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Due to their superior weight-specific mechanical properties, carbon fibre epoxy composites (CFRP) are commonly used in aviation industry. However, their brittle failure behaviour limits the structural integrity and damage tolerance in case of impact (e.g. tool drop, bird strike, hail strike, ramp collision) or crash events. To ensure sufficient robustness, a minimum skin thickness is therefore prescribed for the fuselage, partially exceeding typical service load requirements from ground or flight manoeuvre load cases. A minimum skin thickness is also required for lightning strike protection purposes and to enable state-of-the-art bolted repair technology. Furthermore, the electrical conductivity of CFRP aircraft structures is insufficient for certain applications; additional metal components are necessary to provide electrical functionality (e.g. metal meshes on the outer skin for lightning strike protection, wires for electrical bonding and grounding, overbraiding of cables to provide electromagnetic shielding). The corresponding penalty weights compromise the lightweight potential that is actually given by the structural performance of CFRP over aluminium alloys.
Former research attempts tried to overcome these deficits by modifying the resin system (e.g. by addition of conductive particles or toughening agents) but could not prove sufficient enhancements. A novel holistic approach is the incorporation of highly conductive and ductile continuous metal fibres into CFRP. The basic idea of this hybrid material concept is to take advantage of both the electrical and mechanical capabilities of the integrated metal fibres in order to simultaneously improve the electrical conductivity and the damage tolerance of the composite. The increased density of the hybrid material is over-compensated by omitting the need for additional electrical system installation items and by the enhanced structural performance, enabling a reduction of the prescribed minimum skin thickness. Advantages over state-of-the-art fibre metal laminates mainly arise from design and processing technology aspects.
In this context, the present work focuses on analysing and optimising the structural and electrical performance of such hybrid composites with shares of metal fibres up to 20 vol.%. Bundles of soft-annealed austenitic steel or copper cladded low carbon steel fibres with filament diameters of 60 or 63 µm are considered. The fibre bundles are distinguished by high elongation at break (32 %) and ultimate tensile strength (900 MPa) or high electrical conductivity (2.4 × 10^7 S/m). Comprehensive researches are carried out on the fibre bundles as well as on unidirectional and multiaxial laminates. Both hybrid composites with homogeneous and accumulated steel fibre arrangement are taken into account. Electrical in-plane conductivity, plain tensile behaviour, suitability for bolted joints as well as impact and perforation performance of the composite are analysed. Additionally, a novel non-destructive testing method based on measurement of deformation-induced phase transformation of the metastable austenitic steel fibres is discussed.
The outcome of the conductivity measurements verifies a correlation of the volume conductivity of the composite with the volume share and the specific electrical resistance of the incorporated metal fibres. Compared to conventional CFRP, the electrical conductivity in parallel to the fibre orientation can be increased by one to two orders of magnitude even for minor percentages of steel fibres. The analysis, however, also discloses the challenge of establishing a sufficient connection to the hybrid composite in order to entirely exploit its electrical conductivity.
In case of plain tensile load, the performance of the hybrid composite is essentially affected by the steel fibre-resin-adhesion as well as the laminate structure. Uniaxial hybrid laminates show brittle, singular failure behaviour. Exhaustive yielding of the embedded steel fibres is confined to the arising fracture gap. The high transverse stiffness of the isotropic metal fibres additionally intensifies strain magnification within the resin under transverse tensile load. This promotes (intralaminar) inter-fibre-failure at minor composite deformation. By contrast, multiaxial hybrid laminates exhibit distinctive damage evolution. After failure initiation, the steel fibres extensively yield and sustain the load-carrying capacity of angularly (e.g. ±45°) aligned CFRP plies. The overall material response is thus not only a simple superimposition but a complex interaction of the mechanical behaviour of the composite’s constituents. As a result of this post-damage performance, an ultimate elongation of over 11 % can be proven for the hybrid laminates analysed in this work. In this context, the influence of the steel fibre-resin adhesion on the failure behaviour of the hybrid composite is explicated by means of an analytical model. Long term exposure to corrosive media has no detrimental effect on the mechanical performance of stainless steel fibre reinforced composites. By trend, water uptake increases the maximum elongation at break of the hybrid laminate.
Moreover, the suitability of CFRP for bolted joints can partially be improved by the integration of steel fibres. While the bearing strength basically remains nearly unaffected, the bypass failure behaviour (ε_{max}: +363 %) as well as the head pull-through resistance (E_{a,BPT}: +81 %) can be enhanced. The improvements primarily concern the load-carrying capacity after failure initiation. Additionally, the integrated ductile steel fibres significantly increase the energy absorption capacity of the laminate in case of progressive bearing failure by up to 63 %.
However, the hybrid composite exhibits a sensitive low velocity/low mass impact behaviour. Compared to conventional CFRP, the damage threshold load of very thin hybrid laminates is lower, making them prone for delamination at minor, non-critical impact energies. At higher energy levels, however, the impact-induced delamination spreads less since most of the impact energy is absorbed by yielding of the ductile metal fibres instead of crack propagation. This structural advantage compared to CFRP gains in importance with increasing impact energy. The plastic deformation of the metastable austenitic steel fibres is accompanied by a phase transformation from paramagnetic γ-austenite to ferromagnetic α’-martensite. This change of the magnetic behaviour can be used to detect and evaluate impacts on the surface of the hybrid composite, which provides a simple non-destructive testing method. In case of low velocity/high mass impact, integration of ductile metal fibres into CFRP enables to address spacious areas of the laminate for energy absorption purposes. As a consequence, the perforation resistance of the hybrid composite is significantly enhanced; by addition of approximately 20 vol.% of stainless steel fibres, the perforation strength can be increased by 61 %, while the maximum energy absorption capacity rises by 194 %.

Following the ideas presented in Dahlhaus (2000) and Dahlhaus and Sahm (2000) for time series, we build a Whittle-type approximation of the Gaussian likelihood for locally stationary random fields. To achieve this goal, we extend a Szegö-type formula, for the multidimensional and local stationary case and secondly we derived a set of matrix approximations using elements of the spectral theory of stochastic processes. The minimization of the Whittle likelihood leads to the so-called Whittle estimator \(\widehat{\theta}_{T}\). For the sake of simplicity we assume known mean (without loss of generality zero mean), and hence \(\widehat{\theta}_{T}\) estimates the parameter vector of the covariance matrix \(\Sigma_{\theta}\).
We investigate the asymptotic properties of the Whittle estimate, in particular uniform convergence of the likelihoods, and consistency and Gaussianity of the estimator. A main point is a detailed analysis of the asymptotic bias which is considerably more difficult for random fields than for time series. Furthemore, we prove in case of model misspecification that the minimum of our Whittle likelihood still converges, where the limit is the minimum of the Kullback-Leibler information divergence.
Finally, we evaluate the performance of the Whittle estimator through computational simulations and estimation of conditional autoregressive models, and a real data application.

Computational simulations run on large supercomputers balance their outputs with the need of the scientist and the capability of the machine. Persistent storage is typically expensive and slow, its peformance grows at a slower rate than the processing power of the machine. This forces scientists to be practical about the size and frequency of the simulation outputs that can be later analyzed to understand the simulation states. Flexibility in the trade-offs of flexibilty and accessibility of the outputs of the simulations are critical the success of scientists using the supercomputers to understand their science. In situ transformations of the simulation state to be persistently stored is the focus of this dissertation.
The extreme size and parallelism of simulations can cause challenges for visualization and data analysis. This is coupled with the need to accept pre partitioned data into the analysis algorithms, which is not always well oriented toward existing software infrastructures. The work in this dissertation is focused on improving current work flows and software to accept data as it is, and efficiently produce smaller, more information rich data, for persistent storage that is easily consumed by end-user scientists. I attack this problem from both a theoretical and practical basis, by managing completely raw data to quantities of information dense visualizations and study methods for managing both the creation and persistence of data products from large scale simulations.

In this thesis we address two instances of duality in commutative algebra.
In the first part, we consider value semigroups of non irreducible singular algebraic curves
and their fractional ideals. These are submonoids of Z^n closed under minima, with a conductor and which fulfill special compatibility properties on their elements. Subsets of Z^n
fulfilling these three conditions are known in the literature as good semigroups and their ideals, and their class strictly contains the class of value semigroup ideals. We examine
good semigroups both independently and in relation with their algebraic counterpart. In the combinatoric setting, we define the concept of good system of generators, and we
show that minimal good systems of generators are unique. In relation with the algebra side, we give an intrinsic definition of canonical semigroup ideals, which yields a duality
on good semigroup ideals. We prove that this semigroup duality is compatible with the Cohen-Macaulay duality under taking values. Finally, using the duality on good semigroup ideals, we show a symmetry of the Poincaré series of good semigroups with special properties.
In the second part, we treat Macaulay’s inverse system, a one-to-one correspondence
which is a particular case of Matlis duality and an effective method to construct Artinian k-algebras with chosen socle type. Recently, Elias and Rossi gave the structure of the inverse system of positive dimensional Gorenstein k-algebras. We extend their result by establishing a one-to-one correspondence between positive dimensional level k-algebras and certain submodules of the divided power ring. We give several examples to illustrate
our result.

Annual Report 2017
(2017)

The present situation of control engineering in the context of automated production can be described as a tension field between its desired outcome and its actual consideration. On the one hand, the share of control engineering compared to the other engineering domains has significantly increased within the last decades due to rising automation degrees of production processes and equipment. On the other hand, the control engineering domain is still underrepresented within the production engineering process. Another limiting factor constitutes a lack of methods and tools to decrease the amount of software engineering efforts and to permit the development of innovative automation applications that ideally support the business requirements.
This thesis addresses this challenging situation by means of the development of a new control engineering methodology. The foundation is built by concepts from computer science to promote structuring and abstraction mechanisms for the software development. In this context, the key sources for this thesis are the paradigm of Service-oriented Architecture and concepts from Model-driven Engineering. To mold these concepts into an integrated engineering procedure, ideas from Systems Engineering are applied. The overall objective is to develop an engineering methodology to improve the efficiency of control engineering by a higher adaptability of control software and decreased programming efforts by reuse.

The proliferation of sensors in everyday devices – especially in smartphones – has led to crowd sensing becoming an important technique in many urban applications ranging from noise pollution mapping or road condition monitoring to tracking the spreading of diseases. However, in order to establish integrated crowd sensing environments on a large scale, some open issues need to be tackled first. On a high level, this thesis concentrates on dealing with two of those key issues: (1) efficiently collecting and processing large amounts of sensor data from smartphones in a scalable manner and (2) extracting abstract data models from those collected data sets thereby enabling the development of complex smart city services based on the extracted knowledge.
Going more into detail, the first main contribution of this thesis is the development of methods and architectures to facilitate simple and efficient deployments, scalability and adaptability of crowd sensing applications in a broad range of scenarios while at the same time enabling the integration of incentivation mechanisms for the participating general public. During an evaluation within a complex, large-scale environment it is shown that real-world deployments of the proposed data recording architecture are in fact feasible. The second major contribution of this thesis is the development of a novel methodology for using the recorded data to extract abstract data models which are representing the inherent core characteristics of the source data correctly. Finally – and in order to bring together the results of the thesis – it is demonstrated how the proposed architecture and the modeling method can be used to implement a complex smart city service by employing a data driven development approach.

We continue in this paper the study of k-adaptable robust solutions for combinatorial optimization problems with bounded uncertainty sets. In this concept not a single solution needs to be chosen to hedge against the uncertainty. Instead one is allowed to choose a set of k different solutions from which one can be chosen after the uncertain scenario has been revealed. We first show how the problem can be decomposed into polynomially many subproblems if k is fixed. In the remaining part of the paper we consider the special case where k=2, i.e., one is allowed to choose two different solutions to hedge against the uncertainty. We decompose this problem into so called coordination problems. The study of these coordination problems turns out to be interesting on its own. We prove positive results for the unconstrained combinatorial optimization problem, the matroid maximization problem, the selection problem, and the shortest path problem on series parallel graphs. The shortest path problem on general graphs turns out to be NP-complete. Further, we present for minimization problems how to transform approximation algorithms for the coordination problem to approximation algorithms for the original problem. We study the knapsack problem to show that this relation does not hold for maximization problems in general. We present a PTAS for the corresponding coordination problem and prove that the 2-adaptable knapsack problem is not at all approximable.

The cytosolic Fe65 adaptor protein family, consisting of Fe65, Fe65L1 and Fe65L2 is involved in many intracellular signaling pathways linking via its three interaction domains a continuously growing list of proteins by facilitating functional interactions. One of the most important binding partners of Fe65 family proteins is the amyloid precursor protein (APP), which plays an important role in Alzheimer Disease.
To gain deeper insights in the function of the ubiquitously expressed Fe65 and the brain enriched Fe65L1, the goal of my study was I) to analyze their putative synaptic function in vivo, II) to examine structural analysis focusing on a putative dimeric complex of Fe65, III) to consider the involvement of Fe65 in mediating LRP1 and APP intracellular trafficking in murine hippocampal neurons. By utilizing several behavioral analyses of Fe65 KO, Fe65L1 KO and Fe65/Fe65L1 DKO mice I could demonstrate that the Fe65 protein family is essential for learning and memory as well as grip strength and locomotor activity. Furthermore, immunohistological as well as protein biochemical analysis revealed that the Fe65 protein family is important for neuromuscular junction formation in the peripheral nervous system, which involves binding of APP and acting downstream of the APP signaling pathway. Via Co-immunoprecipitation analysis I could verify that Fe65 is capable to form dimers ex vivo, which exclusively occur in the cytosol and upon APP expression are shifted to membrane compartments forming trimeric complexes. The influence of the loss of Fe65 and/or Fe65L1 on APP and/or LRP1 transport characteristics in axons could not be verified, possibly conditioned by the compensatory effect of Fe65L2. However, I could demonstrate that LRP1 affects the APP transport independently of Fe65 by shifting APP into slower types of vesicles leading to changed processing and endocytosis of APP.
The outcome of my thesis advanced our understanding of the Fe65 protein family, especially its interplay with APP physiological function in synapse formation and synaptic plasticity.

This paper presents a case study of duty rostering for physicians at a department of orthopedics and trauma surgery. We provide a detailed description of the rostering problem faced and present an integer programming model that has been used in practice for creating duty rosters at the department for more than a year. Using real world data, we compare the model output to a manually generated roster as used previously by the department and analyze the quality of the rosters generated by the model over a longer time span. Moreover, we demonstrate how unforeseen events such as absences of scheduled physicians are handled.