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Mobile-Commerce-Studie
(2000)
Zur Schätzung der Exposition von Oberflächengewässern durch Pflanzenschutzmittel werden PEC-Werte mit Hilfe eines probabilistischen Verfahrens ermittelt. Hierfür werden zunächst verschiedene Regressionsanalysen zur Modellierung der Abdrift durchgeführt. Anschließend wird die ausgewählte Abdriftverteilung mit verschiedenen Verteilungsansätzen für die Aufwandmenge und das Gewässervolumen kombiniert.
Neue technologische Entwicklungen basieren immer mehr auf einer
zunehmenden Mathematisierung, gerade in den Ingenieurwissenschaften.
Nicht erst seit PISA ist jedoch zu beobachten, dass sich das
belastbare mathematische Grundwissen vieler Studienanfänger in den letzten Jahren verringert hat.
Im vorliegenden Beitrag wird dieses Spannungsfeld, in dem sich die Ingenieurmathematik befindet, aus Sicht von Fachhochschuldozenten beschrieben. Ausgehend von den Ausbildungszielen der Ingenieurmathematik werden Anforderungen an die Schulmathematik abgeleitet.
Diese Anforderungen werden beispielhaft für die Einführung und den Umgang mit den mathematischen Objekten Zahlen, Terme, Gleichungen und Funktionen konkretisiert.
Ziel ist eine Sensibilisierung von Mathematiklehrerinnen und -lehrern, um ihre Schulabsolventinnen und -absolventen besser für ein zukünftiges ingenieurwissenschaftliches Studium zu rüsten.
Data management is a challenge in both scientific and technical environments. Therefore researchers have developed a special interest in this field. Modern approaches (i.e. Subversion, CVS) already offer authoring and versioning in distributed systems. However this might be insufficient in a vast number of scenarios, where not only the data resulting from a process, but also data which describes the process that generated those results is crucial.
This thesis introduces and demonstrates a novel method for learning qualitative models of the world by an autonomous robot. The method makes possible generation of qualitative models that can be used for prediction as well as directing the experiments to improve the model. The qualitative models form the knowledge representation of the robot and consists of qualitative trees and non-deterministic finite automaton. An efficient exploration algorithm that lets the robot collect the most relevant learning samples is also introduced. To demonstrate the use of the methodology, representation and algorithm, two experiments are described. The first experiment is conducted using a mobile robot and a ball, where the robot observes the ball and learns the effect of its actions on the observed attributes of the world. The second experiment is conducted using a mobile robot and five boxes, two non-movable boxes and three movable boxes. The robot experiments actively with the objects and observes the changes in the attributes of the world. The main difference with the two experiments is that the first one tries to learn by observation while the second tries to learn by experimentation. In both experiments the robot learns qualitative models from its actions and observations. Although the primary objective of the robot is to improve itself by being able to predict the outcome of its actions, the models Learned were also used at each step of the learning process to direct the experiments so that the model converges to the final model as quickly as possible.
XPERSIF: a software integration framework & architecture for robotic learning by experimentation
(2008)
The integration of independently-developed applications into an efficient system, particularly in a distributed setting, is the core issue addressed in this work. Cooperation between researchers across various field boundaries in order to solve complex problems has become commonplace. Due to the multidisciplinary nature of such efforts, individual applications are developed independent of the integration process. The integration of individual applications into a fully-functioning architecture is a complex and multifaceted task. This thesis extends a component-based architecture, previously developed by the authors, to allow the integration of various software applications which are deployed in a distributed setting. The test bed for the framework is the EU project XPERO, the goal of which is robot learning by experimentation. The task at hand is the integration of the required applications, such as planning of experiments, perception of parametrized features, robot motion control and knowledge-based learning, into a coherent cognitive architecture. This allows a mobile robot to use the methods involved in experimentation in order to learn about its environment. To meet the challenge of developing this architecture within a distributed, heterogeneous environment, the authors specified, defined, developed, implemented and tested a component-based architecture called XPERSIF. The architecture comprises loosely-coupled, autonomous components that offer services through their well-defined interfaces and form a service-oriented architecture. The Ice middleware is used in the communication layer. Its deployment facilitates the necessary refactoring of concepts. One fully specified and detailed use case is the successful integration of the XPERSim simulator which constitutes one of the kernel components of XPERO.The results of this work demonstrate that the proposed architecture is robust and flexible, and can be successfully scaled to allow the complete integration of the necessary applications, thus enabling robot learning by experimentation. The design supports composability, thus allowing components to be grouped together in order to provide an aggregate service. Distributed simulation enabled real time tele-observation of the simulated experiment. Results show that incorporating the XPERSim simulator has substantially enhanced the speed of research and the information flow within the cognitive learning loop.