Overview of methods to analyse dynamic data

This book gives an overview of existing data analysis methods to analyse the dynamic data obtained from full scale testing, with their advantages and drawbacks. The overview of full scale testing and dynamic data analysis is limited to energy performance characterization of either building components or whole buildings. The methods range from averaging and regression methods to dynamic approaches based on system identification techniques. These methods are discussed in relation to their application in following in situ measurements: -measurement of thermal transmittance of building components based on heat flux meters; -measurement of thermal and solar transmittance of building components tested in outdoor calorimetric test cells; -measurement of heat transfer coefficient and solar aperture of whole buildings based on co-heating or transient heating tests; -characterisation of the energy performance of whole buildings based on energy use monitoring

A hybrid multiagent approach for global trajectory optimization

In this paper we consider a global optimization method for space trajectory design problems. The method, which actually aims at finding not only the global minimizer but a whole set of low-lying local minimizers(corresponding to a set of different design options), is based on a domain decomposition technique where each subdomain is evaluated through a procedure based on the evolution of a population of agents. The method is applied to two space trajectory design problems and compared with existing deterministic and stochastic global optimization methods

Atmospheric PSF Interpolation for Weak Lensing in Short Exposure Imaging Data

A main science goal for the Large Synoptic Survey Telescope (LSST) is to measure the cosmic shear signal from weak lensing to extreme accuracy. One difficulty, however, is that with the short exposure time ($\simeq$15 seconds) proposed, the spatial variation of the Point Spread Function (PSF) shapes may be dominated by the atmosphere, in addition to optics errors. While optics errors mainly cause the PSF to vary on angular scales similar or larger than a single CCD sensor, the atmosphere generates stochastic structures on a wide range of angular scales. It thus becomes a challenge to infer the multi-scale, complex atmospheric PSF patterns by interpolating the sparsely sampled stars in the field. In this paper we present a new method, PSFent, for interpolating the PSF shape parameters, based on reconstructing underlying shape parameter maps with a multi-scale maximum entropy algorithm. We demonstrate, using images from the LSST Photon Simulator, the performance of our approach relative to a 5th-order polynomial fit (representing the current standard) and a simple boxcar smoothing technique. Quantitatively, PSFent predicts more accurate PSF models in all scenarios and the residual PSF errors are spatially less correlated. This improvement in PSF interpolation leads to a factor of 3.5 lower systematic errors in the shear power spectrum on scales smaller than $\sim13'$, compared to polynomial fitting. We estimate that with PSFent and for stellar densities greater than $\simeq1/{\rm arcmin}^{2}$, the spurious shear correlation from PSF interpolation, after combining a complete 10-year dataset from LSST, is lower than the corresponding statistical uncertainties on the cosmic shear power spectrum, even under a conservative scenario.Comment: 18 pages,12 figures, accepted by MNRA

Grammar-based Representation and Identification of Dynamical Systems

In this paper we propose a novel approach to identify dynamical systems. The method estimates the model structure and the parameters of the model simultaneously, automating the critical decisions involved in identification such as model structure and complexity selection. In order to solve the combined model structure and model parameter estimation problem, a new representation of dynamical systems is proposed. The proposed representation is based on Tree Adjoining Grammar, a formalism that was developed from linguistic considerations. Using the proposed representation, the identification problem can be interpreted as a multi-objective optimization problem and we propose a Evolutionary Algorithm-based approach to solve the problem. A benchmark example is used to demonstrate the proposed approach. The results were found to be comparable to that obtained by state-of-the-art non-linear system identification methods, without making use of knowledge of the system description.Comment: Submitted to European Control Conference (ECC) 201

Nonlinear Stochastic Modelling of Antimicrobial resistance in Bacterial Populations

This thesis applies mathematical modelling and statistical methods to investigate the dynamics and mechanisms of bacterial evolution. More specifically it is concerned with the evolution of antibiotic resistance in bacteria populations, which is an increasing problem for the treatment of infections in humans and animals. To prevent the evolution and spread of resistance, there is a need for further understanding of its dynamics. A grey-box modelling approach based on stochastic differential equations is the main and innovative method applied to study bacterial systems in this thesis. Through the stochastic differential equation approach, knowledge of continuous dynamical systems can be combined with strong statistical methods. Hereby, important tools for model development, parameter estimation, and model validation are provided when in connection with data. The data available for the model development consist mainly of optical density measurements of bacterial concentrations. At high cell densities the optical density measurements will be effected by shadow effects from the bacteria leading to an underestimation of the concentration. To circumvent this problem a exponential calibration curve has been applied for all the data. This new curve was found to performthe best calibration in a comparison with other earlier suggested curves. In this thesis a new systematic framework for model improvement based on the grey-box modelling approach is proposed, and applied to find a model for bacterial growth in an environment with multiple substrates. Models based on stochastic differential equations are also used in studies of mutation and conjugation. Mutation and conjugation are important mechanisms for the development of resistance. Earlier models for conjugation have described systems where the substrate is present in abundant amounts, but in this thesis a model for conjugation in exhaustible media has been proposed. The role of mutators for bacterial evolution is another topic studied in this thesis. Mutators are characterized by having a high mutation rate and are believed to play an important role for the evolution of resistance. When growing under stressed conditions, such as in the presence of antibiotics, mutators are considered to have an advantages in comparison to non-mutators. This has been supported by a mathematical model for competing growth between a mutator and a non-mutator population. The growth rates of the two populations were initially compared by a maximum likelihood approach and the growth rates were found to be equal. Thereafter a model for the competing growth was developed. The models showthat mutatorswill obtain a higher fitness by adapting faster to an environment with antibiotics than the non-mutators. In another study a new hypothesis for the long term role of mutator bacteria is tested. This model suggests that mutators can work as "genetic work stations", where multiple mutations occur and subsequently are transmitted to the non-mutator population by conjugation. Another study in this thesis is concerned with the spread of colonization with resistant bacteria between patients in a hospital and people in the related catchment population. The resistance considered is extended-spectrumbeta-lactamases, and it is the first time a model has been developed for the spread of this type of resistance. Different transfer mechanisms are studied and quantified with the model. Simulations of the model indicates that cross-transfer of resistance between patients is the most important mechanism of transfer. The mathematical models developed in this thesis have helped to an improved understanding of the evolution and spread of resistance. They are thus a prime example of the strength of combining microbiology and experiments with modelling