TY  - CHAP
A1  - Hoegen, Anne von
A1  - Doncker, Rik W. De
A1  - Bragard, Michael
A1  - Hoegen, Svenja von
T1  - Problem-Based Learning in Automation Engineering: Performing a Remote Laboratory Session Serving Various Educational Attainments
T2  - 2021 IEEE Global Engineering Education Conference (EDUCON)
Y1  - 2021
U6  - http://dx.doi.org/10.1109/EDUCON46332.2021.9453925
SP  - 1605
EP  - 1614
ER  - 
TY  - CHAP
A1  - Ferrein, Alexander
A1  - Meeßen, Marcus
A1  - Limpert, Nicolas
A1  - Schiffer, Stefan
ED  - Lepuschitz, Wilfried
T1  - Compiling ROS Schooling Curricula via Contentual Taxonomies
T2  - Robotics in Education
Y1  - 2021
SN  - 978-3-030-67411-3
U6  - http://dx.doi.org/10.1007/978-3-030-67411-3_5
N1  - RiE: International Conference on Robotics in Education (RiE);
Advances in Intelligent Systems and Computing book series (AISC, volume 1316)
SP  - 49
EP  - 60
PB  - Springer
CY  - Cham
ER  - 
TY  - CHAP
A1  - El Moussaoui, Noureddine
A1  - Kassmi, Khalil
A1  - Alexopoulos, Spiros
A1  - Schwarzer, Klemens
A1  - Chayeb, Hamid
A1  - Bachiri, Najib
T1  - Simulation studies on a new innovative design of a hybrid solar distiller MSDH alimented with a thermal and photovoltaic energy
T2  - Materialstoday: Proceedings
Y1  - 2021
U6  - http://dx.doi.org/10.1016/j.matpr.2021.03.115
SN  - 2214-7853
ER  - 
TY  - CHAP
A1  - Schulte, Maximilian
A1  - Eggert, Mathias
T1  - Predicting hourly bitcoin prices based on long short-term memory neural networks
T2  - Proceedings of the International Conference on Wirtschaftsinformatik (WI) 2021
N2  - 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 %.
Y1  - 2021
N1  - 16th International Conference on Wirtschaftsinformatik,
March 2021, Essen, Germany
ER  - 
TY  - CHAP
A1  - Funke, Harald
A1  - Beckmann, Nils
A1  - Keinz, Jan
A1  - Horikawa, Atsushi
T1  - 30 years of dry low NOx micromix combustor research for hydrogen-rich fuels: an overview of past and present activities
T2  - Proceedings of the ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition, September 21–25, 2020, Virtual, Online. Vol.: 4B: Combustion, Fuels, and Emissions
KW  - Micromix
KW  - Hydrogen
KW  - Fuel-flexibility
KW  - NOx
KW  - Emissions
Y1  - 2021
SN  - 978-0-7918-8413-3
U6  - http://dx.doi.org/10.1115/GT2020-16328
N1  - Paper No. GT2020-16328, V04BT04A069
PB  - American Society of Mechanical Engineers (ASME)
ER  - 
TY  - CHAP
A1  - Heuermann, Holger
A1  - Harzheim, Thomas
A1  - Mühmel, Marc
T1  - A maritime harmonic radar search and rescue system using passive and active tags
T2  - 2020 17th European Radar Conference (EuRAD)
KW  - Harmonic Radar
KW  - Rescue System
KW  - Frequency Doubler
KW  - Transponder
KW  - Tag
Y1  - 2021
SN  - 978-2-87487-061-3
U6  - http://dx.doi.org/10.1109/EuRAD48048.2021.00030
N1  - Proceedings of the 17th European Radar Conference, 
13th - 15th January 2021, Utrecht, Netherlands
SP  - 73
EP  - 76
PB  - IEEE
ER  -