00 Informatik, Wissen, Systeme
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Whilst Paul de Casteljau is now famous for his fundamental algorithm of curve and surface approximation, little is known about his other findings. This article offers an insight into his results in geometry, algebra and number theory. Related to geometry, his classical algorithm is reviewed as an index reduction of a polar form. This idea is used to show de Casteljau's algebraic way of smoothing, which long went unnoticed. We will also see an analytic polar form and its use in finding the intersection of two curves. The article summarises unpublished material on metric geometry. It includes theoretical advances, e.g., the 14-point strophoid or a way to link Apollonian circles with confocal conics, and also practical applications such as a recurrence for conjugate mirrors in geometric optics. A view on regular polygons leads to an approximation of their diagonals by golden matrices, a generalisation of the golden ratio. Relevant algebraic findings include matrix quaternions (and anti-quaternions) and their link with Lorentz' equations. De Casteljau generalised the Euclidean algorithm and developed an automated method for approximating the roots of a class of polynomial equations. His contributions to number theory not only include aspects on the sum of four squares as in quaternions, but also a view on a particular sum of three cubes. After a review of a complete quadrilateral in a heptagon and its angles, the paper concludes with a summary of de Casteljau's key achievements. The article contains a comprehensive bibliography of de Casteljau's works, including previously unpublished material.
Data Mining und Knowledge Discovery in Databases (KDD) sind Forschungs- und Anwendungsgebiete, die sich mit der Extraktion von nutzbarem Wissen aus Daten befassen. Dazu werden unter anderem Methoden des maschinellen Lernens eingesetzt. Die Induktive Logikprogrammierung ist ein Teilgebiet des Maschinellen Lernens, dessen Gegenstand das Lernen aus multi-relational und prädikatenlogisch repräsentierten Daten ist, während andere Lernverfahren üblicherweise Daten voraussetzen, die in Form einer einzelnen Attribut-Wert-Tabelle vorliegen. Der Einsatz von ILP-Methoden für KDD und Data Mining wird auch als Relationales Data Mining bezeichnet. Ein Anwendungsgebiet von KDD und Data Mining ist die Teilgruppenanalyse. Dabei wird eine Population von Fällen, die in einer Datenbank repräsentiert sind, nach besonders interessanten Teilgruppen der Population durchsucht, indem mögliche Teilgruppen generiert und mithilfe geeigneter Interessantheitsfunktionen bewertet werden. Die vorliegende Arbeit hat sich zum Ziel gesetzt, Methoden zur sicheren Beschränkung des Suchraums bei der Suche nach interessanten Teilgruppen in multi-relationalen Daten zu erarbeiten und zu evaluieren. Dazu wird ein Verfahren zur Suche nach interessanten Teilgruppen in multi-relationalen Datenbanken entwickelt, das verschiedene Methoden zur Suchraumbeschränkung integriert. Die verschiedenen Methoden zur Suchraumbeschränkung werden in Experimenten evaluiert und das entwickelte Verfahren zur Bearbeitung eines echten Data Mining-Problems eingesetzt. Die Arbeit bietet im einzelnen: (1) eine Formalisierung der Teilgruppenanalyse im Rahmen der ILP, (2) Optimumschätzfunktionen zu ausgewählten Interessantheitsfunktionen, (3) eine Erweiterung des bekannten Apriori-Suchverfahrens zur Warenkorbanalyse, die es erlaubt, die von Apriori durchsuchte Hypothesensprache einzuschränken, (4) einen ILP-Sprachbias für die Teilgruppenanalyse, der die Anwendung der Teilmengenbedingung des Apriori-Suchverfahrens zur Beschränkung eines ILP-Suchraums erlaubt, (5) einen SQL-Sprachbias für die Teilgruppenanalyse in multi-relationalen Datenbanken, (6) einen Ansatz zur Integration der Suchraumbeschränkung anhand von Taxonomien in einen Apriori-artigen Suchalgorithmus, (7) eine Methode zur Behandlung diskretisierter numerischer Attribute, die die Suchraumbeschränkung anhand von Allgemeinerbeziehungen zwischen Intervallen vereinheitlicht mit der Suchraumbeschränkung anhand von Taxonomien, (8) Experimente zur Wirksamkeit der verschiedenen Möglichkeiten zur Suchraumbeschränkung, (9) die Anwendung der entwickelten Ansätze auf ein echtes Data Mining-Problem mit Bank-Daten und ausführliche Vergleiche mit verwandten Arbeiten. Die Experimente wurden mit einer prototypischen Implementation der in dieser Arbeit entwickelten Ansätze durchgeführt. Dabei haben sich Teilmengenbedingung und Optimumschätzfunktionen als wirkungsvolle und zuverlässige Methoden zur Beschränkung des Suchraums erwiesen, während der Beitrag der Taxonomien zur Suchraumbeschränkung zwischen verschiedenen Anwendungen stark schwankte und in einigen Fällen nur gering war. Ein wichtiges Ergebnis der Versuche ist, daß die Teilmengenbedingung, die bisher nur zur Suchraumbeschränkung in ein-relationalen Datenbanken eingesetzt werden konnte, für multi-relationale Datenbanken und ILP-Sprachen genauso wirkungsvoll sein kann wie für ein-relationale Datenbanken.
VBA bietet das Potenzial, effektive Digitalisierungslösungen mit geringem Aufwand zu realisieren. “VBA für Office-Automatisierung und Digitalisierung" zeigt mit vielen Codebeispielen die Automatisierung von Excel, Word, Outlook, PowerPoint, SAP ERP und SOLIDWORKS und das Zusammenwirken dieser Systeme. Auch Webservices und Rest APIs werden mit VBA angesprochen und erschließen interessante Möglichkeiten bis hin zu KI. Das Buch erläutert wichtige Konzepte und gibt viele Tipps, um VBA-Anwendungen mit einfachen Mitteln unternehmenstauglich und administrierbar zu gestalten.
One goal of research activities is finding ways to manage the growing complexity of embedded systems using self- configuration methods. While autonomous configuration could potentially be used in safety-critical and real-time systems, the basic requirements are not yet in place. This paper will outline a concept for the real autonomous configuration of TDMA-based communication processes, which currently does not exist. The paper initially addresses the TDMA-specific framework conditions and a potential solution. The issue of the mandatory a-priori known schedule is resolved using a generic schedule, because a simple method based on "free-slot-reserved-for- further-nodes" is not feasible. The most difficult part the startup was implemented through the generic schedule and an ID-based collision resolution process. To demonstrate the viability of the concept, the configuration method was implemented using a FlexRay communication system. This also satisfied the goal of eliminating the need for additional hardware and preserving the fault tolerant multimaster structure of the FlexRay system. The functionality of the concept was validated under different scenarios. The configuration times were analyzed, the results of which are also detailed here.
Disentangling Human-AI Hybrids: Conceptualizing the Interworking of Humans and AI-Enabled Systems
(2023)
Artificial intelligence (AI) offers great potential in organizations. The path to achieving this potential will involve human-AI interworking, as has been confirmed by numerous studies. However, it remains to be explored which direction this interworking of human agents and AI-enabled systems ought to take. To date, research still lacks a holistic understanding of the entangled interworking that characterizes human-AI hybrids, so-called because they form when human agents and AI-enabled systems closely collaborate. To enhance such understanding, this paper presents a taxonomy of human-AI hybrids, developed by reviewing the current literature as well as a sample of 101 human-AI hybrids. Leveraging weak sociomateriality as justificatory knowledge, this study provides a deeper understanding of the entanglement between human agents and AI-enabled systems. Furthermore, a cluster analysis is performed to derive archetypes of human-AI hybrids, identifying ideal–typical occurrences of human-AI hybrids in practice. While the taxonomy creates a solid foundation for the understanding and analysis of human-AI hybrids, the archetypes illustrate the range of roles that AI-enabled systems can play in those interworking scenarios.
This paper is a summary of the creation and data usage of an autonomous model vehicle which was recreated in CarMaker[1]. The simulation data come to use when following students of this semester project will develop algorithms and simulations which are impractical to test or train in real life. This paper starts with a summary of the background information around the model vehicle and the software CarMaker[1], then reproduces the construction process, digs deeper in ROS[6] and finishes with the extraction of the data.
To address 5G design targets, massive MIMO and mmWave communication are enabling technologies. Luckily, in many respects these two technologies share a symbiotic integration. Accordingly, a logical step is to integrate mmWave communications and massive MIMO to form ”mmWave-massive MIMO” which substantially increases user throughput, improve spectral and energy efficiencies, increase the capacity of mobile networks and achieve high multiplexing gains. Thus, this work analyses the concepts, performances, comparison and discussion of these technologies called: massive MIMO, mmWave Communications and mmWave-massive MIMO systems jointly. Besides, outcomes of extensive researches, emerging trends together with their respective benefits, challenges, proposed solutions and their comparative analysis is addressed. The performance of hybrid beamforming architecture with a fully digital and analog beamforming techniques are also analyzed. Analytical and simulation results show that the low-complexity hybrid analog-digital precoding achieves all round comparable precoding gains for mmWave-Massive MIMO technology.
The fifth-generation (5G) wireless communication system requires massive connectivity with high data rates and low latency. One of the technologies to meet these requirements is mm Wave massive MIMO. This work, therefore, aspires to have an in-depth look at the channel estimation and beamforming techniques jointly with their respective architectures for mm Wave massive MIMO system. In particular; sparse, compressed sensing, machine learning and array signal processing based channel estimation are addressed from 5G channel estimation techniques. On the other hand, beamforming techniques like hybrid beamforming and the low-complexity hybrid block diagonalization schemes with their mathematical analysis are included. This work also discusses in detail the challenges, optimization methods and mitigation techniques of pilot contamination, signal detection, channel estimation and hybrid beamforming for mm Wave massive MIMO system. The result asserts that partially connected block-diagonal hybrid bema forming with array signal processing based channel estimation is more optimal than the others with respect to over all performance, complexity and energy consumption. Finally, open research directions and challenges are pointed out.
In general, current systems for the Digital Factory implement a product-process-resource (PPR) data model in a monolithic rich-client/server architecture with a single database persistence layer. Common data objects are the product bills of material, descriptions of the production processes, or the resource structure, e.g. bill of equipment. The main drawback of the current monolithic architecture is the slow rate of development, which prevents fast adoption of the software to the new production planning process (i.e., due to new technologies for the transformation of the automotive industry with the goal of electrification) is not possible. Furthermore, time-consuming and error-prone export-import operations characterize the collaboration of the engineering supply chain. Mercedes-Benz has created a new IT system architecture for their Digital Factory. The core idea of this architecture is a module-based approach. Each planning step has its own module, e.g. product analysis, layout planning or cost calculation. One single module consists of a server-based business logic, a web-based user interface and its own database. Each module is the source of master data objects that originate from the corresponding planning step and refers to data objects from predecessor planning steps. The single modules communicate mostly via KAFKA. The usage of a model based application engine allows the fast creation of different modules. Best-of-breed third-party systems for specific planning steps can be integrated into the system architecture. Web technologies allow suppliers to access the Mercedes-Benz systems directly for a fully integrated supplier collaboration. Roll-out has started and has already led to significant efficiencies.
Das Buch vermittelt die Tourismus- und Reisewirtschaft als eine globale Branche der angewandten Wirtschaftsinformatik. Sie erfordert multimediale Informations- und Kommunikationssysteme, Management-, Vertriebs- und Verarbeitungssysteme im Rahmen IT-basierter Prozesse. Fachleute der Angewandten Informatik sollen die Strukturen und Anforderungen verstehen, um innovative Systeme entwickeln und bereitstellen zu können. Fachleute des Tourismus- und Reisemanagements sollen innovative informationstechnologische Entwicklungen beurteilen sowie IT-Investitionen entscheiden können, um sie erfolgreich und resilient einzusetzen.
Neben der umfassenden Aktualisierung erhalten die Mobilitätswende, der Online-Handel, die Vernetzung in Sozialen Medien, Big Data, Künstliche Intelligenz, Mixed Reality u.a.m in dieser dritten Auflage einen erweiterten Fokus. Das Buch unterstützt die Lehre und Forschung sowie die Unternehmenspraxis.
- Gesamtschau der IT-Systeme in der Tourismus- und Reiseverkehrswirtschaft mit entscheidungsrelevanter Tiefe und Zukunftsorientierung
- Integration der Leistungsprozesse von der Kundenkommunikation bis zur Hintergrundverarbeitung und Datenanalyse
Ziel des Lehrbuches ist es, einen umfassenden Einblick in das gesamte Spektrum elektronischer Informations-, Kommunikations- und Reservierungssysteme im Tourismus zu geben. Das Lehrbuch umfasst die Inhalte der Vorlesungen mit Übungen an Hochschulen aller Ebenen. Das Lehrbuch richtet sich an Studierende des Bachelor- und Masterstudiums der Studienrichtung Tourismus sowie an Führungskräfte und Mitarbeiter/innen von Unternehmen der Reise- und Tourismuswirtschaft. Fächer des IT-basierten Informationsmanagements werden an Hochschulen bereits im Grundstudium gelehrt und im Haupt- und Masterstudium vertieft.
Das Lehrbuch gibt umfassend Einblick in das Spektrum elektronischer Informations-, Kommunikations- und Reservierungssysteme im Tourismus.
Aktuelle Trends im E-Tourismus sowie wesentliche Systeme der Reisemittler (besonders globale Distributionssysteme) und Leistungsanbieter (Flug, Hotel etc.) werden behandelt. Ein weitreichender Überblick zum Yield-, Vertriebskanal- und Kundenbeziehungsmanagement stellt wesentliche Prozesse ausführlich dar.
- Aktuelle Trends im eTourismus wie Customer Journey, eMarketing, eCommerce, Geoinformationen, mTourismus
- Fallbeispiele mit exemplarischem Charakter
- Anregungen für zukünftige Realisierungsanwendungen als Inspiration für die Zukunft des eTourismus
Beim offenen Internet geht es um die Schaffung von Regeln zur Wahrung der gleichberechtigten und nichtdiskriminierenden Behandlung des Datenverkehrs bei der Bereitstellung von Internetzugangsdiensten
und damit verbundener Rechte der Endnutzer. Im Mittelpunkt der aktuellen Regelungen steht das Best-Effort-Prinzip. Demnach sollen Provider alle Datenpakete unabhängig von Inhalt, Anwendung, Herkunft und Ziel gleich behandeln und schnellstmöglich durch ihre Infrastruktur transportieren. Gestattet wird den Zugangsanbietern ein angemessenes Verkehrsmanagement, um das Netz integer und sicher zu halten oder um eine drohende Netzüberlastung zu vermeiden. Provider können Spezialdienste zusätzlich zum traditionellen Internet anbieten, um ein spezielles Qualitätsniveau zu gewährleisten. Dafür muss ausreichend Netzkapazität bereitstehen.
Home health applications have evolved over the last few decades. Assistive systems such as a data platform in connection with health devices can allow for health-related data to be automatically transmitted to a database. However, there remain significant challenges concerning intermodular communication. Central among them is the challenge of achieving interoperability, the ability of devices to communicate and share data with each other. A major goal of this project was to extend an existing data platform (COMES®) and establish working interoperability by connecting assistive devices with differing approaches. We describe this process for a sleep monitoring and a physical exercise device. Furthermore, we aimed to test this setup and the implementation with a data platform in both a laboratory and an in-home setting with 11 elderly participants. The platform modification was realized, and the relevant changes were made so that the incoming data could be processed by the data platform, as well as visually displayed in real-time. Data was recorded by the respective device and transmitted into the data server with minor disruptions. Our observations affirmed that difficulties and data loss are far more likely to occur with increasing technical complexity, in the event of instable internet connection, or when the device setup requires (elderly) subjects to take specific steps for proper functioning. We emphasize the importance for tests and evaluations of home health technologies in real-life circumstances.
Demographic change is advancing inexorably. The number of people in need of care is growing rapidly, while at the same time there is already a shortage of several thousand caregivers to provide adequate, needs-based nursing care. Intelligent assistance systems can make a contribution to solving this problem. In this article, diverse assistance systems from the areas of nursing care, health care, rehabilitation and training, and mobility are presented as examples. These different components, equipped with the appropriate sensors, data transmission units and interfaces, can all be integrated into a system platform and thus form a comprehensive intelligent, digital assistance system or a mobile diagnosis and therapy platform.
This paper is a summary oft he traffic light detection for an autonomous model vehicle which was created for a student semester project. The traffic light detection comes to use when the model vehicle takes part in the VDI-Cup or other future Cups. The VDI-Cup will be discribed later on. The structure of this paper start with a summary, followed by the discribtion oft the VDI-Cup. After that we will explain how we implemented the traffic light detection and finishes with the conclusion of our implementation.
Collecting the training set required for building a robust neural network for autonomous driving requires large amount of data. It is nearly impossible to collect this data only by recording the driving of real world vehicles. There is no organization or company that is able to provide the resources needed to tackle this task.[1] Therefore the approach currently used is to generate the large amount of training data by simulating virtual cars in computer simulations that try to mirror real world road traffic as close as possible. Besides the huge amount it is also important that the data generated varies quiet a lot, otherwise the neural network can not learn to adapt to the many different situations that occur in real world road traffic every day.[2] Therefore it would be great to record the virtual car driving on as many different tracks as possible. To solve this issue this paper proposes a fast and simple iterative algorithm that can be used to procedural generate tracks that can be used for the recording and training of autonomous driving cars.
Lane detection is an essential part for an autonomous car to function. With a lane departure warning system many modern cars are already equipped with a lane detection system. But models and methods to predict lanes are numerous and often follow different approaches to solve the lane detection problem. This paper wants to give an overview on some of the state of the art models and methods for lane detection that lately achieved good results on public datasets. The paper will also look at training and testing a SCNN [10] using the free Colaboratory service from Google [29].
In the last decade autonomous driving has evolved from a science fictional dream to an everyday reality. With the advance of more and more companies bringing their versions of self-driving cars on the street it is just a matter of time before the majority of transportation will be in the hand of computers. But with the deadly car accident involving a self-driving Uber car back in 2018 there is also the question about how reliable autonomous driving really is and how we can validate and test the safety of this new road user 1. An uprising approach towards creating robust and adaptable neural networks is called domain randomization. This paper explores the possibility of using this method to create training data with driving simulations. It will propose a list of important criteria and factors affecting the selection of a fitting simulation. Furthermore it will present a track generator which is able to create useful tracks and export them to a format which can be used by several common simulations used in the field of autonomous driving research.