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DamokleS 4.0
(2019)
Dieser interne Bericht beschreibt die Zielsetzung, Durchführung und Auswertung des Projektes Damokles 4.0. Das Projekt zielt darauf ab, neue, digitale Technologien in die Schwerindustrie einzuführen um Produktionsprozesse zu modernisieren. Unter Einsatz neuer Technologien, insbesondere mobiler Geräte, soll ein cyberphyiskalisches System (CPS) eine kontextbasierte und künstlich intelligente Unterstützung der Mitarbeiter in den Bereichen der Schwerindustrie ermöglichen. Hierzu werden typische Anwendungsfälle und die damit verbundenen Szenarien zur Unterstützung der Mitarbeiter auf Basis von neuen, flexiblen, adaptiven und mobilen Technologien, wie Augmented Reality und künstlicher Intelligenz, modelliert. Um den Prototypen einer AR-Anwendung und einer kamerabasierte Personenverfolgung zu entwickeln, hat die Hochschule Ruhr West im kleinen Technikum am Campus Bottrop eine entsprechende industrielle Umgebung simuliert. Die Projektergebnisse zeigen die Anwendbarkeit der vorgeschlagenen Softwareansätze und die Ergebnisse einer Untersuchung der psychologischen Einflüsse auf die Mitarbeiter.
RELEVANCE & RESEARCH QUESTION: Currently the effectiveness of Virtual Reality (VR) and Augmented Reality (AR) systems as practice teaching methods are virtually uncharted. The proof that these systems can provide the same or better learning outcomes than a text instructed practical task could represent a significant benefit for educational activities. METHODS & DATA: To fathom the effectiveness, an experimental study with the three conditions (VR, AR and a real setup) were used to teach participant how to assemble a standard computer. Each condition was divided into two parts: part one in which participants were confronted with their specific scenario, part two in which participants had to go through a real practice after one week. The learning outcome was determined by the designation of hardware parts, a quiz that queried their function and the correct assembling of the components in addition to needed time. Apart from the mere performance, the acceptance of such application in academic context and difference in evaluation by men and women were of interest. RESULTS: Results concerning the Learning Outcome showed that participants from the VR condition outperformed those learned from the real setup ((M=10.0, SD=0.0) [virtual reality] vs. (M=8.95, SD=1.27) [control]). Furthermore, results from the assembling duration assessment demonstrated that VR Group Participants completed their tasks 6.62% faster than the control group. Regarding the identification of Hardware Parts, both groups scored a significant improvement during the post condition compared to the first test run, indicating a learning progress. However, due to the VR group achieving a better outcome in average answers and a more significant difference between the trials, the results indicate a better performance by participants assigned to the VR condition. ADDED VALUE: The results revealed that VR and AR systems could exceed text-based approach in terms of learning outcome performance. The effectiveness of the systems implicates a major benefit for the educational landscape, as learning content that is not realizable in terms of cost, distance or logistics could be designed as an immersive and engaging experience.
Relax yourself - Using Virtual Reality to enhance employees mental health and work performance
(2019)
This paper presents work-in-progress aiming to develop an actively adapting virtual reality (VR) relaxation application. Due to the immersive nature of VR technologies, people can escape from their real environment and get into a relaxing state. Goal of the application is to adapt to the users' physiological signals to foster the positive effect. Until now, a first version of the VR application was constructed and is currently evaluated in an experiment. Preliminary results of this study demonstrate that people appreciate the immersion into the virtual environment and escape from reality. Moreover, participants highlighted the option to adapt users' needs and preferences. Based on the final study data, the constructed application will be enhanced with regard to adoption and surrounding factors.
Das vorliegende Paper gibt einen Überblick über das Verhalten von modernen, autonom navigierenden Fahrzeugen in Baustellen. Dabei werden besondere Herausforderungen für die autonome Navigation im Baustellenbereich benannt. Außerdem wird ein Überblick über die Sensorausstattung und die Fahrerassistenzsysteme von modernen Fahrzeugen gegeben und es werden Technologien vorgestellt, die für eine Verbesserung der autonomen Navigation durch Baustellen genutzt werden können. Es wird ein Versuch durchgeführt, der aufzeigt, wie zuverlässig moderne Fahrzeuge durch Baustellensituationen navigieren können. Dabei werden Schwachstellen, wie bspw. die mangelnde Verfügbarkeit von Fahrerassistenzsystemen bei niedrigen Geschwindigkeiten, aufgedeckt.
In this review, we describe current Machine Learning approaches to hand gesture recognition with depth data from time-of-flight sensors. In particular, we summarise the achievements on a line of research at the Computational Neuroscience laboratory at the Ruhr West University of Applied Sciences. Relating our results to the work of others in this field, we confirm that Convolutional Neural Networks and Long Short-Term Memory yield most reliable results. We investigated several sensor data fusion techniques in a deep learning framework and performed user studies to evaluate our system in practice. During our course of research, we gathered and published our data in a novel benchmark dataset (REHAP), containing over a million unique three-dimensional hand posture samples.
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 hochwertigen 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.
Industry 4.0 is known as the fourth industrial revolution which refers to the integration of technologies that make the factories interoperable by seamlessly connecting machines, employees and sensors for communication. In Industry 4.0, one of the key features is the use of new technologies to recognize the current context. Thus, the employees are supported with contextual information for speeding up decision-making during various processes related to planning, production, maintenance, etc. As a contribution to this area, the work described here aims to introduce a cyber-physical system (CPS) approach to provide context-based and intelligent support to employees in heavy industries using new technologies, especially in the field of mobile devices. In this work, mobile device sensors and image processing techniques are used to recognize the context which requires specific support. In addition, new scenarios and associated processes are developed to support the employees on the basis of new, flexible, adaptive and mobile technologies.
Artificial Intelligence Driven Human-Machine Collaboration Scenarios in Virtual Reality (Poster)
(2018)
A self-driving car that operates on the SAE automation level 3 or 4 can navigate through different traffic conditions without human input. If such a system is on its operating limits, it will emit a takeover request before shutting down. This request will likely generate a physical response of the driver. Our goal is to shed light on the stress perception of drivers in various scenarios. To this end, we have carried out a feasibility study for preparation. Two subjects drove an autonomous vehicle and during the ride ECG signals were recorded, and afterwards evaluated. Unfortunately, the stress reaction to takeover requests could not be investigated, due to the poor function of the autonomous driving mode from the vehicle, however the reaction to autopilot misconduct without warning to the driver could be investigated instead.
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.