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- Kaiserslautern - Fachbereich Wirtschaftswissenschaften (19)
- Kaiserslautern - Fachbereich Physik (9)
Distributed message-passing systems have become ubiquitous and essential for our daily lives. Hence, designing and implementing them correctly is of utmost importance. This is, however, very challenging at the same time. In fact, it is well-known that verifying such systems is algorithmically undecidable in general due to the interplay of asynchronous communication (messages are buffered) and concurrency. When designing communication in a system, it is natural to start with a global protocol specification of the desired communication behaviour. In such a top-down approach, the implementability problem asks, given such a global protocol, if the specified behaviour can be implemented in a distributed setting without additional synchronisation. This problem has been studied from two perspectives in the literature. On the one hand, there are Multiparty Session Types (MSTs) from process algebra, with global types to specify protocols. Key to the MST approach is a so-called projection operator, which takes a global type and tries to project it onto every participant: if successful, the local specifications are safe to use. This approach is efficient but brittle. On the other hand, High-level Message Sequence Charts (HMSCs) study the implementability problem from an automata-theoretic perspective. They employ very few restrictions on protocol specifications, making the implementability problem for HMSCs undecidable in general. The work in this thesis is the first to formally build a bridge between the world of MSTs and HMSCs. To start, we present a generalised projection operator for sender-driven choice. This allows a sender to send to different receivers when branching, which is crucial to handle common communication patterns from distributed computing. Despite this first step, we also show that the classical MST projection approach is inherently incomplete. We present the first formal encoding from global types to HMSCs. With this, we prove decidability of the implementability problem for global types with sender-driven choice. Furthermore, we develop the first direct and complete projection operator for global types with sender-driven choice, using automata-theoretic techniques, and show its effectiveness with a prototype implementation. We are the first to provide an upper bound for the implementability problem for global types with sender-driven (or directed) choice and show it to be in PSPACE. We also provide a session type system that uses the results from our projection operator. Last, we introduce protocol state machines (PSMs) – an automata-based protocol specification formalism – that subsume both global types from MSTs and HMSCs with regard to expressivity. We use transformations on PSMs to show that many of the syntactic restrictions of global types are not restrictive in terms of protocol expressivity. We prove that the implementability problem for PSMs with mixed choice, which requires no dedicated sender for a branch but solely all labels to be distinct, is undecidable in general. With our results on expressivity, this answers an open question: the implementability problem for mixed-choice global types is undecidable in general.
Coastal port-industrial areas are becoming increasingly significant due to urban shrinkage, population
decline, and climate change. To address social and economic issues and enhance climate resilience, it
is crucial to anticipate urban shrinkage in both stable and growing coastal areas that are undergoing
economic transformation. Urban planning can better understand the dynamics of planning for urban
shrinkage and climate resilience, as port-industrial areas have a large economic impact on nearby
coastal communities.
This dissertation examines the long-term implications of urban shrinkage in coastal port-industrial
areas in the context of climate change and sea level rise in England. The research problem is that
current urban policy does not adequately address the challenges of urban shrinkage and climate
resilience in these areas. The research questions are: What are the population changes in local areas
in England? What effect does population decline have on changing urbanisation patterns in older
industrial areas? What type of adaptation efforts were made in North East Lincolnshire, England, and
Bremerhaven, Germany, in response to the 2013 tidal surge, and how did this affect urban
shrinkage?
The dissertation applies an integrated concept of Shrinkage-Resilience as a framework for analysis.
The methodology includes a review of existing models and frameworks, as well as case studies of
international and local contexts. The findings suggest that between 2013-2019, 68% of older
industrial areas (including coastal ports) in England are undergoing changing urbanisation patterns
relative to population, land use, and green belt areas, and are key areas for urban policy, such as the
Levelling Up agenda. One of the areas, North East Lincolnshire is discussed and compared to
Bremerhaven. These examples demonstrate the link between Shrinkage-Resilience approaches and
their practical implementation in coastal port-industrial areas affected by urban shrinkage.
This research advances the scientific practice of urban planning and policy-making for shrinking cities
by introducing the approach of Shrinkage-Resilience, which emphasises the importance of
considering long-term social, economic, and environmental impacts in urban shrinkage contexts. This
approach is crucial in the transition to a more sustainable and inclusive society, where the welfare of
present and future generations, the environment, and economic development are taken into
account. The dissertation provides recommendations for urban planning to incorporate policy
changes for shrinking cities and coastal port-industrial areas worldwide, to include disaster risk
reduction and climate change adaptation approaches.
To increase situational awareness of the crane operator, the aim of this thesis is to develop a vision-based deep learning object detection from crane load-view using an adaptive perception in the construction area. Conventional worker detection methods are based on simple shape or color features from the worker's appearances. Nonetheless, these methods can fail to recognize the workers who do not wear the protective gears. To find out an image representation of the object from the top view manually or handcrafted feature is crucial. We, therefore, employed deep learning methods to automatically learn those features.
To yield optimal results, deep learning methods require mass amount of data.
Due to the data deficit especially in the construction domain, we developed the photorealistic world to create the data in addition to our samples collected from the real construction area. The simulated platform does not benefit only from diverse data types, but also concurrent research development which speeds up the pipeline at a low cost.
Our research findings indicate that the combination of synthetic and real training samples improved the state-of-the-art detector. In line with previous studies to bridge the gap between synthetic and real data, the results of preprocessed synthetic images are substantially better than using the raw data by approximately 10%.
Finding the right deep learning model for load-view detection is challenging.
By investigating our training data, it becomes evident that the majority of bounding box sizes are very small with a complex background.
In addition, we gave the priority to speed over accuracy based on the construction safety criteria. Finally, RetinaNet is chosen out of the three primary object detection models.
Nevertheless, the data-driven detection algorithm can fail to handle scale invariance, especially for detectors whose input size is changed in an extremely wide range.
The adaptive zoom feature can enhance the quality of the worker detection.
To avoid further data gathering and extensive retraining, the proposed automatic zoom method of the load-view crane camera supports the deep learning algorithm, specifically in the high scale variant problem. The finite state machine is employed for control strategies to adapt the zoom level to cope not only with inconsistent detection but also abrupt camera movement during lifting operation. Consequently, the detector is able to detect a small size object by smooth continuous zoom control without additional training.
The adaptive zoom control not only enhances the performance of the top-view object detection but also reduces the interaction of the crane operator with camera system, reducing the risk of fatality during load lifting operation.
Aquatic habitats are closely linked to the adjacent riparian area. Fluxes of nutrients, energy and matter through emerging aquatic insects are a key component of the aquatic subsidy to terrestrial systems. In fact, adult insects serve as high-quality prey for riparian predators. Stressors impacting the aquatic subsidy can thus translate to consequences for the receiving terrestrial food web, while mechanistic knowledge is extremely limited. Against this background, this thesis aimed at (i) assessing the impact of a model stressor specifically targeting insect emergence, that is the mosquito control agent Bacillus thuringiensis var. israelensis, on quantity, temporal dynamics and (ii) quality of emerging aquatic insects. For this purpose, outdoor floodplain pond mesocosms (n = 6) were employed. Since emergence is, in most cases, no point event but occurs over a longer period emergence was monitored over 3.5 months. The model stressor, i.e., Bti applied three times during spring at 2.88 × 10^9 ITU/ha, shifted the emergence time of aquatic insects, especially of non-biting midges (Diptera: Chironomidae), by ten days with a 26% reduced peak, while the nutrient content was not altered. On this basis, (ii) the propagation of the effects in aquatic subsidy emergence to riparian predators was investigated. Stable isotope analyses were used to assess the diet of a model predator, that is the web-building riparian spider Tetragnatha extensa. Results suggested changes in the composition of the spider’s diet to replace missing Chironomidae by other aquatic and terrestrial prey organisms pointing to further negative consequences. Finally, the thesis aimed at (iii) the understanding of processes underlying an altered emergence of aquatic subsidy mainly consisting of chironomids. Using a laboratory-based test design, populations of Chironomus riparius (n = 6) were assessed for their sensitivity towards Bti under different food qualities (high and low nutritious) before and after a long-term (six months) Bti exposure. Signs of phenotypic adaptation were observed in emergence time and nutrient content over multiple generations, resulting in changes in chironomids’ quantity and quality as food source. Overall, it can be concluded that direct and indirect effects of an aquatic stressor, as well as the adaptive response to it, can alter ecosystems at different levels, including individual, population and community level. Furthermore, this thesis highlights the importance of a temporal perspective when investigating the impact of aquatic stressors beyond ecosystem boundaries. It illustrates potential bottom-up effects on riparian predators through altered emergence of aquatic insects, feeding our understanding of meta-ecosystems and how stressors and their effects are transferred across systems. These insights will support efforts to protect and conserve natural ecosystems.
The German energy mix, which provides an overview of the sources of electricity available in Germany, is changing as a result of the expansion of renewable energy sources. With this shift towards sustainable energy sources such as wind and solar power, the electricity market situation is also in flux. Whereas in the past there were few uncertainties in electricity generation and only demand was subject to stochastic uncertainties, generation is now subject to stochastic fluctuations as well, especially due to weather dependency. To provide a supportive framework for this different situation, the electricity market has introduced, among other things, the intraday market, products with half-hourly and quarter-hourly time slices, and a modified balancing energy market design. As a result, both electricity price forecasting and optimization issues remain topical.
In this thesis, we first address intraday market modeling and intraday index forecasting. To do so, we move to the level of individual bids in the intraday market and use them to model the limit order books of intraday products. Based on statistics of the modeled limit order books, we present a novel estimator for the intraday indices. Especially for less liquid products, the order book statistics contain relevant information that allows for significantly more accurate predictions in comparison to the benchmark estimator.
Unlike the intraday market, the day ahead market allows smaller companies without their own trading department to participate since it is operated as a market with daily auctions. We optimize the flexibility offer of such a small company in the day ahead market and model the prices with a stochastic multi-factor model already used in the industry. To make this model accessible for stochastic optimization, we discretize it in time and space using scenario trees. Here we present existing algorithms for scenario tree generation as well as our own extensions and adaptations. These are based on the nested distance, which measures the distance between two distributions of stochastic processes. Based on the resulting scenario trees, we apply the stochastic optimization methods of stochastic programming, dynamic programming, and reinforcement learning to illustrate in which context the methods are appropriate.
Virtual Possibilities: Exploring the Role of Emerging Technologies in Work and Learning Environments
(2024)
The present work aims to investigate whether virtual reality can support learning as well as vocational work environments. To this end, four studies were conducted, with the first set investigating the demands for vocational workers and the impact of input methods on participant performance. These studies laid the foundation needed to create studies incorporating virtual reality research. The second set of studies was concerned with the impact of virtual reality on learning performance as well as the influence of binaural stimuli presentation on task performance. Results of each study are discussed individually and in conjunction with one another. The four studies are further supplemented with further research conducted by the author as well as an analysis of the growing field of virtual reality-based research. The thesis closes by embedding the discussed work into the scientific landscape and tries to give an outlook for virtual reality-based use cases in the future.
In recent years, there has been a growing need for accurate 3D scene reconstruction. Recent developments in the automotive industry have led to the increased use of ADAS where 3D reconstruction techniques are used, for example, as part of a collision detection system. For such applications, scene geometry reconstruction is usually performed in the form of depth estimation, where distances to scene objects are obtained.
In general, depth estimation systems can be divided into active and passive. Both systems have their advantages and disadvantages, but passive systems are usually cheaper to produce and easier to assemble and integrate than active systems. Passive systems can be stereo- or multiple-view based. Up to a certain limit, increasing the number of views in multi-view systems usually results in improved depth estimation accuracy.
One potential problem for ensuring the reliability of multi-view systems is the need to accurately estimate the orientation of their optical sensors. One way to ensure sensor placement for multi-view systems is to rigidly fix the sensors at the manufacturing stage. Unlike arbitrary sensor placement, using of a simplified and known sensor placement geometry further simplifies the depth estimation.
We meet with the concept of light field, which parameterizes all visible light passing through all viewpoints by their intersection with angular and spatial planes. When applied to computer vision, this gives us a 2D set of 2D images, where the physical distances between each image are fixed and proportional to each other.
Existing light field depth estimation methods provide good accuracy, which is suitable for industrial applications. However, the main problems of these methods are related to their running time and resource requirements. Most of the algorithms presented in the literature are typically sharpened for accuracy, can only be run on high-performance machines and often require a significant amount of time to process and obtain results.
Real-world applications often have running time requirements. Also, often there is a power-consumption limitation. In this dissertation, we investigate the problem of building a depth estimation system with an light field camera that satisfies the operating time and power consumption constraints without significant loss of estimation accuracy.
First, an algorithm for calibrating light field cameras is proposed, together with an algorithm for automatic calibration refinement, that works on arbitrary captured scenes. An algorithm for classical geometric depth estimation using light field cameras is proposed. Ways to optimize the algorithm for real-time use without significant loss of accuracy are presented. Finally, the ways how the presented depth estimation methods can be extended using modern deep learning paradigms under the two previously mentioned constraints are shown.
With the expansion of the electromobility and wind energy, the number of frequency inverter-controlled electric motors and generators is increasing. In parallel, the number of the rolling bearing failures caused by inverter-induced parasitic currents also shows an increasing trend. In order to determine the electrical state of the rolling bearing, to develop preventive measures against damages caused by parasitic currents and to support system-level calculations, electrical rolling bearing models have been developed. The models are based on the electrical insulating ability of the lubricant film that develops in the rolling contacts. For the capacitance calculation of the rolling contacts, different correction factors were developed to simplify the complex tribological and electrical interactions of this region. The state-of-the-art correction factors vary widely, and their validity range also differ significantly, which leads to uncertainty in their general application and to the demand for further investigations of this field. In the present work, a combined simulation method is developed that can determine the rolling bearing capacitance of axially loaded rolling bearings. The simulation consists of an electrically extended EHL simulation for calculating the capacitance of the rolling contact, and an electrical FEM simulation for the capacitance calculation of the non-contact regions. With the combination of the resulted capacitance values of the two simulation methods, the total rolling bearing capacitance can be determined with high accuracy and without using correction factors. In addition, due to experimental investigations, the different capacitance sources of the rolling bearing are identified. After the validation of the combined simulation method, it can be applied for the investigation of the different capacitance sources, i.e., to determine their significance compared to the total rolling bearing capacitance. The developed simulation method allows a detailed analysis of the rolling bearing capacitances, taking into account influencing factors that could not be considered before (e.g., oil quantity in the environment of the rolling bearing). As a result, the accurate calculation of the rolling bearing capacitance can improve the prediction of the harmful parasitic currents and help to develop preventive measures against them.
Knowledge workers face an ever increasing flood of information in their daily work. They live in a “multi-tasking craziness”, involving activities like creating, finding, processing, assessing or organizing information while constantly switching from one context to another, each being associated with different tasks, documents, mails, etc. Hence, their personal information sphere consisting of file, mail and bookmark folders as well as their content, calendar entries, etc. is cluttered with information that has become irrelevant. Finding important information thus gets harder and much of previously gained knowledge is practically lost.
This thesis explores new ways of solving this problem by investigating the potential of self-(re)organizing and especially forgetting-enabled personal knowledge assistants in the given scenario. It utilizes so-called Managed Forgetting, which is an escalating set of measures to overcome the binary keep-or-delete paradigm, ranging from temporal hiding, to condensation, to adaptive reorganization, synchronization, archiving and deletion. Managed Forgetting is combined with two other major ideas: First, it uses the Semantic Desktop as an ecosystem, which brings Semantic Web and thus knowledge graph technologies to a user’s desktop, making it possible to capture and represent major parts of a user’s personal mental model in a machine-understandable way and exploit it in many different applications. Second, the system uses explicated context information – so-called Context Spaces: context is seen as an explicit interaction element users can work with (i.e. a “tangible” object similar to a folder) and in (immersion). The thesis is structured according to the basic interaction cycle with such a system, ranging from evidence collection to information extraction and context elicitation, followed by information value assessment and the actual support measures consisting of self-(re)organization decisions (back-end) and user interface updates (front-end). The system’s data foundation are personal or group knowledge graphs as well as native data. This work makes contributions to all of these aspects, whereas several of them have been investigated and developed in interdisciplinary research with cognitive scientists. On a more general level, searching and trust in such highly autonomous assistants have also been investigated.
In summary, a self-(re)organizing and especially forgetting-enabled support system for information management and knowledge work has been realized. Its different features vary in maturity: the most mature ones are already in practical use (also in industry), while the latest are just well elaborated (position papers) or rough ideas. Different evaluation strategies have been applied ranging from mere data-driven experiments to various user studies. Some of them were rather short-term with controlled laboratory conditions, others less controlled but spanning several months. Different benefits of working with such a system could be quantified, e.g. cognitive offloading effects and reduced task switching/resumption time. Other benefits were gathered qualitatively, e.g. tidiness of the information sphere and its better alignment with the user’s mental model. The presented approach has been shown to hold a lot of potential. In some aspects, however, only first steps have been taken towards tapping it, e.g. several support measures can be further refined and automation further increased.
This thesis focuses on the operation of reliability-constrained routes in wireless ad-hoc networks. A complete communication protocol that is capable of guaranteeing a statistical minimum reliability level would have to support several functionalities: first, routes that are capable of supporting the specified Quality of Service requirement have to be discovered. During operation of discovered routes, the current Quality of Service level has to be monitored continuously. Whenever significant deviations are detected and the required level of Quality of Service is endangered, route maintenance has to ensure continuous operation. All four functionalities, route discovery, route operation, route maintenance and collection and distribution of network status information, will be addressed in this thesis.
In the first part of the thesis, we propose a new approach for Quality-of- Service routing in wireless ad-hoc networks called rmin-routing, with the provision of statistical minimum route reliability as main route selection criterion. To achieve specified minimum route reliabilities, we improve the reliability of individual links by well-directed retransmissions, to be applied during the operation of routes. To select among a set of candidate routes, we define and apply route quality criteria concerning network load.
High-quality information about the network status is essential for the discovery and operation of routes and clusters in wireless ad-hoc networks. This requires permanent observation and assessment of nodes, links, and link metrics, and the exchange of gathered status data. In the second part of the thesis, we present cTEx, a configurable topology explorer for wireless ad-hoc networks that efficiently detects and exchanges high-quality network status information during operation.
In the third part, we propose a decentralized algorithm for the discovery and operation of reliability-constrained routes in wireless ad-hoc networks called dRmin-routing. The algorithm uses locally available network status information about network topology and link properties that is collected proactively in order to discover a preliminary route candidate. This is followed by a distributed, reactive search along this preselected route to remove imprecisions of the locally recorded network status before making a final route selection. During route operation, dRmin-routing monitors routes and performs different kinds of route repair actions to maintain route reliability in order to overcome varying link reliabilities.