123 research outputs found
Symbolische Interpretation Technischer Zeichnungen
Gescannte und vektorisierte technische Zeichnungen werden automatisch unter Nutzung eines Netzes von Modellen in eine hochwertige Datenstruktur migriert. Die Modelle beschreiben die Inhalte der Zeichnungen hierarchisch und deklarativ. Modelle für einzelne Bestandteile der Zeichnungen können paarweise unabhängig entwickelt werden. Dadurch werden auch sehr komplexe Zeichnungsklassen wie Elektroleitungsnetze oder Gebäudepläne zugänglich. Die Modelle verwendet der neue, sogenannte Y-Algorithmus: Hypothesen über die Deutung lokaler Zeichnungsinhalte werden hierarchisch generiert. Treten bei der Nutzung konkurrierender Modelle Konflikte auf, werden diese protokolliert. Mittels des Konfliktbegriffes können konsistente Interpretationen einer kompletten Zeichnung abstrakt definiert und während der Analyse einer konkreten Zeichnung bestimmt werden. Ein wahrscheinlichkeitsbasiertes Gütemaß bewertet jede dieser alternativen, globalen Interpretationen. Das Suchen einer bzgl. dieses Maßes optimalen Interpretation ist ein NP-hartes Problem. Ein Branch and Bound-Algorithmus stellt die adäquate Lösung dar
Current state of ASoC design methodology
This paper gives an overview of the current state of ASoC design
methodology and presents preliminary results on evaluating the learning
classifier system XCS for the control of a QuadCore. The ASoC design
methodology can determine system reliability based on activity, power and
temperature analysis, together with reliability block diagrams. The
evaluation of the XCS shows that in the evaluated setup, XCS can find
optimal operating points, even in changed environments or with changed
reward functions. This even works, though limited, without the genetic
algorithm the XCS uses internally. The results motivate us to continue
the evaluation for more complex setups
Pruning population size in XCS for complex problems
In this report, we show how to prune the population size of the Learning Classifier System XCS for complex problems. We say a problem is complex, when the number of specified bits of the optimal start classifiers (the prob lem dimension) is not constant. First, we derive how to estimate an equiv- alent problem dimension for complex problems based on the optimal start classifiers. With the equivalent problem dimension, we calculate the optimal maximum population size just like for regular problems, which has already been done. We empirically validate our results.
Furthermore, we introduce a subsumption method to reduce the number of classifiers. In contrast to existing methods, we subsume the classifiers after the learning process, so subsuming does not hinder the evolution of optimal classifiers, which has been reported previously. After subsumption, the number of classifiers drops to about the order of magnitude of the optimal classifiers while the correctness rate nearly stays constant
Collective PV-RCNN: A Novel Fusion Technique using Collective Detections for Enhanced Local LiDAR-Based Perception
Comprehensive perception of the environment is crucial for the safe operation
of autonomous vehicles. However, the perception capabilities of autonomous
vehicles are limited due to occlusions, limited sensor ranges, or environmental
influences. Collective Perception (CP) aims to mitigate these problems by
enabling the exchange of information between vehicles. A major challenge in CP
is the fusion of the exchanged information. Due to the enormous bandwidth
requirement of early fusion approaches and the interchangeability issues of
intermediate fusion approaches, only the late fusion of shared detections is
practical. Current late fusion approaches neglect valuable information for
local detection, this is why we propose a novel fusion method to fuse the
detections of cooperative vehicles within the local LiDAR-based detection
pipeline. Therefore, we present Collective PV-RCNN (CPV-RCNN), which extends
the PV-RCNN++ framework to fuse collective detections. Code is available at
https://github.com/ekut-esComment: accepted at IEEE ITSC 202
Ways of improving the precision of eye tracking data: Controlling the influence of dirt and dust on pupil detection
Eye-tracking technology has to date been primarily employed in research. With recent advances in aordable video-based devices, the implementation of gaze-aware smartphones, and marketable driver monitoring systems, a considerable step towards pervasive eye-tracking has been made. However, several new challenges arise with the usage of eye-tracking in the wild and will need to be tackled to increase the acceptance of this technology. The main challenge is still related to the usage of eye-tracking together with eyeglasses, which in combination with reflections for changing illumination conditions will make a subject "untrackable". If we really want to bring the technology to the consumer, we cannot simply exclude 30% of the population as potential users only because they wear eyeglasses, nor can we make them clean their glasses and the device regularly. Instead, the pupil detection algorithms need to be made robust to potential sources of noise. We hypothesize that the amount of dust and dirt on the eyeglasses and the eye-tracker camera has a significant influence on the performance of currently available pupil detection algorithms. Therefore, in this work, we present a systematic study of the eect of dust and dirt on the pupil detection by simulating various quantities of dirt and dust on eyeglasses. Our results show 1) an overall high robustness to dust in an o-focus layer. 2) the vulnerability of edge-based methods to even small in-focus dust particles. 3) a trade-o between tolerated particle size and particle amount, where a small number of rather large particles showed only a minor performance impact
Error detection techniques applicable in an architecture framework and design methodology for autonomic SoCs
This work-in-progress paper surveys error detection techniques for transient, timing, permanent and logical errors in system-on-chip (SoC) design and discusses their applicability in the design of monitors for our Autonomic SoC architecture framework. These monitors will be needed to deliver necessary signals to achieve fault-tolerance, self-healing and self-calibration in our Autonomic SoC architecture. The framework combines the monitors with a welltailored design methodology that explores how the Autonomic SoC (ASoC) can cope with malfunctioning subcomponents.1st IFIP International Conference on Biologically Inspired Cooperative Computing - Chip-DesignRed de Universidades con Carreras en Informática (RedUNCI
Relic Abundance of LKP Dark Matter in UED model including Effects of Second KK Resonances
We reevaluate the thermal relic density of the Kaluza-Klein (KK) dark matter
in universal extra dimension models. In particular, we consider the effect of
the resonance caused by second KK particles on the density. We find that the
annihilation cross sections relevant to the density are significantly enhanced
due to the resonance when the Higgs boson mass is large enough (m_h \gtrsim 200
GeV). As a result, the mass of the dark matter particle consistent with the
WMAP observation is increased compared to the result which does not include any
resonance.Comment: 15 pages, 6 figure
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