@article{RoethPielenWolffetal.2018, author = {R{\"o}th, Thilo and Pielen, Michael and Wolff, Klaus and L{\"u}diger, Thomas}, title = {Urbane Fahrzeugkonzepte f{\"u}r die Shared Mobility}, series = {Automobiltechnische Zeitschrift - ATZ}, volume = {120}, journal = {Automobiltechnische Zeitschrift - ATZ}, number = {1}, publisher = {Springer Vieweg}, address = {Wiesbaden}, issn = {0001-2785}, doi = {10.1007/s35148-017-0176-8}, pages = {18 -- 23}, year = {2018}, abstract = {Urbane Mobilit{\"a}tskonzepte der Zukunft erfordern neue Unternehmensformen, idealerweise aus Old Economy und New Economy, sowie eine enge Anbindung an die gesellschaftsrelevante Zukunftsforschung. F{\"u}r neue Fahrzeugkonzepte des Carsharing bedeutet dies, dass alle kostenverursachenden Faktoren erfasst und analysiert werden m{\"u}ssen. Die FH Aachen, share2drive und FEV geben einen Ausblick auf die zuk{\"u}nftige Fahrzeugklasse der Personal Public Vehicles als „Rolling Device".}, language = {de} } @inproceedings{PaslighFunkeRoethetal.2010, author = {Pasligh, N. and Funke, D. and R{\"o}th, Thilo and Krack, R.}, title = {Leichtbau Quertrager als Stahlblech-Aluminiumdruckguss-Hybrid - Von der numerischen Berechnung bis zum realen Prototypen}, series = {VDI BERICHTE}, booktitle = {VDI BERICHTE}, publisher = {VDI Verlag}, address = {D{\"u}sseldorf}, isbn = {978-3-18-092107-5}, pages = {688 Seiten}, year = {2010}, language = {de} } @article{WeiheErnstRoethetal.2013, author = {Weihe, Stefan and Ernst, Ansgar and R{\"o}th, Thilo and Proksch, Johannes}, title = {Aluminium-Stahl-Verbundguss im Nutzfahrzeugbau}, series = {ATZ - Automobiltechnische Zeitschrift}, volume = {115}, journal = {ATZ - Automobiltechnische Zeitschrift}, number = {4}, publisher = {Springer Fachmedien Wiesbaden}, issn = {2192-8800 (Online)}, pages = {312 -- 316}, year = {2013}, abstract = {In modernen Fahrzeugkarosserien der Großserie kommen zunehmend Materialmischbauweisen zur Anwendung. In Zusammenarbeit der Daimler AG, der Tower Automotive Holding GmbH, der Imperia GmbH sowie der Partnerunternehmen KSM Castings GmbH und Schaufler Tooling GmbH \& Co. KG wird das Leichtbaupotenzial von Aluminiumverbundguss-Stahlblech-Hybriden am Beispiel des vorderen Dachquertr{\"a}gers des Mercedes-Benz Viano/Vito ausf{\"u}hrlich untersucht.}, language = {de} } @article{KreyerMuellerEsch2020, author = {Kreyer, J{\"o}rg and M{\"u}ller, Marvin and Esch, Thomas}, title = {A Calculation Methodology for Predicting Exhaust Mass Flows and Exhaust Temperature Profiles for Heavy-Duty Vehicles}, series = {SAE International Journal of Commercial Vehicles}, volume = {13}, journal = {SAE International Journal of Commercial Vehicles}, number = {2}, publisher = {SAE International}, address = {Warrendale, Pa.}, issn = {1946-3928}, doi = {10.4271/02-13-02-0009}, pages = {129 -- 143}, year = {2020}, abstract = {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.}, language = {en} } @article{SerrorHackHenzeetal.2021, author = {Serror, Martin and Hack, Sacha and Henze, Martin and Schuba, Marko and Wehrle, Klaus}, title = {Challenges and Opportunities in Securing the Industrial Internet of Things}, series = {IEEE Transactions on Industrial Informatics}, volume = {17}, journal = {IEEE Transactions on Industrial Informatics}, number = {5}, publisher = {IEEE}, address = {New York}, issn = {1941-0050}, doi = {10.1109/TII.2020.3023507}, pages = {2985 -- 2996}, year = {2021}, language = {en} } @inproceedings{MerkensHebel2021, author = {Merkens, Torsten and Hebel, Christoph}, title = {Sharing mobility concepts - flexible, sustainable, smart}, series = {Proceedings of the 1st UNITED - Southeast Asia Automotive Interest Group (SAIG) International Conference}, booktitle = {Proceedings of the 1st UNITED - Southeast Asia Automotive Interest Group (SAIG) International Conference}, isbn = {978-3-902103-94-9}, pages = {43 -- 44}, year = {2021}, language = {en} } @inproceedings{WuKemper2016, author = {Wu, Ziyi and Kemper, Hans}, title = {The optimal 48 V - battery pack for a specific load profile of a heavy duty vehicle}, series = {8. Internationale Fachtagung Kraftwerk Batterie : 26. - 27. April 2016, M{\"u}nster, Deutschland}, booktitle = {8. Internationale Fachtagung Kraftwerk Batterie : 26. - 27. April 2016, M{\"u}nster, Deutschland}, year = {2016}, language = {en} } @inproceedings{NeuJanserKhatibietal.2016, author = {Neu, Eugen and Janser, Frank and Khatibi, Akbar A. and Orifici, Adrian C.}, title = {In-flight vibration-based structural health monitoring of aircraft wings}, series = {30th Congress of the internatonal council of the aeronautical sciences : 25.-30. September 2016, Daejeon, Korea}, booktitle = {30th Congress of the internatonal council of the aeronautical sciences : 25.-30. September 2016, Daejeon, Korea}, pages = {10 Seiten}, year = {2016}, abstract = {This work presents a methodology for automated damage-sensitive feature extraction and anomaly detection under multivariate operational variability for in-flight assessment of wings. The method uses a passive excitation approach, i. e. without the need for artificial actuation. The modal system properties (natural frequencies and damping ratios) are used as damage-sensitive features. Special emphasis is placed on the use of Fiber Bragg Grating (FBG) sensing technology and the consideration of Operational and Environmental Variability (OEV). Measurements from a wind tunnel investigation with a composite cantilever equipped with FBG and piezoelectric sensors are used to successfully detect an impact damage. In addition, the feasibility of damage localisation and severity estimation is evaluated based on the coupling found between damageand OEV-induced feature changes.}, language = {en} } @article{PetersonRoethUibel2017, author = {Peterson, Leif Arne and R{\"o}th, Thilo and Uibel, Thomas}, title = {Einsatz von Holzwerkstoffen in Fahrzeugstrukturen}, series = {Bauen mit Holz}, journal = {Bauen mit Holz}, number = {3}, publisher = {Bruderverlag}, address = {K{\"o}ln}, issn = {0005-6545}, pages = {32 -- 38}, year = {2017}, language = {de} } @incollection{RoethDeutskensKreiskoetheretal.2018, author = {R{\"o}th, Thilo and Deutskens, Christoph and Kreisk{\"o}ther, Kai and Heimes, Heiner Hans and Schittny, Bastian and Ivanescu, Sebastian and Kleine B{\"u}ning, Max and Reinders, Christian and Wessels, Saskia and Haunreiter, Andreas and Reisgen, Uwe and Thiele, Regina and Hameyer, Kay and Doncker, Rik W. de and Sauer, Uwe and Hoek, Hauke van and H{\"u}bner, Mareike and Hennen, Martin and Stolze, Thilo and Vetter, Andreas and Hagedorn, J{\"u}rgen and M{\"u}ller, Dirk and Rewitz, Kai and Wesseling, Mark and Flieger, Bj{\"o}rn}, title = {Entwicklung von elektrofahrzeugspezifischen Systemen}, series = {Elektromobilit{\"a}t}, booktitle = {Elektromobilit{\"a}t}, publisher = {Springer Vieweg}, address = {Berlin, Heidelberg}, isbn = {978-3-662-53137-2}, doi = {10.1007/978-3-662-53137-2_6}, pages = {279 -- 386}, year = {2018}, abstract = {Die Batterie ist eine der absolut zentralen Komponenten des Elektrofahrzeugs. Die serielle Entwicklung und Produktion dieser Batterien und die Verbesserung der Leistungen wird entscheidend f{\"u}r den Erfolg der Elektromobilit{\"a}t sein. Die Batterie ist jedoch nicht das einzige elektrofahrzeugspezifische System, das neu entwickelt, umkonzipiert oder verbessert werden muss. So sind ebenso die Entwicklung der neuen Fahrzeugstruktur sowie des elektrifizierten Antriebsstranges Teil dieses Kapitels. Weiterhin wird ein Blick auf das bedeutende Thema des Thermomanagements geworfen.}, language = {de} } @inproceedings{DinghoferHartung2020, author = {Dinghofer, Kai and Hartung, Frank}, title = {Analysis of Criteria for the Selection of Machine Learning Frameworks}, series = {2020 International Conference on Computing, Networking and Communications (ICNC)}, booktitle = {2020 International Conference on Computing, Networking and Communications (ICNC)}, publisher = {IEEE}, address = {New York, NY}, doi = {10.1109/ICNC47757.2020.9049650}, pages = {373 -- 377}, year = {2020}, abstract = {With the many achievements of Machine Learning in the past years, it is likely that the sub-area of Deep Learning will continue to deliver major technological breakthroughs [1]. In order to achieve best results, it is important to know the various different Deep Learning frameworks and their respective properties. This paper provides a comparative overview of some of the most popular frameworks. First, the comparison methods and criteria are introduced and described with a focus on computer vision applications: Features and Uses are examined by evaluating papers and articles, Adoption and Popularity is determined by analyzing a data science study. Then, the frameworks TensorFlow, Keras, PyTorch and Caffe are compared based on the previously described criteria to highlight properties and differences. Advantages and disadvantages are compared, enabling researchers and developers to choose a framework according to their specific needs.}, language = {en} } @inproceedings{KreyerMuellerEsch2020, author = {Kreyer, J{\"o}rg and M{\"u}ller, Marvin and Esch, Thomas}, title = {A Map-Based Model for the Determination of Fuel Consumption for Internal Combustion Engines as a Function of Flight Altitude}, publisher = {DGLR}, address = {Bonn}, doi = {10.25967/490162}, pages = {13 Seiten}, year = {2020}, abstract = {In addition to very high safety and reliability requirements, the design of internal combustion engines (ICE) in aviation focuses on economic efficiency. The objective must be to design the aircraft powertrain optimized for a specific flight mission with respect to fuel consumption and specific engine power. Against this background, expert tools provide valuable decision-making assistance for the customer. In this paper, a mathematical calculation model for the fuel consumption of aircraft ICE is presented. This model enables the derivation of fuel consumption maps for different engine configurations. Depending on the flight conditions and based on these maps, the current and the integrated fuel consumption for freely definable flight emissions is calculated. For that purpose, an interpolation method is used, that has been optimized for accuracy and calculation time. The mission boundary conditions flight altitude and power requirement of the ICE form the basis for this calculation. The mathematical fuel consumption model is embedded in a parent program. This parent program presents the simulated fuel consumption by means of an example flight mission for a representative airplane. The focus of the work is therefore on reproducing exact consumption data for flight operations. By use of the empirical approaches according to Gagg-Farrar [1] the power and fuel consumption as a function of the flight altitude are determined. To substantiate this approaches, a 1-D ICE model based on the multi-physical simulation tool GT-Suite® has been created. This 1-D engine model offers the possibility to analyze the filling and gas change processes, the internal combustion as well as heat and friction losses for an ICE under altitude environmental conditions. Performance measurements on a dynamometer at sea level for a naturally aspirated ICE with a displacement of 1211 ccm used in an aviation aircraft has been done to validate the 1-D ICE model. To check the plausibility of the empirical approaches with respect to the fuel consumption and performance adjustment for the flight altitude an analysis of the ICE efficiency chain of the 1-D engine model is done. In addition, a comparison of literature and manufacturer data with the simulation results is presented.}, language = {en} } @article{KhayyamJamaliBabHadiasharetal.2020, author = {Khayyam, Hamid and Jamali, Ali and Bab-Hadiashar, Alireza and Esch, Thomas and Ramakrishna, Seeram and Jalil, Mahdi and Naebe, Minoo}, title = {A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modelling with Application in Industry 4.0}, series = {IEEE Access}, journal = {IEEE Access}, publisher = {IEEE}, address = {New York, NY}, isbn = {2169-3536}, doi = {10.1109/ACCESS.2020.2999898}, pages = {1 -- 12}, year = {2020}, abstract = {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.}, language = {en} } @inproceedings{NikolovskiRekeElsenetal.2021, author = {Nikolovski, Gjorgji and Reke, Michael and Elsen, Ingo and Schiffer, Stefan}, title = {Machine learning based 3D object detection for navigation in unstructured environments}, series = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)}, booktitle = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)}, publisher = {IEEE}, isbn = {978-1-6654-7921-9}, doi = {10.1109/IVWorkshops54471.2021.9669218}, pages = {236 -- 242}, year = {2021}, abstract = {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.}, language = {en} } @inproceedings{FiedlerGottschlichMuellerMelcher2021, author = {Fiedler, Gerda and Gottschlich-M{\"u}ller, Birgit and Melcher, Karin}, title = {Online-Pr{\"u}fungen mit STACK Aufgaben}, series = {Tagungsband ASIM Workshop STS/GMMS/EDU 2021}, booktitle = {Tagungsband ASIM Workshop STS/GMMS/EDU 2021}, editor = {Liu-Henke, Xiaobo and Durak, Umut}, publisher = {ARGESIM Verlag}, address = {Wien}, isbn = {978-3-901608-69-8}, doi = {10.11128/arep.45}, pages = {173 -- 178}, year = {2021}, abstract = {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 f{\"u}r eine Open-Book Online Pr{\"u}fung geeignet, da eine faire Pr{\"u}fungssituation gew{\"a}hrleistet werden kann.}, language = {de} } @article{HahnHebelManz2022, author = {Hahn, Geogr W. and Hebel, Christoph and Manz, W.}, title = {Die neuen Empfehlungen f{\"u}r Verkehrsnachfragemodellierung im Personenverkehr}, series = {Straßenverkehrstechnik}, volume = {66}, journal = {Straßenverkehrstechnik}, number = {10}, publisher = {Kirschbaum Verlag GmbH}, address = {Bonn}, issn = {0039-2219}, doi = {10.53184/SVT10-2022-1}, pages = {721 -- 726}, year = {2022}, abstract = {Die neu erschienenen „Empfehlungen zum Einsatz von Verkehrsnachfragemodellen f{\"u}r den Personenverkehr" liefern erstmals als Empfehlungspapier der Forschungsgesellschaft f{\"u}r Straßen- und Verkehrswesen einen umfassenden {\"U}berblick zu den verschiedenen Aspekten der Modellierung und geben dem Fachplaner konkrete Hilfestellung f{\"u}r die Konzeption von Nachfragemodellen. Das Empfehlungspapier zielt unter anderem darauf ab, die Erwartungen und das Anspruchsniveau in Hinblick auf Sachgerechtigkeit der Modelle, die erzielbare Modellqualit{\"a}t und den Detaillierungsgrad der Modellaussagen zu harmonisieren.}, language = {de} } @article{EmigHebelSchwark2022, author = {Emig, J. and Hebel, Christoph and Schwark, A.}, title = {Einsatzbereiche f{\"u}r Verkehrsnachfragemodelle}, series = {Straßenverkehrstechnik}, volume = {66}, journal = {Straßenverkehrstechnik}, number = {10}, publisher = {Kirschbaum Verlag GmbH}, address = {Bonn}, issn = {0039-2219}, doi = {10.53184/SVT10-2022-2}, pages = {727 -- 736}, year = {2022}, abstract = {In der Praxis bestehen vielf{\"a}ltige Einsatzbereiche f{\"u}r Verkehrsnachfragemodelle. Mit ihnen k{\"o}nnen Kenngr{\"o}ßen des Verkehrsangebots und der Verkehrsnachfrage f{\"u}r den heutigen Zustand wie auch f{\"u}r zuk{\"u}nftige Zust{\"a}nde bereitgestellt werden, um so die Grundlagen f{\"u}r verkehrsplanerische Entscheidungen zu liefern. Die neuen „Empfehlungen zum Einsatz von Verkehrsnachfragemodellen f{\"u}r den Personenverkehr" (EVNM-PV) (FGSV 2022) veranschaulichen anhand von typischen Planungsaufgaben, welche differenzierten Anforderungen daraus f{\"u}r die Modellkonzeption und -erstellung resultieren. Vor dem Hintergrund der konkreten Aufgabenstellung sowie deren spezifischer planerischer Anforderungen bildet die abzuleitende Modellspezifikation die verabredete Grundlage zwischen Auftraggeber und Modellersteller f{\"u}r die konkrete inhaltliche, fachliche Ausgestaltung des Verkehrsmodells.}, language = {de} } @article{PfaffGidaszewskiSchmidt2020, author = {Pfaff, Raphael and Gidaszewski, Lars and Schmidt, Bernd}, title = {Ber{\"u}cksichtigung von No Fault Found im Diagnose- und Instandhaltungssystem von Schienenfahrzeugen}, series = {ETR - Eisenbahntechnische Rundschau}, journal = {ETR - Eisenbahntechnische Rundschau}, number = {5}, publisher = {DVV Media Group}, address = {Hamburg}, issn = {0013-2845}, pages = {56 -- 59}, year = {2020}, abstract = {Intermittierende und nicht reproduzierbare Fehler, auch als No Fault Found bezeichnet, treten in praktisch allen Bereichen auf und sorgen f{\"u}r hohe Kosten. Diese sind h{\"a}ufig auf unpr{\"a}zise Fehlerbeschreibungen zur{\"u}ckzuf{\"u}hren. Im vorliegenden Beitrag werden Anpassungen der Vorgehensweise bei der Entwicklung und Anpassungen des Diagnosesystems vorgeschlagen.}, language = {de} } @article{HoevelerBauknechtWolfetal.2020, author = {Hoeveler, B. and Bauknecht, Andr{\´e} and Wolf, C. Christian and Janser, Frank}, title = {Wind-Tunnel Study of a Wing-Embedded Lifting Fan Remaining Open in Cruise Flight}, series = {Journal of Aircraft}, volume = {57}, journal = {Journal of Aircraft}, number = {4}, publisher = {AIAA}, address = {Reston, Va.}, issn = {1533-3868}, doi = {10.2514/1.C035422}, year = {2020}, abstract = {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 G{\"o}ttingen of DLR, German Aerospace Center in G{\"o}ttingen 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.}, language = {en} } @article{KhayyamJamaliBabHadiasharetal.2020, author = {Khayyam, Hamid and Jamali, Ali and Bab-Hadiashar, Alireza and Esch, Thomas and Ramakrishna, Seeram and Jalili, Mahdi and Naebe, Minoo}, title = {A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modeling with Application in Industry 4.0}, series = {IEEE Access}, volume = {8}, journal = {IEEE Access}, number = {Art. 9108222}, publisher = {IEEE}, address = {New York, NY}, issn = {2169-3536}, doi = {10.1109/ACCESS.2020.2999898}, pages = {111381 -- 111393}, year = {2020}, abstract = {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.}, language = {en} }