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Predicting hourly bitcoin prices based on long short-term memory neural networks (2021)
Maximilian Schulte ; Mathias Eggert
Bitcoin is a cryptocurrency and is considered a high-risk asset class whose price changes are difficult to predict. Current research focusses on daily price movements with a limited number of predictors. The paper at hand aims at identifying measurable indicators for Bitcoin price movement s and the development of a suitable forecasting model for hourly changes. The paper provides three research contributions. First, a set of significant indicators for predicting the Bitcoin price is identified. Second, the results of a trained Long Short-term Memory (LSTM) neural network that predicts price changes on an hourly basis is presented and compared with other algorithms. Third, the results foster discussions of the applicability of neural nets for stock price predictions. In total, 47 input features for a period of over 10 months could be retrieved to train a neural net that predicts the Bitcoin price movements with an error rate of 3.52 %.
Auslegungstool zur energieeffizienten Kühlung von Gebäuden (2020)
Andreas Dümmler ; Kerstin Oetringer ; Joachim Göttsche
Neues Modell zur 1D-Simulation der indirekten Verdunstungskühlung (2020)
Kerstin Oetringer ; Andreas Dümmler ; Joachim Göttsche
30 years of dry low NOx micromix combustor research for hydrogen-rich fuels: an overview of past and present activities (2021)
Harald Funke ; Nils Beckmann ; Jan Keinz ; Atsushi Horikawa
Automated design space exploration of the hydrogen fueled "Micromix" combustor technology (2017)
Anis Haj Ayed ; Constantin J. D. Striegan ; Karsten Kusterer ; Harald Funke ; M. Kazari ; Atsushi Horikawa ; Kunio Okada
Combined with the use of renewable energy sources for its production, Hydrogen represents a possible alternative gas turbine fuel for future low emission power generation. Due to its different physical properties compared to other fuels such as natural gas, well established gas turbine combustion systems cannot be directly applied for Dry Low NOx (DLN) Hydrogen combustion. This makes the development of new combustion technologies an essential and challenging task for the future of hydrogen fueled gas turbines. The newly developed and successfully tested “DLN Micromix” combustion technology offers a great potential to burn hydrogen in gas turbines at very low NOx emissions. Aiming to further develop an existing burner design in terms of increased energy density, a redesign is required in order to stabilise the flames at higher mass flows and to maintain low emission levels. For this purpose, a systematic design exploration has been carried out with the support of CFD and optimisation tools to identify the interactions of geometrical and design parameters on the combustor performance. Aerodynamic effects as well as flame and emission formation are observed and understood time- and cost-efficiently. Correlations between single geometric values, the pressure drop of the burner and NOx production have been identified as a result. This numeric methodology helps to reduce the effort of manufacturing and testing to few designs for single validation campaigns, in order to confirm the flame stability and NOx emissions in a wider operating condition field.
A maritime harmonic radar search and rescue system using passive and active tags (2021)
Holger Heuermann ; Thomas Harzheim ; Marc Mühmel
Evaluation of the failure criterion for masonry by homogenisation (2004)
M. Mistler ; Christoph Butenweg ; A. Anthoine
Modeling and upscaling of a pilot bayonettube reactor for indirect solar mixed methane reforming (2020)
Carlos Rendon ; Christian Schwager ; Mona Ghiasi ; Pascal Schmitz ; Fakhri Bohang ; Ricardo Alexander Chico Caminos ; Cristiano Teixeira Boura ; Ulf Herrmann
Operational experience and behaviour of a parabolic trough collector system with concrete thermal energy storage for process steam generation in Cyprus (2020)
Johannes Sattler ; Ricardo Alexander Chico Caminos ; Nicolas Ürlings ; Siddharth Dutta ; Victor Ruiz ; Soteris Kalogirou ; Panayiotis Ktistis ; Rafaela Agathokleous ; Christian Jung ; Spiros Alexopoulos ; Vikrama Nagababu Atti ; Cristiano Teixeira Boura ; Ulf Herrmann
Gamified Virtual Reality Training Environment for the Manufacturing Industry (2020)
Jessica Ulmer ; Sebastian Braun ; Chi-Tsun Cheng ; Steve Dowey ; Jörg Wollert
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