Land use changes have become a major contributor to the anthropogenic global change. The ongoing dispersion and concentration of the human species, being at their orders unprecedented, have indisputably altered Earth’s surface and atmosphere. The effects are so salient and irreversible that a new geological epoch, following the interglacial Holocene, has been announced: the Anthropocene. While its onset is by some scholars dated back to the Neolithic revolution, it is commonly referred to the late 18th century. The rapid development since the industrial revolution and its implications gave rise to an increasing awareness of the extensive anthropogenic land change and led to an urgent need for sustainable strategies for land use and land management. By preserving of landscape and settlement patterns at discrete points in time, archival geospatial data sources such as remote sensing imagery and historical geotopographic maps, in particular, could give evidence of the dynamic land use change during this crucial period. In this context, this thesis set out to explore the potentials of retrospective geoinformation for monitoring, communicating, modeling and eventually understanding the complex and gradually evolving processes of land cover and land use change. Currently, large amounts of geospatial data sources such as archival maps are being worldwide made online accessible by libraries and national mapping agencies. Despite their abundance and relevance, the usage of historical land use and land cover information in research is still often hindered by the laborious visual interpretation, limiting the temporal and spatial coverage of studies. Thus, the core of the thesis is dedicated to the computational acquisition of geoinformation from archival map sources by means of digital image analysis. Based on a comprehensive review of literature as well as the data and proposed algorithms, two major challenges for long-term retrospective information acquisition and change detection were identified: first, the diversity of geographical entity representations over space and time, and second, the uncertainty inherent to both the data source itself and its utilization for land change detection. To address the former challenge, image segmentation is considered a global non-linear optimization problem. The segmentation methods and parameters are adjusted using a metaheuristic, evolutionary approach. For preserving adaptability in high level image analysis, a hybrid model- and data-driven strategy, combining a knowledge-based and a neural net classifier, is recommended. To address the second challenge, a probabilistic object- and field-based change detection approach for modeling the positional, thematic, and temporal uncertainty adherent to both data and processing, is developed. Experimental results indicate the suitability of the methodology in support of land change monitoring. In conclusion, potentials of application and directions for further research are given.

In der heutigen Zeit spielt die Automatisierung eine immer bedeutendere Rolle, speziell im Bereich der Robotik entwickeln sich immer neue Einsatzgebiete, in denen der Mensch durch autonome Fahrzeuge ersetzt wird. Dabei orientiert sich der Großteil der eingesetzten Roboter an Streckenmarkierungen, die in den Einsatzumgebungen installiert sind. Bei diesen Systemen gibt es jedoch einen hohen Installationsaufwand, was die Entwicklung von Robotersystemen, die sich mithilfe ihrer verbauten Sensorik orientieren, vorantreibt. Es existiert zwar eine Vielzahl an Robotern die dafür verwendet werden können. Die Entwicklung der Steuerungssoftware ist aber immer noch Teil der Forschung. Für die Steuerung wird eine Umgebungskarte benötigt, an der sich der Roboter orientieren kann. Hierfür eignen sich besonders SLAM-Verfahren, die simultanes Lokalisieren und Kartographieren durchführen. Dabei baut der Roboter während seiner Bewegung durch den Raum mithilfe seiner Sensordaten eine Umgebungskarte auf und lokalisiert sich daran, um seine Position auf der Karte exakt zu bestimmen. Im Laufe dieser Arbeit wurden über 30 verschiedene SLAM Implementierungen bzw. Umsetzungen gefunden die das SLAM Problem lösen. Diese sind jedoch größtenteils an spezielle Systembzw. Umgebungsvoraussetzungen angepasste eigenständige Implementierungen. Es existiert keine öffentlich zugängliche Übersicht, die einen Vergleich aller bzw. des Großteils der Verfahren, z.B. in Bezug auf ihre Funktionsweise, Systemvoraussetzungen (Sensorik, Roboterplattform), Umgebungsvoraussetzungen (Indoor, Outdoor, ...), Genauigkeit oder Geschwindigkeit, gibt. Viele dieser SLAMs besitzen Implementierungen und Dokumentationen in denen ihre Einsatzgebiete, Testvoraussetzungen oder Weiterentwicklungen im Vergleich zu anderen SLAMVerfahren beschrieben werden, was aber bei der großen Anzahl an Veröffentlichungen das Finden eines passenden SLAM-Verfahrens nicht erleichtert. Bei einer solchen Menge an SLAM-Verfahren und Implementierungen stellen sich aus softwaretechnologischer Sicht folgende Fragen: 1. Besteht die Möglichkeit einzelne Teile des SLAM wiederzuverwenden? 2. Besteht die Möglichkeit einzelne Teile des SLAM dynamisch auszutauschen? Mit dieser Arbeit wird das Ziel verfolgt, diese beiden Fragen zu beantworten. Hierfür wird zu Beginn eine Übersicht über alle gefundenen SLAMs aufgebaut um diese in ihren grundlegenden Eigenschaften zu unterscheiden. Aus der Vielzahl von Verfahren werden die rasterbasierten Verfahren, welche Laserscanner bzw. Tiefenbildkamera als Sensorik verwenden, als zu untersuchende Menge ausgewählt. Diese Teilmenge an SLAM-Verfahren wird hinsichtlich ihrer nichtfunktionalen Eigenschaften genauer untersucht und versucht in Komponenten zu unterteilen, welche in mehreren verschiedenen Implementierungen wiederverwendet werden können. Anhand der extrahierten Komponenten soll ein Featurebaum aufgebaut werden, der dem Anwender einen Überblick und die Möglichkeit bereitstellt SLAM-Verfahren nach speziellen Kriterien (Systemvoraussetzungen, Umgebungen, ...) zusammenzusetzen bzw. zur Laufzeit anzupassen. Dafür müssen die verfügbaren SLAM Implementierungen und dazugehörigen Dokumentationen in Bezug auf ihre Gemeinsamkeiten und Unterschiede analysiert werden.

This thesis deals with the optimal control of PDEs. After a brief introduction in the theory of elliptic and parabolic PDEs, we introduce a software that solves systems of PDEs by the finite elements method. In the second chapter we derive optimality conditions in terms of function spaces, i.e. a systems of PDEs coupled by some pointwise relations. Now we present algorithms to solve the optimality systems numerically and present some numerical test cases. A further chapter deals with the so called lack of adjointness, an issue of gradient methods applied on parabolic optimal control problems. However, since optimal control problems lead to large numerical schemes, model reduction becomes popular. We analyze the proper orthogonal decomposition method and apply it to our model problems. Finally, we apply all considered techniques to a real world problem.:Introduction The state equation Optimal control and optimality conditions Algorithms The \"lack of adjointness\" Numerical examples Efficient solution of PDEs and KKT- systems A real world application Functional analytical basics Codes of the examples

Searching is a fundamental task to support research. Current search engines are keyword-based. Semantic technologies promise a next generation of semantic search engines, which will be able to answer questions. Current approaches either apply natural language processing to unstructured text or they assume the existence of structured statements over which they can reason. This work provides a system for combining the classical keyword-based search engines with semantic annotation. Conventional search results are annotated using a customized annotation algorithm, which takes the textual properties and requirements such as speed and scalability into account. The biomedical background knowledge consists of the GeneOntology and Medical Subject Headings and other related entities, e.g. proteins/gene names and person names. Together they provide the relevant semantic context for a search engine for the life sciences. We develop the system GoWeb for semantic web search and evaluate it using three benchmarks. It is shown that GoWeb is able to aid question answering with success rates up to 79%. Furthermore, the system also includes semantic hyperlinks that enable semantic browsing of the knowledge space. The semantic hyperlinks facilitate the use of the eScience infrastructure, even complex workflows of composed web services. To complement the web search of GoWeb, other data source and more specialized information needs are tested in different prototypes. This includes patents and intranet search. Semantic search is applicable for these usage scenarios, but the developed systems also show limits of the semantic approach. That is the size, applicability and completeness of the integrated ontologies, as well as technical issues of text-extraction and meta-data information gathering. Additionally, semantic indexing as an alternative approach to implement semantic search is implemented and evaluated with a question answering benchmark. A semantic index can help to answer questions and address some limitations of GoWeb. Still the maintenance and optimization of such an index is a challenge, whereas GoWeb provides a straightforward system.

Im bisherigen Auslegungsprozess wird ein Bahnsystem in der Regel in Teilsysteme zerlegt, die nacheinander und für sich betrachtet entworfen werden. Das Verhalten des Gesamtsystems im geplanten täglichen Betrieb wird nur für wenige Varianten mittels Simulation überprüft. In dieser Arbeit wird der Ansatz vorgestellt, ein elektrisches Bahnsystem als Optimierungsaufgabe zu modellieren und diese mit einem geeigneten mathematischen Suchverfahren zu lösen, um Wechselwirkungen im Gesamtsystem bereits während der Dimensionierung berücksichtigen zu können. Zu diesem Zweck wird ein mehrkriterieller genetischer Algorithmus mit Zugfahrtsimulation und Netzberechnung gekoppelt, um ein für elektrische Bahnen entwickeltes Optimierungsmodell zu lösen. Am Beispiel einer realen Metrostrecke wird das Verfahren auf seine Eignung getestet und die erzielten Ergebnisse bewertet. / In the previous design process the electric railway system was subdivided into subsystems that are conceived one after the other and independent of each other. The performance of the complete railway system under realistic operation conditions can only be verified for some very few variants using simulation tools. The paper presents an approach to formulate an electric railway system as a self-contained optimization problem solved by means of a mathematical optimization method in order to consider interactions within the system in the early stage of the design process. Therefore a multi-objective genetic algorithm is coupled with both train simulation and electrical network calculation solving an optimization model specially designed for electrical railway systems. The proposed method is tested on an actual metro system. The results of this case study are presented and evaluated.

Die Dissertation behandelt methodische und algorithmische Aspekte der Spektralelementemethode zur räumlichen Diskretisierung partieller Differentialgleichungen. Die Weiterentwicklung einer symmetriebasierten Faktorisierung ermöglicht effiziente Operatoren für Tetraederelemente. Auf Grundlage einer umfassenden Leistungsanalyse werden Engpässe in der Implementierung der Operatoren identifiziert und durch algorithmische Modifikationen der Methode eliminiert.

Description Logics (DLs) belong to a successful family of knowledge representation formalisms with two key assets: formally well-defined semantics which allows to represent knowledge in an unambiguous way and automated reasoning which allows to infer implicit knowledge from the one given explicitly. This thesis investigates various reasoning techniques for tractable DLs in the EL family which have been implemented in the CEL system. It suggests that the use of the lightweight DLs, in which reasoning is tractable, is beneficial for ontology design and maintenance both in terms of expressivity and scalability. The claim is supported by a case study on the renown medical ontology SNOMED CT and extensive empirical evaluation on several large-scale biomedical ontologies.

Manufacturing process development is under constant pressure to achieve a good yield for stable processes. The development of new technologies, especially in the field of photomask and semiconductor development, is at its phys- ical limits. In this area, data, e.g. sensor data, has to be collected and analyzed for each process in order to ensure process quality. With increasing complexity of manufactur- ing processes, the volume of data that has to be evaluated rises accordingly. The complexity and data volume exceeds the possibility of a manual data analysis. At this point, data mining techniques become interesting. The application of current techniques is complex because most of the data is captured with sensor measurement tools. Therefore, every measured value contains a specific error. In this paper we propose an error-aware extension of the density-based al- gorithm DBSCAN. Furthermore, we present some quality measures which could be utilized for further interpretation of the determined clustering results. With this new cluster algorithm, we can ensure that masks are classified into the correct cluster with respect to the measurement errors, thus ensuring a more likely correlation between the masks.

Modern application domains such as Composite Event Recognition (CER) and real-time Analytics require the ability to dynamically refresh query results under high update rates. Traditional approaches to this problem are based either on the materialization of subresults (to avoid their recomputation) or on the recomputation of subresults (to avoid the space overhead of materialization). Both techniques have recently been shown suboptimal: instead of materializing results and subresults, one can maintain a data structure that supports efficient maintenance under updates and can quickly enumerate the full query output, as well as the changes produced under single updates. Unfortunately, these data structures have been developed only for aggregate-join queries composed of equi-joins, limiting their applicability in domains such as CER where temporal joins are commonplace. In this paper, we present a new approach for dynamically evaluating queries with multi-way θ-joins under updates that is effective in avoiding both materialization and recomputation of results, while supporting a wide range of applications. To do this we generalize Dynamic Yannakakis, an algorithm for dynamically processing acyclic equi-join queries. In tandem, and of independent interest, we generalize the notions of acyclicity and free-connexity to arbitrary θ-joins. We instantiate our framework to the case where θ-joins are only composed of equalities and inequalities (<, ≤, =, >, ≥) and experimentally compare this algorithm, called IEDyn, to state of the art CER systems as well as incremental view maintenance engines. IEDyn performs consistently better than the competitor systems with up to two orders of magnitude improvements in both time and memory consumption.

This paper presents novel tree-based search algorithms that exploit the SIMD instructions found in virtually all modern processors. The algorithms are a natural extension of binary search: While binary search performs one comparison at each iteration, thereby cutting the search space in two halves, our algorithms perform k comparisons at a time and thus cut the search space into k pieces. On traditional processors, this so-called k-ary search procedure is not beneficial because the cost increase per iteration offsets the cost reduction due to the reduced number of iterations. On modern processors, however, multiple scalar operations can be executed simultaneously, which makes k-ary search attractive. In this paper, we provide two different search algorithms that differ in terms of efficiency and memory access patterns. Both algorithms are first described in a platform independent way and then evaluated on various state-of-the-art processors. Our experiments suggest that k-ary search provides significant performance improvements (factor two and more) on most platforms.