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    A 3D DCE-US Interconnected Voxel Analysis Framework for Bedside Characterization of Tumor Vascular Properties in Liver Malignancies
    Liver cancer results in a significant number of deaths worldwide. Characterization of patient-specific tumor vasculature and perfusion at bedside can enable better, individualized treatments. We propose a reconstruction method based on a two-compartment contrast flow field model to characterize vascular properties through the analysis of 3D dynamic contrast-enhanced ultrasound measurements. Our inversion method determines patient-specific parameter functions by minimizing the mismatch of simulated and measured contrast concentrations. The analysis is performed on synthetic as well as experimental animal data. To assess clinical relevance, we investigate reconstruction of physiologically interpretable quantities such as arterial blood flow and perfusion.
      74
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    SPP 2265: Limit theorems for stabilising functionals
    Many random geometric systems are constructed from point processes. Their global quantities can frequently be expressed as sums of contributions of the underlying points, so-called scores. Stabilization means that the score of a point only depends on the other points of the point process in a random neighborhood. Such sums of scores are called stabilising functionals and play an important role in stochastic geometry. They arise, for example, in the context of spatial random graphs, random tessellations or random polytopes. This project concerns stabilising functionals of underlying Poisson or binomial point processes and studies their asymptotic behaviour as the number of points goes to infinity. Here the variances of stabilizing functionals can have different orders, which is crucial for their analysis. Limit theorems for stabilizing functionals are an active topic of research for more than twenty years and can be applied to many different problems. The goal of this project is to significantly extend and complement the limit theory for stabilizing functionals in several directions: (i) multivariate quantitative central limit theorems, (ii) discretized normal approximation, (iii) normal approximation in total variation distance and (iv) functional central limit theorems. It is planned to derive abstract results that are valid for large classes of stabilizing functionals and, in particular, for different variance orders. These findings will be applied to many prominent examples from stochastic geometry such as edge-length functionals and component counts of spatial random graphs, edge-length functionals of Voronoi tessellations, the volume of the Voronoi set approximation, numbers of maximal points as well as intrinsic volumes and numbers of k-faces of random polytopes. The proofs of some of the intended results will rely on Stein’s method or its combination with Malliavin calculus.
      57
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    CRC 1615 - Central project Z-INF: Big data processing for SMART reactors
    This project integrates the services and research necessary to process the very large amounts of data produced in SMART reactors. For the research part, novel approaches to establish data stream processing in the fog will be developed. A special focus is on integrated solutions which consider aspects like networking quality when setting up a data stream processing topology. The goal is to integrate edge- and cloud-based computational resources in order to process very large amounts of data coming from SMART reactors and similar smart systems.
      107
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    CRC 1615 - Project A08: Lagrangian devices with a validated model in multi-particle tracking
    Project A08 will develop Lagrangian devices that can function as sensors and actuators and are substantially smaller than existing Lagrangian sensors. To enable accurate tracking of multiple particles, we will integrate sensor readings from the devices with a newly developed real-time model for their trajectories. This will deliver a high fidelity tracking mechanism that is robust and can cope with issues like noise, missing or ambiguous data and other adverse effects. Ultimately, this will allow the sensors to provide a comprehensive view of the internal state of a chemical reactor and enable their use for closed-loop control.
      154
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    SPP 2402: Extrapolative digital greybox models for describing and predicting the macroscopic system behavior of TiAlN-coated cutting tools
    Tools with a hard coating make up the majority of cutting tools used today. They protect the substrate from abrasive wear, increase chemical resistance and reduce the coefficient of friction, resulting in increased tool life during turning and milling operations. Various empirical or physically based models exist for the estimation of tool life. Since the properties of these coatings depend on a large number of manufacturing parameters, no consistent models exist to predict the wear behavior of hard coatings. The objective of the proposed research project is to gain knowledge about the tribological cause-and-effect relationships of the wear behavior of coated cutting tools in interaction with the workpiece material and the process parameters under consideration of the particular mechanical, chemical-structural and tribological properties of the coating and to map this knowledge into a model. Therefor greybox models shall be used. These models are rely on artificial neural networks (blackbox) that use training data based on process parameters, measurements and metadata from machining tests. Physical and empirical models for wear rate prediciton and remaining tool life are additionally integrated into these models (whitebox). This allows the model, with the limited scope of training data due to the high experimental effort, to predict physically meaningful solutions. In machining tests, which are partially automated by integrating force, temperature, structure-borne sound and surface measurement techniques as well as microscopy in the workspace of a CNC lathe, a continuously growing database is created for the simultaneous model building. In order to minimize the experimental effort and thus the material and energy costs, the next experimental parameters are determined in each case on the basis of the current training state of the model using Active Learning. The recorded measurement and metadata are available in a wide variety of formats, such as image data, continuous measurement or discrete measurement points. They are processed and stored with their metadata in an digital lab book. Subsequently, the data is homogenized and reduced to obtain a balanced data set for training the neural networks. For final validation, blind tests, with cutting parameters and coating unknown to the model, are performed to confirm the interpolation and extrapolation capabilities of the greybox model.
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