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We present a pipeline for recognizing dynamic freehand gestures on mobile devices based on extracting depth information coming from a single Time-of-Flight sensor. Hand gestures are recorded with a mobile 3D sensor, transformed frame by frame into an appropriate 3D descriptor and fed into a deep LSTM network for recognition purposes. LSTM being a recurrent neural model, it is uniquely suited for classifying explicitly time-dependent data such as hand gestures. For training and testing purposes, we create a small database of four hand gesture classes, each comprising 40 × 150 3D frames. We conduct experiments concerning execution speed on a mobile device, generalization capability as a function of network topology, and classification ability ‘ahead of time’, i.e., when the gesture is not yet completed. Recognition rates are high (>95%) and maintainable in real-time as a single classification step requires less than 1 ms computation time, introducing freehand gestures for mobile systems.
In this contribution we present a novel approach to transform data from time-of-flight (ToF) sensors to be interpretable by Convolutional Neural Networks (CNNs). As ToF data tends to be overly noisy depending on various factors such as illumination, reflection coefficient and distance, the need for a robust algorithmic approach becomes evident. By spanning a three-dimensional grid of fixed size around each point cloud we are able to transform three-dimensional input to become processable by CNNs. This simple and effective neighborhood-preserving methodology demonstrates that CNNs are indeed able to extract the relevant information and learn a set of filters, enabling them to differentiate a complex set of ten different gestures obtained from 20 different individuals and containing 600.000 samples overall. Our 20-fold cross-validation shows the generalization performance of the network, achieving an accuracy of up to 98.5% on validation sets comprising 20.000 data samples. The real-time applicability of our system is demonstrated via an interactive validation on an infotainment system running with up to 40fps on an iPad in the vehicle interior.
Applying step heating thermography to wind turbine rotor blades as a non-destructive testing method
(2017)
Increasing economic viability and safety through structural health monitoring of wind turbines
(2017)
Serious accidents with property damage or even human casualties, result from structural flaws in wind turbine rotor blades. Common maintenance practices result in long downtimes and do not lead to the required results. Therefore, the Ruhr West University of Applied Sciences and the iQbis Consulting GmbH, currently research a new structural health monitoring method for wind turbine rotor blades. The goal of this project is to build a sensor system that can detect structural weaknesses inside of rotor blades without the need of downtime for industrial climbers. This technology has the potential to prevent accidents, save lives, extend the useful life of wind turbines and optimize the production of green energy.
Checking wind turbines for damage is a common problem for operators of wind parks, as regular inspections are legally required in many countries and prevention is economically viable. While some of the common forms of damage are easily visible on the surface, structural problems can remain invisible for years before they eventually result in catastrophic failure of a rotor blade. Common forms of testing fibre composite parts like ultrasonic testing or X-ray tests are impractical due to the large dimensions of wind turbine components and their limited accessibility for any short-range methods. Active thermographic inspection of wind turbines is a promising approach to testing for structural flaws beneath the surface of rotor blades. As part of an ongoing research project, a setup for testing the general viability of this method was built and used to compare different thermographic cameras. A sample cut from a discarded rotor blade was modified to emulate structural damage. The results are promising for the development of a cost effective on-site testing system.
Autonomous driving is one of the future visions in which many vehicle manufacturers are working with high pressure.
Nowadays, it is already supported partially by high-class vehicles. A completely autonomous journey is indeed the goal, but in cars for
the public road traffic still not available. Automatic lane keeping assistants, speed regulators as well as shield and obstacle detections
are parts or precursors on the way to completely autonomous driving.
The American vehicle manufacturer Tesla is not only known for its electric drive, but also for the fact that high-pressure work is carried out on the autonomous drive. Tesla is thus the only vehicle manufacturer to use its users as so-called beta testers for its assistance systems. The progress and the function of the currently available Model S in the field of assistance systems and autonomic driving is documented and described in this paper. It is shown how good or bad the test vehicle manages scenarios in normal road traffic situations
with the assistance systems, e.g. lane keeping assistant, speed control, lane change and distance assistant, and which scenarios can
not be managed by the vehicle itself.
Das kEFIR‐Projekt untersucht die praktische Anwendung von thermographischen Verfahren zur Analyse der strukturellen Integrität von Windkraftrotorblättern. Das Projekt entstand in Zusammenarbeit der Hochschule Ruhr West (HRW) mit der IQbis Consulting GmbH im Rahmen eines ZIM‐Förderprojekts des Bundesministeriums für Wirtschaft und Energie (BMWi). Hintergrund ist die zunehmende Anzahl von Windkraftanlagen (WKA) und der somit steigende Wartungsaufwand. Um einen reibungslosen Betrieb dieser Anlagen zu gewährleisten und damit den besonderen Anforderungen an die Verfügbarkeit energieerzeugender Anlagen sicherzustellen, ist ein Bedarf an qualitativ hochwertigen Fehleranalysesystemen für im Betrieb befindlicher WKA von besonderer Bedeutung. Erfahrungsgemäß ist der Zeitaufwand für diese Inspektionen mit aktuellen Mitteln sehr groß und wird üblicherweise mit mehreren Arbeitstagen kalkuliert. Die Reproduzierbarkeit der gewonnenen Daten ist bei den derzeitigen Methoden meist nicht gewährleistet. Um frühzeitig auf Instabilitäten oder Schäden in den Rotorblättern einer WKA aufmerksam zu werden, ist die Entwicklung eines schnellen und qualitativ hoch wertigen Fehleranalysesystems von zentraler Bedeutung. Ein Forschungsschwerpunkt in diesem Zusammenhang ist die Entwicklung von geeigneten bildgebenden und berührungslosen Verfahren, welche bei den Inspektionen eingesetzt werden können. Beispielsweise erlaubt der Einsatz thermographischer Sensoren eine Analyse nicht nur der Rotorblattoberfläche, sondern auch ihrer inneren Struktur. Weiterhin ist aufgrund des schnell wachsenden Marktes bei unbemannten Luftfahrzeugen, wie beispielsweise positionsstabiler Quatrocoptersysteme, eine zusätzliche Möglichkeit gegeben, die Inspektion von Windenergieanlagen mit Hilfe mobiler, kompakter und fliegender Analysesysteme zu unterstützen.