Virtual Communities of Practice: Design and Development of Support Platforms and Participation Fostering

Mestrado em Engenharia Informática - Área de Especialização em Sistemas Gráficos e Multimédia

Modelling a cable structure for a new wind energy production device

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Mecânica Especialização em Concepção e Produção

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.

Hyperspectral imagery framework for unmixing and dimensionality estimation

In hyperspectral imagery a pixel typically consists mixture of spectral signatures of reference substances, also called endmembers. Linear spectral mixture analysis, or linear unmixing, aims at estimating the number of endmembers, their spectral signatures, and their abundance fractions. This paper proposes a framework for hyperpsectral unmixing. A blind method (SISAL) is used for the estimation of the unknown endmember signature and their abundance fractions. This method solve a non-convex problem by a sequence of augmented Lagrangian optimizations, where the positivity constraints, forcing the spectral vectors to belong to the convex hull of the endmember signatures, are replaced by soft constraints. The proposed framework simultaneously estimates the number of endmembers present in the hyperspectral image by an algorithm based on the minimum description length (MDL) principle. Experimental results on both synthetic and real hyperspectral data demonstrate the effectiveness of the proposed algorithm.

Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. 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. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.

Liderança e estratégia em contexto de inovação nas organizações de saúde : estudos de caso

RESUMO - A presente investigação procura descrever e compreender como a estratégia influencia a liderança e como esta por sua vez interage nos processos de inovação e mudança, em organizações de saúde. Desconhecem-se estudos anteriores, em Portugal, sobre este problema de investigação e da respectiva problemática teórica. Trata-se de um estudo exploratório e descritivo que envolveu 5 organizações de saúde, 4 portuguesas e 1 espanhola, 4 hospitais (dois privados e uma unidade local de saúde). Utilizou-se uma abordagem mista de investigação (qualitativa e quantitativa), que permitiu compreender, através do estudo de caso, como se articulam a estratégia, a liderança e a inovação nessas cinco organizações de saúde. Os resultados do estudo empírico foram provenientes da recolha de dados efectuada através de observação directa e estruturada, entrevistas com actores-chave, documentos em suporte de papel e digital, e ainda inquérito por questionário de auto-resposta a uma amostra (n=165) de actores do line e do staff (Administradores, Directores de Serviço/Departamento, Enfermeiros Chefe e Técnicos Coordenadores) das cinco organizações de saúde. Tanto o modelo de Miles & Snow (estratégia organizacional), como o modelo dos valores contrastantes de Quinn (cultura organizacional e liderança), devidamente adaptados, mostram-se heurísticos e provam poder aplicar-se às organizações de saúde, apesar a sua complexidade e especificidade. Tanto as organizações do sector público como do sector privado e organizações públicas concessionadas (parcerias público privadas) podem ser acompanhadas e monitorizadas nos seus processos de inovação e mudança, associados aos tipos de cultura, liderança ou estratégia organizacionais adoptadas. As organizações de saúde coabitam num continuum, onde o ambiente (quer interno quer externo) e o tempo são factores decisivos que condicionam a estratégia a adoptar. Também aqui, em função da realidade dinâmica e complexa onde a organização se move, não há tipologias puras. Há, sim, uma grande plasticidade e flexibilidade organizacionais. Quanto aos líderes, exercem habitualmente a autoridade formal, pela via da circular normativa. Não são pares (nem primi inter pares), colocam-se por vezes numa posição de superioridade, quando o mais adequado seria a relação de parceria, cooperação e procura de consensos, com todos os colaboradores, afim de serem eles os verdadeiros protagonistas e facilitadores da mudança e das inovações. Como factores facilitadores da inovação e da mudança, encontrámos nas organizações de saúde estudadas o seguinte: facilidade de aprender; visão/missão adequadas; ausência de medo de falhar; e como factores inibidores: falta de articulação entre serviços/departamentos; estrutura organizacional (no sector público muito verticalizada e no sector privado mais horizontalizada); resistência à mudança; falta de tempo; falha no tempo de reacção (o tempo útil para a tomada de decisão é, por vezes, ultrapassado). --------ABSTRACT - The present research seeks to describe and understand how strategy influences leadership and how this in turn interacts in the process of innovation and change in health organizations. Previous studies on these topics are unknown in Portugal, about this research problem and its theoretical problem. This is an exploratory and descriptive study that involved 5 health organizations, 4 Portuguese and 1 Spanish. We used a mixed approach of research (qualitative and quantitative), which enabled us to understand, through case study, how strategy and leadership were articulated with innovation in these five health organizations. The results of the empirical study came from data collection through direct observation, interviews with key actors, documents and survey questionnaire answered by 165 participants of line and staff (Administrators, Medical Directors of Service /Department, Head Nurses and Technical Coordinators) of the five health organizations. Despite their complexity and specificity, both the model of Miles & Snow (organizational strategy) and the model of the Competing Values Framework of Quinn (organizational culture and leadership), suitably adapted, have proven heuristic power and able to be apply to healthcare organizations. Both public sector organizations, private and public organizations licensed (public-private partnerships) can be tracked and monitored in their processes of innovation and change in order to understand its kind of culture, leadership or organizational strategy adopted. Health organizations coexist in a continuum, where the environment (internal and external) and time are key factors which determine the strategy to adopt. Here too depending on the dynamic and complex reality where the organization moves, there are no pure types. There is indeed a great organizational plasticity and flexibility. Leaders usually carry the formal authority by circular normative. They are not pairs (or primi inter pares). Instead they are, sometimes, in a position of superiority, when the best thing is partnership, collaboration, cooperation, building consensus and cooperation with all stakeholders, in order that they are the real protagonists and facilitators of change and innovation. As factors that facilitate innovation and change, we found in health organizations studied, the following: ease of learning; vision / mission appropriate; absence of fear of failure, and as inhibiting factors: lack of coordination between agencies / departments; organizational structure (in the public sector it is too vertical and in the private sector it is more horizontal); resistance to change; lack of time and failure in the reaction time (the time for decision making is sometimes exceeded).

Algebraic structure for interaction on mixed models

Binary operations on commutative Jordan algebras, CJA, can be used to study interactions between sets of factors belonging to a pair of models in which one nests the other. It should be noted that from two CJA we can, through these binary operations, build CJA. So when we nest the treatments from one model in each treatment of another model, we can study the interactions between sets of factors of the first and the second models.

Symbolic dynamics and big bang bifurcation in Weibull-Gompertz-Fréchet's growth models

In this paper, motivated by the interest and relevance of the study of tumor growth models, a central point of our investigation is the study of the chaotic dynamics and the bifurcation structure of Weibull-Gompertz-Fréchet's functions: a class of continuousdefined one-dimensional maps. Using symbolic dynamics techniques and iteration theory, we established that depending on the properties of this class of functions in a neighborhood of a bifurcation point PBB, in a two-dimensional parameter space, there exists an order regarding how the infinite number of periodic orbits are born: the Sharkovsky ordering. Consequently, the corresponding symbolic sequences follow the usual unimodal kneading sequences in the topological ordered tree. We verified that under some sufficient conditions, Weibull-Gompertz-Fréchet's functions have a particular bifurcation structure: a big bang bifurcation point PBB. This fractal bifurcations structure is of the so-called "box-within-a-box" type, associated to a boxe ω1, where an infinite number of bifurcation curves issues from. This analysis is done making use of fold and flip bifurcation curves and symbolic dynamics techniques. The present paper is an original contribution in the framework of the big bang bifurcation analysis for continuous maps.

Automatização de testes à framework myMIS

Atualmente, verifica-se um aumento na necessidade de software feito à medida do cliente, que se consiga adaptar de forma rápida as constantes mudanças da sua área de negócio. Cada cliente tem os seus problemas concretos que precisa de resolver, não lhe sendo muitas vezes possível dispensar uma elevada quantidade de recursos para atingir os fins pretendidos. De forma a dar resposta a estes problemas surgiram várias arquiteturas e metodologias de desenvolvimento de software, que permitem o desenvolvimento ágil de aplicações altamente configuráveis, que podem ser personalizadas por qualquer utilizador das mesmas. Este dinamismo, trazido para as aplicações sobre a forma de modelos que são personalizados pelos utilizadores e interpretados por uma plataforma genérica, cria maiores desafios no momento de realizar testes, visto existir um número de variáveis consideravelmente maior que numa aplicação com uma arquitetura tradicional. É necessário, em todos os momentos, garantir a integridade de todos os modelos, bem como da plataforma responsável pela sua interpretação, sem ser necessário o desenvolvimento constante de aplicações para suportar os testes sobre os diferentes modelos. Esta tese debruça-se sobre uma aplicação, a plataforma myMIS, que permite a interpretação de modelos orientados à gestão, escritos numa linguagem específica de domínio, sendo realizada a avaliação do estado atual e definida uma proposta de práticas de testes a aplicar no desenvolvimento da mesma. A proposta resultante desta tese permitiu verificar que, apesar das dificuldades inerentes à arquitetura da aplicação, o desenvolvimento de testes de uma forma genérica é possível, podendo as mesmas lógicas ser utilizadas para o teste de diversos modelos distintos.

Genetic structure of Neisseria meningitidis serogroup C epidemic strains in South Brazil

In the present study we report the results of an analysis, based on serotyping, multilocus enzyme electrophoresis (MEE), and ribotyping of N. meningitidis serogroup C strains isolated from patients with meningococcal disease (MD) in Rio Grande do Sul (RS) and Santa Catarina (SC) States, Brazil, as the Center of Epidemiology Control of Ministry of Health detected an increasing of MD cases due to this serogroup in the last two years (1992-1993). We have demonstrated that the MD due to N.meningitidis serogroup C strains in RS and SC States occurring in the last 4 years were caused mainly by one clone of strains (ET 40), with isolates indistinguishable by serogroup, serotype, subtype and even by ribotyping. One small number of cases that were not due to an ET 40 strains, represent closely related clones that probably are new lineages generated from the ET 40 clone referred as ET 11A complex. We have also analyzed N.meningitidis serogroup C strains isolated in the greater São Paulo in 1976 as representative of the first post epidemic year in that region. The ribotyping method, as well as MEE, could provide useful information about the clonal characteristics of those isolates and also of strains isolated in south Brazil. The strains from 1976 have more similarity with the actual endemic than epidemic strains, by the ribotyping, sulfonamide sensitivity, and MEE results. In conclusion, serotyping with monoclonal antibodies (C:2b:P1.3), MEE (ET 11 and ET 11A complex), and ribotyping by using ClaI restriction enzyme (Rb2), were useful to characterize these epidemic strains of N.meningitidis related to the increased incidence of MD in different States of south Brazil. It is mostly probable that these N.meningitidis serogroup C strains have poor or no genetic corelation with 1971-1975 epidemic serogroup C strains. The genetic similarity of members of the ET 11 and ET 11A complex were confirmed by the ribotyping method by using three restriction endonucleases.

Criação de uma Framework Modular Vocacionada para o Desenvolvimento de Videojogos

Os videojogos são cada vez mais uma das maiores áreas da indústria de entretenimento, tendo esta vindo a expandir-se de ano para ano. Para além disso, os videojogos estão cada vez mais presentes no nosso dia-adia, quer através dos dispositivos móveis ou das novas consolas. Com base nesta premissa, é seguro de afirmar que o investimento neste campo trará mais ganhos do que perdas. Esta Dissertação tem como objetivo o estudo do estado da indústria dos videojogos, tendo como principal foco a conceção de um videojogo, a partir duma Framework Modular, desenvolvida também no âmbito desta Dissertação. Para isso, é feito um estudo sobre o estado da arte tecnológico, onde várias ferramentas de criação de videojogos foram estudadas e analisadas, de forma a perceber as forças e fraquezas de cada uma, e um estudo sobre a arte do negócio, ficando assim com uma ideia mais concreta dos vários pontos necessários para a criação de um videojogo. De seguida são discutidos os diferentes géneros de videojogos existentes e é conceptualizado um pequeno videojogo, tendo ainda em conta os diferentes tipos de interfaces que são mais utilizados na indústria dos videojogos, de forma a entender qual será a forma mais viável, conforme o género, e as diferentes mecânicas presentes no videojogo a criar. A Framework Modular é desenvolvida tendo em conta toda a análise previamente realizada, e o videojogo conceptualizado. Esta tem como grande objetivo uma elevada personalização e manutenibilidade, sendo que todos os módulos implementados podem ser substituídos por outros sem criar conflitos entre si. Finalmente, de forma a unir todos os temas analisados ao longo desta Dissertação, é ainda desenvolvido um Protótipo de forma a comprovar o bom funcionamento da Framework, aplicando todas as decisões previamente feitas.

An integer programming framework for sequencing cutting patterns based on interval graph completion

We derived a framework in integer programming, based on the properties of a linear ordering of the vertices in interval graphs, that acts as an edge completion model for obtaining interval graphs. This model can be applied to problems of sequencing cutting patterns, namely the minimization of open stacks problem (MOSP). By making small modifications in the objective function and using only some of the inequalities, the MOSP model is applied to another pattern sequencing problem that aims to minimize, not only the number of stacks, but also the order spread (the minimization of the stack occupation problem), and the model is tested.

A Data Mining Framework for Electric Load Profiling

This paper consists in the characterization of medium voltage (MV) electric power consumers based on a data clustering approach. It is intended to identify typical load profiles by selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The best partition is selected using several cluster validity indices. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ behavior. The data-mining-based methodology presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partitions. To validate our approach, a case study with a real database of 1.022 MV consumers was used.

Stochastic Framework for Strategic Decision-making of Load-serving Entities for Day-ahead Market

The deregulation of electricity markets has diversified the range of financial transaction modes between independent system operator (ISO), generation companies (GENCO) and load-serving entities (LSE) as the main interacting players of a day-ahead market (DAM). LSEs sell electricity to end-users and retail customers. The LSE that owns distributed generation (DG) or energy storage units can supply part of its serving loads when the nodal price of electricity rises. This opportunity stimulates them to have storage or generation facilities at the buses with higher locational marginal prices (LMP). The short-term advantage of this model is reducing the risk of financial losses for LSEs in DAMs and its long-term benefit for the LSEs and the whole system is market power mitigation by virtually increasing the price elasticity of demand. This model also enables the LSEs to manage the financial risks with a stochastic programming framework.

Demand Response Programs Definition Using Demand Price Elasticity to Define Consumers Aggregation for an Improved Remuneration Structure

The positioning of the consumers in the power systems operation has been changed in the recent years, namely due to the implementation of competitive electricity markets. Demand response is an opportunity for the consumers’ participation in electricity markets. Smart grids can give an important support for the integration of demand response. The methodology proposed in the present paper aims to create an improved demand response program definition and remuneration scheme for aggregated resources. The consumers are aggregated in a certain number of clusters, each one corresponding to a distinct demand response program, according to the economic impact of the resulting remuneration tariff. The knowledge about the consumers is obtained from its demand price elasticity values. The illustrative case study included in the paper is based on a 218 consumers’ scenario.