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Linear and nonlinear response functions (RF) are extracted for the climate system and the carbon cycle represented by the MPI-ESM and cGENIE models, respectively. Appropriately designed simulations are run for this purpose. Joining these RFs, we have a climate emulator with carbon emissions as the forcing and any desired observable quantity (provided the data is saved), such as the surface air temperature or precipitation, as the predictand. Like e.g. for atmospheric CO2 concentration, we also have RFs for the solar constant as a forcing — mimicking solar radiation management (SRM) geoengineering. We consider two application cases. 1. One is based on the Paris 2015 agreement, determining the necessary least amount of SRM geoengineering needed to keep the global mean surface air temperature below a certain threshold, e.g. 1.5 or 2 [oC], given a certain amount of carbon emission abatement (ABA) and carbon dioxide removal (CDR) geoengineering. 2. The other application considers the conservation of the Greenland ice sheet (GrIS). Using a zero-dimensional simplification of a complex ice sheet model, we determine (a) if we need SRM given some ABA and CDR, and, if possible, (b) the required least amount of SRM to avoid the collapse of the GrIS. Keeping temperatures below 2 [oC] even is hardly possible without sustained SRM (1.); however, the collapse of the GrIS can be avoided applying SRM even for moderate levels of CDR and ABA, an overshoot being affordable (2.).
Classification of point clouds by different types of geometric primitives is an essential part in the reconstruction process of CAD geometry. We use support vector machines (SVM) to label patches in point clouds with the class labels tori, ellipsoids, spheres, cones, cylinders or planes. For the classification features based on different geometric properties like point normals, angles, and principal curvatures are used. These geometric features are estimated in the local neighborhood of a point of the point cloud. Computing these geometric features for a random subset of the point cloud yields a feature distribution. Different features are combined for achieving best classification results. To minimize the time consuming training phase of SVMs, the geometric features are first evaluated using linear discriminant analysis (LDA).
LDA and SVM are machine learning approaches that require an initial training phase to allow for a subsequent automatic classification of a new data set. For the training phase point clouds are generated using a simulation of a laser scanning device. Additional noise based on an laser scanner error model is added to the point clouds. The resulting LDA and SVM classifiers are then used to classify geometric primitives in simulated and real laser scanned point clouds.
Compared to other approaches, where all known features are used for classification, we explicitly compare novel against known geometric features to prove their effectiveness.
Volterra and Wiener series
(2011)
Volterra and Wiener series are two classes of polynomial representations of nonlinear systems. They are perhaps the best understood and most widely used nonlinear system representations in signal processing and system identification. A Volterra or Wiener representation can be thought of as a natural extension of the classical linear system representation. In addition to the convolution of the input signal with the system's impulse response, the system representation includes a series of nonlinear terms that contain products of increasing order of the input signal with itself. It can be shown that these polynomial extension terms allow for representing a large class of nonlinear systems which basically encompasses all systems with scalar outputs that are time-invariant and have noninfinite memory.
Three-dimensional ship localization with only one camera is a challenging task due to the loss of depth information caused by perspective projection. In this paper, we propose a method to measure distances based on the assumption that ships lie on a flat surface. This assumption allows to recover depth from a single image using the principle of inverse perspective. For the 3D ship detection task, we use a hybrid approach that combines image detection with a convolutional neural network, camera geometry and inverse perspective. Furthermore, a novel calculation of object height is introduced. Experiments show that the monocular distance computation works well in comparison to a Velodyne lidar. Due to its robustness, this could be an easy-to-use baseline method for detection tasks in navigation systems.