A sensor classification strategy for robotic manipulators

In practice the robotic manipulators present some degree of unwanted vibrations. The advent of lightweight arm manipulators, mainly in the aerospace industry, where weight is an important issue, leads to the problem of intense vibrations. On the other hand, robots interacting with the environment often generate impacts that propagate through the mechanical structure and produce also vibrations. In order to analyze these phenomena a robot signal acquisition system was developed. The manipulator motion produces vibrations, either from the structural modes or from endeffector impacts. The instrumentation system acquires signals from several sensors that capture the joint positions, mass accelerations, forces and moments, and electrical currents in the motors. Afterwards, an analysis package, running off-line, reads the data recorded by the acquisition system and extracts the signal characteristics. Due to the multiplicity of sensors, the data obtained can be redundant because the same type of information may be seen by two or more sensors. Because of the price of the sensors, this aspect can be considered in order to reduce the cost of the system. On the other hand, the placement of the sensors is an important issue in order to obtain the suitable signals of the vibration phenomenon. Moreover, the study of these issues can help in the design optimization of the acquisition system. In this line of thought a sensor classification scheme is presented. Several authors have addressed the subject of the sensor classification scheme. White (White, 1987) presents a flexible and comprehensive categorizing scheme that is useful for describing and comparing sensors. The author organizes the sensors according to several aspects: measurands, technological aspects, detection means, conversion phenomena, sensor materials and fields of application. Michahelles and Schiele (Michahelles & Schiele, 2003) systematize the use of sensor technology. They identified several dimensions of sensing that represent the sensing goals for physical interaction. A conceptual framework is introduced that allows categorizing existing sensors and evaluates their utility in various applications. This framework not only guides application designers for choosing meaningful sensor subsets, but also can inspire new systems and leads to the evaluation of existing applications. Today’s technology offers a wide variety of sensors. In order to use all the data from the diversity of sensors a framework of integration is needed. Sensor fusion, fuzzy logic, and neural networks are often mentioned when dealing with problem of combing information from several sensors to get a more general picture of a given situation. The study of data fusion has been receiving considerable attention (Esteban et al., 2005; Luo & Kay, 1990). A survey of the state of the art in sensor fusion for robotics can be found in (Hackett & Shah, 1990). Henderson and Shilcrat (Henderson & Shilcrat, 1984) introduced the concept of logic sensor that defines an abstract specification of the sensors to integrate in a multisensor system. The recent developments of micro electro mechanical sensors (MEMS) with unwired communication capabilities allow a sensor network with interesting capacity. This technology was applied in several applications (Arampatzis & Manesis, 2005), including robotics. Cheekiralla and Engels (Cheekiralla & Engels, 2005) propose a classification of the unwired sensor networks according to its functionalities and properties. This paper presents a development of a sensor classification scheme based on the frequency spectrum of the signals and on a statistical metrics. Bearing these ideas in mind, this paper is organized as follows. Section 2 describes briefly the robotic system enhanced with the instrumentation setup. Section 3 presents the experimental results. Finally, section 4 draws the main conclusions and points out future work.

Wind-CSP short-term coordination by Milp approach

This paper is on the maximization of total profit in a day-ahead market for a price-taker producer needing a short-term scheduling for wind power plants coordination with concentrated solar power plants, having thermal energy storage systems. The optimization approach proposed for the maximization of profit is a mixed-integer linear programming problem. The approach considers not only transmission grid constraints, but also technical operating constraints on both wind and concentrated solar power plants. Then, an improved short-term scheduling coordination is provided due to the more accurate modelling presented in this paper. Computer simulation results based on data for the Iberian wind and concentrated solar power plants illustrate the coordination benefits and show the effectiveness of the approach.

On the use of particle swarm optimization to maximize bending stiffness of functionally graded structures

Functionally graded materials are a type of composite materials which are tailored to provide continuously varying properties, according to specific constituent's mixing distributions. These materials are known to provide superior thermal and mechanical performances when compared to the traditional laminated composites, because of this continuous properties variation characteristic, which enables among other advantages, smoother stresses distribution profiles. Therefore the growing trend on the use of these materials brings together the interest and the need for getting optimum configurations concerning to each specific application. In this work it is studied the use of particle swarm optimization technique for the maximization of a functionally graded sandwich beam bending stiffness. For this purpose, a set of case studies is analyzed, in order to enable to understand in a detailed way, how the different optimization parameters tuning can influence the whole process. It is also considered a re-initialization strategy, which is not a common approach in particle swarm optimization as far as it was possible to conclude from the published research works. As it will be shown, this strategy can provide good results and also present some advantages in some conditions. This work was developed and programmed on symbolic computation platform Maple 14. (C) 2013 Elsevier B.V. All rights reserved.

Incidence and risk factors for intimate partner violence during the postpartum period

OBJECTIVE To estimate the incidence and identify risk factors for intimate partner violence during postpartum.METHODS This prospective cohort study was conducted with women, aged between 18-49 years, enrolled in the Brazilian Family Health Strategy in Recife, Northeastern Brazil, between 2005 and 2006. Of the 1.057 women interviewed during pregnancy and postpartum, 539 women, who did not report violence before or during pregnancy, were evaluated. A theoretical-conceptual framework was built with three levels of factors hierarchically ordered: women’s and partners’ sociodemografic and behavioral characteristics, and relationship dynamics. Incidence and risk factors of intimate partner violence were estimated by Poisson Regression.RESULTS The incidence of violence during postpartum was 9.3% (95%CI 7.0;12.0). Isolated psychological violence was the most common (4.3%; 95%CI 2.8;6.4). The overlapping of psychological with physical violence occurred at 3.3% (95%CI 2.0;5.3) and with physical and/or sexual in almost 2.0% (95%CI 0.8;3.0) of cases. The risk of partner violence during postpartum was increased for women with a low level of education (RR = 2.6; 95%CI 1.3;5.4), without own income (RR = 1.7; 95%CI 1.0;2.9) and those who perpetrated physical violence against their partner without being assaulted first (RR = 2.0; 95%CI 1.2;3.4), had a very controlling partner (RR = 2.5; 95%CI 1.1;5.8), and had frequent fights with their partner (RR = 1.7; 95%CI 1.0;2.9).CONCLUSIONS The high incidence of intimate partner violence during postpartum and its association with aspects of the relationship’s quality between the couple, demonstrated the need for public policies that promote conflict mediation and enable forms of empowerment for women to address the cycle of violence.

Constraining multi-Higgs flavour models

To study a flavour model with a non-minimal Higgs sector one must first define the symmetries of the fields; then identify what types of vacua exist and how they may break the symmetries; and finally determine whether the remnant symmetries are compatible with the experimental data. Here we address all these issues in the context of flavour models with any number of Higgs doublets. We stress the importance of analysing the Higgs vacuum expectation values that are pseudo-invariant under the generators of all subgroups. It is shown that the only way of obtaining a physical CKM mixing matrix and, simultaneously, non-degenerate and non-zero quark masses is requiring the vacuum expectation values of the Higgs fields to break completely the full flavour group, except possibly for some symmetry belonging to baryon number. The application of this technique to some illustrative examples, such as the flavour groups Delta (27), A(4) and S-3, is also presented.

Surface morphology and phase transformations of femtosecond laser-processed saphire

The morphological and structural modifications induced in sapphire by surface treatment with femtosecond laser radiation were studied. Single-crystal sapphire wafers cut parallel to the (0 1 2) planes were treated with 560 fs, 1030 nm wavelength laser radiation using wide ranges of pulse energy and repetition rate. Self-ordered periodic structures with an average spatial periodicity of similar to 300 nm were observed for fluences slightly higher than the ablation threshold. For higher fluences the interaction was more disruptive and extensive fracture, exfoliation, and ejection of ablation debris occurred. Four types of particles were found in the ablation debris: (a) spherical nanoparticles about 50 nm in diameter; (b) composite particles between 150 and 400 nm in size; (c) rounded resolidified particles about 100-500 nm in size; and (d) angular particles presenting a lamellar structure and deformation twins. The study of those particles by selected area electron diffraction showed that the spherical nanoparticles and the composite particles are amorphous, while the resolidified droplets and the angular particles, present a crystalline a-alumina structure, the same of the original material. Taking into consideration the existing ablation theories, it is proposed that the spherical nanoparticles are directly emitted from the surface in the ablation plume, while resolidified droplets are emitted as a result of the ablation process, in the liquid phase, in the low intensity regime, and by exfoliation, in the high intensity regime. Nanoparticle clusters are formed by nanoparticle coalescence in the cooling ablation plume. (C) 2013 Elsevier B.V. All rights reserved.

Efficient feature selection filters for high-dimensional data

Feature selection is a central problem in machine learning and pattern recognition. On large datasets (in terms of dimension and/or number of instances), using search-based or wrapper techniques can be cornputationally prohibitive. Moreover, many filter methods based on relevance/redundancy assessment also take a prohibitively long time on high-dimensional. datasets. In this paper, we propose efficient unsupervised and supervised feature selection/ranking filters for high-dimensional datasets. These methods use low-complexity relevance and redundancy criteria, applicable to supervised, semi-supervised, and unsupervised learning, being able to act as pre-processors for computationally intensive methods to focus their attention on smaller subsets of promising features. The experimental results, with up to 10(5) features, show the time efficiency of our methods, with lower generalization error than state-of-the-art techniques, while being dramatically simpler and faster.

Machine learning Vasicek model calibration with Gaussian processes

In this article, we calibrate the Vasicek interest rate model under the risk neutral measure by learning the model parameters using Gaussian processes for machine learning regression. The calibration is done by maximizing the likelihood of zero coupon bond log prices, using mean and covariance functions computed analytically, as well as likelihood derivatives with respect to the parameters. The maximization method used is the conjugate gradients. The only prices needed for calibration are zero coupon bond prices and the parameters are directly obtained in the arbitrage free risk neutral measure.

Response rate in the Study of Cardiovascular Risks in Adolescents – ERICA

ABSTRACT OBJECTIVE To describe the response rate and characteristics of people who either took part or not in from the Study of Cardiovascular Risks in Adolescents (ERICA) , according to information subsets. METHODS ERICA is a school-based, nation-wide investigation with a representative sample of 12 to 17-year-old adolescents attending public or private schools in municipalities with over 100,000 inhabitants in Brazil. Response rate of eligible subjects were calculated according to macro-regions, sex, age, and type of school (public or private). We also calculated the percentages of replacement schools in comparison with the ones originally selected as per the sample design, according to the types of schools in the macro-regions. The subjects and non-subjects were compared according to sex, age, and average body mass indices (kg/m2). RESULTS We had 102,327 eligible adolescents enrolled in the groups drawn. The highest percentage of complete information was obtained for the subset of the questionnaire (72.9%). Complete information regarding anthropometric measurements and the ones from the questionnaire were obtained for 72.0% of the adolescents, and the combination of these data with the 24-hour dietary recall were obtained for 70.3% of the adolescents. Complete information from the questionnaire plus biochemical blood evaluation data were obtained for 52.5% of the morning session adolescents (selected for blood tests). The response percentage in private schools was higher than the one in public schools for most of the combination of information. The ratio of older and male adolescents non-participants was higher than the ratio among participants. CONCLUSIONS The response rate for non-invasive procedures was high. The response rate for blood collection – an invasive procedure that requires a 12-hour fasting period and the informed consent form from legal guardians – was lower. The response rate observed in public schools was lower than in the private ones, and that may reflect lower school frequency of registered students.

Pt/Carbon materials as Bi-Functional catalysts for N-decane hydroisomerization

The activity and selectivity of bi-functional carbon-supported platinum catalysts for the hydroisomerization of n-alkanes have been studied. The influence of the properties of the carbon support on the performance of the catalysts were investigated by incorporating the metallic function on a series of carbons with varied porosity (microporous: GL-50 from Norit, and mesoporous: CMK-3) and surface chemistry (modified by wet oxidation). The characterization results achieved with H-2 chemisorption and TEM showed differences in surface metal concentrations and metal-support interactions depending on the support composition. The highest metal dispersion was achieved after oxidation of the carbon matrix in concentrated nitric acid, suggesting that the presence of surface functional sites distributed in inner and outer surface favors a homogeneous metal distribution. On the other hand, the higher hydrogenating activity of the catalysts prepared with the mesoporous carbon pointed out that a fast molecular traffic inside the pores plays an important role in the catalysts performance. For n-decane hydroisomerization of long chain n-alkanes, higher activities were obtained for the catalysts with an optimized acidity and metal dispersion along with adequate porosity, pointing out the importance of the support properties in the performance of the catalysts.

Visualization of clusters in geo-referenced data using three-dimensional self-organizing maps

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

Optimal design for active damping in sandwich structures using the Direct MultiSearch method

This paper addresses the problem of optimal positioning of surface bonded piezoelectric patches in sandwich plates with viscoelastic core and laminated face layers. The objective is to maximize a set of modal loss factors for a given frequency range using multiobjective topology optimization. Active damping is introduced through co-located negative velocity feedback control. The multiobjective topology optimization problem is solved using the Direct MultiSearch Method. An application to a simply supported sandwich plate is presented with results for the maximization of the first six modal loss factors. The influence of the finite element mesh is analyzed and the results are, to some extent, compared with those obtained using alternative single objective optimization.

Maximização da valorização energética em aterros sanitários recorrendo a equipamentos baseados no Ciclo Orgânico de Rankine (ORC)

Atualmente, os aterros sanitários representam uma solução para a gestão e tratamento dos resíduos sólidos urbanos. Da deposição, ocorrem duas formas de emissões ao longo do tempo, a produção de biogás e de lixiviados, que resultam sobretudo da decomposição da matéria orgânica. Um dos principais constituintes do biogás é o metano, o qual tem elevado poder calorífico. O presente trabalho aborda, a maximização da valorização energética em aterros sanitários, recorrendo a equipamentos baseados no Ciclo Orgânico de Rankine (ORC) para a produção de eletricidade. É apresentado como caso de estudo a central de valorização energética da Suldouro, em Sermonde, que produz eletricidade a partir do biogás resultante da decomposição da matéria orgânica depositada em aterro. O biogás é utilizado como combustível para os motogeradores utilizados para o seu aproveitamento energético, sendo que apenas cerca de 40% do potencial energético contido no biogás é transformado em eletricidade, registando-se perdas sobretudo nas emissões dos gases de exaustão e na água de arrefecimento dos motores. Para avaliação do potencial da recuperação energética dos gases de escape é avaliado o desempenho termodinâmico do ciclo ORC. Para tal foi desenvolvida uma ferramenta em MATLAB utilizando como modelo a configuração do ORC com recuperador de calor. O cálculo das propriedades termodinâmicas dos fluidos foi obtido através da criação de uma sub-rotina que chama o programa CoolProp. Este programa restitui propriedades como a entalpia, entropia, pressões e temperaturas em cada ponto do ciclo, permitindo assim ao utilizador otimizar o tempo na obtenção de resultados. A avaliação económica é fundamental na tomada de decisões por parte do investidor e dos financiadores do projeto. É então apresentada a análise económica e efetuada uma análise de sensibilidade, onde foram efetuadas variações nos vetores mais importantes de forma a poder avaliar-se o impacto em termos da sua rentabilidade. A ferramenta desenvolvida permite obter de forma prática, os três indicadores económicos extremamente influentes no que se refere à tomada de decisão. A utilização dos sistemas ORC e os seus benefícios não se esgotam na maximização dos aproveitamentos da valorização energética em aterros sanitários. Também a recuperação de calor para a produção de energia elétrica pode ter um impacto importante em muitos setores intensivos de energia, contribuindo significativamente para a redução do consumo e aumentando a eficiência de todo o processo de produção.

Gestão integrada de ativos num contexto real

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

Aplicação do princípio das ondas de Elliott à bolsa portuguesa

Tese para a obtenção do grau de Doutor em Economia, especialidade de Economia da Empresa