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Year of publication
- 2019 (65) (remove)
Landwirtschaftliche Biogasanlagen leisten mit ca. 9.300 Anlagen und einem Anteil von 5,3% an der Stromerzeugung, einen Beitrag zur Erzeugung Erneuer-barer Energien in Deutschland. Die Optimierung dieser Anlagen fördert die nachhaltige Bereitstellung von Strom, Wärme und BioErdgas.
Das Ergebnis dieser Forschungsarbeit ist die Entwicklung eines mehrmethodi-schen Bewertungsansatzes zur Beschreibung der Qualität der Eingangs-substrate als Teil einer ganzheitlichen Prozessoptimierung. Dies gelingt durch die kombinierte Nutzung klassischer Analysesätze, der Nutzung organolepti-scher Parameter – der humansensorischen Sinnenprüfung – und der Integration von prozess- und substratspezifischem Erfahrungswissen. Anhand von halbtechnischen Versuchen werden Korrelationen und Kausalitäten zwi-schen chemisch-physikalischen, biologischen, organoleptischen und erfahrungsbezogenen Parametern erforscht. Die Entwicklung einer Fallbasis mit Hilfe des Fallbasierten Schließens, einer Form Künstlicher Intelligenz, zeigt das Entwicklungs- und Integrationspotenzial der Automatisierung auf, insbesondere auch im Hinblick auf neue Ansätze z.B. Industrie 4.0. Erste Lösungen zur Bewältigung der identifizierten Herausforderungen der mehrmethodischen Prozessbewertung werden vorgestellt.
Abschließend wird ein Ausblick auf den weiteren Forschungsbedarf gegeben und die Übertragbarkeit des mehrmethodischen Bewertungsansatzes auf andere Anwendungsfelder z.B. Bioabfallbehandlung, Kläranlagen angeregt.
Urban planners are often challenged with the task of developing design solutions which must meet multiple, and often contradictory, criteria. In this paper, we investigated the trade-offs between social, psychological, and energy potential of the fundamental elements of urban form: the street network and the building massing. Since formal mehods to evaluate urban form from the psychological and social point of view are not readily available, we developed a methodological framework to quantify these criteria as the first contribution in this paper. To evaluate the psychological potential, we conducted a three-tiered empirical study starting from real world environments and then abstracting them to virtual environments. In each context, the implicit (physiological) response and explicit (subjective) response of pedestrians were measured. To quantify the social potential, we developed a street network centrality-based measure of social accessibility.
For the energy potential, we created an energy model to analyze the impact of pure geometric form on the energy demand of the building stock. The second contribution of this work is a method to identify distinct clusters of urban form and, for each, explore the trade-offs between the select design criteria. We applied this method to two case studies identifying nine types of urban form and their respective potential trade-offs, which are directly applicable for the assessment of strategic decisions regarding urban form during the early planning stages.
In der vorliegenden Arbeit wird die Bedeutung und Funktion des fotografischen Bildes und im Besonderen der Handyfotografie im Kontext von Internet und sozialen Onlinenetzwerken beschrieben, untersucht und diskutiert.
Im Spannungsfeld zwischen sozialwissenschaftlichen Theorien, foto- und medientheoretischem Diskurs sowie ästhetisch künstlerischen Perspektiven wird ausgelotet, welche konkreten visuellen und sozialen Formen die gegenwärtige mobile digitale Bildpraxis angenommen hat, wie sich ihre Gebrauchsweise innerhalb des globalen gesellschaftlichen Alltags darstellt, und in welcher Weise ihr spezielles Zusammenspiel aus „shooting“, „uploading“, „sharing“ zu neuen menschlichen Handlungsmustern und zur Schaffung neuer kultureller Realitäten beiträgt.
FCS-MBFLEACH: Designing an Energy-Aware Fault Detection System for Mobile Wireless Sensor Networks
(2019)
Wireless sensor networks (WSNs) include large-scale sensor nodes that are densely distributed over a geographical region that is completely randomized for monitoring, identifying, and analyzing physical events. The crucial challenge in wireless sensor networks is the very high dependence of the sensor nodes on limited battery power to exchange information wirelessly as well as the non-rechargeable battery of the wireless sensor nodes, which makes the management and monitoring of these nodes in terms of abnormal changes very difficult. These anomalies appear under faults, including hardware, software, anomalies, and attacks by raiders, all of which affect the comprehensiveness of the data collected by wireless sensor networks. Hence, a crucial contraption should be taken to detect the early faults in the network, despite the limitations of the sensor nodes. Machine learning methods include solutions that can be used to detect the sensor node faults in the network. The purpose of this study is to use several classification methods to compute the fault detection accuracy with different densities under two scenarios in regions of interest such as MB-FLEACH, one-class support vector machine (SVM), fuzzy one-class, or a combination of SVM and FCS-MBFLEACH methods. It should be noted that in the study so far, no super cluster head (SCH) selection has been performed to detect node faults in the network. The simulation outcomes demonstrate that the FCS-MBFLEACH method has the best performance in terms of the accuracy of fault detection, false-positive rate (FPR), average remaining energy, and network lifetime compared to other classification methods.
The design of engineering structures takes place today and in the past on the basis of static calculations. The consideration of uncertainties in the model quality becomes more and more important with the development of new construction methods and design requirements. In addition to the traditional forced-based approaches, experiences and observations about the deformation behavior of components and the overall structure under different exposure conditions allow the introduction of novel detection and evaluation criteria.
The proceedings at hand are the result from the Bauhaus Summer School Course: Forecast Engineering held at the Bauhaus-Universität Weimar, 2017. It summarizes the results of the conducted project work, provides the abstracts of the contributions by the participants, as well as impressions from the accompanying programme and organized cultural activities.
The special character of this course is in the combination of basic disciplines of structural engineering with applied research projects in the areas of steel and reinforced concrete structures, earthquake and wind engineering as well as informatics and linking them to mathematical methods and modern tools of visualization. Its innovative character results from the ambitious engineering tasks and advanced
modeling demands.
Institute of Structural Engineering, Institute of Structural Mechanics, as well as Institute for Computing, Mathematics and Physics in Civil Engineering at the faculty of civil engineering at the Bauhaus-Universität Weimar presented special topics of structural engineering to highlight the broad spectrum of civil engineering in the field of modeling and simulation.
The summer course sought to impart knowledge and to combine research with a practical context, through a challenging and demanding series of lectures, seminars and project work. Participating students were enabled to deal with advanced methods and its practical application.
The extraordinary format of the interdisciplinary summer school offers the opportunity to study advanced developments of numerical methods and sophisticated modelling techniques in different disciplines of civil engineering for foreign and domestic students, which go far beyond traditional graduate courses.
The proceedings at hand are the result from the Bauhaus Summer School course: Forecast Engineering held at the Bauhaus-Universität Weimar, 2018. It summarizes the results of the conducted project work, provides the abstracts/papers of the contributions by the participants, as well as impressions from the accompanying programme and organized cultural activities.
The p-Laplace equation is a nonlinear generalization of the well-known Laplace equation. It is often used as a model problem for special types of nonlinearities, and therefore it can be seen as a bridge between very general nonlinear equations and the linear Laplace equation, too. It appears in many problems for instance in the theory of non-Newtonian fluids and fluid dynamics or in rockfill dam problems, as well as in special problems of image restoration and image processing.
The aim of this thesis is to solve the p-Laplace equation for 1 < p < 2, as well as for 2 < p < 3 and to find strong solutions in the framework of Clifford analysis. The idea is to apply a hypercomplex integral operator and special function theoretic methods to transform the p-Laplace equation into a p-Dirac equation. We consider boundary value problems for the p-Laplace equation and transfer them to boundary value problems for a p-Dirac equation. These equations will be solved iteratively by applying Banach’s fixed-point principle. Applying operator-theoretical methods for the p-Dirac equation, the existence and uniqueness of solutions in certain Sobolev spaces will be proved.
In addition, using a finite difference approach on a uniform lattice in the plane, the fundamental solution of the Cauchy-Riemann operator and its adjoint based on the fundamental solution of the Laplacian will be calculated. Besides, we define gener- alized discrete Teodorescu transform operators, which are right-inverse to the discrete Cauchy-Riemann operator and its adjoint in the plane. Furthermore, a new formula for generalized discrete boundary operators (analogues of the Cauchy integral operator) will be considered. Based on these operators a new version of discrete Borel-Pompeiu formula is formulated and proved.
This is the basis for an operator calculus that will be applied to the numerical solution of the p-Dirac equation. Finally, numerical results will be presented showing advantages and problems of this approach.
In diesem Dokument gehen wir den gesamten Fragebogen für die vollständige Gemeinwohlbilanz (kurz GB, katalanisch Balanç Comunitari bzw. BC) Frage für Frage durch und erklären, was mit jeder einzelnen Frage gemeint ist und welche Informationen anzugeben sind. Es wird daher sehr empfohlen, vor dem Ausfüllen des Fragebogens in der App einen Blick auf den gesamten Fragenkatalog zu werfen, weil dies die Erhebung der Daten und die Verteilung der Aufgaben auf die verschiedenen mit der Erstellung der GB befassten Personen erleichtert. Die GB ist ein Selbstevaluierungstool, mit dem Organisationen ihre Tätigkeit unter Umwelt-, Sozial- und Governance-Aspekten bewerten können.
Modern immersive telepresence systems enable people at different locations to meet in virtual environments using realistic three-dimensional representations of their bodies. For the realization of such a three-dimensional version of a video conferencing system, each user is continuously recorded in 3D. These 3D recordings are exchanged over the network between remote sites. At each site, the remote recordings of the users, referred to as 3D video avatars, are seamlessly integrated into a shared virtual scenery and displayed in stereoscopic 3D for each user from his or her perspective.
This thesis reports on algorithmic and technical contributions to modern immersive telepresence systems and presents the design, implementation and evaluation of the first immersive group-to-group telepresence system in which each user is represented as realistic life-size 3D video avatar. The system enabled two remote user groups to meet and collaborate in a consistent shared virtual environment. The system relied on novel methods for the precise calibration and registration of color- and depth- sensors (RGBD) into the coordinate system of the application as well as an advanced distributed processing pipeline that reconstructs realistic 3D video avatars in real-time. During the course of this thesis, the calibration of 3D capturing systems was greatly improved. While the first development focused on precisely calibrating individual RGBD-sensors, the second stage presents a new method for calibrating and registering multiple color and depth sensors at a very high precision throughout a large 3D capturing volume. This method was further refined by a novel automatic optimization process that significantly speeds up the manual operation and yields similarly high accuracy. A core benefit of the new calibration method is its high runtime efficiency by directly mapping from raw depth sensor measurements into an application coordinate system and to the coordinates of its associated color sensor. As a result, the calibration method is an efficient solution in terms of precision and applicability in virtual reality and immersive telepresence applications. In addition to the core contributions, the results of two case studies which address 3D reconstruction and data streaming lead to the final conclusion of this thesis and to directions of future work in the rapidly advancing field of immersive telepresence research.
This thesis addresses an adaptive higher-order method based on a Geometry Independent Field approximatTion(GIFT) of polynomial/rationals plines over hierarchical T-meshes(PHT/RHT-splines).
In isogeometric analysis, basis functions used for constructing geometric models in computer-aided design(CAD) are also employed to discretize the partial differential equations(PDEs) for numerical analysis. Non-uniform rational B-Splines(NURBS) are the most commonly used basis functions in CAD. However, they may not be ideal for numerical analysis where local refinement is required.
The alternative method GIFT deploys different splines for geometry and numerical analysis. NURBS are utilized for the geometry representation, while for the field solution, PHT/RHT-splines are used. PHT-splines not only inherit the useful properties of B-splines and NURBS, but also possess the capabilities of local refinement and hierarchical structure. The smooth basis function properties of PHT-splines make them suitable for analysis purposes. While most problems considered in isogeometric analysis can be solved efficiently when the solution is smooth, many non-trivial problems have rough solutions. For example, this can be caused by the presence of re-entrant corners in the domain. For such problems, a tensor-product basis (as in the case of NURBS) is less suitable for resolving the singularities that appear since refinement propagates throughout the computational domain. Hierarchical bases and local refinement (as in the case of PHT-splines) allow for a more efficient way to resolve these singularities by adding more degrees of freedom where they are necessary. In order to drive the adaptive refinement, an efficient recovery-based error estimator is proposed in this thesis. The estimator produces a recovery solution which is a more accurate approximation than the computed numerical solution. Several two- and three-dimensional numerical investigations with PHT-splines of higher order and continuity prove that the proposed method is capable of obtaining results with higher accuracy, better convergence, fewer degrees of freedom and less computational cost than NURBS for smooth solution problems. The adaptive GIFT method utilizing PHT-splines with the recovery-based error estimator is used for solutions with discontinuities or singularities where adaptive local refinement in particular domains of interest achieves higher accuracy with fewer degrees of freedom. This method also proves that it can handle complicated multi-patch domains for two- and three-dimensional problems outperforming uniform refinement in terms of degrees of freedom and computational cost.