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 movements 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 - Conference Proceedings Turbo Expo: Power for Land, Sea and Air, Volume 4B: Combustion, Fuels, and Emissions N2 - The paper presents an overview of the past and present of low-emission combustor research with hydrogen-rich fuels at Aachen University of Applied Sciences. In 1990, AcUAS started developing the Dry-Low-NOx Micromix combustion technology. Micromix reduces NOx emissions using jet-in-crossflow mixing of multiple miniaturized fuel jets and combustor air with an inherent safety against flashback. At first, pure hydrogen as fuel was investigated with lab-scale applications. Later, Micromix prototypes were developed for the use in an industrial gas turbine Honeywell/Garrett GTCP-36-300, proving low NOx characteristics during real gas turbine operation, accompanied by the successful definition of safety laws and control system modifications. Further, the Micromix was optimized for the use in annular and can combustors as well as for fuel-flexibility with hydrogen-methane-mixtures and hydrogen-rich syngas qualities by means of extensive experimental and numerical simulations. In 2020, the latest Micromix application will be demonstrated in a commercial 2 MW-class gas turbine can-combustor with full-scale engine operation. The paper discusses the advances in Micromix research over the last three decades. KW - Micromix KW - Hydrogen KW - Fuel-flexibility KW - NOx KW - Emissions Y1 - 2021 SN - 978-0-7918-8413-3 U6 - https://doi.org/10.1115/GT2020-16328 N1 - ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition September 21–25, 2020, Virtual, Online N1 - Paper No. GT2020-16328, V04BT04A069 PB - ASME CY - New York, NY ER - TY - JOUR A1 - Ayed, Anis Haj A1 - Kusterer, Karsten A1 - Funke, Harald A1 - Keinz, Jan A1 - Bohn, D. T1 - CFD based exploration of the dry-low-NOx hydrogen micromix combustion technology at increased energy densities JF - Propulsion and Power Research KW - Micromix combustion KW - Hydrogen gas turbine KW - Hydrogen combustion KW - High hydrogen combustion KW - Dry-low-NOx (DLN) combustion Y1 - 2017 SN - 2212-540X U6 - https://doi.org/10.1016/j.jppr.2017.01.005 VL - 6 IS - 1 SP - 15 EP - 24 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Ayed, Anis Haj A1 - Striegan, Constantin J. D. A1 - Kusterer, Karsten A1 - Funke, Harald A1 - Kazari, M. A1 - Horikawa, Atsushi A1 - Okada, Kunio T1 - Automated design space exploration of the hydrogen fueled "Micromix" combustor technology N2 - 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. Y1 - 2017 N1 - Proceedings of the 1st Global Power and Propulsion Forum GPPF 2017, Jan 16-18, 2017, Zurich, Switzerland SP - 1 EP - 8 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) N2 - This article introduces a new maritime search and rescue system based on S-band illumination harmonic radar (HR). Passive and active tags have been developed and tested attached to life jackets and a rescue boat. This system was able to detect and range the active tags up to a range of 5800 m in tests on the Baltic Sea with an antenna input power of only 100 W. All electronic GHz components of the system, excluding the S-band power amplifier, were custom developed for this purpose. Special attention is given to the performance and conceptual differences between passive and active tags used in the system and integration with a maritime X-band navigation radar is demonstrated. KW - Harmonic Radar KW - Rescue System KW - Frequency Doubler KW - Transponder KW - Tag Y1 - 2021 SN - 978-2-87487-061-3 U6 - https://doi.org/10.1109/EuRAD48048.2021.00030 N1 - 17th European Radar Conference, 13th - 15th January 2021, Utrecht, Netherlands SP - 73 EP - 76 PB - IEEE CY - New York, NY ER - TY - JOUR A1 - Engemann, Heiko A1 - Du, Shengzhi A1 - Kallweit, Stephan A1 - Cönen, Patrick A1 - Dawar, Harshal T1 - OMNIVIL - an autonomous mobile manipulator for flexible production JF - Sensors Y1 - 2020 SN - 1424-8220 U6 - https://doi.org/10.3390/s20247249 N1 - Special issue: Sensor Networks Applications in Robotics and Mobile Systems VL - 20 IS - 24, art. no. 7249 SP - 1 EP - 30 PB - MDPI CY - Basel ER - TY - CHAP A1 - Mistler, M. A1 - Butenweg, Christoph A1 - Anthoine, A. T1 - Evaluation of the failure criterion for masonry by homogenisation T2 - Proceedings of the Seventh International Conference on Computational Structures Technology : [Lisbon, Portugal, 7 - 9 September 2004] / ed. by B. H. V. Topping and C.A. Mota Soares Y1 - 2004 SN - 0-948749-95-4 U6 - https://doi.org/10.4203/ccp.79.201 PB - Civil-Comp Press CY - Stirling ER - TY - CHAP A1 - Engemann, Heiko A1 - Du, Shengzhi A1 - Kallweit, Stephan A1 - Ning, Chuanfang A1 - Anwar, Saqib T1 - AutoSynPose: Automatic Generation of Synthetic Datasets for 6D Object Pose Estimation T2 - Machine Learning and Artificial Intelligence. Proceedings of MLIS 2020 N2 - We present an automated pipeline for the generation of synthetic datasets for six-dimension (6D) object pose estimation. Therefore, a completely automated generation process based on predefined settings is developed, which enables the user to create large datasets with a minimum of interaction and which is feasible for applications with a high object variance. The pipeline is based on the Unreal 4 (UE4) game engine and provides a high variation for domain randomization, such as object appearance, ambient lighting, camera-object transformation and distractor density. In addition to the object pose and bounding box, the metadata includes all randomization parameters, which enables further studies on randomization parameter tuning. The developed workflow is adaptable to other 3D objects and UE4 environments. An exemplary dataset is provided including five objects of the Yale-CMU-Berkeley (YCB) object set. The datasets consist of 6 million subsegments using 97 rendering locations in 12 different UE4 environments. Each dataset subsegment includes one RGB image, one depth image and one class segmentation image at pixel-level. Y1 - 2020 SN - 978-1-64368-137-5 U6 - https://doi.org/10.3233/FAIA200770 N1 - Frontiers in Artificial Intelligence and Applications. Vol 332 SP - 89 EP - 97 PB - IOS Press CY - Amsterdam ER - TY - THES A1 - Bronder, Thomas T1 - Label-free detection of tuberculosis DNA with capacitive field-effect biosensors Y1 - 2020 U6 - https://doi.org/10.17192/z2021.0056 N1 - Dissertation, Universität, Marburg 2020 PB - Philipps-Universität Marburg CY - Marburg ER - TY - JOUR A1 - Givanoudi, Stella A1 - Cornelis, Peter A1 - Rasschaert, Geertrui A1 - Wackers, Gideon A1 - Iken, Heiko A1 - Rolka, David A1 - Yongabi, Derick A1 - Robbens, Johan A1 - Schöning, Michael Josef A1 - Heyndrickx, Marc A1 - Wagner, Patrick T1 - Selective Campylobacter detection and quantification in poultry: A sensor tool for detecting the cause of a common zoonosis at its source JF - Sensors and Actuators B: Chemical Y1 - 2021 U6 - https://doi.org/10.1016/j.snb.2021.129484 SN - 0925-4005 IS - In Press, Journal Pre-proof SP - Article 129484 PB - Elsevier CY - Amsterdam ER -