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Due to the robustness and flexibility of hydraulic components, hydraulic control systems are used in a wide range of applications under various environmental conditions. However, the coverage of this broad field of applications often comes with a loss of performance. Especially when conditions and working points change often, hydraulic control systems cannot work at their optimum. Flexible electronic controllers in combination with techniques from the field of machine learning have the potential to overcome these issues. By applying a reinforcement learning algorithm, this paper examines whether learned controllers can compete with an expert-tuned solution. Thereby, the method is thoroughly validated by using simulations and experiments as well.

This paper describes the failure detection system of an electro-hydraulic actuator with dual modular redundancy based on a hybrid twin TM concept. Hybrid twin TM is a combination of virtual twin that operates in parallel with the actuator and represents its ideal behaviour, and a digital twin that identifies possible failures using the sensor readings residuals. Simulation-based system reliability analysis helps to generate a dataset for training the digital twin using machine learning algorithms. A systematic failure detection approach based on decision trees and the process of analysing the quality of the result is described.

Mining for association rules and frequent patterns is a central activity in data mining. However, most existing algorithms are only moderately suitable for real-world scenarios. Most strategies use parameters like minimum support, for which it can be very difficult to define a suitable value for unknown datasets. Since most untrained users are unable or unwilling to set such technical parameters, we address the problem of replacing the minimum-support parameter with top-n strategies. In our paper, we start by extending a top-n implementation of the ECLAT algorithm to improve its performance by using heuristic search strategy optimizations. Also, real-world datasets are often distributed and modern database architectures are switching from expensive SMPs to cheaper shared-nothing blade servers. Thus, most mining queries require distribution handling. Since partitioning can be forced by user-defined semantics, it is often forbidden to transform the data. Therefore, we developed an adaptive top-n frequent-pattern mining algorithm that simplifies the mining process on real distributions by relaxing some requirements on the results. We first combine the PARTITION and the TPUT algorithms to handle distributed top-n frequent-pattern mining. Then, we extend this new algorithm for distributions with real-world data characteristics. For frequent-pattern mining algorithms, equal distributions are important conditions, and tiny partitions can cause performance bottlenecks. Hence, we implemented an approach called MAST that defines a minimum absolute-support threshold. MAST prunes patterns with low chances of reaching the global top-n result set and high computing costs. In total, our approach simplifies the process of frequent-pattern mining for real customer scenarios and data sets. This may make frequent-pattern mining accessible for very new user groups. Finally, we present results of our algorithms when run on the SAP NetWeaver BW Acceleratorwith standard and real business datasets.

Die vorliegende Arbeit untersucht den Entwurf und die Anwendung selbstlernender Regler als intelligente Reglerkomponente im Wirkungsablauf eines Regelkreises für regelungstechnische Anwendungen. Der aufwändige Prozess der Analyse des dynamischen Systems und der Reglersynthese, welche die klassischen Entwurfsmuster der Regelungstechnik benötigen, wird dabei ersetzt durch eine lernende Reglerkomponente. Diese kann mit sehr wenig Wissen über den zu regelnden Prozess eingesetzt werden und lernt direkt durch Interaktion eine präzise Regelung auf extern vorgegebene Führungsgrößen. Der Lernvorgang basiert dabei auf einem Optimierungsprozess mit einem leistungsfähigen Batch-Reinforcement-Lernverfahren, dem ´Neural Fitted Q-Iteration´. Dieses Verfahren wird auf seine Verwendung als selbstlernender Regler untersucht. Für die in den Untersuchungen festgestellten Unzulänglichkeiten des Verfahrens bezüglich der geforderten präzisen, zeitoptimalen Regelung werden verbesserte Vorgehensweisen entwickelt, die ebenfalls auf ihre Leistungsfähigkeit untersucht werden.Für typische regelungstechnische Problemstellungen sind die diskreten Aktionen des NFQ-Verfahrens nicht ausreichend, um eine präzise Regelung auf beliebige Führungsgrößen zu erzeugen.Durch die Entwicklung einer Erweiterung des NFQ für kontinuierliche Aktionen wird die Genauigkeit und Leistungsfähigkeit der selbstlernenden Regler drastisch erhöht, ohne die benötigte Interaktionszeit am Prozess zu erhöhen.An ausgewählten Problemen der Regelung linearer und nichtlinearer Prozesse wird die Leistungsfähigkeit der entwickelten Verfahren empirisch evaluiert. Es zeigt sich dabei, dass die hier entwickelten selbstlernenden Regler mit wenigen Minuten Interaktionszeit an einem Prozess eine präzise Regelungsstrategie für beliebige externe Führungsgrößen lernen, ohne dass Expertenwissen über den Prozess vorliegt.

Die vorliegende Arbeit widmet sich der Untersuchung und Weiterentwicklung selbständig lernender maschineller Lernverfahren (Reinforcement Lernen) in der Anwendung auf visuelle Wahrnehmungen. Zuletzt wurden mit der Einführung speicherbasierter Methoden in das Reinforcement Lernen große Fortschritte beim Lernen an realen Systemen erzielt, aber der Umgang mit hochkomplexen visuellen Eingabedaten, wie sie z.B. von einer digitalen Kamera aufgezeichnet werden, stellt weiterhin ein ungelöstes Problem dar. Bestehende Methoden sind auf den Umgang mit niedrigdimensionalen Zustandsbeschreibungen beschränkt, was eine Anwendung dieser Verfahren direkt auf den Strom von Bilddaten bisher ausschließt und den vorgeschalteten Einsatz klassischer Methoden des Bildverstehens zur Extraktion und geeigneten Kodierung der relevanten Informationen erfordert. Einen Ausweg bietet der Einsatz von so genannten `tiefen Autoencodern'. Diese mehrschichtigen neuronalen Netze ermöglichen es, selbstorganisiert niedrigdimensionale Merkmalsräume zur Repräsentation hochdimensionaler Eingabedaten zu erlernen und so eine klassische, aufgabenspezifische Bildanalyse zu ersetzen. In typischen Objekterkennungsaufgaben konnten auf Basis dieser erlernten Repräsentationen bereits beeindruckende Ergebnisse erzielt werden. Im Rahmen der vorliegenden Arbeit werden nun die tiefen Autoencodernetze auf ihre grundsätzliche Tauglichkeit zum Einsatz im Reinforcement Lernen untersucht. Mit dem ``Deep Fitted Q''-Algorithmus wird ein neuer Algorithmus entwickelt, der das Training der tiefen Autoencodernetze auf effiziente Weise in den Reinforcement Lernablauf integriert und so den Umgang mit visuellen Wahrnehmungen beim Strategielernen ermöglicht. Besonderes Augenmerk wird neben der Dateneffizienz auf die Stabilität des Verfahrens gelegt. Im Anschluss an eine Diskussion der theoretischen Aspekte des Verfahrens wird eine ausführliche empirische Evaluation der erzeugten Merkmalsräume und der erlernten Strategien an simulierten und realen Systemen durchgeführt. Dabei gelingt es im Rahmen der vorliegenden Arbeit mit Hilfe der entwickelten Methoden erstmalig, Strategien zur Steuerung realer Systeme direkt auf Basis der unvorverarbeiteten Bildinformationen zu erlernen, wobei von außen nur das zu erreichende Ziel vorgegeben werden muss.

Glioblastoma multiforme (GMB) is an extremely aggressive and invasive brain cancer with a median survival of less than one year. In addition, due to its anaplastic nature the histological classification of this cancer is not simple. These characteristics make this disease an interesting and important target for new methodologies of analysis and classification. In recent years, molecular information has been used to segregate and analyze GBM patients, but generally this methodology utilizes single-`omic' data to perform the classification or multi-’omic’ data in a sequential manner. In this project, a novel approach for the classification and analysis of patients with GBM is presented. The main objective of this work is to find clusters of patients with distinctive profiles using multi-’omic’ data with a real integrative methodology. During the last years, the TCGA consortium has made publicly available thousands of multi-’omic’ samples for multiple cancer types. Thanks to this, it was possible to obtain numerous GBM samples (> 300) with data for gene and microRNA expression, CpG sites methylation and copy-number variation (CNV). To achieve our objective, a mixture of linear models were built for each gene using its expression as output and a mixture of multi-`omic' data as covariates. Each model was coupled with a lasso penalization scheme, and thanks to the mixture nature of the model, it was possible to fit multiple submodels to discover different linear relationships in the same model. This complex but interpretable method was used to train over \numprint{10000} models. For \texttildelow \numprint{2400} cases, two or more submodels were obtained. Using the models and their submodels, 6 different clusters of patients were discovered. The clusters were profiled based on clinical information and gene mutations. Through this analysis, a clear separation between the younger patients and with higher survival rate (Clusters 1, 2 and 3) and those from older patients and lower survival rate (Clusters 4, 5 and 6) was found. Mutations in the gene IDH1 were found almost exclusively in Cluster 2, additionally, Cluster 5 presented a hypermutated profile. Finally, several genes not previously related to GBM showed a significant presence in the clusters, such as C15orf2 and CHEK2. The most significant models for each clusters were studied, with a special focus on their covariants. It was discovered that the number of shared significant models were very small and that the well known GBM related genes appeared as significant covariates for plenty of models, such as EGFR1 and TP53. Along with them, ubiquitin-related genes (UBC and UBD) and NRF1, which have not been linked to GBM previously, had a very significant role. This work showed the potential of using a mixture of linear models to integrate multi-’omic’ data and to group patients in order to profile them and find novel markers. The resulting clusters showed unique profiles and their significant models and covariates were comprised by well known GBM related genes and novel markers, which present the possibility for new approaches to study and attack this disease. The next step of the project is to improve several elements of the methodology to achieve a more detail analysis of the models and covariates, in particular taking into account the regression coefficients of the submodels.

Epilepsy is the most common neurological disorder and an accurate forecast of seizures would help to overcome the patient’s uncertainty and helplessness. In this contribution, we present and discuss a novel methodology for the classification of intracranial electroencephalography (iEEG) for seizure prediction. Contrary to previous approaches, we categorically refrain from an extraction of hand-crafted features and use a convolutional neural network (CNN) topology instead for both the determination of suitable signal characteristics and the binary classification of preictal and interictal segments. Three different models have been evaluated on public datasets with long-term recordings from four dogs and three patients. Overall, our findings demonstrate the general applicability. In this work we discuss the strengths and limitations of our methodology.

The use of computers and algorithms to deal with human language, in both spoken and written form, is summarized by the term natural language processing (nlp). Modeling language in a way that is suitable for computers plays an important role in nlp. One idea is to use formalisms from theoretical computer science for that purpose. For example, one can try to find an automaton to capture the valid written sentences of a language. Finding such an automaton by way of examples is called training. In this work, we also consider the structure of sentences by making use of trees. We use weighted tree automata (wta) in order to deal with such tree structures. Those devices assign weights to trees in order to, for example, distinguish between good and bad structures. The well-known expectation-maximization algorithm can be used to train the weights for a wta while the state behavior stays fixed. As a way to adapt the state behavior of a wta, state splitting, i.e. dividing a state into several new states, and state merging, i.e. replacing several states by a single new state, can be used. State splitting, state merging, and the expectation maximization algorithm already were combined into the state splitting and merging algorithm, which was successfully applied in practice. In our work, we formalized this approach in order to show properties of the algorithm. We also examined a new approach – the count-based state merging algorithm – which exclusively relies on state merging. When dealing with trees, another important tool is binarization. A binarization is a strategy to code arbitrary trees by binary trees. For each of three different binarizations we showed that wta together with the binarization are as powerful as weighted unranked tree automata (wuta). We also showed that this is still true if only probabilistic wta and probabilistic wuta are considered.:How to Read This Thesis 1. Introduction 1.1. The Contributions and the Structure of This Work 2. Preliminaries 2.1. Sets, Relations, Functions, Families, and Extrema 2.2. Algebraic Structures 2.3. Formal Languages 3. Language Formalisms 3.1. Context-Free Grammars (CFGs) 3.2. Context-Free Grammars with Latent Annotations (CFG-LAs) 3.3. Weighted Tree Automata (WTAs) 3.4. Equivalences of WCFG-LAs and WTAs 4. Training of WTAs 4.1. Probability Distributions 4.2. Maximum Likelihood Estimation 4.3. Probabilities and WTAs 4.4. The EM Algorithm for WTAs 4.5. Inside and Outside Weights 4.6. Adaption of the Estimation of Corazza and Satta [CS07] to WTAs 5. State Splitting and Merging 5.1. State Splitting and Merging for Weighted Tree Automata 5.1.1. Splitting Weights and Probabilities 5.1.2. Merging Probabilities 5.2. The State Splitting and Merging Algorithm 5.2.1. Finding a Good π-Distributor 5.2.2. Notes About the Berkeley Parser 5.3. Conclusion and Further Research 6. Count-Based State Merging 6.1. Preliminaries 6.2. The Likelihood of the Maximum Likelihood Estimate and Its Behavior While Merging 6.3. The Count-Based State Merging Algorithm 6.3.1. Further Adjustments for Practical Implementations 6.4. Implementation of Count-Based State Merging 6.5. Experiments with Artificial Automata and Corpora 6.5.1. The Artificial Automata 6.5.2. Results 6.6. Experiments with the Penn Treebank 6.7. Comparison to the Approach of Carrasco, Oncina, and Calera-Rubio [COC01] 6.8. Conclusion and Further Research 7. Binarization 7.1. Preliminaries 7.2. Relating WSTAs and WUTAs via Binarizations 7.2.1. Left-Branching Binarization 7.2.2. Right-Branching Binarization 7.2.3. Mixed Binarization 7.3. The Probabilistic Case 7.3.1. Additional Preliminaries About WSAs 7.3.2. Constructing an Out-Probabilistic WSA from a Converging WSA 7.3.3. Binarization and Probabilistic Tree Automata 7.4. Connection to the Training Methods in Previous Chapters 7.5. Conclusion and Further Research A. Proofs for Preliminaries B. Proofs for Training of WTAs C. Proofs for State Splitting and Merging D. Proofs for Count-Based State Merging Bibliography List of Algorithms List of Figures List of Tables Index Table of Variable Names