diff erent ways of calculation: On the one hand, a real-time calculation within the game engine and, on the other hand, an external calculation using the structural analysis software SOFiSTiK and an API provided by a parallel research project. The preceding concept includes a problem analysis where the structural and content-related challenges of teaching engineering mechanics are discussed. Afterwards, fi ndings from expert interviews with students, teachers, tutors and e-learning experts are used to develop the application scenarios. The application aims to overcome the rigid boundaries between the reality and abstraction level in engineering mechanics. A fi rst evaluation of the application showed that augmented reality is suitable for the representation and working on questions of engineering mechanics in a real environment. The prerequisites for its widespread use are given, since the application only requires an augmented reality-capable smartphone and an Internet connection
Gertrude Berliner Collection 1881-1990
No full textThis mostly unorganized collection holds manuscripts, drawings and correspondence, as well as some vital records, photographs and published materials pertaining to the author Gertrude Berliner and her family in Vienna, Austria, and in Hanover, Germany. Most of her writings deal with family and emigration, personal recollections and reminiscences of childhood and adulthood.The author Gertrude Berliner was born 1909 in Vienna, Austria, into a Jewish family originating from Hannover, Germany. After "Anschluss" in 1938, she left for England and later immigrated to the USA.Photographs removed to Photograph CollectionAsch, Schalom ; Rhina ; Bruchsteiner family ; Tyndall, Paul Camille ; Mosler, Ilse ; Brenholz, Johanna ; Brings, Helenedigitize
Curtailing Political Parties Efficiently: The Policy Decision to Abolish Party Chapters in South Korea
No full textThis article deals with the policy decision process leading to the abolishment of party chapters in South Korea. Why and how the 'party on the ground' came to be banned by law twenty years after formal democratization is a puzzling question, since the institution of party chapters is key to achieving the central task that political parties have of translating the political will of the people into actual policy, and because parties are (therefore) constitutionally required to have the 'necessary means' to do so. While the justification for the abolishment can obviously be traced back to corruption and abuse of power at the election-district level, a systematic analysis of the decision-making process has been largely neglected in academic literature. The author of this article, however, has scrutinized the policy decision from a long-term perspective, doing so by way of discourse analysis in order to obtain a grounded understanding of the dynamics behind it and to provide insights for further theoretical inquiry and possible practical application
Incorporating sufficient physical information into artificial neural networks: a guaranteed improvement via physics-based Rao-Blackwellization
No full textThe concept of Rao-Blackwellization is employed to improve predictions of
artificial neural networks by physical information. The error norm and the
proof of improvement are transferred from the original statistical concept to a
deterministic one, using sufficient information on physics-based conditions.
The proposed strategy is applied to material modeling and illustrated by
examples of the identification of a yield function, elasto-plastic steel
simulations, the identification of driving forces for quasi-brittle damage and
rubber experiments. Sufficient physical information is employed, e.g., in the
form of invariants, parameters of a minimization problem, dimensional analysis,
isotropy and differentiability. It is proven how intuitive accretion of
information can yield improvement if it is physically sufficient, but also how
insufficient or superfluous information can cause impairment. Opportunities for
the improvement of artificial neural networks are explored in terms of the
training data set, the networks' structure and output filters. Even crude
initial predictions are remarkably improved by reducing noise, overfitting and
data requirements
Optimizing artificial neural networks for mechanical problems by physics-based Rao-Blackwellization: example of a hyperelastic microsphere model
No full textThe Rao-Blackwell scheme provides an algorithm on how to implement sufficient information into statistical models and is adopted here to deterministic material modeling. Even crude initial predictions are improved significantly by Rao-Blackwellization, which is proven by an error inequality. This is first illustrated by an analytical example of hyperelasticity utilizing knowledge on principal stretches. Rao-Blackwellization improves a 1-d uniaxial strain-energy relation into a 3-d relation that resembles the classical micro-sphere approach. The presented scheme is moreover ideal for data-based approaches, because it supplements existing predictions with additional physical information. A second example hence illustrates the application of Rao-Blackwellization to an artificial neural network to improve its prediction on load paths, which were absent in the original training process
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