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Today publications are digitally available which enables researchers to search the text and often also the content of tables. On the contrary, images cannot be searched which is not a problem for most fields, but in chemistry most of the information are contained in images, especially structure diagrams. Next to the "normal" chemical structures, which represent exactly one molecule, there also exist generic structures, so called Markush structures. These contain variable parts and additional textual information which enable them to represent several molecules at once. This can vary between just a few and up to thousands or even millions. This ability lead to a spread of Markush structures in patents, because it enables patents to protect entire families of molecules at once. Next to the prevention of an enumeration of all structures it also has the advantage that, if a Markush structure is used in a patent, it is much harder to determine whether a specific structure is protected by it or not. To solve the question about the protection of a structure, it is necessary to search the patents. Appropriate databases for this task already do exist, but are filled manually. An automatic processing does not yet exist. In this project a Markush structure reconstruction prototype is developed which is able to reconstruct bitmaps including Markush structures (meaning a depiction of the structure and a text part describing the generic parts) into a digital format and save them in the newly developed context-free grammar based file format extSMILES. This format is searchable due to its context-free grammar based design. To be able to develop a Markush structure reconstruction prototype, an in depth analysis of the concept of Markush structures and their requirements for a reconstruction process was performed. Thereby it is stated, that the common connection table concept of the existing file formats is not able to store Markush structures. Especially challenging are conditions for most of the formats. Thus, a context-free grammar based file format is developed, which extends the SMILES format. This extSMILES called format assures the searchability of the results by its context-free grammar based concept, and is able to store all information contained in Markush structures. In addition it is generic, extendable and easily understandable. The developed prototype for the Markush structure reconstruction uses extSMILES as output format and is based on the chemical structure recognition tool chemoCR and the Unstructured Information Management Architecture UIMA. For chemoCR modules are developed which enable it to recognize and assemble Markush structures as well as to return the reconstruction result in extSMILES. For UIMA on the other hand, a pipeline is developed, which is able to analyse and translate the input text files to extSMILES. The results of both tools then are combined and presented in chemoCR. An evaluation of the prototype is performed on a representative set of twelve structures of interest and low image quality which contain all typical Markush elements. Trivial structures containing only one R-group are not evaluated. Due to the challenging nature of the images, no Markush structure could be correctly reconstructed. But by regarding the assumption, that R-group definitions which are described by natural language are excluded from the task, and under the condition that the core structure reconstruction is improved, the rate of success can be increased to 58.4%.
This work extends the affordance-inspired robot control architecture introduced in the MACS project [35] and especially its approach to integrate symbolic planning systems given in [24] by providing methods to automated abstraction of affordances to high-level operators. It discusses how symbolic planning instances can be generated automatically based on these operators and introduces an instantiation method to execute the resulting plans. Preconditions and effects of agent behaviour are learned and represented in Gärdenfors conceptual spaces framework. Its notion of similarity is used to group behaviours to abstract operators based on the affordance-inspired, function-centred view on the environment. Ways on how the capabilities of conceptual spaces to map subsymbolic to symbolic representations to generate PDDL planning domains including affordance-based operators are discussed. During plan execution, affordance-based operators are instantiated by agent behaviour based on the situation directly before its execution. The current situation is compared to past ones and the behaviour that has been most successful in the past is applied. Execution failures can be repaired by action substitution. The concept of using contexts to dynamically change dimension salience as introduced by Gärdenfors is realized by using techniques from the field of feature selection. The approach is evaluated using a 3D simulation environment and implementations of several object manipulation behaviours.
Das WebDAV-Protokoll (Web-based Distributed Authoring and Versioning) ermöglicht die Bearbeitung und Verwaltung von Dateien auf einem Web-Server. Aus technischer Sicht ist WebDAV eine Erweiterung des HTTP-Protokolls. Durch die rasche Zunahme und den steigenden Verbreitungsgrad von WebDAV-basierten Anwendungen, wie etwa Dokumentenmanagementsystemen, steigen auch die Anforderungen an deren Zuverlässigkeit. Die voll umfassende Unterstützung von Transaktionen, d.h. die Zusammenfassung einer Menge von Verarbeitungsschritten zu einer logischen Einheit, würde hierzu einen wichtigen Beitrag leisten. Die für Transaktionen geforderten Eigenschaften, die gleichzeitig auch deren Hauptvorteile darstellen, werden durch das bekannte Akronym ACID beschrieben, welches für Atomarität (atomicity), Konsistenz (consistency), Isoliertheit (isolation) und Dauerhaftigkeit (durability) steht. Zurzeit unterstützt das WebDAV-Protokoll allerdings nur die Punkte Konsistenz und Dauerhaftigkeit, eine komplette und vor allem standardkonforme Unterstützung der ACID-Eigenschaften von Transaktionen ist nicht gegeben. Im Rahmen dieser Arbeit wurde nun ein Transaktionsmodell für den WebDAVStandard erarbeitet. Das Modell ermöglicht es, eine Menge von Dateioperationen transaktionsbasiert durchzuführen. Das Modell unterstützt dabei zur Sicherstellung der Serialisierbarkeit sowohl optimistische als auch pessimistische Verfahren. Die Unterstützung des optimistischen Verfahrens wurde dabei von der IETF (Internet Engineering Task Force) als zulässiges und sinnvolles Vorgehen zur Realisierung von Transaktionen mittels WebDAV bestätigt. Für die pessimistischen Verfahren wurde im Rahmen dieser Arbeit aufgezeigt, wie die bestehenden Konzepte des WebDAV-Standards erweitert werden müssen, um dies ebenfalls umsetzen zu können. Um die getroffene Entwurfsentscheidung zu verifizieren, wurde eine prototypische Implementierung des Modells vorgenommen. Hierbei wurde, nach einer entsprechenden Evaluierung und Bewertung, die optimistische Nebenläufigkeitskontrolle umgesetzt. Clientseitig setzt die Implementierung auf der Jackrabit-Library auf, die serverseitige Implementierung verwendet als Grundlage den WebDAV-Server von Subversion.
This master thesis describes a supervised approach to the detection and the identification of humans in TV-style video sequences. In still images and video sequences, humans appear in different poses and views, fully visible and partly occluded, with varying distances to the camera, at different places, under different illumination conditions, etc. This diversity in appearance makes the task of human detection and identification to a particularly challenging problem. A possible solution of this problem is interesting for a wide range of applications such as video surveillance and content-based image and video processing. In order to detect humans in views ranging from full to close-up view and in the presence of clutter and occlusion, they are modeled by an assembly of several upper body parts. For each body part, a detector is trained based on a Support Vector Machine and on densely sampled, SIFT-like feature points in a detection window. For a more robust human detection, localized body parts are assembled using a learned model for geometric relations based on Gaussians. For a flexible human identification, the outward appearance of humans is captured and learned using the Bag-of-Features approach and non-linear Support Vector Machines. Probabilistic votes for each body part are combined to improve classification results. The combined votes yield an identification accuracy of about 80% in our experiments on episodes of the TV series "Buffy the Vampire Slayer". The Bag-of-Features approach has been used in previous work mainly for object classification tasks. Our results show that this approach can also be applied to the identification of humans in video sequences. Despite the difficulty of the given problem, the overall results are good and encourage future work in this direction.
This thesis investigates the benefit of rubrics for grading short answers using an active learning mechanism. Automating short answer grading using Natural Language Processing (NLP) is one of the active research areas in the education domain. This could save time for the evaluator and invest more time in preparing for the lecture. Most of the research on short answer grading was treated as a similarity task between reference and student answers. However, grading based on reference answers does not account for partial grades and does not provide feedback. Also, the grading is automatic that tries to replace the evaluator. Hence, using rubrics for short answer grading with active learning eliminates the drawbacks mentioned earlier.
Initially, the proposed approach is evaluated on the Mohler dataset, popularly used to benchmark the methodology. This phase is used to determine the parameters for the proposed approach. Therefore, the approach with the selected parameter exceeds the performance of current State-Of-The-Art (SOTA) methods resulting in the Pearson correlation value of 0.63 and Root Mean Square Error (RMSE) of 0.85. The proposed approach has surpassed the SOTA methods by almost 4%.
Finally, the benchmarked approach is used to grade the short answer based on rubrics instead of reference answers. The proposed approach evaluates short answers from Autonomous Mobile Robot (AMR) dataset to provide scores and feedback (formative assessment) based on the rubrics. The average performance of the dataset results in the Pearson correlation value of 0.61 and RMSE of 0.83. Thus, this research has proven that rubrics-based grading achieves formative assessment without compromising performance. In addition, the rubrics have the advantage of generalizability to all answers.
Distributed computing environments allow collaborative problem solving across teams and organisations. A fundamental precondition for collaboration is the ability to find available participants and be able to exchange information. One way to approach this conceptual formulation are central directories or registry services. A major disadvantage of centralized components is, that they limit the flexibility to form ad hoc networks that are targeted to solve a specific problem. To facilitate flexible and dynamic collaborations, ideas from decentralized and self-organising networks can be combined with concepts of service oriented computing. This project aims to investigate potential solutions for dynamic discovery of network participants and outlines how to manage challenges associated with the development of a discovery protocol for distributed systems. During the course of this project a prototypical implementation was created that integrates into the open source distributed, collaborative problem solving environment RCE [9]. It is currently developed at the German Aerospace Center (DLR) but is planned to make the framework available to broader community.
Distributed systems comprise distributed computing systems, distributed information systems, and distributed pervasive systems. They are often very complex and their implementation is challenging. Intensive and continuous testing is indispensable to ensure reliability and high quality of a distributed system. The testing process should have a high degree of automation, not only on lower levels (i.e. unit and module testing), but also on higher testing levels (e.g. system, integration, and acceptance tests). To achieve automation on higher testing levels virtual infrastructure components (e.g. virtual machines, virtual networks) that are offered as a Service (IaaS) can be employed. The elasticity of on-demand computation resources fits well together with the varying resource demands of automated test execution.
A methodology for automated acceptance testing of distributed systems that uses virtual infrastructure is presented. It is founded on a task-oriented model that is used to abstract concurrency and asynchronous, remote communication in distributed systems. The model is used as groundwork for a domain-specific language that allows expressing tests for distributed systems in the form of scenarios. On the one hand, test scenarios are executable and, therefore, fully automated. On the other hand, test scenarios represent requirements to the system under test making an automated, example-based verification possible.
A prototypical implementation is used to apply the developed methodology in the context of two different case studies. The first case study uses RCE as an example of a distributed, workflow-driven integration environment for scientific computing. The second one uses MongoDB as an example of a document-oriented database system that offers distributed data storage through master-slave replication. The results of the experimental evaluation indicate that the developed acceptance testing methodology is a useful approach to design, build, and execute tests for distributed systems with high quality and a high degree of automation.
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]
The objective of this thesis is to implement a computer game based motivation system for maximal strength testing on the Biodex System 3 Isokinetic Dynamometer. The prototype game has been designed to improve the peak torque produced in an isometric knee extensor strength test. An extensive analysis is performed on a torque data set from a previous study. The torque responses for five second long maximal voluntary contractions of the knee extensor are analyzed to understand torque response characteristics of different subjects. The parameters identifed in the data analysis are used in the implementation of the 'Shark and School of Fish' game. The behavior of the game for different torque responses is analyzed on a different torque data set from the previous study. The evaluation shows that the game rewards and motivates continuously over a repetition to reach the peak torque value. The evaluation also shows that the game rewards the user more if he overcomes a baseline torque value within the first second and then gradually increase the torque to reach peak torque.
Das Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme (IAIS) betreibt seit mehreren Jahren auf dem Campus Schloss Birlinghoven in Sankt Augustin angewandte Forschung in den Bereichen Multisensordatenanalyse und Datenvisualisierung.
Im Rahmen einer mehrjährigen Kooperation zwischen dem Fraunhofer-IAIS und der Wehrtechnischen Dienststelle 71 (WTD71) wurde das Seeraumüberwachungssystem iLEXX entwickelt. Es soll den Benutzer auf auffällige Situationen hinweisen und ihm kontextabhängig alle notwendigen Handlungsoptionen zur weiteren Aufklärung der Situation oder der Abwehr einer Bedrohung aufzeigen. Das iLEXX-System verarbeitet eine Vielzahl von Sensordaten und Ereignissen. Abhängig vom Szenario kommen hier mehrere tausend Updates pro Sekunde zusammen, die in Echtzeit vorverarbeitet und visualisiert werden müssen.
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.
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.
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 service robotics, tasks without the involvement of objects are barely applicable, like in searching, fetching or delivering tasks. Service robots are supposed to capture efficiently object related information in real world scenes while for instance considering clutter and noise, and also being flexible and scalable to memorize a large set of objects. Besides object perception tasks like object recognition where the object’s identity is analyzed, object categorization is an important visual object perception cue that associates unknown object instances based on their e.g. appearance or shape to a corresponding category. We present a pipeline from the detection of object candidates in a domestic scene over the description to the final shape categorization of detected candidates. In order to detect object related information in cluttered domestic environments an object detection method is proposed that copes with multiple plane and object occurrences like in cluttered scenes with shelves. Further a surface reconstruction method based on Growing Neural Gas (GNG) in combination with a shape distribution-based descriptor is proposed to reflect shape characteristics of object candidates. Beneficial properties provided by the GNG such as smoothing and denoising effects support a stable description of the object candidates which also leads towards a more stable learning of categories. Based on the presented descriptor a dictionary approach combined with a supervised shape learner is presented to learn prediction models of shape categories.
Experimental results, of different shapes related to domestically appearing object shape categories such as cup, can, box, bottle, bowl, plate and ball, are shown. A classification accuracy of about 90% and a sequential execution time of lesser than two seconds for the categorization of an unknown object is achieved which proves the reasonableness of the proposed system design. Additional results are shown towards object tracking and false positive handling to enhance the robustness of the categorization. Also an initial approach towards incremental shape category learning is proposed that learns a new category based on the set of previously learned shape categories.
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.
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.
As cameras are ubiquitous in autonomous systems, object detection is a crucial task. Object detectors are widely used in applications such as autonomous driving, healthcare, and robotics. Given an image, an object detector outputs both the bounding box coordinates as well as classification probabilities for each object detected. The state-of-the-art detectors are treated as black boxes due to their highly non-linear internal computations. Even with unprecedented advancements in detector performance, the inability to explain how their outputs are generated limits their use in safety-critical applications in particular. It is therefore crucial to explain the reason behind each detector decision in order to gain user trust, enhance detector performance, and analyze their failure.
Previous work fails to explain as well as evaluate both bounding box and classification decisions individually for various detectors. Moreover, no tools explain each detector decision, evaluate the explanations, and also identify the reasons for detector failures. This restricts the flexibility to analyze detectors. The main contribution presented here is an open-source Detector Explanation Toolkit (DExT). It is used to explain the detector decisions, evaluate the explanations, and analyze detector errors. The detector decisions are explained visually by highlighting the image pixels that most influence a particular decision. The toolkit implements the proposed approach to generate a holistic explanation for all detector decisions using certain gradient-based explanation methods. To the author’s knowledge, this is the first work to conduct extensive qualitative and novel quantitative evaluations of different explanation methods across various detectors. The qualitative evaluation incorporates a visual analysis of the explanations carried out by the author as well as a human-centric evaluation. The human-centric evaluation includes a user study to understand user trust in the explanations generated across various explanation methods for different detectors. Four multi-object visualization methods are provided to merge the explanations of multiple objects detected in an image as well as the corresponding detector outputs in a single image. Finally, DExT implements the procedure to analyze detector failures using the formulated approach.
The visual analysis illustrates that the ability to explain a model is more dependent on the model itself than the actual ability of the explanation method. In addition, the explanations are affected by the object explained, the decision explained, detector architecture, training data labels, and model parameters. The results of the quantitative evaluation show that the Single Shot MultiBox Detector (SSD) is more faithfully explained compared to other detectors regardless of the explanation methods. In addition, a single explanation method cannot generate more faithful explanations than other methods for both the bounding box and the classification decision across different detectors. Both the quantitative and human-centric evaluations identify that SmoothGrad with Guided Backpropagation (GBP) provides more trustworthy explanations among selected methods across all detectors. Finally, a convex polygon-based multi-object visualization method provides more human-understandable visualization than other methods.
The author expects that DExT will motivate practitioners to evaluate object detectors from the interpretability perspective by explaining both bounding box and classification decisions.
High-dimensional and multi-variate data from dynamical systems such as turbulent flows and wind turbines can be analyzed with deep learning due to its capacity to learn representations in lower-dimensional manifolds. Two challenges of interest arise from data generated from these systems, namely, how to anticipate wind turbine failures and how to better understand air flow through car ventilation systems. There are deep neural network architectures that can project data into a lower-dimensional space with the goal of identifying and understanding patterns that are not distinguishable in the original dimensional space. Learning data representations in lower dimensions via non-linear mappings allows one to perform data compression, data clustering (for anomaly detection), data reconstruction and synthetic data generation.
In this work, we explore the potential that variational autoencoders (VAE) have to learn low-dimensional data representations in order to tackle the problems posed by the two dynamical systems mentioned above. A VAE is a neural network architecture that combines the mechanisms of the standard autoencoder and variational bayes. The goal here is to train a neural network to minimize a loss function defined by a reconstruction term together with a variational term defined as a Kulback-Leibler (KL) divergence.
The report discusses the results obtained for the two different data domains: wind turbine time series and turbulence data from computational fluid dynamics (CFD) simulations.
We report on the reconstruction, clustering and unsupervised anomaly detection of wind turbine multi-variate time series data using a variant of a VAE called Variational Recurrent Autoencoder (VRAE). We trained a VRAE to cluster normal and abnormal wind turbine series (two class problem) as well as normal and multiple abnormal series (multi-class problem). We found that the model is capable of distinguishing between normal and abnormal cases by reducing the dimensionality of the input data and projecting it to two dimensions using techniques such as Principal Component Analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). A set of anomaly scoring methods is applied on top of these latent vectors in order to compute unsupervised clustering. We have achieved an accuracy of up to 96% with the KM eans + + algorithm.
We also report the data reconstruction and generation results of two dimensional turbulence slices corresponding to CFD simulation of a HVAC air duct. For this, we have trained a Convolutional Variational Autoencoder (CVAE). We have found that the model is capable of reconstructing laminar flows up to a certain degree of resolution as well generating synthetic turbulence data from the learned latent distribution.
Chipkarten im Mobilfunk
(2002)
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.
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.
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.
The introduction of gestures as a supplementary input modality has become of increasing interest to human computer interaction design, especially for 3D computer environments. This thesis describes the concepts and development of a gesture recognition system based on the machine learning technique of Hidden Markov Models. Well-known from the field of speech recognition, this statistical method is employed in this thesis to represent and recognize predefined gestures. Within this work, gestures are defined as symbols, such as simple geometric shapes or Roman letters. They are extracted from a stream of three-dimensional optical tracking data which is resampled, reduced to 2D and quantized to be used as input to discrete Hidden Markov Models. A set of prerecorded training data is used to learn the parameters of the models and recognition is achieved by evaluating the trained models. The devised system was used to augment an existing virtual reality prototype application which serves as a demonstration and development platform for the VRGeo consortium. The consortium's goal is to investigate and utilize the benefits of virtual reality technology for the oil and gas industry. An isolated test of the system with seven gestures showed accuracies of up to 98.57% and the review from experts in the fields of virtual reality and geophysics was predominantly positive.
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.