Collective intelligence on dynamic manufacturing scheduling optimization

Swarm Intelligence (SI) is a growing research field of Artificial Intelligence (AI). SI is the general term for several computational techniques which use ideas and get inspiration from the social behaviours of insects and of other animals. This paper presents hybridization and combination of different AI approaches, like Bio-Inspired Techniques (BIT), Multi-Agent systems (MAS) and Machine Learning Techniques (ML T). The resulting system is applied to the problem of jobs scheduling to machines on dynamic manufacturing environments.

Programa de manutenção de aeronaves das companhias de aviação de baixo custo

Este trabalho aborda o Programa de Manutenção de Aeronaves das companhias de aviação de baixo custo, tendo como foco a análise e identificação dos requisitos legais e das metodologias de desenvolvimento de um Programa de Manutenção de uma aeronave e a comparação dos custos de manutenção de companhias de baixo custo com as companhias regulares. A aplicação eficaz de um programa de manutenção, para além de reduzir os seus custos, tem um impacto positivo na segurança, economia da manutenção e na fiabilidade de despacho. A metodologia utilizada foi a análise de informação de publicações e artigos. Com base na revisão de literaturas especializadas, fez-se uma selecção dos diversos aspectos necessários para se obter um Programa de Manutenção, o que permitiu construir o caso de estudo e efectuar a análise dos custos inerentes de manutenção de um operador aéreo de baixo custo e de um operador aéreo regular. Os resultados da análise permitiram chegar a veracidade da hipótese de que do ponto de vista de manutenção é igualmente seguro ou não seguro voar numa companhia de baixo custo e numa companhia regular, assim independentemente do tipo de companhia, ambas devem cumprir os requisitos para aprovação do PMA imposto pela autoridade aeronáutica, para garantir a aeronavegabilidade das aeronaves, ou seja, a sua segurança para a condição de voo, sem pôr em causa o carimbo baixo custo ou regular da companhia.

Cooperation mechanism for team-work based multi-agent system in dynamic scheduling through meta-heuristics

This paper describes a Multi-agent Scheduling System that assumes the existence of several Machines Agents (which are decision-making entities) distributed inside the Manufacturing System that interact and cooperate with other agents in order to obtain optimal or near-optimal global performances. Agents have to manage their internal behaviors and their relationships with other agents via cooperative negotiation in accordance with business policies defined by the user manager. Some Multi Agent Systems (MAS) organizational aspects are considered. An original Cooperation Mechanism for a Team-work based Architecture is proposed to address dynamic scheduling using Meta-Heuristics.

Mulheres em morte-cor: os objetos que fazem e desfazem corpos e cancros metastáticos

Este texto sintetiza o último capítulo da investigação de doutoramento – Objetos feitos de cancro: a cultura material como pedaço de doença em histórias de mulheres contadas pela arte. Através de uma reflexão em torno dos objetos e materialidades que ganham forma e relevo em projetos artísticos referentes à experiência feminina do cancro, esta tese propõe conceitos alternativos de cultura material e de doença oncológica. Rejeita-se uma separação ou diferenciação entre dimensões materiais e intangíveis na doença, entendendo-se os objetos de cultura material como pedaços de cancro, ou seja, enquanto partes constitutivas das ideias, sensações, emoções e gestos que fazem a experiência do corpo doente. Objetos hospitalares, domésticos e pessoais, de uso coletivo ou individual, onde se incluem materialidades descartáveis, vestuário, mobiliário, equipamento e máquinas, compõem uma lista de realidades que se encastram nas experiências do corpo em diagnóstico, internamento, tratamento, reconstrução, remissão, recorrência, metastização e morte. Dando nome a esta continuidade indivisa, propus os conceitos “objeto nosoencastrável” e “doença modular”, pretendendo, na forma como defino as coisas, os mesmos encaixes que existem na realidade vivida. Para compreender a ação, os usos e os sentidos dos objetos que fazem e são pedaços de cancro(s), o campo de trabalho desta investigação abrangeu as imagens e os textos explicativos de cento e cinquenta projetos artísticos produzidos por ou com mulheres que viveram a experiência desta doença. Expostos na Internet, os exercícios criativos, amadores ou profissionais, de fotografia comercial e artística, pintura, desenho, colagem, modelagem, escultura, costura e tricô serviram de terreno narrativo e visual, permitindo-me encontrar a versão émica dos encaixes entre cultura material e doença. Tocar a continuidade entre objetos e cancros, juntando os saberes do corpo, da arte e da antropologia, assentou numa abordagem teórica e metodológica onde ensaiei o potencial heurístico daquilo a que chamo a “terceira metade das coisas e do conhecimento”.

Dynamic power- and failure-aware cloud resources allocation for Sets of independent tasks

Cloud computing is increasingly being adopted in different scenarios, like social networking, business applications, scientific experiments, etc. Relying in virtualization technology, the construction of these computing environments targets improvements in the infrastructure, such as power-efficiency and fulfillment of users’ SLA specifications. The methodology usually applied is packing all the virtual machines on the proper physical servers. However, failure occurrences in these networked computing systems can induce substantial negative impact on system performance, deviating the system from ours initial objectives. In this work, we propose adapted algorithms to dynamically map virtual machines to physical hosts, in order to improve cloud infrastructure power-efficiency, with low impact on users’ required performance. Our decision making algorithms leverage proactive fault-tolerance techniques to deal with systems failures, allied with virtual machine technology to share nodes resources in an accurately and controlled manner. The results indicate that our algorithms perform better targeting power-efficiency and SLA fulfillment, in face of cloud infrastructure failures.

Modelling a Rotating Shaft as an Elastically Restrained Bernoulli-Euler Beam

Industrial rotating machines may be exposed to severe dynamic excitations due to resonant working regimes. Dealing with the bending vibration, problem of a machine rotor, the shaft - and attached discs - can be simply modelled using the Bernoulli-Euler beam theory, as a continuous beam subjected to a specific set of boundary conditions. In this study, the authors recall Rayleigh's method to propose an iterative strategy, which allows for the determination of natural frequencies and mode shapes of continuous beams taking into account the effect of attached concentrated masses and rotational inertias, including different stiffness coefficients at the right and the left end sides. The algorithm starts with the exact solutions from Bernoulli-Euler's beam theory, which are then updated through Rayleigh's quotient parameters. Several loading cases are examined in comparison with the experimental data and examples are presented to illustrate the validity of the model and the accuracy of the obtained values.

Construção de um robô trepador com locomoção através de rodas e adesão através de meios magnéticos

Mestrado em Engenharia Electrotécnica e de Computadores

Aplicação web para tratamento estatístico de dados de teste

Mestrado em Engenharia Electrotécnica e de Computadores

Sistema de detecção de intrusão em redes informáticas

Mestrado em Engenharia Electrotécnica e de Computadores

Android as a cloud ticket validator

Trabalho Final de Mestrado para a obtenção do grau de Mestre em Engenharia Informática e de Computadores

Melhoria do processo com recurso a metodologia Kaizen

Mestrado em Engenharia Electrotécnica e de Computadores

Espresso coffee residues: a valuable source of unextracted compounds

Espresso spent coffee grounds were chemically characterized to predict their potential, as a source of bioactive compounds, by comparison with the ones from the soluble coffee industry. Sampling included a total of 50 samples from 14 trademarks, collected in several coffee shops and prepared with distinct coffee machines. A high compositional variability was verified, particularly with regard to such water-soluble components as caffeine, total chlorogenic acids (CGA), and minerals, supported by strong positive correlations with total soluble solids retained. This is a direct consequence of the reduced extraction efficiency during espresso coffee preparation, leaving a significant pool of bioactivity retained in the extracted grounds. Besides the lipid (12.5%) and nitrogen (2.3%) contents, similar to those of industrial coffee residues, the CGA content (478.9 mg/100 g), for its antioxidant capacity, and its caffeine content (452.6 mg/100 g), due to its extensive use in the food and pharmaceutical industries, justify the selective assembly of this residue for subsequent use.

Classification and staging of chronic liver disease from multimodal data

Chronic liver disease (CLD) is most of the time an asymptomatic, progressive, and ultimately potentially fatal disease. In this study, an automatic hierarchical procedure to stage CLD using ultrasound images, laboratory tests, and clinical records are described. The first stage of the proposed method, called clinical based classifier (CBC), discriminates healthy from pathologic conditions. When nonhealthy conditions are detected, the method refines the results in three exclusive pathologies in a hierarchical basis: 1) chronic hepatitis; 2) compensated cirrhosis; and 3) decompensated cirrhosis. The features used as well as the classifiers (Bayes, Parzen, support vector machine, and k-nearest neighbor) are optimally selected for each stage. A large multimodal feature database was specifically built for this study containing 30 chronic hepatitis cases, 34 compensated cirrhosis cases, and 36 decompensated cirrhosis cases, all validated after histopathologic analysis by liver biopsy. The CBC classification scheme outperformed the nonhierachical one against all scheme, achieving an overall accuracy of 98.67% for the normal detector, 87.45% for the chronic hepatitis detector, and 95.71% for the cirrhosis detector.

The usefulness of ultrasound in the classification of chronic liver disease

Chronic Liver Disease is a progressive, most of the time asymptomatic, and potentially fatal disease. In this paper, a semi-automatic procedure to stage this disease is proposed based on ultrasound liver images, clinical and laboratorial data. In the core of the algorithm two classifiers are used: a k nearest neighbor and a Support Vector Machine, with different kernels. The classifiers were trained with the proposed multi-modal feature set and the results obtained were compared with the laboratorial and clinical feature set. The results showed that using ultrasound based features, in association with laboratorial and clinical features, improve the classification accuracy. The support vector machine, polynomial kernel, outperformed the others classifiers in every class studied. For the Normal class we achieved 100% accuracy, for the chronic hepatitis with cirrhosis 73.08%, for compensated cirrhosis 59.26% and for decompensated cirrhosis 91.67%.

Chronic liver disease staging classification based on ultrasound, clinical and laboratorial data

In this work the identification and diagnosis of various stages of chronic liver disease is addressed. The classification results of a support vector machine, a decision tree and a k-nearest neighbor classifier are compared. Ultrasound image intensity and textural features are jointly used with clinical and laboratorial data in the staging process. The classifiers training is performed by using a population of 97 patients at six different stages of chronic liver disease and a leave-one-out cross-validation strategy. The best results are obtained using the support vector machine with a radial-basis kernel, with 73.20% of overall accuracy. The good performance of the method is a promising indicator that it can be used, in a non invasive way, to provide reliable information about the chronic liver disease staging.