000 Informatik, Informationswissenschaft, allgemeine Werke
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Autonomous mobile robots operating in unknown terrain have to guide
their drive decisions through local perception. Local mapping and
traversability analysis is essential for safe rover operation and low level
locomotion. This thesis deals with the challenge of building a local,
robot centric map from ultra short baseline stereo imagery for height
and traversability estimation.
Several grid-based, incremental mapping algorithms are compared and
evaluated in a multi size, multi resolution framework. A new, covariance
based mapping update is introduced, which is capable of detecting sub-
cellsize obstacles and abstracts the terrain of one cell as a first order
surface.
The presented mapping setup is capable of producing reliable ter-
rain and traversability estimates under the conditions expected for the
Cooperative Autonomous Distributed Robotic Exploreration (CADRE)
mission.
Algorithmic- and software architecture design targets high reliability
and efficiency for meeting the tight constraints implied by CADRE’s
small on-board embedded CPU.
Extensive evaluations are conducted to find possible edge-case scenar-
ios in the operating envelope of the map and to confirm performance
parameters. The research in this thesis targets the CADRE mission, but
is applicable to any form of mobile robotics which require height- and
traversability mapping.
Wireless communication networks already comprise an integral part of both the private and industrial sectors and are successfully replacing existing wired networks. They enable the development of novel applications and offer greater flexibility and efficiency. Although some efforts are already underway in the aerospace sector to deploy wireless communication networks on board spacecraft, none of these projects have yet succeeded in replacing the hard-wired state-of-the-art architecture for intra-spacecraft communication. The advantages are evident as the reduction of the wiring harness saves time, mass, and costs, and makes the whole integration process more flexible. It also allows for easier scaling when interconnecting different systems.
This dissertation deals with the design and implementation of a wireless network architecture to enhance intra-spacecraft communications by breaking with the state-of-the-art standards that have existed in the space industry for decades. The potential and benefits of this novel wireless network architecture are evaluated, an innovative design using ultra-wideband technology is presented. It is combined with a Medium Access Control (MAC) layer tailored for low-latency and deterministic networks supporting even mission-critical applications. As demonstrated by the Wireless Compose experiment on the International Space Station (ISS), this technology is not limited to communications but also enables novel positioning applications.
To adress the technological challenges, extensive studies have been carried out on electromagnetic compatibility, space radiation, and data robustness. The architecture was evaluated from various perspectives and successfully demonstrated in space.
Overall, this research highlights how a wireless network can improve and potentially replace existing state-of-the-art communication systems on board spacecraft in future missions. And it will help to adapt and ultimately accelerate the implementation of wireless networks in space systems.
Graphs provide a key means to model relationships between entities.
They consist of vertices representing the entities,
and edges representing relationships between pairs of entities.
To make people conceive the structure of a graph,
it is almost inevitable to visualize the graph.
We call such a visualization a graph drawing.
Moreover, we have a straight-line graph drawing
if each vertex is represented as a point
(or a small geometric object, e.g., a rectangle)
and each edge is represented as a line segment between its two vertices.
A polyline is a very simple straight-line graph drawing,
where the vertices form a sequence according to which the vertices are connected by edges.
An example of a polyline in practice is a GPS trajectory.
The underlying road network, in turn, can be modeled as a graph.
This book addresses problems that arise
when working with straight-line graph drawings and polylines.
In particular, we study algorithms
for recognizing certain graphs representable with line segments,
for generating straight-line graph drawings,
and for abstracting polylines.
In the first part, we first examine,
how and in which time we can decide
whether a given graph is a stick graph,
that is, whether its vertices can be represented as
vertical and horizontal line segments on a diagonal line,
which intersect if and only if there is an edge between them.
We then consider the visual complexity of graphs.
Specifically, we investigate, for certain classes of graphs,
how many line segments are necessary for any straight-line graph drawing,
and whether three (or more) different slopes of the line segments
are sufficient to draw all edges.
Last, we study the question,
how to assign (ordered) colors to the vertices of a graph
with both directed and undirected edges
such that no neighboring vertices get the same color
and colors are ascending along directed edges.
Here, the special property of the considered graph is
that the vertices can be represented as intervals
that overlap if and only if there is an edge between them.
The latter problem is motivated by an application
in automated drawing of cable plans with vertical and horizontal line segments,
which we cover in the second part.
We describe an algorithm that
gets the abstract description of a cable plan as input,
and generates a drawing that takes into account
the special properties of these cable plans,
like plugs and groups of wires.
We then experimentally evaluate the quality of the resulting drawings.
In the third part, we study the problem of abstracting (or simplifying)
a single polyline and a bundle of polylines.
In this problem, the objective is to remove as many vertices as possible from the given polyline(s)
while keeping each resulting polyline sufficiently similar to its original course
(according to a given similarity measure).
The ongoing and evolving usage of networks presents two critical challenges for current and future networks that require attention: (1) the task of effectively managing the vast and continually increasing data traffic and (2) the need to address the substantial number of end devices resulting from the rapid adoption of the Internet of Things. Besides these challenges, there is a mandatory need for energy consumption reduction, a more efficient resource usage, and streamlined processes without losing service quality. We comprehensively address these efforts, tackling the monitoring and quality assessment of streaming applications, a leading contributor to the total Internet traffic, as well as conducting an exhaustive analysis of the network performance within a Long Range Wide Area Network (LoRaWAN), one of the rapidly emerging LPWAN solutions.
Deep Learning (DL) models are trained on a downstream task by feeding (potentially preprocessed) input data through a trainable Neural Network (NN) and updating its parameters to minimize the loss function between the predicted and the desired output. While this general framework has mainly remained unchanged over the years, the architectures of the trainable models have greatly evolved. Even though it is undoubtedly important to choose the right architecture, we argue that it is also beneficial to develop methods that address other components of the training process. We hypothesize that utilizing domain knowledge can be helpful to improve DL models in terms of performance and/or efficiency. Such model-agnostic methods can be applied to any existing or future architecture. Furthermore, the black box nature of DL models motivates the development of techniques to understand their inner workings. Considering the rapid advancement of DL architectures, it is again crucial to develop model-agnostic methods.
In this thesis, we explore six principles that incorporate domain knowledge to understand or improve models. They are applied either on the input or output side of the trainable model. Each principle is applied to at least two DL tasks, leading to task-specific implementations. To understand DL models, we propose to use Generated Input Data coming from a controllable generation process requiring knowledge about the data properties. This way, we can understand the model’s behavior by analyzing how it changes when one specific high-level input feature changes in the generated data. On the output side, Gradient-Based Attribution methods create a gradient at the end of the NN and then propagate it back to the input, indicating which low-level input features have a large influence on the model’s prediction. The resulting input features can be interpreted by humans using domain knowledge.
To improve the trainable model in terms of downstream performance, data and compute efficiency, or robustness to unwanted features, we explore principles that each address one of the training components besides the trainable model. Input Masking and Augmentation directly modifies the training input data, integrating knowledge about the data and its impact on the model’s output. We also explore the use of Feature Extraction using Pretrained Multimodal Models which can be seen as a beneficial preprocessing step to extract useful features. When no training data is available for the downstream task, using such features and domain knowledge expressed in other modalities can result in a Zero-Shot Learning (ZSL) setting, completely eliminating the trainable model. The Weak Label Generation principle produces new desired outputs using knowledge about the labels, giving either a good pretraining or even exclusive training dataset to solve the downstream task. Finally, improving and choosing the right Loss Function is another principle we explore in this thesis. Here, we enrich existing loss functions with knowledge about label interactions or utilize and combine multiple task-specific loss functions in a multitask setting.
We apply the principles to classification, regression, and representation tasks as well as to image and text modalities. We propose, apply, and evaluate existing and novel methods to understand and improve the model. Overall, this thesis introduces and evaluates methods that complement the development and choice of DL model architectures.
The ongoing and evolving usage of networks presents two critical challenges for current and future networks that require attention: (1) the task of effectively managing the vast and continually increasing data traffic and (2) the need to address the substantial number of end devices resulting from the rapid adoption of the Internet of Things. Besides these challenges, there is a mandatory need for energy consumption reduction, a more efficient resource usage, and streamlined processes without losing service quality. We comprehensively address these efforts, tackling the monitoring and quality assessment of streaming applications, a leading contributor to the total Internet traffic, as well as conducting an exhaustive analysis of the network performance within a Long Range Wide Area Network (LoRaWAN), one of the rapidly emerging LPWAN solutions.
Environmental issues have emerged especially since humans burned fossil fuels, which led to air pollution and climate change that harm the environment. These issues’ substantial consequences evoked strong efforts towards assessing the state of our environment.
Various environmental machine learning (ML) tasks aid these efforts. These tasks concern environmental data but are common ML tasks otherwise, i.e., datasets are split (training, validatition, test), hyperparameters are optimized on validation data, and test set metrics measure a model’s generalizability. This work focuses on the following environmental ML tasks: Regarding air pollution, land use regression (LUR) estimates air pollutant concentrations at locations where no measurements are available based on measured locations and each location’s land use (e.g., industry, streets). For LUR, this work uses data from London (modeled) and Zurich (measured). Concerning climate change, a common ML task is model output statistics (MOS), where a climate model’s output for a study area is altered to better fit Earth observations and provide more accurate climate data. This work uses the regional climate model (RCM) REMO and Earth observations from the E-OBS dataset for MOS. Another task regarding climate is grain size distribution interpolation where soil properties at locations without measurements are estimated based on the few measured locations. This can provide climate models with soil information, that is important for hydrology. For this task, data from Lower Franconia is used.
Such environmental ML tasks commonly have a number of properties: (i) geospatiality, i.e., their data refers to locations relative to the Earth’s surface. (ii) The environmental variables to estimate or predict are usually continuous. (iii) Data can be imbalanced due to relatively rare extreme events (e.g., extreme precipitation). (iv) Multiple related potential target variables can be available per location, since measurement devices often contain different sensors. (v) Labels are spatially often only sparsely available since conducting measurements at all locations of interest is usually infeasible. These properties present challenges but also opportunities when designing ML methods for such tasks.
In the past, environmental ML tasks have been tackled with conventional ML methods, such as linear regression or random forests (RFs). However, the field of ML has made tremendous leaps beyond these classic models through deep learning (DL). In DL, models use multiple layers of neurons, producing increasingly higher-level feature representations with growing layer depth. DL has made previously infeasible ML tasks feasible, improved the performance for many tasks in comparison to existing ML models significantly, and eliminated the need for manual feature engineering in some domains due to its ability to learn features from raw data. To harness these advantages for environmental domains it is promising to develop novel DL methods for environmental ML tasks.
This thesis presents methods for dealing with special challenges and exploiting opportunities inherent to environmental ML tasks in conjunction with DL. To this end, the proposed methods explore the following techniques: (i) Convolutions as in convolutional neural networks (CNNs) to exploit reoccurring spatial patterns in geospatial data. (ii) Posing the problems as regression tasks to estimate the continuous variables. (iii) Density-based weighting to improve estimation performance for rare and extreme events. (iv) Multi-task learning to make use of multiple related target variables. (v) Semi–supervised learning to cope with label sparsity. Using these techniques, this thesis considers four research questions: (i) Can air pollution be estimated without manual feature engineering? This is answered positively by the introduction of the CNN-based LUR model MapLUR as well as the off-the-shelf LUR solution OpenLUR. (ii) Can colocated pollution data improve spatial air pollution models? Multi-task learning for LUR is developed for this, showing potential for improvements with colocated data. (iii) Can DL models improve the quality of climate model outputs? The proposed DL climate MOS architecture ConvMOS demonstrates this. Additionally, semi-supervised training of multilayer perceptrons (MLPs) for grain size distribution interpolation is presented, which can provide improved input data. (iv) Can DL models be taught to better estimate climate extremes? To this end, density-based weighting for imbalanced regression (DenseLoss) is proposed and applied to the DL architecture ConvMOS, improving climate extremes estimation. These methods show how especially DL techniques can be developed for environmental ML tasks with their special characteristics in mind. This allows for better models than previously possible with conventional ML, leading to more accurate assessment and better understanding of the state of our environment.
In produzierenden Unternehmen werden verschiedene Vorgehensweisen zur Planung, Überwachung und Steuerung von Produktionsabläufen eingesetzt. Einer dieser Methoden wird als Vorgangsknotennetzplantechnik bezeichnet. Die einzelnen Produktionsschritte werden als Knoten definiert und durch Pfeile miteinander verbunden. Die Pfeile stellen die Beziehungen der jeweiligen Vorgänge zueinander und damit den Produktionsablauf dar. Diese Technik erlaubt den Anwendern einen umfassenden Überblick über die einzelnen Prozessrelationen. Zusätzlich können mit ihr Vorgangszeiten und Produktfertigstellungszeiten ermittelt werden, wodurch eine ausführliche Planung der Produktion ermöglicht wird. Ein Nachteil dieser Technik begründet sich in der alleinigen Darstellung einer ausführbaren Prozessabfolge. Im Falle eines Störungseintritts mit der Folge eines nicht durchführbaren Vorgangs muss von dem originären Prozess abgewichen werden. Aufgrund dessen wird eine Neuplanung erforderlich. Es werden Alternativen für den gestörten Vorgang benötigt, um eine Fortführung des Prozesses ungeachtet der Störung zu erreichen. Innerhalb dieser Arbeit wird daher eine Erweiterung der Vorgangsknotennetzplantechnik beschrieben, die es erlaubt, ergänzend zu dem geplanten Soll-Prozess Alternativvorgänge für einzelne Vorgänge darzulegen. Diese Methode wird als Maximalnetzplan bezeichnet. Die Alternativen werden im Falle eines Störungseintritts automatisch evaluiert und dem Anwender in priorisierter Reihenfolge präsentiert. Durch die Verwendung des Maximalnetzplans kann eine aufwendige Neuplanung vermieden werden. Als Anwendungsbeispiel dient ein Montageprozess, mithilfe dessen die Verwendbarkeit der Methode dargelegt wird. Weiterführend zeigt eine zeitliche Analyse zufallsbedingter Maximalnetzpläne eine Begründung zur Durchführung von Alternativen und damit den Nutzen des Maximalnetzplans auf. Zusätzlich sei angemerkt, dass innerhalb dieser Arbeit verwendete Begrifflichkeiten wie Anwender, Werker oder Mitarbeiter in maskuliner Schreibweise niedergeschrieben werden. Dieses ist ausschließlich der Einfachheit geschuldet und nicht dem Zweck der Diskriminierung anderer Geschlechter dienlich. Die verwendete Schreibweise soll alle Geschlechter ansprechen, ob männlich, weiblich oder divers.
Serverless computing is an emerging cloud computing paradigm that offers a highlevel
application programming model with utilization-based billing. It enables the
deployment of cloud applications without managing the underlying resources or
worrying about other operational aspects. Function-as-a-Service (FaaS) platforms
implement serverless computing by allowing developers to execute code on-demand
in response to events with continuous scaling while having to pay only for the
time used with sub-second metering. Cloud providers have further introduced
many fully managed services for databases, messaging buses, and storage that also
implement a serverless computing model. Applications composed of these fully
managed services and FaaS functions are quickly gaining popularity in both industry
and in academia.
However, due to this rapid adoption, much information surrounding serverless
computing is inconsistent and often outdated as the serverless paradigm evolves.
This makes the performance engineering of serverless applications and platforms
challenging, as there are many open questions, such as: What types of applications
is serverless computing well suited for, and what are its limitations? How should
serverless applications be designed, configured, and implemented? Which design
decisions impact the performance properties of serverless platforms and how can
they be optimized? These and many other open questions can be traced back to an
inconsistent understanding of serverless applications and platforms, which could
present a major roadblock in the adoption of serverless computing.
In this thesis, we address the lack of performance knowledge surrounding serverless
applications and platforms from multiple angles: we conduct empirical studies
to further the understanding of serverless applications and platforms, we introduce
automated optimization methods that simplify the operation of serverless applications,
and we enable the analysis of design tradeoffs of serverless platforms by
extending white-box performance modeling.
Detecting anomalies in transaction data is an important task with a high potential to avoid financial loss due to irregularities deliberately or inadvertently carried out, such as credit card fraud, occupational fraud in companies or ordering and accounting errors. With ongoing digitization of our world, data-driven approaches, including machine learning, can draw benefit from data with less manual effort and feature engineering. A large variety of machine learning-based anomaly detection methods approach this by learning a precise model of normality from which anomalies can be distinguished. Modeling normality in transactional data, however, requires to capture distributions and dependencies within the data precisely with special attention to numerical dependencies such as quantities, prices or amounts.
To implicitly model numerical dependencies, Neural Arithmetic Logic Units have been proposed as neural architecture. In practice, however, these have stability and precision issues.
Therefore, we first develop an improved neural network architecture, iNALU, which is designed to better model numerical dependencies as found in transaction data. We compare this architecture to the previous approach and show in several experiments of varying complexity that our novel architecture provides better precision and stability.
We integrate this architecture into two generative neural network models adapted for transaction data and investigate how well normal behavior is modeled. We show that both architectures can successfully model normal transaction data, with our neural architecture improving generative performance for one model.
Since categorical and numerical variables are common in transaction data, but many machine learning methods only process numerical representations, we explore different representation learning techniques to transform categorical transaction data into dense numerical vectors. We extend this approach by proposing an outlier-aware discretization, thus incorporating numerical attributes into the computation of categorical embeddings, and investigate latent spaces, as well as quantitative performance for anomaly detection.
Next, we evaluate different scenarios for anomaly detection on transaction data. We extend our iNALU architecture to a neural layer that can model both numerical and non-numerical dependencies and evaluate it in a supervised and one-class setting. We investigate the stability and generalizability of our approach and show that it outperforms a variety of models in the balanced supervised setting and performs comparably in the one-class setting. Finally, we evaluate three approaches to using a generative model as an anomaly detector and compare the anomaly detection performance.