Damage evaluation of drilled carbon/epoxy laminates based on area assessment methods

The characteristics of carbon fibre reinforced laminates had widened their use, from aerospace to domestic appliances. A common characteristic is the need of drilling for assembly purposes. It is known that a drilling process that reduces the drill thrust force can decrease the risk of delamination. In this work, delamination assessment methods based on radiographic data are compared and correlated with mechanical test results (bearing test).

Optimizing the renewable generation mix in the portuguese power system based on temporal and spatial diversity

Renewable energy sources (RES) have unique characteristics that grant them preference in energy and environmental policies. However, considering that the renewable resources are barely controllable and sometimes unpredictable, some challenges are faced when integrating high shares of renewable sources in power systems. In order to mitigate this problem, this paper presents a decision-making methodology regarding renewable investments. The model computes the optimal renewable generation mix from different available technologies (hydro, wind and photovoltaic) that integrates a given share of renewable sources, minimizing residual demand variability, therefore stabilizing the thermal power generation. The model also includes a spatial optimization of wind farms in order to identify the best distribution of wind capacity. This methodology is applied to the Portuguese power system.

A qualitative assessment of modularity in CaesarJ components based on implementations of design patterns

Tese de Mestrado em Engenharia Informática

High-performance flexible hybrid field-effect transistors based on cellulose fiber paper

IEEE Electron Device Letters, VOL. 29, NO. 9,

Parallel GPU architecture for hyperspectral unmixing based on augmented Lagrangian method

Hyperspectral imaging has become one of the main topics in remote sensing applications, which comprise hundreds of spectral bands at different (almost contiguous) wavelength channels over the same area generating large data volumes comprising several GBs per flight. This high spectral resolution can be used for object detection and for discriminate between different objects based on their spectral characteristics. One of the main problems involved in hyperspectral analysis is the presence of mixed pixels, which arise when the spacial resolution of the sensor is not able to separate spectrally distinct materials. Spectral unmixing is one of the most important task for hyperspectral data exploitation. However, the unmixing algorithms can be computationally very expensive, and even high power consuming, which compromises the use in applications under on-board constraints. In recent years, graphics processing units (GPUs) have evolved into highly parallel and programmable systems. Specifically, several hyperspectral imaging algorithms have shown to be able to benefit from this hardware taking advantage of the extremely high floating-point processing performance, compact size, huge memory bandwidth, and relatively low cost of these units, which make them appealing for onboard data processing. In this paper, we propose a parallel implementation of an augmented Lagragian based method for unsupervised hyperspectral linear unmixing on GPUs using CUDA. The method called simplex identification via split augmented Lagrangian (SISAL) aims to identify the endmembers of a scene, i.e., is able to unmix hyperspectral data sets in which the pure pixel assumption is violated. The efficient implementation of SISAL method presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory.

Scenarios Generation for Multi-Agent simulation of Electricity Markets based on Intelligent Data Analysis

This document presents a tool able to automatically gather data provided by real energy markets and to generate scenarios, capture and improve market players’ profiles and strategies by using knowledge discovery processes in databases supported by artificial intelligence techniques, data mining algorithms and machine learning methods. It provides the means for generating scenarios with different dimensions and characteristics, ensuring the representation of real and adapted markets, and their participating entities. The scenarios generator module enhances the MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) simulator, endowing a more effective tool for decision support. The achievements from the implementation of the proposed module enables researchers and electricity markets’ participating entities to analyze data, create real scenarios and make experiments with them. On the other hand, applying knowledge discovery techniques to real data also allows the improvement of MASCEM agents’ profiles and strategies resulting in a better representation of real market players’ behavior. This work aims to improve the comprehension of electricity markets and the interactions among the involved entities through adequate multi-agent simulation.

Adaptive Penalty and Barrier function based on Fuzzy Logic

Optimization methods have been used in many areas of knowledge, such as Engineering, Statistics, Chemistry, among others, to solve optimization problems. In many cases it is not possible to use derivative methods, due to the characteristics of the problem to be solved and/or its constraints, for example if the involved functions are non-smooth and/or their derivatives are not know. To solve this type of problems a Java based API has been implemented, which includes only derivative-free optimization methods, and that can be used to solve both constrained and unconstrained problems. For solving constrained problems, the classic Penalty and Barrier functions were included in the API. In this paper a new approach to Penalty and Barrier functions, based on Fuzzy Logic, is proposed. Two penalty functions, that impose a progressive penalization to solutions that violate the constraints, are discussed. The implemented functions impose a low penalization when the violation of the constraints is low and a heavy penalty when the violation is high. Numerical results, obtained using twenty-eight test problems, comparing the proposed Fuzzy Logic based functions to six of the classic Penalty and Barrier functions are presented. Considering the achieved results, it can be concluded that the proposed penalty functions besides being very robust also have a very good performance.

E-Health individual adoption - empirical model based on UTAUT 2.

The future of health care delivery is becoming more citizen-centred, as today’s user is more active, better informed and more demanding. The European Commission is promoting online health services and, therefore, member states will need to boost deployment and use of online services. This makes e-health adoption an important field to be studied and understood. This study applied the extended unified theory of acceptance and usage technology (UTAUT2) to explain patients’ individual adoption of e-health. An online questionnaire was administrated Portugal using mostly the same instrument used in UTAUT2 adapted to e-health context. We collected 386 valid answers. Performance expectancy, effort expectancy, social influence, and habit had the most significant explanatory power over behavioural intention and habit and behavioural intention over technology use. The model explained 52% of the variance in behavioural intention and 32% of the variance in technology use. Our research helps to understand the desired technology characteristics of ehealth. By testing an information technology acceptance model, we are able to determine what is more valued by patients when it comes to deciding whether to adopt e-health systems or not.

Al-doped ZnO ceramic sputtering targets based on nanocrystalline powders produced by emulsion detonation synthesis – deposition and application as a transparent conductive oxide material

Transparent conducting oxides (TCOs) have been largely used in the optoelectronic industry due to their singular combination of low electrical resistivity and high optical transmittance. They are usually deposited by magnetron sputtering systems being applied in several devices, specifically thin film solar cells (TFSCs). Sputtering targets are crucial components of the sputtering process, with many of the sputtered films properties dependent on the targets characteristics. The present thesis focuses on the development of high quality conductive Al-doped ZnO (AZO) ceramic sputtering targets based on nanostructured powders produced by emulsion detonation synthesis method (EDSM), and their application as a TCO. In this sense, the influence of several processing parameters was investigated from the targets raw-materials synthesis to the application of sputtered films in optoelectronic devices. The optimized manufactured AZO targets present a final density above 99 % with controlled grain size, an homogeneous microstructure with a well dispersed ZnAl2O4 spinel phase, and electrical resistivities of ~4 × 10-4 Ωcm independently on the Al-doping level among 0.5 and 2.0 wt. % Al2O3. Sintering conditions proved to have a great influence on the properties of the targets and their performance as a sputtering target. It was demonstrated that both deposition process and final properties of the films are related with the targets characteristics, which in turn depends on the initial powder properties. In parallel, the influence of several deposition parameters in the film´s properties sputtered from these targets was investigated. The sputtered AZO TCOs showed electrical properties at room temperature that are superior to simple oxides and comparable to a reference TCO – indium tin oxide (ITO), namely low electrical resistivity of 5.45 × 10-4 Ωcm, high carrier mobility (29.4 cm2V-1s-1), and high charge carrier concentration (3.97 × 1020 cm-3), and also average transmittance in the visible region > 80 %. These superior properties allowed their successful application in different optoelectronic devices.

Assessing the production of jet mix columns using alkali activated waste based on mechanical and financial performance and CO2 (eq) emissions

Recent research has proved the potential of alkaline activated fly-ash for soil stabilisation. However, such studies have not focused on the link between financial, mechanical and environmental aspects of this solution, but only on their absolute mechanical properties. The present paper characterises the mechanical behaviour of a large spectrum of activator-ash-soil combinations used to build jet mixing columns, analysing also the cost and CO2 (eq) emissions. The concern with these two vectors forced a decrease in the quantity of stabilising agent added to the soil, relatively to previous research, and the effects of such low quantities have not yet been published. However, the results clearly showed a significant improve in strength, still well above the average values expected when improving the stressstrain behaviour of a weak soil. Uniaxial compressive strength tests were used to assess the effects of the fly-ash percentage, the alkalieash ratio and the water content. The carbon calculator recently developed by the European Federation of Foundation Contractors and the Deep Foundations Institute was used to quantify the CO2 (eq) emissions associated with this technique. The financial cost was estimated based on the experience of a major Portuguese contractor. For comparison purposes, soil cement mixtures were also analysed, using similar conditions and tools used for the soil-ash analysis. Results showed that the cement and ash solutions are very similar in terms of overall performance, with some advantage of the former regarding financial cost, and a significant advantage of the latter regarding the CO2 (eq) emissions. This new grout, although it is in an embryonic stage, it has the potential for broader developments in the field.

State of the art and open questions on cathode preparation based on carbon coated lithium iron phosphate

One important component with particular relevance in battery performance is the cathode, being one of the main responsible elements for cell capacity and cycle life. Carbon coated lithium iron phosphate, C-LiFePO4, active material is one of the most promising cathode materials for the next generation of large scale lithium ion battery applications and strong research efforts are being devoted to it, due to its excellent characteristics, including high capacity, ~170 mAh/g, and safety. This review summarizes the main developments on C-LiFePO4 based cathode film preparation and performance. The effect of the binder, conductive additive, relationship between active material-binder-conductive additive and drying step, in the electrode film fabrication and performance is presented and discussed. Finally, after the presentation of the cell types fabricated with C-LiFePO4 active material and their performance, some conclusions and guidelines for further investigations are outlined.

Effect of ionic liquid anion type in the performance of solid polymer electrolytes based on Poly(Vinylidene fluoride-trifluoroethylene)

The effect of different anions within the ionic liquid in the characteristics of solid polymer electrolytes (SPEs) based on P(VDF-TrFE) has been investigated. 1-ethyl-3-methylimidazolium acetate, [C2mim][OAc], 1-ethyl-3-methylimidazolium triflate, [C2mim][(CF3SO3)3], 1-ethyl-3-methylimidazolium lactate, [C2mim][Lactate], 1-ethyl-3-methylimidazolium thiocyanate, [C2mim][SNC] and 1-ethyl-3-methylimidazolium hydrogen sulphate [C2mim][HSO4] have been used in SPE prepared by thermally induced phase separation (TIPS). The polymer phase, thermal and electrochemical properties of the SPE have been determined. The thermal and electrical properties of the SPEs strongly depend on the selected IL, as determined by their different interactions with the polymer matrix. The room temperature ionic conductivity increases in the following way for the different anions: [SNC] > [CF3SO3)3] > [HSO4] > [Lactate] > [OAc], which is mainly dependent on the viscosity of the ionic liquid.

MOEA/VAN: Multiobjective Evolutionary Algorithm based on Vector Angle Neighborhood

Natural selection favors the survival and reproduction of organisms that are best adapted to their environment. Selection mechanism in evolutionary algorithms mimics this process, aiming to create environmental conditions in which artificial organisms could evolve solving the problem at hand. This paper proposes a new selection scheme for evolutionary multiobjective optimization. The similarity measure that defines the concept of the neighborhood is a key feature of the proposed selection. Contrary to commonly used approaches, usually defined on the basis of distances between either individuals or weight vectors, it is suggested to consider the similarity and neighborhood based on the angle between individuals in the objective space. The smaller the angle, the more similar individuals. This notion is exploited during the mating and environmental selections. The convergence is ensured by minimizing distances from individuals to a reference point, whereas the diversity is preserved by maximizing angles between neighboring individuals. Experimental results reveal a highly competitive performance and useful characteristics of the proposed selection. Its strong diversity preserving ability allows to produce a significantly better performance on some problems when compared with stat-of-the-art algorithms.

Assessment of distinctive road runoff quality in coastal areas, based on a monitoring case study

High levels of marine salt deposition present in coastal areas have a relevant effect on road runoff characteristics. This study assesses this effect with the purpose of identifying the relationships between monitored water quality parameters and intrinsic site variables. To achieve this objective, an extensive monitoring program was conducted on a Portuguese coastal highway. The study included 30 rainfall events, in different weather, traffic, and salt deposition conditions. The evaluations of various water quality parameters were carried out in over 200 samples. In addition, the meteorological, hydrological, and traffic parameters were continuously measured. The salt deposition rates were determined by means of a wet candle device, which is an innovative feature of the monitoring program. The relation between road runoff pollutants and independent variables associated with weather, traffic, and salt deposition conditions was assessed. Significant correlations among pollutants were observed. A high salinity concentration and its influence on the road runoff were confirmed. Furthermore, the concentrations of the most relevant pollutants seemed to be very dependent on some meteorological variables, particularly the duration of the antecedent dry period prior to each rainfall event and the average wind speed.

Mechanical characterization of bio-epoxy ecocomposites reinforced with fibrous structures based on natural fibers

This work intends to evaluate the mechanical properties of eco-composites reinforced with natural fiber fabrics in different fibrous arrangements, with a thermoset matrix of natural origin. When integrated by hand lay-up process, the composites obtained present excellent mechanical characteristics combined with environment friendly features, being able to be used in various industrial sectors.