TY - CHAP A1 - Nikolovski, Gjorgji A1 - Reke, Michael A1 - Elsen, Ingo A1 - Schiffer, Stefan T1 - Machine learning based 3D object detection for navigation in unstructured environments T2 - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) N2 - In this paper we investigate the use of deep neural networks for 3D object detection in uncommon, unstructured environments such as in an open-pit mine. While neural nets are frequently used for object detection in regular autonomous driving applications, more unusual driving scenarios aside street traffic pose additional challenges. For one, the collection of appropriate data sets to train the networks is an issue. For another, testing the performance of trained networks often requires tailored integration with the particular domain as well. While there exist different solutions for these problems in regular autonomous driving, there are only very few approaches that work for special domains just as well. We address both the challenges above in this work. First, we discuss two possible ways of acquiring data for training and evaluation. That is, we evaluate a semi-automated annotation of recorded LIDAR data and we examine synthetic data generation. Using these datasets we train and test different deep neural network for the task of object detection. Second, we propose a possible integration of a ROS2 detector module for an autonomous driving platform. Finally, we present the performance of three state-of-the-art deep neural networks in the domain of 3D object detection on a synthetic dataset and a smaller one containing a characteristic object from an open-pit mine. KW - 3D object detection KW - LiDAR KW - autonomous driving KW - Deep learning KW - Three-dimensional displays Y1 - 2021 SN - 978-1-6654-7921-9 U6 - http://dx.doi.org/10.1109/IVWorkshops54471.2021.9669218 N1 - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops), 11-17 July 2021, Nagoya, Japan. SP - 236 EP - 242 PB - IEEE ER - TY - CHAP A1 - Fiedler, Gerda A1 - Gottschlich-Müller, Birgit A1 - Melcher, Karin ED - Liu-Henke, Xiaobo ED - Durak, Umut T1 - Online-Prüfungen mit STACK Aufgaben T2 - Tagungsband ASIM Workshop STS/GMMS/EDU 2021 N2 - Wir stellen hier exemplarisch STACK Aufgaben vor, die frei von der Problematik sind, welche sich durch diverse Kommunikationswege und (webbasierte) Computer Algebra Systeme (CAS) ergibt. Daher sind sie insbesondere für eine Open-Book Online Prüfung geeignet, da eine faire Prüfungssituation gewährleistet werden kann. Y1 - 2021 SN - 978-3-901608-69-8 U6 - http://dx.doi.org/10.11128/arep.45 N1 - Virtueller Workshop, ASIM STS/GMMS & EDU 2021, 11.-12. März 2021 PB - ARGESIM Verlag CY - Wien ER - TY - CHAP A1 - Schopen, Oliver A1 - Kemper, Hans A1 - Esch, Thomas T1 - Development of a comparison methodology and evaluation matrix for electrically driven compressors in ICE and FC T2 - Proceedings of the 1st UNITED – Southeast Asia Automotive Interest Group (SAIG) International Conference N2 - In addition to electromobility and alternative drive systems, a focus is set on electrically driven compressors (EDC), with a high potential for increasing the efficiency of internal combustion engines (ICE) and fuel cells [01]. The primary objective is to increase the ICE torque, provided independently of the ICE speed by compressing the intake air and consequently the ICE filling level supported by the compressor. For operation independent from the ICE speed, the EDC compressor is decoupled from the turbine by using an electric compressor motor (CM) instead of the turbine. ICE performances can be increased by the use of EDC where individual compressor parameters are adapted to the respective application area [02] [03]. This task contains great challenges, increased by demands with regard to pollutant reduction while maintaining constant performance and reduced fuel consumption. The FH-Aachen is equipped with an EDC test bench which enables EDC-investigations in various configurations and operating modes. Characteristic properties of different compressors can be determined, which build the basis for a comparison methodology. Subject of this project is the development of a comparison methodology for EDC with an associated evaluation method and a defined overall evaluation method. For the application of this comparison methodology, corresponding series of measurements are carried out on the EDC test bench using an appropriate test device. KW - electro mobility KW - fuel cell KW - internal combustion engine KW - electrically driven compressors Y1 - 2021 SN - 978-3-902103-94-9 N1 - 1st UNITED-SAIG International Conference, 21-22 APR 2021, Chulalongkorn University, Thailand SP - 45 EP - 46 PB - FH Joanneum CY - Graz ER - TY - CHAP A1 - Dey, Thomas A1 - Elsen, Ingo A1 - Ferrein, Alexander A1 - Frauenrath, Tobias A1 - Reke, Michael A1 - Schiffer, Stefan ED - Makedon, Fillia T1 - CO2 Meter: a do-it-yourself carbon dioxide measuring device for the classroom T2 - PETRA 2021: The 14th PErvasive Technologies Related to Assistive Environments Conference N2 - In this paper we report on CO2 Meter, a do-it-yourself carbon dioxide measuring device for the classroom. Part of the current measures for dealing with the SARS-CoV-2 pandemic is proper ventilation in indoor settings. This is especially important in schools with students coming back to the classroom even with high incidents rates. Static ventilation patterns do not consider the individual situation for a particular class. Influencing factors like the type of activity, the physical structure or the room occupancy are not incorporated. Also, existing devices are rather expensive and often provide only limited information and only locally without any networking. This leaves the potential of analysing the situation across different settings untapped. Carbon dioxide level can be used as an indicator of air quality, in general, and of aerosol load in particular. Since, according to the latest findings, SARS-CoV-2 can be transmitted primarily in the form of aerosols, carbon dioxide may be used as a proxy for the risk of a virus infection. Hence, schools could improve the indoor air quality and potentially reduce the infection risk if they actually had measuring devices available in the classroom. Our device supports schools in ventilation and it allows for collecting data over the Internet to enable a detailed data analysis and model generation. First deployments in schools at different levels were received very positively. A pilot installation with a larger data collection and analysis is underway. KW - embedded hardware KW - sensor networks KW - information systems KW - education KW - do-it-yourself Y1 - 2021 SN - 9781450387927 U6 - http://dx.doi.org/10.1145/3453892.3462697 N1 - PETRA '21: The 14th PErvasive Technologies Related to Assistive Environments Conference Corfu Greece 29 June 2021- 2 July 2021 SP - 292 EP - 299 PB - Association for Computing Machinery CY - New York ER - TY - JOUR A1 - Pfaff, Raphael A1 - Gidaszewski, Lars A1 - Schmidt, Bernd T1 - Berücksichtigung von No Fault Found im Diagnose- und Instandhaltungssystem von Schienenfahrzeugen JF - ETR - Eisenbahntechnische Rundschau N2 - Intermittierende und nicht reproduzierbare Fehler, auch als No Fault Found bezeichnet, treten in praktisch allen Bereichen auf und sorgen für hohe Kosten. Diese sind häufig auf unpräzise Fehlerbeschreibungen zurückzuführen. Im vorliegenden Beitrag werden Anpassungen der Vorgehensweise bei der Entwicklung und Anpassungen des Diagnosesystems vorgeschlagen. Y1 - 2020 SN - 0013-2845 IS - 5 SP - 56 EP - 59 PB - DVV Media Group CY - Hamburg ER - TY - JOUR A1 - Khayyam, Hamid A1 - Jamali, Ali A1 - Bab-Hadiashar, Alireza A1 - Esch, Thomas A1 - Ramakrishna, Seeram A1 - Jalili, Mahdi A1 - Naebe, Minoo T1 - A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modeling with Application in Industry 4.0 JF - IEEE Access N2 - To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements. Y1 - 2020 U6 - http://dx.doi.org/10.1109/ACCESS.2020.2999898 SN - 2169-3536 VL - 8 IS - Art. 9108222 SP - 111381 EP - 111393 PB - IEEE CY - New York, NY ER - TY - JOUR A1 - Hoeveler, B. A1 - Bauknecht, André A1 - Wolf, C. Christian A1 - Janser, Frank T1 - Wind-Tunnel Study of a Wing-Embedded Lifting Fan Remaining Open in Cruise Flight JF - Journal of Aircraft N2 - It is investigated whether a nonrotating lifting fan remaining uncovered during cruise flight, as opposed to being covered by a shutter system, can be realized with limited additional drag and loss of lift during cruise flight. A wind-tunnel study of a wing-embedded lifting fan has been conducted at the Side Wind Test Facility Göttingen of DLR, German Aerospace Center in Göttingen using force, pressure, and stereoscopic particle image velocimetry techniques. The study showed that a step on the lower side of the wing in front of the lifting fan duct increases the lift-to-drag ratio of the whole model by up to 25% for all positive angles of attack. Different sizes and inclinations of the step had limited influence on the surface pressure distribution. The data indicate that these parameters can be optimized to maximize the lift-to-drag ratio. A doubling of the curvature radius of the lifting fan duct inlet lip on the upper side of the wing affected the lift-to-drag ratio by less than 1%. The lifting fan duct inlet curvature can therefore be optimized to maximize the vertical fan thrust of the rotating lifting fan during hovering without affecting the cruise flight performance with a nonrotating fan. Y1 - 2020 U6 - http://dx.doi.org/10.2514/1.C035422 SN - 1533-3868 VL - 57 IS - 4 PB - AIAA CY - Reston, Va. ER - TY - RPRT A1 - Thoma, Andreas A1 - Laarmann, Lukas A1 - Merkens, Torsten A1 - Franzke, Till A1 - Möhren, Felix A1 - Buttermann, Lilly A1 - van der Weem, Dirk A1 - Fischer, Maximilian A1 - Misch, Philipp A1 - Böhme, Mirijam A1 - Röth, Thilo A1 - Hebel, Christoph A1 - Ritz, Thomas A1 - Franke, Marina A1 - Braun, Carsten T1 - Entwicklung eines intermodalen Mobilitätskonzeptes für die Pilotregion NRW/Rhein-Maas Euregio und Schaffung voller Kundenakzeptanz durch Transfer von Standards aus dem PKW-Bereich auf ein Flugtaxi : Schlussbericht : Projektakronym: SkyCab (Kategorie B) : Laufzeit in Monaten: 6 : Hauptthema: Kategorie B: Innovative Ideen mit Bezug zu UAS/Flugtaxis Y1 - 2020 N1 - Förderkennzeichen BMVI 45UAS1027A-F PB - FH Aachen CY - Aachen ER - TY - JOUR A1 - Khayyam, Hamid A1 - Jamali, Ali A1 - Bab-Hadiashar, Alireza A1 - Esch, Thomas A1 - Ramakrishna, Seeram A1 - Jalil, Mahdi A1 - Naebe, Minoo T1 - A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modelling with Application in Industry 4.0 JF - IEEE Access N2 - To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements. Y1 - 2020 SN - 2169-3536 U6 - http://dx.doi.org/10.1109/ACCESS.2020.2999898 SP - 1 EP - 12 PB - IEEE CY - New York, NY ER - TY - JOUR A1 - Kreyer, Jörg A1 - Müller, Marvin A1 - Esch, Thomas T1 - A Calculation Methodology for Predicting Exhaust Mass Flows and Exhaust Temperature Profiles for Heavy-Duty Vehicles JF - SAE International Journal of Commercial Vehicles N2 - The predictive control of commercial vehicle energy management systems, such as vehicle thermal management or waste heat recovery (WHR) systems, are discussed on the basis of information sources from the field of environment recognition and in combination with the determination of the vehicle system condition. In this article, a mathematical method for predicting the exhaust gas mass flow and the exhaust gas temperature is presented based on driving data of a heavy-duty vehicle. The prediction refers to the conditions of the exhaust gas at the inlet of the exhaust gas recirculation (EGR) cooler and at the outlet of the exhaust gas aftertreatment system (EAT). The heavy-duty vehicle was operated on the motorway to investigate the characteristic operational profile. In addition to the use of road gradient profile data, an evaluation of the continuously recorded distance signal, which represents the distance between the test vehicle and the road user ahead, is included in the prediction model. Using a Fourier analysis, the trajectory of the vehicle speed is determined for a defined prediction horizon. To verify the method, a holistic simulation model consisting of several hierarchically structured submodels has been developed. A map-based submodel of a combustion engine is used to determine the EGR and EAT exhaust gas mass flows and exhaust gas temperature profiles. All simulation results are validated on the basis of the recorded vehicle and environmental data. Deviations from the predicted values are analyzed and discussed. Y1 - 2020 U6 - http://dx.doi.org/10.4271/02-13-02-0009 SN - 1946-3928 VL - 13 IS - 2 SP - 129 EP - 143 PB - SAE International CY - Warrendale, Pa. ER -