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- Abdalmoaty, Mohamed, 1986-
(författare)
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Identification of Stochastic Nonlinear Dynamical Models Using Estimating Functions
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Data-driven modeling of stochastic nonlinear systems is recognized as a very challenging problem, even when reduced to a parameter estimation problem. A main difficulty is the intractability of the likelihood function, which renders favored estimation methods, such as the maximum likelihood method, analytically intractable. During the last decade, several numerical methods have been developed to approximately solve the maximum likelihood problem. A class of algorithms that attracted considerable attention is based on sequential Monte Carlo algorithms (also known as particle filters/smoothers) and particle Markov chain Monte Carlo algorithms. These algorithms were able to obtain impressive results on several challenging benchmark problems; however, their application is so far limited to cases where fundamental limitations, such as the sample impoverishment and path degeneracy problems, can be avoided.This thesis introduces relatively simple alternative parameter estimation methods that may be used for fairly general stochastic nonlinear dynamical models. They are based on one-step-ahead predictors that are linear in the observed outputs and do not require the computations of the likelihood function. Therefore, the resulting estimators are relatively easy to compute and may be highly competitive in this regard: they are in fact defined by analytically tractable objective functions in several relevant cases. In cases where the predictors are analytically intractable due to the complexity of the model, it is possible to resort to {plain} Monte Carlo approximations. Under certain assumptions on the data and some conditions on the model, the convergence and consistency of the estimators can be established. Several numerical simulation examples and a recent real-data benchmark problem demonstrate a good performance of the proposed method, in several cases that are considered challenging, with a considerable reduction in computational time in comparison with state-of-the-art sequential Monte Carlo implementations of the ML estimator.Moreover, we provide some insight into the asymptotic properties of the proposed methods. We show that the accuracy of the estimators depends on the model parameterization and the shape of the unknown distribution of the outputs (via the third and fourth moments). In particular, it is shown that when the model is non-Gaussian, a prediction error method based on the Gaussian assumption is not necessarily more accurate than one based on an optimally weighted parameter-independent quadratic norm. Therefore, it is generally not obvious which method should be used. This result comes in contrast to a current belief in some of the literature on the subject. Furthermore, we introduce the estimating functions approach, which was mainly developed in the statistics literature, as a generalization of the maximum likelihood and prediction error methods. We show how it may be used to systematically define optimal estimators, within a predefined class, using only a partial specification of the probabilistic model. Unless the model is Gaussian, this leads to estimators that are asymptotically uniformly more accurate than linear prediction error methods when quadratic criteria are used. Convergence and consistency are established under standard regularity and identifiability assumptions akin to those of prediction error methods.Finally, we consider the problem of closed-loop identification when the system is stochastic and nonlinear. A couple of scenarios given by the assumptions on the disturbances, the measurement noise and the knowledge of the feedback mechanism are considered. They include a challenging case where the feedback mechanism is completely unknown to the user. Our methods can be regarded as generalizations of some classical closed-loop identification approaches for the linear time-invariant case. We provide an asymptotic analysis of the methods, and demonstrate their properties in a simulation example.
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| 2. |
- Abdalmoaty, Mohamed, 1986-
(författare)
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Learning Stochastic Nonlinear Dynamical Systems Using Non-stationary Linear Predictors
- 2017
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The estimation problem of stochastic nonlinear parametric models is recognized to be very challenging due to the intractability of the likelihood function. Recently, several methods have been developed to approximate the maximum likelihood estimator and the optimal mean-square error predictor using Monte Carlo methods. Albeit asymptotically optimal, these methods come with several computational challenges and fundamental limitations.The contributions of this thesis can be divided into two main parts. In the first part, approximate solutions to the maximum likelihood problem are explored. Both analytical and numerical approaches, based on the expectation-maximization algorithm and the quasi-Newton algorithm, are considered. While analytic approximations are difficult to analyze, asymptotic guarantees can be established for methods based on Monte Carlo approximations. Yet, Monte Carlo methods come with their own computational difficulties; sampling in high-dimensional spaces requires an efficient proposal distribution to reduce the number of required samples to a reasonable value.In the second part, relatively simple prediction error method estimators are proposed. They are based on non-stationary one-step ahead predictors which are linear in the observed outputs, but are nonlinear in the (assumed known) input. These predictors rely only on the first two moments of the model and the computation of the likelihood function is not required. Consequently, the resulting estimators are defined via analytically tractable objective functions in several relevant cases. It is shown that, under mild assumptions, the estimators are consistent and asymptotically normal. In cases where the first two moments are analytically intractable due to the complexity of the model, it is possible to resort to vanilla Monte Carlo approximations. Several numerical examples demonstrate a good performance of the suggested estimators in several cases that are usually considered challenging.
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| 3. |
- Acharya, B. S., et al.
(författare)
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Introducing the CTA concept
- 2013
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Ingår i: Astroparticle physics. - : Elsevier BV. - 0927-6505 .- 1873-2852. ; 43, s. 3-18
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- The Cherenkov Telescope Array (CTA) is a new observatory for very high-energy (VHE) gamma rays. CTA has ambitions science goals, for which it is necessary to achieve full-sky coverage, to improve the sensitivity by about an order of magnitude, to span about four decades of energy, from a few tens of GeV to above 100 TeV with enhanced angular and energy resolutions over existing VHE gamma-ray observatories. An international collaboration has formed with more than 1000 members from 27 countries in Europe, Asia, Africa and North and South America. In 2010 the CTA Consortium completed a Design Study and started a three-year Preparatory Phase which leads to production readiness of CTA in 2014. In this paper we introduce the science goals and the concept of CTA, and provide an overview of the project. (C) 2013 Elsevier B.V. All rights reserved.
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| 4. |
- Ahlgren, Per, 1960-, et al.
(författare)
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A bibliometric analysis of battery research with the BATTERY 2030+ roadmap as point of departure
- 2022
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In this bibliometric study, we analyze the six battery research subfields identified in the BATTERY 2030+ roadmap: Battery Interface Genome, Materials Acceleration Platform, Recyclability, Smart functionalities: Self-healing, Smart functionalities: Sensing, and Manufacturability. In addition, we analyze the entire research field related to BATTERY 2030+ as a whole, using two operationalizations. We (a) evaluate the European standing in the subfields/the BATTERY 2030+ field in comparison to the rest of the world, and (b) identify strongholds of the subfields/the BATTERY 2030+ field across Europe. For each subfield and the field as a whole, we used seed articles, i.e. articles listed in the BATTERY 2030+ roadmap or cited by such articles, in order to generate additional, similar articles located in an algorithmically obtained classification system. The output of the analysis is publication volumes, field normalized citation impact values with comparisons between country/country aggregates and between organizations, co-publishing networks between countries and organizations, and keyword co-occurrence networks. For the results related to (a), the performance of EU & associated (countries) is similar to China and the aggregate Japan-South Korea-Singapore and well below North America regarding citation impact and with respect to the field as a whole. Exceptions are, however, the subfields Battery Interface Genome and Recyclability. For the results related to (b), there is a large variability in the EU & associated organizations regarding volume in the different subfields. For citation impact, examples of high-performing EU & associated organizations are ETH Zurich and Max Planck Society for the Advancement of Science.
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| 8. |
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Arbete pågår : - i tankens mönster och kroppens miljöer
- 2008
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Samlingsverk (redaktörskap) (övrigt vetenskapligt/konstnärligt)abstract
- Work is always in progress, somewhere, in some form. In a historical perspective the view of work has changed, as have the contexts where work takes place. Still there are strongly rooted images of what work is – in the patterns of thought and material conditions.This book embarks from the idea that work is something both immaterial and material. It discusses work as a conception and cultural norm, but also as something very tangible and concrete.Researchers from an array of scientific fields have gathered together around these questions. The idea has been to twist and turn the conceptions of what work is and – embarking from one’s own discipline – to contribute new perspectives to this topic.
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| 9. |
- Audren, A., et al.
(författare)
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Damage recovery in ZnO by post-implantation annealing
- 2010
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Ingår i: Nuclear Instruments and Methods in Physics Research Section B. - : Elsevier BV. - 0168-583X .- 1872-9584. ; 268:11-12, s. 1842-1846
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- ZnO bulk samples were implanted with 200 key-Co ions at room temperature with two fluences, 1 x 10(16) and 8 x 10(16) cm(-2), and then annealed in air for 30 min at different temperatures up to 900 degrees C. After the implantation and each annealing step, the samples were analyzed by Rutherford backscattering spectrometry (RBS) in random and channeling directions to follow the evolution of the disorder profile. The RBS spectra reveal that disorder is created during implantation in proportion to the Co fluence. The thermal treatments induce a disorder recovery, which is however, not complete after annealing at 900 degrees C, where about 15% of the damage remains. To study the Co profile evolution during annealing, the samples were, in addition to RBS, characterized by secondary ion mass spectrometry (SIMS). The results show that Co diffusion starts at 800 degrees C, but also that a very different behavior is seen for Co concentrations below and above the solubility limit. (C) 2010 Elsevier B.V. All rights reserved.
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