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Currently, car assistant systems mainly try to prevent accidents. Increasing built-in car technology also extends the potential applications in vehicles. Future cars might have virtual windshields that augment the traffic or individual virtual assistants interacting with the user. In this paper, we explore the potential of an assistant system that helps the car’s occupants to calm down and reduce stress when they experience an accident in front of them. We present requirements from a discussion (N= 11) and derive a system design from them. Further, we test the system design in a video-based simulator study (N= 43). Our results indicate that an accident support system increases perceived control and trust and helps to calm down the user.
"Quarter agile" aims to promote older people's social participation and community
via physical and cognitive training which the participants also help create. The project relies heavily on the use of smartphones as training support. Loneliness
and loss of physical and cognitive skills are to be prevented by means of training
and participation in groups. We want to investigate the effects of technology-
assisted training on physical and cognitive performance and social participation of
older people. "Quarter agile" is geared towards healthy people ages 65 and up who are residents of the specified neighborhood.
To analyze the electric field around bipolar resectoscopes, used in urology, in terms of reasons for late complications after a surgical treatment a flexible multielectrode system was developed to measure the 3-D potential distribution. A high spatial resolution is achieved with the least possible individual measurements under the conditions of a quasi-static electric field. A flexible arrangement and positioning of the measuring points in the vertical direction of the experimental environment enable an adjustable spatial resolution and the selection of the region of interest. The existing influence of the multielectrode system on the measuring results is described and a correction method is presented to achieve significant results. Thus, the multielectrode system is usable for a comparative study of bipolar resectoscopes varying in the arrangement of resection and return electrode.
Im Rahmen des diesjährigen Workshop Automotive HMI werden wieder eine Vielzahl an Vorträgen aus dem Bereich automobiler Mensch-Maschine Schnittstellen präsentiert. Des Weiteren ist wie in den beiden letzten Jahren ein Interaktiver Innovationsworkshop Teil des Programms. Das Motto der Mensch und Computer 2014 lautet „Interaktiv Unterwegs “. Dies passt hervorragend zum Thema des Workshops.
In this demo paper we present a new visualization technique for dynamic networks. It displays the time slices of the dynamic network using two dimensional graph layouting algorithms and stacks these in the third dimension to show the development over time. The visualization ensures that the same node always has the same position in each time slice so that it is easy to follow its development. It also allows filtering data and influencing node appearance based on properties. Additionally we offer a two dimensional comparison view for two time slices which highlights changes in graph structure and (if available) in measures of nodes. The presented visualization technique is implemented using Web technology and is available in a Web-based analytics workbench. We demonstrate the benefits of these techniques by an analysis of a data set from a learning community.
4. Workshop Automotive HMI
(2015)
Benutzerschnittstellen im Fahrzeug stellen eine besondere Herausforderung in Konzeption und Entwicklung dar, steht doch eine sichere Bedienung in allen Fahrsituationen sowohl von Fahrerassistenzsystemen als auch von Komfort-und Unterhaltungsfunktionen im Vordergrund. Zugleich treffen durch zunehmende Vernetzung die langen Entwicklungszyklen von Kraftfahrzeugen auf die hochdynamische Welt von Mobiltelefonen und Internet. Ein-und Ausgabetechnologien gehören des Weiteren zu den zentralen Mitteln der Hersteller, die Wertigkeit der im Fahrzeug eingebauten Systeme hervorzuheben und sich gegenüber der Konkurrenz abzuheben. Dafür werden in diesem Workshop Konzepte und technische Lösungen von Designern, Entwicklern und Human Factors Experten aus Hochschulen, Forschungsinstituten und der Automobilindustrie vorgestellt und diskutiert.
5th Workshop Automotive HMI
(2016)
Benutzerschnittstellen im Fahrzeug stellen eine besondere Herausforderung in Konzeption und Entwicklung dar, steht doch eine sichere Bedienung in allen Fahrsituationen von Fahrerassistenzsystemen wie auch Komfort- und Unterhaltungsfunktionen im Vordergrund. Zugleich treffen durch zunehmende Vernetzung die langen Entwicklungszyklen von Kraftfahrzeugen auf die hochdynamische Welt von Mobiltelefonen und Internet. Ein- und Ausgabetechnologien gehören des Weiteren zu den zentralen Mitteln der Hersteller, die Wertigkeit der im Fahrzeug eingebauten Systeme hervorzuheben. Passend zu dem Tagungsmotto „Sozial Digital – Gemeinsam Auf Neuen Wegen“ wurden in diesem Workshop insbesondere Arbeiten und Visionen präsentiert, die das Automobil bzw. HMIs im Fahrzeug als Teil einer vernetzten digitalen Welt verstehen – einer neuen Art eines sozialen Mensch-Maschine Ökosystems. Die zentrale Frage, die im Workshop diskutiert wurde war, wie Systeme in Zukunft aussehen müssen, um sowohl den Menschen als auch die Maschine optimal zu unterstützen (angelehnt an das MABA-MABA Paradigma von Fitts, 1954). Der Workshop war wiederum interdisziplinär aufgesetzt und hat Konzepte und technische Lösungen von und mit Designern, Entwicklern und „Human Factors“-Experten aus Universitäten/Hochschulen, Forschungsinstituten und der Automobilindustrie aus ganzheitlicher Sicht diskutiert.
Im Zentrum dieses Workshops stehen Erkenntnisse zur Mensch-Computer-Interaktion in sicherheitskritischen Anwendungsgebieten. Da in solchen Feldern – etwa Katastrophenmanagement, Verkehr, Produktion oder Medizin – immer häufiger MCI stattfindet, sind viele wissenschaftliche Gebiete, unter anderem die Informatik, zunehmend gefragt. Die Herausforderung besteht darin, bestehende Ansätze und Methoden zu diskutieren, anzupassen und innovative Lösungsansätze zu entwickeln.
Automotive user interfaces and, in particular, automated vehicle technology pose a plenty of challenges to researchers, vehicle manufacturers, and third-party suppliers to support all diverse facets of user needs. To give an example, they emerge from the variation of different usergroups ranging from inexperienced, thrill-seeking young novice drivers to elderly drivers with all their natural limitations. To allow assessing the quality of automotive user interfaces and automated driving technology already during development and within virtual test processes, the proposed workshop is dedicated to the quest of finding objective, quantifiable quality criteria for describing future driving experiences. The workshop is intended for HCI, AutomotiveUI, and “Human Factors" researchers and practitioners as well for designers and developers. In adherence to the conference main topic “Spielend einfach interagieren “, this workshop calls in particular for contributions in the area of human factors and ergonomics (user acceptance, trust, user experience, driving fun, natural user interfaces, etc.) and artificial intelligence (predictive HMIs, adaptive systems, intuitive interaction).
Automotive user interfaces and automated vehicle technology pose numerous challenges to support all diverse facets of user needs. These range from inexperienced, thrill-seeking, young novice drivers to elderly drivers with a mostly opposite set of preferences together with their natural limitations. To allow assessing the (hedonic) quality of automotive user interfaces and automated driving technology (i. e., UX) already during development, the proposed workshop is dedicated to the quest of finding objective, quantifiable criteria to describe future driving experiences. The workshop is intended for HCI, AutomotiveUI, and “Human Factors” researchers and practitioners as well for designers and developers. In adherence to the conference main topic “Interaktion – Verbindet – Alle”, this workshop calls in particular for contributions in the areas of human factors and ergonomics (user acceptance, trust, user experience, driving fun, natural user interfaces, etc.) with focus on hedonic quality and design of user experience to enhance the safety feeling in ADS.
Even though many aspects of automated driving have not yet become reality, many human factors issues have already been investigated. However, recent discussions revealed common misconceptions in both research and society about vehicle automation and the levels of automation levels. This might be due to the fact that automated driving functions are misnamed (cf. Autopilot) and that vehicles integrate functions at different automation levels (L1 lane keeping assistant, L2/L3 traffic jam assist, L4 valet parking). The user interface is one of the most critical issues in the interaction between humans and vehicles--and diverging mental models might be a major challenge here. Today's (manual) vehicles are ill-suited for appropriate HMI testing for automated vehicles. Instead, virtual or mixed reality might be a much better playground to test new interaction concepts in an automated driving setting.
E-Learning and openness in education are receiving ever increasing attention in businesses as well as in academia. However, these practices have only to small extent been introduced in public administrations. The study addresses this gap by presenting a literature review on Open Educational Resources [OER] and E-Learning in the public sector. The main goal of the article is to identify challenges to open E-Learning in public administrations. Experiences will be conceptualized as barriers which need to be considered when introducing open E-Learning systems and programs in administrations. The main outcome is a systematic review of lessons learned, presented as a contextualized Barrier Framework which is suitable to analyze requirements when introducing E-Learning and OER in public administrations.
CoRA is a robotic assistant whose task is to collaborate with a human operator on simple manipulation or handling tasks. Its sensory channels comprising vision, audition, haptics, and force sensing are used to extract perceptual information about speech, gestures and gaze of the operator, and object recognition. The anthropomorphic robot arm makes goal-directed movements to pick up and hand-over objects. The human operator may mechanically interact with the arm by pushing it away (haptics) or by taking an object out of the robot’s gripper (force sensing). The design objective has been to exploit the human operator’s intuition by modeling the mechanical structure, the senses, and the behaviors of the assistant on human anatomy, human perception, and human motor behavior.
Given the success of convolutional neural networks (CNNs) during recent years in numerous object recognition tasks, it seems logical to further extend their applicability to the treatment of three-dimensional data such as point clouds provided by depth sensors. To this end, we present an approach exploiting the CNN’s ability of automated feature generation and combine it with a novel 3D feature computation technique, preserving local information contained in the data. Experiments are conducted on a large data set of 600.000 samples of hand postures obtained via ToF (time-of-flight) sensors from 20 different persons, after an extensive parameter search in order to optimize network structure. Generalization performance, measured by a leave-one-person-out scheme, exceeds that of any other method presented for this specific task, bringing the error for some persons down to 1.5 %.
This contribution presents a novel approach of utilizing Time-of-Flight (ToF) technology for mid-air hand gesture recognition on mobile devices. ToF sensors are capable of providing depth data at high frame rates independent of illumination making any kind of application possible for in- and outdoor situations. This comes at the cost of precision regarding depth measurements and comparatively low lateral resolution. We present a novel feature generation technique based on a rasterization of the point clouds which
realizes fixed-sized input making Deep Learning approaches applicable using Convolutional Neural Networks. In order to increase precision we introduce several methods to reduce noise and normalize the input to overcome difficulties in scaling. Backed by a large-scale database of about half
a million data samples taken from different individuals our
contribution shows how hand gesture recognition is realiz-
able on commodity tablets in real-time at frame rates of up to 17Hz. A leave-one out cross-validation experiment
demonstrates the feasibility of our approach with classification errors as low as 1,5% achieved persons unknown to the model.
To reduce the number of traffic accidents and to increase the drivers comfort, the thought of designing driver assistance systems rose in the past years. Principal problems are caused by having a moving observer (ego motion) in predominantly natural surroundings. In this paper we present a solution for a flexible architecture for a driver assistance system. The architecture can be subdivided into four different parts: the object-related analysis, the knowledge base, the behavior-based scene interpretation, and the behavior planning unit. The object-related analysis is fed with data by the sensors (e.g., vision, radar). The sensor data are preprocessed (flexible sensor fusion) and evaluated (saliency map) searching for object-related information (positions, types of objects, etc.). The knowledge base is represented by static and dynamic knowledge. It consists of a set of rules (e.g. , traffic rules, physical laws), additional information (i.e., GPS, lane-information) and it is implicitly used by algorithms in the system. The scene interpretation combines the information extracted by the object related analysis and inspects the information for contradictions. It is strongly connected to the behavior planning using only information needed for the actual task. In the scene interpretation consistent representations (i.e., bird's eye view) are organized and interpreted as well as a scene analysis is performed. The results of the scene interpretation are used for decision making in behavior planning, which is controlled by the actual task. The influence of behavior planning on the behavior of the guided vehicle is limited to advices as no mechanical control (e.g. , control of the steering angle) was implemented. An Intelligent Cruise Control (ICC) is shown as a spin-off for using this architecture.
We present a novel approach of distributing matrix multiplications among GPU-equipped nodes in a cluster system. In this context we discuss the induced challenges and possible solutions. Additionally we state an algorithm which outperforms optimized GPU BLAS libraries for small matrices. Furthermore we provide a novel theoretical model for distributing algorithms within homogeneous computation systems with multiple hierarchies. In the context of this model we develop an algorithm which can find the optimal distribution parameters for each involved subalgorithm. We provide a detailed analysis of the algorithms space and time complexities and justify its use with a structured evaluation within a small GPU-equipped Beowulf cluster.
A Large and Quick Induction Field Scanner for Examining the Interior of Extended Objects or Humans
(2017)
This study describes the techniques and signal properties of a large, powerful, and linear-scanning 1.5 MHz induction field scanner. The mechanical system is capable of quickly reading the volume of relative large objects, e.g., a test person. The general approach mirrors Magnetic Induction Tomography (MIT), but the details differ considerably from currently-described MIT systems: the setup is asymmetrical, and it operates in gradiometric modalities, either with coaxial excitation with destructive interference or with a single excitation loop and tilted receivers. Following this approach, the primary signals were almost completely nulled, and test objects' real or imaginary imprint was obtained directly. The coaxial gradiometer appeared advantageous: exposure to strong fields was reduced due to destructive interference. Meanwhile, the signals included enhanced components at higher spatial frequencies, thereby obtaining a gradually improved capability for localization. For robust signals, the excitation field can be powered towards the rated limits of human exposure to time-varying magnetic fields. Repeated measurements assessed the important signal integrity, which is affected by the scanner´s imperfections, particularly any motions or respiratory changes in living beings during or between repeated scans. The currently achieved and overall figure of merit for artifacts was 58 dB for inanimate test objects and 44 dB for a test person. Both numbers should be understood as worst case levels: a repeated scan with intermediate breathing and drift/dislocations requires 50 seconds, whereas a single measurement (with respiratory arrest) takes only about 5 seconds.
A light-weight real-time ap- plicable hand gesture recognition system for automotive applications
(2015)
We present a novel approach for improved hand-gesture recognition by a single time-of-flight(ToF) sensor in an automotive environment. As the sensor's lateral resolution is comparatively low, we employ a learning approach comprising multiple processing steps, including PCA-based cropping, the computation of robust point cloud descriptors and training of a Multilayer perceptron (MLP) on a large database of samples. A sophisticated temporal fusion technique boosts the overall robustness of recognition by taking into account data coming from previous classification steps. Overall results are very satisfactory when evaluated on a large benchmark set of ten different hand poses, especially when it comes to generalization on previously unknown persons.
In this paper we describe a session management system for setting up various collabora- tive classroom ,scenarios. The approach ,is addressing the additional workload ,of administrating classroom networks on the teacher, which is an important aspect for teachers' willingness to im- plement technology enhanced,learning in schools. The system facilitates preparation of classroom scenarios and the adhoc installation of networked collaborative sessions. We provided a graphical interface, which is usable for administration, monitoring, and for specification of a wide variety of different classroom ,situations with group work. The resulting graphical specifications are well suited to be re-used in the more formal learning design format IMS/LD; this is achieved by a auto- matable transformation of the scenarios to LD documents. Keywords: Collaborative classroom scenarios, lightweight classroom orchestration, learning de- sign, shared workspaces.
We present a novel hierarchical approach to multi-class classification which is generic in that it can be applied to different classification models (e.g., support vector machines, perceptrons), and makes no explicit assumptions about the probabilistic structure of the problem as it is usually done in multi-class classification. By adding a cascade of additional classifiers, each of which receives the previous classifier's output in addition to regular input data, the approach harnesses unused information that manifests itself in the form of, e.g., correlations between predicted classes. Using multilayer perceptrons as a classification model, we demonstrate the validity of this approach by testing it on a complex ten-class 3D gesture recognition task.
Knowledge of fundamentals of human-computer interaction resp. usability engineering is getting more and more important in technical domains. However this interdisciplinary field of work and corresponding degree programs are not broadly known. Therefore at the Hochschule Ruhr West, University of Applied Sciences, a program was developed to give teen-aged pupils insights into this area in a project-based learning environment with professional tools. Within the last 18 month this project was successfully conducted several times with participants of different age.
We present a system for 3D hand gesture recognition based on low-cost time-of-flight(ToF) sensors intended for outdoor use in automotive human-machine interaction. As signal quality is impaired compared to Kinect-type sensors, we study several ways to improve performance when a large number of gesture classes is involved. Our system fuses data coming from two ToF sensors which is used to build up a large database and subsequently train a multilayer perceptron (MLP). We demonstrate that we are able to reliably classify a set of ten hand gestures in real-time and describe the setup of the system, the utilised methods as well as possible application scenarios.
We present a system for efficient dynamic hand gesture recognition based on a single time-of-flight sensor. As opposed to other approaches, we simply rely on depth data to interpret user movement with the hand in mid-air. We set up a large database to train multilayer perceptrons (MLPs) which are subsequently used for classification of static hand poses that define the targeted dynamic gestures. In order to remain robust against noise and to balance the low sensor resolution, PCA is used for data cropping and highly descriptive features, obtainable in real-time, are presented. Our simple yet efficient definition of a dynamic hand gesture shows how strong results are achievable in an automotive environment allowing for interesting and sophisticated applications to be realized.
We present a novel method to perform multi-class pattern classification with neural networks and test it on a challenging 3D hand gesture recognition problem. Our method consists of a standard one-against-all (OAA) classification, followed by another network layer classifying the resulting class scores, possibly augmented by the original raw input vector. This allows the network to disambiguate hard-to-separate classes as the distribution of class scores carries considerable information as well, and is in fact often used for assessing the confidence of a decision. We show that by this approach we are able to significantly boost our results, overall as well as for particular difficult cases, on the hard 10-class gesture classification task.
The influence of national culture on knowledge sharing has important implications for all organizations. However, the existing frameworks only cover a subset of relevant factors or limit the research of the framework to either organizational or national level. Hence, a more encompassing framework is needed. The question this articles answers is how does national culture influence knowledge sharing. Based on extensive literature review and interviews carried out in Finland and Japan, this article sets forth a foundation for a new framework. The framework details how national culture influences individual level and organizational level factors and technical tools. Additionally, the framework includes a new dimension, time-dimension, which is usually disregarded in knowledge sharing research. For researchers and practitioners, the derived framework provides key insight on relevant factors on knowledge sharing and national culture. Finally, future research directions are discussed.
We present a publicly available benchmark database for the problem of hand posture recognition from noisy depth data and fused RGB-D data obtained from low-cost time-of-flight (ToF) sensors. The database is the most extensive database of this kind containing over a million data samples (point clouds) recorded from 35 different individuals for ten different static hand postures. This captures a great amount of variance, due to person-related factors, but also scaling, translation and rotation are explicitly represented. Benchmark results achieved with a standard classification algorithm are computed by cross-validation both over samples and persons, the latter implying training on all persons but one and testing on the remaining one. An important result using this database is that cross-validation performance over samples (which is the standard procedure in machine learning) is systematically higher than cross-validation performance over persons, which is to our mind the true application-relevant measure of generalization performance.
This paper discusses the efforts carried out related to the design and development of a web-based framework that allows designing, deploying and executing mobile data collecting applications. Furthermore, it also allows analyzing and presenting the data that is generated during the mentioned process. The fact that the framework is completely web-based provides a platform independent execution of the mobile application on any mobile device with a web-browser. As a result that the whole life-cycle of creating, executing and discussing a mobile learning activity is implemented in pure web-based manner separates this work from similar efforts. In the course of this work, the current state of development of two of the components, the authoring tool and the mobile application is presented. This framework was introduced to teachers in an activity to follow up an initial study. On the basis of a workshop with teachers, we performed an explorative study regarding the technology acceptance and usability of two components of the proposed framework. The results are discussed and analyzed in this paper.
Understanding user needs and behavior in automated vehicles (AVs) while traveling is essential for future in-vehicle interface and service design. Since AVs are not yet market-ready, current knowledge about AV use and perception is based on observations in other transportation modes, interviews, or surveys about the hypothetical situation. In this paper, we close this gap by presenting real-world insights into the attitude towards highly automated driving and non-driving-related activities (NDRAs). Using a Wizard of Oz AV, we conducted a real-world driving study (N= 12) with six rides per participant during multiple days. We provide insights into the users’ perceptions and behavior. We found that (1) the users’ trust a human driver more than a system,(2) safety is the main acceptance factor, and (3) the most popular NDRAs were being idle and the use of the smartphone.
Web based security applications have become increasingly important in the past years. Especially in times of blockchain based crypto currencies, user authentication is a critical aspect for the overall security, integrity and acceptance of such systems. While blockchain technologies provide a decentralized approach, the client side still largely relies on centralized security approaches. Those centralized approaches are easier to implement, but at the same time bear the risk of usual security flaws. Therefore, this paper presents a decentralized approach for increasing the security by adding a decentralized two-factor authentication mechanism to the execution of
operations.
This paper deals with the question how to integrate smart devices in Java appli-
cations. It will outline how different smart devices can be used to enrich learning
environments, we will point to some of the problems one has to face while dealing
with smart devices, a differentiation of smart devices will be done and we will give an
overview about existing Java Virtual Machines available for different smart devices.
Furthermore we will tackle the question of the communication between different smart
devices and also between different kinds of smart devices. An outlook to the future
work will also be given at the end of this work
This paper describes a system which allows platform independent access to quizzes of the popular learning platform Moodle. The main focus is on the software architecture which is implemented on the base of platform independent technology like Web Services, HTML5 and JavaScript. Another aspect is the user interface which was developed with the goal to run on a broad range of mobile devices from small mobile phones up to large tablets.
The development of web based applications gained enormous interests in recent years. Most of formerly desktop based applications nowadays provide at least a web based version or are completely re-implemented as web based applications. Nevertheless, from the development point of view, there are still a lot of strategies for the development of web based applications borrowed from the development strategies for desktop applications. Therefore, this paper concentrates on the description of an approach that allows to re-use a from the development of desktop applications well-known Design Pattern with a distinct enhancement for web based applications.
The use of Web Services in modern software development is widely accepted and provides (integrated in an according architecture) a fast, flexible and scalable way for the implementation of modern software products. On the other hand, the development of mobile applications, so called apps, becomes more and more important. While using Web Services also from mobile devices is an already accepted scheme in the development of mobile apps, there is not much work done yet for providing Web Services on mobile devices. Therefore, this paper presents a new perspective to Web Services that could be run on mobile devices and, by this, become mobile Web Services.
In recent years, the number of reasonable powerful mobile devices increased. In 2011, the number of smartphones(e.g.)increased to more than 300 million units. A lot of research has already been conducted with respect of mobile devices acting as Cloud Service consumers, but
still not much effort is put on mobile devices in the role of Cloud Service providers. Therefore, this paper presents an approach that allows to utilize mobile devices like smart phones or tablets as Cloud Service providers. In order to make this a reasonable approach, some of the occurring problems are discussed and it is shown how the presented architecture is able to overcome these problems. Last
but not least, this paper
describes some performance
tests of the chosen implementa
tion for mobile Web Services.
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.
Advances in Human-Robot Interaction" provides a unique collection of recent research in human-robot interaction. It covers the basic important research areas ranging from multi-modal interfaces, interpretation, interaction, learning, or motion coordination to topics such as physical interaction, systems, and architectures. The book addresses key issues of human-robot interaction concerned with perception, modelling, control, planning and cognition, covering a wide spectrum of applications. This includes interaction and communication with robots in manufacturing environments and the collaboration and co-existence with assistive robots in domestic environments. Among the presented examples are a robotic bartender, a new programming paradigm for a cleaning robot, or an approach to interactive teaching of a robot assistant in manufacturing environment. This carefully edited book reports on contributions from leading German academic institutions and industrial companies brought together within MORPHA, a 4 year project on interaction and communication between humans and anthropomorphic robot assistants.
Aktiv im Alter
(2016)
Die Prognosen für den demografischen Wandel sind eindeutig: In den kommenden Jahren wird es immer mehr Menschen über 65 Jahre geben. Damit verbunden sind große Herausforderungen für die Gesellschaft und ihre Sozialsysteme, aber auch für viele Angehörige, die ihre Verwandten im Alter pflegen. Doch nicht alle älteren Menschen leben im Kreise ihrer Familie oder können sich Fremdbetreuung durch Pflegedienste leisten. Häufig übernehmen Nachbarn oder Freunde aus der Umgebung diese Aufgabe. Für diese Menschen wird das Wohnquartier zum zentralen Gesundheitsstandort.
Im besten Fall können sie dort ihren Alltag noch lange selbstständig bewältigen und ihre sozialen Kontakte aufrechterhalten. Das soll bald eine App unterstützen. Sie ist Teil eines Trainingsprogramms, das die Hochschule für Gesundheit (hsg) im Verbund mit der Hochschule Ruhr West erarbeitet. Der Name des Projekts ist Programm: „Quartier agil – Aktiv vor Ort“. Mit Übungen zum kognitiven und körperlichen Training, Angeboten für Gruppenaktivitäten, Kommunikationsforen und Funktionen zur Selbstkontrolle wollen die Forscherinnen und Forscher
ältere Menschen fit halten.
Die spezifischen Herausforderungen des Fachgebiets bedürfen jedoch auch weiterhin einer Diskussion und der Entwicklung neuer Methoden und Ansätze zur Gestaltung von Informationssystemen. Diese sollen dieses Jahr adressiert werden. Generell fokussieren wir eher auf die Effekte von Technologien auf realweltliche Praktiken, als auf die isolierte Technologie. Auch der auf diesen Beiträgen basierende Workshop legt aktuelle Entwicklungen und Fragestellungen offen und gibt neue Impulse für das Forschungsgebiet. Der Workshop wird dabei zweigeteilt gestaltet: Innerhalb des ersten Teils wird den Vortragenden die Möglichkeit gegeben die eigenen Forschungsarbeiten zu präsentieren. Dabei sind sowohl designorientierte, praxisbasierte Analysen und Studien, als auch entwickelte und evaluierte Prototypen neuer Technologien von Interesse. Es wird den Vortragenden die Möglichkeit gegeben die eigenen Forschungsarbeiten teilweise in einem eher frühen Stadium in kompakter Form zu präsentieren und anschließend in Hinblick auf deren Weiterentwicklung diskutieren.
Massive open online courses (MOOCs) become more and more popular. These course formats are typically highly flexible and attract large groups of learners from heterogeneous backgrounds. So far research in this area concentrating on success factors for low dropout rates and high satisfaction on the side of the learners in MOOCs is scarce. In this chapter, we describe experiences of a large online course offered to students of two large German universities. Based on theory drawn from a social psychological perspective on the relevance of social interaction for learning, we describe the background, structure, and specific elements of the MOOC-like course. We outline evaluation results of both small group collaboration (in workshops) and mass interaction (via forum and wiki usage) as well as results of the general evaluation of the overall course concept. We argue that the specific mixture of small and large group interaction as well as teacher- and learner-generated content is especially promising with regard to satisfaction, learning outcomes, and course completion rates.
In recent years the diversity and the ownership of mobile devices steadily increased while the prices for this kind of devices decreased to a level that allows many students to own reasonably powerful devices. As mobile devices are also being used in learning scenarios, the challenge of today is the integration of multiple heterogeneous devices into existing and upcoming learning scenarios. This paper describes an architecture that allows easy integration of various kinds of mobile and non-mobile devices. The presented architecture will be exemplified by a group discussion scenario in a heterogeneous learning environment. The paper concludes with the description of a pilot study using the described system.
In the context of existing approaches to cluster computing we present a newly developed modular framework `SimpleHydra' for rapid deployment and management of Beowulf clusters. Instead of focusing only the pure computation tasks on homogeneous clusters (i.e. clusters with identically set up nodes), this framework aims to ease the configuration of heterogeneous clusters and to provide a low-level / high-level object-oriented API for low-latency distributed computing. Our framework does not make any restrictions regarding the hardware and minimizes the use of external libraries to the case of special modules. In addition to that our framework enables the user to develop highly dynamic cluster topologies. We describe the framework's general structure as well as time critical elements, give application examples in the `Big-Data' context during a research project and briefly discuss additional features. Furthermore we give a thorough theoretical time/space complexity analysis of our implemented methods and general approaches.
Systems for automated image analysis are useful for a variety of tasks. Their importance is still growing due to technological advances and increased social acceptance. Especially driver assistance systems have reached a high level of sophistication. Fully or partly autonomously guided vehicles, particularly for road traffic, require highly reliable algorithms due to the conditions imposed by natural environments. At the Institut fur Neuroinformatik, methods for analyzing driving relevant scenes by computer vision are developed in cooperation with several partners from the automobile industry. We present a system extracting important information from an image taken by a CCD camera installed at the rear-view mirror in a car. The approach is divided into a sequential and a parallel phase of sensor and information processing. Three main tasks, namely initial segmentation (object detection), object tracking and object classification are realized by integration in the sequential phase and by fusion in the parallel phase. The main advantage of this approach is integrative coupling of different algorithms providing partly redundant information. q 2000 Elsevier Science B.V. All rights reserved.
This paper describes an educational application that combines handhelds (PDAs) and programmable Lego bricks in a classroom scenario that deals with the problem of letting a robot escape from a maze. It is specific to our setting that the problem can be solved both in the physical world by steering a Lego robot and in a simulated software environment on a PDA or on a PC. This approach enables the students to generate successful sets of rules in the simulation and to test these sets of rules later in physical mazes, or to create new types of mazes as challenges for known rule sets. In this paper we describe the technical setting for this scenario, different pedagogical scenarios and we will report an evaluation with a group of students in a school environment.
In this study, we looked at the competencies and changes in the competency spectrum required for global start-ups in the digital age. Specifically, we explored intergenerational collaboration as an intervention in which experienced business-people from senior adult groups support young entrepreneurs. We conducted a Delphi study with 20 experts from different disciplines, considering the study context. The results of this study shed light on understanding the necessary competencies of entrepreneurs for intergenerationally supported start-up innovation by providing 27 competencies categorized as follows: intergenerational safety facilitation, cultural awareness, virtues for growth, effectual creativity, technical expertise, responsive teamwork, values-based organization, and sustainable network development. In addition, the study results also reveal the competency priorities and the minimum requirements for each competency group based on the global innovation process and can be used to develop a readiness assessment for start-up entrepreneurs.
Diese Arbeit konzentriert sich auf die Erstellung einer umfassenden technischen Dokumentation für ein Medizinprodukt der Klasse IIa gemäß den Anforderungen der Medical Device Regulation (MDR) 2017/745. Das Hauptziel besteht darin, eine konforme Dokumentation zu entwickeln, die alle wesentlichen Aspekte der Produktentwicklung, Herstellung und Verwendung abdeckt. Besonderes Augenmerk wird darauf gelegt, die regulatorischen Anforderungen der MDR zu erfüllen, um die Sicherheit und Wirksamkeit des Produkts zu gewährleisten und eine Zulassung für den europäischen Markt zu ermöglichen. Die verschiedenen Bestandteile der technischen Dokumentation, einschließlich klinischer Bewertungen, Risikomanagement, Validierungstests, Konformitätsbewertungen und Kennzeichnung, werden gründlich untersucht. Es wird eine systematische und strukturierte Herangehensweise präsentiert, um eine erfolgreiche Konformitätsbewertung für das hier beschriebene Medizinprodukt der Klasse IIa zu erreichen. Die Ergebnisse dieser Arbeit tragen dazu bei, die Qualität und Sicherheit des hier bearbeiteten Medizinproduktes der Klasse IIa zu verbessern und somit die Patientensicherheit zu gewährleisten.
Die transurethrale Resektion der Prostata (TUR-P) ist der Gold Standard für die endoskopisch-chirurgische Behandlung der Benignen Prostatahyperplasie (BPH). Unbekannt ist jedoch, ob bei der bipolaren TUR-P die Möglichkeit von elektrothermischen Verletzung von benachbartem nichtopertiertem Gewebe besteht. Grund hierfür könnten höhere Ausgangsleistungen der Chirurgie-Generatoren sein. Deshalb werden experimentell gemessene Werte der räumlichen Verlustleistungsdichte um ein bipolares Resektoskop als Quellterm für die Berechnung der Temperaturänderung im operationsnahen Gewebe verwendet. Die Temperaturberechnung basiert auf der numerischen Lösung der Wärmeleitungsgleichung nach Penne. Für die gewählten Randbedingungen und Eingabeparameter sind keine signifikanten Temperaturerhöhungen im Berechnungsgebiet am proximalen Ende des Resektoskop-Schaftes festzustellen. Um ein umfassendes Verständnis zu gewinnen sollen weiterführende Untersuchungen mit einer Variation der Randbedingungen und Eingabeparameter durchgeführt werden
The task of object detection in the automotive sector can be performed by evaluating various
sensor data. The evaluation of LiDAR data for the detection of objects is a special challenge for
which systems with neural networks can be used. These neural networks are trained by means of a
data set. If you want to use the net with your own recordings or another data set, it is important
to know how well these systems work in combination with data from another sensor. This allows
the results to be estimated in advance and compared with the results of previous experiments.
In this work the sensor dependence of a LiDAR based object recognition with neural networks
will be analysed. The detector used in this work is PointRCNN [1], which was designed for the
KITTI dataset [2]. To check the sensor dependency, the ’AEV Autonomous Driving Dataset’
(A2D2) dataset [3] was selected as a further dataset. After an introduction to PointRCNN and its
functionality, the data of both datasets are analysed. Then the data of the second dataset will be
ported into the format of the KITTI dataset so that they can be used with PointRCNN. Through
experiments with varying combinations of training and validation data it shall be investigated to
what extent trained models can be transferred to other sensor data or datasets. Therefore, it shall
be investigated how strong the dependence of the detector (PointRCNN) on the used sensors is.
The results show that PointRCNN can be evaluated with a different dataset than the training
dataset while still being able to detect objects. The point density of the datasets plays a decisive
role for the quality of the detection. Therefore it can be said that PointRCNN has a sensor
dependency that varies with the nature of the point cloud and its density.
Keywords: LiDAR data, 3D object recognition, laser scanner, sensor dependency, PointRCNN,
PointNet++, PointNet, KITTI Dataset, AEV Autonomous Driving Dataset, A2D2 Dataset
Der deutsche stationäre Einzelhandel gerät immer mehr unter Druck. Seit nunmehr fast einem Jahr bestimmt die Covid-19-Pandemie weltweit das menschliche Leben. Unter den Maßnahmen zum Schutz der deutschen Bevölkerung leidet auch die deutsche Wirtschaft. Vor allem den stationären Einzelhandel trifft es in dieser Zeit sehr. Leere Städte und geschlossene Läden sind schon fast zur Normalität geworden. Doch nicht erst seit Covid-19 erlebt der deutsche stationäre Einzelhandel finanzielle Einbußen. Fortschritte auf Gebieten der modernen Technologien wie „Big Data“ und die voranschreitende Digitalisierung kommen vor allem dem Onlinehandel, der auch von der Covid-19-Pandemie profitiert, zugute. Verbunden mit den sich verändernden Bedürfnissen der deutschen Bevölkerung an den Handel, greift der Online-Handel
den stationären Einzelhandel durch den Ausbau seiner Marktanteile an. Jedoch verspricht ein modernes, aber nicht neues Technologiegebiet dem stationären Einzelhandel Besserung. Die Nutzung von Künstlicher Intelligenz könnte dem Einzelhandel dazu verhelfen, selber Gewinne
aus den anderen modernen Technologiegebieten zu erzielen, sich den veränderten Bedürfnissen des Kunden anzupassen und dem Druck des Onlinehandels stand zu halten. Die vorliegende Arbeit setzt sich mit der Bewertung des Chancenpotenzials Künstlicher Intelligenz für die Zukunft des deutschen stationären Einzelhandels auseinander. Damit wird versucht die Frage, ob der Einsatz von KI-Anwendungen dem deutschen stationären Einzelhandel dazu verhelfen wird, die oben beschriebenen Herausforderungen zu bewältigen, zu beantworten.
Um dem Leser ein fundiertes Verständnis zu vermitteln, basiert die Ermittlung des Potenzials auf einer detaillierten Erläuterung der Künstlichen Intelligenz sowie deren Fähigkeiten und Chancen, aber auch ihrer Risiken und Hürden auf dem zukünftigen Weg der Implementierung.
Auf diesem Fundament wird dann mit Hilfe einer literarischen Analyse die Bewertung vorgenommen. Bisher von der Literatur wenig berücksichtigt sind Veränderungen der Situation des deutschen stationären Einzelhandels durch die Auswirkungen der noch immer anhaltenden Covid-19-Pandemie. Die Ergebnisse der Literaturanalyse werden daher durch die Durchführung und Auswertung von Experteninterviews, als Methode der qualitativen Primärforschung,
auf Aktualität und Übereinstimmung mit Erkenntnissen aus der Praxis überprüft.
Mit Dara Kossok-Spieß, Referentin des Handelsverbands Deutschland, Niels Will und Frederic Kerber, beide im Einsatz für praxisnahe Forschungsprojekte des deutschen Forschungsinstituts für Künstliche Intelligenz, sind sowohl Vertreter beider Interessengruppen – der Anwendung sowie der Forschung – vertreten. Hierdurch konnten neue Erkenntnisse über die zukünftigen Auswirkungen der Covid-19-Pandemie auf den deutschen stationären Einzelhandelsmarkt gewonnen werden. Außerdem konnten Barrieren, die in naher Zukunft durch die Zusammenarbeit der Anwender mit der Forschung, gelöst werden müssen, damit Künstliche Intelligenz flächenübergreifend in den deutschen stationären Einzelhandel einziehen kann, ermittelt werden. Die vorliegende Arbeit richtet sich daher an alle Personen, die ein Interesse an der Bewertung des Technologiegebiets Künstlicher Intelligenz besitzen und/oder sich für die Zukunft des deutschen stationären Einzelhandels interessieren.
Analyse dynamischer Szenen
(1999)
In diesem Artikel wird die Analyse dynamischer Szenen im Rahmen einer flexiblen Architektur zur Lösung von Fahrerassistenzaufgaben in Kraftfahrzeugen vorgestellt. Die Lösung unterschiedlicher Aufgaben mit verwandten Ansätzen bedingt einen hohen Grad an Modularität und Flexibilität. Nur so können die gestellten Aufgaben mit den vorhandenen Algorithmen optimal gelöst werden. In der vorgestellten Architektur wird eine objektbezogene Analyse von Sensordaten, eine verhaltensbasierte Szeneninterpretation und eine Verhaltensplanung durchgeführt. Eine globale Wissensbasis, auf der jedes einzelne Modul arbeitet, beinhaltet die Beschreibung physikalischer Zusammenhänge, Verhaltensregeln für den Straßenverkehr, sowie Objekt- und Szenenwissen.
Externes Wissen (z.B. GPS – Global Positioning System) kann ebenfalls in die Wissensbasis eingebunden werden. Als Anwendungsbeispiel der Verhaltensplanung ist ein intelligenter Tempomat realisiert.
Analyse von Unsicherheiten künstlicher neuronaler Netze und Integration in die Objektverfolgung
(2022)
Over the last few years, the development of assistance systems for motor vehicles has shifted from comfort functions to control tasks. Increasingly, these control tasks are also being transferred to semi-autonomous systems. One safety-critical aspect is the correct and reliable observation of the immediate environment by the vehicle. These observations can be used, among other things, to set up models for tracking objects. Due to recent research, topics such as uncertainties for object detections and the calibration of artificial neural networks are now emerging.
The goal of this work is to investigate the possibility of processing positional uncertainties of a detector in a multiple object tracking approach and the eects on the tracking of objects. Additionally, the calibration of the used detector will be evaluated and corrected if necessary. The eects of the calibration on the tracking results will also be investigated in this context. After an investigation of the procedure used to generate the position uncertainties of the detector, a connection to the multiple object tracking was made and an approach to process the uncertainties based on a Kalman filter was developed. The confidence of the detections was also remodeled. For this purpose, the confidence was interpreted as the existence probability and processed using a Bayes Filter to reflect the existence of the tracks. In addition, appropriate calibration methods for the position uncertainties and confidence were selected and incorporated into the tracking procedure. The validation of the presented approaches was performed on a data set for driving situations.
The evaluation of the results showed that a processing of the position uncertainties generated by a detector is feasible in the presented tracking approach. The interpretation of the confidence as existence probability leads to good results. Calibration of the confidence further improves the results. However, the calibration of the position uncertainties led to worse results. Further inves-tigation of other calibration methods for the position uncertainties is needed.
Keywords: Multiple Object Tracking, Kalman Filter, Neural Network Calibration
One of the technical building blocks of Cloud Computing infrastructures are Web Services. With respect to mobile devices their role as Web Service consumers is widely accepted and today a large number of mobile applications already consume Web Services in order to fulfill their task. Still, not much research is conducted, as yet, to allow deploying Web Services on mobile devices and thus uses these kinds of devices as Web Service providers. This paper presents an analysis of one already implemented approach for provisioning mobile Web Services with respect to energy/battery consumption. Here, after shortly presenting the implementation for the provisioning of mobile Web Services an evaluation of the battery consumption that results in using the approach is presented. Last but not least, an improvement with respect to the battery consumption is presented. The performance test shows that the improved approach provides a reasonable way to introduce Web Service provisioning for mobile devices.
Analysis of dynamic scenes
(2000)
In this paper the proposed architecture for a dynamic scene analysis is illustrated by a driver assistance system. To reduce the number of traffic accidents and to increase the drivers comfort, the thought of designing driver assistance systems rose in the past years. Principal problems are caused by having a moving observer (ego motion) in predominantly natural surroundings. In this paper we present a solution for a flexible architecture for a driver assistance system. The architecture can be subdivided into four different parts: the object-related analysis, the knowledge base, the behavior-based scene interpretation, and the behavior planning unit. The object-related analysis is fed with data by the sensors (vision, radar). The sensor data are preprocessed (flexible sensor fusion) and evaluated (saliency map) searching for object-related information (positions, types of objects, etc.). The knowledge base is represented by static and dynamic knowledge. It consists of a set of rules (traffic rules, physical laws), additional information (GPS, lane-information) and it is implicitly used by algorithms in the system. The scene interpretation combines the information extracted by the
object-related analysis and inspects the information for contradictions. It is strongly connected to the behavior planning using only information needed for the actual task. In the scene interpretation consistent representations (i.e., bird’s eye view) are organized and interpreted as well as a scene analysis is performed. The results of the scene interpretation are used for decision making in behavior planning, which is controlled by the actual task.
Photoluminescence (PL) in GaN or InGaN layers monitored during epitaxial growth at high temperatures permits a quasi-continuous in situ characterization of opto-electronic properties. Therefore, epitaxial parameters can now be optimized at the earliest possible stage. A pulsed and high-power UV laser was required for PL excitation at high temperatures. Herein, the underlying nonlinear mechanism was studied via time-resolved PL experiments and rate equation-based modeling. A temperature-activated and saturable path for quenching over defects was identified. Beyond the saturation threshold, reasonably-intensive PL sets in. At high temperatures not only is the near band gap-PL present, but also—as a new observation—a defect-assisted PL emerges. Apart from these specific electronic transitions in high-temperature PL of GaN, a simple, but reasonably predictive model of the luminescent thin film has been set up to track down interference fringes in the PL spectra. It is worth mentioning that the spectral PL modulation (aiming at the Purcell effect) is often mixed up with ordinary Fabry–Pérot interference. A distinction has become key to properly analyze the spectral signatures of high-temperature PL in order to provide a reliable in situ characterization of GaN layers during epitaxial growth
Die transurethrale Resektion der Prostata (TUR-P) ist der Gold Standard für die endoskopisch-chirurgische Behandlung der Benignen Prostatahyperplasie (BPH). Unbekannt ist jedoch, ob bei der bipolaren TUR-P die Möglichkeit von elektrothermischen Verletzung von benachbar-
tem nichtopertiertem Gewebe besteht. Grund hierfür könnten höhere Ausgangsleistungen der Chirurgie-Generatoren sein. Deshalb werden experimentell gemessene Werte der räumlichen Verlustleistungsdichte um ein bipolares Resektoskop als Quellterm für die Berechnung der Temperaturänderung im operationsnahen Gewebe verwendet. Die Temperaturberechnung basiert auf der numerischen Lösung der Wärmeleitungsgleichung nach Penne. Für die gewählten Randbedingungen und Eingabeparameter sind keine signifikanten Temperaturerhöhungen im Berechnungsgebiet am proximalen Ende des Resektoskop-Schaftes festzustellen. Um ein umfassendes Verständnis zu gewinnen sollen weiterführende Untersuchungen mit einer Variation der Randbedingungen und Eingabeparameter durchgeführt werden.
Anonymity-preserving Methods for Client-side Filtering in Position-based Collaboration Approaches
(2017)
In dieser Arbeit wurde eine Motorsteuerung für mikrofluidische Peristaltikpumpen in Lab-on-a-Chip Systemen entwickelt. Neben der dafür notwendigen elektrischen Schaltung wurde viel Wert auf die softwareseitige Umsetzung gelegt. Zusätzlich zu der reinen Vorgabe von essentiellen Größen, wie beispielsweise der Drehzahl und dem damit geförderten Volumen, wird ein vielseitiges und zuverlässiges Steuerungssystem vorgestellt, das versucht Schwankungen im geförderten Flüssigkeitsstrom zu reduzieren. Um schon vor dem ersten Betrieb, des parallel zu dieser Arbeit gefertigten mechanischen Aufbau, die Leistung sowie Ausmaße der Schwankungen abzuschätzen, wurde der zu erwartende Volumenstrom auf Basis der geometrischen Ausmaße modelliert. Dadurch können Algorithmen zur Glättung des Flusses bereits in einer frühen Phase der Softwareentwicklung berücksichtigt werden. Für eine bessere Charakterisierung des mechanischen Aufbaus und Überwachung des Betriebs wurde das System um eine sensorlose Erkennung von Bewegungen des Motors ergänzt. Somit kann unter anderem die Zuverlässigkeit und Dimensionierung der verwendeten Motoren überprüft werden. Zusätzlich wurde der Prozess zum Verschweißen von thermoplastischer Elastomer Folie mit dem mikrofluidischen Chip optimiert.
CORA is a robotic assistant whose task is to collaborate with a human operator on simple manipulation or handling tasks. Its sensory channels comprising vision, audition, haptics, and force sensing are used to extract perceptual information about speech, gestures and gaze of the operator, and object recognition. The anthropomorphic robot arm makes goal-directed movements to pick up and hand over objects. The human operator may mechanically interact with the arm by pushing it away (haptics) or by taking an object out of the robot's gripper (force sensing). The design objective has been to exploit the human operator's intuition by modeling the mechanical structure, the senses, and the behaviors of the assistant on human anatomy, human perception, and human motor behavior.
Das Ziel der vorliegenden Arbeit ist es, die Eignung von MeshLab in einem Reverse-Engineering-Projekt zu überprüfen. Dazu wurden vor Beginn sechs Kriterien aufgestellt, auf die MeshLab untersucht wird. Das Ergebnis zeigt, dass MeshLab fünf von sechs Kriterien erfüllt und somit für einen Einsatz geeignet ist.
MeshLab ist ein Teil der Datenaufbereitung des Reverse Engineering. Es ist ein kostenloses Programm und somit in Kombination mit einem günstigen Scanner für einen Einsatz in Reverse-Engineering-Projekten mit einem geringen Kostenaufwand einsetzbar.
The aim of this bachelor thesis is to verify the suitability of MeshLab in a Reverse-Engineering-Project. Before the beginning six criterias were set up on which MeshLab is examined. The result shows that MeshLab fulfills five of six criterias and is therefore suitable for use.
MeshLab is a part of the data preparation from the Reverse Engineering. It is a free programm and in combination with a cheap scanner, it can be used in a Revere-Engineering-Project with a low Budget.
Applying step heating thermography to wind turbine rotor blades as a non-destructive testing method
(2017)
Artificial Intelligence Driven Human-Machine Collaboration Scenarios in Virtual Reality (Poster)
(2018)
This work aims to generate synthetic electromyographic (EMG) signals using Generative Adversarial Network (GAN). GANs are considered as one of the most exciting and promising approaches in deep learning [6], offering the possibility to generate artificial data based on real data. GAN consists of two main parts, a discriminator that attempts to differentiate between the generated data and the original data, and a generator that tries to fool the discriminator by generating data which looks like real data, the GAN works by staging a two-player
minimax game between generator and discriminator networks. To achieve the objective of generating realistic artificial electromyographic signals, two different architectures are considered for the generator and the discriminator networks of the GAN model: Long short-term memory (LSTM), which can avoid the long-term dependency problem and remembers information over a long period of time, and convolutional neural network (CNN), which is a powerful tool at automatic feature extraction. Different combinations of CNN and LSTM including hybrid model are experimented within the GAN using the same training data-set. The results and performances of each combination are compared and reviewed. The generated artificial EMG signals can be used to
simulate real muscle activity situations to for example improve muscle signal controlled prostheses using artificial data that may include conditions that does not exist in real data. This method of artificial data generation is not limited to EMG signals, the network can also be used to generate other synthetic biomedical signals such as electroencephalogram (EEG) or electrocardiogram (ECG) that can be practically used for testing algorithms and classifiers.
Aspekte der Datensammlung beim Einsatz von Convolutional Neural Network für das autonome Fahren
(2022)
Die vorliegende Bachelorarbeit thematisiert, ob ein Convolutional Neural Network für das autonome Fahren geeignet ist und wie der gesamte Datenprozess für das Convolutional Neural Network Modell aussehen könnte. Dazu wurden mehrere Forschungsfragen gestellt.
Um die Forschungsfragen zu beantworten, wurde eine quantitative Literaturarbeit mit aktuellen wissenschaftlichen Kenntnissen durchgeführt. Die Bachelorarbeit ist in der deutschen Sprache geschrieben, aus diesem Grund orientiert sich die Bachelorarbeit an die deutschsprachige Bevölkerung.
Die quantitative Literaturarbeit zeigte, dass die größten Herausforderungen für das Convolutional Neural Network Training in der Datenspeicherung und der Datenverarbeitung liegen. Ebenfalls zeigte die quantitative Literaturarbeit, dass das Convolutional Neural Network durch die qualifizierte Wahl von Methoden und Daten eine bessere Erfolgsquote als das menschliche Verhalten für das autonome Fahren nachweist.
Dies zeigt, dass der Einsatz von Convolutional Neural Network Modellen mit einem optimalen Datenprozess und der Dateninfrastruktur für das autonome Fahren empfehlens-wert sein kann.
In this work methods are described, which are used for an individual adaption of a dialog system. Anyway, an automatic real-time capable visual user attention estimation for a face to face human machine interaction is described. Furthermore, an emotion estimation is presented, which combines a visual and an acoustic method. Both, the attention estimation and the visual emotion estimation based on Active Appearance Models (AAMs). Certainly, for the attention estimation Multilayer Perceptrons (MLPs) are used to map the Active Appearance Parameters (AAM-Parameters) onto the current head pose. Afterwards, the chronology of the head poses is classified as attention or inattention. In the visual emotion estimation the AAM-Parameter will be classified by a Support-Vector-Machine (SVM). The acoustic emotion estimation also use a SVM to classifies emotion related audio signal features into the 5 basis emotions (neutral, happy, sad, anger, surprise). Afterward, a Bayes network is used to combine the results of the visual and the acoustic estimation in the decision level. The visual attention estimation as well as the emotion estimation will be used in service robotic to allow a more natural and human like dialog. Furthermore, the human head pose is very efficient interpreted as head nodding or shaking by the use of adaptive statistical moments. Especially, the head movement of many demented people are restricted, so they often only use their eyes to look around. For that reason, this work examine a simple gaze estimation with the help of an ordinary webcam. Moreover, a full body user re-identification method is described, which allows an individual state estimation of several people for hight dynamic situations. In this work an appearance based method is described, which allows a fast people re-identification over a short time span to allow the usage of individual parameter.
Public transportation will become highly automated in the future, and at some point, human drivers are no longer necessary. Today many people are skeptical about such scenarios of autonomous public transport (abbr.: APT). In this paper, we assess users’ subjective priority of different factors that lead to personal acceptance or rejection of APT using an adapted online version of the Q-Methodology with 44 participants. We found four prototypical attitudes to which subgroups of participants relate: 1) technical enthusiasts, 2) social skeptics, 3) service-oriented non-enthusiasts, and 4) technology-oriented non-enthusiasts. We provide an unconventional perspective on APT acceptance that helps practitioners prioritize design requirements and communicate, targeting users’ specific attitudes.
To enable a robotic assistant to autonomously reach for and transport objects while avoiding obstacles we have generalized the attractor dynamics approach established for vehicles to trajectory formation in robot arms. This approach is able to deal with the time-varying environments that occur when a human operator moves in a shared workspace. Stable fixed-points (attractors) for the heading direction of the end-effector shift during movement and are being tracked by the system. This enables the attractor dynamics aoproach to avoid the spurious states that hamper potential field methods. Separating planning and control computationally, the approach is also simpler to implement. The stability properties of the movement plan enable to approach to deal with fluctuating and imprecise sensory information. We implement this approach on a seven degree of freedom anthropomorphic arm reaching for objects on a working surface. We use an exact solution of the inverse kinematics, which enables us to steer the spatial position of the elbow clear of obstacles. The straight-line trajectories of the end-effector that result far from obstacles make the movement goals of the robotic assistant predictable, improving man-machine interaction.
Auf gute Zusammenarbeit
(2008)
Die vorliegende Arbeit untersucht die Eigenschaft Authentizität auf der Video-Plattform TikTok als möglichen Erfolgsfaktor zur Steigerung der Markenbekanntheit. Sie beantwortet drei Forschungsfragen, welche zunächst mithilfe einer Sekundärforschung untersucht werden. Dabei wird der Begriff Markenauthentizität in Bezug auf TikTok erörtert und die Grundlagen des Social Media Marketings erforscht. Die erarbeiteten Erkenntnisse und Methoden bilden die Grundlage für die Formulierung von vier Hypothesen.
Zur Überprüfung der Hypothesen folgt im Anschluss eine empirische Forschung in Form einer Online-Umfrage, bei welchem das Unternehmen Abihome GmbH als Fallbeispiel dient. Das Unternehmen eignet sich aufgrund seiner noch ausbaufähigen Präsenz auf der Plattform TikTok für dieses Vorhaben. Die gewonnenen Daten der empirischen Studie werden mit denen der Sekundärforschung kombiniert und ausgewertet, um Handlungsempfehlungen für Unternehmen abzuleiten, welche ihre Brand Awareness mithilfe eines authentischen Auftrittes auf TikTok optimieren wollen.
Die breite Einführung autonomer Fahrzeuge, ob für den Individualverkehr oder auch den öffentlichen Nahverkehr, ist nur noch eine Frage der Zeit. Dies bedeutet unweigerlich, dass in absehbarer Zeit alle Verkehrsteilnehmer*innen mit dieser Art von Fahrzeugen in Berührung kommen werden. In diesem Artikel soll diskutiert werden, wie Ansätze des Positive Computing helfen können, die Ausgestaltung der automatisierten Fahrzeuge so vorzunehmen, dass sie zum Wohlbefinden der Menschen in Verkehrssituationen beitragen.