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Closed-loop platoon simulation with cooperative intelligent transportation systems based on vehicle-to-X communication (2021)
Max-Arno Meyer ; Christian Granrath ; Günter Feyerl ; Johannes Richenhagen ; Jakob Kaths ; Jakob Andert
Direct plastic structural design under random strength and random load by chance constrained programming (2021)
Ngoc Trinh Tran ; Manfred Staat
Capacitive Field-Effect Biosensor Studying Adsorption of Tobacco Mosaic Virus Particles (2021)
Melanie Jablonski ; Arshak Poghossian ; Robin Severin ; Michael Keusgen ; Christian Wege ; Michael J. Schöning
Selective Campylobacter detection and quantification in poultry: A sensor tool for detecting the cause of a common zoonosis at its source (2021)
Stella Givanoudi ; Peter Cornelis ; Geertrui Rasschaert ; Gideon Wackers ; Heiko Iken ; David Rolka ; Derick Yongabi ; Johan Robbens ; Michael J. Schöning ; Marc Heyndrickx ; Patrick Wagner
Capacitive Field‐Effect Biosensor Applied for the Detection of Acetoin in Alcoholic Beverages and Fermentation Broths (2021)
Melanie Jablonski ; Felix Münstermann ; Jasmina Nork ; Denise Molinnus ; Lukas Muschallik ; Johannes Bongaerts ; Torsten Wagner ; Michael Keusgen ; Petra Siegert ; Michael J. Schöning
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
A maritime harmonic radar search and rescue system using passive and active tags (2021)
Holger Heuermann ; Thomas Harzheim ; Marc Mühmel
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 %.
Ein pazifistisches Kriegerdenkmal der Moderne. Das Totenkreuz von Rudolf Schwarz (2021)
Anke Fissabre
Lignins Isolated via Catalyst-Free Organosolv Pulping from Miscanthus x giganteus, M. sinensis, M. robustus and M. nagara: A Comparative Study (2021)
Michel Bergs ; Yulia Monakhova ; Bernd W. Diehl ; Christopher Konow ; Georg Völkering ; Ralf Pude ; Margit Schulze
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