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Yield Curves and Chance-Risk Classification: Modeling, Forecasting, and Pension Product Portfolios
(2021)
This dissertation consists of three independent parts: The yield curve shapes generated by interest rate models, the yield curve forecasting, and the application of the chance-risk classification to a portfolio of pension products. As a component of the capital market model, the yield curve influences the chance-risk classification which was introduced to improve the comparability of pension products and strengthen consumer protection. Consequently, all three topics have a major impact on this essential safeguard.
Firstly, we focus on the obtained yield curve shapes of the Vasicek interest rate models. We extend the existing studies on the attainable yield curve shapes in the one-factor Vasicek model by analysis of the curvature. Further, we show that the two-factor Vasicek model can explain significantly more effects that are observed at the market than its one-factor variant. Among them is the occurrence of dipped yield curves.
We further introduce a general change of measure framework for the Monte Carlo simulation of the Vasicek model under a subjective measure. This can be used to avoid the occurrence of a far too high frequency of inverse yield curves with growing time.
Secondly, we examine different time series models including machine learning algorithms forecasting the yield curve. For this, we consider statistical time series models such as autoregression and vector autoregression. Their performances are compared with the performance of a multilayer perceptron, a fully connected feed-forward neural network. For this purpose, we develop an extended approach for the hyperparameter optimization of the perceptron which is based on standard procedures like Grid and Random Search but allows to search a larger hyperparameter space. Our investigation shows that multilayer perceptrons outperform statistical models for long forecast horizons.
The third part deals with the chance-risk classification of state-subsidized pension products in Germany as well as its relevance for customer consulting. To optimize the use of the chance-risk classes assigned by Produktinformationsstelle Altersvorsorge gGmbH, we develop a procedure for determining the chance-risk class of different portfolios of state-subsidized pension products under the constraint that the portfolio chance-risk class does not exceed the customer's risk preference. For this, we consider a portfolio consisting of two new pension products as well as a second one containing a product already owned by the customer as well as the offer of a new one. This is of particular interest for customer consulting and can include other assets of the customer. We examine the properties of various chance and risk parameters as well as their corresponding mappings and show that a diversification effect exists. Based on the properties, we conclude that the average final contract values have to be used to obtain the upper bound of the portfolio chance-risk class. Furthermore, we develop an approach for determining the chance-risk class over the contract term since the chance-risk class is only assigned at the beginning of the accumulation phase. On the one hand, we apply the current legal situation, but on the other hand, we suggest an approach that requires further simulations. Finally, we translate our results into recommendations for customer consultation.
Wreath product groups \(C_\ell \wr \mathfrak{S}_n\) have a rich combinatorial representation theory coming from the symmetric group case and involving partitions, Young tableaux, and Specht modules. To such a wreath product group \(W\), one can associate various algebras and geometric objects: Hecke algebras, quantum groups, Hilbert schemes, Calogero--Moser spaces, and (restricted) rational Cherednik algebras. Over the years, surprising connections have been made between a lot of these objects, with many of these connections having been traced back to combinatorial constructions and properties of the group \(W\) itself.
In this thesis, we have studied one of the algebras, namely the restricted rational Cherednik algebra \(\overline{\mathsf{H}}_\mathbf{c}(W)\), in order to find combinatorial models which describe certain representation theoretical phenomena around \(\overline{\mathsf{H}}_\mathbf{c}(W)\). In particular, we generalize a result by Gordon and describe the graded \(W\)-characters of the simple modules of \(\overline{\mathsf{H}}_\mathbf{c}(W)\) for generic parameter \(\mathbf{c}\) using Haiman's wreath Macdonald polynomials. These graded \(W\)-characters turn out to be specializations of Haiman's wreath Macdonald polynomials. In the non-generic parameter case, we use recent results by Maksimau to combinatorially express an inductive rule of \(\overline{\mathsf{H}}_\mathbf{c}(W)\)-modules first described by Bellamy. We use our results in type \(B\) to describe the (ungraded) \(B_n\)-character of simple \(\overline{\mathsf{H}}_\mathbf{c}(B_n)\)-modules associated to bipartitions with one empty part. Afterwards, we relate this combinatorial induction to various other algebras and families of \(W\)-characters found in the literature such as Lusztig's constructible characters, as well as detail some connections between generic and non-generic parameter using wreath Macdonald polynomials.
In this thesis we extend the worst-case modeling approach as first introduced by Hua and Wilmott (1997) (option pricing in discrete time) and Korn and Wilmott (2002) (portfolio optimization in continuous time) in various directions.
In the continuous-time worst-case portfolio optimization model (as first introduced by Korn and Wilmott (2002)), the financial market is assumed to be under the threat of a crash in the sense that the stock price may crash by an unknown fraction at an unknown time. It is assumed that only an upper bound on the size of the crash is known and that the investor prepares for the worst-possible crash scenario. That is, the investor aims to find the strategy maximizing her objective function in the worst-case crash scenario.
In the first part of this thesis, we consider the model of Korn and Wilmott (2002) in the presence of proportional transaction costs. First, we treat the problem without crashes and show that the value function is the unique viscosity solution of a dynamic programming equation (DPE) and then construct the optimal strategies. We then consider the problem in the presence of crash threats, derive the corresponding DPE and characterize the value function as the unique viscosity solution of this DPE.
In the last part, we consider the worst-case problem with a random number of crashes by proposing a regime switching model in which each state corresponds to a different crash regime. We interpret each of the crash-threatened regimes of the market as states in which a financial bubble has formed which may lead to a crash. In this model, we prove that the value function is a classical solution of a system of DPEs and derive the optimal strategies.
In 2002, Korn and Wilmott introduced the worst-case scenario optimal portfolio approach.
They extend a Black-Scholes type security market, to include the possibility of a
crash. For the modeling of the possible stock price crash they use a Knightian uncertainty
approach and thus make no probabilistic assumption on the crash size or the crash time distribution.
Based on an indifference argument they determine the optimal portfolio process
for an investor who wants to maximize the expected utility from final wealth. In this thesis,
the worst-case scenario approach is extended in various directions to enable the consideration
of stress scenarios, to include the possibility of asset defaults and to allow for parameter
uncertainty.
Insurance companies and banks regularly have to face stress tests performed by regulatory
instances. In the first part we model their investment decision problem that includes stress
scenarios. This leads to optimal portfolios that are already stress test prone by construction.
The solution to this portfolio problem uses the newly introduced concept of minimum constant
portfolio processes.
In the second part we formulate an extended worst-case portfolio approach, where asset
defaults can occur in addition to asset crashes. In our model, the strictly risk-averse investor
does not know which asset is affected by the worst-case scenario. We solve this problem by
introducing the so-called worst-case crash/default loss.
In the third part we set up a continuous time portfolio optimization problem that includes
the possibility of a crash scenario as well as parameter uncertainty. To do this, we combine
the worst-case scenario approach with a model ambiguity approach that is also based on
Knightian uncertainty. We solve this portfolio problem and consider two concrete examples
with box uncertainty and ellipsoidal drift ambiguity.
Distributed systems are omnipresent nowadays and networking them is fundamental for the continuous dissemination and thus availability of data. Provision of data in real-time is one of the most important non-functional aspects that safety-critical networks must guarantee. Formal verification of data communication against worst-case deadline requirements is key to certification of emerging x-by-wire systems. Verification allows aircraft to take off, cars to steer by wire, and safety-critical industrial facilities to operate. Therefore, different methodologies for worst-case modeling and analysis of real-time systems have been established. Among them is deterministic Network Calculus (NC), a versatile technique that is applicable across multiple domains such as packet switching, task scheduling, system on chip, software-defined networking, data center networking and network virtualization. NC is a methodology to derive deterministic bounds on two crucial performance metrics of communication systems:
(a) the end-to-end delay data flows experience and
(b) the buffer space required by a server to queue all incoming data.
NC has already seen application in the industry, for instance, basic results have been used to certify the backbone network of the Airbus A380 aircraft.
The NC methodology for worst-case performance analysis of distributed real-time systems consists of two branches. Both share the NC network model but diverge regarding their respective derivation of performance bounds, i.e., their analysis principle. NC was created as a deterministic system theory for queueing analysis and its operations were later cast in a (min,+)-algebraic framework. This branch is known as algebraic Network Calculus (algNC). While algNC can efficiently compute bounds on delay and backlog, the algebraic manipulations do not allow NC to attain the most accurate bounds achievable for the given network model. These tight performance bounds can only be attained with the other, newly established branch of NC, the optimization-based analysis (optNC). However, the only optNC analysis that can currently derive tight bounds was proven to be computationally infeasible even for the analysis of moderately sized networks other than simple sequences of servers.
This thesis makes various contributions in the area of algNC: accuracy within the existing framework is improved, distributivity of the sensor network calculus analysis is established, and most significantly the algNC is extended with optimization principles. They allow algNC to derive performance bounds that are competitive with optNC. Moreover, the computational efficiency of the new NC approach is improved such that this thesis presents the first NC analysis that is both accurate and computationally feasible at the same time. It allows NC to scale to larger, more complex systems that require formal verification of their real-time capabilities.
Crowd condition monitoring concerns the crowd safety and concerns business performance metrics. The research problem to be solved is a crowd condition estimation approach to enable and support the supervision of mass events by first-responders and marketing experts, but is also targeted towards supporting social scientists, journalists, historians, public relations experts, community leaders, and political researchers. Real-time insights of the crowd condition is desired for quick reactions and historic crowd conditions measurements are desired for profound post-event crowd condition analysis.
This thesis aims to provide a systematic understanding of different approaches for crowd condition estimation by relying on 2.4 GHz signals and its variation in crowds of people, proposes and categorizes possible sensing approaches, applies supervised machine learning algorithms, and demonstrates experimental evaluation results. I categorize four sensing approaches. Firstly, stationary sensors which are sensing crowd centric signals sources. Secondly, stationary sensors which are sensing other stationary signals sources (either opportunistic or special purpose signal sources). Thirdly, a few volunteers within the crowd equipped with sensors which are sensing other surrounding crowd centric device signals (either individually, in a single group or collaboratively) within a small region. Fourthly, a small subset of participants within the crowd equipped with sensors and roaming throughout a whole city to sense wireless crowd centric signals.
I present and evaluate an approach with meshed stationary sensors which were sensing crowd centric devices. This was demonstrated and empirically evaluated within an industrial project during three of the world-wide largest automotive exhibitions. With over 30 meshed stationary sensors in an optimized setup across 6400m2 I achieved a mean absolute error of the crowd density of just 0.0115
people per square meter which equals to an average of below 6% mean relative error from the ground truth. I validate the contextual crowd condition anomaly detection method during the visit of chancellor Mrs. Merkel and during a large press conference during the exhibition. I present the approach of opportunistically sensing stationary based wireless signal variations and validate this during the Hannover CeBIT exhibition with 80 opportunistic sources with a crowd condition estimation relative error of below 12% relying only on surrounding signals in influenced by humans. Pursuing this approach I present an approach with dedicated signal sources and sensors to estimate the condition of shared office environments. I demonstrate methods being viable to even detect low density static crowds, such as people sitting at their desks, and evaluate this on an eight person office scenario. I present the approach of mobile crowd density estimation by a group of sensors detecting other crowd centric devices in the proximity with a classification accuracy of the crowd density of 66 % (improvement of over 22% over a individual sensor) during the crowded Oktoberfest event. I propose a collaborative mobile sensing approach which makes the system more robust against variations that may result from the background of the people rather than the crowd condition with differential features taking information about the link structure between actively scanning devices, the ratio between values observed by different devices, ratio of discovered crowd devices over time, team-wise diversity of discovered devices, number of semi- continuous device visibility periods, and device visibility durations into account. I validate the approach on multiple experiments including the Kaiserslautern European soccer championship public viewing event and evaluated the collaborative mobile sensing approach with a crowd condition estimation accuracy of 77 % while outperforming previous methods by 21%. I present the feasibility of deploying the wireless crowd condition sensing approach to a citywide scale during an event in Zurich with 971 actively sensing participants and outperformed the reference method by 24% in average.
Reading as a cultural skill is acquired over a long period of training. This thesis supports the idea that reading is based on specific strategies that result from modification and coordination of earlier developed object recognition strategies. The reading-specific processing strategies are considered to be more analytic compared to object recognition strategies, which are described as holistic. To enable proper reading skills these strategies have to become automatized. Study 1 (Chapter 4) examined the temporal and visual constrains of letter recognition strategies. In the first experiment two successively presented stimuli (letters or non-letters) had to be classified as same or different. The second stimulus could either be presented in isolation or surrounded by a shape, which was either similar (congruent) or different (incongruent) in its geometrical properties to the stimulus itself. The non-letter pairs were presented twice as often as the letter pairs. The results demonstrated a preference for the holistic strategy also in letters, even if the non- letter set was presented twice as often as the letter set, showing that the analytic strategy does not replace the holistic one completely, but that the usage of both strategies is task-sensitive. In Experiment 2, we compared the Global Precedence Effect (GPE) for letters and non-letters in central viewing, with the global stimulus size close to the functional visual field in whole word reading (6.5◦ of visual angle) and local stimuli close to the critical size for fluent reading of individual letters (0.5◦ of visual angle). Under these conditions, the GPE remained robust for non-letters. For letters, however, it disappeared: letters showed no overall response time advantage for the global level and symmetric congruence effects (local-to-global as well as global-to-local interference). These results indicate that reading is based on resident analytic visual processing strategies for letters. In Study 2 (Chapter 5) we replicated the latter result with a large group of participants as part of a study in which pairwise associations of non-letters and phonological or non-phonological sounds were systematically trained. We investigated whether training would eliminate the GPE also for non-letters. We observed, however, that the differentiation between letters and non-letter shapes persists after training. This result implies that pairwise association learning is not sufficient to overrule the process differentiation in adults. In addition, subtle effects arising in the letter condition (due to enhanced power) enable us to further specify the differentiation in processing between letters and non-letter shapes. The influence of reading ability on the GPE was examined in Study 3 (Chapter 6). Children with normal reading skills and children with poor reading skills were instructed to detect a target in Latin or Hebrew Navon letters. Children with normal reading skills showed a GPE for Latin letters, but not for Hebrew letters. In contrast, the dyslexia group did not show GPE for either kind of stimuli. These results suggest that dyslexic children are not able to apply the same automatized letter processing strategy as children with normal reading skills do. The difference between the analytic letter processing and the holistic non-letter processing was transferred to the context of whole word reading in Study 4 (Chapter 7). When participants were instructed to detect either a letter or a non-letter in a mixed character string, for letters the reaction times and error rates increased linearly from the left to the right terminal position in the string, whereas for non-letters a symmetrical U-shaped function was observed. These results suggest, that the letter-specific processing strategies are triggered automatically also for more word-like material. Thus, this thesis supports and expands prior results of letter-specific processing and gives new evidences for letter-specific processing strategies.
In an overall effort to contribute to the steadily expanding EO literature, this cumulative dissertation aims to help the literature to advance with greater clarity, comprehensive modeling, and more robust research designs. To achieve this, the first paper of this dissertation focuses on the consistency and coherence in variable choices and modeling considerations by conducting a systematic quantitative review of the EO-performance literature. Drawing on the plethora of previous EO studies, the second paper employs a comprehensive meta-analytic structural equation modeling approach (MASEM) to explore the potential for unique component-level relationships among EO’s three core dimensions in antecedent to outcome relationships. The third paper draws on these component-level insights and performs a finer-grained replication of the seminal MASEM of Rosenbusch, Rauch, and Bausch (2013) that proposes EO as a full mediator between the task environment and firm performance. The fourth and final paper of this cumulative dissertation illustrates exigent endogeneity concerns inherent in observational EO-performance research and provides guidance on how researchers can move towards establishing causal relationships.
Wetting of a solid surface with liquids is an important parameter in the chemical engineering process such as distillation, absorption and desorption. The degree of wetting in packed columns mainly contributes in the generating of the effective interfacial area and then enhancing of the heat and mass transfer process. In this work the wetting of solid surfaces was studied in real experimental work and virtually through three dimensional CFD simulations using the multiphase flow VOF model implemented in the commercial software FLUENT. That can be used to simulate the stratified flows [1]. The liquid rivulet flow which is a special case of the film flow and mostly found in packed columns has been discussed. Wetting of a solid flat and wavy metal plate with rivulet liquid flow was simulated and experimentally validated. The local rivulet thickness was measured using an optically assisted mechanical sensor using a needle which is moved perpendicular to the plate surface with a step motor and in the other two directions using two micrometers. The measured and simulated rivulet profiles were compared to some selected theoretical models founded in the literature such as Duffy & Muffatt [2], Towell & Rothfeld [3] and Al-Khalil et al. [4]. The velocity field in a cross section of a rivulet flow and the non-dimensional maximum and mean velocity values for the vertical flat plate was also compared with models from Al-Khalil et al. [4] and Allen & Biggin [5]. Few CFD simulations for the wavy plate case were compared to the experimental findings, and the Towel model for a flat plate [3]. In the second stage of this work 3-D CFD simulations and experimental study has been performed for wetting of a structured packing element and packing sheet consisting of three elements from the type Rombopak 4M, which is a product of the company Kuhni, Switzerland. The hydrodynamics parameters of a packed column, e. i. the degree of wetting, the interfacial area and liquid hold-up have been depicted from the CFD simulations for different liquid systems and liquid loads. Flow patterns on the degree of wetting have been compared to that of the experiments, where the experimental values for the degree of wetting were estimated from the snap shooting of the flow on the packing sheet in a test rig. A new model to describe the hydrodynamics of packed columns equipped with Rombopak 4M was derived with help of the CFD–simulation results. The model predicts the degree of wetting, the specific or interfacial area and liquid hold-up at different flow conditions. This model was compared to Billet & Schultes [6], the SRP model Rocha et al. [7-9], to Shi & Mersmann [10] and others. Since the pressure drop is one of the most important parameter in packed columns especially for vacuum operating columns, few CFD simulations were performed to estimate the dry pressure drop in a structured and flat packing element and were compared to the experimental results. It was found a good agreement from one side, between the experimental and the CFD simulation results, and from the other side between the simulations and theoretical models for the rivulet flow on an inclined plate. The flow patterns and liquid spreading behaviour on the packing element agrees well with the experimental results. The VOF (Volume of Fluid) was found very sensitive to different liquid properties and can be used in optimization of the packing geometries and revealing critical details of wetting and film flow. An extension of this work to perform CFD simulations for the flow inside a block of the packing to get a detailed picture about the interaction between the liquid and packing surfaces is recommended as further perspective.
This research explores the development of web based reference software for
characterisation of surface roughness for two-dimensional surface data. The reference software used for verification of surface characteristics makes the evaluation methods easier for clients. The algorithms used in this software
are based on International ISO standards. Most software used in industrial measuring
instruments may give variations in the parameters calculated due to numerical changes in
calculation. Such variations can be verified using the proposed reference software.
The evaluation of surface roughness is carried out in four major steps: data capture, data
align, data filtering and parameter calculation. This work walks through each of these steps
explaining how surface profiles are evaluated by pre-processing steps called fitting and
filtering. The analysis process is then followed by parameter evaluation according to DIN EN
ISO 4287 and DIN EN ISO 13565-2 standards to extract important information from the
profile to characterise surface roughness.
Wearable activity recognition aims to identify and assess human activities with the help
of computer systems by evaluating signals of sensors which can be attached to the human
body. This provides us with valuable information in several areas: in health care, e.g. fluid
and food intake monitoring; in sports, e.g. training support and monitoring; in entertainment,
e.g. human-computer interface using body movements; in industrial scenarios, e.g.
computer support for detected work tasks. Several challenges exist for wearable activity
recognition: a large number of nonrelevant activities (null class), the evaluation of large
numbers of sensor signals (curse of dimensionality), ambiguity of sensor signals compared
to the activities and finally the high variability of human activity in general.
This thesis develops a new activity recognition strategy, called invariants classification,
which addresses these challenges, especially the variability in human activities. The
core idea is that often even highly variable actions include short, more or less invariant
sub-actions which are due to hard physical constraints. If someone opens a door, the
movement of the hand to the door handle is not fixed. However the door handle has to
be pushed to open the door. The invariants classification algorithm is structured in four
phases: segmentation, invariant identification, classification, and spotting. The segmentation
divides the continuous sensor data stream into meaningful parts, which are related
to sub-activities. Our segmentation strategy uses the zero crossings of the central difference
quotient of the sensor signals, as segment borders. The invariant identification finds
the invariant sub-activities by means of clustering and a selection strategy dependent on
certain features. The classification identifies the segments of a specific activity class, using
models generated from the invariant sub-activities. The models include the invariant
sub-activity signal and features calculated on sensor signals related to the sub-activity. In
the spotting, the classified segments are used to find the entire activity class instances in
the continuous sensor data stream. For this purpose, we use the position of the invariant
sub-activity in the related activity class instance for the estimation of the borders of the
activity instances.
In this thesis, we show that our new activity recognition strategy, built on invariant
sub-activities, is beneficial. We tested it on three human activity datasets with wearable
inertial measurement units (IMU). Compared to previous publications on the same
datasets we got improvement in the activity recognition in several classes, some with a
large margin. Our segmentation achieves a sensible method to separate the sensor data in
relation to the underlying activities. Relying on sub-activities makes us independent from
imprecise labels on the training data. After the identification of invariant sub-activities,
we calculate a value called cluster precision for each sensor signal and each class activity.
This tells us which classes can be easily classified and which sensor channels support
the classification best. Finally, in the training for each activity class, our algorithm selects
suitable signal channels with invariant sub-activities on different points in time and
with different length. This makes our strategy a multi-dimensional asynchronous motif
detection with variable motif length.
The thesis is concerned with the modelling of ionospheric current systems and induced magnetic fields in a multiscale framework. Scaling functions and wavelets are used to realize a multiscale analysis of the function spaces under consideration and to establish a multiscale regularization procedure for the inversion of the considered operator equation. First of all a general multiscale concept for vectorial operator equations between two separable Hilbert spaces is developed in terms of vector kernel functions. The equivalence to the canonical tensorial ansatz is proven and the theory is transferred to the case of multiscale regularization of vectorial inverse problems. As a first application, a special multiresolution analysis of the space of square-integrable vector fields on the sphere, e.g. the Earth’s magnetic field measured on a spherical satellite’s orbit, is presented. By this, a multiscale separation of spherical vector-valued functions with respect to their sources can be established. The vector field is split up into a part induced by sources inside the sphere, a part which is due to sources outside the sphere and a part which is generated by sources on the sphere, i.e. currents crossing the sphere. The multiscale technqiue is tested on a magnetic field data set of the satellite CHAMP and it is shown that crustal field determination can be improved by previously applying our method. In order to reconstruct ionspheric current systems from magnetic field data, an inversion of the Biot-Savart’s law in terms of multiscale regularization is defined. The corresponding operator is formulated and the singular values are calculated. Based on the konwledge of the singular system a regularzation technique in terms of certain product kernels and correponding convolutions can be formed. The method is tested on different simulations and on real magnetic field data of the satellite CHAMP and the proposed satellite mission SWARM.
Nanotechnology is now recognized as one of the most promising areas for technological
development in the 21st century. In materials research, the development of
polymer nanocomposites is rapidly emerging as a multidisciplinary research activity
whose results could widen the applications of polymers to the benefit of many different
industries. Nanocomposites are a new class of composites that are particle-filled
polymers for which at least one dimension of the dispersed particle is in the nanometer
range. In the related area polymer/clay nanocomposites have attracted considerable
interest because they often exhibit remarkable property improvements when
compared to virgin polymer or conventional micro- and macro- composites.
The present work addresses the toughening and reinforcement of thermoplastics via
a novel method which allows us to achieve micro- and nanocomposites. In this work
two matrices are used: amorphous polystyrene (PS) and semi-crystalline polyoxymethylene
(POM). Polyurethane (PU) was selected as the toughening agent for POM
and used in its latex form. It is noteworthy that the mean size of rubber latices is
closely matched with that of conventional toughening agents, impact modifiers.
Boehmite alumina and sodium fluorohectorite (FH) were used as reinforcements.
One of the criteria for selecting these fillers was that they are water swellable/
dispersible and thus their nanoscale dispersion can be achieved also in aqueous
polymer latex. A systematic study was performed on how to adapt discontinuousand
continuous manufacturing techniques for the related nanocomposites.
The dispersion of nanofillers was characterized by transmission, scanning electron
and atomic force microcopy (TEM, SEM and AFM respectively), X-ray diffraction
(XRD) techniques, and discussed. The crystallization of POM was studied by means
of differential scanning calorimetry and polarized light optical microscopy (DSC and
PLM, respectively). The mechanical and thermomechanical properties of the composites
were determined in uniaxial tensile, dynamic-mechanical thermal analysis
(DMTA), short-time creep tests, and thermogravimetric analysis (TGA).
PS composites were produced first by a discontinuous manufacturing technique,
whereby FH or alumina was incorporated in the PS matrix by melt blending with and
without latex precompounding of PS latex with the nanofiller. It was found that direct melt mixing (DM) of the nanofillers with PS resulted in micro-, whereas the latex mediated
pre-compounding (masterbatch technique, MB) in nanocomposites. FH was
not intercalated by PS when prepared by DM. On the other hand, FH was well dispersed
(mostly intercalated) in PS via the PS latex-mediated predispersion of FH following
the MB route. The nanocomposites produced by MB outperformed the DM
compounded microcomposites in respect to properties like stiffness, strength and
ductility based on dynamic-mechanical and static tensile tests. It was found that the
resistance to creep (summarized in master curves) of the nanocomposites were improved
compared to those of the microcomposites. Master curves (creep compliance
vs. time), constructed based on isothermal creep tests performed at different temperatures,
showed that the nanofiller reinforcement affects mostly the initial creep
compliance.
Next, ternary composites composed of POM, PU and boehmite alumina were produced
by melt blending with and without latex precompounding. Latex precompounding
served for the predispersion of the alumina particles. The related MB was produced
by mixing the PU latex with water dispersible boehmite alumina. The composites
produced by the MB technique outperformed the DM compounded composites in
respect to most of the thermal and mechanical characteristics.
Toughened and/or reinforced PS- and POM-based composites have been successfully
produced by a continuous extrusion technique, too. This technique resulted in
good dispersion of both nanofillers (boehmite) and impact modifier (PU). Compared
to the microcomposites obtained by conventional DM, the nanofiller dispersion became
finer and uniform when using the water-mediated predispersion. The resulting
structure markedly affected the mechanical properties (stiffness and creep resistance)
of the corresponding composites. The impact resistance of POM was highly
enhanced by the addition of PU rubber when manufactured by the continuous extrusion
manufacturing technique. This was traced to the dispersed PU particle size being
in the range required from conventional, impact modifiers.
In urban planning, sophisticated simulation models are key tools to estimate future population growth for measuring the impact of planning decisions on urban developments and the environment. Simulated population projections usually result in large, macro-scale, multivariate geospatial data sets. Millions of records have to be processed, stored, and visualized to help planners explore and analyze complex population patterns. We introduce a database driven framework for visualizing geospatial multidimensional simulation data based on the output from UrbanSim, a software for the analysis and planning of urban developments. The designed framework is extendable and aims at integrating empirical-stochastic methods and urban simulation models with techniques developed for information visualization and cartography. First, we develop an empirical model for the estimation of residential building types based on demographic household characteristics. The predicted dwelling type information is important for the analysis of future material use, carbon footprint calculations, and for visualizing simultaneously the results of land usage, density, and other significant parameters in 3D space. Our model uses multinomial logistic regression to derive building types at different scales. The estimated regression coefficients are applied to UrbanSim output in order to predict residential building types. The simulation results and the estimated building types are managed in an object-relational geodatabase. From the database, density, building types, and significant demographic variables are visually encoded as scalable, georeferenced 3D geometries and displayed on top of aerial photographs in a Google Earth visual synthesis. The geodatabase can be accessed and the visualization parameters can be chosen through a web-based user interface. The geometries are encoded in KML, Google's markup language, as ready-to-visualize data sets. The goal is to enhance human cognition by displaying abstract representations of multidimensional data sets in a realistic context and thus to support decision making in planning processes.
Due to the steadily growing flood of data, the appropriate use of visualizations for efficient data analysis is as important today as it has never been before. In many application domains, the data flood is based on processes that can be represented by node-link diagrams. Within such a diagram, nodes may represent intermediate results (or products), system states (or snapshots), milestones or real (and possibly georeferenced) objects, while links (edges) can embody transition conditions, transformation processes or real physical connections. Inspired by the engineering sciences application domain and the research project “SinOptiKom: Cross-sectoral optimization of transformation processes in municipal infrastructures in rural areas”, a platform for the analysis of transformation processes has been researched and developed based on a geographic information system (GIS). Caused by the increased amount of available and interesting data, a particular challenge is the simultaneous visualization of several visible attributes within one single diagram instead of using multiple ones. Therefore, two approaches have been developed, which utilize the available space between nodes in a diagram to display additional information.
Motivated by the necessity of appropriate result communication with various stakeholders, a concept for a universal, dashboard-based analysis platform has been developed. This web-based approach is conceptually capable of displaying data from various data sources and has been supplemented by collaboration possibilities such as sharing, annotating and presenting features.
In order to demonstrate the applicability and usability of newly developed applications, visualizations or user interfaces, extensive evaluations with human users are often inevitable. To reduce the complexity and the effort for conducting an evaluation, the browser-based evaluation framework (BREF) has been designed and implemented. Through its universal and flexible character, virtually any visualization or interaction running in the browser can be evaluated with BREF without any additional application (except for a modern web browser) on the target device. BREF has already proved itself in a wide range of application areas during the development and has since grown into a comprehensive evaluation tool.
The visualization of numerical fluid flow datasets is essential to the engineering processes that motivate their computational simulation. To address the need for visual representations that convey meaningful relations and enable a deep understanding of flow structures, the discipline of Flow Visualization has produced many methods and schemes that are tailored to a variety of visualization tasks. The ever increasing complexity of modern flow simulations, however, puts an enormous demand on these methods. The study of vortex breakdown, for example, which is a highly transient and inherently three-dimensional flow pattern with substantial impact wherever it appears, has driven current techniques to their limits. In this thesis, we propose several novel visualization methods that significantly advance the state of the art in the visualization of complex flow structures. First, we propose a novel scheme for the construction of stream surfaces from the trajectories of particles embedded in a flow. These surfaces are extremely useful since they naturally exploit coherence between neighboring trajectories and are highly illustrative in nature. We overcome the limitations of existing stream surface algorithms that yield poor results in complex flows, and show how the resulting surfaces can be used a building blocks for advanced flow visualization techniques. Moreover, we present a visualization method that is based on moving section planes that travel through a dataset and sample the flow. By considering the changes to the flow topology on the plane as it moves, we obtain a method of visualizing topological structures in three-dimensional flows that are not accessible by conventional topological methods. On the same algorithmic basis, we construct an algorithm for the tracking of critical points in such flows, thereby enabling the treatment of time-dependent datasets. Last, we address some problems with the recently introduced Lagrangian techniques. While conceptually elegant and generally applicable, they suffer from an enormous computational cost that we significantly use by developing an adaptive approximation algorithm. This allows the application of such methods on very large and complex numerical simulations. Throughout this thesis, we will be concerned with flow visualization aspect of general practical significance but we will particularly emphasize the remarkably challenging visualization of the vortex breakdown phenomenon.
In urban planning, both measuring and communicating sustainability are among the most recent concerns. Therefore, the primary emphasis of this thesis concerns establishing metrics and visualization techniques in order to deal with indicators of sustainability.
First, this thesis provides a novel approach for measuring and monitoring two indicators of sustainability - urban sprawl and carbon footprints – at the urban neighborhood scale. By designating different sectors of relevant carbon emissions as well as different household categories, this thesis provides detailed information about carbon emissions in order to estimate impacts of daily consumption decisions and travel behavior by household type. Regarding urban sprawl, a novel gridcell-based indicator model is established, based on different dimensions of urban sprawl.
Second, this thesis presents a three-step-based visualization method, addressing predefined requirements for geovisualizations and visualizing those indicator results, introduced above. This surface-visualization combines advantages from both common GIS representation and three-dimensional representation techniques within the field of urban planning, and is assisted by a web-based graphical user interface which allows for accessing the results by the public.
In addition, by focusing on local neighborhoods, this thesis provides an alternative approach in measuring and visualizing both indicators by utilizing a Neighborhood Relation Diagram (NRD), based on weighted Voronoi diagrams. Thus, the user is able to a) utilize original census data, b) compare direct impacts of indicator results on the neighboring cells, and c) compare both indicators of sustainability visually.
Nowadays, the increasing demand for ever more customizable products has emphasized the need for more flexible and fast-changing manufacturing systems. In this environment, simulation has become a strategic tool for the design, development, and implementation of such systems. Simulation represents a relatively low-cost and risk-free alternative for testing the impact and effectiveness of changes in different aspects of manufacturing systems.
Systems that deal with this kind of data for its use in decision making processes are known as Simulation-Based Decision Support Systems (SB-DSS). Although most SB-DSS provide a powerful variety of tools for the automatic and semi-automatic analysis of simulations, visual and interactive alternatives for the manual exploration of the results are still open to further development.
The work in this dissertation is focused on enhancing decision makers’ analysis capabilities by making simulation data more accessible through the incorporation of visualization and analysis techniques. To demonstrate how this goal can be achieved, two systems were developed. The first system, viPhos – standing for visualization of Phos: Greek for light –, is a system that supports lighting design in factory layout planning. viPhos combines simulation, analysis, and visualization tools and techniques to facilitate the global and local (overall factory or single workstations, respectively) interactive exploration and comparison of lighting design alternatives.
The second system, STRAD - standing for Spatio-Temporal Radar -, is a web-based systems that considers the spatio/attribute-temporal analysis of event data. Since decision making processes in manufacturing also involve the monitoring of the systems over time, STRAD enables the multilevel exploration of event data (e.g., simulated or historical registers of the status of machines or results of quality control processes).
A set of four case studies and one proof of concept prepared for both systems demonstrate the suitability of the visualization and analysis strategies adopted for supporting decision making processes in diverse application domains. The results of these case studies indicate that both, the systems as well as the techniques included in the systems can be generalized and extended to support the analysis of different tasks and scenarios.
Due to remarkable technological advances in the last three decades the capacity of computer systems has improved tremendously. Considering Moore's law, the number of transistors on integrated circuits has doubled approximately every two years and the trend is continuing. Likewise, developments in storage density, network bandwidth, and compute capacity show similar patterns. As a consequence, the amount of data that can be processed by today's systems has increased by orders of magnitude. At the same time, however, the resolution of screens has hardly increased by a factor of ten. Thus, there is a gap between the amount of data that can be processed and the amount of data that can be visualized. Large high-resolution displays offer a way to deal with this gap and provide a significantly increased screen area by combining the images of multiple smaller display devices. The main objective of this dissertation is the development of new visualization and interaction techniques for large high-resolution displays.
This dissertation focuses on the visualization of urban microclimate data sets,
which describe the atmospheric impact of individual urban features. The application
and adaptation of visualization and analysis concepts to enhance the
insight into observational data sets used this specialized area are explored, motivated
through application problems encountered during active involvement
in urban microclimate research at the Arizona State University in Tempe, Arizona.
Besides two smaller projects dealing with the analysis of thermographs
recorded with a hand-held device and visualization techniques used for building
performance simulation results, the main focus of the work described in
this document is the development of a prototypic tool for the visualization
and analysis of mobile transect measurements. This observation technique involves
a sensor platform mounted to a vehicle, which is then used to traverse
a heterogeneous neighborhood to investigate the relationships between urban
form and microclimate. The resulting data sets are among the most complex
modes of in-situ observations due to their spatio-temporal dependence, their
multivariate nature, but also due to the various error sources associated with
moving platform observations.
The prototype enables urban climate researchers to preprocess their data,
to explore a single transect in detail, and to aggregate observations from multiple
traverses conducted over diverse routes for a visual delineation of climatic
microenvironments. Extending traditional analysis methods, the suggested visualization
tool provides techniques to relate the measured attributes to each
other and to the surrounding land cover structure. In addition to that, an
improved method for sensor lag correction is described, which shows the potential
to increase the spatial resolution of measurements conducted with slow
air temperature sensors.
In summary, the interdisciplinary approach followed in this thesis triggers
contributions to geospatial visualization and visual analytics, as well as to urban
climatology. The solutions developed in the course of this dissertation are
meant to support domain experts in their research tasks, providing means to
gain a qualitative overview over their specific data sets and to detect patterns,
which can then be further analyzed using domain-specific tools and methods.