On a Limit of Perturbed Conservation Laws with Diffusion and Non-Positive Dispersion

We consider a conservation law perturbed by a linear diffusion and a general form of non-positive dispersion. We prove the convergence of the corresponding solution to the entropy weak solution of the hyperbolic conservation law.

Measure-valued mass evolution problems with flux boundary conditions and solution-dependent velocities

In this paper we prove well-posedness for a measure-valued continuity equation with solution-dependent velocity and flux boundary conditions, posed on a bounded one-dimensional domain. We generalize the results of an earlier paper [J. Differential Equations, 259 (2015), pp. 10681097] to settings where the dynamics are driven by interactions. In a forward-Euler-like approach, we construct a time-discretized version of the original problem and employ those results as a building block within each subinterval. A limit solution is obtained as the mesh size of the time discretization goes to zero. Moreover, the limit is independent of the specific way of partitioning the time interval [0, T]. This paper is partially based on results presented in Chapter 5 of [Evolution Equations for Systems Governed by Social Interactions, Ph.D. thesis, Eindhoven University of Technology, 2015], while a number of issues that were still open there are now resolved.

A new entropy measure based on the Renyi entropy rate using Gaussian kernels

The concept of entropy rate is well defined in dynamical systems theory but is impossible to apply it directly to finite real world data sets. With this in mind, Pincus developed Approximate Entropy (ApEn), which uses ideas from Eckmann and Ruelle to create a regularity measure based on entropy rate that can be used to determine the influence of chaotic behaviour in a real world signal. However, this measure was found not to be robust and so an improved formulation known as the Sample Entropy (SampEn) was created by Richman and Moorman to address these issues. We have developed a new, related, regularity measure which is not based on the theory provided by Eckmann and Ruelle and proves a more well-behaved measure of complexity than the previous measures whilst still retaining a low computational cost.

A novel entropy measure for analysis of the electrocardiogram

There has been much recent research into extracting useful diagnostic features from the electrocardiogram with numerous studies claiming impressive results. However, the robustness and consistency of the methods employed in these studies is rarely, if ever, mentioned. Hence, we propose two new methods; a biologically motivated time series derived from consecutive P-wave durations, and a mathematically motivated regularity measure. We investigate the robustness of these two methods when compared with current corresponding methods. We find that the new time series performs admirably as a compliment to the current method and the new regularity measure consistently outperforms the current measure in numerous tests on real and synthetic data.

About a Vanishing Viscosity-Capillarity Method

We consider a class of nonlinear dissipative-dispersive perturbations of the scalar conservation law @tu + div f (u) = 01 and we study the convergence of the approximated solutions to its entropy solution. In particular, we obtain conditions under which the balance between dissipation and dispersion gives rise to the convergence (by DiPerna's measure-valued solution technique).

Case base reduction using solution-space metrics

In this paper we propose a case base reduction technique which uses a metric defined on the solution space. The technique utilises the Generalised Shepard Nearest Neighbour (GSNN) algorithm to estimate nominal or real valued solutions in case bases with solution space metrics. An overview of GSNN and a generalised reduction technique, which subsumes some existing decremental methods, such as the Shrink algorithm, are presented. The reduction technique is given for case bases in terms of a measure of the importance of each case to the predictive power of the case base. A trial test is performed on two case bases of different kinds, with several metrics proposed in the solution space. The tests show that GSNN can out-perform standard nearest neighbour methods on this set. Further test results show that a caseremoval order proposed based on a GSNN error function can produce a sparse case base with good predictive power.

Application of the entropy technique and genetic algorithms to construction site layout planning of medium-size projects

Genetic algorithms (GAs) have been introduced into site layout planning as reported in a number of studies. In these studies, the objective functions were defined so as to employ the GAs in searching for the optimal site layout. However, few studies have been carried out to investigate the actual closeness of relationships between site facilities; it is these relationships that ultimately govern the site layout. This study has determined that the underlying factors of site layout planning for medium-size projects include work flow, personnel flow, safety and environment, and personal preferences. By finding the weightings on these factors and the corresponding closeness indices between each facility, a closeness relationship has been deduced. Two contemporary mathematical approaches - fuzzy logic theory and an entropy measure - were adopted in finding these results in order to minimize the uncertainty and vagueness of the collected data and improve the quality of the information. GAs were then applied to searching for the optimal site layout in a medium-size government project using the GeneHunter software. The objective function involved minimizing the total travel distance. An optimal layout was obtained within a short time. This reveals that the application of GA to site layout planning is highly promising and efficient.

Inversão gravimétrica do relevo do embasamento usando regularização entrópica

Apresentamos um novo método de inversão gravimétrica para estimar o relevo descontínuo do embasamento de bacias sedimentares, cujos pacotes sedimentares podem ter contraste de densidade constante ou variando hiperbolicamente com a profundidade. O método combina a maximização da medida de entropia de ordem zero e a minimização da medida de entropia de primeira ordem do vetor de soluções (profundidades do embasamento). O modelointerpretativo consiste de um conjunto de primas retangulares verticais justapostos, com o contraste de densidade conhecido, cujas espessuras representam as profundidades do embasamento e são os parâmetros a serem determinados. A minimização da entropia de ordem um favorece soluções apresentando descontinuidades abruptas e a maximização da entropia de ordem zero é empregada apenas para evitar sua minimização excessiva. O método foi aplicado a dados sintéticos simulando: (i) bacias intracratônicas com o relevo do embasamento suave ou apresentando descontinuidades abruptas localizadas e (ii) bacias marginais falhadas. No caso de embasamento suave, a comparação dos resultados com aqueles obtidos através da suavidade global e da suavidade ponderada mostrou que ambos os métodos delinearam o embasamento. No caso de embasamentos falhados, seja em bacias intracratônicas ou marginais, as soluções obtidas com a regularização entrópica e a suavidade ponderada foram equivalentes, delineando o embasamento e as descontinuidades, o que não ocorreu com a suavidade global. A regularização entrópica, no entanto, não requereu informação a priori sobre a profundidade, diferentemente da suavidade ponderada. Tanto o método proposto como os métodos das suavidades global e ponderada foram aplicadas a quatro conjuntos de dados reais. O primeiro é proveniente da Ponte do Poema que está localizada no campus da Universidade Federal do Pará, Belém, Pará, permitiu certificar a aplicabilidade prática do método, uma vez que o relevo abaixo da ponte é acessível. O segundo, da porção norte do Steptoe Valley, Nevada, Estados Unidos. As soluções obtidas com a regularização entrópica e com a suavidade ponderada mostram um embasamento apresentando diversas descontinuidades verticais, o que não aconteceu com a suavidade global. O terceiro conjunto provém do graben de San Jacinto, situado na Califórnia, Estados Unidos, que levou a soluções em que o graben é assimétrico e apenas a suavidade ponderada apresentou solução com fundo achatado. O quarto conjunto de dados é oriundo do graben do Büyük Menderes, localizado na Turquia ocidental. As soluções do método proposto e da suavidade ponderada apresentaram grandes deslocamentos verticais, confirmados pela geologia, o que não aconteceu com a suavidade global.

Entropic information for travelling solitons in Lorentz and CPT breaking systems

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Approximate entropy of network parameters

We study the notion of approximate entropy within the framework of network theory. Approximate entropy is an uncertainty measure originally proposed in the context of dynamical systems and time series. We first define a purely structural entropy obtained by computing the approximate entropy of the so-called slide sequence. This is a surrogate of the degree sequence and it is suggested by the frequency partition of a graph. We examine this quantity for standard scale-free and Erdös-Rényi networks. By using classical results of Pincus, we show that our entropy measure often converges with network size to a certain binary Shannon entropy. As a second step, with specific attention to networks generated by dynamical processes, we investigate approximate entropy of horizontal visibility graphs. Visibility graphs allow us to naturally associate with a network the notion of temporal correlations, therefore providing the measure a dynamical garment. We show that approximate entropy distinguishes visibility graphs generated by processes with different complexity. The result probes to a greater extent these networks for the study of dynamical systems. Applications to certain biological data arising in cancer genomics are finally considered in the light of both approaches.

Analysis of nocturnal oxygen saturation recordings using kernel entropy to assist in sleep apnea-hypopnea diagnosis

In this study, a new entropy measure known as kernel entropy (KerEnt), which quantifies the irregularity in a series, was applied to nocturnal oxygen saturation (SaO 2) recordings. A total of 96 subjects suspected of suffering from sleep apnea-hypopnea syndrome (SAHS) took part in the study: 32 SAHS-negative and 64 SAHS-positive subjects. Their SaO 2 signals were separately processed by means of KerEnt. Our results show that a higher degree of irregularity is associated to SAHS-positive subjects. Statistical analysis revealed significant differences between the KerEnt values of SAHS-negative and SAHS-positive groups. The diagnostic utility of this parameter was studied by means of receiver operating characteristic (ROC) analysis. A classification accuracy of 81.25% (81.25% sensitivity and 81.25% specificity) was achieved. Repeated apneas during sleep increase irregularity in SaO 2 data. This effect can be measured by KerEnt in order to detect SAHS. This non-linear measure can provide useful information for the development of alternative diagnostic techniques in order to reduce the demand for conventional polysomnography (PSG). © 2011 IEEE.

Processing of laser Doppler flowmetry signals from healthy subjects and patients with varicose veins: information categorization approach based on intrinsic mode functions and entropy computation

International audience

A Fuzzy Approximation Scheme for Sequential Learning in Pattern Recognition

An adaptive learning scheme, based on a fuzzy approximation to the gradient descent method for training a pattern classifier using unlabeled samples, is described. The objective function defined for the fuzzy ISODATA clustering procedure is used as the loss function for computing the gradient. Learning is based on simultaneous fuzzy decisionmaking and estimation. It uses conditional fuzzy measures on unlabeled samples. An exponential membership function is assumed for each class, and the parameters constituting these membership functions are estimated, using the gradient, in a recursive fashion. The induced possibility of occurrence of each class is useful for estimation and is computed using 1) the membership of the new sample in that class and 2) the previously computed average possibility of occurrence of the same class. An inductive entropy measure is defined in terms of induced possibility distribution to measure the extent of learning. The method is illustrated with relevant examples.

Branching Particle Systems and Superprocess

In this paper, we describe a system of particles that perform independent random motions in space and at the end of their lifetimes give birth to a random number of offspring. We show that the system in the large density, small mass, rapid branching or long time scale limit converges to a measure-valued diffusion called the superprocess.