Dynamic range prediction for an electric vehicle

Electric Vehicles (EVs) have limited energy storage capacity and the maximum autonomy range is strongly dependent of the driver's behaviour. Due to the fact of that batteries cannot be recharged quickly during a journey, it is essential that a precise range prediction is available to the driver of the EV. With this information, it is possible to check if the desirable destination is achievable without a stop to charge the batteries, or even, if to reach the destination it is necessary to perform an optimized driving (e.g., cutting the air-conditioning, among others EV parameters). The outcome of this research work is the development of an Electric Vehicle Assistant (EVA). This is an application for mobile devices that will help users to take efficient decisions about route planning, charging management and energy efficiency. Therefore, it will contribute to foster EVs adoption as a new paradigm in the transportation sector.

Electric vehicle assistant based in driver profile

This paper presents the outcomes of a research work consisting in the development of an Electric Vehicle Assistant (EVA), which creates and stores a driver profile where are contained the driving behaviours related with the EV energy consumption, the EV battery charging information, and the performed routes. This is an application for mobile devices that is able to passively track the driver behaviour and to access several information related with the EV in real time. It is also proposed a range prediction approach based on probability to take into account unpredictable effects of personal driving style, traffic or weather.

Detection of road accident accumulation zones with a visual analytics approach

Nowadays, road accidents are a major public health problem, which increase is forecasted if road safety is not treated properly, dying about 1.2 million people every year around the globe. In 2012, Portugal recorded 573 fatalities in road accidents, on site, revealing the largest decreasing of the European Union for 2011, along with Denmark. Beyond the impact caused by fatalities, it was calculated that the economic and social costs of road accidents weighted about 1.17% of the Portuguese gross domestic product in 2010. Visual Analytics allows the combination of data analysis techniques with interactive visualizations, which facilitates the process of knowledge discovery in sets of large and complex data, while the Geovisual Analytics facilitates the exploration of space-time data through maps with different variables and parameters that are under analysis. In Portugal, the identification of road accident accumulation zones, in this work named black spots, has been restricted to annual fixed windows. In this work, it is presented a dynamic approach based on Visual Analytics techniques that is able to identify the displacement of black spots on sliding windows of 12 months. Moreover, with the use of different parameterizations in the formula usually used to detect black spots, it is possible to identify zones that are almost becoming black spots. Through the proposed visualizations, the study and identification of countermeasures to this social and economic problem can gain new grounds and thus the decision- making process is supported and improved.

Using customer lifetime value to improve the prediction of bank deposit subscription in telemarketing campaigns

Customer lifetime value (LTV) enables using client characteristics, such as recency, frequency and monetary (RFM) value, to describe the value of a client through time in terms of profitability. We present the concept of LTV applied to telemarketing for improving the return-on-investment, using a recent (from 2008 to 2013) and real case study of bank campaigns to sell long- term deposits. The goal was to benefit from past contacts history to extract additional knowledge. A total of twelve LTV input variables were tested, un- der a forward selection method and using a realistic rolling windows scheme, highlighting the validity of five new LTV features. The results achieved by our LTV data-driven approach using neural networks allowed an improvement up to 4 pp in the Lift cumulative curve for targeting the deposit subscribers when compared with a baseline model (with no history data). Explanatory knowledge was also extracted from the proposed model, revealing two highly relevant LTV features, the last result of the previous campaign to sell the same product and the frequency of past client successes. The obtained results are particularly valuable for contact center companies, which can improve pre- dictive performance without even having to ask for more information to the companies they serve.

A proactive intelligent decision support system for predicting the popularity of online news

"Lecture notes in computer science series, ISSN 0302-9743, vol. 9273"

An integrated system for human action recognition from video using hidden Markov model

In this paper, we present an integrated system for real-time automatic detection of human actions from video. The proposed approach uses the boundary of humans as the main feature for recognizing actions. Background subtraction is performed using Gaussian mixture model. Then, features are extracted from silhouettes and Vector Quantization is used to map features into symbols (bag of words approach). Finally, actions are detected using the Hidden Markov Model. The proposed system was validated using a newly collected real- world dataset. The obtained results show that the system is capable of achieving robust human detection, in both indoor and outdoor environments. Moreover, promising classification results were achieved when detecting two basic human actions: walking and sitting.

Predicting plateau pressure in intensive medicine for ventilated patients

Barotrauma is identified as one of the leading diseases in Ventilated Patients. This type of problem is most common in the Intensive Care Units. In order to prevent this problem the use of Data Mining (DM) can be useful for predicting their occurrence. The main goal is to predict the occurence of Barotrauma in order to support the health professionals taking necessary precautions. In a first step intensivists identified the Plateau Pressure values as a possible cause of Barotrauma. Through this study DM models (classification) where induced for predicting the Plateau Pressure class (>=30 cm

Sistema de predição de avarias em máquinas de unidades fabris globalmente dispersas

Tese de Doutoramento Ramo Engenharia Industrial e de Sistemas

Instrumentos de políticas públicas do turismo: uma análise empírica dos municípios portugueses

Tese de Doutoramento em Ciências da Administração

Educação bilingue em Moçambique: um estudo de caso, na Província de Gaza, centrado nas práticas pedagógicas do ensino básico

Em contextos como o de Moçambique que apresenta uma diversidade linguística, o uso de mais de uma língua no ensino é quase que obrigatório. Assim, urge a necessidade de formalizar a Educação Bilingue (EB) nas escolas moçambicanas. A modalidade de EB em Moçambique é de transição e manutenção, nas primeiras classes (1ª, 2ª e 3ª) usa-se a língua local como meio de instrução e o Português é uma disciplina, a partir da 4ª classe há uma inversão. Dessa estratégia o aluno deve estar em condições na 5ª classe de enfrentar o exame nacional, que é feito em língua oficial Portuguesa, para transitar do 1º para o 2º grau. O trabalho tem como problema esta interrogação: De que modo o programa curricular de Educação Bilingue se traduz nas práticas pedagógicas nas escolas do Ensino Básico que usam a língua Portuguesa e Changana no distrito de Bilene, na província de Gaza? O estudo empírico é de natureza qualitativa e quantitativa, com a utilização de técnicas de recolha de dados como a análise documental, entrevistas, questionário e provas escrita e oral aos alunos. Do estudo conclui-se: i) Não existe um programa de base especificamente para EB; ii) Há incongruência entre o programa e as práticas pedagógicas e iii) Os alunos apresentam maior dificuldades de compreensão, na escrita do que na oralidade, nas provas de conhecimento nas disciplinas básicas (Português e Matemática) em língua Portuguesa.

Sistema inteligente para análise do posicionamento de árbitros de futebol

Dissertação de mestrado em Sistemas de Informação

Pervasive patient timeline

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Avaliação de desempenho docente: Contributos para as práticas de avaliação na escola

Tese de Doutoramento em Ciências da Educação (Especialidade em Desenvolvimento Curricular)

Pervasive business intelligence: a marketing intelligence framework proposal

Doctoral Thesis in Information Systems and Technologies Area of Information Systems and Technology

Automatically estimating iSAX parameters

The Symbolic Aggregate Approximation (iSAX) is widely used in time series data mining. Its popularity arises from the fact that it largely reduces time series size, it is symbolic, allows lower bounding and is space efficient. However, it requires setting two parameters: the symbolic length and alphabet size, which limits the applicability of the technique. The optimal parameter values are highly application dependent. Typically, they are either set to a fixed value or experimentally probed for the best configuration. In this work we propose an approach to automatically estimate iSAX’s parameters. The approach – AutoiSAX – not only discovers the best parameter setting for each time series in the database, but also finds the alphabet size for each iSAX symbol within the same word. It is based on simple and intuitive ideas from time series complexity and statistics. The technique can be smoothly embedded in existing data mining tasks as an efficient sub-routine. We analyze its impact in visualization interpretability, classification accuracy and motif mining. Our contribution aims to make iSAX a more general approach as it evolves towards a parameter-free method.