Global analytic expansion of solution for a class of linear parabolic systems with coupling of first order derivatives terms

We derive global analytic representations of fundamental solutions for a class of linear parabolic systems with full coupling of first order derivative terms where coefficient may depend on space and time. Pointwise convergence of the global analytic expansion is proved. This leads to analytic representations of solutions of initial-boundary problems of first and second type in terms of convolution integrals or convolution integrals and linear integral equations. The results have both analytical and numerical impact. Analytically, our representations of fundamental solutions of coupled parabolic systems may be used to define generalized stochastic processes. Moreover, some classical analytical results based on a priori estimates of elliptic equations are a simple corollary of our main result. Numerically, accurate, stable and efficient schemes for computation and error estimates in strong norms can be obtained for a considerable class of Cauchy- and initial-boundary problems of parabolic type. Furthermore, there are obvious and less obvious applications to finance and physics. Warning: The argument given in the current version is only valid in special cases (essentially the scalar case). A more involved argument is needed for systems and will be communicated soon in a replacement,Comment: 24 pages, the paper needs some correction and is under substantial revisio

Applications of Krylov Subspace and Balanced Truncation Model Order Reduction in Power Systems

Dynamic representations of power systems usually result in the order of hundreds or even thousands of buses. Therefore, reduction of these dynamic representations is convenient. Two applications of model order reduction in power systems are discussed in this thesis. First, Krylov subspace-based method is applied to the IEEE-123 Node Test Feeder in the context of distribution-level power systems simulation. Second, a Balanced Truncation-based model reduction is implemented in the 3-Machine 9-Bus system for designing a power system controller in the context of generation- and transmission-level power systems. First, for the IEEE-123 Node Test Feeder, a two-sided Arnoldi algorithm is proposed to compute the basis of the Krylov subspace-based model reduction. The two-sided Arnoldi algorithm was found to decrease the deviation between the reduced and full-order model. Second, for the 3-Machine 9-Bus, a linear quadratic regulator (LQR) controller is designed based on the reduced model. The selection method of Q and R matrices is adopted from [1]. The resulting controller is shown to damp the oscillations of the open-loop system

Test generation from P systems using model checking

This paper presents some testing approaches based on model checking and using different testing criteria. First, test sets are built from different Kripke structure representations. Second, various rule coverage criteria for transitional, non-deterministic, cell-like P systems, are considered in order to generate adequate test sets. Rule based coverage criteria (simple rule coverage, context-dependent rule coverage and variants) are defined and, for each criterion, a set of LTL (Linear Temporal Logic) formulas is provided. A codification of a P system as a Kripke structure and the sets of LTL properties are used in test generation: for each criterion, test cases are obtained from the counterexamples of the associated LTL formulas, which are automatically generated from the Kripke structure codification of the P system. The method is illustrated with an implementation using a specific model checker, NuSMV. (C) 2010 Elsevier Inc. All rights reserved

Homogeneous behaviors

Recently a smooth compactification of the space of linear systems with $n$ states, $m$ inputs and $p$ outputs has been discovered. In this paper we obtain a concrete interpretation of this compactification as a space of discrete-time behaviors. We use both homogeneous polynomial representations and generalized first-order representations, and provide a realization theory to link these to each other

MODEL ORDER REDUCTION OF NONLINEAR DYNAMIC SYSTEMS USING MULTIPLE PROJECTION BASES AND OPTIMIZED STATE-SPACE SAMPLING

Model order reduction (MOR) is a very powerful technique that is used to deal with the increasing complexity of dynamic systems. It is a mature and well understood field of study that has been applied to large linear dynamic systems with great success. However, the continued scaling of integrated micro-systems, the use of new technologies, and aggressive mixed-signal design has forced designers to consider nonlinear effects for more accurate model representations. This has created the need for a methodology to generate compact models from nonlinear systems of high dimensionality, since only such a solution will give an accurate description for current and future complex systems.The goal of this research is to develop a methodology for the model order reduction of large multidimensional nonlinear systems. To address a broad range of nonlinear systems, which makes the task of generalizing a reduction technique difficult, we use the concept of transforming the nonlinear representation into a composite structure of well defined basic functions from multiple projection bases.We build upon the concept of a training phase from the trajectory piecewise-linear (TPWL) methodology as a practical strategy to reduce the state exploration required for a large nonlinear system. We improve upon this methodology in two important ways: First, with a new strategy for the use of multiple projection bases in the reduction process and their coalescence into a unified base that better captures the behavior of the overall system; and second, with a novel strategy for the optimization of the state locations chosen during training. This optimization technique is based on using the Hessian of the system as an error bound metric.Finally, in order to treat the overall linear/nonlinear reduction task, we introduce a hierarchical approach using a block projection base. These three strategies together offer us a new perspective to the problem of model order reduction of nonlinear systems and the tracking or preservation of physical parameters in the final compact model

Identification of Structural Models as a Problem of Group Representation Theory

It has been shown that symmetries of moment functions of stochastic processes play an important role in identification of systems. They provide the group-theoretic method of choice of the model structure and model parameters. In the first stage the group-theoretic analysis of some fundamental concepts of stochastic dynamics: stochastic processes and functional series of Volterra-Wiener type has been undertaken. The analysis of group representations of the moment functions of order m for stochastic processes is the basic, original concept of the work. The following groups: symmetric Sm, special affine SAff(m), general linear GL(n, R), GL(n,C) and their subgroups play the main role in the models. In the second stage the informational entropy has been introduced as a measure of the randomness in the identified models. The group-theoretic approach underlines the unity of the nonlinear system identification and leads to useful engineering results in the range of the second-order (stochastic) theory