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In order to help journalists investigate inside large audiovisual archives, as maintained by news broadcast agencies, the multimedia data must be indexed by text-based search engies. By automatically creating a transcript through automatic speech recognition (ASR), the spoken word becomes accessible to text search, and queries for keywords are made possible. But stil, important contextual information like the identity of the speaker is not captured. Especially when gathering original footage in the political domain, the identity of the speaker can be the most important query constraint, although this name may not be prominent in the words spoken. It is thus desireable to have this information provided explicitely to the search engine. To provide this information, the archive must be an alyzed by automatic Speaker Identification (SID). While this research topic has seen substantial gains in accuracy and robustness over last years, it has not yet established itself as a helpful, large-scale tool outside the research community. This thesis sets out to establish a workflow to provide automatic speaker identification. Its application is to help journalists searching on speeches given in the German parliament (Bundestag). This is a contribution to the News-Stream 3.0 project, a BMBF funded research project that addresses accessibility of various data sources for journalists.
Das Deutsche Zentrum für Luft- und Raumfahrt (DLR) führt viele Forschungen und Studien im Bereich der Luft- und Raumfahrt durch. Dabei spielen die Studien für die Gesundheit und Medizin auch eine sehr wichtige Rolle bei der DLR. Zu diesem Zweck führt die DLR die Artificial Gravity bed rest study (AGBRESA) im Auftrag der European Space Agency (esa) und in Kooperation der NASA durch. In dieser Studie werden die negativen Auswirkungen der Schwerelosigkeit auf dem Menschen im Weltall simuliert. Dabei werden Experimente durchgeführt, um die negative Auswirkungen entgegenzuwirken. Die Ergebnisse der Experimente werden in der DLR digital, aber auch auf Papier dokumentiert. In diesem Master-Projekt habe ich nun die Aufgabe, die Papierprotokolle für den Bereich der Blutabnahme und der Labordokumentation in eine digitale Form zu ersetzen.
Graphbasierte Diskussionen sind eine Form von Online-Diskussionen, bei denen eine Diskussion als Graph visualisiert wird. Beispielhafte Diskussionsanwendungen sind unter anderem Belvedere [SWCP95], FreeStyler [Gas03] oder Digalo [LK06]. Graphen dieser Art sind, was bestimmte Eigenschaften betrifft, vergleichbar mit Petri-Netzen [Pet62]. So gibt es bei Beiden gewichtete, gerichtete Kanten sowie Knoten verschiedenen Typs, die jeweils bestimmte Eigenschaften besitzen. Im Gegensatz zu einem Petri-Netz, das immer ein bipartiter Graph ist, können bei einem Diskussionsgraphen jedoch prinzipiell alle Knoten miteinander verbunden werden. Moderatoren solcher Diskussionen sind oftmals mit dem Problem konfrontiert, dass sie mehrere Diskussionen gleichzeitig beobachten wollen, was jedoch aufgrund der Komplexität der Struktur von Diskussionsgraphen kaum effizient möglich ist.
This thesis proposes a multi-label classification approach using the Multimodal Transformer (MulT) [80] to perform multi-modal emotion categorization on a dataset of oral histories archived at the Haus der Geschichte (HdG). Prior uni-modal emotion classification experiments conducted on the novel HdG dataset provided less than satisfactory results. They uncovered issues such as class imbalance, ambiguities in emotion perception between annotators, and lack of representative training data to perform transfer learning [28]. Hence, the objectives of this thesis were to achieve better results by performing a multi-modal fusion and resolving the problems arising from class imbalance and annotator-induced bias in emotion perception. A further objective was to assess the quality of the novel HdG dataset and benchmark the results using SOTA techniques. Through a literature survey on the challenges, models, and datasets related to multi-modal emotion recognition, we created a methodology utilizing the MulT along with a multi-label classification approach. This approach produced a considerable improvement in the overall emotion recognition by obtaining an average AUC of 0.74 and Balanced-accuracy of 0.70 on the HdG dataset, which is comparable to state-of-the-art (SOTA) results on other datasets. In this manner, we were also able to benchmark the novel HdG dataset as well as introduce a novel multi-annotator learning approach to understand each annotator’s relative strengths and weaknesses for emotion perception. Our evaluation results highlight the potential benefits of the novel multi-annotator learning approach in improving overall performance by resolving the problems arising from annotator-induced bias and variation in the perception of emotions. Complementing these results, we performed a further qualitative analysis of the HdG annotations with a psychologist to study the ambiguities found in the annotations. We conclude that the ambiguities in annotations may have resulted from a combination of several socio-psychological factors and systemic issues associated with the process of creating these annotations. As these problems are also present in most multi-modal emotion recognition datasets, we conclude that the domain could benefit from a set of annotation guidelines to create standardized datasets.
Semantic Image Segmentation Combining Visible and Near-Infrared Channels with Depth Information
(2015)
Image understanding is a vital task in computer vision that has many applications in areas such as robotics, surveillance and the automobile industry. An important precondition for image understanding is semantic image segmentation, i.e. the correct labeling of every image pixel with its corresponding object name or class. This thesis proposes a machine learning approach for semantic image segmentation that uses images from a multi-modal camera rig. It demonstrates that semantic segmentation can be improved by combining different image types as inputs to a convolutional neural network (CNN), when compared to a single-image approach. In this work a multi-channel near-infrared (NIR) image, an RGB image and a depth map are used. The detection of people is further improved by using a skin image that indicates the presence of human skin in the scene and is computed based on NIR information. It is also shown that segmentation accuracy can be enhanced by using a class voting method based on a superpixel pre-segmentation. Models are trained for 10-class, 3-class and binary classification tasks using an original dataset. Compared to the NIR-only approach, average class accuracy is increased by 7% for 10-class, and by 22% for 3-class classification, reaching a total of 48% and 70% accuracy, respectively. The binary classification task, which focuses on the detection of people, achieves a classification accuracy of 95% and true positive rate of 66%. The report at hand describes the proposed approach and the encountered challenges and shows that a CNN can successfully learn and combine features from multi-modal image sets and use them to predict scene labeling.
This report presents an approach on a quadrotor dynamics stabilization based on ICP SLAM. Because the quadrotor lacks sensory information to detect its horizontal drift an additional sensor as Hokuyo-UTM has been used to perform on-line ICP-based SLAM. The obtained position estimates were used in control loops to maintain desired position and orientation of the vehicle. Such attitude parameters as height, yaw and position in space were controlled based on the laser data. As a result the quadrotor demonstrated two significant for autonomous navigation capabilities: performance of on-line SLAMon a flying vehicle and maintaining desired position in 3D space. Visual approach on optical flow based on Pyramid Lucas-Kanade algorithm has been touched and tested in different environmental conditions though hasn't been implemented in the control loop. Also the performance of the Hokuyo laser scanner and the related to it ICP SLAM algorithm have been tested in different environmental conditions indoors, outdoors and in presence of smoke. Results are presented and discussed. The requirement of performing on-line SLAM algorithm and to carry quite heavy equipment for it forced to seek a solution to increase the payload of the quadrotor with its computational power. A new hardware and distributed software architectures are therefore presented in the report.
Object detection concerns the classification and localization of objects in an image. To cope with changes in the environment, such as when new classes are added or a new domain is encountered, the detector needs to update itself with the new information while retaining knowledge learned in the past. Previous works have shown that training the detector solely on new data would produce a severe "forgetting" effect, in which the performance on past tasks deteriorates through each new learning phase. However, in many cases, storing and accessing past data is not possible due to privacy concerns or storage constraints. This project aims to investigate promising continual learning strategies for object detection without storing and accessing past training images and labels. We show that by utilizing the pseudo-background trick to deal with missing labels, and knowledge distillation to deal with missing data, the forgetting effect can be significantly reduced in both class-incremental and domain-incremental scenarios. Furthermore, an integration of a small latent replay buffer can result in a positive backward transfer, indicating the enhancement of past knowledge when new knowledge is learned.
Modern engineering relies heavily on utilizing computer technologies. This is especially true for thermoplastic manufacturing, such as blow molding. A crucial milestone for digitalization is the continuous integration of data in unified or interoperable systems. While new simulation technologies are constantly developed, data management standards such as STEP fail at integrating them. On the other hand, industrial standards such as ”VMAP” manage to improve interoperability for Small and Medium-sized Enterprises. However, they do not provide Simulation Process and Data Management (SPDM) technologies. For SPDM integration of VMAP data, Ontology-Based Data Access is used to allow continuing the digital thread in custom semantic-based open-source solutions. An ontology of the database format (VMAP) was generated alongside an expandable knowledge graph of data access methods. A Python-based software architecture was developed, automatically using the semantic representations of database format and data access to query data and metadata within the VMAP file. The result is a software architecture template that can be adapted for other data standards and integrated into semantic data management systems. It allows semantic queries on simulation data down to element-wise resolution without integrating the whole model information. The architecture can instantiate a file in a knowledge graph, query a file’s metadatum and, in case it is not yet available, find a semantically represented process that allows the creation and instantiation of the required metadatum. See Figure 1. The results of this thesis can be expected to form a basis for semantic SPDM tools.
This project focuses on object detection in dense volume data. There are several types of dense volume data, namely Computed Tomography (CT) scan, Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI). This work focuses on CT scans. CT scans are not limited to the medical domain; they are also used in industries. CT scans are used in airport baggage screening, assembly lines, and the object detection systems in these places should be able to detect objects fast. One of the ways to address the issue of computational complexity and make the object detection systems fast is to use low-resolution images. Low-resolution CT scanning is fast. The entire process of scanning and detection can be made faster by using low-resolution images. Even in the medical domain, to reduce the rad iation dose, the exposure time of the patient should be reduced. The exposure time of patients could be reduced by allowing low-resolution CT scans. Hence it is essential to find out which object detection model has better accuracy as well as speed at low-resolution CT scans. However, the existing approaches did not provide details about how the model would perform when the resolution of CT scans is varied. Hence in this project, the goal is to analyze the impact of varying resolution of CT scans on both the speed and accuracy of the model. Three object detection models, namely RetinaNet, YOLOv3, and YOLOv5, were trained at various resolutions. Among the three models, it was found that YOLOv5 has the best mAP and f1 score at multiple resolutions on the DeepLesion dataset. RetinaNet model h as the least inference time on the DeepLesion dataset. From the experiments, it could be asserted that sacrificing mean average precision (mAP) to improve inference time by reducing resolution is feasible.
Für die Durchführung größerer Projekte innerhalb des DLR ist es häufig notwendig, dass sich Wissenschaftler fachübergreifend in Themengebiete einarbeiten müssen. Im Rahmen dieser Einarbeitung führen Wissenschaftler Recherchen in fremden Fachbereichen durch. Das DLR hat zu diesem Zweck das Wissensportal KnowledgeFinder entwickelt. Dieses Framework setzt klassische Suchverfahren zum Auffinden von Informationen in beliebigen Datenbeständen ein. Wenn Wissenschaftler in fremden Fachbereichen recherchieren, dann fällt es ihnen aufgrund des oberflächlichen Einblicks oftmals schwer, zielgerichtet nach Informationen zu suchen. Die im KnowledgeFinder eingesetzten klassischen Suchverfahren, die auf textueller und struktureller Ähnlichkeit basieren, können bei diesen unspezifischen Suchanfragen nur bedingt beim Auffinden von relevanten Informationen helfen. Aufgrund von Mehrdeutigkeiten und unterschiedlichen Kontexten stoße solche Verfahren oftmals an ihre Grenzen. Semantische Technologien haben zum Ziel diesen Mangel zu beheben. Hier wird neben der textuellen und strukturellen Ähnlichkeit zusätzlich die Dimension der Bedeutung betrachtet. In dieser Masterthesis wurde untersucht, ob die Suchergebnisqualität des KnowledgeFinder durch den Einsatz semantischer Technologien verbessert werden kann. Innerhalb einer Machbarkeitsstudie wurde dazu das KnowledgeFinder Framework um semantische Suchverfahren erweitert. Diese Verfahren sollen die fachübergreifende Recherche von DLR-Wissenschaftlern erleichtern, indem sie ihnen helfen, passende Suchergebnisse in den entsprechenden Fachbereichen zu finden.
Die Matrix-Vektor-Multiplikation für dünn besetzte Matrizen (SpMV) stellt für weitreichende wissenschaftliche Anwendungen eine der Kernoperationen des High-Performance-Computing-Bereichs dar. Für die verteilte Berechnung mit immer beliebter werdenden hybriden Rechenclustern kommt dabei die Frage nach einer geeigneten Partitionierungsstrategie für die Verteilung von Daten und Berechnung auf. Diese Arbeit beschäftigt sich damit welchen Einfluss die Struktur der Matrix und die unterschiedlichen Prozessortypen auf die Leistung der SpMV haben und schlägt ein Modell vor, um für diese eine lastbalancierte Verteilung zu erreichen. Wesentliche Bestandteile sind dabei die Laufzeitvorhersage für aktuelle CPUs und GPUs basierend auf einem abgewandelten Roofline-Modell sowie die bewährte Methode der Graph-Partitionierung.
In einem Grid steht Benutzern mit entsprechendem Zugang eine Vielzahl verteilter Ressourcen zur Verfügung. Die daraus entstehenden wirtschaftlichen und technischen Vorteile rechtfertigen die Portierung von bestehenden Desktop-Anwendungen. Die vorliegende Arbeit befasst sich mit der Fragestellung, welche Einflussfaktoren bei der Portierung von Desktop-Anwendungen in ein Grid eine Rolle spielen können und wie diese in Hinblick auf die Machbarkeit zu bewerten sind. Basierend auf den zugrunde liegenden Softwarearchitekturen werden Architekturmerkmale von Desktop-Anwendungen identifiziert und Hypothesen darüber entwickelt, welche Aspekte den Portierungsprozess beeinflussen. Am Beispiel der Portierung der Anwendung „DataFinder“ der Abteilung Verteilte Systeme und Komponentensoftware des DLR werden die entwickelten Hypothesen überprüft. Die Erkenntnisse aus der Beispielportierung werden ausführlich dargestellt und anschließend kritisch diskutiert.
In dieser Arbeit wird im Rahmen von FFE+, einem internen Projekt des Deutschen Zentrums für Luft- und Raumfahrt, eine entscheidungsbasierte Fertigungsstrategie für die Herstellung einer Mikrogasturbinenblisk aus oxidkeramischem Faserverbundwerkstoff entwickelt. Hierfür soll das vakuumbasierte Infusionsverfahren der Abteilung Struktur- und Funktionskeramik des Instituts für Werksstoffforschung verwendet werden. Zunächst wird der theoretische Hintergrund des Materials und die davon etablierte Verarbeitung betrachtet. Aus Basis dieser Grundlage können das System und Funktionen der oxidkeramischen Blisk im Sinne der methodischen Prozessentwicklung bestimmt werden. Die darin formulierten Anforderungen und Bewertungskriterien lassen eine aufwandsreduzierte Entwurfsphase von Konzepten oder Lösungsprinzipien zu. Hierbei ist die Faserstruktur der maßgeblicher Einflussfaktor in der Lösungsfindung. Nach der Bewertung, Validierung und Anpassung der Ergebnisse wird die Fertigungsstrategie auf dem best-bewerteten Konzept und den bisherigen Projekten der Abteilung entworfen. Zusätzlich ist in dieser Arbeit eine Machbarkeitsstudie am Institut für Flugzeugbau der Universität Stuttgart von einem bislang unbekannten Verfahren zur Herstellung oxidkeramischer Faserpreforms durchgeführt worden. Da eine Aussage über die Materialkennwerte für eine sichere Funktionsgewährleistung notwendig ist, sind Materialversuche bei Raum- und Hochtemperatur geplant. Das abschließende Ziel einer Prozessketten-Grundlage von Projekten mit dem vakuumbasierten Infusionsverfahren des Instituts für Werkstoffforschung fasst die Ergebnisse von dieser Arbeit und anderen Erfahrungsberichten zusammen.
The aim of this master thesis was to probe the view of Bonn’s citizens on the smart city project of the German city. A literature review helped defining the smart city term and identifying the smart city concept that is mostly used in Germany. This can be summarized as an urban planning concept using information and communication technology to build citizen centric, sustainable cities. According to this, a smart city should include transparent communication and participation of its citizens. The websites and different publications of Bonn were researched to understand its smart city strategy and vision. This revealed inconsistencies. To resolve these inconsistencies, three representatives of the city were inter-viewed. Based on the knowledge gained up to this point, two groups of Bonn’s inhabitants discussed the Smart City Bonn and presented their perception of it. With the help of this methodology, the following results were obtained. Communication and participation of the city are in many cases in line with the current recommendations for a smart city. Bonn has apparently recognized the relevance of these aspects in theory but should also implement them more consistently in practice. Currently the city council publishes contradictory information and does not plan to incorporate the sight of Bonn’s citizens to develop the smart city strat-egy in the first place, as it is recommended in common literature.
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.
The task of this thesis is to develop an OGC-compliant Sensor Observation Service (SOS) { a component of the SWE { for GPS related sensor data in this context. It should, in contrast to existing implementations, support full mobility of the sensors and be con gurable with respect to adding di erent kinds of sensors. In particular, mobile phones should be considered as sensors, which transmit their data to the SOS server through the transactional SOS interface.
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.
In the field of autonomous robotics, sensors have played a major role in defining the scope of technology and to a great extent, limitations of it as well. This cycle of constant updates and hence technological advancement has made given birth to some serious industries which were once inconceivable. Industries like autonomous driving which has a serious impact on safety and security of people, also has an equally harsh implication on the dynamics and economics of the market. With sensors like LiDAR and RADAR delivering 3D measurements as point clouds, there is a necessity to process the raw measurements directly and many research groups are working on the same. A sizable research has gone in solving the task of object detection on 2D images. In this thesis we aim to develop a LiDAR based 3D object detection scheme. We combine the ideas of PointPillars and feature pyramid networks from 2D vision to propose Pillar-FPN. The proposed method directly takes 3D point clouds as input and outputs a 3D bounding box. Our pipeline consists of multiple variations of proposed Pillar-FPN at the feature fusion level that are described in the results section. We have trained our model on the KITTI train dataset and evaluated it on KITTI validation dataset.
Augmented Reality (AR) findet heutzutage sehr viele Anwendungsbereiche. Durch die Überlagerung von virtuellen Informationen mit der realen Umgebung eignet sich diese Technologie besonders für die Unterstützung der Benutzer bei technischen Wartungs- oder Reparaturvorgängen. Damit die virtuellen Daten korrekt mit der realen Welt überlagert werden, müssen Position und Orientierung der Kamera durch ein Trackingverfahren ermittelt werden. In dieser Arbeit wurde für diesen Zweck ein markerloses, modellbasiertes Trackingsystem implementiert. Während einer Initialisierungs-Phase wird die Kamerapose mithilfe von kalibrierten Referenzbildern, sogenannten Keyframes, bestimmt. In einer darauffolgenden Tracking-Phase wird das zu trackende Objekt weiterverfolgt. Evaluiert wurde das System an dem 1:1 Trainingsmodell des biologischen Forschungslabors Biolab, welches von der Europäischen Weltraumorganisation ESA zur Verfügung gestellt wurde.