Diagnóstico cardíaco a partir de dados acústicos e clínicos

Este documento foi redigido no âmbito da dissertação do Mestrado em Engenharia Informática na área de Arquiteturas, Sistemas e Redes, do Departamento de Engenharia Informática, do ISEP, cujo tema é diagnóstico cardíaco a partir de dados acústicos e clínicos. O objetivo deste trabalho é produzir um método que permita diagnosticar automaticamente patologias cardíacas utilizando técnicas de classificação de data mining. Foram utilizados dois tipos de dados: sons cardíacos gravados em ambiente hospitalar e dados clínicos. Numa primeira fase, exploraram-se os sons cardíacos usando uma abordagem baseada em motifs. Numa segunda fase, utilizamos os dados clínicos anotados dos pacientes. Numa terceira fase, avaliamos a combinação das duas abordagens. Na avaliação experimental os modelos baseados em motifs obtiveram melhores resultados do que os construídos a partir dos dados clínicos. A combinação das abordagens mostrou poder ser vantajosa em situações pontuais.

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.

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.

Demand response programs definition supported by clustering and classification techniques

The growing importance and influence of new resources connected to the power systems has caused many changes in their operation. Environmental policies and several well know advantages have been made renewable based energy resources largely disseminated. These resources, including Distributed Generation (DG), are being connected to lower voltage levels where Demand Response (DR) must be considered too. These changes increase the complexity of the system operation due to both new operational constraints and amounts of data to be processed. Virtual Power Players (VPP) are entities able to manage these resources. Addressing these issues, this paper proposes a methodology to support VPP actions when these act as a Curtailment Service Provider (CSP) that provides DR capacity to a DR program declared by the Independent System Operator (ISO) or by the VPP itself. The amount of DR capacity that the CSP can assure is determined using data mining techniques applied to a database which is obtained for a large set of operation scenarios. The paper includes a case study based on 27,000 scenarios considering a diversity of distributed resources in a 33 bus distribution network.

Analysis of financial data series using fractional Fourier transform and multidimensional scaling

The goal of this study is the analysis of the dynamical properties of financial data series from worldwide stock market indexes during the period 2000–2009. We analyze, under a regional criterium, ten main indexes at a daily time horizon. The methods and algorithms that have been explored for the description of dynamical phenomena become an effective background in the analysis of economical data. We start by applying the classical concepts of signal analysis, fractional Fourier transform, and methods of fractional calculus. In a second phase we adopt the multidimensional scaling approach. Stock market indexes are examples of complex interacting systems for which a huge amount of data exists. Therefore, these indexes, viewed from a different perspectives, lead to new classification patterns.

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.

Recomendações personalizadas de alunos em sistemas de hipermédia adaptativa educacional usando Data Mining

O aumento de tecnologias disponíveis na Web favoreceu o aparecimento de diversas formas de informação, recursos e serviços. Este aumento aliado à constante necessidade de formação e evolução das pessoas, quer a nível pessoal como profissional, incentivou o desenvolvimento área de sistemas de hipermédia adaptativa educacional - SHAE. Estes sistemas têm a capacidade de adaptar o ensino consoante o modelo do aluno, características pessoais, necessidades, entre outros aspetos. Os SHAE permitiram introduzir mudanças relativamente à forma de ensino, passando do ensino tradicional que se restringia apenas ao uso de livros escolares até à utilização de ferramentas informáticas que através do acesso à internet disponibilizam material didático, privilegiando o ensino individualizado. Os SHAE geram grande volume de dados, informação contida no modelo do aluno e todos os dados relativos ao processo de aprendizagem de cada aluno. Facilmente estes dados são ignorados e não se procede a uma análise cuidada que permita melhorar o conhecimento do comportamento dos alunos durante o processo de ensino, alterando a forma de aprendizagem de acordo com o aluno e favorecendo a melhoria dos resultados obtidos. O objetivo deste trabalho foi selecionar e aplicar algumas técnicas de Data Mining a um SHAE, PCMAT - Mathematics Collaborative Educational System. A aplicação destas técnicas deram origem a modelos de dados que transformaram os dados em informações úteis e compreensíveis, essenciais para a geração de novos perfis de alunos, padrões de comportamento de alunos, regras de adaptação e pedagógicas. Neste trabalho foram criados alguns modelos de dados recorrendo à técnica de Data Mining de classificação, abordando diferentes algoritmos. Os resultados obtidos permitirão definir novas regras de adaptação e padrões de comportamento dos alunos, poderá melhorar o processo de aprendizagem disponível num SHAE.

Optimization of River Water Quality Surveys by Multivariate Analysis of Physicochemical, Bacteriological and Ecotoxicological Data

This study aims to optimize the water quality monitoring of a polluted watercourse (Leça River, Portugal) through the principal component analysis (PCA) and cluster analysis (CA). These statistical methodologies were applied to physicochemical, bacteriological and ecotoxicological data (with the marine bacterium Vibrio fischeri and the green alga Chlorella vulgaris) obtained with the analysis of water samples monthly collected at seven monitoring sites and during five campaigns (February, May, June, August, and September 2006). The results of some variables were assigned to water quality classes according to national guidelines. Chemical and bacteriological quality data led to classify Leça River water quality as “bad” or “very bad”. PCA and CA identified monitoring sites with similar pollution pattern, giving to site 1 (located in the upstream stretch of the river) a distinct feature from all other sampling sites downstream. Ecotoxicity results corroborated this classification thus revealing differences in space and time. The present study includes not only physical, chemical and bacteriological but also ecotoxicological parameters, which broadens new perspectives in river water characterization. Moreover, the application of PCA and CA is very useful to optimize water quality monitoring networks, defining the minimum number of sites and their location. Thus, these tools can support appropriate management decisions.

Using data mining techniques to support breast cancer diagnosis

More than ever, there is an increase of the number of decision support methods and computer aided diagnostic systems applied to various areas of medicine. In breast cancer research, many works have been done in order to reduce false-positives when used as a double reading method. In this study, we aimed to present a set of data mining techniques that were applied to approach a decision support system in the area of breast cancer diagnosis. This method is geared to assist clinical practice in identifying mammographic findings such as microcalcifications, masses and even normal tissues, in order to avoid misdiagnosis. In this work a reliable database was used, with 410 images from about 115 patients, containing previous reviews performed by radiologists as microcalcifications, masses and also normal tissue findings. Throughout this work, two feature extraction techniques were used: the gray level co-occurrence matrix and the gray level run length matrix. For classification purposes, we considered various scenarios according to different distinct patterns of injuries and several classifiers in order to distinguish the best performance in each case described. The many classifiers used were Naïve Bayes, Support Vector Machines, k-nearest Neighbors and Decision Trees (J48 and Random Forests). The results in distinguishing mammographic findings revealed great percentages of PPV and very good accuracy values. Furthermore, it also presented other related results of classification of breast density and BI-RADS® scale. The best predictive method found for all tested groups was the Random Forest classifier, and the best performance has been achieved through the distinction of microcalcifications. The conclusions based on the several tested scenarios represent a new perspective in breast cancer diagnosis using data mining techniques.

A Data-mining-based Methodology to support MV Electricity Customers' Characterization

This paper presents an electricity medium voltage (MV) customer characterization framework supportedby knowledge discovery in database (KDD). The main idea is to identify typical load profiles (TLP) of MVconsumers and to develop a rule set for the automatic classification of new consumers. To achieve ourgoal a methodology is proposed consisting of several steps: data pre-processing; application of severalclustering algorithms to segment the daily load profiles; selection of the best partition, corresponding tothe best consumers’ segmentation, based on the assessments of several clustering validity indices; andfinally, a classification model is built based on the resulting clusters. To validate the proposed framework,a case study which includes a real database of MV consumers is performed.

Terminal defect analysis and classification methodology for automotive industry

The impact of end customer quality complaints with direct relationship with automotive components has presented negative trend at European level for the entire automotive industry. Thus, this research proposal is to concentrate efforts on the most important items of Pareto chart and understand the failure type and the mechanism involved, link and impact of the project and parameters on the process, ending it with the development of one of the company’s most desired tool, that hosted this project – European methodology of terminals defects classification, and listing real opportunities for improvement based on measurement and analysis of actual data. Through the development of terminals defects classification methodology, which is considered a valuable asset to the company, all the other companies of the YAZAKI’s group will be able to characterize terminals as brittle or ductile, in order to put in motion, more efficiently, all the other different existing internal procedures for the safeguarding of the components, improving manufacturing efficiency. Based on a brief observation, nothing can be said in absolute sense, concerning the failure causes. Base materials, project, handling during manufacture and storage, as well as the cold work performed by plastic deformation, all play an important role. However, it was expected that this failure has been due to a combination of factors, in detriment of the existence of a single cause. In order to acquire greater knowledge about this problem, unexplored by the company up to the date of commencement of this study, was conducted a thorough review of existing literature on the subject, real production sites were visited and, of course, the actual parts were tested in lab environment. To answer to many of the major issues raised throughout the investigation, were used extensively some theoretical concepts focused on the literature review, with a view to realizing the relationship existing between the different parameters concerned. Should here be stated that finding technical studies on copper and its alloys is really hard, not being given all the desirable information. This investigation has been performed as a YAZAKI Europe Limited Company project and as a Master Thesis for Instituto Superior de Engenharia do Porto, conducted during 9 months between 2012/2013.

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.