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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.
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
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
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.
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.
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.
So far, researchers have used a wellbeing-centered approach to catalyze successful intergenerational collaboration (IGC) in innovative activities. However, due to the subject’s multidisciplinary nature, there is still a dearth of comprehensive research devoted to constructing the IGC system. Thus, the purpose of this study is to fill a research void by providing a conceptual framework for information technology (IT) system designers to use as a jumping-off point for designing an IGC system with a wellbeing-oriented design. A systematic literature study was conducted to identify relevant terms and develop a conceptual framework based on a review of 75 selected scientific papers. The result consists of prominent thematic linkages and a conceptual framework related to design technology for IGC systems. The conceptual framework provides a comprehensive overview of IGC systems in the innovation process by identifying five barrier dimensions and using six wellbeing determinants as IGC catalysts. Moreover, this study discusses future directions for research on IGC systems. This study offers a novel contribution by shifting the technology design process from an age-based design approach to wellbeing-driven IGC systems. Additional avenues for investigation were revealed through the analysis of the study’s findings.
Fahrerassistenzsysteme werden eingesetzt, um dem Fahrer
eines Kraftfahrzeugs Handlungsabläufe abzunehmen. Diese Handlungsabläufe
werden definiert durch eine Aufgabenstellung, die vom Fahrer an das Fahrerassi-
stenzsystem übergeben oder systembedingt gelöst wird. Bei komplexen Fahreras-
sistenzsystemen ist an eine autonome Navigation im Straßenverkehr gedacht. Es
wird ein neues Verfahren vorgestellt, welches eine Bewegungssteuerung eines
autonomen Fahrzeugs durchführen kann. Es werden der Lenkwinkel und die Ge-
schwindigkeit beeinflußt. Für diese Aufgabe wird ein dynamischer Ansatz aus
dem Bereich der neuronalen Felder gewählt. Relevante Attribute für den Fahrt-
verlauf auf unterschiedlichem Abstraktionsniveau können dabei einfach (additiv)
verarbeitet werden.
So far, electronic data interchange (EDI) has been primarily used by large companies. They increasingly pressure their business partners to participate in or connect to their EDI infrastructure. Companies, which do not use EDI so far, face the challenge of imple-mentation. Questions, such as the choice of the right EDI approach and the right EDI standard, have to be answered. In addition, there are often high investment costs. Small- and medium-sized enterprises (SMEs) are particularly affected due to their limited re-sources and financial means in comparison to those of large enterprises. Based on a structured literature research, information on the state of the art as well as research was consolidated and the opportunities and risks of EDI for small and medium-sized enter-prises were examined. The results show that EDI offers a variety of opportunities ranging from process optimization to competitive advantages, but that these also depend on the degree of integration. The understanding of the own benefits as well as the support of the management plays an important role for the successful adoption, implementation and integration of EDI.
Keywords: EDI, interorganizational systems, SME, system integration, data interchange
We describe the general concept, system architecture, hardware, and the behavioral abilities of Cora (Cooperative Robot Assistant, see Fig. 1), an autonomous non mobile robot assistant. Outgoing from our basic assumption that the behavior to perform determines the internal and external structure of the behaving system, we have designed Cora anthropomorphic to allow for humanlike behavioral strategies in solving complex tasks. Although Cora was built as a prototype of a service robot system to assist a human partner in industrial assembly tasks, we will show that Cora’s behavioral abilities are also conferrable in a household environment. After the description of the hardware platform and the basic concepts of our approach, we present some experimental results by means of an assembly task.
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.
Für Firmen spielt Kundensegmentierung zur Verbesserung ihrer
Absatzmöglichkeiten eine zunehmend größere Rolle. Dabei zeigt sich die Wahl der optimalen Methode zur Datenanalyse und Kundensegmentierung aus vielfältigen Gründen als entscheidende Voraussetzung für den Erfolg.
Das Ziel der vorliegenden Arbeit ist es, am Beispiel des Datensatzes aus dem Bereich des E-Commerce Customer Segmentation zu untersuchen, ob die Anwendung von Deep Learning gegenüber den dort mit klassischem Machine Learning durchgeführten Segmentierungen bessere Ergebnisse erzielt. Die dabei gewonnenen Erkenntnisse ermöglichen es, Kriterien für die optimale Methodenwahl näher zu bestimmen. Dazu ist es erforderlich, beim Datensatz die gleiche Datenvorverarbeitung wie in der Referenzarbeit zu verwenden, um die Ergebnisse des Deep Learning Modells mit jenen des Machine Learning Modells vergleichbar zu machen.
Der Vergleich ergab, dass die Performance beim Deep Learning Verfahren
mittig zwischen den Ergebnissen der anderen Machine Learning Algorithmen liegt. Die Performance ist den klassischen Machine Learning Verfahren bei der
hier vorhandenen Größe des Datensatzes nicht überlegen. Daraus folgt, dass bei ähnlicher Performance die sonstigen Voraussetzungen der Methoden, wie zum Beispiel die Komplexität der Netzwerkarchitektur, die Trainingsgeschwindigkeit und die Hardwarevoraussetzungen, eine
entscheidende Rolle spielen. Die Erörterung verschiedener weiterer Methoden des Deep Learning deutet darauf hin, dass der Aufwand, damit gute Ergebnisse bei heterogenen Daten der Kundensegmentierung zu erreichen, noch nicht überzeugt.
Learning the German language is one of the most critical challenges for refugee children in Germany. It is a prerequisite to allow communication and integration into the educational system. To solve the underlying problem, we conceptualized a set of principles for the design of language learning systems to support collaboration between teachers and refugee children, using a Design Science Research approach. The proposed design principles offer functional and non-functional requirements of systems, including the integration of open educational resources, different media types to develop visual and audio narratives that can be linked to the cultural and social background. This study also illustrates the use of the proposed design principles by providing a working prototype of a learning system. In this, refugee children can learn the language collaboratively and with freely accessible learning resources. Furthermore, we discuss the proposed design principles with various socio-technical aspects of the well-being determinants to promote a positive system design for different cultural and generational settings. Overall, despite some limitations, the implemented design principles can optimize the potential of open educational resources for the research context and derive further recommendations for further research.
Background:
Detection of influential actors in social media such as Twitter or Facebook plays an important role for improving the quality and efficiency of work and services in many fields such as education and marketing.
Methods:
The work described here aims to introduce a new approach that characterizes the influence of actors by the strength of attracting new active members into a networked community. We present a model of influence of an actor that is based on the attractiveness of the actor in terms of the number of other new actors with which he or she has established relations over time.
Results:
We have used this concept and measure of influence to determine optimal seeds in a simulation of influence maximization using two empirically collected social networks for the underlying graphs.
Conclusions:
Our empirical results on the datasets demonstrate that our measure stands out as a useful measure to define the attractors comparing to the other influence measures.
This study proposes a framework for the collaborative development of global start-up innovators in a multigenerational digital environment. Intergenerational collaboration has been identified as a strategy to support entrepreneurs during their formative years. However, integrating and fostering intergenerational collaboration remains elusive. Therefore, this study aims to identify competencies for successful global start- ups through intergenerational knowledge transfer. We used a systematic literature review to identify a competency set consisting of growth virtues, effectual creativity, technical domain, responsive teamwork, values-based organization, sustainable networking, cultural awareness, and facilitating intergenerational safety. The competency framework serves as a foundation for knowledge management research on the global innovation readiness of people to collaborate across generations in the digital age.
In this document a reliable data streaming mechanism for a TDMA LPWAN application is developed by adapting a link layer solution for power line communication, published at the International Symposium on Power Line Communications and its Applications (ISPLC) 2015. A C++ implementation of the services link layer is provided and demonstrated
working at a packet error rate of 50%.
Die Nutzung von Robotern hat stark zugenommen und die Wirkung auf Menschen grundlegend
verändert. Roboter wurden in unsere Gesellschaft eingeführt, jedoch berichten Studien darüber,
dass ihre soziale Rolle immer noch unklar ist (Bartneck & Hu, 2008).
Es gibt es immer mehr Filme, die das Bild von Robotern prägen, zudem gibt es immer mehr
Roboter, die in verschiedenen Kontexten eingesetzt werden, zum Beispiel die Robbe Paro, die Patient:innen in Altenheimen hilft (Schneider, 2021). Auch in Einkaufszentren können Roboter
eingesetzt werden. Hierbei konnte eine Studie in Japan in einer Shopping-Mall zeigen, dass Menschen einen Roboter missbrauchen, vor allem wenn sie sich unbeobachtet fühlen (Nomura et al., 2014). Durch die zunehmende Präsenz von Robotern geriet dieses Phänomen des Robot-
Abuse zunehmend in den wissenschaftlichen Fokus.
Thematisch lässt sich der Gegenstand dieser Abschlussarbeit in ebendiesen Bereich von Robot-
Abuse einordnen, denn sie bezieht sich explizit auf das Empathieverhalten der
Teilnehmer:innen der in dieser Arbeit durchgeführten Studie während eines beobachteten
Robotermissbrauchs. Dabei wird zusätzlich betrachtet, wie sich der Grad der
Anthropomorphisierung auswirkt und wie er wahrgenommen wird. Zudem werden die
unterschiedlichen Wahrnehmungen der Geschlechter betrachtet. Vor diesem Hintergrund soll folgende Forschungsfrage beantwortet werden: Gibt es einen Unterschied im Empathieempfinden in Bezug auf das Geschlecht? Das Ziel der vorliegenden Arbeit ist es dabei,
die Unterschiede im Empathieverhalten bei Robotern mit verschiedenen Graden der
Anthropomorphisierung während eines nonverbalen Robotermissbrauchs zu untersuchen und
einen Überblick über die vorhandene Literatur zu schaffen.
Dafür wird eine Onlinestudie mit einem 2x3-Between-Subjects-Design durchgeführt. In der
Studie werden drei Roboter verwendet: NAO, MiRo-E und der Staubsaugroboter Kobold. Die Teilnehmer:innen sehen eines von drei selbst erstellten Videos, in denen der jeweilige Roboter
getreten wird. Zudem wurde eine Literaturrecherche zu den Themen Anthropomorphismus,
Robot-Abuse, Geschlechterunterschiede und Empathie durchgeführt, um die theoretische Herleitung zu erläutern und aufzuarbeiten.
In dieser Bachelorarbeit werden dafür zunächst die relevanten theoretischen Grundlagen
betrachtet. Diesbezüglich werden in Kapitel 2 die Begriffe Anthropomorphismus, Robot-Abuse,
Geschlechterunterschiede und Empathie erläutert. Anschließend werden die Ergebnisse ausführlich aufgezeigt und diskutiert. Zum Schluss wird in einem Fazit die Eingrenzung dieser
Studie und ein Ausblick auf eine weitere Studie aufgezeigt.
In recent years, the healthcare industry has increasingly relied on modern technologies.
Conventional methods are supported by Big Data methods or are being
investigated in the research. Has Big Data become more relevant in medicine in recent years? What does the future look like? In which medical subject area is Big Data being applied? These questions will be clarified during the thesis. In the first part, the usage of Big Data in medicine is shown and then, by using a bibliometric analysis, the importance and development of Big Data in medicine is presented.
Afterwards, there is a discussion of the results followed with a summary and the future perspective. This thesis gives an overview about the currently technological possibilities and the potentials of Big Data in healthcare and medicine.
Enabling decentral collaborative innovation processes -a web based real time collaboration platform
(2018)
The main goal of this paper is to define a collaborative innovation process as well as a supporting tool. It is motivated through the increasing competition on global markets and the resultant propagation of decentralized projects with a high demand of innovative collaboration in global contexts. It bases on a project accomplished by the author group. A detailed literature review and the action design research methodology of the project led to an enhanced process model for decentral collaborative innovation processes and a basic realization of a browser based real time tool to enable these processes.The initial evaluation in a practical distributed setting has shown that the created tool is a useful way to support collaborative innovation processes.
Diese Arbeit beschäftigt sich mit der Erstellung einer Administratoroberfläche für die Lehre bei Photovoltaik (PV)-Praktika in der virtuellen Realität (VR). Die erstellte Umgebung bietet, mittels Bildschirmspiegelungen, Möglichkeiten zur didaktischen Anleitung und Unterstützung der Studierenden. Das Thema wurde aufgrund einer bestehenden Lehranwendung in der VR bedeutungsvoll und zeigt deutliches Potenzial. Diese Lehranwendung wird bereits umfassend und verpflichtend in den Praktika eingesetzt. Sie bietet einen praxisnahen Aufbau von Solaranlagen und erhöht gefahrlos die Experimentierfreudigkeit. Mit ihr lassen sich die aufgebauten Anlagen technisch prüfen, simulieren und bewerten. Zudem werden die beiden Möglichkeiten zur Unterstützung der Studierenden beurteilt. Als Ergebnis wird die Umsetzung der nahezu automatisierten Administratorober-fläche verdeutlicht und ein Usability-Test aus den Praktika evaluiert.
Schlagwörter: Administratoroberfläche, Bildschirmspiegelung, C, Didaktik, im-mersiv, Oculus Quest 2, Photovoltaik, Python, Tkinter, virtuelle Realität