004 Datenverarbeitung; Informatik
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With the progress in robotics research the human machine interfaces reach more and more the status of being the major limiting factor for the overall system performance of a system for remote navigation and coordination of robots. In this monograph it is elaborated how mixed reality technologies can be applied for the user interfaces in order to increase the overall system performance. Concepts, technologies, and frameworks are developed and evaluated in user studies which enable for novel user-centered approaches to the design of mixed-reality user interfaces for remote robot operation. Both the technological requirements and the human factors are considered to achieve a consistent system design. Novel technologies like 3D time-of-flight cameras are investigated for the application in the navigation tasks and for the application in the developed concept of a generic mixed reality user interface. In addition it is shown how the network traffic of a video stream can be shaped on application layer in order to reach a stable frame rate in dynamic networks. The elaborated generic mixed reality framework enables an integrated 3D graphical user interface. The realized spatial integration and visualization of available information reduces the demand for mental transformations for the human operator and supports the use of immersive stereo devices. The developed concepts make also use of the fact that local robust autonomy components can be realized and thus can be incorporated as assistance systems for the human operators. A sliding autonomy concept is introduced combining force and visual augmented reality feedback. The force feedback component allows rendering the robot's current navigation intention to the human operator, such that a real sliding autonomy with seamless transitions is achieved. The user-studies prove the significant increase in navigation performance by application of this concept. The generic mixed reality user interface together with robust local autonomy enables a further extension of the teleoperation system to a short-term predictive mixed reality user interface. With the presented concept of operation, it is possible to significantly reduce the visibility of system delays for the human operator. In addition, both advantageous characteristics of a 3D graphical user interface for robot teleoperation- an exocentric view and an augmented reality view – can be combined.
The first step towards aerial planetary exploration has been made. Ingenuity shows extremely promising results, and new missions are already underway. Rotorcraft are capable of flight. This capability could be utilized to support the last stages of Entry, Descent, and Landing. Thus, mass and complexity could be scaled down.
Autorotation is one method of descent. It describes unpowered descent and landing, typically performed by helicopters in case of an engine failure. MAPLE is suggested to test these procedures and understand autorotation on other planets. In this series of experiments, the Ingenuity helicopter is utilized. Ingenuity would autorotate a ”mid-air-landing” before continuing with normal flight. Ultimately, the collected data shall help to understand autorotation on Mars and its utilization for interplanetary exploration.
Deep learning enables enormous progress in many computer vision-related tasks. Artificial Intel- ligence (AI) steadily yields new state-of-the-art results in the field of detection and classification. Thereby AI performance equals or exceeds human performance. Those achievements impacted many domains, including medical applications.
One particular field of medical applications is gastroenterology. In gastroenterology, machine learning algorithms are used to assist examiners during interventions. One of the most critical concerns for gastroenterologists is the development of Colorectal Cancer (CRC), which is one of the leading causes of cancer-related deaths worldwide. Detecting polyps in screening colonoscopies is the essential procedure to prevent CRC. Thereby, the gastroenterologist uses an endoscope to screen the whole colon to find polyps during a colonoscopy. Polyps are mucosal growths that can vary in severity.
This thesis supports gastroenterologists in their examinations with automated detection and clas- sification systems for polyps. The main contribution is a real-time polyp detection system. This system is ready to be installed in any gastroenterology practice worldwide using open-source soft- ware. The system achieves state-of-the-art detection results and is currently evaluated in a clinical trial in four different centers in Germany.
The thesis presents two additional key contributions: One is a polyp detection system with ex- tended vision tested in an animal trial. Polyps often hide behind folds or in uninvestigated areas. Therefore, the polyp detection system with extended vision uses an endoscope assisted by two additional cameras to see behind those folds. If a polyp is detected, the endoscopist receives a vi- sual signal. While the detection system handles the additional two camera inputs, the endoscopist focuses on the main camera as usual.
The second one are two polyp classification models, one for the classification based on shape (Paris) and the other on surface and texture (NBI International Colorectal Endoscopic (NICE) classification). Both classifications help the endoscopist with the treatment of and the decisions about the detected polyp.
The key algorithms of the thesis achieve state-of-the-art performance. Outstandingly, the polyp detection system tested on a highly demanding video data set shows an F1 score of 90.25 % while working in real-time. The results exceed all real-time systems in the literature. Furthermore, the first preliminary results of the clinical trial of the polyp detection system suggest a high Adenoma Detection Rate (ADR). In the preliminary study, all polyps were detected by the polyp detection system, and the system achieved a high usability score of 96.3 (max 100). The Paris classification model achieved an F1 score of 89.35 % which is state-of-the-art. The NICE classification model achieved an F1 score of 81.13 %.
Furthermore, a large data set for polyp detection and classification was created during this thesis. Therefore a fast and robust annotation system called Fast Colonoscopy Annotation Tool (FastCAT) was developed. The system simplifies the annotation process for gastroenterologists. Thereby the
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gastroenterologists only annotate key parts of the endoscopic video. Afterward, those video parts are pre-labeled by a polyp detection AI to speed up the process. After the AI has pre-labeled the frames, non-experts correct and finish the annotation. This annotation process is fast and ensures high quality. FastCAT reduces the overall workload of the gastroenterologist on average by a factor of 20 compared to an open-source state-of-art annotation tool.
LoRaWAN Network Planning in Smart Environments: Towards Reliability, Scalability, and Cost Reduction
(2022)
The goal in this work is to present a guidance for LoRaWAN planning to improve overall reliability for message transmissions and scalability. At the end, the cost component is discussed. Therefore, a five step approach is presented that helps to plan a LoRaWAN deployment step by step: Based on the device locations, an initial gateway placement is suggested followed by in-depth frequency and channel access planning. After an initial planning phase, updates for channel access and the initial gateway planning is suggested that should also be done periodically during network operation. Since current gateway placement approaches are only studied with random channel access, there is a lot of potential in the cell planning phase. Furthermore, the performance of different channel access approaches is highly related on network load, and thus cell size and sensor density. Last, the influence of different cell planning ideas on expected costs are discussed.
Social patterns and roles can develop when users talk to intelligent voice assistants (IVAs) daily. The current study investigates whether users assign different roles to devices and how this affects their usage behavior, user experience, and social perceptions. Since social roles take time to establish, we equipped 106 participants with Alexa or Google assistants and some smart home devices and observed their interactions for nine months. We analyzed diverse subjective (questionnaire) and objective data (interaction data). By combining social science and data science analyses, we identified two distinct clusters—users who assigned a friendship role to IVAs over time and users who did not. Interestingly, these clusters exhibited significant differences in their usage behavior, user experience, and social perceptions of the devices. For example, participants who assigned a role to IVAs attributed more friendship to them used them more frequently, reported more enjoyment during interactions, and perceived more empathy for IVAs. In addition, these users had distinct personal requirements, for example, they reported more loneliness. This study provides valuable insights into the role-specific effects and consequences of voice assistants. Recent developments in conversational language models such as ChatGPT suggest that the findings of this study could make an important contribution to the design of dialogic human–AI interactions.
A bipartite graph G=(U,V,E) is convex if the vertices in V can be linearly ordered such that for each vertex u∈U, the neighbors of u are consecutive in the ordering of V. An induced matching H of G is a matching for which no edge of E connects endpoints of two different edges of H. We show that in a convex bipartite graph with n vertices and m weighted edges, an induced matching of maximum total weight can be computed in O(n+m) time. An unweighted convex bipartite graph has a representation of size O(n) that records for each vertex u∈U the first and last neighbor in the ordering of V. Given such a compact representation, we compute an induced matching of maximum cardinality in O(n) time. In convex bipartite graphs, maximum-cardinality induced matchings are dual to minimum chain covers. A chain cover is a covering of the edge set by chain subgraphs, that is, subgraphs that do not contain induced matchings of more than one edge. Given a compact representation, we compute a representation of a minimum chain cover in O(n) time. If no compact representation is given, the cover can be computed in O(n+m) time. All of our algorithms achieve optimal linear running time for the respective problem and model, and they improve and generalize the previous results in several ways: The best algorithms for the unweighted problem versions had a running time of O(n\(^{2}\)) (Brandstädt et al. in Theor. Comput. Sci. 381(1–3):260–265, 2007. https://doi.org/10.1016/j.tcs.2007.04.006). The weighted case has not been considered before.
Digitization and transcription of historic documents offer new research opportunities for humanists and are the topics of many edition projects. However, manual work is still required for the main phases of layout recognition and the subsequent optical character recognition (OCR) of early printed documents. This paper describes and evaluates how deep learning approaches recognize text lines and can be extended to layout recognition using background knowledge. The evaluation was performed on five corpora of early prints from the 15th and 16th Centuries, representing a variety of layout features. While the main text with standard layouts could be recognized in the correct reading order with a precision and recall of up to 99.9%, also complex layouts were recognized at a rate as high as 90% by using background knowledge, the full potential of which was revealed if many pages of the same source were transcribed.
Die Entwicklung eines wissensbasierten Systems, speziell eines Diagnosesystems, ist eine Teildisziplin der künstlichen Intelligenz und angewandten Informatik. Im Laufe der Forschung auf diesem Gebiet wurden verschiedene Lösungsansätze mit unterschiedlichem Erfolg bei der Anwendung in der Kraftfahrzeugdiagnose entwickelt. Diagnosesysteme in Vertragswerkstätten, das heißt in Fahrzeughersteller gebundenen Werkstätten, wenden hauptsächlich die fallbasierte Diagnostik an. Zum einen hält sich hier die Fahrzeugvielfalt in Grenzen und zum anderen besteht eine Meldepflicht bei neuen, nicht im System vorhandenen Fällen. Die freien Werkstätten verfügen nicht über eine solche Datenbank. Somit ist der fallbasierte Ansatz schwer umsetzbar. In freien Werkstätten - Fahrzeughersteller unabhängigen Werkstätten - basiert die Fehlersuche hauptsächlich auf Fehlerbäumen. Wegen der wachsenden Fahrzeugkomplexität, welche wesentlich durch die stark zunehmende Anzahl der durch mechatronische Systeme realisierten Funktionen bedingt ist, und der steigenden Typenvielfalt ist die geführte Fehlersuche in freien Werkstätten nicht immer zielführend. Um die Unterstützung des Personals von freien Werkstätten bei der zukünftigen Fehlersuche zu gewährleisten, werden neue Generationen von herstellerunabhängigen Diagnosetools benötigt, die die Probleme der Variantenvielfalt und Komplexität lösen. In der vorliegenden Arbeit wird ein Lösungsansatz vorgestellt, der einen qualitativen, modellbasierten Diagnoseansatz mit einem auf heuristischem Diagnosewissen basierenden Ansatz vereint. Neben der Grundlage zur Wissenserhebung werden in dieser Arbeit die theoretische Grundlage zur Beherrschung der Variantenvielfalt sowie die Tests für die erstellten Diagnosemodelle behandelt. Die Diagnose ist symptombasiert und die Inferenzmechanismen zur Verarbeitung des Diagnosewissens sind eine Kombination aus Propagierung der abweichenden physikalischen Größen im Modell und der Auswertung des heuristischen Wissens. Des Weiteren werden in dieser Arbeit verschiedene Aspekte der Realisierung der entwickelten theoretischen Grundlagen dargestellt, zum Beispiel: Systemarchitektur, Wissenserhebungsprozess, Ablauf des Diagnosevorgangs in den Werkstätten. Die Evaluierung der entwickelten Lösung bei der Wissenserhebung in Form von Modellerstellungen und Modellierungsworkshops sowie Feldtests dient nicht nur zur Bestätigung des entwickelten Ansatzes, sondern auch zur Ideenfindung für die Integration der entwickelten Tools in die existierende IT-Infrastruktur.
Data mining has proved its significance in various domains and applications. As an important subfield of the general data mining task, subgroup mining can be used, e.g., for marketing purposes in business domains, or for quality profiling and analysis in medical domains. The goal is to efficiently discover novel, potentially useful and ultimately interesting knowledge. However, in real-world situations these requirements often cannot be fulfilled, e.g., if the applied methods do not scale for large data sets, if too many results are presented to the user, or if many of the discovered patterns are already known to the user. This thesis proposes a combination of several techniques in order to cope with the sketched problems: We discuss automatic methods, including heuristic and exhaustive approaches, and especially present the novel SD-Map algorithm for exhaustive subgroup discovery that is fast and effective. For an interactive approach we describe techniques for subgroup introspection and analysis, and we present advanced visualization methods, e.g., the zoomtable that directly shows the most important parameters of a subgroup and that can be used for optimization and exploration. We also describe various visualizations for subgroup comparison and evaluation in order to support the user during these essential steps. Furthermore, we propose to include possibly available background knowledge that is easy to formalize into the mining process. We can utilize the knowledge in many ways: To focus the search process, to restrict the search space, and ultimately to increase the efficiency of the discovery method. We especially present background knowledge to be applied for filtering the elements of the problem domain, for constructing abstractions, for aggregating values of attributes, and for the post-processing of the discovered set of patterns. Finally, the techniques are combined into a knowledge-intensive process supporting both automatic and interactive methods for subgroup mining. The practical significance of the proposed approach strongly depends on the available tools. We introduce the VIKAMINE system as a highly-integrated environment for knowledge-intensive active subgroup mining. Also, we present an evaluation consisting of two parts: With respect to objective evaluation criteria, i.e., comparing the efficiency and the effectiveness of the subgroup discovery methods, we provide an experimental evaluation using generated data. For that task we present a novel data generator that allows a simple and intuitive specification of the data characteristics. The results of the experimental evaluation indicate that the novel SD-Map method outperforms the other described algorithms using data sets similar to the intended application concerning the efficiency, and also with respect to precision and recall for the heuristic methods. Subjective evaluation criteria include the user acceptance, the benefit of the approach, and the interestingness of the results. We present five case studies utilizing the presented techniques: The approach has been successfully implemented in medical and technical applications using real-world data sets. The method was very well accepted by the users that were able to discover novel, useful, and interesting knowledge.
Knowledge encoding in game mechanics: transfer-oriented knowledge learning in desktop-3D and VR
(2019)
Affine Transformations (ATs) are a complex and abstract learning content. Encoding the AT knowledge in Game Mechanics (GMs) achieves a repetitive knowledge application and audiovisual demonstration. Playing a serious game providing these GMs leads to motivating and effective knowledge learning. Using immersive Virtual Reality (VR) has the potential to even further increase the serious game’s learning outcome and learning quality. This paper compares the effectiveness and efficiency of desktop-3D and VR in respect to the achieved learning outcome. Also, the present study analyzes the effectiveness of an enhanced audiovisual knowledge encoding and the provision of a debriefing system. The results validate the effectiveness of the knowledge encoding in GMs to achieve knowledge learning. The study also indicates that VR is beneficial for the overall learning quality and that an enhanced audiovisual encoding has only a limited effect on the learning outcome.