DC Fuzzy Multicriteria Approach to Increase the Probability of Delivering Power in Distribution Networks

This paper proposes a methodology to increase the probability of delivering power to any load point through the identification of new investments. The methodology uses a fuzzy set approach to model the uncertainty of outage parameters, load and generation. A DC fuzzy multicriteria optimization model considering the Pareto front and based on mixed integer non-linear optimization programming is developed in order to identify the adequate investments in distribution networks components which allow increasing the probability of delivering power to all customers in the distribution network at the minimum possible cost for the system operator, while minimizing the non supplied energy cost. To illustrate the application of the proposed methodology, the paper includes a case study which considers an 33 bus distribution network.

Methodology for collaborative enterprise reference ontology building

Dissertation presented at Faculdade de Ciências e Tecnologia of Universidade Nova de Lisboa to obtain the Master degree in Electrical Engineering and Computer Science

Models and tools for value systems analysis in collaborative environments

Dissertation to obtain the degree of Doctor in Electrical and Computer Engineering, specialization of Collaborative Networks

Synthesis about a collaborative project on “Technology Assessment of Autonomous System

The project started in 2009 with the support of DAAD in Germany and CRUP in Portugal under the “Collaborative German-Portuguese University Actions” programme. One central goal is the further development of a theory of technology assessment applied to robotics and autonomous systems in general that reflects in its methodology the changing conditions of knowledge production in modern societies and the emergence of new robotic technologies and of associated disruptive changes. Relevant topics here are handling broadened future horizons and new clusters of science and technology (medicine, engineering, interfaces, industrial automation, micro-devices, security and safety), as well as new governance structures in policy decision making concerning research and development (R

Adaptive filtering for high quality HMM based speech synthesis

In this work an adaptive filtering scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for Hidden Markov Model (HMM) based speech synthesis quality enhancement. The objective is to improve signal smoothness across HMMs and their related states and to reduce artifacts due to acoustic model's limitations. Both speech and artifacts are modelled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. Themodel parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The quality enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. The system's performance has been evaluated using mean opinion score tests and the proposed technique has led to improved results.

Recommendation of Tourism Resources Supported by Crowdsourcing

Context-aware recommendation of personalised tourism resources is possible because of personal mobile devices and powerful data filtering algorithms. The devices contribute with computing capabilities, on board sensors, ubiquitous Internet access and continuous user monitoring, whereas the filtering algorithms provide the ability to match the profile (interests and the context) of the tourist against a large knowledge bases of tourism resources. While, in terms of technology, personal mobile devices can gather user-related information, including the user context and access multiple data sources, the creation and maintenance of an updated knowledge base of tourism-related resources requires a collaborative approach due to the heterogeneity, volume and dynamic nature of the resources. The current PhD thesis aims to contribute to the solution of this problem by adopting a Crowdsourcing approach for the collaborative maintenance of the knowledge base of resources, Trust and Reputation for the validation of uploaded resources as well as publishers, Big Data for user profiling and context-aware filtering algorithms for the personalised recommendation of tourism resources.

Comparative analysis of multicriteria decision making methods

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores

Drivers of participation in collaborative consumption ventures

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Real-time collaborative editing of OutSystems DSL models

Real-time collaborative editing systems are common nowadays, and their advantages are widely recognized. Examples of such systems include Google Docs, ShareLaTeX, among others. This thesis aims to adopt this paradigm in a software development environment. The OutSystems visual language lends itself very appropriate to this kind of collaboration, since the visual code enables a natural flow of knowledge between developers regarding the developed code. Furthermore, communication and coordination are simplified. This proposal explores the field of collaboration on a very structured and rigid model, where collaboration is made through the copy-modify-merge paradigm, in which a developer gets its own private copy from the shared repository, modifies it in isolation and later uploads his changes to be merged with modifications concurrently produced by other developers. To this end, we designed and implemented an extension to the OutSystems Platform, in order to enable real-time collaborative editing. The solution guarantees consistency among the artefacts distributed across several developers working on the same project. We believe that it is possible to achieve a much more intense collaboration over the same models with a low negative impact on the individual productivity of each developer.

Exploring Literary Landscapes: From Texts to Spatiotemporal Analysis through Collaborative Work and GIS

This article argues that the study of literary representations of landscapes can be aided and enriched by the application of digital geographic technologies. As an example, the article focuses on the methods and preliminary findings of LITESCAPE.PT—Atlas of Literary Landscapes of Mainland Portugal, an on-going project that aims to study literary representations of mainland Portugal and to explore their connections with social and environmental realities both in the past and in the present. LITESCAPE.PT integrates traditional reading practices and ‘distant reading’ approaches, along with collaborative work, relational databases, and geographic information systems (GIS) in order to classify and analyse excerpts from 350 works of Portuguese literature according to a set of ecological, socioeconomic, temporal and cultural themes. As we argue herein this combination of qualitative and quantitative methods—itself a response to the difficulty of obtaining external funding—can lead to (a) increased productivity, (b) the pursuit of new research goals, and (c) the creation of new knowledge about natural and cultural history. As proof of concept, the article presents two initial outcomes of the LITESCAPE.PT project: a case study documenting the evolving literary geography of Lisbon and a case study exploring the representation of wolves in Portuguese literature.

Community indentification in peer-t-peer collaborative consumption services

Even though collaborative consumption (CC) is gaining economic importance, research in CC is still in its infancy. Consumers’ reasons for participating have already been investigated but little research on consequences of participation has been conducted. This article examines whether interactions between customers in peer-to-peer CC services influence the willingness to coproduce service outcomes. Drawing on social exchange theory, it is proposed that this effect is mediated by consumers’ identification with the brand community. Furthermore, continuance intention in CC is introduced as a second stage moderator. In a cross-sectional study, customers of peer-to-peer accommodation sharing are surveyed. While customer-to-customer interactions were found to have a positive effect on brand community identification, brand community identification did not positively affect co-production intention. Surprisingly, the effect of brand community identification on co-production intention was negative. Moreover, continuance intention of customers did not moderate this relationship. Bearing in mind current challenges for researchers and companies, theoretical and managerial implications are discussed.

A spatial multicriteria model for urban accessibility mapping

Accessibility is nowadays an important issue for the development of cities. It is seen as a priority in order toguarantee equal access to fundamental rights, to improve the quality of life of citizens and to ensure that everyone, regardless of age, mobility or ability, have equal access to all the resources and benefits cities have to offer. Consequently, factors closely related to the accessibility have gained a higher relevance for identifying and assessing the location of urban facilities. The main goal of the paper is to present an accessibility evaluation model applied in Santarém, in Brazil, a city located midway between the larger cities of Belem and Manaus. The research instruments, sampling method and data analysis proposed for mapping urban accessibility are described. Daily activities were used to identify and group key destinations. The model was implemented within a geographic information system and integrates the individualâ s perspective through the definition of each key destination weight, reflecting their significance for daily activities in the urban area. Accessibility to key destinations was mapped over 24 districts of the city of Santarém. The results of this model application can support city administration decision-making for new investments in order to improve urban quality of life.

Dynamic group formation in mobile computer supported collaborative learning environment

Forming suitable learning groups is one of the factors that determine the efficiency of collaborative learning activities. However, only a few studies were carried out to address this problem in the mobile learning environments. In this paper, we propose a new approach for an automatic, customized, and dynamic group formation in Mobile Computer Supported Collaborative Learning (MCSCL) contexts. The proposed solution is based on the combination of three types of grouping criteria: learner’s personal characteristics, learner’s behaviours, and context information. The instructors can freely select the type, the number, and the weight of grouping criteria, together with other settings such as the number, the size, and the type of learning groups (homogeneous or heterogeneous). Apart from a grouping mechanism, the proposed approach represents a flexible tool to control each learner, and to manage the learning processes from the beginning to the end of collaborative learning activities. In order to evaluate the quality of the implemented group formation algorithm, we compare its Average Intra-cluster Distance (AID) with the one of a random group formation method. The results show a higher effectiveness of the proposed algorithm in forming homogenous and heterogeneous groups compared to the random method.

BeSmart2: A multicriteria decision aid application

This paper presents an improved version of an application whose goal is to provide a simple and intuitive way to use multicriteria decision methods in day-to-day decision problems. The application allows comparisons between several alternatives with several criteria, always keeping a permanent backup of both model and results, and provides a framework to incorporate new methods in the future. Developed in C#, the application implements the AHP, SMART and Value Functions methods.

Enhancing text classification to improve information filtering

Text classification, information filtering, semi-supervised learning, quality control