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Die Informationsbroschüre „3D-Druck – Prozessmanagement für individualisierte Massenprodukte“ richtet sich an 3D-Druckdienstleister, die additive Fertigungsverfahren (Additive Manufacturing) nutzen, sowie an IT-Dienstleister, die eine Plattform für den Datenaustausch und die Individualisierung anbieten.
Mit der additiven 3D-Drucktechnologie erfolgt die Fertigung von Produkten durch einen schichtweisen Aufbau. In dieser Broschüre werden mögliche Potenziale im Bereich der Herstellung mittels additiver Fertigungsverfahren aufgezeigt.
Die Informationsbroschüre „Anforderungen an die Gestaltung multimodaler Mobilitätsanwendungen“ richtet sich an IT-Dienstleister. In dieser Broschüre werden mögliche Potenziale im Bereich des allgemeinen Mobilitätsmanagements aufgezeigt. Automobilhersteller vernetzten sich zunehmend mit Technologie-Unternehmen. Es geht nicht nur um die besondere Entwicklung von spezieller Elektronik- und Softwarelösungen für Navigations- und Entertainmentsysteme oder auch Fahrassistenz-Systemen in modernen PKW, sondern um einen übergreifenden Design- und Interaktionsansatz für miteinander vernetzte Geräte.
Benutzerzentrierte Entwicklung mobiler Unternehmenssoftware, Teil 2 : die Iterative Entwicklung
(2008)
Information Channels
(2000)
Production and distribution of personalized information services employing mass customization
(2003)
Mobile CRM-Systeme : Customer Relationship Management zur Unterstützung des Vertriebsaußendienstes
(2003)
Mobile Unternehmenssoftware
(2006)
The initial idea of Robotic Process Automation (RPA) is the automation of business processes through a simple emulation of user input and output by software robots. Hence, it can be assumed that no changes of the used software systems and existing Enterprise Architecture (EA) is
required. In this short, practical paper we discuss this assumption based on a real-life implementation project. We show that a successful RPA implementation might require architectural work during analysis, implementation, and migration. As practical paper we focus on exemplary lessons-learned and new questions related to RPA and EA.
In this paper we report on an architecture for a self-driving car that is based on ROS2. Self-driving cars have to take decisions based on their sensory input in real-time, providing high reliability with a strong demand in functional safety. In principle, self-driving cars are robots. However, typical robot software, in general, and the previous version of the Robot Operating System (ROS), in particular, does not always meet these requirements. With the successor ROS2 the situation has changed and it might be considered as a solution for automated and autonomous driving. Existing robotic software based on ROS was not ready for safety critical applications like self-driving cars. We propose an architecture for using ROS2 for a self-driving car that enables safe and reliable real-time behaviour, but keeping the advantages of ROS such as a distributed architecture and standardised message types. First experiments with an automated real passenger car at lower and higher speed-levels show that our approach seems feasible for autonomous driving under the necessary real-time conditions.
Rugged terrain robot designs are important for field robotics missions. A number of commercial platforms are available, however, at an impressive price. In this paper, we describe the hardware and software component of a low-cost wheeled rugged-terrain robot. The robot is based on an electric children quad bike and is modified to be driven by wire. In terms of climbing properties, operation time and payload it can compete with some of the commercially available platforms, but at a far lower price.
Finding a good system topology with more than a handful of components is a
highly non-trivial task. The system needs to be able to fulfil all expected load cases, but at the
same time the components should interact in an energy-efficient way. An example for a system
design problem is the layout of the drinking water supply of a residential building. It may be
reasonable to choose a design of spatially distributed pumps which are connected by pipes in at
least two dimensions. This leads to a large variety of possible system topologies. To solve such
problems in a reasonable time frame, the nonlinear technical characteristics must be modelled
as simple as possible, while still achieving a sufficiently good representation of reality. The
aim of this paper is to compare the speed and reliability of a selection of leading mathematical
programming solvers on a set of varying model formulations. This gives us empirical evidence
on what combinations of model formulations and solver packages are the means of choice with the current state of the art.
The UN sets the goal to ensure access to water and sanitation for all people by 2030. To address this goal, we present a multidisciplinary approach for designing water supply networks for slums in large cities by applying mathematical optimization. The problem is modeled as a mixed-integer linear problem (MILP) aiming to find a network describing the optimal supply infrastructure. To illustrate the approach, we apply it on a small slum cluster in Dhaka, Bangladesh.
Ensuring access to water and sanitation for all is Goal No. 6 of the 17 UN Sustainability Development Goals to transform our world. As one step towards this goal, we present an approach that leverages remote sensing data to plan optimal water supply networks for informal urban settlements. The concept focuses on slums within large urban areas, which are often characterized by a lack of an appropriate water supply. We apply methods of mathematical optimization aiming to find a network describing the optimal supply infrastructure. Hereby, we choose between different decentral and central approaches combining supply by motorized vehicles with supply by pipe systems. For the purposes of illustration, we apply the approach to two small slum clusters in Dhaka and Dar es Salaam. We show our optimization results, which represent the lowest cost water supply systems possible. Additionally, we compare the optimal solutions of the two clusters (also for varying input parameters, such as population densities and slum size development over time) and describe how the result of the optimization depends on the entered remote sensing data.