TY - CHAP A1 - Schulze, Sven A1 - Mühleisen, M. A1 - Feyerl, Günter T1 - Adaptive energy management strategy for a heavy-duty truck with a P2-hybrid topology T2 - 18. Internationales Stuttgarter Symposium. Proceedings Y1 - 2018 U6 - https://doi.org/10.1007/978-3-658-21194-3 SP - 75 EP - 89 PB - Springer Vieweg CY - Wiesbaden 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 - https://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 - TY - JOUR A1 - Serror, Martin A1 - Hack, Sacha A1 - Henze, Martin A1 - Schuba, Marko A1 - Wehrle, Klaus T1 - Challenges and Opportunities in Securing the Industrial Internet of Things JF - IEEE Transactions on Industrial Informatics Y1 - 2021 U6 - https://doi.org/10.1109/TII.2020.3023507 SN - 1941-0050 VL - 17 IS - 5 SP - 2985 EP - 2996 PB - IEEE CY - New York ER - TY - CHAP A1 - Merkens, Torsten A1 - Hebel, Christoph T1 - Sharing mobility concepts – flexible, sustainable, smart T2 - Proceedings of the 1st UNITED – Southeast Asia Automotive Interest Group (SAIG) International Conference KW - Sharing mobility KW - electro mobility KW - business models KW - mobility behaviour Y1 - 2021 SN - 978-3-902103-94-9 N1 - 1st UNITED-SAIG International Conference, 21-22 APR 2021, Chulalongkorn University, Thailand SP - 43 EP - 44 ER - TY - CHAP A1 - Wu, Ziyi A1 - Kemper, Hans T1 - The optimal 48 V – battery pack for a specific load profile of a heavy duty vehicle T2 - 8. Internationale Fachtagung Kraftwerk Batterie : 26. – 27. April 2016, Münster, Deutschland Y1 - 2016 ER - TY - CHAP A1 - Neu, Eugen A1 - Janser, Frank A1 - Khatibi, Akbar A. A1 - Orifici, Adrian C. T1 - In-flight vibration-based structural health monitoring of aircraft wings T2 - 30th Congress of the internatonal council of the aeronautical sciences : 25.-30. September 2016, Daejeon, Korea N2 - 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. Y1 - 2016 ER - TY - CHAP A1 - Dinghofer, Kai A1 - Hartung, Frank T1 - Analysis of Criteria for the Selection of Machine Learning Frameworks T2 - 2020 International Conference on Computing, Networking and Communications (ICNC) N2 - 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. Y1 - 2020 U6 - https://doi.org/10.1109/ICNC47757.2020.9049650 N1 - 2020 International Conference on Computing, Networking and Communications (ICNC), 17-20 February 2020, Big Island, HI, USA SP - 373 EP - 377 PB - IEEE CY - New York, NY ER - TY - CHAP A1 - Kreyer, Jörg A1 - Müller, Marvin A1 - Esch, Thomas T1 - A Map-Based Model for the Determination of Fuel Consumption for Internal Combustion Engines as a Function of Flight Altitude N2 - 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. Y1 - 2020 U6 - https://doi.org/10.25967/490162 N1 - 68. Deutscher Luft- und Raumfahrtkongress 30.09.-02.10.2019, Darmstadt PB - DGLR CY - Bonn 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 - https://doi.org/10.1109/ACCESS.2020.2999898 SP - 1 EP - 12 PB - IEEE CY - New York, NY ER - 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 - https://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 - 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 - https://doi.org/10.2514/1.C035422 SN - 1533-3868 VL - 57 IS - 4 PB - AIAA CY - Reston, Va. 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 - https://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 - Pfaff, Raphael A1 - Enning, Manfred A1 - Sutter, Stefan T1 - A risk‑based approach to automatic brake tests for rail freight service: incident analysis and realisation concept JF - SN Applied Sciences N2 - This study reviews the practice of brake tests in freight railways, which is time consuming and not suitable to detect certain failure types. Public incident reports are analysed to derive a reasonable brake test hardware and communication architecture, which aims to provide automatic brake tests at lower cost than current solutions. The proposed solutions relies exclusively on brake pipe and brake cylinder pressure sensors, a brake release position switch as well as radio communication via standard protocols. The approach is embedded in the Wagon 4.0 concept, which is a holistic approach to a smart freight wagon. The reduction of manual processes yields a strong incentive due to high savings in manual labour and increased productivity. KW - Freight rail KW - Brake test KW - Incident analysis KW - Train composition KW - Brake set-up Y1 - 2022 U6 - https://doi.org/10.1007/s42452-022-05007-x SN - 2523-3971 N1 - Corresponding author: Raphael Pfaff VL - 4 IS - 4 SP - 1 EP - 14 PB - Springer CY - Cham ER - TY - JOUR A1 - Neu, Eugen A1 - Janser, Frank A1 - Khatibi, Akbar A. A1 - Orifici, Adrian C. T1 - Fully Automated Operational Modal Analysis using multi-stage clustering JF - Mechanical Systems and Signal Processing Y1 - 2017 U6 - https://doi.org/10.1016/j.ymssp.2016.07.031 SN - 0888-3270 VL - Vol. 84, Part A SP - 308 EP - 323 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Neu, Eugen A1 - Janser, Frank A1 - Khatibi, Akbar A. A1 - Orifici, Adrian C. T1 - Automated modal parameter-based anomaly detection under varying wind excitation JF - Structural Health Monitoring N2 - Wind-induced operational variability is one of the major challenges for structural health monitoring of slender engineering structures like aircraft wings or wind turbine blades. Damage sensitive features often show an even bigger sensitivity to operational variability. In this study a composite cantilever was subjected to multiple mass configurations, velocities and angles of attack in a controlled wind tunnel environment. A small-scale impact damage was introduced to the specimen and the structural response measurements were repeated. The proposed damage detection methodology is based on automated operational modal analysis. A novel baseline preparation procedure is described that reduces the amount of user interaction to the provision of a single consistency threshold. The procedure starts with an indeterminate number of operational modal analysis identifications from a large number of datasets and returns a complete baseline matrix of natural frequencies and damping ratios that is suitable for subsequent anomaly detection. Mahalanobis distance-based anomaly detection is then applied to successfully detect the damage under varying severities of operational variability and with various degrees of knowledge about the present operational conditions. The damage detection capabilities of the proposed methodology were found to be excellent under varying velocities and angles of attack. Damage detection was less successful under joint mass and wind variability but could be significantly improved through the provision of the currently encountered operational conditions. Y1 - 2016 U6 - https://doi.org/10.1177/1475921716665803 SN - 1475-9217 VL - 15 IS - 6 SP - 1 EP - 20 PB - Sage CY - London ER - TY - CHAP A1 - Schuba, Marko A1 - Höfken, Hans-Wilhelm A1 - Linzbach, Sophie T1 - An ICS Honeynet for Detecting and Analyzing Cyberattacks in Industrial Plants T2 - 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET) N2 - Cybersecurity of Industrial Control Systems (ICS) is an important issue, as ICS incidents may have a direct impact on safety of people or the environment. At the same time the awareness and knowledge about cybersecurity, particularly in the context of ICS, is alarmingly low. Industrial honeypots offer a cheap and easy to implement way to raise cybersecurity awareness and to educate ICS staff about typical attack patterns. When integrated in a productive network, industrial honeypots may not only reveal attackers early but may also distract them from the actual important systems of the network. Implementing multiple honeypots as a honeynet, the systems can be used to emulate or simulate a whole Industrial Control System. This paper describes a network of honeypots emulating HTTP, SNMP, S7communication and the Modbus protocol using Conpot, IMUNES and SNAP7. The nodes mimic SIMATIC S7 programmable logic controllers (PLCs) which are widely used across the globe. The deployed honeypots' features will be compared with the features of real SIMATIC S7 PLCs. Furthermore, the honeynet has been made publicly available for ten days and occurring cyberattacks have been analyzed KW - Conpot KW - honeypot KW - honeynet KW - ICS KW - cybersecurity Y1 - 2022 SN - 978-1-6654-4231-2 SN - 978-1-6654-4232-9 U6 - https://doi.org/10.1109/ICECET52533.2021.9698746 N1 - 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET). 09-10 December 2021. Cape Town, South Africa. PB - IEEE ER - TY - CHAP A1 - Christian, Esser A1 - Montag, Tim A1 - Schuba, Marko A1 - Allhof, Manuel T1 - Future critical infrastructure and security - cyberattacks on charging stations T2 - 31st International Electric Vehicle Symposium & Exhibition and International Electric Vehicle Technology Conference (EVS31 & EVTeC 2018) Y1 - 2018 SN - 978-1-5108-9157-9 SP - 665 EP - 671 PB - Society of Automotive Engineers of Japan (JSAE) CY - Tokyo ER - TY - JOUR A1 - Pfaff, Raphael T1 - Braking distance prediction for vehicle consist in low-speed on-sight operation: a Monte Carlo approach JF - Railway Engineering Science N2 - The first and last mile of a railway journey, in both freight and transit applications, constitutes a high effort and is either non-productive (e.g. in the case of depot operations) or highly inefficient (e.g. in industrial railways). These parts are typically managed on-sight, i.e. with no signalling and train protection systems ensuring the freedom of movement. This is possible due to the rather short braking distances of individual vehicles and shunting consists. The present article analyses the braking behaviour of such shunting units. For this purpose, a dedicated model is developed. It is calibrated on published results of brake tests and validated against a high-definition model for low-speed applications. Based on this model, multiple simulations are executed to obtain a Monte Carlo simulation of the resulting braking distances. Based on the distribution properties and established safety levels, the risk of exceeding certain braking distances is evaluated and maximum braking distances are derived. Together with certain parameters of the system, these can serve in the design and safety assessment of driver assistance systems and automation of these processes. KW - Freight rail KW - Shunting KW - Braking curves KW - Brake set-up KW - Driver assistance system Y1 - 2023 U6 - https://doi.org/10.1007/s40534-023-00303-7 SN - 2662-4753 (eISSN) SN - 2662-4745 (Print) VL - 31 IS - 2 SP - 135 EP - 144 PB - SpringerOpen ER - TY - CHAP A1 - Galdi, Chiara A1 - Hartung, Frank A1 - Dugelay, Jean-Luc T1 - Videos versus still images: Asymmetric sensor pattern noise comparison on mobile phones T2 - Electronic Imaging N2 - Nowadays, the most employed devices for recoding videos or capturing images are undoubtedly the smartphones. Our work investigates the application of source camera identification on mobile phones. We present a dataset entirely collected by mobile phones. The dataset contains both still images and videos collected by 67 different smartphones. Part of the images consists in photos of uniform backgrounds, especially collected for the computation of the RSPN. Identifying the source camera given a video is particularly challenging due to the strong video compression. The experiments reported in this paper, show the large variation in performance when testing an highly accurate technique on still images and videos. KW - Image Forensics KW - Mobile Phones KW - Image Database Y1 - 2017 U6 - https://doi.org/10.2352/ISSN.2470-1173.2017.7.MWSF-331 SN - 2470-1173 N1 - IS&T International Symposium on Electronic Imaging 2017 Media Watermarking, Security, and Forensics 2017 SP - 100 EP - 103 PB - Society for Imaging Science and Technology CY - Springfield, Virginia ER - TY - JOUR A1 - Schulze, Sven A1 - Feyerl, Günter A1 - Pischinger, Stefan T1 - Advanced ECMS for hybrid electric heavy-duty trucks with predictive battery discharge and adaptive operating strategy under real driving conditions JF - Energies N2 - To fulfil the CO2 emission reduction targets of the European Union (EU), heavy-duty (HD) trucks need to operate 15% more efficiently by 2025 and 30% by 2030. Their electrification is necessary as conventional HD trucks are already optimized for the long-haul application. The resulting hybrid electric vehicle (HEV) truck gains most of the fuel saving potential by the recuperation of potential energy and its consecutive utilization. The key to utilizing the full potential of HEV-HD trucks is to maximize the amount of recuperated energy and ensure its intelligent usage while keeping the operating point of the internal combustion engine as efficient as possible. To achieve this goal, an intelligent energy management strategy (EMS) based on ECMS is developed for a parallel HEV-HD truck which uses predictive discharge of the battery and adaptive operating strategy regarding the height profile and the vehicle mass. The presented EMS can reproduce the global optimal operating strategy over long phases and lead to a fuel saving potential of up to 2% compared with a heuristic strategy. Furthermore, the fuel saving potential is correlated with the investigated boundary conditions to deepen the understanding of the impact of intelligent EMS for HEV-HD trucks. KW - Energy management strategies KW - ECMS KW - CO2 emission reduction targets KW - Driving cycle recognition KW - Predictive battery discharge Y1 - 2023 U6 - https://doi.org/10.3390/en16135171 SN - 1996-1073 N1 - The article belongs to the Special Issue "Energy Management Strategies of Electrified Vehicles toward the Real-World Driving". VL - 16 IS - 13 PB - MDPI CY - Basel ER -