Vorwort

Ohne Zusammenfassung     

Optimizing counter-terror operations: Should one fight fire with “fire” or “water”?

This paper deals dynamically with the question of how recruitment to terror organizations is influenced by counter-terror operations. This is done within an optimal control model, where the key state is the (relative) number of terrorists and the key controls are two types of counter-terror tactics, one (“water”) that does not and one (“fire”) that does provoke recruitment of new terrorists. The model is nonlinear and does not admit analytical solutions, but an efficient numerical implementation of Pontryagin's minimum principle allows for solution with base case parameters and considerable sensitivity analysis. Generally, this model yields two different steady states, one where the terror organization is nearly eradicated and one with a high number of terrorists. Whereas water strategies are used at almost any time, it can be optimal not to use fire strategies if the number of terrorists is below a certain threshold.     

Approximability of Clausal Constraints

We study a family of problems, called Maximum Solution (Max Sol), where the objective is to maximise a linear goal function over the feasible integer assignments to a set of variables subject to a set of constraints. When the domain is Boolean (i.e. restricted to {0,1}), the maximum solution problem is identical to the well-studied Max Ones problem, and the complexity and approximability is completely understood for all restrictions on the underlying constraints. We continue this line of research by considering the Max Sol problem for relations defined by regular signed logic over finite subsets of the natural numbers; the complexity of the corresponding decision problem has recently been classified by Creignou et al. (Theory Comput. Syst. 42(2):239–255, 2008). We give sufficient conditions for when such problems are polynomial-time solvable and we prove that they are APX-hard otherwise. Similar dichotomies are also obtained for variants of the Max Sol problem.     

Induction and Synchronous Motors Compared for System Stability

A comparison is made between synchronous motors and induction motors with regard to industrial power system stability. Representation of motor loads in stability studies is reviewed. The specific system studied is an infinite bus supplying a combination induction and synchronous motor load through a transmission line and transformer. The system is subjected to a fault at the transformer and critical values of fault clearing time are computer calculated for ratios of synchronous motor load to induction motor load from zero to unity. The effect of variation of motor parameters on clearing times is discussed.     

Convex optimization of measurement allocation for magnetic tracking systems

Magnetic tracking is a popular technique that exploits static and low-frequency magnetic fields for positioning of quasi-stationary objects. One important system design aspect, which substantially influences the performance of the tracking system, is how to collect as much information as possible with a given number of measurements. In this work, we optimize the allocation of measurements given a large number of possible measurements of a generic magnetic tracking system that exploits time-division multiplexing. We exploit performance metrics based on the Fisher information matrix. In particular, the performance metrics measure worst-case or average performance in a measurement domain, i.e. the domain where the tracking is to be performed. An optimization problem with integer variables is formulated. By relaxing the constraint that the variables should be integer, a convex optimization problem is obtained. The two performance metrics are compared for several realistic measurement scenarios with planar transmitter constellations. The results show that the worst performance is obtained in the most distant parts of the measurement domain. Furthermore, measurement allocations optimized for worst-case performance require measurements in a larger area than measurement allocations optimized for average performance.     

Style-Controllable Speech-Driven Gesture Synthesis Using Normalising Flows

Automatic synthesis of realistic gestures promises to transform the fields of animation, avatars and communicative agents. In off-line applications, novel tools can alter the role of an animator to that of a director, who provides only high-level input for the desired animation; a learned network then translates these instructions into an appropriate sequence of body poses. In interactive scenarios, systems for generating natural animations on the fly are key to achieving believable and relatable characters. In this paper we address some of the core issues towards these ends. By adapting a deep learning-based motion synthesis method called MoGlow, we propose a new generative model for generating state-of-the-art realistic speech-driven gesticulation. Owing to the probabilistic nature of the approach, our model can produce a battery of different, yet plausible, gestures given the same input speech signal. Just like humans, this gives a rich natural variation of motion. We additionally demonstrate the ability to exert directorial control over the output style, such as gesture level, speed, symmetry and spacial extent. Such control can be leveraged to convey a desired character personality or mood. We achieve all this without any manual annotation of the data. User studies evaluating upper-body gesticulation confirm that the generated motions are natural and well match the input speech. Our method scores above all prior systems and baselines on these measures, and comes close to the ratings of the original recorded motions. We furthermore find that we can accurately control gesticulation styles without unnecessarily compromising perceived naturalness. Finally, we also demonstrate an application of the same method to full-body gesticulation, including the synthesis of stepping motion and stance.     

Initial rapid wetting in metallic systems

The initial rapid wetting of a solid surface by a liquid phase is an important step in many industrial processes. Liquid-phase sintering of powder metallurgical steels is one such industrial process, where metallic powders of micrometer size are used. Investigating the dynamic wetting of a high-temperature metallic drop of micrometer size experimentally is very challenging. Here, a phase-field-based numerical model is first implemented and verified by accurately capturing the initial dynamic wetting of millimeter-sized metal drops and then the model is extended to predict the dynamic wetting of a micrometer-sized metal drop. We found, in accordance with recent observations, that contact line friction is required for accurate simulation of dynamic wetting. Our results predict the wetting time for a micrometer-sized metal drop and also indicate that the dynamic wetting patterns at the micro- and millimeter length scales are qualitatively similar. We also found that the wetting process is much faster for a micrometer-sized metal drop compared to a millimeter-sized metal drop.     

Approximating curves and their offsets using biarcs and Pythagorean hodograph quintics

This paper compares two techniques for the approximation of the offsets to a given planar curve. The two methods are based on approximate conversion of the planar curve into circular splines and Pythagorean hodograph (PH) splines, respectively. The circular splines are obtained using a novel variant of biarc interpolation, while the PH splines are constructed via Hermite interpolation of C¹ boundary data.We analyze the approximation order of both conversion procedures. As a new result, the C¹ Hermite interpolation with PH quintics is shown to have approximation order 4 with respect to the original curve, and 3 with respect to its offsets. In addition, we study the resulting data volume, both for the original curve and for its offsets. It is shown that PH splines outperform the circular splines for increasing accuracy, due to the higher approximation order.