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In dieser vorliegenden Arbeit wurde der photolytische und photokatalytische Abbau von Lignin untersucht. Eine Charakterisierung des verwendeten Photoreaktors wurde mittels Kalium-Ferrioxalat-Aktinometrie durchgeführt. Zur Analyse der abgebauten Lignine wurde eine Optimierung einer bereits bestehenden Methode zur Bestimmung des Hydroxylgehaltes erarbeitet. Die Bestimmung der Hydroxylgehalte erfolgte demnach bei Raumtemperatur nach einer Acetylierungsdauer von 72 h und zeigte eine Abnahme der Hydroxylgehalte mit andauernder UV-Bestrahlung. Selbige Beobachtung konnte mit Hilfe der ATR-IR-Spektroskopie gemacht werden. Zusätzlich konnte die Bildung von Carbonsäuren und der Abbau von aromatischen Strukturen detektiert werden. Der Abbau aromatischer Strukturen konnte ebenfalls durch UV-VIS-Spektroskopie gezeigt werden. Eine Vermutung, dass es sich bei dem Abbauprozess um einen oxidativen Mechanismus handelt, konnte mit dem Abbau von Hydroxylgruppen über eine Bildung von Carbonsäuren zu Kohlenstoffdioxid bestätigt werden. Eine Freisetzung von Kohlenstoffdioxid konnte durch eine Bestimmung des IC festgestellt werden. Die Ergebnisse der Gel-Permeations-Chromatographie zusammen mit einer TOC-Analyse zeigen einen Abbau der molaren Masse des Lignins auf. Es konnten Fragmente mit einer Molmasse ähnlich der Monomere des Lignins gefunden werden. Der eingesetzte Photokatalysator wurde via Röntgenbeugung untersucht und konnte als das hoch photokatalytisch aktive P25 von Degussa identifiziert werden. Trotz des Einsatzes verschiedener Katalysatorkonzentrationen in einem Bereich von 0-0,5 g L^(-1) konnte kein Einfluss des Photokatalysators auf den Abbauprozess des Lignins beobachtet werden.
Diese Arbeit beschäftigt sich mit der Entwicklung eines, für die kontrollierte Freisetzung hydrophiler Wirkstoffe geeigneten, Verkapselungssystems mit dem Ziel die Freisetzung osteospezifischer P2-Liganden zu verzögern, um bei der Behandlung von Knochendefekten kritischer Größe die Bildung neuen Knochengewebes zu gewährleisten. Hierfür werden, unter Anwendung der immersiven Layer-by-Layer-Beschichtung, mit den Modell-Substanzen Adenosintriphosphat und Suramin versetzte, Alginat sowie κ-Carrageen-Kapseln mit Chitosan und Lignosulfonat beschichtet und auf ihr Freisetzungsverhalten hin untersucht.
This work aims to create a natural language generation (NLG) base for further development of systems for automatic examination questions generation and automatic summarization in Hochschule Bonn-Rhein-Sieg and Fraunhofer IAIS, respectively. Nowadays both tasks are very relevant. The first can significantly simplify the university teachers' work and the second to be of assistance for a faster retrieval of knowledge from an excessively large amount of information that people often work with. We focus on the search for an efficient and robust approach to the controlled NLG problem. Therefore, though the initial idea of the project was the usage of the generative adversarial neural networks (GANs), we switched our attention to more robust and easily-controllable autoencoders. Thus, in this work we implement an autoencoder for unsupervised discovery of latent space representations of text, and show the ability of the system to generate new sentences based on this latent space. Apart from that, we apply Gaussian mixture techniques in order to obtain meaningful text clusters and thereby try to create a tool that would allow us to generate sentences relevant to the semantics of the Gaussian clusters, e.g. positive or negative reviews or examination questions on certain topic. The developed system is tested on several datasets and compared to GANs' performance.
Die letzten zwei Jahrzehnte wurden durch das exponentielle Wachstum der zur Verfügung stehenden Daten geprägt. Täglich produzieren Menschen und Maschinen mehr und mehr Daten, die oftmals in verteilten Datenspeichern abgelegt werden. Anwendungsgebiete lassen sich beispielsweise in der Physik und Astronomie finden, wo immense Datenmengen von Teilchenbeschleunigern oder Satelliten erzeugt werden, die gespeichert und verarbeitet werden müssen. Aus diesen Datenmengen können weder vom Menschen direkt noch durch traditionelle Analysemethoden neue Erkenntnisse gewonnen werden. Zur Verarbeitung dieser Datenmassen sind parallele sowie verteilte Datenanalyseverfahren notwendig. [MTT18,NEKH+18]
Neural network based object detectors are able to automatize many difficult, tedious tasks. However, they are usually slow and/or require powerful hardware. One main reason is called Batch Normalization (BN) [1], which is an important method for building these detectors. Recent studies present a potential replacement called Self-normalizing Neural Network (SNN) [2], which at its core is a special activation function named Scaled Exponential Linear Unit (SELU). This replacement seems to have most of BNs benefits while requiring less computational power. Nonetheless, it is uncertain that SELU and neural network based detectors are compatible with one another. An evaluation of SELU incorporated networks would help clarify that uncertainty. Such evaluation is performed through series of tests on different neural networks. After the evaluation, it is concluded that, while indeed faster, SELU is still not as good as BN for building complex object detector networks.
Interactive Object Detection
(2019)
The success of state-of-the-art object detection methods depend heavily on the availability of a large amount of annotated image data. The raw image data available from various sources are abundant but non-annotated. Annotating image data is often costly, time-consuming or needs expert help. In this work, a new paradigm of learning called Active Learning is explored which uses user interaction to obtain annotations for a subset of the dataset. The goal of active learning is to achieve superior object detection performance with images that are annotated on demand. To realize active learning method, the trade-off between the effort to annotate (annotation cost) unlabeled data and the performance of object detection model is minimised.
Random Forests based method called Hough Forest is chosen as the object detection model and the annotation cost is calculated as the predicted false positive and false negative rate. The framework is successfully evaluated on two Computer Vision benchmark and two Carl Zeiss custom datasets. Also, an evaluation of RGB, HoG and Deep features for the task is presented.
Experimental results show that using Deep features with Hough Forest achieves the maximum performance. By employing Active Learning, it is demonstrated that performance comparable to the fully supervised setting can be achieved by annotating just 2.5% of the images. To this end, an annotation tool is developed for user interaction during Active Learning.
Zustandsregelung für ein Mikroflugsystem zur Ansteuerung vorgegebener Wegpunkte in Innenräumen
(2018)
In der Masterarbeit Zustandsregelung für ein Mikroflugsystem zur Ansteuerung vorgegebener Wegpunkte in Innenräumen wird die Entwicklung einer Positionsregelung für ein Mikroflugsystem vorgestellt. Damit ist es möglich, sowohl in einer bekannten als auch unbekannten Umgebung vorgegebene Wegpunkte automatisch anzusteuern. Die Lokalisation des Flugsystems findet mit interner Sensorik sowie mithilfe von zwei Laserscannern statt. Steht bereits eine Karte der Umgebung zur Verfügung, ist es möglich, einen Pfad zu einem vorgegebenen Zielpunkt zu berechnen und diesen Pfad automatisch abzufliegen.
The recent explosion of available audio-visual media is the new challenge for information retrieval research. Audio speech recognition systems translate spoken content to the text domain. There is a need for searching and indexing this data which possesses no logical structure. One possible way to structure it on a high level of abstraction is by finding topic boundaries. Two unsupervised topic segmentation methods were evaluated with real-world data in the course of this work. The first one, TSF, models topic shifts as fluctuations in the similarity function of the transcript. The second one, LCSeg, approaches topic changes as places with the least overlapping lexical chains. Only LCSeg performed close to a similar real-world corpus. Other reported results could not be outperformed. Topic analysis based on the repeated word usage models renders topic changes more ambiguous than expected. This issue has more impact on the segmentation quality than the state-of-the-art ASR word error rate. It could be concluded that it is advisable to develop topic segmentation algorithms with real-world data to avoid potential biases to artificial data. Unlike evaluated approaches based on word usage analysis, methods operating with local contexts can be expected to perform better through emulation of semantic dependencies.
Estimation of Prediction Uncertainty for Semantic Scene Labeling Using Bayesian Approximation
(2018)
With the advancement in technology, autonomous and assisted driving are close to being reality. A key component of such systems is the understanding of the surrounding environment. This understanding about the environment can be attained by performing semantic labeling of the driving scenes. Existing deep learning based models have been developed over the years that outperform classical image processing algorithms for the task of semantic labeling. However, the existing models only produce semantic predictions and do not provide a measure of uncertainty about the predictions. Hence, this work focuses on developing a deep learning based semantic labeling model that can produce semantic predictions and their corresponding uncertainties. Autonomous driving needs a real-time operating model, however the Full Resolution Residual Network (FRRN) [4] architecture, which is found as the best performing architecture during literature search, is not able to satisfy this condition. Hence, a small network, similar to FRRN, has been developed and used in this work. Based on the work of [13], the developed network is then extended by adding dropout layers and the dropouts are used during testing to perform approximate Bayesian inference. The existing works on uncertainties, do not have quantitative metrics to evaluate the quality of uncertainties estimated by a model. Hence, the area under curve (AUC) of the receiver operating characteristic (ROC) curves is proposed and used as an evaluation metric in this work. Further, a comparative analysis about the influence of dropout layer position, drop probability and the number of samples, on the quality of uncertainty estimation is performed. Finally, based on the insights gained from the analysis, a model with optimal configuration of dropout is developed. It is then evaluated on the Cityscape dataset and shown to be outperforming the baseline model with an AUC-ROC of about 90%, while the latter having AUC-ROC of about 80%.