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- tensile membrane structure, lightweight structure, parametric design, visual programming, interoperability, BIM, IFC, RF-COM, Grasshopper3D, Rhinoceros, Revit, Allplan (1)
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Institute
- Fakultät Architektur und Gestaltung (41)
- Fakultät Bauingenieurwesen (104)
- Fakultät Elektrotechnik und Informationstechnik (34)
- Fakultät Informatik (121)
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- Fakultät Wirtschafts-, Kultur- und Rechtswissenschaften (106)
- Institut für Angewandte Forschung - IAF (115)
- Institut für Naturwissenschaften und Mathematik - INM (3)
- Institut für Optische Systeme - IOS (39)
- Institut für Strategische Innovation und Technologiemanagement - IST (60)
Side Channel Attack Resistance of the Elliptic Curve Point Multiplication using Gaussian Integers
(2020)
Elliptic curve cryptography is a cornerstone of embedded security. However, hardware implementations of the elliptic curve point multiplication are prone to side channel attacks. In this work, we present a new key expansion algorithm which improves the resistance against timing and simple power analysis attacks. Furthermore, we consider a new concept for calculating the point multiplication, where the points of the curve are represented as Gaussian integers. Gaussian integers are subset of the complex numbers, such that the real and imaginary parts are integers. Since Gaussian integer fields are isomorphic to prime fields, this concept is suitable for many elliptic curves. Representing the key by a Gaussian integer expansion is beneficial to reduce the computational complexity and the memory requirements of a secure hardware implementation.
Schreiben im Studium
(2020)
Sollte das wissenschaftliche Schreiben als allgemeine Wissenschaftssprache vermittelt werden oder sollten die sprachlichen Anforderungen in den jeweiligen Disziplinen berücksichtigt werden? Die Antwort auf diese Frage hängt auch davon ab, wie stark sich die Wissenschaftssprachen unterscheiden. In der vorliegenden Studie wurden wissenschaftssprachliche Besonderheiten anhand zweier Korpora untersucht, die deutschsprachige Dissertationen der Fächer Betriebswirtschaftslehre (BWL) bzw. Maschinenbau (MB) umfassten und in denen der Gebrauch von Mehrworteinheiten (z.B. im vergleich zu den) verglichen wurde. Die Untersuchung verdeutlicht den unterschiedlichen Gebrauch der Mehrworteinheiten zwischen den Disziplinen: Nur vierzehn der 50 häufigsten Mehrworteinheiten wurden in beiden Korpora genutzt. Die Ergebnisse werden mit Blick auf die Vermittlung des wissenschaftlichen Schreibens erörtert. Es werden Überlegungen zur Weiterentwicklung der Methode angestellt und es wird diskutiert, wie die Korpuslinguistik die Schreibvermittlung im Studium unterstützen könnte.
Ein Beitrag zum Beobachterentwurf und zur sensorlosen Folgeregelung translatorischer Magnetaktoren
(2020)
Pascal Laube presents machine learning approaches for three key problems of reverse engineering of defective structured surfaces: parametrization of curves and surfaces, geometric primitive classification and inpainting of high-resolution textures. The proposed methods aim to improve the reconstruction quality while further automating the process. The contributions demonstrate that machine learning can be a viable part of the CAD reverse engineering pipeline.
Multi-Dimensional Connectionist Classification is amethod for weakly supervised training of Deep Neural Networksfor segmentation-free multi-line offline handwriting recognition.MDCC applies Conditional Random Fields as an alignmentfunction for this task. We discuss the structure and patterns ofhandwritten text that can be used for building a CRF. Since CRFsare cyclic graphical models, we have to resort to approximateinference when calculating the alignment of multi-line text duringtraining, here in the form of Loopy Belief Propagation. This workconcludes with experimental results for transcribing small multi-line samples from the IAM Offline Handwriting DB which showthat MDCC is a competitive methodology.
Forecasting is crucial for both system planning and operations in the energy sector. With increasing penetration of renewable energy sources, increasing fluctuations in the power generation need to be taken into account. Probabilistic load forecasting is a young, but emerging research topic focusing on the prediction of future uncertainties. However, the majority of publications so far focus on techniques like quantile regression, ensemble, or scenario-based methods, which generate discrete quantiles or sets of possible load curves. The conditioned probability distribution remains unknown and can only be estimated when the output is post-processed using a statistical method like kernel density estimation.
Instead, the proposed probabilistic deep learning model uses a cascade of transformation functions, known as normalizing flow, to model the conditioned density function from a smart meter dataset containing electricity demand information for over 4,000 buildings in Ireland. Since the whole probability density function is tractable, the parameters of the model can be obtained by minimizing the negative loglikelihood through the state of the art gradient descent. This leads to the model with the best representation of the data distribution.
Two different deep learning models have been compared, a simple three-layer fully connected neural network and a more advanced convolutional neural network for sequential data processing inspired by the WaveNet architecture. These models have been used to parametrize three different probabilistic models, a simple normal distribution, a Gaussian mixture model, and the normalizing flow model. The prediction horizon is set to one day with a resolution of 30 minutes, hence the models predict 48 conditioned probability distributions.
The normalizing flow model outperforms the two other variants for both architectures and proves its ability to capture the complex structures and dependencies causing the variations in the data. Understanding the stochastic nature of the task in such detail makes the methodology applicable for other use cases apart from forecasting. It is shown how it can be used to detect anomalies in the power grid or generate synthetic scenarios for grid planning.
In order to elaborate inflation and deflation tendencies due to the COVID-19 pandemic and how they are tried to be actively influenced, this paper compares news regarding the measurements of central banks in Europe, USA and Japan. Factors affecting inflation are defined in conjunction with the typical measurements of central banks and conclusions are drawn in respect to differences of the most recent correcting behavior. The paper is concluded by discussing how price levels might develop during and after the crisis.
Production and marketing of cereal grains are some of the main activities in developing countries to ensure food security. However, the food gap is complicated further by high postharvest loss of grains during storage. This study aimed to compare low‐cost modified‐atmosphere hermetic storage structures with traditional practice to minimize quantitative and qualitative losses of grains during storage. The study was conducted in two phases: in the first phase, seven hermetic storage structures with or without smoke infusion were compared, and one selected structure was further validated at scaled‐up capacity in the second phase.
Pop-up Workshopreihe
(2020)
Traditional Western philosophy, cognitive science and traditional HCI frameworks approach the term digital and its implications with an implicit dualism (nature/cul-ture, theory /practice, body/mind, human/machine). What lies between is a feature of our postmodern times, in which different states, conditions or positions merge and co-exist in a new, hybrid reality, a “continuous beta” (Mühlenbeck & Skibicki, 2007) version of becoming .Post-digitality involves the physical dimensions of spatio-temporal engagements. This new ontological paradigm reconceptualizes digital technology through the ex-perience of the human body and its senses, thus emphasizing form-taking, situation-al engagement and practice rather than symbolic, disembodied rationality. This rais-es two questions in particular: how to encourage curiosity, playfulness, serendipity, emergence, discourse and collectivity? How to construct working methods without foregrounding and dividing the subject into an individual that already takes posi-tion? This paper briefly outlines the rhizomatic framework that I developed within my PhD research. This attempts to overcome two prevailing tendencies: first, the one-sided view of scientific approaches to knowledge acquisition and the pure-ly application-oriented handling of materials, technologies and machines; second, the distanced perception of the world. In contrast, my work involves project-driven alchemic curiosity and doing research through artistic design practice. This means thinking through materials, technologies and machinic interactions. Now, at the end of this PhD journey, 10 interdisciplinary projects have emerged from this ontological queer-paradigm that is post-digital–crafting 4.0. Below I illustrate this approach and its outcomes.
Der digitale Seilüberwacher
(2020)
In beispielhafter Zusammenarbeit zwischen Industrie (Geobrugg AG, Romanshorn/Schweiz) und Wissenschaft (WITg Institut für Werkstoffsystemtechnik Thurgau an der Hochschule Konstanz, Tägerwilen/Schweiz) wurde mit Unterstützung der Schweizer staatlichen Innovationsförderung (KTI, hee Innosuisse) ein neues Werkstoff- und Fertigungskonzept für den Bau von Fischzuchtnetzen aus hochfesten nichtrostenden Stahldrähten entwickelt.
Diese Entwicklung wurde 2019 von Swiss Inox, der Schweizer Innovationspreis Prix Inox ausgezeichnet.
A residual neural network was adapted and applied to the Physionet/Computing data in Cardiology Challenge 2020 to detect 24 different classes of cardiac abnormalities from 12-lead. Additive Gaussian noise, signal shifting, and the classification of signal sections of different lengths were applied to prevent the network from overfitting and facilitating generalization. Due to the use of a global pooling layer after the feature extractor, the network is independent of the signal’s length. On the hidden test set of the challenge, the model achieved a validation score of 0.656 and a full test score of 0.27, placing us 15th out of 41 officially ranked teams (Team name: UC_Lab_Kn). These results show the potential of deep neural networks for ap- plication to raw data and a complex multi-class multi-label classification problem, even if the training data is from di- verse datasets and of differing lengths.
Botswana, a new construction project – the Maun Science Park - is to be built with a focus on sustainability and to create a new living space for the rapidly growing population in Africa. The project will be a blueprint for future projects in Africain terms of progress, technology and sustainability. This thesis will deal with its financial framework and will serve as a basis for the development of ways and means of financing such projects.