Fuzzy, integer and fractional-order control: application on a wind turbine benchmark model

This paper presents a comparison between proportional integral control approaches for variable speed wind turbines. Integer and fractional-order controllers are designed using linearized wind turbine model whilst fuzzy controller also takes into account system nonlinearities. These controllers operate in the full load region and the main objective is to extract maximum power from the wind turbine while ensuring the performance and reliability required to be integrated into an electric grid. The main contribution focuses on the use of fractional-order proportional integral (FOPI) controller which benefits from the introduction of one more tuning parameter, the integral fractional-order, taking advantage over integer order proportional integral (PI) controller. A comparison between proposed control approaches for the variable speed wind turbines is presented using a wind turbine benchmark model in the Matlab/Simulink environment. Results show that FOPI has improved system performance when compared with classical PI and fuzzy PI controller outperforms the integer and fractional-order control due to its capability to deal with system nonlinearities and uncertainties. © 2014 IEEE.

From cellulosic based liquid crystalline sheared solutions to 1D and 2D soft materials

Liquid crystalline cellulosic-based solutions described by distinctive properties are at the origin of different kinds of multifunctional materials with unique characteristics. These solutions can form chiral nematic phases at rest, with tuneable photonic behavior, and exhibit a complex behavior associated with the onset of a network of director field defects under shear. Techniques, such as Nuclear Magnetic Resonance (NMR), Rheology coupled with NMR (Rheo-NMR), rheology, optical methods, Magnetic Resonance Imaging (MRI), Wide Angle X-rays Scattering (WAXS), were extensively used to enlighten the liquid crystalline characteristics of these cellulosic solutions. Cellulosic films produced by shear casting and fibers by electrospinning, from these liquid crystalline solutions, have regained wider attention due to recognition of their innovative properties associated to their biocompatibility. Electrospun membranes composed by helical and spiral shape fibers allow the achievement of large surface areas, leading to the improvement of the performance of this kind of systems. The moisture response, light modulated, wettability and the capability of orienting protein and cellulose crystals, opened a wide range of new applications to the shear casted films. Characterization by NMR, X-rays, tensile tests, AFM, and optical methods allowed detailed characterization of those soft cellulosic materials. In this work, special attention will be given to recent developments, including, among others, a moisture driven cellulosic motor and electro-optical devices.

Node adaptation for global collocation with radial basis functions using direct multisearch for multiobjective optimization

Meshless methods are used for their capability of producing excellent solutions without requiring a mesh, avoiding mesh related problems encountered in other numerical methods, such as finite elements. However, node placement is still an open question, specially in strong form collocation meshless methods. The number of used nodes can have a big influence on matrix size and therefore produce ill-conditioned matrices. In order to optimize node position and number, a direct multisearch technique for multiobjective optimization is used to optimize node distribution in the global collocation method using radial basis functions. The optimization method is applied to the bending of isotropic simply supported plates. Using as a starting condition a uniformly distributed grid, results show that the method is capable of reducing the number of nodes in the grid without compromising the accuracy of the solution. (C) 2013 Elsevier Ltd. All rights reserved.

Compact, frequency reconfigurable, printed monopole antenna

This paper proposes a possible implementation of a compact printed monopole antenna, useful to operate in UMTS and WLAN bands. In order to accomplish that, a miniaturization technique based on the application of chip inductors is used in conjunction with frequency reconfiguration capability. The chip inductors change the impedance response of the monopole, allowing to reduce the resonant frequency. In order to be able to operate the antenna in these two different frequencies, an antenna reconfiguration technique based on PIN diodes is applied. This procedure allows the change of the active form of the antenna leading to a shift in the resonant frequency. The prototype measurements show good agreement with the simulation results.

Influence of the Sodium/Proton replacement on the structural, morphological and photocatalytic properties of titanate nanotubes

Titanate nanotubes (TNT) with different sodium contents have been synthesised using a hydrothermal approach and a swift and highly controllable post-washing processes. The influence of the sodium/proton replacement on the structural and morphological characteristics of the prepared materials was analysed. Different optical behaviour was observed depending on the Na+/H+ samples’ content. A band gap energy of 3.27±0.03 eV was estimated for the material with higher sodium content while a value of 2.81±0.02 eV was inferred for the most protonated material, which therefore exhibits an absorption edge in the near visible region. The point of zero charge of the materials was determined and the influence of the sodium content on the adsorption of both cationic and anionic organic dyes was studied. The photocatalytic performance of the TNT samples was evaluated in the rhodamine 6G degradation process. Best photodegradation results were obtained when using the most protonated material as catalyst, although this material has shown the lowest R6G adsorption capability.

Marx-Type solid-state bipolar modulator topologies: Performance comparison

The operation of generalized Marx-type solid-state bipolar modulators is discussed and compared with simplified Marx-derived circuits, to evaluate their capability to deal with various load conditions. A comparative analysis on the number of switches per cell, fiber optic trigger count, losses, and switch hold-off voltages has been made. A circuit topology is obtained as a compromise in terms of operating performance, trigger simplicity, and switching losses. A five-stage laboratory prototype of this circuit has been assembled using 1200 V insulated gate bipolar transistors (IGBTs) and diodes, operating with 1000 V dc input voltage and 1 kHz frequency, giving 5 kV bipolar pulses, with 2.5 mu s pulse width and 5 mu s relaxation time into resistive, capacitive, and inductive loads.

Análise do mercado de serviços de regulação de frequência secundária e terciária no sistema eléctrico português

Dissertação para obtenção do grau de Mestre em Engenharia Electrotécnica Ramo Energia

Processamento sensorial e interacção diádica como promotores de resiliência nas crianças de famílias com baixos rendimentos

A pesquisa sobre resiliência sugere que a criança que se desenvolve em contexto adverso, poderá usufruir de atributos relevantes, pessoais e do ambiente. Neste sentido pretendeu-se estudar, até que ponto, as competências de modulação sensorial da criança e a qualidade das interacções mãe-filho, influenciavam as trajectórias de risco e podiam promover as oportunidades de resiliência da criança. Participaram no estudo 136 crianças, 67 do sexo feminino e 69 do sexo masculino, com idades entre os 7 e os 36 meses. Analisámos a sensibilidade materna em situação de jogo livre recorrendo à escala CARE-Index e o processamento sensorial através do de entrevista baseado no protocolo de Dunn (1997) assente nos quatro padrões de processamento sensorial: baixo registo; sensibilidade sensorial; procura sensorial; evitamento sensorial, construto anteriormente validado. Constituímos, com base nas premissas do modelo de avaliação autêntica, um índex de capacidades, que nos serviu como referencial para a avaliação do risco e da resiliência. Os resultados indicaram que a resiliência infantil em ambiente de pobrezaestava associada a indicadores de elevada sensibilidade materna e a índices adequados de processamento sensorial. A discussão dos resultados enquadrou-se nos modelos actuais e emergentes das influências neurobiológicas e ambientais nos processos de risco e de resiliência.

Clinical classification versus semiquantification with adapted reference values for 123I-FP-CIT SPECT in a nuclear medicine department

Semi quantification (SQ) in DaTScan® studies is broadly used in clinic daily basis, however there is a suspicious about its discriminative capability, and concordance with the diagnostic classification performed by the physician. Aim: Evaluate the discriminate capability of an adapted database and reference's values of healthy controls for the Dopamine Transporters (DAT) with 123I–FP-IT named DBRV adapted to Nuclear Medicine Department's protocol and population of Infanta Cristina's Hospital, and its concordance with the physician classification.

Síntese de um novo polímero conjugado e reticulável e sua aplicação em células fotovoltaicas

Trabalho Final de Mestrado para obtenção do Grau de Mestre em Engenharia Química e Biológica

Métodos "verdes" de produção de nanomateriais que promovem nanotecnologias sustentáveis

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Química

Recurring contact opportunities within groups of devices

The capability to anticipate a contact with another device can greatly improve the performance and user satisfaction not only of mobile social network applications but of any other relying on some form of data harvesting or hoarding. One of the most promising approaches for contact prediction is to extrapolate from past experiences. This paper investigates the recurring contact patterns observed between groups of devices using an 8-year dataset of wireless access logs produced by more than 70000 devices. This effort permitted to model the probabilities of occurrence of a contact at a predefined date between groups of devices using a power law distribution that varies according to neighbourhood size and recurrence period. In the general case, the model can be used by applications that need to disseminate large datasets by groups of devices. As an example, the paper presents and evaluates an algorithm that provides daily contact predictions, based on the history of past pairwise contacts and their duration. Copyright © 2015 ICST.

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

Viscosity and self-diffusion coefficients of dialkyl adipates: a correlation scheme with predictive capabilities

A correlation and predictive scheme for the viscosity and self-diffusivity of liquid dialkyl adipates is presented. The scheme is based on the kinetic theory for dense hard-sphere fluids, applied to the van der Waals model of a liquid to predict the transport properties. A "universal" curve for a dimensionless viscosity of dialkyl adipates was obtained using recently published experimental viscosity and density data of compressed liquid dimethyl (DMA), dipropyl (DPA), and dibutyl (DBA) adipates. The experimental data are described by the correlation scheme with a root-mean-square deviation of +/- 0.34 %. The parameters describing the temperature dependence of the characteristic volume, V-0, and the roughness parameter, R-eta, for each adipate are well correlated with one single molecular parameter. Recently published experimental self-diffusion coefficients of the same set of liquid dialkyl adipates at atmospheric pressure were correlated using the characteristic volumes obtained from the viscosity data. The roughness factors, R-D, are well correlated with the same single molecular parameter found for viscosity. The root-mean-square deviation of the data from the correlation is less than 1.07 %. Tests are presented in order to assess the capability of the correlation scheme to estimate the viscosity of compressed liquid diethyl adipate (DEA) in a range of temperatures and pressures by comparison with literature data and of its self-diffusivity at atmospheric pressure in a range of temperatures. It is noteworthy that no data for DEA were used to build the correlation scheme. The deviations encountered between predicted and experimental data for the viscosity and self-diffusivity do not exceed 2.0 % and 2.2 %, respectively, which are commensurate with the estimated experimental measurement uncertainty, in both cases.