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An important new development within the European ENSEMBLES project has been to explore performance-based weighting of regional climate models (RCMs). Until now, although no weighting has been applied in multi-RCM analyses, one could claim that an assumption of ‘equal weight’ was implicitly adopted. At the same time, different RCMs generate different results, e.g. for various types of extremes, and
We use formaldehyde (HCHO) vertical column measurements from the Scanning Imaging Absorption spectrometer for Atmospheric Chartography (SCIAMACHY) and Ozone Monitoring Instrument (OMI), and a nested-grid version of the GEOS-Chem chemistry transport model, to infer an ensemble of top-down isoprene emission estimates from tropical South America during 2006, using different model configurations and a
We present exploratory data analysis methods to assess inversion estimates using examples based on l(2)- and l(1)-regularization. These methods can be used to reveal the presence of systematic errors such as bias and discretization effects, or to validate assumptions made on the statistical model used in the analysis. The methods include bounds on the performance of randomized estimators of a larg
This paper examines the potential benefits of applying next best view planning to sequential 3D reconstruction from unordered image sequences. A standard sequential structure-and-motion pipeline is extended with active selection of the order in which cameras are resectioned. To this end, approximate covariance propagation is implemented throughout the system, providing running estimates of the unc
Decentralised and scalable conditions for robust stability of networks of heterogenous linear time-invariant (LTI) systems are derived based on integral quadratic constraints. These generalise previous works in the literature with an increased flexibility in the choice of multipliers employed. The results allow for arbitrary interconnection matrices and accommodate multi-input-multi-output systems
In absence of covariance data, Kalman filters are usually initialized by guessing the initial state. Making the variance of the initial state estimate large makes sure that the estimate converges quickly and that the influence of the initial guess soon will be negligible. If, however, only very few measurements are available during the estimation process and an estimate is wanted as soon as possib
The application of feedback control to computing systems is a promising research area, but has to date been hindered by the almost unanimously perceived complexity in creating control-oriented system models. Computing systems are in fact considered very hard to describe with dynamic models allowing for simple and powerful control design tools, so that complex ones need bringing in to the detriment
We consider the problem of modeling and control of the nonlinear dynamics of a micro manipulator, utilized for machining operations in combination with industrial robots. Position control of the micro manipulator is a challenging problem because of the actuation principle, which is based on piezo-actuators with inherent nonlinear behavior. The major nonlinearities in the manipulator are identified
This paper presents a novel on-line approach of merging multiple different predictors of plasma glucose into a single optimized prediction. Various different predictors are merged by recursive weighting into a single prediction using regularized optimization. The approach is evaluated on 12 data sets of type I diabetes data, using three parallel predictors. The performance of the combined predicti
Two mixed-phase precipitation events were observed on 21 September and 20 October 2010 over the southern part of Finland during the Light Precipitation Validation Experiment (LPVEx). These events have been simulated using the Weather Research and Forecasting Model coupled with spectral bin microphysics (WRF-SBM). The detailed ice-melting scheme with prognosis of the liquid water fraction during me
Resource allocation in clouds is mostly done assuming hard requirements, applications either receive the requested resources or fail. Given the dynamic nature of workloads, guaranteeing on-demand allocations requires large spare capacity. Hence, one cannot have a system that is both reliable and efficient. To solve this issue, we introduce Service Level (SL) awareness in clouds, assuming applicati
Robust and reliable formulas for predicting bed load and suspended load were developed for application in the nearshore zone where waves and currents may transport sediment separately or in combination. Also, a routine was included to determine the sediment transport in the swash zone, both in the longshore and cross-shore directions. An important objective of the development was to arrive at gene
Time-dependent ensemble averages, i.e., trajectory-based averages of some observable, are of importance in many fields of science. A crucial objective when interpreting such data is to fit these averages (for instance, squared displacements) with a function and extract parameters (such as diffusion constants). A commonly overlooked challenge in such function fitting procedures is that fluctuations
Driven by the increasing demands on data rate from applications, the wireless communication standard has for decades been evolving approximately at a pace of one generation per ten years. Following this trend, the ambitious plan to replace the current cellular mobile network standard (4G) with the next generation standard (5G) is going through the standardization phase and is getting close to its
The purpose of this paper is to analyse transient dynamics in adaptive control using statistical concentration bounds. For maximal clarity, the study is limited to a linear first order system with a single uncertain parameter. Two types of bounds are given: First we prove probabilistic bounds on the parameter estimation error as a function of time. In particular, we prove that the estimation error
We analyze the local convergence of proximal splitting algorithms to solve optimization problems that are convex besides a rank constraint. For this, we show conditions under which the proximal operator of a function involving the rank constraint is locally identical to the proximal operator of its convex envelope, hence implying local convergence. The conditions imply that the non-convex algorith
Cloud computing infrastructures are powering most of the web hosting services that we use at all times. A recent failure in the Amazon cloud infrastructure made many of the website that we use on a hourly basis unavailable1. This illustrates the importance of cloud applications being able to absorb peaks in workload, and at the same time to tune their power requirements to the power and energy cap
In this paper we consider L1 optimal and H-infinity optimal control problems for a particular class of Positive Bilinear Systems that arise in drug dosage design for HIV treatment. Starting from existent characterizations of the L1-norm for positive systems, a convex formulation for the first problem is provided. As for the H-infinity case, we propose an algorithm based on the iterative solution o
In the framework of robotics, Reinforcement Learning (RL) deals with the learning of a task by the robot itself. This paper presents a hierarchical planning approach in which the robot learns the optimal behavior for different levels. For high-level discrete actions, Q-learning was chosen, whereas for the low level we utilize Policy Improvement with Path Integrals (PI^2) algorithm to learn the par
