Refine
Year of publication
Document Type
- Conference Proceeding (57)
- Article (33)
- Part of a Book (7)
- Patent (3)
- Report (3)
- Book (1)
- Doctoral Thesis (1)
- Poster (1)
Has Fulltext
- no (106) (remove)
Keywords
- Energy storage (4)
- Power plants (4)
- Associated liquids (3)
- Concentrated solar power (3)
- Hybrid energy system (3)
- Electricity generation (2)
- Smart Building (2)
- Solar thermal technologies (2)
- UAV (2)
- electro mobility (2)
- 3D object detection (1)
- Aircraft design (1)
- Android (1)
- Anomalieerkennung (1)
- BIM (1)
- Brake set-up (1)
- Braking curves (1)
- Building Automation (1)
- Bumblebees (1)
- CCD-Bildwandler (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)
- 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)
- Freight rail (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)
- Informationssicherheit (1)
- Informationssicherheitsmanagement (1)
- IoT (1)
- Kalman filter (1)
- Klassifikator <Informatik> (1)
- LiDAR (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)
- Obstacle avoidance (1)
- Open Source (1)
- PEM fuel cells (1)
- PTC (1)
- Parabolic trough collector (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)
- 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)
- 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 (106) (remove)
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.
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.
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.
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.