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The double-LNN Calibration technique for scattering parameter measurements of microstrip devices
(1995)
Compared to peripheral pain, trigeminal pain elicits higher levels of fear, which is assumed to enhance the interruptive effects of pain on concomitant cognitive processes. In this fMRI study we examined the behavioral and neural effects of trigeminal (forehead) and peripheral (hand) pain on visual processing and memory encoding. Cerebral activity was measured in 23 healthy subjects performing a visual categorization task that was immediately followed by a surprise recognition task. During the categorization task subjects received concomitant noxious electrical stimulation on the forehead or hand. Our data show that fear ratings were significantly higher for trigeminal pain. Categorization and recognition performance did not differ between pictures that were presented with trigeminal and peripheral pain. However, object categorization in the presence of trigeminal pain was associated with stronger activity in task-relevant visual areas (lateral occipital complex, LOC), memory encoding areas (hippocampus and parahippocampus) and areas implicated in emotional processing (amygdala) compared to peripheral pain. Further, individual differences in neural activation between the trigeminal and the peripheral condition were positively related to differences in fear ratings between both conditions. Functional connectivity between amygdala and LOC was increased during trigeminal compared to peripheral painful stimulation. Fear-driven compensatory resource activation seems to be enhanced for trigeminal stimuli, presumably due to their exceptional biological relevance.
The Carologistics team participates in the RoboCup Logistics League for the seventh year. The RCLL requires precise vision,
manipulation and path planning, as well as complex high-level decision
making and multi-robot coordination. We outline our approach with an
emphasis on recent modifications to those components.
The team members in 2018 are David Bosen, Christoph Gollok, Mostafa
Gomaa, Daniel Habering, Till Hofmann, Nicolas Limpert, Sebastian Schönitz,
Morian Sonnet, Carsten Stoffels, and Tarik Viehmann.
This paper is based on the last year’s team description.
The continuously growing amount of renewable sources starts compromising the stability of electrical grids. Contradictory to fossil fuel power plants, energy production of wind and photovoltaic (PV) energy is fluctuating. Although predictions have significantly improved, an outage of multi-MW offshore wind farms poses a challenging problem. One solution could be the integration of storage systems in the grid. After a short overview, this paper focuses on two exemplary battery storage systems, including the required power electronics. The grid integration, as well as the optimal usage of volatile energy reserves, is presented for a 5- kW PV system for home application, as well as for a 100- MW medium-voltage system, intended for wind farm usage. The efficiency and cost of topologies are investigated as a key parameter for large-scale integration of renewable power at medium- and low-voltage.
The overall energy efficiency of ventilation systems can be improved by considering not only single components, but by considering as well the interplay between every part of the system. With the help of the method "TOR" ("Technical Operations Research"), which was developed at the Chair of Fluid Systems at TU Darmstadt, it is possible to improve the energy efficiency of the whole system by considering all possible design choices programmatically. We show the ability of this systematic design approach with a ventilation system for buildings as a use case example.
Based on a Mixed-Integer Nonlinear Program (MINLP) we model the ventilation system. We use binary variables to model the selection of different pipe diameters. Multiple fans are model with the help of scaling laws. The whole system is represented by a graph, where the edges represent the pipes and fans and the nodes represents the source of air for cooling and the sinks, that have to be cooled. At the beginning, the human designer chooses a construction kit of different suitable fans and pipes of different diameters and different load cases. These boundary conditions define a variety of different possible system topologies. It is not possible to consider all topologies by hand. With the help of state of the art solvers, on the other side, it is possible to solve this MINLP.
Next to this, we also consider the effects of malfunctions in different components. Therefore, we show a first approach to measure the resilience of the shown example use case. Further, we compare the conventional approach with designs that are more resilient. These more resilient designs are derived by extending the before mentioned model with further constraints, that consider explicitly the resilience of the overall system. We show that it is possible to design resilient systems with this method already in the early design stage and compare the energy efficiency and resilience of these different system designs.
Due to the increasing complexity of software projects, software development is becoming more and more dependent on teams. The quality of this teamwork can vary depending on the team composition, as teams are always a combination of different skills and personality types. This paper aims to answer the question of how to describe a software development team and what influence the personality of the team members has on the team dynamics. For this purpose, a systematic literature review (n=48) and a literature search with the AI research assistant Elicit (n=20) were conducted. Result: A person’s personality significantly shapes his or her thinking and actions, which in turn influences his or her behavior in software development teams. It has been shown that team performance and satisfaction can be strongly influenced by personality. The quality of communication and the likelihood of conflict can also be attributed to personality.
This paper describes the realization of a novel neurocomputer which is based on the concepts of a coprocessor. In contrast to existing neurocomputers the main interest was the realization of a scalable, flexible system, which is capable of computing neural networks of arbitrary topology and scale, with full independence of special hardware from the software's point of view. On the other hand, computational power should be added, whenever needed and flexibly adapted to the requirements of the application. Hardware independence is achieved by a run time system which is capable of using all available computing power, including multiple host CPUs and an arbitrary number of neural coprocessors autonomously. The realization of arbitrary neural topologies is provided through the implementation of the elementary operations which can be found in most neural topologies.
The transition within transportation towards battery electric vehicles can lead to a more sustainable future. To account for the development goal ‘climate action’ stated by the United Nations, it is mandatory, within the conceptual design phase, to derive energy-efficient system designs. One barrier is the uncertainty of the driving behaviour within the usage phase. This uncertainty is often addressed by using a stochastic synthesis process to derive representative driving cycles and by using cycle-based optimization. To deal with this uncertainty, a new approach based on a stochastic optimization program is presented. This leads to an optimization model that is solved with an exact solver. It is compared to a system design approach based on driving cycles and a genetic algorithm solver. Both approaches are applied to find efficient electric powertrains with fixed-speed and multi-speed transmissions. Hence, the similarities, differences and respective advantages of each optimization procedure are discussed.
The course Physics for Electrical Engineering is part of the curriculum of the
bachelor program Electrical Engineering at University of Applied Science Aachen.
Before covid-19 the course was conducted in a rather traditional way with all parts
(lecture, exercise and lab) face-to-face. This teaching approach changed
fundamentally within a week when the covid-19 limitations forced all courses to
distance learning. All parts of the course were transformed to pure distance learning
including synchronous and asynchronous parts for the lecture, live online-sessions
for the exercises and self-paced labs at home. Using these methods, the course was
able to impart the required knowledge and competencies. Taking the teacher’s
observations of the student’s learning behaviour and engagement, the formal and
informal feedback of the students and the results of the exams into account, the new
methods are evaluated with respect to effectiveness, sustainability and suitability for
competence transfer. Based on this analysis strong and weak points of the concept
and countermeasures to solve the weak points were identified. The analysis further
leads to a sustainable teaching approach combining synchronous and asynchronous
parts with self-paced learning times that can be used in a very flexible manner for
different learning scenarios, pure online, hybrid (mixture of online and presence
times) and pure presence teaching.
This chapter describes three general strategies to master uncertainty in technical systems: robustness, flexibility and resilience. It builds on the previous chapters about methods to analyse and identify uncertainty and may rely on the availability of technologies for particular systems, such as active components. Robustness aims for the design of technical systems that are insensitive to anticipated uncertainties. Flexibility increases the ability of a system to work under different situations. Resilience extends this characteristic by requiring a given minimal functional performance, even after disturbances or failure of system components, and it may incorporate recovery. The three strategies are described and discussed in turn. Moreover, they are demonstrated on specific technical systems.
Solution of plane anisotropic elastostatical boundary value problems by singular integral equations
(1982)
Smart pixel : photonic mixer device (PMD) ; new system concept of a 3D-imaging camera-on-a-chip
(1998)
The Volatility Framework is a collection of tools for the analysis of computer RAM. The framework offers a multitude of analysis options and is used by many investigators worldwide. Volatility currently comes with a command line interface only, which might be a hinderer for some investigators to use the tool. In this paper we present a GUI and extensions for the Volatility Framework, which on the one hand simplify the usage of the tool and on the other hand offer additional functionality like storage of results in a database, shortcuts for long Volatility Framework command sequences, and entirely new commands based on correlation of data stored in the database.
Short term effects of magnetic resonance imaging on excitability of the motor cortex at 1.5T and 7T
(2010)
Rationale and Objectives
The increasing spread of high-field and ultra-high-field magnetic resonance imaging (MRI) scanners has encouraged new discussion of the safety aspects of MRI. Few studies have been published on possible cognitive effects of MRI examinations. The aim of this study was to examine whether changes are measurable after MRI examinations at 1.5 and 7 T by means of transcranial magnetic stimulation (TMS).
Materials and Methods
TMS was performed in 12 healthy, right-handed male volunteers. First the individual motor threshold was specified, and then the cortical silent period (SP) was measured. Subsequently, the volunteers were exposed to the 1.5-T MRI scanner for 63 minutes using standard sequences. The MRI examination was immediately followed by another TMS session. Fifteen minutes later, TMS was repeated. Four weeks later, the complete setting was repeated using a 7-T scanner. Control conditions included lying in the 1.5-T scanner for 63 minutes without scanning and lying in a separate room for 63 minutes. TMS was performed in the same way in each case. For statistical analysis, Wilcoxon's rank test was performed.
Results
Immediately after MRI exposure, the SP was highly significantly prolonged in all 12 subjects at 1.5 and 7 T. The motor threshold was significantly increased. Fifteen minutes after the examination, the measured value tended toward normal again. Control conditions revealed no significant differences.
Conclusion
MRI examinations lead to a transient and highly significant alteration in cortical excitability. This effect does not seem to depend on the strength of the static magnetic field.