In dieser Dissertation wird die Entwicklung eines selbstorganisierenden Produktionssystems untersucht, um die Herausforderungen in der Produktionsplanung und -steuerung, insbesondere bei Unsicherheiten, besser zu bewältigen. Die Arbeit analysiert zunächst die Grundlagen der Produktionsplanung und -steuerung, gefolgt von einer detaillierten Untersuchung des interdisziplinären Themas der Selbstorganisation. Basierend darauf wird ein Vorgehen zur Identifikation geeigneter Methoden der Selbstorganisation für eine bestimmte Problemstellung entwickelt und auf Produktionsplanung und -steuerung angewendet. Durch die Bewertung bestehender Referenzarchitekturen und deren Integration in ein Framework für selbstorganisierende Produktion, werden selbstorganisierende Verfahren mit etablierten Verfahren der Produktionsplanung und -steuerung verglichen. Die Arbeit zeigt die Überlegenheit der selbstorganisierenden Systeme bei der Planung unter Unsicherheit und liefert nachweislich Verbesserungen hinsichtlich der sekundären und tertiären Ziele der Produktion. Die Dissertation gliedert sich in acht Kapitel, die jeweils verschiedene Aspekte der Selbstorganisation, Produktionsplanung und -steuerung, sowie die Entwicklung und Evaluation eines selbstorganisierenden Produktionssystems behandeln. Die Ergebnisse dieser Arbeit haben das Potenzial, die Produktionsplanung und -steuerung unter Unsicherheit effektiver zu gestalten und somit eine bedeutende Verbesserung für die Industrie zu bieten.:1. Einleitung 18 1.1. Motivation zur Nutzung von Selbstorganisation in der Produktionsplanung und -steuerung 1.2. Forschungsfragen: Wie kann Selbstorganisation bei der Bewältigung der Aufgaben der PPS helfen? 1.3. Aufbau und Vorgehensweise dieser Arbeit 2. Produktionsplanung und -steuerung 2.1. Einführung in die Produktionsplanung und -steuerung 2.1.1. Planung 2.1.2. Produktion 2.1.3. Produktionsplanung und -steuerung 2.2. Ziele der Produktionsplanung und -steuerung 2.2.1. Primäre Ziele 2.2.2. Sekundäre Ziele 2.2.3. Tertiäre Ziele 2.3. Zentral organisierte Produktionsplanung und -steuerung 2.3.1. Aufbau der zentral organisierten Produktionsplanung und -steuerung 2.3.2. Ablauf der zentral organisierten Produktionsplanung 2.3.3. Ablauf der zentral organisierten Produktionssteuerung 2.4. Unsicherheit in der Produktionsplanung und -steuerung 2.5. Arten von Entscheidungsproblemen in der Produktionsplanung und -steuerung 3. Selbstorganisation 3.1. Selbstorganisation in komplexen Systemen 3.2. Sich selbst organisierenden Systemen zugeschriebene Eigenschaften 3.2.1. Offenheit des Systems 3.2.2. Emergenz des Systems 3.2.3. Nichtlinearität des Systems 3.2.4. Attraktoren im Zustandsraum des Systems 3.2.5. Indeterminismus des Systems 3.2.6. Pfadabhängigkeit des Systems 3.2.7. Autonomie der Elemente im System 3.2.8. Autopoiesis neuer Elemente im System 3.2.9. Anpassungsfähigkeit des Systems 3.3. Von statischen zu sich selbst organisierenden Systemen 4. Vorgehensweise zur Entwicklung eines sich selbst organisierenden Systems zur Produktionsplanung und -steuerung 4.1. Vorgehen zur Entwicklung sich selbst organisierender Systeme 4.2. Elemente eines sich selbst organisierenden Systems 4.3. Umwelteinflüsse und Interaktionsmuster sich selbst organisierender Systeme 4.3.1. Taxonomie der Interaktion zur Koordination 4.3.2. Interaktionsmuster zur Koordination sich selbst organisierender Systeme 4.3.3. Zuordnung von Interaktionsmustern zu Entscheidungsproblemen 4.4. Organisationsstrukturen von Systemen 4.5. Ungewollte Phänomene bei der Verwendung von Selbstorganisation 4.6. Anwendungsgebiete der Selbstorganisation in der Produktionsplanung und -steuerung 5. Referenzarchitekturen für sich selbst organisierende Systeme und für die Produktionsplanung und -steuerung 85 5.1. Anforderungen an eine sich selbst organisierende Produktionsplanung und -steuerung 5.2. Referenzarchitekturen für sich selbst organisierende Systeme 5.2.1. Autonomic Computing 5.2.2. Organic Computing 5.2.3. Multi-Agenten Systeme 5.2.4. Aktor-Modell 5.2.5. Warteschlangennetzwerke 5.3. Referenzarchitekturen für die Produktionsplanung und -steuerung 5.3.1. Product Ressource Order Straff Architecture 5.3.2. ISA-95 5.3.3. Reference Architecture Model for Industry 4.0 5.4. Herausforderungen der Überführung einer sich selbst organisierenden Produktion in die Realität 6. Architektur für eine sich selbst organisierte Produktion 105 6.1. MATE - Manufacturing on Actor Technology 6.2. Elemente der sich selbst organisierenden Produktionsplanung und -steuerung 108 6.3. Umwelteinflüsse auf die sich selbst organisierende Produktionsplanung und -steuerung 6.4. Organisation der Elemente 6.4.1. Koordination der sich selbst organisierenden Produktionsplanung und -steuerung 6.4.2. Verhalten bei der sich selbst organisierten Planung 6.4.3. Verhalten bei der sich selbst organisierten Produktionssteuerung 7. Evaluierung der sich selbst organisierenden Produktionsplanung und -steuerung 122 7.1. Ablauf der Evaluation der Produktionsplanung und -steuerung 7.2. Datenerhebung für verschiedene Produktionen 7.2.1. Möglichkeiten der Datenerhebung 7.2.2. Grundlegendes Vorgehen zur Testdatengenerierung 7.2.3. Strukturbeschreibende Kennzahlen der Produktion 7.3. Generierung der Daten für die Evaluierung der Produktionsplanung und - steuerung 7.3.1. Validierung der generierten Testdaten 7.3.2. Generierung der zu evaluierenden Produktion 7.4. Beschreibung der Ansätze zur zentralen und dezentralen Produktionsplanung und -steuerung zum Zweck des Vergleichs 7.4.1. Zentral organisiertes Planungssystem 7.4.2. Dezentral organisiertes Planungssystem mittels erschöpfender Zuteilung 7.5. Vergleich der Ansätze hinsichtlich der Ziele der Produktionsplanung und - steuerung 7.5.1. Effizienz 7.5.2. Termintreue der Kundenaufträge 7.5.3. Durchlaufzeit der Materialien 7.5.4. Auslastung der Produktionsressourcen 7.5.5. Lagerbelegung über die Zeit 7.5.6. Stabilität 7.5.7. Robustheit 7.5.8. Flexibilität 7.5.9. Anpassungsfähigkeit 7.5.10. Skalierbarkeit 7.6. Vergleich der Ansätze hinsichtlich verschiedener Eigenschaften der Produktion 8. Einordnung und Bewertung der Ergebnisse, Zusammenfassung und Ausblick 8.1. Einordnung der Eigenschaften der sich selbst organisierenden Produktionsplanung und -steuerung 8.2. Bewertung der Ergebnisse der Evaluation der sich selbst organisierenden Produktionsplanung und -steuerung 8.3. Zusammenfassung 8.4. Ausblick

Diffusion Models have achieved state-of-the-art results in image generating tasks, yet face different challenges when used in different domains. We first give a brief overview of the Diffusion Models architecture. Then, we present a new model and architecture called SVGFusion that applies the principles of Diffusion Models to generate Vector Graphics. Vector Graphics have a complex structure and are vastly different than pixel images, and thus the main challenge when working with Vector Graphics is how to represent their complex structure in a way that a Diffusion Model can effectively process. We will explain this and the further challenges that we encountered during the process and how we successfully addressed some of them. We demonstrate the effectiveness of our approach by training a sample model on a decently sized dataset as well as running valuable experiments. Furthermore, we offer useful insights, recommendations and code to researchers who wish to further explore this topic.

This project concerns studying the early development of living organisms. This period is accompanied by dynamic morphogenetic events. There is an increase in the number of cells, changes in the shape of cells and specification of cell fate during this time. Typically, in order to capture the dynamic morphological changes, one can employ a form of microscopy imaging such as Selective Plane Illumination Microscopy (SPIM) which offers a single-cell resolution across time, and hence allows observing the positions, velocities and trajectories of most cells in a developing embryo. Unfortunately, the dynamic genetic activity which underlies these morphological changes and influences cellular fate decision, is captured only as static snapshots and often requires processing (sequencing or imaging) multiple distinct individuals. In order to set the stage for characterizing the factors which influence cellular fate, one must bring the data arising from the above-mentioned static snapshots of multiple individuals and the data arising from SPIM imaging of other distinct individual(s) which characterizes the changes in morphology, into the same frame of reference. In this project, a computational pipeline is established, which achieves the aforementioned goal of mapping data from these various imaging modalities and specimens to a canonical frame of reference. This pipeline relies on the three core building blocks of Instance Segmentation, Tracking and Registration. In this dissertation work, I introduce EmbedSeg which is my solution to performing instance segmentation of 2D and 3D (volume) image data. Next, I introduce LineageTracer which is my solution to performing tracking of a time-lapse (2d+t, 3d+t) recording. Finally, I introduce PlatyMatch which is my solution to performing registration of volumes. Errors from the application of these building blocks accumulate which produces a noisy observation estimate of gene expression for the digitized cells in the canonical frame of reference. These noisy estimates are processed to infer the underlying hidden state by using a Hidden Markov Model (HMM) formulation. Lastly, for wider dissemination of these methods, one requires an effective visualization strategy. A few details about the employed approach are also discussed in the dissertation work. The pipeline was designed keeping imaging volume data in mind, but can easily be extended to incorporate other data modalities, if available, such as single cell RNA Sequencing (scRNA-Seq) (more details are provided in the Discussion chapter). The methods elucidated in this dissertation would provide a fertile playground for several experiments and analyses in the future. Some of such potential experiments and current weaknesses of the computational pipeline are also discussed additionally in the Discussion Chapter.

Embedding small wireless sensors into the environment allows for monitoring physical processes with high spatio-temporal resolutions. Today, these devices are equipped with a battery to supply them with power. Despite technological advances, the high maintenance cost and environmental impact of batteries prevent the widespread adoption of wireless sensors. Battery-free devices that store energy harvested from light, vibrations, and other ambient sources in a capacitor promise to overcome the drawbacks of (rechargeable) batteries, such as bulkiness, wear-out and toxicity. Because of low energy input and low storage capacity, battery-free devices operate intermittently; they are forced to remain inactive for most of the time charging their capacitor before being able to operate for a short time. While it is known how to deal with intermittency on a single device, the coordination and communication among groups of multiple battery-free devices remain largely unexplored. For the first time, the present thesis addresses this problem by proposing new methods and tools to investigate and overcome several fundamental challenges.

Optimized implementations of blocking and nonblocking collective operations are most important for scalable high-performance applications. Offloading such collective operations into the communication layer can improve performance and asynchronous progression of the operations. However, it is most important that such offloading schemes remain flexible in order to support user-defined (sparse neighbor) collective communications. In this work we propose a design for a collective offload unit. Our hardware design is able to execute dependency graph based representations of collective functions. To cope with the scarcity of memory resources we designed a new point to point messaging protocol which does not need to store information about unexpected messages. The offload unit proposed in this thesis could be integrated into high performance networks such as EXTOLL. Our design achieves a clock frequency of 212 MHz on a Xilinx Virtex6 FPGA, while using less than 10% of the available logic slices and less than 30% of the available memory blocks. Due to the specialization of our design we can accelerate important tasks of the message passing framework, such as message matching by a factor of two, compared to a software implementation running on a CPU with a ten times higher clock speed.:1. Task Description 1.1. Theses 2. Introduction 2.1. Motivation 2.2. Outline of this Thesis 2.3. Related Work 2.3.1. NIC Based Packet Forwarding 2.3.2. Hardware Barrier Implementations 2.3.3. ConnectX2 CORE-Direct Collective Offload Support 2.3.4. Collective Offload Support in the Portals 4 API 2.4. Group Operation Assembly Language 2.4.1. GOAL API 2.4.2. Scratchpad Buffer 2.4.3. Schedule Execution 2.5. The EXTOLL Network 2.6. Field Programmable Gate Arrays 3. Dealing with Constrained Resources 3.1. Hardware Limitations 3.2. Common Collective Functions in GOAL 3.3. Schedule Representation for the Hardware GOAL Interpreter 3.4. Executing Large Schedules using a small amount of Memory 3.4.1. Limits of Previously Suggested Approaches 3.4.2. Testing for Deadlocks in Schedules 3.4.3. Transforming Process Local Schedules into Global Schedules 3.4.4. Predetermined Buffer Locations 3.5. Queueing Active Operations in Hardware 3.6. Designing a Low-Memory-Footprint Point to Point Protocol 3.6.1. Arrival Times 3.6.2. Eager Protocol 3.6.3. Rendezvous Protocol 3.6.4. A Protocol without an Unexpected Queue 3.7. Protocol Verification 3.7.1. Capabilities of the Model Checker SPIN 3.7.2. Modeling the Protocol 3.7.3. Limitations of the Basic Protocol 4. The Matching Problem 4.1. Matching on the Host CPU 4.2. Implementation Methodology 4.3. Matching Unit Interface 4.4. Matching Unit Implementation 4.4.1. Slot Management Unit 4.4.2. The Input Consumer 4.4.3. The Output Generator 4.4.4. The Matching Unit 4.5. Slot Management Unit for Non-synchronous Transfers 5. The GOAL Interpreter 5.1. Schedule Interpreter Design 5.1.1. The Active Queue 5.1.2. The Dependency Resolver 5.2. Transceiver Interface 5.3. The Starter 5.3.1. Starting Operations 5.3.2. Processing Incoming Packets 5.3.3. Incoming Non-synchronous Packets 5.3.4. Presorting the Active Queue 5.3.5. Arbitration Units 5.3.6. IN-Filter 5.3.7. Outcommand Manager 5.3.8. Non-synchronous Protocol 5.3.9. Send Protocol 5.3.10. Receive Protocol 5.3.11. Local Operations on FPGA 6 Evaluation 6.1. Performance Analysis 6.2. Future Work 6.3. Conclusions Bibliography

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During laparoscopic liver resection, the limited access to the organ, the small field of view and lack of palpation can obstruct a surgeon’s workflow. Automatic navigation systems could use the images from preoperative volumetric organ scans to help the surgeons find their target (tumors) and risk-structures (vessels) more efficiently. This requires the preoperative data to be fused (or registered) with the intraoperative scene in order to display information at the correct intraoperative position. One key challenge in this setting is the automatic estimation of the organ’s current intra-operative deformation, which is required in order to predict the position of internal structures. Parameterizing the many patient-specific unknowns (tissue properties, boundary conditions, interactions with other tissues, direction of gravity) is very difficult. Instead, this work explores how to employ deep neural networks to solve the registration problem in a data-driven manner. To this end, convolutional neural networks are trained on synthetic data to estimate an organ’s intraoperative displacement field and thus its current deformation. To drive this estimation, visible surface cues from the intraoperative camera view must be supplied to the networks. Since reliable surface features are very difficult to find, the networks are adapted to also find correspondences between the pre- and intraoperative liver geometry automatically. This combines the search for correspondences with the biomechanical behavior estimation and allows the networks to tackle the full non-rigid registration problem in one single step. The result is a model which can quickly predict the volume deformation of a liver, given only sparse surface information. The model combines the advantages of a physically accurate biomechanical simulation with the speed and powerful feature extraction capabilities of deep neural networks. To test the method intraoperatively, a registration pipeline is developed which constructs a map of the liver and its surroundings from the laparoscopic video and then uses the neural networks to fuse the preoperative volume data into this map. The deformed organ volume can then be rendered as an overlay directly onto the laparoscopic video stream. The focus of this pipeline is to be applicable to real surgery, where everything should be quick and non-intrusive. To meet these requirements, a SLAM system is used to localize the laparoscopic camera (avoiding setup of an external tracking system), various neural networks are used to quickly interpret the scene and semi-automatic tools let the surgeons guide the system. Beyond the concrete advantages of the data-driven approach for intraoperative registration, this work also demonstrates general benefits of training a registration system preoperatively on synthetic data. The method lets the engineer decide which values need to be known explicitly and which should be estimated implicitly by the networks, which opens the door to many new possibilities.:1 Introduction 1.1 Motivation 1.1.1 Navigated Liver Surgery 1.1.2 Laparoscopic Liver Registration 1.2 Challenges in Laparoscopic Liver Registration 1.2.1 Preoperative Model 1.2.2 Intraoperative Data 1.2.3 Fusion/Registration 1.2.4 Data 1.3 Scope and Goals of this Work 1.3.1 Data-Driven, Biomechanical Model 1.3.2 Data-Driven Non-Rigid Registration 1.3.3 Building a Working Prototype 2 State of the Art 2.1 Rigid Registration 2.2 Non-Rigid Liver Registration 2.3 Neural Networks for Simulation and Registration 3 Theoretical Background 3.1 Liver 3.2 Laparoscopic Liver Resection 3.2.1 Staging Procedure 3.3 Biomechanical Simulation 3.3.1 Physical Balance Principles 3.3.2 Material Models 3.3.3 Numerical Solver: The Finite Element Method (FEM) 3.3.4 The Lagrangian Specification 3.4 Variables and Data in Liver Registration 3.4.1 Observable 3.4.2 Unknowns 4 Generating Simulations of Deforming Organs 4.1 Organ Volume 4.2 Forces and Boundary Conditions 4.2.1 Surface Forces 4.2.2 Zero-Displacement Boundary Conditions 4.2.3 Surrounding Tissues and Ligaments 4.2.4 Gravity 4.2.5 Pressure 4.3 Simulation 4.3.1 Static Simulation 4.3.2 Dynamic Simulation 4.4 Surface Extraction 4.4.1 Partial Surface Extraction 4.4.2 Surface Noise 4.4.3 Partial Surface Displacement 4.5 Voxelization 4.5.1 Voxelizing the Liver Geometry 4.5.2 Voxelizing the Displacement Field 4.5.3 Voxelizing Boundary Conditions 4.6 Pruning Dataset - Removing Unwanted Results 4.7 Data Augmentation 5 Deep Neural Networks for Biomechanical Simulation 5.1 Training Data 5.2 Network Architecture 5.3 Loss Functions and Training 6 Deep Neural Networks for Non-Rigid Registration 6.1 Training Data 6.2 Architecture 6.3 Loss 6.4 Training 6.5 Mesh Deformation 6.6 Example Application 7 Intraoperative Prototype 7.1 Image Acquisition 7.2 Stereo Calibration 7.3 Image Rectification, Disparity- and Depth- estimation 7.4 Liver Segmentation 7.4.1 Synthetic Image Generation 7.4.2 Automatic Segmentation 7.4.3 Manual Segmentation Modifier 7.5 SLAM 7.6 Dense Reconstruction 7.7 Rigid Registration 7.8 Non-Rigid Registration 7.9 Rendering 7.10 Robotic Operating System 8 Evaluation 8.1 Evaluation Datasets 8.1.1 In-Silico 8.1.2 Phantom Torso and Liver 8.1.3 In-Vivo, Human, Breathing Motion 8.1.4 In-Vivo, Human, Laparoscopy 8.2 Metrics 8.2.1 Mean Displacement Error 8.2.2 Target Registration Error (TRE) 8.2.3 Champfer Distance 8.2.4 Volumetric Change 8.3 Evaluation of the Synthetic Training Data 8.4 Data-Driven Biomechanical Model (DDBM) 8.4.1 Amount of Intraoperative Surface 8.4.2 Dynamic Simulation 8.5 Volume to Surface Registration Network (V2S-Net) 8.5.1 Amount of Intraoperative Surface 8.5.2 Dependency on Initial Rigid Alignment 8.5.3 Registration Accuracy in Comparison to Surface Noise 8.5.4 Registration Accuracy in Comparison to Material Stiffness 8.5.5 Champfer-Distance vs. Mean Displacement Error 8.5.6 In-vivo, Human Breathing Motion 8.6 Full Intraoperative Pipeline 8.6.1 Intraoperative Reconstruction: SLAM and Intraoperative Map 8.6.2 Full Pipeline on Laparoscopic Human Data 8.7 Timing 9 Discussion 9.1 Intraoperative Model 9.2 Physical Accuracy 9.3 Limitations in Training Data 9.4 Limitations Caused by Difference in Pre- and Intraoperative Modalities 9.5 Ambiguity 9.6 Intraoperative Prototype 10 Conclusion 11 List of Publications List of Figures Bibliography

Finding bottlenecks and eliminating them to increase the overall flow of a network often appears in real world applications, such as production planning, factory layout, flow related physical approaches, and even cyber security. In many cases, several edges can form a bottleneck (cascaded bottlenecks). This work presents a visual analytics methodology to analyze these cascaded bottlenecks. The methodology consists of multiple steps: identification of bottlenecks, identification of potential improvements, communication of bottlenecks, interactive adaption of bottlenecks, and a feedback loop that allows users to adapt flow networks and their resulting bottlenecks until they are satisfied with the flow network configuration. To achieve this, the definition of a minimal cut is extended to identify network edges that form a (cascaded) bottleneck. To show the effectiveness of the presented approach, we applied the methodology to two flow network setups and show how the overall flow of these networks can be improved.

The semantic annotation of large multimedia corpora is essential for numerous tasks. Be it for the training of classification algorithms, efficient content retrieval, or for analytical reasoning, appropriate labels are often the first necessity before automatic processing becomes efficient. However, manual labeling of large datasets is time-consuming and tedious. Hence, we present a new visual approach for labeling and retrieval of reports in multimedia news corpora. It combines automatic classifier training based on caption text from news reports with human interpretation to ease the annotation process. In our approach, users can initialize labels with keyword queries and iteratively annotate examples to train a classifier. The proposed visualization displays representative results in an overview that allows to follow different annotation strategies (e.g., active learning) and assess the quality of the classifier. Based on a usage scenario, we demonstrate the successful application of our approach. Therein, users label several topics which interest them and retrieve related documents with high confidence from three years of news reports.

In modern supercomputer architectures, the I/O capabilities do not keep up with the computational speed. Image-based techniques are one very promising approach to a scalable output format for visual analysis, in which a reduced output that corresponds to the visible state of the simulation is rendered in-situ and stored to disk. These techniques can support interactive exploration of the data through image compositing and other methods, but automatic methods of highlighting data and reducing clutter can make these methods more effective. In this paper, we suggest a method of assisted exploration through the combination of feature-centric analysis with image space techniques and show how the reduction of the data to features of interest reduces occlusion in the output for a set of example applications.