Tools for simulation output analysis

This technical report presents a description of the output data files and the tools used to validate and to extract information from the output data files generated by the Repeater-Based Hybrid Wired/Wireless Network Simulator and the Bridge-Based Hybrid Wired/Wireless Network Simulator.

A few what-ifs on using statistical analysis of stochastic simulation runs to extract timeliness properties

Modern real-time systems, with a more flexible and adaptive nature, demand approaches for timeliness evaluation based on probabilistic measures of meeting deadlines. In this context, simulation can emerge as an adequate solution to understand and analyze the timing behaviour of actual systems. However, care must be taken with the obtained outputs under the penalty of obtaining results with lack of credibility. Particularly important is to consider that we are more interested in values from the tail of a probability distribution (near worst-case probabilities), instead of deriving confidence on mean values. We approach this subject by considering the random nature of simulation output data. We will start by discussing well known approaches for estimating distributions out of simulation output, and the confidence which can be applied to its mean values. This is the basis for a discussion on the applicability of such approaches to derive confidence on the tail of distributions, where the worst-case is expected to be.

eTourism – Mobile Data Interface (Sistema de Apoio On-Line ao Turista em Dispositivos Móveis com Adaptação de Tours Personalizados)

O intuito principal desta Tese é criar um interface de Dados entre uma fonte de informação e fornecimento de Rotas para turistas e disponibilizar essa informação através de um sistema móvel interactivo de navegação e visualização desses mesmos dados. O formato tecnológico será portátil e orientado à mobilidade (PDA) e deverá ser prático, intuitivo e multi-facetado, permitindo boa usabilidade a públicos de várias faixas etárias. Haverá uma componente de IA (Inteligência Artificial), que irá usar a informação fornecida para tomar decisões ponderadas tendo em conta uma diversidade de aspectos. O Sistema a desenvolver deverá ser, assim, capaz de lidar com imponderáveis (alterações de rota, gestão de horários, cancelamento de pontos de visita, novos pontos de visita) e, finalmente, deverá ajudar o turista a gerir o seu tempo entre Pontos de Interesse (POI – Points os Interest). Deverá também permitir seguir ou não um dado percurso pré-definido, havendo possibilidade de cenários de exploração de POIs, sugeridos a partir de sugestões in loco, similares a Locais incluídos no trajecto, que se enquadravam no perfil dos Utilizadores. O âmbito geográfico de teste deste projecto será a zona ribeirinha do porto, por ser um ex-líbris da cidade e, simultaneamente, uma zona com muitos desafios ao nível geográfico (com a inclinação) e ao nível do grande número de Eventos e Locais a visitar.

Adherend thickness effect on the tensile fracture toughness of a structural adhesive using an optical data acquisition method

Adhesive bonding is nowadays a serious candidate to replace methods such as fastening or riveting, because of attractive mechanical properties. As a result, adhesives are being increasingly used in industries such as the automotive, aerospace and construction. Thus, it is highly important to predict the strength of bonded joints to assess the feasibility of joining during the fabrication process of components (e.g. due to complex geometries) or for repairing purposes. This work studies the tensile behaviour of adhesive joints between aluminium adherends considering different values of adherend thickness (h) and the double-cantilever beam (DCB) test. The experimental work consists of the definition of the tensile fracture toughness (GIC) for the different joint configurations. A conventional fracture characterization method was used, together with a J-integral approach, that take into account the plasticity effects occurring in the adhesive layer. An optical measurement method is used for the evaluation of crack tip opening and adherends rotation at the crack tip during the test, supported by a Matlab® sub-routine for the automated extraction of these quantities. As output of this work, a comparative evaluation between bonded systems with different values of adherend thickness is carried out and complete fracture data is provided in tension for the subsequent strength prediction of joints with identical conditions.

POSTDATA – Towards publishing European poetry as linked open data

POSTDATA is a 5 year's European Research Council (ERC) Starting Grant Project that started in May 2016 and is hosted by the Universidad Nacional de Educación a Distancia (UNED), Madrid, Spain. The context of the project is the corpora of European Poetry (EP), with a special focus on poetic materials from different languages and literary traditions. POSTDATA aims to offer a standardized model in the philological field and a metadata application profile (MAP) for EP in order to build a common classification of all these poetic materials. The information of Spanish, Italian and French repertoires will be published in the Linked Open Data (LOD) ecosystem. Later we expect to extend the model to include additional corpora. There are a number of Web Based Information Systems in Europe with repertoires of poems available to human consumption but not in an appropriate condition to be accessible and reusable by the Semantic Web. These systems are not interoperable; they are in fact locked in their databases and proprietary software, not suitable to be linked in the Semantic Web. A way to make this data interoperable is to develop a MAP in order to be able to publish this data available in the LOD ecosystem, and also to publish new data that will be created and modeled based on this MAP. To create a common data model for EP is not simple since the existent data models are based on conceptualizations and terminology belonging to their own poetical traditions and each tradition has developed an idiosyncratic analytical terminology in a different and independent way for years. The result of this uncoordinated evolution is a set of varied terminologies to explain analogous metrical phenomena through the different poetic systems whose correspondences have been hardly studied – see examples in González-Blanco & Rodríguez (2014a and b). This work has to be done by domain experts before the modeling actually starts. On the other hand, the development of a MAP is a complex task though it is imperative to follow a method for this development. The last years Curado Malta & Baptista (2012, 2013a, 2013b) have been studying the development of MAP's in a Design Science Research (DSR) methodological process in order to define a method for the development of MAPs (see Curado Malta (2014)). The output of this DSR process was a first version of a method for the development of Metadata Application Profiles (Me4MAP) (paper to be published). The DSR process is now in the validation phase of the Relevance Cycle to validate Me4MAP. The development of this MAP for poetry will follow the guidelines of Me4MAP and this development will be used to do the validation of Me4MAP. The final goal of the POSTDATA project is: i) to be able to publish all the data locked in the WIS, in LOD, where any agent interested will be able to build applications over the data in order to serve final users; ii) to build a Web platform where: a) researchers, students and other final users interested in EP will be able to access poems (and their analyses) of all databases; b) researchers, students and other final users will be able to upload poems, the digitalized images of manuscripts, and fill in the information concerning the analysis of the poem, collaboratively contributing to a LOD dataset of poetry.

SNC / NCRF 24 – Acontecimentos após a data do balanço e suas implicações fiscais e de auditoria

Orientador Prof. Dr. João Domingues Costa

Geostatistical Mapping of Outfall Plume Dispersion Data gathered with an autonomous underwater vehicle

The main purpose of this study was to examine the applicability of geostatistical modeling to obtain valuable information for assessing the environmental impact of sewage outfall discharges. The data set used was obtained in a monitoring campaign to S. Jacinto outfall, located off the Portuguese west coast near Aveiro region, using an AUV. The Matheron’s classical estimator was used the compute the experimental semivariogram which was fitted to three theoretical models: spherical, exponential and gaussian. The cross-validation procedure suggested the best semivariogram model and ordinary kriging was used to obtain the predictions of salinity at unknown locations. The generated map shows clearly the plume dispersion in the studied area, indicating that the effluent does not reach the near by beaches. Our study suggests that an optimal design for the AUV sampling trajectory from a geostatistical prediction point of view, can help to compute more precise predictions and hence to quantify more accurately dilution. Moreover, since accurate measurements of plume’s dilution are rare, these studies might be very helpful in the future for validation of dispersion models.

An architecture for an effective usage of data mining in business intelligence systems

Business Intelligence (BI) is one emergent area of the Decision Support Systems (DSS) discipline. Over the last years, the evolution in this area has been considerable. Similarly, in the last years, there has been a huge growth and consolidation of the Data Mining (DM) field. DM is being used with success in BI systems, but a truly DM integration with BI is lacking. Therefore, a lack of an effective usage of DM in BI can be found in some BI systems. An architecture that pretends to conduct to an effective usage of DM in BI is presented.

Norma internacional de contabilidade [NIC 10 ]: acontecimento após a data de balanço

Revista Fiscal Maio 2006

Data mining contributions to characterize MV consumers and to improve the suppliers-consumers settlements

This paper deals with the establishment of a characterization methodology of electric power profiles of medium voltage (MV) consumers. The characterization is supported on the data base knowledge discovery process (KDD). Data Mining techniques are used with the purpose of obtaining typical load profiles of MV customers and specific knowledge of their customers’ consumption habits. In order to form the different customers’ classes and to find a set of representative consumption patterns, a hierarchical clustering algorithm and a clustering ensemble combination approach (WEACS) are used. Taking into account the typical consumption profile of the class to which the customers belong, new tariff options were defined and new energy coefficients prices were proposed. Finally, and with the results obtained, the consequences that these will have in the interaction between customer and electric power suppliers are analyzed.

Data mining techniques contributions to support electrical vehicle demand response

The introduction of Electric Vehicles (EVs) together with the implementation of smart grids will raise new challenges to power system operators. This paper proposes a demand response program for electric vehicle users which provides the network operator with another useful resource that consists in reducing vehicles charging necessities. This demand response program enables vehicle users to get some profit by agreeing to reduce their travel necessities and minimum battery level requirements on a given period. To support network operator actions, the amount of demand response usage can be estimated using data mining techniques applied to a database containing a large set of operation scenarios. The paper includes a case study based on simulated operation scenarios that consider different operation conditions, e.g. available renewable generation, and considering a diversity of distributed resources and electric vehicles with vehicle-to-grid capacity and demand response capacity in a 33 bus distribution network.

Adaptive tool for automatic data collection of real electricity markets

The study of electricity markets operation has been gaining an increasing importance in last years, as result of the new challenges that the electricity markets restructuring produced. This restructuring increased the competitiveness of the market, but with it its complexity. The growing complexity and unpredictability of the market’s evolution consequently increases the decision making difficulty. Therefore, the intervenient entities are forced to rethink their behaviour and market strategies. Currently, lots of information concerning electricity markets is available. These data, concerning innumerous regards of electricity markets operation, is accessible free of charge, and it is essential for understanding and suitably modelling electricity markets. This paper proposes a tool which is able to handle, store and dynamically update data. The development of the proposed tool is expected to be of great importance to improve the comprehension of electricity markets and the interactions among the involved entities.

Metalearning in ALBidS: A Strategic Bidding System for electricity markets

Metalearning is a subfield of machine learning with special pro-pensity for dynamic and complex environments, from which it is difficult to extract predictable knowledge. The field of study of this work is the electricity market, which due to the restructuring that recently took place, became an especially complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotia-tion entities. The proposed metalearner takes advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that pro-vides decision support to electricity markets’ participating players. Using the outputs of each different strategy as inputs, the metalearner creates its own output, considering each strategy with a different weight, depending on its individual quality of performance. The results of the proposed meth-od are studied and analyzed using MASCEM - a multi-agent electricity market simulator that models market players and simulates their operation in the market. This simulator provides the chance to test the metalearner in scenarios based on real electricity market´s data.

Histogram-based DNA analysis for the visualization of chromosome, genome and species information

We describe a novel approach to explore DNA nucleotide sequence data, aiming to produce high-level categorical and structural information about the underlying chromosomes, genomes and species. The article starts by analyzing chromosomal data through histograms using fixed length DNA sequences. After creating the DNA-related histograms, a correlation between pairs of histograms is computed, producing a global correlation matrix. These data are then used as input to several data processing methods for information extraction and tabular/graphical output generation. A set of 18 species is processed and the extensive results reveal that the proposed method is able to generate significant and diversified outputs, in good accordance with current scientific knowledge in domains such as genomics and phylogenetics.

Data mining techniques to support the classification of MV electricity customers

This paper describes a methodology that was developed for the classification of Medium Voltage (MV) electricity customers. Starting from a sample of data bases, resulting from a monitoring campaign, Data Mining (DM) techniques are used in order to discover a set of a MV consumer typical load profile and, therefore, to extract knowledge regarding to the electric energy consumption patterns. In first stage, it was applied several hierarchical clustering algorithms and compared the clustering performance among them using adequacy measures. In second stage, a classification model was developed in order to allow classifying new consumers in one of the obtained clusters that had resulted from the previously process. Finally, the interpretation of the discovered knowledge are presented and discussed.