Linear polyethylene and polypropylene: problems and opportunities. A report for businessmen,

Mode of access: Internet.

Some factors in the relation of stream and valley sedimentation to flood-control problems.

Mode of access: Internet.

Evaluation of porous materials for boundary-layer control

ASTIA document no. AD 110582.

Multi-valued control problems and mixture density network

We have proposed a novel robust inversion-based neurocontroller that searches for the optimal control law by sampling from the estimated Gaussian distribution of the inverse plant model. However, for problems involving the prediction of continuous variables, a Gaussian model approximation provides only a very limited description of the properties of the inverse model. This is usually the case for problems in which the mapping to be learned is multi-valued or involves hysteritic transfer characteristics. This often arises in the solution of inverse plant models. In order to obtain a complete description of the inverse model, a more general multicomponent distributions must be modeled. In this paper we test whether our proposed sampling approach can be used when considering an arbitrary conditional probability distributions. These arbitrary distributions will be modeled by a mixture density network. Importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The effectiveness of the importance sampling from an arbitrary conditional probability distribution will be demonstrated using a simple single input single output static nonlinear system with hysteretic characteristics in the inverse plant model.

A mixture density network approach to modelling and exploiting uncertainty in nonlinear control problems

In nonlinear and stochastic control problems, learning an efficient feed-forward controller is not amenable to conventional neurocontrol methods. For these approaches, estimating and then incorporating uncertainty in the controller and feed-forward models can produce more robust control results. Here, we introduce a novel inversion-based neurocontroller for solving control problems involving uncertain nonlinear systems which could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. Based on importance sampling from these distributions a novel robust inverse control approach is obtained. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider. A nonlinear multi-variable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non-Gaussian distributions of control signal as well as processes with hysteresis. © 2004 Elsevier Ltd. All rights reserved.

Anti-Periodic Boundary Value Problem for Impulsive Fractional Integro Differential Equations

MSC 2010: 34A37, 34B15, 26A33, 34C25, 34K37

Linear non-local diffusion problems in metric measure spaces

The aim of this paper is to provide a comprehensive study of some linear non-local diffusion problems in metric measure spaces. These include, for example, open subsets in ℝN, graphs, manifolds, multi-structures and some fractal sets. For this, we study regularity, compactness, positivity and the spectrum of the stationary non-local operator. We then study the solutions of linear evolution non-local diffusion problems, with emphasis on similarities and differences with the standard heat equation in smooth domains. In particular, we prove weak and strong maximum principles and describe the asymptotic behaviour using spectral methods.

Integer Linear Programming for Sequence Problems: A general approach to reduce the problem size

Sequence problems belong to the most challenging interdisciplinary topics of the actuality. They are ubiquitous in science and daily life and occur, for example, in form of DNA sequences encoding all information of an organism, as a text (natural or formal) or in form of a computer program. Therefore, sequence problems occur in many variations in computational biology (drug development), coding theory, data compression, quantitative and computational linguistics (e.g. machine translation). In recent years appeared some proposals to formulate sequence problems like the closest string problem (CSP) and the farthest string problem (FSP) as an Integer Linear Programming Problem (ILPP). In the present talk we present a general novel approach to reduce the size of the ILPP by grouping isomorphous columns of the string matrix together. The approach is of practical use, since the solution of sequence problems is very time consuming, in particular when the sequences are long.

Simulações de problemas inversos com aplicações em engenharia nuclear usando técnicas de transporte de partículas neutras monoenergéticas na formulação unidimensional de ordenadas discretas

Neste trabalho, três técnicas para resolver numericamente problemas inversos de transporte de partículas neutras a uma velocidade para aplicações em engenharia nuclear são desenvolvidas. É fato conhecido que problemas diretos estacionários e monoenergéticos de transporte são caracterizados por estimar o fluxo de partículas como uma função-distribuição das variáveis independentes de espaço e de direção de movimento, quando os parâmetros materiais (seções de choque macroscópicas), a geometria, e o fluxo incidente nos contornos do domínio (condições de contorno), bem como a distribuição de fonte interior são conhecidos. Por outro lado, problemas inversos, neste trabalho, buscam estimativas para o fluxo incidente no contorno, ou a fonte interior, ou frações vazio em barras homogêneas. O modelo matemático usado tanto para os problemas diretos como para os problemas inversos é a equação de transporte independente do tempo, a uma velocidade, em geometria unidimensional e com o espalhamento linearmente anisotrópico na formulação de ordenadas discretas (SN). Nos problemas inversos de valor de contorno, dado o fluxo emergente em um extremo da barra, medido por um detector de nêutrons, por exemplo, buscamos uma estimativa precisa para o fluxo incidente no extremo oposto. Por outro lado, nos problemas inversos SN de fonte interior, buscamos uma estimativa precisa para a fonte armazenada no interior do domínio para fins de blindagem, sendo dado o fluxo emergente no contorno da barra. Além disso, nos problemas inversos SN de fração de vazio, dado o fluxo emergente em uma fronteira da barra devido ao fluxo incidente prescrito no extremo oposto, procuramos por uma estimativa precisa da fração de vazio no interior da barra, no contexto de ensaios não-destrutivos para aplicações na indústria. O código computacional desenvolvido neste trabalho apresenta o método espectronodal de malha grossa spectral Greens function (SGF) para os problemas diretos SN em geometria unidimensional para gerar soluções numéricas precisas para os três problemas inversos SN descritos acima. Para os problemas inversos SN de valor de contorno e de fonte interior, usamos a propriedade da proporcionalidade da fuga de partículas; ademais, para os problemas inversos SN de fração de vazio, oferecemos a técnica a qual nos referimos como o método físico da bissecção. Apresentamos resultados numéricos para ilustrar a precisão das três técnicas, conforme descrito nesta tese.

Regularization of time-varying descriptor systems by the asvd

We study linear variable coefficient control problems in descriptor form. Based on a behaviour approach and the general theory for linear differential algebraic systems we give the theoretical analysis and describe numerically stable methods to determine the structural properties of the system.

A new analytical solution for water table fluctuations in coastal aquifers with sloping beaches

The Boussinesq equation appears as the zeroth-order term in the shallow water flow expansion of the non-linear equation describing the flow of fluid in an unconfined aquifer. One-dimensional models based on the Boussinesq equation have been used to analyse tide-induced water table fluctuations in coastal aquifers. Previous analytical solutions for a sloping beach are based on the perturbation parameter, epsilon(N) = alphaepsilon cot beta (in which beta is the beach slope, alpha is the amplitude parameter and epsilon is the shallow water parameter) and are limited to tan(-1) (alphaepsilon) much less than beta less than or equal to pi/2. In this paper, a new higher-order solution to the non-linear boundary value problem is derived. The results demonstrate the significant influence of the higher-order components and beach slope on the water table fluctuations. The relative difference between the linear solution and the present solution increases as 6 and a increase, and reaches 7% of the linear solution. (C) 2003 Elsevier Ltd. All rights reserved.

Optimal bounded control of first-passage failure of strongly non-linear oscillators under combined harmonic and white-noise excitations

A procedure for designing the optimal bounded control of strongly non-linear oscillators under combined harmonic and white-noise excitations for minimizing their first-passage failure is proposed. First, a stochastic averaging method for strongly non-linear oscillators under combined harmonic and white-noise excitations using generalized harmonic functions is introduced. Then, the dynamical programming equations and their boundary and final time conditions for the control problems of maximizing reliability and of maximizing mean first-passage time are formulated from the averaged Ito equations by using the dynamical programming principle. The optimal control law is derived from the dynamical programming equations and control constraint. Finally, the conditional reliability function, the conditional probability density and mean of the first-passage time of the optimally controlled system are obtained from solving the backward Kolmogorov equation and Pontryagin equation. An example is given to illustrate the proposed procedure and the results obtained are verified by using those from digital simulation. (C) 2003 Elsevier Ltd. All rights reserved.

Monte Carlo Algorithms for Linear Problems

MSC Subject Classification: 65C05, 65U05.