Distributed Belief Revision and Environmental Decision Support

This article discusses the development of an Intelligent Distributed Environmental Decision Support System, built upon the association of a Multi-agent Belief Revision System with a Geographical Information System (GIS). The inherent multidisciplinary features of the involved expertises in the field of environmental management, the need to define clear policies that allow the synthesis of divergent perspectives, its systematic application, and the reduction of the costs and time that result from this integration, are the main reasons that motivate the proposal of this project. This paper is organised in two parts: in the first part we present and discuss the developed - Distributed Belief Revision Test-bed - DiBeRT; in the second part we analyse its application to the environmental decision support domain, with special emphasis on the interface with a GIS.

Distributed Belief Revision and Environmental Decision Support

This article discusses the development of an Intelligent Distributed Environmental Decision Support System, built upon the association of a Multi-agent Belief Revision System with a Geographical Information System (GIS). The inherent multidisciplinary features of the involved expertises in the field of environmental management, the need to define clear policies that allow the synthesis of divergent perspectives, its systematic application, and the reduction of the costs and time that result from this integration, are the main reasons that motivate the proposal of this project. This paper is organised in two parts: in the first part we present and discuss the developed ; in the second part we analyse its application to the environmental decision support domain, with special emphasis on the interface with a GIS.

Intelligent Distributed Environmental Decision Support System

This article discusses the development of an Intelligent Distributed Environmental Decision Support System, built upon the association of a Multi-agent Belief Revision System with a Geographical Information System (GIS). The inherent multidisciplinary features of the involved expertises in the field of environmental management, the need to define clear policies that allow the synthesis of divergent perspectives, its systematic application, and the reduction of the costs and time that result from this integration, are the main reasons that motivate the proposal of this project. This paper is organised in two parts: in the first part we present and discuss the developed Distributed Belief Revision Test-bed — DiBeRT; in the second part we analyse its application to the environmental decision support domain, with special emphasis on the interface with a GIS.

Environmental Decision Support: a Distributed Artificial Intelligence Approach

Decision making in any environmental domain is a complex and demanding activity, justifying the development of dedicated decision support systems. Every decision is confronted with a large variety and amount of constraints to satisfy as well as contradictory interests that must be sensibly accommodated. The first stage of a project evaluation is its submission to the relevant group of public (and private) agencies. The individual role of each agency is to verify, within its domain of competence, the fulfilment of the set of applicable regulations. The scope of the involved agencies is wide and ranges from evaluation abilities on the technical or economical domains to evaluation competences on the environmental or social areas. The second project evaluation stage involves the gathering of the recommendations of the individual agencies and their justified merge to produce the final conclusion. The incorporation and accommodation of the consulted agencies opinions is of extreme importance: opinions may not only differ, but can be interdependent, complementary, irreconcilable or, simply, independent. The definition of adequate methodologies to sensibly merge, whenever possible, the existing perspectives while preserving the overall legality of the system, will lead to the making of sound justified decisions. The proposed Environmental Decision Support System models the project evaluation activity and aims to assist developers in the selection of adequate locations for their projects, guaranteeing their compliance with the applicable regulations.

O papel do inapem no fomento do empreendedorismo na provincia do Kwanza Norte-Angola

Dissertação de Mestrado apresentada ao Instituto de Contabilidade e Administração do Porto para a obtenção do grau de Mestre em Empreendedorismo e Internacionalização, sob orientação de Celsa Maria Carvalho Machado e Adalmiro Álvaro Malheiro de Castro Andrade Pereira

Evaluating the quality and the success of online courses

Measuring the quality of a b-learning environment is critical to determine the success of a b-learning course. There are a lot of materials related to the quality process, namely different approaches and perspectives but none of them is specific of the product of a b-learning context. In this paper we identify the indicators that should be analyzed in order to determine the quality of a b-learning course, since its success reflect not only the student’s perception, but also what should be taken into account. B-Learning environments are relatively new and combine educational characteristics with technological elements that support the learning process and the training delivery. Our main objective is to know what a high quality b-learning environment is in students’’ perception and what are the main quality dimensions of these courses, in the perspective of the products and services offered. After a literature review concerning the quality process and in particular the b-learning quality field, a structure that provides the main elements that should be evaluated by students when we are measuring the quality and the success of b-learning product/services was created. The structure obtained was applied to a case study of the Polytechnic Institute of Oporto. Results presented will help institutions to deliver services with more quality and improve their long-term competitiveness.

Decision Support Concerning Demand Response Programs Design and Use – A Conceptual Framework and Simulation Tool

Demand response can play a very relevant role in the context of power systems with an intensive use of distributed energy resources, from which renewable intermittent sources are a significant part. More active consumers participation can help improving the system reliability and decrease or defer the required investments. Demand response adequate use and management is even more important in competitive electricity markets. However, experience shows difficulties to make demand response be adequately used in this context, showing the need of research work in this area. The most important difficulties seem to be caused by inadequate business models and by inadequate demand response programs management. This paper contributes to developing methodologies and a computational infrastructure able to provide the involved players with adequate decision support on demand response programs and contracts design and use. The presented work uses DemSi, a demand response simulator that has been developed by the authors to simulate demand response actions and programs, which includes realistic power system simulation. It includes an optimization module for the application of demand response programs and contracts using deterministic and metaheuristic approaches. The proposed methodology is an important improvement in the simulator while providing adequate tools for demand response programs adoption by the involved players. A machine learning method based on clustering and classification techniques, resulting in a rule base concerning DR programs and contracts use, is also used. A case study concerning the use of demand response in an incident situation is presented.

Personal learning environments: integration of Web 2.0 applications and content management systems

This paper presents an ongoing project that implements a platform for creating personal learning environments controlled by students, integrating Web 2.0 applications and content management systems, enabling the safe use of content created in Web 2.0 applications, allowing its publication in the infrastructure controlled by the HEI. Using this platform, students can develop their personal learning environment (PLE) integrated with the Learning Management System (LMS) of the HEI, enabling the management of their learning and, simultaneously, creating their e-portfolio with digital content developed for Course Units (CU). All this can be maintained after the student completes his academic studies, since the platform will remain accessible to students even after they leave the HEI and lose access to its infrastructure. The platform will enable the safe use of content created in Web 2.0 applications, allowing its protected publication in the infrastructure controlled by HEI, thus contributing to the adaptation of the L&T paradigm to the Bologna process.

Aplicação da metodologia de “lean management” no processo de desenvolvimento de produto

O presente trabalho de dissertação teve como objetivo a implementação de metodologias de Lean Management e avaliação do seu impacto no processo de Desenvolvimento de Produto. A abordagem utilizada consistiu em efetuar uma revisão da literatura e levantamento do Estado da Arte para obter a fundamentação teórica necessária à implementação de metodologias Lean. Prosseguiu com o levantamento da situação inicial da organização em estudo ao nível das atividades de desenvolvimento de produto, práticas de gestão documental e operacional e ainda de atividades de suporte através da realização de inquéritos e medições experimentais. Este conhecimento permitiu criar um modelo de referência para a implementação de Lean Management nesta área específica do desenvolvimento de produto. Após implementado, este modelo foi validado pela sua experimentação prática e recolha de indicadores. A implementação deste modelo de referência permitiu introduzir na Unidade de Desenvolvimento de Produto e Sistemas (DPS) da organização INEGI, as bases do pensamento Lean, contribuindo para a criação de um ambiente de Respeito pela Humanidade e de Melhoria Contínua. Neste ambiente foi possível obter ganhos qualitativos e quantitativos nas várias áreas em estudo, contribuindo de forma global para um aumento da eficiência e eficácia da DPS. Prevê-se que este aumento de eficiência represente um aumento da capacidade instalada na Organização, pela redução anual de 2290 horas de desperdício (6.5% da capacidade total da unidade) e pela redução significativa em custos operacionais. Algumas das implementações de melhoria propostas no decorrer deste trabalho, após verificado o seu sucesso, extravasaram a unidade em estudo e foram aplicadas transversalmente à da organização. Foram também obtidos ganhos qualitativos, tais como a normalização de práticas de gestão documental e a centralização e agilização de fluxos de informação. Isso permitiu um aumento de qualidade dos serviços prestados pela redução de correções e retrabalho. Adicionalmente, com o desenvolvimento de uma nova ferramenta que permite a monitorização do estado atual dos projetos a nível da sua percentagem de execução (cumprimento de objetivos), prazos e custos, bem como a estimação das datas de conclusão dos projetos possibilitando o replaneamento do projeto bem como a detecção atempada de desvios. A ferramenta permite também a criação de um histórico que identifica o esforço horário associado à realização das atividades/tarefas das várias áreas de Desenvolvimento de Produto e desta forma pode ser usada como suporte à orçamentação futura de atividades similares. No decorrer do projeto, foram também criados os mecanismos que permitem o cálculo de indicadores das competências técnicas e motivações intrínsecas individuais da equipa DPS. Estes indicadores podem ser usados na definição por parte dos gestores dos projetos da composição das equipas de trabalho, dos executantes de tarefas individuais do projeto e dos destinatários de ações de formação. Com esta informação é expectável que se consiga um maior aproveitamento do potencial humano e como consequência um aumento do desempenho e da satisfação pessoal dos recursos humanos da organização. Este caso de estudo veio demonstrar que o potencial de melhoria dos processos associados ao desenvolvimento de produto através de metodologias de Lean Management é muito significativo, e que estes resultam em ganhos visíveis para a organização bem como para os seus elementos individualmente.

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.

Short-term Wind Forecasting to Support Virtual Power Player Operation

This paper proposes a wind speed forecasting model that contributes to the development and implementation of adequate methodologies for Energy Resource Man-agement in a distribution power network, with intensive use of wind based power generation. The proposed fore-casting methodology aims to support the operation in the scope of the intraday resources scheduling model, name-ly with a time horizon of 10 minutes. A case study using a real database from the meteoro-logical station installed in the GECAD renewable energy lab was used. A new wind speed forecasting model has been implemented and it estimated accuracy was evalu-ated and compared with a previous developed forecast-ing model. Using as input attributes the information of the wind speed concerning the previous 3 hours enables to obtain results with high accuracy for the wind short-term forecasting.

Decision support tool for Virtual Power Players: Hybrid Particle Swarm Optimization applied to Day-ahead Vehicle-To-Grid Scheduling

This paper presents a decision support tool methodology to help virtual power players (VPPs) in the Smart Grid (SGs) context to solve the day-ahead energy resource scheduling considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G). The main focus is the application of a new hybrid method combing a particle swarm approach and a deterministic technique based on mixedinteger linear programming (MILP) to solve the day-ahead scheduling minimizing total operation costs from the aggregator point of view. A realistic mathematical formulation, considering the electric network constraints and V2G charging and discharging efficiencies is presented. Full AC power flow calculation is included in the hybrid method to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Reinforcement Learning Based on the Bayesian Theorem for Electricity Markets Decision Support

This paper presents the applicability of a reinforcement learning algorithm based on the application of the Bayesian theorem of probability. The proposed reinforcement learning algorithm is an advantageous and indispensable tool for ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to electricity market negotiating players. ALBidS uses a set of different strategies for providing decision support to market players. These strategies are used accordingly to their probability of success for each different context. The approach proposed in this paper uses a Bayesian network for deciding the most probably successful action at each time, depending on past events. The performance of the proposed methodology is tested using electricity market simulations in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). MASCEM provides the means for simulating a real electricity market environment, based on real data from real electricity market operators.

Strategic Bidding for Electricity Markets Negotiation Using Support Vector Machines

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which simulates the electricity markets environment. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated by being included in ALBidS and then compared with the application of an Artificial Neural Network, originating promising results. The proposed approach is tested and validated using real electricity markets data from MIBEL - Iberian market operator.

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.