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This paper gives a global solution to an H-infinity control problem for systems with symmetric state matrix and state-dependent input matrix. A simple, closed-form expression for the minimizing controller is obtained. This is in contrast to already established theory in which nonlinear H-infinity problems are solved locally. The result is then illustrated through an example, and the potential for
This paper presents tractable two parameter stochastic processes of the drift-diffusion class in order to model economic processes with a focus on income. Starting from the resulting closed-form, cross-sectional distributions, easy-to-interpret expressions for mobility and inequality (including the popular Gini-coefficient) are derived. The general processes are applied to discuss income mobility
A well-balanced exploration-exploitation trade-off is crucial for successful acquisition functions in Bayesian optimization. However, there is a lack of quantitative measures for exploration, making it difficult to analyze and compare different acquisition functions. This work introduces two novel approaches - observation traveling salesman distance and observation entropy - to quantify the explor
Bayesian optimization (BO ) is an effective method for optimizing expensive-to-evaluate black-box functions. While high-dimensional problems can be particularly challenging, due to the multitude of parameter choices and the potentially high number of data points required to fit the model, this limitation can be addressed if the problem satisfies simplifying assumptions. Axis-aligned subspace appro
This thesis concerns control of capacity-constrained networks. These systems involve many agents interconnected by a resource distribution network. The capacity to generate and distribute this resource is constrained. This applies, for instance, to power grids, communication networks, smart surveillance camera networks, and district heating networks. District heating networks in particular are the
For linear time-invariant systems with uncertain parameters belonging to a finite set, we present a purely eterministic approach to multiple-model estimation and propose an algorithm based on the minimax criterion using constrained quadratic programming. The estimator tends to learn the dynamics of the system, and once the uncertain parameters have been sufficiently estimated, the estimator behave
Weakly-hard models have been used to analyse real-time systems subject to patterns of deadline hits and misses. However, the tools that are available in the literature have a set of shortcomings. The analysis they offer is limited to a single weaklyhard constraint and to patterns that specify the number of misses, rather than the number of hits. Furthermore, the scalability of the tools is limited
A corpus study and a combined behavioural and neurophysiological study tested how phonetic and phonological features of the Danish creaky voice feature ‘stød’ influence predictive processing. Being associated with certain word endings, stød and its modal voice counterpart non-stød can cue upcoming speech. Stød has two phases. The first shows phonetic differences in pitch while the second, characte
In this paper, we apply an optimal Linear Quadratic (LQ) controller, which has an inherent structure that allows for a distributed implementation, to an irrigation network. The network consists of a water reservoir and connected water canals. The goal is to keep the levels close to the set-points when farmers take out water. The LQ controller is designed using a first-order approximation of the ca
Nondestructive testing of composite materials is important in aerospace applications, and mm-wave imaging has been increasingly used for this purpose. Imaging is traditionally performed using Fourier methods, with inverse methods being an alternative. This communication presents a mm-wave imaging method with an inverse approach intended for nondestructive testing of singly curved composite panels
We present property probes, a mechanism for helping a developer explore partial program analysis results in terms of the source program interactively while the program is edited. A node locator data structure is introduced that maps between source code spans and program representation nodes, and that helps identify probed nodes in a robust way, after modifications to the source code. We have devel
Objective: The association between work-related moderate-to-vigorous physical activity (MVPA) and higher levels of stress response is recognized, but whether this association is moderated by regular exercise remains unclear. This cross-sectional study investigated whether exercise-based physical activity (PA) associates with lower levels of stress responses moderated by work-related MVPA. Methods:
Design of buildings is a complex task in which ideas are sketched and communicated, by representations that are incrementally elaborated from the early rough sketches to the final design. We claim, that today’s model–based design tools are restricted from fully supporting this process as they are founded on the principle that objects are instances of static types. Such systems do not offer work wi
Context: It is an enigma that agile projects can succeed ‘without requirements’ when weak requirements engineering is a known cause for project failures. While agile development projects often manage well without extensive requirements test cases are commonly viewed as requirements and detailed requirements are documented as test cases.Objective: We have investigated this agile practice of using t
High-dimensional problems have long been considered the Achilles' heel of Bayesian optimization. Spurred by the curse of dimensionality, a large collection of algorithms aim to make it more performant in this setting, commonly by imposing various simplifying assumptions on the objective. In this paper, we identify the degeneracies that make vanilla Bayesian optimization poorly suited to high-dimen
