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Was ist dran am Hype um die Cloud? Während Gartner Research bereits von einem Abwärts trend spricht, sehen Prof. Alda und Prof. Bonne von der Hochschule Bonn-Rhein-Sieg viele gewinnbringende Anwendungsszenarien in der Praxis. Insbesondere auf den Finance- und Accounting-Bereich lassen sich die positiven Vorteile einer Cloud- Lösung übertragen.
The Anomalous X‐ray Pulsar 4U 0142+61 is the only neutron star where it is believed that one of the long searched‐for ‘fallback’ disks has been detected in the mid‐IR by Wang et al. [1] using Spitzer. Such a disk originates from material falling back to the NS after the supernova. We search for cold circumstellar material in the 90 GHz continuum using the Plateau de Bure Interferometer. No millimeter flux is detected at the position of 4U 0142+61, the upper flux limit is 150 μJy corresponding to the 3σ noise rms level. The re‐processed Spitzer MIPS 24μm data presented previously by Wang et al. [2] show some indication of flux enhancement at the position of the neutron star, albeit below the 3σ statistical significance limit. At far infrared wavelengths the source flux densities are probably below the Herschel confusion limits.
We report on submillimetre bolometer observations of the isolated neutron star RX J1856.5−3754 using the Large Apex Bolometer Camera bolometer array on the Atacama Pathfinder Experiment telescope. No cold dust continuum emission peak at the position of RX J1856.5−3754 was detected. The 3σ flux density upper limit of 5 mJy translates into a cold dust mass limit of a few earth masses. We use the new submillimetre limit, together with a previously obtained H-band limit, to constrain the presence of a gaseous, circumpulsar disc. Adopting a simple irradiated disc model, we obtain a mass accretion limit of Graphic and a maximum outer disc radius of ∼1014 cm. By examining the projected proper motion of RX J1856.5−3754, we speculate about a possible encounter of the neutron star with a dense fragment of the CrA molecular cloud a few thousand years ago.
In Artificial Intelligence, numerous learning paradigms have been developed over the past decades. In most cases of embodied and situated agents, the learning goal for the artificial agent is to „map“ or classify the environment and the objects therein [1, 2], in order to improve navigation or the execution of some other domain-specific task. Dynamic environments and changing tasks still pose a major challenge for robotic learning in real-world domains. In order to intelligently adapt its task strategies, the agent needs cognitive abilities to more deeply understand its environment and the effects of its actions. In order to approach this challenge within an open-ended learning loop, the XPERO project (http://www.xpero.org) explores the paradigm of Learning by Experimentation to increase the robot's conceptual world knowledge autonomously. In this setting, tasks which are selected by an actionselection mechanism are interrupted by a learning loop in those cases where the robot identifies learning as necessary for solving a task or for explaining observations. It is important to note that our approach targets unsupervised learning, since there is no oracle available to the agent, nor does it have access to a reward function providing direct feedback on the quality of its learned model, as e.g. in reinforcement learning approaches. In the following sections we present our framework for integrating autonomous robotic experimentation into such a learning loop. In section 1 we explain the different modules for stimulation and design of experiments and their interaction. In section 2 we describe our implementation of these modules and how we applied them to a real world scenario to gather target-oriented data for learning conceptual knowledge. There we also indicate how the goaloriented data generation enables machine learning algorithms to revise the failed prediction model.
Domestic Robotics
(2008)
Domestic Robotics
(2016)
Ice accumulation in the blades of wind turbines can cause them to describe anomalous rotations or no rotations at all, thus affecting the generation of electricity and power output. In this work, we investigate the problem of ice accumulation in wind turbines by framing it as anomaly detection of multi-variate time series. Our approach focuses on two main parts: first, learning low-dimensional representations of time series using a Variational Recurrent Autoencoder (VRAE), and second, using unsupervised clustering algorithms to classify the learned representations as normal (no ice accumulated) or abnormal (ice accumulated). We have evaluated our approach on a custom wind turbine time series dataset, for the two-classes problem (one normal versus one abnormal class), we obtained a classification accuracy of up to 96$\%$ on test data. For the multiple-class problem (one normal versus multiple abnormal classes), we present a qualitative analysis of the low-dimensional learned latent space, providing insights into the capacities of our approach to tackle such problem. The code to reproduce this work can be found here https://github.com/agrija9/Wind-Turbines-VRAE-Paper.
It has been well proved that deep networks are efficient at extracting features from a given (source) labeled dataset. However, it is not always the case that they can generalize well to other (target) datasets which very often have a different underlying distribution. In this report, we evaluate four different domain adaptation techniques for image classification tasks: DeepCORAL, DeepDomainConfusion, CDAN and CDAN+E. These techniques are unsupervised given that the target dataset dopes not carry any labels during training phase. We evaluate model performance on the office-31 dataset. A link to the github repository of this report can be found here: https://github.com/agrija9/Deep-Unsupervised-Domain-Adaptation.
Self-supervised learning has proved to be a powerful approach to learn image representations without the need of large labeled datasets. For underwater robotics, it is of great interest to design computer vision algorithms to improve perception capabilities such as sonar image classification. Due to the confidential nature of sonar imaging and the difficulty to interpret sonar images, it is challenging to create public large labeled sonar datasets to train supervised learning algorithms. In this work, we investigate the potential of three self-supervised learning methods (RotNet, Denoising Autoencoders, and Jigsaw) to learn high-quality sonar image representation without the need of human labels. We present pre-training and transfer learning results on real-life sonar image datasets. Our results indicate that self-supervised pre-training yields classification performance comparable to supervised pre-training in a few-shot transfer learning setup across all three methods. Code and self-supervised pre-trained models are be available at https://github.com/agrija9/ssl-sonar-images
Die nachhaltige Organisation des Verkehrs soll auf kostengünstige, umweltfreundliche und nutzerfreundlichere Nahverkehrskonzepte, die möglichst viele Bürger zur Nutzung des ÖPNV einladen, abzielen. Vor diesem Hintergrund ist es das Ziel dieses Beitrags, instrumentelle Ansatzpunkte für ein Nachhaltigkeitscontrolling in ÖPNV-Unternehmen aufzuzeigen. Hierzu werden nachfolgend die Berücksichtigung der Nachhaltigkeit bei Investitionsentscheidungen, das Carbon Accounting (Transparenz über CO2-Emissionen), die Integration der ökologischen, ökonomischen und sozialen Nachhaltigkeitsdimension bei der Berichterstattung und die Einbindung der genannten Instrumente in ein Managementsystem skizziert. Die Nachhaltigkeitsdimensionen Ökologie, Ökonomie und Soziales lassen sich gut mit Hilfe mehrdimensionaler, integrierter Managementsysteme in der Organisation verankern und systematisch in interne Strukturen und Prozesse einbetten. Integrierte Managementsysteme können so eine wichtige Voraussetzung für ein effizientes Nachhaltigkeitscontrolling sein.
Das Roboter-Baukastensystem ProfiBot vom Fraunhofer Institut IAIS wird in Zusammenarbeit mit der Hochschule für Technik und Wirtschaft (HTW-Saarbrücken) und der Firma HighTec EDV-Systeme GmbH aus Saarbrücken zu einer mobilen Roboterplattform für die Ausbildung an Hochschulen weiterentwickelt. In dieser Diplomarbeit wird das vorgegebene eingebettete System und ein Echtzeitbetriebssystem der Firma HighTec EDV-Systeme GmbH benutzt, um die Regelung der Motoren und der Fahrzeugbewegungen zu implementieren. Ein Benutzer kann die mobile Roboterplattform mithilfe einer Schnittstelle zur Anwendungsprogrammierung (API) auf Basis von physikalischen Größen ansteuern und aktuelle Zustände abfragen. Um die mobile Roboterplattform flexibel benutzen zu können, werden mit einer zusätzlichen Elektronik digitale und analoge Ein- und Ausgänge des eingebetteten Systems auf die Anwendungen der Robotik angepasst. Neben einem Programmstartschalter und Status-LEDs können vier Schaltleisten zur Kollisionserkennung und analoge Sensoren mit Einheitssignal angeschlossen werden. Zuletzt wird die Reglerstruktur kalibriert und getestet.
Problem Fersenbeinfraktur
(2007)