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京都大学 / 新制・課程博士 / 博士(工学) / 甲第23157号 / 工博第4801号 / 新制||工||1751(附属図書館) / 京都大学大学院工学研究科材料工学専攻 / (主査)教授 辻 伸泰, 教授 乾 晴行, 教授 安田 秀幸 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

We study minimization problems with respect to a one-parameter family of generalized relative entropies. These relative entropies, which we call relative -entropies (denoted I (P; Q)), arise as redundancies under mismatched compression when cumulants of compression lengths are considered instead of expected compression lengths. These parametric relative entropies are a generalization of the usual relative entropy (Kullback-Leibler divergence). Just like relative entropy, these relative -entropies behave like squared Euclidean distance and satisfy the Pythagorean property. We explore the geometry underlying various statistical models and its relevance to information theory and to robust statistics. The thesis consists of three parts. In the first part, we study minimization of I (P; Q) as the first argument varies over a convex set E of probability distributions. We show the existence of a unique minimizer when the set E is closed in an appropriate topology. We then study minimization of I on a particular convex set, a linear family, which is one that arises from linear statistical constraints. This minimization problem generalizes the maximum Renyi or Tsallis entropy principle of statistical physics. The structure of the minimizing probability distribution naturally suggests a statistical model of power-law probability distributions, which we call an -power-law family. Such a family is analogous to the exponential family that arises when relative entropy is minimized subject to the same linear statistical constraints. In the second part, we study minimization of I (P; Q) over the second argument. This minimization is generally on parametric families such as the exponential family or the - power-law family, and is of interest in robust statistics ( > 1) and in constrained compression settings ( < 1). In the third part, we show an orthogonality relationship between the -power-law family and an associated linear family. As a consequence of this, the minimization of I (P; ), when the second argument comes from an -power-law family, can be shown to be equivalent to a minimization of I ( ; R), for a suitable R, where the first argument comes from a linear family. The latter turns out to be a simpler problem of minimization of a quasi convex objective function subject to linear constraints. Standard techniques are available to solve such problems, for example, via a sequence of convex feasibility problems, or via a sequence of such problems but on simpler single-constraint linear families.

This thesis examines the utility of mathematical complexity measures for the analysis of stratospheric dynamics. Through theoretical considerations and tests with artificial data sets, e.g., the iteration of the logistic map, suitable parameters are determined for the application of the statistical entropy measures sample entropy (SE) and Rényi entropy (RE) to methane (a long-lived stratospheric tracer) data from simulations of the SOCOL chemistry-climate model. The SE is shown to be useful for quantifying the variability of recurring patterns in a time series and is able to identify tropical patterns similar to those reported by previous studies of the ``tropical pipe'' region. However, the SE is found to be unsuitable for use in polar regions, due to the non-stationarity of the methane data at extra-tropical latitudes. It is concluded that the SE cannot be used to analyse climate complexity on a global scale. The focus is turned to the RE, which is a complexity measure of probability distribution functions (PDFs). Using the second order RE and a normalisation factor, zonal PDFs of ten consecutive days of methane data are created with a Bayesian optimal binning technique. From these, the RE is calculated for every day (moving 10-day window). The results indicate that the RE is a promising tool for identifying stratospheric mixing barriers. In Southern Hemisphere winter and early spring, RE produces patterns similar to those found in other studies of stratospheric mixing. High values of RE are found to be indicative of the strong fluctuations in tracer distributions associated with relatively unmixed air in general, and with gradients in the vicinity of mixing barriers, in particular. Lower values suggest more thoroughly mixed air masses. The analysis is extended to eleven years of model data. Realistic inter-annual variability of some of the RE structures is observed, particularly in the Southern Hemisphere. By calculating a climatological mean of the RE for this period, additional mixing patterns are identified in the Northern Hemisphere. The validity of the RE analysis and its interpretation is underlined by showing that qualitatively similar patterns can be seen when using observational satellite data of a different tracer. Compared to previous techniques, the RE has the advantage that it requires significantly less computational effort, as it can be used to derive dynamical information from model or measurement tracer data without relying on any additional input such as wind fields. The results presented in this thesis strongly suggest that the RE is a useful new metric for analysing stratospheric mixing and its variability from climate model data. Furthermore, it is shown that the RE measure is very robust with respect to data gaps, which makes it ideal for application to observations. Hence, using the RE for comparing observations of tracer distributions with those from model simulations potentially presents a novel approach for analysing mixing in the stratosphere.

This thesis examines the utility of mathematical complexity measures for the analysis of stratospheric dynamics. Through theoretical considerations and tests with artificial data sets, e.g., the iteration of the logistic map, suitable parameters are determined for the application of the statistical entropy measures sample entropy (SE) and Rényi entropy (RE) to methane (a long-lived stratospheric tracer) data from simulations of the SOCOL chemistry-climate model. The SE is shown to be useful for quantifying the variability of recurring patterns in a time series and is able to identify tropical patterns similar to those reported by previous studies of the ``tropical pipe'' region. However, the SE is found to be unsuitable for use in polar regions, due to the non-stationarity of the methane data at extra-tropical latitudes. It is concluded that the SE cannot be used to analyse climate complexity on a global scale. The focus is turned to the RE, which is a complexity measure of probability distribution functions (PDFs). Using the second order RE and a normalisation factor, zonal PDFs of ten consecutive days of methane data are created with a Bayesian optimal binning technique. From these, the RE is calculated for every day (moving 10-day window). The results indicate that the RE is a promising tool for identifying stratospheric mixing barriers. In Southern Hemisphere winter and early spring, RE produces patterns similar to those found in other studies of stratospheric mixing. High values of RE are found to be indicative of the strong fluctuations in tracer distributions associated with relatively unmixed air in general, and with gradients in the vicinity of mixing barriers, in particular. Lower values suggest more thoroughly mixed air masses. The analysis is extended to eleven years of model data. Realistic inter-annual variability of some of the RE structures is observed, particularly in the Southern Hemisphere. By calculating a climatological mean of the RE for this period, additional mixing patterns are identified in the Northern Hemisphere. The validity of the RE analysis and its interpretation is underlined by showing that qualitatively similar patterns can be seen when using observational satellite data of a different tracer. Compared to previous techniques, the RE has the advantage that it requires significantly less computational effort, as it can be used to derive dynamical information from model or measurement tracer data without relying on any additional input such as wind fields. The results presented in this thesis strongly suggest that the RE is a useful new metric for analysing stratospheric mixing and its variability from climate model data. Furthermore, it is shown that the RE measure is very robust with respect to data gaps, which makes it ideal for application to observations. Hence, using the RE for comparing observations of tracer distributions with those from model simulations potentially presents a novel approach for analysing mixing in the stratosphere.

The overall goal of this thesis is to better understand changes in the spatial pattern of urban growth and its impact on landscape configuration by conducting a case study in Greater Chengdu, an inland megacity in China. The objectives are as follows: 1) Quantifying changes in the spatial pattern of the study area between 2003 and 2013; 2) Evaluating the degree of urban sprawl over that period; 3) Evaluating urban expansion dynamics; and 4) Examining and defining the types of urban growth. Satellite imagery was employed to distinguish and identify different land surface categories. Integrated remote sensing and GIS (Geographic Information System) technique was used to analyse both qualitative and quantitative perspectives regarding the objectives. The results indicate that the urban area of Greater Chengdu doubled from 525.5 km2 to 1191.85 km2 during 2003 to 2013. The geographic footprint demonstrates that the distribution of the built-up area was dispersed and continues to grow more dispersed. The dominant type of urban growth is outward expansion, by which the city grew within a 10 km to 25 km radius surrounding the city center. A substantial infill phenomenon exists between a 5 km and 10 km radius from the city center. The urban core boundary expanded outward by 5 km, while the fringe of suburban area expanded outward by 10 km during the time period, which both indicate a substantial outward expansion over the city. The significant contribution of this study could benefit to many aspects such as comparative studies between cities or continuous studies relevant to urban growth.

Thesis (PhD)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: We investigate the procedure of assigning probabilities to logical statements. The simplest case is that of equilibrium statistical mechanics and its fundamental assumption of equally likely states. Rederiving the formulation led us to question the assumption of logical independence inherent to the construction and speci cally its inability to update probability when data becomes available. Consequently we replace the assumption of logical independence with De Finetti's concept of exchangeability. To use the corresponding representation theorems of De Finetti requires us to assign prior distributions for some general parameter spaces. We propose the use of stability properties to identify suitable prior distributions. The combination of exchangeable likelihoods and corresponding prior distributions results in more general evidence distribution assignments. These new evidence assignments generalise the Shannon entropy to other entropy measures. The goal of these entropy formulations is to provide a general framework for constructing models. / AFRIKAANSE OPSOMMING: Ons ondersoek the prosedure om waarskynlikhede aan logiese stellings toe te ken. Die eenvoudigste geval is die van ewewig-statistiese meganika en die ooreenkomstige fundamentele aanname van ewekansige toestande. Hera eiding van die standaard formulering lei ons tot die bevraagtekening van die aanname van logiese onafhanklikheid en spesi ek die onmoontlikheid van opdatering van waarskynlikheid wanneer data beskikbaar raak. Gevolglik vervang ons die aanname van logiese onafhanklikheid met De Finetti se aanname van omruilbaarheid. Om die ooreenkomstige voorstelling stellings te gebruik moet ons a priori verdelings konstrueer vir 'n paar algemene parameter-ruimtes. Ons stel voor dat stabiliteits-eienskappe gebruik moet word om geskikte a priori distribusies te identi seer. Die kombinase van omruilbare aanneemlikheids funksies en die ooreenkomstige a priori verdelings lei ons tot nuwe toekennings van getuienis-verdelings. Hierdie nuwe getuienesverdelings is n veralgemening van Shannon se entropie na ander entropie-maatstawwe. Die doel van hierdie entropie formalismes is om 'n raamwerk vir modelkonstruksie te verskaf.

We present a mathematical study of two conservative systems in fluid mechanics. First, we study a fragmentation model for sprays. The model takes into account the break-up of spray droplets due to drag forces. In particular, we establish the existence of global weak solutions to a system of incompressible Navier-Stokes equations coupled with a Boltzmann-like kinetic equation. We assume the particles initially have bounded radii and bounded velocities relative to the gas, and we show that those bounds remain as the system evolves. One interesting feature of the model is the apparent accumulation of particles with arbitrarily small radii. As a result, there can be no nontrivial hydrodynamical equilibrium for this system. Next, with an interest in understanding hydrodynamical limits in discontinuous regimes, we study a classical model for shock waves. Specifically, we consider scalar nonviscous conservation laws with strictly convex flux in one spatial dimension, and we investigate the behavior of bounded L² perturbations of shock wave solutions to the Riemann problem using the relative entropy method. We show that up to a time-dependent translation of the shock, the L² norm of a perturbed solution relative to the shock wave is bounded above by the L² norm of the initial perturbation. Finally, we include some preliminary relative entropy estimates which are suitable for a study of shock wave solutions to n x n systems of conservation laws having a convex entropy. / text

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Ecological networks are complex systems forming hierarchical structures in which energy and matter is transferred between the network’s compartments. Predicting energy flows in food webs usually involves complex parameter-rich models. In this thesis, the application of the principle of maximum entropy (MaxEnt) to obtain least biased probability distributions based on prior knowledge is proposed as an alternative to predict the most likely energy flows in food webs from the network topology alone. This approach not only simplifies the characterisation of food web flow patterns based on little empirical knowledge but can also be used to investigate the role of bottom-up and top-down controlling forces in ecosystems resulting from the emergent phenomena based on the complex interactions on the level of species and individuals. The integrative measure of “flow extent”, incorporating both bottom- up and top-down controlling forces on ecosystems, is proposed as a principle behind ecosystem evolution and evaluated against empirical data on food web structure. It could be demonstrated that the method of predicting energy flow with the help of MaxEnt is very flexible, applicable to many different setting and types of questions in ecology, and therefore providing a powerful tool for modelling the energy transfer in ecosystems. Further research has to show in how far the most likely flow patterns are realised in real-word ecosystems. The concept of flow extent maximisation as a selection principle during ecosystem evolution can enhance the understanding of emergent phenomena in complex ecosystems and maybe help to draw a link between thermodynamics and ecology.