Refine
Year of publication
Document Type
- Conference Proceeding (57)
- Article (39)
- Part of a Book (7)
- Patent (3)
- Report (3)
- Book (1)
- Doctoral Thesis (1)
- Poster (1)
Keywords
- Energy storage (4)
- Power plants (4)
- UAV (4)
- Associated liquids (3)
- Concentrated solar power (3)
- Hybrid energy system (3)
- Brake set-up (2)
- Electricity generation (2)
- Freight rail (2)
- Obstacle avoidance (2)
- Smart Building (2)
- Solar thermal technologies (2)
- electro mobility (2)
- 3D object detection (1)
- Aircraft design (1)
- Aircraft sizing (1)
- Android (1)
- Anomalieerkennung (1)
- Automotive safety approach (1)
- Autonomy (1)
- BIM (1)
- Brake test (1)
- Braking curves (1)
- Building Automation (1)
- Bumblebees (1)
- CCD-Bildwandler (1)
- CFD (1)
- CO2 (1)
- CO2 emission reduction targets (1)
- Camera system (1)
- Capacity Building Higher Education (1)
- Carbon Dioxide (1)
- Ceramics (1)
- Cloud passages (1)
- Commercial Vehicle (1)
- Common Rail Injection System (1)
- Conpot (1)
- Control optimization (1)
- Correlations (1)
- Cost function (1)
- Crashworthiness (1)
- Cybersecurity (1)
- Cybersicherheit (1)
- DNI forecast (1)
- DNI forecasting (1)
- Decision theory (1)
- Deep learning (1)
- Design rules (1)
- Diesel Engine (1)
- DiggiTwin (1)
- Digital triage (1)
- Digitalisierung (1)
- Direct normal irradiance forecast (1)
- District data model (1)
- District energy planning platform (1)
- Drag (1)
- Dreidimensionale Bildverarbeitung (1)
- Driver assistance system (1)
- Driving cycle recognition (1)
- Dynamic simulation (1)
- ECMS (1)
- Education (1)
- Electrochemistry (1)
- Energy Disaggregation (1)
- Energy management strategies (1)
- Energy system planning (1)
- Erasmus+ United (1)
- European Framework and South East Asia (1)
- European Transient Cycle (1)
- Flight control (1)
- Full-vehicle crash test (1)
- Geometry (1)
- Global change (1)
- Green aircraft (1)
- Heliostat Field Calibration (1)
- Heliostats (1)
- Home Assistant (1)
- Home Automation Platform (1)
- Human factors (1)
- Hybrid-electric aircraft (1)
- ICS (1)
- IT-Sicherheit (1)
- Image Database (1)
- Image Forensics (1)
- Incident analysis (1)
- Informationssicherheit (1)
- Informationssicherheitsmanagement (1)
- IoT (1)
- Kalman filter (1)
- Klassifikator <Informatik> (1)
- LiDAR (1)
- Local path planning (1)
- MAV (1)
- Machine Learning (1)
- Malaysian Automotive Industry (1)
- Malaysian automotive industry (1)
- Measuring instruments (1)
- Mobile Phones (1)
- Molten salt receiver (1)
- Molten salt receiver system (1)
- Molten salt receiver system, (1)
- Molten salt solar tower (1)
- Multi-objective optimization (1)
- Nowcasting (1)
- Objekterkennung (1)
- Open Source (1)
- PEM fuel cells (1)
- PTC (1)
- Parabolic trough collector (1)
- Parasitic drag (1)
- Path planning (1)
- Photovoltaics (1)
- Predictive battery discharge (1)
- Process prediction (1)
- Quadrocopter (1)
- Renewable energy integration (1)
- Selective Catalytic Reduction (1)
- Sharing mobility (1)
- Shunting (1)
- Solar irradiance (1)
- Star design (1)
- Statistics (1)
- Technology Transfer (1)
- Thermal Energy Storage (1)
- Three-dimensional displays (1)
- Train composition (1)
- Transient flux distribution (1)
- Triage-app (1)
- Two-phase modelling (1)
- UTeM Engineering Knowledge Transfer Unit (1)
- Uncertainty analysis (1)
- Unmanned Air Vehicle (1)
- Unmanned aerial vehicle (1)
- Vorverarbeitung (1)
- adaptive systems (1)
- artificial intelligence (1)
- assistance system (1)
- autonomous driving (1)
- aviation application (1)
- business models (1)
- control system (1)
- cybersecurity (1)
- digitalization (1)
- dissemination (1)
- do-it-yourself (1)
- eVTOL development (1)
- eVTOL safety (1)
- education (1)
- electrically driven compressors (1)
- embedded hardware (1)
- energy transition (1)
- fuel cell (1)
- fuel cell systems (1)
- fuel cell vehicle (1)
- gamification (1)
- health management systems (1)
- honeynet (1)
- honeypot (1)
- information systems (1)
- intelligent control (1)
- intelligent energy management (1)
- internal combustion engine (1)
- machine learning (1)
- manufacturing (1)
- mobility behaviour (1)
- open educational resources (1)
- optimization system (1)
- renewable energies (1)
- sensor networks (1)
- technology transfer (1)
- Überwachung & Optimierung (1)
Institute
- ECSM European Center for Sustainable Mobility (112) (remove)
Aim of the AXON2 project (Adaptive Expert System for Object Recogniton using Neuml Networks) is the development of an object recognition system (ORS) capable of recognizing isolated 3d objects from arbitrary views. Commonly, classification is based on a single feature extracted from the original image. Here we present an architecture adapted from the Mixtures of Eaqerts algorithm which uses multiple neuml networks to integmte different features. During tmining each neural network specializes in a subset of objects or object views appropriate to the properties of the corresponding feature space. In recognition mode the system dynamically chooses the most relevant features and combines them with maximum eficiency. The remaining less relevant features arz not computed and do therefore not decelerate the-recognition process. Thus, the algorithm is well suited for ml-time applications.
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.
Assistance systems have been widely adopted in the manufacturing sector to facilitate various processes and tasks in production environments. However, existing systems are mostly equipped with rigid functional logic and do not provide individual user experiences or adapt to their capabilities. This work integrates human factors in assistance systems by adjusting the hardware and instruction presented to the workers’ cognitive and physical demands. A modular system architecture is designed accordingly, which allows a flexible component exchange according to the user and the work task. Gamification, the use of game elements in non-gaming contexts, has been further adopted in this work to provide level-based instructions and personalised feedback. The developed framework is validated by applying it to a manual workstation for industrial assembly routines.
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.
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.
This paper addresses the pixel based classification of three dimensional objects from arbitrary views. To perform this task a coding strategy, inspired by the biological model of human vision, for pixel data is described. The coding strategy ensures that the input data is invariant against shift, scale and rotation of the object in the input domain. The image data is used as input to a class of self organizing neural networks, the Kohonen-maps or self-organizing feature maps (SOFM). To verify this approach two test sets have been generated: the first set, consisting of artificially generated images, is used to examine the classification properties of the SOFMs; the second test set examines the clustering capabilities of the SOFM when real world image data is applied to the network after it has been preprocessed to be invariant against shift, scale and rotation. It is shown that the clustering capability of the SOFM is strongly dependant on the invariance coding of the images.
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.
Next-generation aircraft designs often incorporate multiple large propellers attached along the wingspan. These highly flexible dynamic systems can exhibit uncommon aeroelastic instabilities, which should be carefully investigated to ensure safe operation. The interaction between the propeller and the wing is of particular importance. It is known that whirl flutter is stabilized by wing motion and wing aerodynamics. This paper investigates the effect of a propeller onto wing flutter as a function of span position and mounting stiffness between the propeller and wing. The analysis of a comparison between a tractor and pusher configuration has shown that the coupled system is more stable than the standalone wing for propeller positions near the wing tip for both configurations. The wing fluttermechanism is mostly affected by the mass of the propeller and the resulting change in eigenfrequencies of the wing. For very weak mounting stiffnesses, whirl flutter occurs, which was shown to be stabilized compared to a standalone propeller due to wing motion. On the other hand, the pusher configuration is, as to be expected, the more critical configuration due to the attached mass behind the elastic axis.
Next-generation aircraft designs often incorporate multiple large propellers attached along the wingspan (distributed electric propulsion), leading to highly flexible dynamic systems that can exhibit aeroelastic instabilities. This paper introduces a validated methodology to investigate the aeroelastic instabilities of wing–propeller systems and to understand the dynamic mechanism leading to wing and whirl flutter and transition from one to the other. Factors such as nacelle positions along the wing span and chord and its propulsion system mounting stiffness are considered. Additionally, preliminary design guidelines are proposed for flutter-free wing–propeller systems applicable to novel aircraft designs. The study demonstrates how the critical speed of the wing–propeller systems is influenced by the mounting stiffness and propeller position. Weak mounting stiffnesses result in whirl flutter, while hard mounting stiffnesses lead to wing flutter. For the latter, the position of the propeller along the wing span may change the wing mode shapes and thus the flutter mechanism. Propeller positions closer to the wing tip enhance stability, but pusher configurations are more critical due to the mass distribution behind the elastic axis.
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.
In order to realistically predict and optimize the actual performance of a concentrating solar power (CSP) plant sophisticated simulation models and methods are required. This paper presents a detailed dynamic simulation model for a Molten Salt Solar Tower (MST) system, which is capable of simulating transient operation including detailed startup and shutdown procedures including drainage and refill. For appropriate representation of the transient behavior of the receiver as well as replication of local bulk and surface temperatures a discretized receiver model based on a novel homogeneous two-phase (2P) flow modelling approach is implemented in Modelica Dymola®. This allows for reasonable representation of the very different hydraulic and thermal properties of molten salt versus air as well as the transition between both. This dynamic 2P receiver model is embedded in a comprehensive one-dimensional model of a commercial scale MST system and coupled with a transient receiver flux density distribution from raytracing based heliostat field simulation. This enables for detailed process prediction with reasonable computational effort, while providing data such as local salt film and wall temperatures, realistic control behavior as well as net performance of the overall system. Besides a model description, this paper presents some results of a validation as well as the simulation of a complete startup procedure. Finally, a study on numerical simulation performance and grid dependencies is presented and discussed.
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 Dachquerträgers des Mercedes-Benz Viano/Vito ausführlich untersucht.
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
In the past, CSP and PV have been seen as competing technologies. Despite massive reductions in the electricity generation costs of CSP plants, PV power generation is - at least during sunshine hours - significantly cheaper. If electricity is required not only during the daytime, but around the clock, CSP with its inherent thermal energy storage gets an advantage in terms of LEC. There are a few examples of projects in which CSP plants and PV plants have been co-located, meaning that they feed into the same grid connection point and ideally optimize their operation strategy to yield an overall benefit. In the past eight years, TSK Flagsol has developed a plant concept, which merges both solar technologies into one highly Integrated CSP-PV-Hybrid (ICPH) power plant. Here, unlike in simply co-located concepts, as analyzed e.g. in [1] – [4], excess PV power that would have to be dumped is used in electric molten salt heaters to increase the storage temperature, improving storage and conversion efficiency. The authors demonstrate the electricity cost sensitivity to subsystem sizing for various market scenarios, and compare the resulting optimized ICPH plants with co-located hybrid plants. Independent of the three feed-in tariffs that have been assumed, the ICPH plant shows an electricity cost advantage of almost 20% while maintaining a high degree of flexibility in power dispatch as it is characteristic for CSP power plants. As all components of such an innovative concept are well proven, the system is ready for commercial market implementation. A first project is already contracted and in early engineering execution.