Optimization algorithms in FMRF model-based segmentation for LIDAR data and co-registered bands

In this paper, a fuzzy Markov random field (FMRF) model is used to segment land-objects into free, grass, building, and road regions by fusing remotely, sensed LIDAR data and co-registered color bands, i.e. scanned aerial color (RGB) photo and near infra-red (NIR) photo. An FMRF model is defined as a Markov random field (MRF) model in a fuzzy domain. Three optimization algorithms in the FMRF model, i.e. Lagrange multiplier (LM), iterated conditional mode (ICM), and simulated annealing (SA), are compared with respect to the computational cost and segmentation accuracy. The results have shown that the FMRF model-based ICM algorithm balances the computational cost and segmentation accuracy in land-cover segmentation from LIDAR data and co-registered bands.

Land-cover classification from airborne LIDAR data fused with aerial optical images

Airborne LIght Detection And Ranging (LIDAR) provides accurate height information for objects on the earth, which makes LIDAR become more and more popular in terrain and land surveying. In particular, LIDAR data offer vital and significant features for land-cover classification which is an important task in many application domains. In this paper, an unsupervised approach based on an improved fuzzy Markov random field (FMRF) model is developed, by which the LIDAR data, its co-registered images acquired by optical sensors, i.e. aerial color image and near infrared image, and other derived features are fused effectively to improve the ability of the LIDAR system for the accurate land-cover classification. In the proposed FMRF model-based approach, the spatial contextual information is applied by modeling the image as a Markov random field (MRF), with which the fuzzy logic is introduced simultaneously to reduce the errors caused by the hard classification. Moreover, a Lagrange-Multiplier (LM) algorithm is employed to calculate a maximum A posteriori (MAP) estimate for the classification. The experimental results have proved that fusing the height data and optical images is particularly suited for the land-cover classification. The proposed approach works very well for the classification from airborne LIDAR data fused with its coregistered optical images and the average accuracy is improved to 88.9%.

Probabilistic downscaling of remote sensing data with applications for multi-scale biogeochemical flux modeling

Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes.

Testing common nonlinear features in vector nonlinear autoregressive models

This paper studies a special class of vector smooth-transition autoregressive (VSTAR) models that contains common nonlinear features (CNFs), for which we proposed a triangular representation and developed a procedure of testing CNFs in a VSTAR model. We first test a unit root against a stable STAR process for each individual time series and then examine whether CNFs exist in the system by Lagrange Multiplier (LM) test if unit root is rejected in the first step. The LM test has standard Chi-squared asymptotic distribution. The critical values of our unit root tests and small-sample properties of the F form of our LM test are studied by Monte Carlo simulations. We illustrate how to test and model CNFs using the monthly growth of consumption and income data of United States (1985:1 to 2011:11).

Tree-structured smooth transition models

The goal of this paper is to introduce a class of tree-structured models that combines aspects of regression trees and smooth transition regression models. The model is called the Smooth Transition Regression Tree (STR-Tree). The main idea relies on specifying a multiple-regime parametric model through a tree-growing procedure with smooth transitions among different regimes. Decisions about splits are entirely based on a sequence of Lagrange Multiplier (LM) tests of hypotheses.

Application of a meshless method in electromagnetics

An improved meshless method is presented with an emphasis on the detailed description of this new computational technique and its numerical implementations by investigating the usefulness of a commonly neglected parameter in this paper. Two approaches to enforce essential boundary conditions are also thoroughly investigated. Numerical tests on a mathematical function is carried out as a means of validating the proposed method. It will be seen that the proposed method is more robust than the conventional ones. Applications in solving electromagnetic problems are also presented.

Optimal control problem for deflection plate with crack

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Interior-exterior point method with global convergence strategy for solving the reactive optimal power flow problem

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Integrable deformations of strings on symmetric spaces

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Analysis of optimal control problems for the incompressible MHD equations and implementation in a finite element multiphysics code

This thesis deals with the study of optimal control problems for the incompressible Magnetohydrodynamics (MHD) equations. Particular attention to these problems arises from several applications in science and engineering, such as fission nuclear reactors with liquid metal coolant and aluminum casting in metallurgy. In such applications it is of great interest to achieve the control on the fluid state variables through the action of the magnetic Lorentz force. In this thesis we investigate a class of boundary optimal control problems, in which the flow is controlled through the boundary conditions of the magnetic field. Due to their complexity, these problems present various challenges in the definition of an adequate solution approach, both from a theoretical and from a computational point of view. In this thesis we propose a new boundary control approach, based on lifting functions of the boundary conditions, which yields both theoretical and numerical advantages. With the introduction of lifting functions, boundary control problems can be formulated as extended distributed problems. We consider a systematic mathematical formulation of these problems in terms of the minimization of a cost functional constrained by the MHD equations. The existence of a solution to the flow equations and to the optimal control problem are shown. The Lagrange multiplier technique is used to derive an optimality system from which candidate solutions for the control problem can be obtained. In order to achieve the numerical solution of this system, a finite element approximation is considered for the discretization together with an appropriate gradient-type algorithm. A finite element object-oriented library has been developed to obtain a parallel and multigrid computational implementation of the optimality system based on a multiphysics approach. Numerical results of two- and three-dimensional computations show that a possible minimum for the control problem can be computed in a robust and accurate manner.

Relação espacial dos óbitos e internações por tuberculose com indicadores sociais em Ribeirão Preto (SP)

Objetivo: Propôs-se analisar a relação espacial dos óbitos e internações evitáveis por TB com indicadores sociais em Ribeirão Preto/SP. Métodos: Trata-se de um estudo ecológico em que foram considerados os casos de óbitos e internações, tendo como causa básica do óbito e motivo principal da internação, a tuberculose (CID A15.0 a A19.9), ocorridos na zona urbana de Ribeirão Preto e registrados respectivamente no Sistema de Informação sobre Mortalidade e no Sistema de Internação Hospitalar do Sistema Único de Saúde no período de 2006 a 2012. Foi realizada a análise univariada das variáveis sociodemográficas e operacionais dos casos investigados. Para construção dos indicadores sociais utilizou-se a análise de componentes principais, sendo selecionados dados das áreas de abrangência do município, considerando os dados do Censo Demográfico de 2010. A geocodificação dos casos foi processada no TerraView versão 4.2.2. Recorreu-se à regressão linear múltipla, pelo método dos mínimos quadrados e à regressão espacial para análise da relação de dependência espacial entre os indicadores sociais e as taxas de mortalidade e de internações por TB. A autocorrelação nos resíduos da regressão linear múltipla foi testada por meio do Teste Global de Moran, as análises foram realizadas considerando os softwares Arcgis-versão 10.1, Statistica versão 12.0, OpenGeoDa versão 1.0 e R versão 3.2.3. Para o diagnóstico do melhor modelo de regressão espacial, utilizou-se o teste Multiplicador de Lagrange. Em todos os testes, foi fixado o nivel de significancia de alfa em 5% (p< 0,05). Resultados: Foram registrados 50 casos de óbitos e 196 casos de internações por TB. A maioria dos casos registrados em ambos os sistemas se deu em pessoas do sexo masculino (n=41; 82%/n=146; 74,5%) e com a forma clínica pulmonar (n=44; 80,0%/n=138; 67,9%). Na construção dos indicadores sociais, três novas variáveis surgiram, apresentando respectivamente variância total de 46,2%, 18,7% e 14,6% sendo denominadas como indicadores de renda, desigualdade social e equidade social. Na modelagem para verificar relação espacial entre os óbitos e os indicadores sociais observou-se que a equidade social foi indicador estatisticamente significativo (p=0,0013) com relação negativa a mortalidade, sendo o Modelo da Defasagem Espacial o melhor método para testar a dependência espacial, com valor de ? (rho) estimado em 0,53 e altamente significativo (p=0,0014). Já na modelagem da relação espacial entre as internações por tuberculose e os indicadores sociais, o indicador de renda apresentou-se estatisticamente significativo (p=0,015) com relação negativa a internação e o melhor método para testar a dependência espacial também foi o Modelo da Defasagem Espacial com valor de ? (rho) estimado em 0,80 e altamente significativo (p<0,0001). Conclusão: O estudo contribuiu no avanço do conhecimento de que a mortalidade e as internações por tuberculose são eventos socialmente determinados, o que sugere investimento por parte da gestão

Controlador integrado baseado em conceito híbrido e controle geométrico para embarcações posicionadas dinamicamente.

O conceito de controle híbrido é aplicado à operação de alívio entre um FPWSO e um navio aliviador. Ambos os navios mantêm suas posições e aproamentos pelo resultado da ação do seu Sistema de Posicionamento Dinâmico (SPD). O alívio dura cerca de 24 horas para ser concluído. Durante este período, o estado de mar pode se alterar e os calados estão sendo constantemente alterados. Um controlador híbrido é projetado para permitir modificacões dos parâmetros de controle/observação se alguma alteração significante do estado de mar e/ou calado das embarcações ocorrer. O principal objetivo dos controladores é manter o posicionamento relativo entre os navios com o intuito de evitar perigosa proximidade ou excesso de tensão no cabo. Com isto em mente, uma nova estratégia de controle que atue integradamente em ambos os navios é proposta baseda em geometria diferencial. Observadores não lineares baseados em passividade são aplicados para estimar a posição, a velocidade e as forças externas de mares calmos até extremos. O critério para troca do controle/observação é baseado na variação do calado e no estado de mar. O calado é assumido conhecido e o estado de mar é estimado pela frequência de pico do espectro do movimento de primeira ordem dos navios. Um modelo de perturbação é proposto para encontrar o número de controladores do sistema híbrido. A equivalência entre o controle geométrico e o controlador baseado em Multiplicadores de Lagrange é demonstrada. Assumindo algumas hipóteses, a equivalência entre os controladores geométrico e o PD é também apresentada. O desempenho da nova estratégia é avaliada por meio de simulações numéricas e comparada a um controlador PD. Os resultados apresentam muito bom desempenho em função do objetivo proposto. A comparação entre a abordagem geométrica e o controlador PD aponta um desempenho muito parecido entre eles.

Design of sway frames

The aim of the work presented in this thesis is to produce a direct method to design structures subject to deflection constraints at the working loads. The work carried out can be divided into four main parts. In the first part, a direct design procedure for plane steel frames subjected to sway limitations is proposed. The stiffness equations are modified so that the sway in each storey is equal to some specified values. The modified equations are then solved by iteration to calculate the cross-sectional properties of the columns as well as the other joint displacements. The beam sections are selected initially and then altered in an effort to reduce the total material cost of the frame. A linear extrapolation technique is used to reduce this cost. In this design, stability functions are used so that the effect of axial loads in the members are taken into consideration. The final reduced cost design is checked for strength requirements and the members are altered accordingly. In the second part, the design method is applied to the design of reinforced concrete frames in which the sway in the columns play an active part in the design criteria. The second moment of area of each column is obtained by solving the modified stiffness equations and then used to calculate the mlnlmum column depth required. Again the frame has to be checked for all the ultimate limit state load cases. In the third part, the method is generalised to design pin-jointed space frames for deflection limitatlions. In these the member areas are calculated so that the deflection at a specified joint is equal to its specified value. In the final part, the Lagrange multiplier technique is employed to obtain an optimum design for plane rigidly jointed steel frames. The iteration technique is used here to solve the modified stiffness equations as well as derivative equations obtained in accordance to the requirements of the optimisation method.

An autoregressive conditional filtering process to remove intraday seasonal volatility and its application to testing the noisy rational expectations model

We develop a new autoregressive conditional process to capture both the changes and the persistency of the intraday seasonal (U-shape) pattern of volatility in essay 1. Unlike other procedures, this approach allows for the intraday volatility pattern to change over time without the filtering process injecting a spurious pattern of noise into the filtered series. We show that prior deterministic filtering procedures are special cases of the autoregressive conditional filtering process presented here. Lagrange multiplier tests prove that the stochastic seasonal variance component is statistically significant. Specification tests using the correlogram and cross-spectral analyses prove the reliability of the autoregressive conditional filtering process. In essay 2 we develop a new methodology to decompose return variance in order to examine the informativeness embedded in the return series. The variance is decomposed into the information arrival component and the noise factor component. This decomposition methodology differs from previous studies in that both the informational variance and the noise variance are time-varying. Furthermore, the covariance of the informational component and the noisy component is no longer restricted to be zero. The resultant measure of price informativeness is defined as the informational variance divided by the total variance of the returns. The noisy rational expectations model predicts that uninformed traders react to price changes more than informed traders, since uninformed traders cannot distinguish between price changes caused by information arrivals and price changes caused by noise. This hypothesis is tested in essay 3 using intraday data with the intraday seasonal volatility component removed, as based on the procedure in the first essay. The resultant seasonally adjusted variance series is decomposed into components caused by unexpected information arrivals and by noise in order to examine informativeness.

An Autoregressive Conditional Filtering Process to remove Intraday Seasonal Volatility and its Application to Testing the Noisy Rational Expectations Model

We develop a new autoregressive conditional process to capture both the changes and the persistency of the intraday seasonal (U-shape) pattern of volatility in essay 1. Unlike other procedures, this approach allows for the intraday volatility pattern to change over time without the filtering process injecting a spurious pattern of noise into the filtered series. We show that prior deterministic filtering procedures are special cases of the autoregressive conditional filtering process presented here. Lagrange multiplier tests prove that the stochastic seasonal variance component is statistically significant. Specification tests using the correlogram and cross-spectral analyses prove the reliability of the autoregressive conditional filtering process. In essay 2 we develop a new methodology to decompose return variance in order to examine the informativeness embedded in the return series. The variance is decomposed into the information arrival component and the noise factor component. This decomposition methodology differs from previous studies in that both the informational variance and the noise variance are time-varying. Furthermore, the covariance of the informational component and the noisy component is no longer restricted to be zero. The resultant measure of price informativeness is defined as the informational variance divided by the total variance of the returns. The noisy rational expectations model predicts that uninformed traders react to price changes more than informed traders, since uninformed traders cannot distinguish between price changes caused by information arrivals and price changes caused by noise. This hypothesis is tested in essay 3 using intraday data with the intraday seasonal volatility component removed, as based on the procedure in the first essay. The resultant seasonally adjusted variance series is decomposed into components caused by unexpected information arrivals and by noise in order to examine informativeness.