A 800 kW I-V curve

This paper presents the measurement of the I-V curve of an 800 kW PV generator by means of an own-made capacitive load. Along the lines of some previous works, it is shown that an I-V curve analysis can also be applied to big PV generators and that, when measuring the operating conditions with reference modules and taking some precautions (especially regarding the operating cell temperature), it is still a useful tool for characterizing them and therefore can be incorporated into maintenance procedures. As far as we know, this is the largest I-V curve measured so far.

Gini coefficient, dissimilarity index and Lorenz curve for the spanish port system by type of goods

This paper shows the Gini Coefficient, the dissimilarity Index and the Lorenz Curve for the Spanish Port System by type of goods from 1960 to the year 2010 for business units: Total traffic, Liquid bulk cargo, Solid bulk cargo, General Merchandise and Container (TEUs) with the aim of carcaterizar the Spanish port systems in these periods and propose future strategies.

A 500-kW pv generator I-V curve

This paper presents the measurement of the I-V curve of a 500-kW PV generator by means of an own-made capacitive load. It is shown that I-V curve analysis can also be applied to big PV generators and that when measuring the operation conditions with reference modules and taking some precautions (especially regarding the operation cell temperature), it is still a useful tool for characterizing them and therefore can be incorporated into maintenance procedures. As far as we know, this is the largest I-V curve measured so far.

Massively parallelizable list-mode reconstruction using a Monte Carlo-based elliptical Gaussian model

Purpose: A fully three-dimensional (3D) massively parallelizable list-mode ordered-subsets expectation-maximization (LM-OSEM) reconstruction algorithm has been developed for high-resolution PET cameras. System response probabilities are calculated online from a set of parameters derived from Monte Carlo simulations. The shape of a system response for a given line of response (LOR) has been shown to be asymmetrical around the LOR. This work has been focused on the development of efficient region-search techniques to sample the system response probabilities, which are suitable for asymmetric kernel models, including elliptical Gaussian models that allow for high accuracy and high parallelization efficiency. The novel region-search scheme using variable kernel models is applied in the proposed PET reconstruction algorithm. Methods: A novel region-search technique has been used to sample the probability density function in correspondence with a small dynamic subset of the field of view that constitutes the region of response (ROR). The ROR is identified around the LOR by searching for any voxel within a dynamically calculated contour. The contour condition is currently defined as a fixed threshold over the posterior probability, and arbitrary kernel models can be applied using a numerical approach. The processing of the LORs is distributed in batches among the available computing devices, then, individual LORs are processed within different processing units. In this way, both multicore and multiple many-core processing units can be efficiently exploited. Tests have been conducted with probability models that take into account the noncolinearity, positron range, and crystal penetration effects, that produced tubes of response with varying elliptical sections whose axes were a function of the crystal's thickness and angle of incidence of the given LOR. The algorithm treats the probability model as a 3D scalar field defined within a reference system aligned with the ideal LOR. Results: This new technique provides superior image quality in terms of signal-to-noise ratio as compared with the histogram-mode method based on precomputed system matrices available for a commercial small animal scanner. Reconstruction times can be kept low with the use of multicore, many-core architectures, including multiple graphic processing units. Conclusions: A highly parallelizable LM reconstruction method has been proposed based on Monte Carlo simulations and new parallelization techniques aimed at improving the reconstruction speed and the image signal-to-noise of a given OSEM algorithm. The method has been validated using simulated and real phantoms. A special advantage of the new method is the possibility of defining dynamically the cut-off threshold over the calculated probabilities thus allowing for a direct control on the trade-off between speed and quality during the reconstruction.

An application of the finite element method to curve fitting

An application of the Finite Element Method (FEM) to the solution of a geometric problem is shown. The problem is related to curve fitting i.e. pass a curve trough a set of given points even if they are irregularly spaced. Situations where cur ves with cusps can be encountered in the practice and therefore smooth interpolatting curves may be unsuitable. In this paper the possibilities of the FEM to deal with this type of problems are shown. A particular example of application to road planning is discussed. In this case the funcional to be minimized should express the unpleasent effects of the road traveller. Some comparative numerical examples are also given.

Linear latent force models using Gaussian Processes

Purely data-driven approaches for machine learning present difficulties when data are scarce relative to the complexity of the model or when the model is forced to extrapolate. On the other hand, purely mechanistic approaches need to identify and specify all the interactions in the problem at hand (which may not be feasible) and still leave the issue of how to parameterize the system. In this paper, we present a hybrid approach using Gaussian processes and differential equations to combine data-driven modeling with a physical model of the system. We show how different, physically inspired, kernel functions can be developed through sensible, simple, mechanistic assumptions about the underlying system. The versatility of our approach is illustrated with three case studies from motion capture, computational biology, and geostatistics.

Fully adaptive gaussian mixture metropolis-hastings algorithm

Markov Chain Monte Carlo methods are widely used in signal processing and communications for statistical inference and stochastic optimization. In this work, we introduce an efficient adaptive Metropolis-Hastings algorithm to draw samples from generic multimodal and multidimensional target distributions. The proposal density is a mixture of Gaussian densities with all parameters (weights, mean vectors and covariance matrices) updated using all the previously generated samples applying simple recursive rules. Numerical results for the one and two-dimensional cases are provided.

Development of Shape-Optimization Tools for the Aerodynamic Design of Turbomachinery Blades

The aim of this work is to develop an automated tool for the optimization of turbomachinery blades founded on an evolutionary strategy. This optimization scheme will serve to deal with supersonic blades cascades for application to Organic Rankine Cycle (ORC) turbines. The blade geometry is defined using parameterization techniques based on B-Splines curves, that allow to have a local control of the shape. The location in space of the control points of the B-Spline curve define the design variables of the optimization problem. In the present work, the performance of the blade shape is assessed by means of fully-turbulent flow simulations performed with a CFD package, in which a look-up table method is applied to ensure an accurate thermodynamic treatment. The solver is set along with the optimization tool to determine the optimal shape of the blade. As only blade-to-blade effects are of interest in this study, quasi-3D calculations are performed, and a single-objective evolutionary strategy is applied to the optimization. As a result, a non-intrusive tool, with no need for gradients definition, is developed. The computational cost is reduced by the use of surrogate models. A Gaussian interpolation scheme (Kriging model) is applied for the estimated n-dimensional function, and a surrogate-based local optimization strategy is proved to yield an accurate way for optimization. In particular, the present optimization scheme has been applied to the re-design of a supersonic stator cascade of an axial-flow turbine. In this design exercise very strong shock waves are generated in the rear blade suction side and shock-boundary layer interaction mechanisms occur. A significant efficiency improvement as a consequence of a more uniform flow at the blade outlet section of the stator is achieved. This is also expected to provide beneficial effects on the design of a subsequent downstream rotor. The method provides an improvement to gradient-based methods and an optimized blade geometry is easily achieved using the genetic algorithm.

Genetic algorithms and Gaussian Bayesian networks to uncover the predictive core set of bibliometric indices

The diversity of bibliometric indices today poses the challenge of exploiting the relationships among them. Our research uncovers the best core set of relevant indices for predicting other bibliometric indices. An added difficulty is to select the role of each variable, that is, which bibliometric indices are predictive variables and which are response variables. This results in a novel multioutput regression problem where the role of each variable (predictor or response) is unknown beforehand. We use Gaussian Bayesian networks to solve the this problem and discover multivariate relationships among bibliometric indices. These networks are learnt by a genetic algorithm that looks for the optimal models that best predict bibliometric data. Results show that the optimal induced Gaussian Bayesian networks corroborate previous relationships between several indices, but also suggest new, previously unreported interactions. An extended analysis of the best model illustrates that a set of 12 bibliometric indices can be accurately predicted using only a smaller predictive core subset composed of citations, g-index, q2-index, and hr-index. This research is performed using bibliometric data on Spanish full professors associated with the computer science area.

Canonical Correlation Analysis for Interpreting Airborne Laser Scanning Metrics along the Lorenz Curve of Tree Size Inequality

Canonical Correlation Analysis for Interpreting Airborne Laser Scanning Metrics along the Lorenz Curve of Tree Size Inequality

Análise de acetona em ar exalado: desenvolvimento de método eletroanalítico e algoritmo para processamento de sinais

Propõe-se método novo e completo para análise de acetona em ar exalado envolvendo coleta com pré-concentração em água, derivatização química e determinação eletroquímica assistida por novo algoritmo de processamento de sinais. Na literatura recente a acetona expirada vem sendo avaliada como biomarcador para monitoramento não invasivo de quadros clínicos como diabetes e insuficiência cardíaca, daí a importância da proposta. Entre as aminas que reagem com acetona para formar iminas eletroativas, estudadas por polarografia em meados do século passado, a glicina apresentou melhor conjunto de características para a definição do método de determinação por voltametria de onda quadrada sem a necessidade de remoção de oxigênio (25 Hz, amplitude de 20 mV, incremento de 5 mV, eletrodo de gota de mercúrio). O meio reacional, composto de glicina (2 mol·L-1) em meio NaOH (1 mol·L-1), serviu também de eletrólito e o pico de redução da imina em -1,57 V vs. Ag|AgCl constituiu o sinal analítico. Para tratamento dos sinais, foi desenvolvido e avaliado um algoritmo inovador baseado em interpolação de linha base por ajuste de curvas de Bézier e ajuste de gaussiana ao pico. Essa combinação permitiu reconhecimento e quantificação de picos relativamente baixos e largos sobre linha com curvatura acentuada e ruído, situação em que métodos convencionais falham e curvas do tipo spline se mostraram menos apropriadas. A implementação do algoritmo (disponível em http://github.com/batistagl/chemapps) foi realizada utilizando programa open source de álgebra matricial integrado diretamente com software de controle do potenciostato. Para demonstrar a generalidade da extensão dos recursos nativos do equipamento mediante integração com programação externa em linguagem Octave (open source), implementou-se a técnica da cronocoulometria tridimensional, com visualização de resultados já tratados em projeções de malha de perspectiva 3D sob qualquer ângulo. A determinação eletroquímica de acetona em fase aquosa, assistida pelo algoritmo baseado em curvas de Bézier, é rápida e automática, tem limite de detecção de 3,5·10-6 mol·L-1 (0,2 mg·L-1) e faixa linear que atende aos requisitos da análise em ar exalado. O acetaldeído, comumente presente em ar exalado, em especial, após consumo de bebidas alcoólicas, dá origem a pico voltamétrico em -1,40 V, contornando interferência que prejudica vários outros métodos publicados na literatura e abrindo possibilidade de determinação simultânea. Resultados obtidos com amostras reais são concordantes com os obtidos por método espectrofotométrico, em uso rotineiro desde o seu aperfeiçoamento na dissertação de mestrado do autor desta tese. Em relação à dissertação, também se otimizou a geometria do dispositivo de coleta, de modo a concentrar a acetona num volume menor de água gelada e prover maior conforto ao paciente. O método completo apresentado, englobando o dispositivo de amostragem aperfeiçoado e o novo e efetivo algoritmo para tratamento automático de sinais voltamétricos, está pronto para ser aplicado. Evolução para um analisador portátil depende de melhorias no limite de detecção e facilidade de obtenção eletrodos sólidos (impressos) com filme de mercúrio, vez que eletrodos de bismuto ou diamante dopado com boro, entre outros, não apresentaram resposta.

Comparison Between Dcrit Considering the Abrupt Variation and Inflexion in the Concrete Mercury Intrusion Porosimetry Curve

Mercury intrusion porosimetry (MIP) has been widely used to evaluate the quality of concrete through the pore size distribution parameters. Two of these parameters are the critical pore diameter (Dcrit) and the percentage of the most interconnected net of pores compared to the total volume of pores. Some researchers consider Dcrit as the diameter obtained from the inflexion point of the cumulative mercury intrusion curve while others consider Dcrit as the diameter obtained from the point of abrupt variation in the same curve. This study aims to analyze two groups of concretes of varying w/c ratios, one cast with pozzolanic cement and another with high initial strength cement, in order to determine which of these diameters feature a better correlation with the quality parameters of the concretes. The concrete quality parameters used for the evaluations were (1) the w/c ratios and (2) chloride diffusion coefficients measured at approximately 90 days. MIP cumulative distributions of the same concretes were also measured at about 90 days, and Dcrit values were determined (1) from the point of abrupt variation and alternatively, (2) from the inflexion point of each of these plots. It was found that Dcrit values measured from the point of abrupt variation were useful indicators of the quality of the concrete, but the Dcrit values based on the inflexion points were not. Hence, it is recommended that Dcrit and the percentage of the most interconnected volume of pores should be obtained considering the point of abrupt variation of the cumulative curve of pore size distribution.

A Proper Yield Curve for Greece to Kick-Start Financial Intermediation. ECMI Policy Brief No. 21, 10 December 2013

At present, the market is severely mispricing Greece’s sovereign risk relative to the country’s fundamentals. As a result of the mispricing, financial intermediation in Greece has become dysfunctional and the privatisation of state-owned assets has stalled. This mispricing is partially due to an illiquid and fragmented government yield curve. A well-designed public liability management exercise can lead to a more efficient pricing of Greece’s government bonds and thereby help restore stable and affordable financing for the country’s private sector, which is imperative in order to overcome Greece’s deep recession. This paper proposes three measures to enhance the functioning of the Greek government debt market: i) Greece should issue a new five-year bond, ii) it should consolidate the 20 individual series of government bonds into four liquid securities and iii) it should offer investors a swap of these newly created bonds into dollar-denominated securities. Each of these measures would be beneficial to the Hellenic Republic, since the government would be able to reduce the face value and the net present value of its debt stock. Furthermore, this exercise would facilitate the resumption of market access, which is a necessary condition for continuous multilateral disbursements to Greece.

On ANOVA Decompositions of Kernels and Gaussian Random Field Paths

The FANOVA (or “Sobol’-Hoeffding”) decomposition of multivariate functions has been used for high-dimensional model representation and global sensitivity analysis. When the objective function f has no simple analytic form and is costly to evaluate, computing FANOVA terms may be unaffordable due to numerical integration costs. Several approximate approaches relying on Gaussian random field (GRF) models have been proposed to alleviate these costs, where f is substituted by a (kriging) predictor or by conditional simulations. Here we focus on FANOVA decompositions of GRF sample paths, and we notably introduce an associated kernel decomposition into 4 d 4d terms called KANOVA. An interpretation in terms of tensor product projections is obtained, and it is shown that projected kernels control both the sparsity of GRF sample paths and the dependence structure between FANOVA effects. Applications on simulated data show the relevance of the approach for designing new classes of covariance kernels dedicated to high-dimensional kriging.