Self-scheduling and bidding strategies of thermal units with stochastic emission constraints

This paper is on the self-scheduling problem for a thermal power producer taking part in a pool-based electricity market as a price-taker, having bilateral contracts and emission-constrained. An approach based on stochastic mixed-integer linear programming approach is proposed for solving the self-scheduling problem. Uncertainty regarding electricity price is considered through a set of scenarios computed by simulation and scenario-reduction. Thermal units are modelled by variable costs, start-up costs and technical operating constraints, such as: forbidden operating zones, ramp up/down limits and minimum up/down time limits. A requirement on emission allowances to mitigate carbon footprint is modelled by a stochastic constraint. Supply functions for different emission allowance levels are accessed in order to establish the optimal bidding strategy. A case study is presented to illustrate the usefulness and the proficiency of the proposed approach in supporting biding strategies. (C) 2014 Elsevier Ltd. All rights reserved.

Physical-chemical and mineralogical characterization of fine aggregates from construction and demolition waste recycling plants

Construction and demolition waste (CDW) represents around 31% of all waste produced in the European Union. It is today acknowledged that the consumption of raw materials in the construction industry is a non-sustainable activity. It is thus necessary to reduce this consumption, and the volume of CDW dumped, by using this waste as a source of raw materials for the production of recycled aggregates. One potential use of these aggregates is their incorporation in reinforced concrete as a replacement of natural aggregates. A concrete that incorporates these aggregates and still performs well requires them to be fully characterized so that their behaviour within the concrete can be predicted. Coarse recycled aggregates have been studied quite thoroughly, because they are simpler to reintroduce in the market as a by-product, and so has the performance of concrete made with them. This paper describes the main results of research designed to characterize the physical and chemical properties of fine recycled aggregates for concrete production and their relationship with mineralogical composition and preprocessing. The constraints of the incorporation of fine aggregates in reinforced concrete are discussed. It is shown that, unless a developed processing diagram is used, this application is not feasible. (C) 2013 Elsevier Ltd. All rights reserved.

Scheduling of a hydro producer considering head-dependency, price scenarios and risk-aversion

In this paper, a mixed-integer quadratic programming approach is proposed for the short-term hydro scheduling problem, considering head-dependency, discontinuous operating regions and discharge ramping constraints. As new contributions to earlier studies, market uncertainty is introduced in the model via price scenarios, and risk aversion is also incorporated by limiting the volatility of the expected profit through the conditional value-at-risk. Our approach has been applied successfully to solve a case Study based on one of the main Portuguese cascaded hydro systems, requiring a negligible computational time.

Feature Discretization with Relevance and Mutual Information Criteria

Feature discretization (FD) techniques often yield adequate and compact representations of the data, suitable for machine learning and pattern recognition problems. These representations usually decrease the training time, yielding higher classification accuracy while allowing for humans to better understand and visualize the data, as compared to the use of the original features. This paper proposes two new FD techniques. The first one is based on the well-known Linde-Buzo-Gray quantization algorithm, coupled with a relevance criterion, being able perform unsupervised, supervised, or semi-supervised discretization. The second technique works in supervised mode, being based on the maximization of the mutual information between each discrete feature and the class label. Our experimental results on standard benchmark datasets show that these techniques scale up to high-dimensional data, attaining in many cases better accuracy than existing unsupervised and supervised FD approaches, while using fewer discretization intervals.

Scheduling of head-dependent cascaded hydro systems: Mixed-integer quadratic programming approach

This paper is on the problem of short-term hydro, scheduling, particularly concerning head-dependent cascaded hydro systems. We propose a novel mixed-integer quadratic programming approach, considering not only head-dependency, but also discontinuous operating regions and discharge ramping constraints. Thus, an enhanced short-term hydro scheduling is provided due to the more realistic modeling presented in this paper. Numerical results from two case studies, based on Portuguese cascaded hydro systems, illustrate the proficiency of the proposed approach.

Hydro energy systems management in Portugal: Profit-based evaluation of a mixed-integer nonlinear approach

In this paper, a novel mixed-integer nonlinear approach is proposed to solve the short-term hydro scheduling problem in the day-ahead electricity market, considering not only head-dependency, but also start/stop of units, discontinuous operating regions and discharge ramping constraints. Results from a case study based on one of the main Portuguese cascaded hydro energy systems are presented, showing that the proposedmixed-integer nonlinear approach is proficient. Conclusions are duly drawn. (C) 2010 Elsevier Ltd. All rights reserved.

Imunohistoquímica no diagnóstico da glomerulonefrite membranosa: estudo comparativo de binómios cromogênio + coloração de contraste

A glomerulonefrite membranosa faz parte das doenças glomerulares que provocam glomerulonefrite crônica, apresentando-se como uma das causas da doença renal terminal. As técnicas de imunofluorescência são o gold standard no estudo imunológico desta patologia em biópsia renal, através da deteção de imunocomplexos (e.g. IgG e C3) e do seu padrão de distribuição granular característico. No entanto, a imunofluorescência não permite uma contextualização histológica e os fluorocromos utilizados possuem um reduzido tempo de atividade, ao contrário das técnicas imunoenzimáticas que utilizam cromogénios coloridos precipitados que permitem a obtenção de uma marcação permanente e a sua contextualização histológica por via da utilização de eficientes colorações de contraste. Com a finalidade de contribuir para a qualidade do diagnóstico da glomerulonefrite membranosa, em biópsias renais, procurou-se, com esta pesquisa, identificar uma técnica imunoenzimática, através da conjugação entre diferentes cromogênios e colorações de contraste, que permita a deteção de depósitos de IgG e C3, com padrão granular. Foram constituídos diferentes binômios cromogênio + coloração, com os cromogênios 3,3›- Diaminobenzidine Tetrahydrochloride e 3-Amino-9-ethylcarbazole e as colorações Periodic Acid Schiff, Periodic Acid Methenamine Silver e Hematoxilina. Foram utilizadas 72 secções de tecido provenientes de seis de casos de biópsias renais com diagnóstico de glomerulonefrite membranosa, fixados em formalina a 10% e incluídos em parafina. A recolha de dados foi realizada por observação microscópica com preenchimento de uma grelha de classificação dos parâmetros: preservação da morfologia, intensidade da marcação específica, quantidade relativa de estruturas marcadas, marcação inespecífica/fundo, contraste e padrão da marcação, que permitiu a classificação dos binómios estudados num score quantitativo de 0-100 pontos. O binômio que apresentou melhores resultados foi 3-Amino-9-ethylcarbazole + Hematoxilina (score 71,81) e o binômio 3,3›- Diaminobenzidine Tetrahydrochloride+Periodic Acid Methenamine Silver (score 7,81), apresentou os piores resultados. O resultado do teste Kruskal-Wallis indica-nos a presença de diferenças estatísticas entre os binómios em estudo (p=0,000). A Hematoxilina pode ser considerada a coloração mais eficaz, pois cumpriu a sua função de auxiliar e facilitar a observação do tipo de padrão com os dois cromogênios utilizados. O cromogênio 3-Amino-9-ethylcarbazole apresentou resultados semelhantes aos produzidos pelo 3,3›-Diaminobenzidine Tetrahydrochloride, no entanto, permitiu identificar em todos os casos o padrão granular de imunomarcação, ao contrário do que aconteceu com este último.

Dosimetria e análise de incertezas em braquiterapia ginecológica

Mestrado em Radiações Aplicadas às tecnologias da Saúde

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.

Exploiting the bin-class histograms for feature selection on discrete data

In machine learning and pattern recognition tasks, the use of feature discretization techniques may have several advantages. The discretized features may hold enough information for the learning task at hand, while ignoring minor fluctuations that are irrelevant or harmful for that task. The discretized features have more compact representations that may yield both better accuracy and lower training time, as compared to the use of the original features. However, in many cases, mainly with medium and high-dimensional data, the large number of features usually implies that there is some redundancy among them. Thus, we may further apply feature selection (FS) techniques on the discrete data, keeping the most relevant features, while discarding the irrelevant and redundant ones. In this paper, we propose relevance and redundancy criteria for supervised feature selection techniques on discrete data. These criteria are applied to the bin-class histograms of the discrete features. The experimental results, on public benchmark data, show that the proposed criteria can achieve better accuracy than widely used relevance and redundancy criteria, such as mutual information and the Fisher ratio.

Optimal generation scheduling of wind-CSP systems in day-ahead electricity markets

This paper presents a coordination approach to maximize the total profit of wind power systems coordinated with concentrated solar power systems, having molten-salt thermal energy storage. Both systems are effectively handled by mixed-integer linear programming in the approach, allowing enhancement on the operational during non-insolation periods. Transmission grid constraints and technical operating constraints on both systems are modeled to enable a true management support for the integration of renewable energy sources in day-ahead electricity markets. A representative case study based on real systems is considered to demonstrate the effectiveness of the proposed approach. © IFIP International Federation for Information Processing 2015.

Blind hyperspectral unmixing

Hyperspectral unmixing methods aim at the decomposition of a hyperspectral image into a collection endmember signatures, i.e., the radiance or reflectance of the materials present in the scene, and the correspondent abundance fractions at each pixel in the image. This paper introduces a new unmixing method termed dependent component analysis (DECA). This method is blind and fully automatic and it overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical based approaches. DECA is based on the linear mixture model, i.e., each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. These abundances are modeled as mixtures of Dirichlet densities, thus enforcing the non-negativity and constant sum constraints, imposed by the acquisition process. The endmembers signatures are inferred by a generalized expectation-maximization (GEM) type algorithm. The paper illustrates the effectiveness of DECA on synthetic and real hyperspectral images.

Scotogenic model for co-bimaximal mixing

We present a scotogenic model, i.e. a one-loop neutrino mass model with dark right-handed neutrino gauge singlets and one inert dark scalar gauge doublet eta, which has symmetries that lead to co-bimaximal mixing, i.e. to an atmospheric mixing angle theta(23) = 45 degrees and to a CP-violating phase delta = +/-pi/2, while the mixing angle theta(13) remains arbitrary. The symmetries consist of softly broken lepton numbers L-alpha (alpha = e, mu, tau), a non-standard CP symmetry, and three L-2 symmetries. We indicate two possibilities for extending the model to the quark sector. Since the model has, besides eta, three scalar gauge doublets, we perform a thorough discussion of its scalar sector. We demonstrate that it can accommodate a Standard Model-like scalar with mass 125 GeV, with all the other charged and neutral scalars having much higher masses.