Fachbereich Elektrotechnik und Informationstechnik
Refine
Year of publication
- 2020 (29) (remove)
Institute
Has Fulltext
- no (29)
Document Type
- Conference Proceeding (13)
- Article (10)
- Book (2)
- Part of a Book (2)
- Report (1)
- Review (1)
Keywords
- MINLP (3)
- Experimental validation (2)
- Anwendungsorientierter Forschungsansatz (1)
- Case study (1)
- Change culture (1)
- Cooling system (1)
- Design Science Research (1)
- E-Learning (1)
- Efficiency optimization (1)
- Engineering optimisation (1)
- Engineering optimization (1)
- Enhanced Telecom Operations Map (1)
- Industrial optimisation (1)
- Interactive process mining (1)
- Lean thinking (1)
- Literaturanalyse (1)
- Literaturrecherche (1)
- MILP (1)
- Methodology (1)
- Mixed-integer programming (1)
- Network design (1)
- OR 2019 (1)
- Objective data (1)
- Piecewise linearization (1)
- Powertrain (1)
- Praxisprojekte (1)
- Process engineering (1)
- Pumping systems (1)
- Referenzmodellierung (1)
- Resilience assessment (1)
- Resilience metric graph theory (1)
- Resilient infrastructure (1)
- Technical Operations Research (1)
- Text Analytics (1)
- Text Mining (1)
- Water distribution system (1)
- Water supply system (1)
- anticipation strategy (1)
- business analytics (1)
- decision analytics (1)
- digital economy (1)
- fault detection (1)
- mathematical optimization (1)
- water supply system (1)
Is part of the Bibliography
- no (29)
The field of Cognitive Robotics aims at intelligent decision making of autonomous robots. It has matured over the last 25 or so years quite a bit. That is, a number of high-level control languages and architectures have emerged from the field. One concern in this regard is the action language GOLOG. GOLOG has been used in a rather large number of applications as a high-level control language ranging from intelligent service robots to soccer robots. For the lower level robot software, the Robot Operating System (ROS) has been around for more than a decade now and it has developed into the standard middleware for robot applications. ROS provides a large number of packages for standard tasks in robotics like localisation, navigation, and object recognition. Interestingly enough, only little work within ROS has gone into the high-level control of robots. In this paper, we describe our approach to marry the GOLOG action language with ROS. In particular, we present our architecture on inte grating golog++, which is based on the GOLOG dialect Readylog, with the Robot Operating System. With an example application on the Pepper service robot, we show how primitive actions can be easily mapped to the ROS ActionLib framework and present our control architecture in detail.
Safety of subjects during radiofrequency exposure in ultra-high-field magnetic resonance imaging
(2020)
Magnetic resonance imaging (MRI) is one of the most important medical imaging techniques. Since the introduction of MRI in the mid-1980s, there has been a continuous trend toward higher static magnetic fields to obtain i.a. a higher signal-to-noise ratio. The step toward ultra-high-field (UHF) MRI at 7 Tesla and higher, however, creates several challenges regarding the homogeneity of the spin excitation RF transmit field and the RF exposure of the subject. In UHF MRI systems, the wavelength of the RF field is in the range of the diameter of the human body, which can result in inhomogeneous spin excitation and local SAR hotspots. To optimize the homogeneity in a region of interest, UHF MRI systems use parallel transmit systems with multiple transmit antennas and time-dependent modulation of the RF signal in the individual transmit channels. Furthermore, SAR increases with increasing field strength, while the SAR limits remain unchanged. Two different approaches to generate the RF transmit field in UHF systems using antenna arrays close and remote to the body are investigated in this letter. Achievable imaging performance is evaluated compared to typical clinical RF transmit systems at lower field strength. The evaluation has been performed under consideration of RF exposure based on local SAR and tissue temperature. Furthermore, results for thermal dose as an alternative RF exposure metric are presented.
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.
Cyberspace is "the environment formed by physical and non-physical components to store, modify, and exchange data using computer networks" (NATO CCDCOE). Beyond that, it is an environment where people interact. IT attacks are hostile, non-cooperative interactions that can be described with conflict theory. Applying conflict theory to IT security leads to different objectives for end-user education, requiring different formats like agency-based competence developing games.
The development of resilient technical systems is a challenging task, as the system should adapt automatically to unknown disturbances and component failures. To evaluate different approaches for deriving resilient technical system designs, we developed a modular test rig that is based on a pumping system. On the basis of this example
system, we present metrics to quantify resilience and an algorithmic approach to improve resilience. This approach enables the pumping system to automatically react on unknown disturbances and to reduce the impact of component failures. In this case, the system is able to automatically adapt its topology by activating additional valves. This enables the system to still reach a minimum performance, even in case of failures. Furthermore, timedependent disturbances are evaluated continuously, deviations from the original state are automatically detected and anticipated in the future. This allows to reduce the impact of future disturbances and leads to a more resilient
system behaviour.