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This thesis studies concurrency control and composition of transactions in computing environments with long living transactions where local data autonomy of transactions is indispensable. This kind of computing architecture is referred to as a Disconnected System where reads are segregated -disconnected- from writes enabling local data autonomy. Disconnecting reads from writes is inspired by Bertrand Meyer's "Command Query Separation" pattern. This thesis provides a simple yet precise definition for a Disconnected System with a focus on transaction management. Concerning concurrency control, transaction management frameworks implement a'one concurrency control mechanism fits all needs strategy'. This strategy, however, does not consider specific characteristics of data access. The thesis shows the limitations of this strategy if transaction load increases, transactions are long lived, local data autonomy is required, and serializability is aimed at isolation level. For example, in optimistic mechanisms the number of aborts suddenly increases if load increases. In pessimistic mechanisms locking causes long blocking times and is prone to deadlocks. These findings are not new and a common solution used by database vendors is to reduce the isolation. This thesis proposes the usage of a novel approach. It suggests choosing the concurrency control mechanism according to the semantics of data access of a certain data item. As a result a transaction may execute under several concurrency control mechanisms. The idea is to introduce lanes similar to a motorway where each lane is dedicated to a certain class of vehicle with the same characteristics. Whereas disconnecting reads and writes sets the traffic's direction, the semantics of data access defines the lanes. This thesis introduces four concurrency control classes capturing the semantics of data access and each of them has an associated tailored concurrency control mechanism. Class O (the optimistic class) implements a first-committer-wins strategy, class R (the reconciliation class) implements a first-n-committers-win strategy, class P (the pessimistic class) implements a first-reader-wins strategy, and class E (the escrow class) implements a first-n-readers-win strategy. In contrast to solutions that adapt the concurrency control mechanism during runtime, the idea is to classify data during the design phase of the application and adapt the classification only in certain cases at runtime. The result of the thesis is a transaction management framework called O|R|P|E. A performance study based on the TPC-C benchmark shows that O|R|P|E has a better performance and a considerably higher commit rate than other solutions. Moreover, the thesis shows that in O|R|P|E aborts are due to application specific limitations, i.e., constraint violations and not due to serialization conflicts. This is a result of considering the semantics.
Compared to diesel or gasoline, using compressed natural gas as a fuel allows for significantly decreased carbon dioxide emissions. With the benefits of this technology fully exploited, substantial increases of engine efficiency can be expected in the near future. However, this will lead to exhaust gas temperatures well below the range required for the catalytic removal of residual methane, which is a strong greenhouse gas. By combination with a countercurrent heat exchanger, the temperature level of the catalyst can be raised significantly in order to achieve sufficient levels of methane conversion with minimal additional fuel penalty. This thesis provides fundamental theoretical background of these so-called heat-integrated exhaust purification systems. On this basis, prototype heat exchangers and appropriate operating strategies for highly dynamic operation in passenger cars are developed and evaluated.
Melamine Formaldehyde (MF) resins are thermosetting synthetic materials. The present work deals with the evaluation of the impregnation process, modification of resin structure and abrasion resistant applications. During the industrial process paper is impregnated by aqueous oligomers. The drying procedure and the corresponding residual volatile content is a crucial step during production, because of its influence on the later surface quality. Standard measurement routines do not differentiate between physical and chemical origin. Using TGA and DSC methods, the evaporation of water could be characterized as a clear separation of solvent evaporation and the release of water during condensation. The method could be used to upgrade current quality control as well as reaction condition tuning. According to the characteristics of duroplastic material, the formed network is very dense but also brittle. Challenging applications require highly modified resins in order to decrease the network density. Substances from bio renewable resources offer chemical possibilities for covalent crosslinking. Several substance classes have been tested for compatibility via hydroxyl groups or amines. The addition of polyols under appropriate reaction conditions showed chemical incorporation into the MF prepolymer. NMR methods have been used to characterize the resins. The synthesized polymers represent a suitable alternative for the usage in challenging furniture and flooring laminate applications. MF applications for scratch and wear resistant surfaces are commonly reinforced by multiple layer setups with inorganic particles. Fulfilling normative requirements a one sheet setup of decorative paper has been developed and tested. The incorporation of special corundum particles directly on the decorative printed paper combined with a new coating system resulted in surfaces of the requested quality for wear resistance surfaces.
Increasing concerns regarding the world´s natural resources and sustainability continue to be a major issue for global development. As a result several political initiatives and strategies for green or resource-efficient growth both on national and international levels have been proposed. A core element of these initiatives is the promotion of an increase of resource or material productivity. This dissertation examines material productivity developments in the OECD and BRICS countries between 1980 and 2008. By applying the concept of convergence stemming from economic growth theory to material productivity the analysis provides insights into both aspects: material productivity developments in general as well potentials for accelerated improvements in material productivity which consequently may allow a reduction of material use globally. The results of the convergence analysis underline the importance of policy-making with regard to technology and innovation policy enabling the production of resource-efficient products and services as well as technology transfer and diffusion.
Unter der Zielsetzung der multimodalen, ortsaufgelösten optischen Spektroskopie für die markierungsfreie Charakterisierung biologischer Materialien nach Morphologie und Chemie werden vier Themenschwerpunkte behandelt.
1. Theorie der elastischen / inelastischen Lichtstreuung und laterale Auflösung in der Mikroskopie
2. Erweiterung eines Raman Mikroskops zu einem multimodalen spektralen Imaging System (MSIS) mit Photonenmigrations-Technologie
3. Erweiterung des MSIS zu Super-Resolution Raman Mikroskopie mit einer Festkörper-Immersionslinse
4. Anwendung des entwickelten MSIS auf biologische Materialien
Saving energy and protecting the environment became fundamental for society and politics, why several laws were enacted to increase the energy-efficiency. Furthermore, the growing number of vehicles and drivers leaded to more accidents and fatalities on the roads, why road safety became an important factor as well. Due to the increasing importance of energy-efficiency and safety, car manufacturers started to optimise the vehicle in terms of energy-effciency and safety. However, energy-efficiency and road safety can be also increased by adapting the driving behaviour to the given driving situation. This thesis presents a concept of an adaptive and rule based driving system that tries to educate the driver in energy-efficient and safe driving by showing recommendations on time. Unlike existing driving-systems, the presented driving system considers energy-efficiency and safety relevant driving rules, the individual driving behaviour and the driver condition. This allows to avoid the distraction of the driver and to increase the acceptance of the driving system, while improving the driving behaviour in terms of energy-efficiency and safety. A prototype of the driving system was developed and evaluated. The evaluation was done on a driving simulator using 42 test drivers, who tested the effect of the driving system on the driving behaviour and the effect of the adaptiveness of the driving system on the user acceptance. It has been proven during the evaluation that the energy-efficiency and safety can be increased, when the driving system was used. Furthermore, it has been proven that the user acceptance of the driving system increases when the adaptive feature was turned on. A high user acceptance of the driving system allows a steady usage of the driving system and, thus, a steady improvement of the driving behaviour in terms of energy-efficiency and safety.
Knowledge is an important resource, whose transfer is still not completely understood. The underlying belief of this thesis is that knowledge cannot be transferred directly from one person to another but must be converted for the transfer and therefore is subject to loss of knowledge and misunderstanding. This thesis proposes a new model for knowledge transfer and empirically evaluates this model. The model is based on the belief that knowledge must be encoded by the sender to transfer it to the receiver, who has to decode the message to obtain knowledge.
To prepare for the model this thesis provides an overview about models for knowledge transfer and factors that influence knowledge transfer. The proposed theoretical model for knowledge transfer is implemented in a prototype to demonstrate its applicability. The model describes the influence of the four layers, namely code, syntactic, semantic, and pragmatic layers, on the encoding and decoding of the message. The precise description of the influencing factors and the overlapping knowledge from sender and receiver facilitate its implementation.
The application area of the layered model for knowledge transfer was chosen to be business process modelling. Business processes incorporate an important knowledge resource of an organisation as they describe the procedures for the production of products and services. The implementation in a software prototype allows a precise description of the process by adding semantic to the simple business process modelling language used.
This thesis contributes to the body of knowledge by providing a new model for knowledge transfer, which shows the process of knowledge transfer in greater detail and highlights influencing factors. The implementation in the area of business process modelling reveals the support provided by the model. An expert evaluation indicates that the implementation of the proposed model supports knowledge transfer in business process modelling. The results of the qualitative evaluation are supported by the findings of a qualitative evaluation, performed as a quasi-experiment with a pre-test/post-test design and two experimental groups and one control group. Mann-Whitney U tests indicated that the group that used the tool that implemented the layered model performed significantly better in terms of completeness (the degree of completeness achieved in the transfer) in comparison with the group that used a standard BPM tool (Z = 3.057, p = 0.002, r = 0.59) and the control group that used pen and paper (Z = 3.859, p < 0.001, r = 0.72). The experiment indicates that the implementation of the layered model supports the creation of a business process and facilitates a more precise representation.
Data collected from internet applications are mainly stored in the form of transactions. All transactions of one user form a sequence, which shows the user´s behaviour on the site. Nowadays, it is important to be able to classify the behaviour in real time for various reasons: e.g. to increase conversion rate of customers while they are in the store or to prevent fraudulent transactions before they are placed. However, this is difficult due to the complex structure of the data sequences (i.e. a mix of categorical and continuous data types, constant data updates) and the large amounts of data that are stored. Therefore, this thesis studies the classification of complex data sequences. It surveys the fields of time series analysis (temporal data mining), sequence data mining or standard classification algorithms. It turns out that these algorithms are either difficult to be applied on data sequences or do not deliver a classification: Time series need a predefined model and are not able to handle complex data types; sequence classification algorithms such as the apriori algorithm family are not able to utilize the time aspect of the data. The strengths and weaknesses of the candidate algorithms are identified and used to build a new approach to solve the problem of classification of complex data sequences. The problem is thereby solved by a two-step process. First, feature construction is used to create and discover suitable features in a training phase. Then, the blueprints of the discovered features are used in a formula during the classification phase to perform the real time classification. The features are constructed by combining and aggregating the original data over the span of the sequence including the elapsed time by using a calculated time axis. Additionally, a combination of features and feature selection are used to simplify complex data types. This allows catching behavioural patterns that occur in the course of time. This new proposed approach combines techniques from several research fields. Part of the algorithm originates from the field of feature construction and is used to reveal behaviour over time and express this behaviour in the form of features. A combination of the features is used to highlight relations between them. The blueprints of these features can then be used to achieve classification in real time on an incoming data stream. An automated framework is presented that allows the features to adapt iteratively to a change in underlying patterns in the data stream. This core feature of the presented work is achieved by separating the feature application step from the computational costly feature construction step and by iteratively restarting the feature construction step on the new incoming data. The algorithm and the corresponding models are described in detail as well as applied to three case studies (customer churn prediction, bot detection in computer games, credit card fraud detection). The case studies show that the proposed algorithm is able to find distinctive information in data sequences and use it effectively for classification tasks. The promising results indicate that the suggested approach can be applied to a wide range of other application areas that incorporate data sequences.
Within the scope of the present cumulative doctoral thesis six scientific papers were published which illustrates that modern reaction model-free (=isoconversional) kinetic analysis (ICKA) methods represents a universal and effective tool for the controlled processing of thermosetting materials. In order to demonstrate the universal applicability of ICKA methods, the thermal cure of different thermosetting materials having a very broad range of chemical composition (melamine-formaldehyde resins, epoxy resins, polyester-epoxy resins, and acrylate/epoxy resins) were analyzed and mathematically modelled. Some of the materials were based on renewable resources (an epoxy resin was made from hempseed oil; linseed oil was modified into an acrylate/epoxy resin). With the aid of ICKA methods not only single-step but also complex multi-step reactions were modelled precisely. The analyzed thermosetting materials were combined with wood, wood-based products, paper, and plant fibers which are processed to various final products. Some of the thermosetting materials were applied as coating (in form of impregnated décor papers or powder and wet coatings respectively) on wood substrates and the epoxy resin from hempseed oil was mixed with plant fibers and processed into bio-based composites for lightweight applications. From the final products mechanical, thermal, and surface properties were determined. The activation energy as function of cure conversion derived from ICKA methods was utilized to predict accurately the thermal curing over the course of time for arbitrary cure conditions. Furthermore the cure models were used to establish correlations between the cross-linking during processing into products and the properties of the final products. Therewith it was possible to derive the process time and temperature that guarantee optimal cross-linking as well as optimal product properties
Reconstructing 3D face shape from a single 2D photograph as well as from video is an inherently ill-posed problem with many ambiguities. One way to solve some of the ambiguities is using a 3D face model to aid the task. 3D morphable face models (3DMMs) are amongst the state of the art methods for 3D face reconstruction, or so called 3D model fitting. However, current existing methods have severe limitations, and most of them have not been trialled on in-the-wild data. Current analysis-by- synthesis methods form complex non linear optimisation processes, and optimisers often get stuck in local optima. Further, most existing methods are slow, requiring in the order of minutes to process one photograph.
This thesis presents an algorithm to reconstruct 3D face shape from a single image as well as from sets of images or video frames in real-time. We introduce a solution for linear fitting of a PCA shape identity model and expression blendshapes to 2D facial landmarks. To improve the accuracy of the shape, a fast face contour fitting algorithm is introduced. These different components of the algorithm are run in iteration, resulting in a fast, linear shape-to- landmarks fitting algorithm. The algorithm, specifically designed to fit to landmarks obtained from in-the-wild images, by tackling imaging conditions that occur in in-the-wild images like facial expressions and the mismatch of 2D–3D contour correspondences, achieves the shape reconstruction accuracy of much more complex, nonlinear state of the art methods, while being multiple orders of magnitudes faster.
Second, we address the problem of fitting to sets of multiple images of the same person, as well as monocular video sequences. We extend the proposed shape-to-landmarks fitting to multiple frames by using the knowledge that all images are from the same identity. To recover facial texture, the approach uses texture from the original images, instead of employing the often-used PCA albedo model of a 3DMM. We employ an algorithm that merges texture from multiple frames in real-time based on a weighting of each triangle of the reconstructed shape mesh.
Last, we make the proposed real-time 3D morphable face model fitting algorithm available as open-source software. In contrast to ubiquitous available 2D-based face models and code, there is a general lack of software for 3D morphable face model fitting, hindering a widespread adoption. The library thus constitutes a significant contribution to the community.