Positive Semidefiniteness and Positive Definiteness of a Linear Parametric Interval Matrix

We consider a symmetric matrix, the entries of which depend linearly on some parameters. The domains of the parameters are compact real intervals. We investigate the problem of checking whether for each (or some) setting of the parameters, the matrix is positive definite (or positive semidefinite). We state a characterization in the form of equivalent conditions, and also propose some computationally cheap sufficient\,/\,necessary conditions. Our results extend the classical results on positive (semi-)definiteness of interval matrices. They may be useful for checking convexity or non-convexity in global optimization methods based on branch and bound framework and using interval techniques

Guaranteed Verification of Dynamic Systems

This work introduces a new specification and verification approach for dynamic systems. The introduced approach is able to provide type II error free results by definition, i.e. there are no hidden faults in the verification result. The approach is based on Kaucher interval arithmetic to enclose the measurement in a bounded error sense. The developed methods are proven mathematically to provide a reliable verification for a wide class of safety critical systems

Guaranteed Verification of Dynamic Systems

Diese Arbeit beschreibt einen neuen Spezifikations- und Verifikationsansatz für dynamische Systeme. Der neue Ansatz ermöglicht dabei Ergebnisse, die per Definition frei von Fehlern 2. Art sind. Dies bedeutet, dass das Ergebnis der Verifikation keine versteckten Fehler enthalten kann. Somit können zuverlässige Ergebnisse für die Analyse von sicherheitskritischen Systemen generiert werden. Dazu wird ein neues Verständnis von mengenbasierter Konsistenz dynamischer Systeme mit einer gegebenen Spezifikation eingeführt. Dieses basiert auf der Verwendung von Kaucher Intervall Arithmetik zur Einschließung von Messdaten. Konsistenz wird anhand der vereinigten Lösungsmenge der Kaucher Arithmetik definiert. Dies führt zu mathematisch garantierten Ergebnissen. Die resultierende Methode kann das spezifizierte Verhalten eines dynamischen System auch im Falle von Rauschen und Sensorungenauigkeiten anhand von Messdaten verifizieren. Die mathematische Beweisbarkeit der Konsistenz wird für eine große Klasse von Systemen gezeigt. Diese beinhalten zeitinvariante, intervallartige und hybride Systeme, wobei letztere auch zur Beschreibung von Nichtlinearitäten verwendet werden können. Darüber hinaus werden zahlreiche Erweiterungen dargestellt. Diese führen bis hin zu einem neuartigen iterativen Identifikations- und Segmentierungsverfahren für hybride Systeme. Dieses ermöglicht die Verfikation hybrider Systeme auch ohne Wissen über Schaltzeitpunkte. Die entwickelten Verfahren können darüber hinaus zur Diagnose von dynamischen Systemen verwendet werden, falls eine ausreichend schnelle Berechnung der Ergebnisse möglich ist. Die Verfahren werden erfolgreich auf eine beispielhafte Variation verschiedener Tanksysteme angewendet. Die neuen Theorien, Methoden und Algortihmen dieser Arbeit bilden die Grundlage für eine zuverlässige Analyse von hochautomatisierten sicherheitskritischen Systemen

Guaranteed Verification of Dynamic Systems

This work introduces a new specification and verification approach for dynamic systems. The introduced approach is able to provide type II error free results by definition, i.e. there are no hidden faults in the verification result. The approach is based on Kaucher interval arithmetic to enclose the measurement in a bounded error sense. The developed methods are proven mathematically to provide a reliable verification for a wide class of safety critical systems

Interval linear systems as a necessary step in fuzzy linear systems

International audienceThis article clarifies what it means to solve a system of fuzzy linear equations, relying on the fact that they are a direct extension of interval linear systems of equations, already studied in a specific interval mathematics literature. We highlight four distinct definitions of a systems of linear equations where coefficients are replaced by intervals, each of which based on a generalization of scalar equality to intervals. Each of the four extensions of interval linear systems has a corresponding solution set whose calculation can be carried out by a general unified method based on a relatively new concept of constraint intervals. We also consider the smallest multidimensional intervals containing the solution sets. We propose several extensions of the interval setting to systems of linear equations where coefficients are fuzzy intervals. This unified setting clarifies many of the anomalous or inconsistent published results in various fuzzy interval linear systems studies

The elastoplastic analysis of planar frames for large displacements by mathematical programming

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Tools to analyze cell signaling models

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2004.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (v. 2, leaves 345-369).Diseases such as diabetes, some forms of cancer, hyper-tension, auto-immune diseases, and some viral diseases are characterized by complex interactions within the human body. Efforts to understand and treat these diseases have only been partially successful. There is currently a huge commercial and academic effort devoted to computational biology to address the shortfalls of qualitative biology. This research has become relevant due to the vast amounts of data now available from high-throughput techniques such as gene-chips, combinatorial chemistry, and fast gene sequencing. The goal of computational biology is to use quantitative models to test complex scientific hypotheses or predict desirable interventions. Consequently, it is important that the model is built to the minimum fidelity required to meet a specific goal, otherwise valuable effort is wasted. Unlike traditional chemical engineering, computational biology does not solely depend on deterministic models of chemical behavior. There is also widespread use of many types of statistical models, stochastic models, electro-static models, and mechanical models. All of these models are inferred from noisy data. It is therefore important to develop techniques to aide the model builder in their task of verifying and using these models to make quantitative predictions. The goal of this thesis is to develop tools for analysing the qualitative and quantitative characteristics of cell-signaling models. The qualitative behavior of deterministic models is studied in the first part of this thesis and the quantitative behavior of stochastic models is studied in the second part. A kinetic model of cell signaling is a common example of a deterministic model used in computational biology.(cont.) Usually such a model is derived from first-principles. The differential equations represent species conservation and the algebraic equations represent rate equations and equations to estimate rate constants. The researcher faces two key challenges once the model has been formulated: it is desirable to summarize a complex model by the phenomena it exhibits, and it is necessary to check whether the qualitative behavior of the model is verified by experimental observation. The key result of this research is a method to rearrange an implicit index one DAE into state-space form efficiently, amenable to standard control engineering analysis. Control engineering techniques can then be used to determine the time constants, poles, and zeros of the system, thus summarizing all the qualitative behavior of the system. The second part of the thesis focuses on the quantitative analysis of cell migration. It is hypothesized that mammalian cell migration is driven by responses to external chemical, electrical and mechanical stimulus. It is desirable to be able to quantify cell migration (speed, frequency of turning) to correlate output to experimental conditions (ligand concentration, cell type, cell medium, etc). However, the local concentration of signaling molecules and receptors is sufficiently low that a continuum model of cell migration is inadequate, i.e., it is only possible to describe cell motion in a probabilistic fashion ...by David Michael Collins.Ph.D