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Ever since the 1980s, researchers in computer science and robotics have been working on making autonomous cars. Due to recent breakthroughs in research and devel- opment, such as the Bertha Benz Project [ZBS+14], the goal of fully autonomous vehicles seems closer than ever before. Yet a lot of questions remain unanswered. Especially now that the automotive industry moves towards autonomous systems in series production vehicles, the task of precise localization has to be solved with automotive grade sensors and keep memory and processing consumption at a mini- mum. This thesis investigates the Simultaneous Localization and Mapping (SLAM) prob- lem for autonomous driving scenarios on a parking lot using low cost automotive sensors. The main focus is herby devoted to the RAdio Detection And Ranging (RADAR) sensor, which has not been widely analyzed in an autonomous driving scenario so far, even though they are abundant in the automotive industry for ap- plications such as Adaptive Cruise Control (ACC). Due to the high noise floor, the radar sensor has widely been disregarded in the Intelligent Transportation Systems and Robotics communities with regards to SLAM applications. However in this thesis, it is shown that the RADAR sensor proves to be an affordable, robust and precise sensor, when modeling its physical properties correctly. In this regard, a GraphSLAM based framework is introduced, which extracts features from the RADAR sensor and generates an optimized map of the surroundings using the RADAR sensor alone. This framework is used to enable crowd based localization, which is not limited to the RADAR sensor alone. By integrating an automotive Light Detection and Ranging (LiDAR) and stereo camera sensor, a robust and precise localization system can be built that that is suitable for autonomous driving even in complex parking lot scenarios. It it is thereby shown that the RADAR sensor is strongly contributing to obtaining good results in a sensor fusion setup. These results were obtained on an extensive dataset on a parking lot, which has been recorded over the course of several months. It contains different weather conditions, different configurations of parked cars and a multitude of different trajectories to validate the approaches described in this thesis and to come to the conclusion that the RADAR sensor is a reliable sensor in series autonomous driving systems, both in a multi sensor framework and as a single component for localization.
In Folge der gegenwärtigen Digitalisierung in der produzierenden Industrie werden Anwendungen oder Services mit potentiell positiven Auswirkungen auf Faktoren wie Effektivität und Arbeitsqualität entwickelt. Ein geeigneter Ansatz zur Stärkung motivierender Aspekte im Arbeitskontext kann Gamification darstellen. In dieser Arbeit ist die initiale Konzeption und Evaluation eines Gamification-Ansatzes für Anwender eines KI-Service zur Maschinenoptimierung dargestellt und möglichen Anforderungen an ein Konzept zur Motivationssteigerung extrahiert.
In dieser Ausarbeitung wird eine zeitliche Vorhersage von Erdbeben getroffen. Hierfür werden mit einem Datensatz aus Labor-Erdbeben Convolutional Neural Networks (CNN) trainiert. Die trainierten Netzwerke geben Vorhersagen, indem sie einen Input an seismischen Daten klassifizieren. Durch das Klassifizieren kann das CNN die zeitliche Entfernung zum nächsten Erdbeben vorhersagen. Es werden hierfür zwei Ansätze miteinander verglichen. Beim ersten Ansatz werden die Originaldaten in ein CNN gegeben. Beim zweiten Ansatz wird vor dem CNN eine Vorverarbeitung der Daten mit den Mel Frequency Cepstral Coefficients (MFCC) durchgeführt. Es zeigt sich, dass mit beiden Ansätzen eine gute Klassifikation möglich ist. Die Kombination aus MFCC und CNN liefert die besseren quantitativen Ergebnisse. Hierbei konnte eine Genauigkeit von 65 % erreicht werden.
Semi-automated image data labelling using AprilTags as a pre-processing step for machine learning
(2019)
Data labelling is a pre-processing step to prepare data for machine learning. There are many ways to collect and prepare this data, but these are usually associated with a greater effort. This paper presents an approach to semi-automated image data labelling using AprilTags. The AprilTags attached to the object, which contain a unique ID, make it possible to link the object surfaces to a particular class. This approach will be implemented and used to label data of a stackable box.
The data is evaluated by training a You Only Look Once (YOLO) net, with a subsequent evaluation of the detection results. These results show that the semi-automatically collected and labelled data can certainly be used for machine learning. However, if concise features of an object surface are covered by the AprilTag, there is a risk that the concerned class will not be recognized. It can be assumed that the labelled data can not only be used for YOLO, but also for other machine learning approaches.
Bereits zum elften Mal findet nun die Studierendenkonferenz Informatics Inside statt. Als Teil des Masterstudiengangs Human-Centered Computing organisieren Masterstudierende selbständig eine vollumfängliche wissenschaftliche Konferenz. Die Informatik ist nach wie vor ständigem Wandel unterworfen. Unsere Studierenden tragen diesem Wandel bei, indem sie in ihrer wissenschaftllichen Vertiefung aktuelle Problemstellungen durch innovative Konzepte lösen. Inzwischen ist die Informatik aber auch nicht immer sofort sichtbar. Das merken wir immer dann, wenn etwas nicht wie vorgesehen funktioniert. Das diesjährige Motto der Informatics Inside ist experience (IT);, verdeckt als Funktionsaufruf:).
The Eleventh International Conference on Advances in Databases, Knowledge, and Data Applications (DBKDA 2019), held between June 02, 2019 to June 06, 2019 - Athens, Greece, continued a series of international events covering a large spectrum of topics related to advances in fundamentals on databases, evolution of relation between databases and other domains, data base technologies and content processing, as well as specifics in applications domains databases.
Advances in different technologies and domains related to databases triggered substantial improvements for content processing, information indexing, and data, process and knowledge mining. The push came from Web services, artificial intelligence, and agent technologies, as well as from the generalization of the XML adoption.
High-speed communications and computations, large storage capacities, and loadbalancing for distributed databases access allow new approaches for content processing with incomplete patterns, advanced ranking algorithms and advanced indexing methods.
Evolution on e-business, ehealth and telemedicine, bioinformatics, finance and marketing, geographical positioning systems put pressure on database communities to push the ‘de facto’ methods to support new requirements in terms of scalability, privacy, performance, indexing, and heterogeneity of both content and technology.
We welcomed academic, research and industry contributions. The conference had the followingtracks:
Knowledgeanddecisionbase
Databasestechnologies
Datamanagement
GraphSM: Large-scale Graph Analysis, Management and Applications
Small and Medium Enterprises (SMEs) which play substantial role in the development of any economy have been on the rise in the recent periods. Consequently, these enterprises are faced with a myriad of challenges which could potentially be solved through adoption of technology. Nonetheless, it has been observed that the new technological uptake among SMEs remains limited with the majority of them opting to maintain the status quo with regards to technology awareness and innovation strategies.
In a literature review, this paper explores three major dynamics curtailing adoption of new technologies by SMEs in the manufacturing: Knowledge absorptive capacity and management factors, organisational structures as well as technological awareness. Firstly, with regards to knowledge absorptive capacity and management factors, this study shows how these factors drive innovation potentials in SMEs.
Secondly, with regards to technological awareness factors, this study documents how perceived usefulness, costs, network and infrastructure, education and skills, training and attitude as well as knowledge influence adoption of new technologies among SMEs in the world. Lastly, the study concludes by analysing how organisational structures drive innovation potentials of SMEs in the wake of swift and profound technological changes in the market.
The relevance of technology knowledge in digital transformation especially in small and mediumsized enterprises (SMEs) that are still largely dependent on physical human capital has become increasingly obvious. This is due to the rapid revolution in business environment coupled with increased living examples of firms disrupted by advancement in technological knowledge. Consequently, we find it progressively vital for SMEs to spot and mitigate both threats and take advantage of opportunities arising from digital transformation dynamism.
Our study aims at exploring the relevance of technology knowledge in SMEs for digital transformation to uncover the opportunities, roadmaps, and models that SMEs can take advantage of in the digital transformation and gain a competitive edge.
We conclude that irrespective relevance of technology knowledge for digital transformation coupled with its low costs and accessibility, SMEs are yet to realize the full potential of technological knowledge. This is mainly due to technologies appearing, changing and also vanishing so rapidly in the digital age, that gaining proper understanding without dedicated resources is utterly difficult for SMEs - making them less competitive as incumbent large firms in the market.
The energy turnaround, digitalization and decreasing revenues forces enterprises in the energy domain to develop new business models. Business models for renewable energy are compound on different logic than business models for larger scale power plants. Following a design science research approach, we examined the business models of three enterprises in the energy domain in a first step. We identified that these business models result in complex ecosystems with multiple actors and difficult relationships between them. One cause is the fast changing and complicated state regulation in Germany. In order to solve the problem, we captured together with the partners of the enterprises the requirements in a second phase. Further we developed the prototype Business Model Configurator (BMConfig) based on the e3Value Ontology on the metamodelling platform ADOxx. We demonstrate the feasibility of our approach in business model of energy efficiency service based on smart meter data.