10,552 research outputs found
Easy-Implementable On-line Identification Method for a First-Order System Including a Time-Delay
This paper proposes a simple yet effective on-line identification method for a first-order system including a time-delay. This method is based on the Laplace transformation in a real number domain and is able to estimate both coefficients of the first-order system and the time-delay simultaneously. An accuracy of the identification was investigated through a simulation. As a result, precise estimation of the method was confirmed compared to an orthodox on-line estimation technique that utilized a bilinear-model. Moreover, a guideline for a tuning of their parameters used in the method is shown. Applying the method to an actual sensor identification, issues under the practical usage were investigated, and the countermeasure was mentioned
Regulation Theory
This paper reviews the design of regulation loops for power converters. Power
converter control being a vast domain, it does not aim to be exhaustive. The
objective is to give a rapid overview of the main synthesis methods in both
continuous- and discrete-time domains.Comment: 23 pages, contribution to the 2014 CAS - CERN Accelerator School:
Power Converters, Baden, Switzerland, 7-14 May 201
Structure Learning in Coupled Dynamical Systems and Dynamic Causal Modelling
Identifying a coupled dynamical system out of many plausible candidates, each
of which could serve as the underlying generator of some observed measurements,
is a profoundly ill posed problem that commonly arises when modelling real
world phenomena. In this review, we detail a set of statistical procedures for
inferring the structure of nonlinear coupled dynamical systems (structure
learning), which has proved useful in neuroscience research. A key focus here
is the comparison of competing models of (ie, hypotheses about) network
architectures and implicit coupling functions in terms of their Bayesian model
evidence. These methods are collectively referred to as dynamical casual
modelling (DCM). We focus on a relatively new approach that is proving
remarkably useful; namely, Bayesian model reduction (BMR), which enables rapid
evaluation and comparison of models that differ in their network architecture.
We illustrate the usefulness of these techniques through modelling
neurovascular coupling (cellular pathways linking neuronal and vascular
systems), whose function is an active focus of research in neurobiology and the
imaging of coupled neuronal systems
Advanced signal processing methods in dynamic contrast enhanced magnetic resonance imaging
Tato dizertační práce představuje metodu zobrazování perfúze magnetickou rezonancí, jež je výkonným nástrojem v diagnostice, především v onkologii. Po ukončení sběru časové sekvence T1-váhovaných obrazů zaznamenávajících distribuci kontrastní látky v těle začíná fáze zpracování dat, která je předmětem této dizertace. Je zde představen teoretický základ fyziologických modelů a modelů akvizice pomocí magnetické rezonance a celý řetězec potřebný k vytvoření obrazů odhadu parametrů perfúze a mikrocirkulace v tkáni. Tato dizertační práce je souborem uveřejněných prací autora přispívajícím k rozvoji metodologie perfúzního zobrazování a zmíněného potřebného teoretického rozboru.This dissertation describes quantitative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI), which is a powerful tool in diagnostics, mainly in oncology. After a time series of T1-weighted images recording contrast-agent distribution in the body has been acquired, data processing phase follows. It is presented step by step in this dissertation. The theoretical background in physiological and MRI-acquisition modeling is described together with the estimation process leading to parametric maps describing perfusion and microcirculation properties of the investigated tissue on a voxel-by-voxel basis. The dissertation is divided into this theoretical analysis and a set of publications representing particular contributions of the author to DCE-MRI.
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