Low-cost vibration sensors: tendencies and applications in condition monitoring of machines and structures

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

From cheating to teaching: a path for conversion of illegal gambling machines

Video poker machines, a former symbol of fraud and gambling in Brazil, are now being converted into computer-based educational tools for Brazilian public primary schools and also for governmental and non-governmental institutions dealing with communities of poverty and social exclusion, in an attempt to reduce poverty risks (decrease money spent on gambling) and promote social inclusion (increase access and motivation to education). Thousands of illegal gambling machines are seized by federal authorities, in Brazil, every year, and usually destroyed at the end of the criminal apprehension process. This paper describes a project developed by the University of Southern Santa Catarina, Brazil, responsible for the conversion process of gambling machines, and the social inclusion opportunities derived from it. All project members worked on a volunteer basis, seeking to promote social inclusion of Brazilian young boys and girls, namely through digital inclusion. So far, the project has been able to convert over 200 gambling machines and install them in over 40 public primary schools, thus directly benefiting more than 12,000 schoolchildren. The initial motivation behind this project was technology based, however the different options arising from the conversion process of the gambling machines have also motivated a rather innovative and unique experience in allowing schoolchildren and young people with special (educational) needs to access to computer-based pedagogical applications. The availability of these converted machines also helps to place Information and Communication Technologies (ICT) in the very daily educational environment of these children and youngsters, thus serving social and cultural inclusion aspects, by establishing a dialogue with the community and their technological expectations, and also directly contributing to their digital literacy.

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.

Solar Intensity Forecasting using Artificial Neural Networks and Support Vector Machines

This paper presents several forecasting methodologies based on the application of Artificial Neural Networks (ANN) and Support Vector Machines (SVM), directed to the prediction of the solar radiance intensity. The methodologies differ from each other by using different information in the training of the methods, i.e, different environmental complementary fields such as the wind speed, temperature, and humidity. Additionally, different ways of considering the data series information have been considered. Sensitivity testing has been performed on all methodologies in order to achieve the best parameterizations for the proposed approaches. Results show that the SVM approach using the exponential Radial Basis Function (eRBF) is capable of achieving the best forecasting results, and in half execution time of the ANN based approaches.

Short-term Wind Speed Forecasting using Support Vector Machines

Wind speed forecasting has been becoming an important field of research to support the electricity industry mainly due to the increasing use of distributed energy sources, largely based on renewable sources. This type of electricity generation is highly dependent on the weather conditions variability, particularly the variability of the wind speed. Therefore, accurate wind power forecasting models are required to the operation and planning of wind plants and power systems. A Support Vector Machines (SVM) model for short-term wind speed is proposed and its performance is evaluated and compared with several artificial neural network (ANN) based approaches. A case study based on a real database regarding 3 years for predicting wind speed at 5 minutes intervals is presented.

Optimização da aprovação de produtos para afinação nas tinting machines

Dissertação para obtenção do Grau de Mestre em Engenharia Química e Bioquímica

Peptidoglycan assembly machines: The Staphylococcus aureus Penicillin-Binding Proteins

Dissertation presented to obtain the Ph.D degree in Biology

Avatars and the «Imitation Game» – can machines smile?

Considering Alan Turing’s challenge in «Computing Machinery and Intelligence» (1950) – can machines play the «imitation game»? – it is proposed that the requirements of the Turing test are already implicitly being used for checking the credibility of virtual characters and avatars. Like characters, Avatars aim to visually express emotions (the exterior signs of the existence of feeling) and its creators have to resort to emotion codes. Traditional arts have profusely contributed for this field and, together with the science of anatomy, shaped the grounds for current Facial Action Coding System (FACS) and their databases. However, FACS researchers have to improve their «instruction tables» so that the machines will be able, in a near future, to be programmed to carry out the operation of recognizing human expressions (face and body) and classify them adequately. For the moment, the reproductions have to resort to the copy of real life expressions, and the presente smile of avatars comes from mirroring their human users.

Application of superconducting bulks and stacks of tapes in electrical machines

The present dissertation focuses on the research of the recent approach of innovative high-temperature superconducting stacked tapes in electrical ma-chines applications, taking into account their potential benefits as an alternative for the massive superconducting bulks, mainly related with geometric and me-chanical flexibility. This work was developed in collaboration with Institut de Ciència de Ma-terials de Barcelona (ICMAB), and is related with evaluation of electrical and magnetic properties of the mentioned superconducting materials, namely: analysis of magnetization of a bulk sample through simulations carried out in the finite elements COMSOL software; measurement of superconducting tape resistivity at liquid nitrogen and room temperatures; and, finally, development and testing of a frequency controlled superconducting motor with rotor built by superconducting tapes. In the superconducting state, results showed a critical current density of 140.3 MA/m2 (or current of 51.15 A) on the tape and a 1 N∙m developed motor torque, independent from the rotor position angle, typical in hysteresis motors.

Smart machines in day-to-day financial management:

Starting from Novabase’s challenge to launch in the UK Millennials a personal financial advisor mobile application, this work project aims to build a planning model to frame a business side of a launch strategy for mobile application in similar market and category. This study culminates on the design of SPOSTAC planning model. The created framework is intended to effectively and efficiently plan a launch strategy, being structured based on seven sequential elements: Situation, Product, Objectives, Strategy, Tactics, Action, and Control.

An investigation of safety design practices of metal machines

BACKGROUND: Machinery safety issues are a challenge facing manufacturers who are supposed to create and provide products in a better and faster way. In spite of their construction and technological advance, they still contribute to many potential hazards for operators and those nearby. OBJECTIVE: The aim of this study is to investigate safety aspects of metal machinery offered for sale on Internet market according to compliance with minimum and fundamental requirements. METHODS: The study was carried out with the application of a checklist prepared on the basis of Directive 2006/42/EC and Directive 2009/104/EC and regulations enforcing them into Polish law. RESULTS: On the basis of the study it was possible to reveal the safety aspects that were not met in practice. It appeared that in the case of minimum requirements the most relevant problems concerned information, signal and control elements, technology and machinery operations, whereas as far as fundamental aspects are concerned it was hard to assure safe work process. CONCLUSIONS: In spite of the fact that more and more legal acts binding in the Member Countries of the European Union are being introduced to alleviate the phenomenon, these regulations are often not fulfilled.

Designing next generation car washing machines with rotary brushes

Tese de Doutoramento - Leaders for Technical Industries (LTI) - MIT Portugal

Modelado multi-dominio de máquinas eléctricas. Multi-domain modeling of electric machines

El objetivo general de este proyecto es desarrollar nuevos modelos multi-dominio de máquinas eléctricas para aplicaciones al control y al diagnóstico de fallas. Se propone comenzar con el modelo electromagnético del motor de inducción en base a circuitos magnéticos equivalentes (MEC) validándolo por medio de simulación y de resultados experimentales. Como segundo paso se pretende desarrollas modelos térmicos y mecánicos con el objetivo que puedan ser acoplados al modelo electromagnético y de esta estudiar la interacción de los dominios y se validará mediante resultados de simulación y experimentales el modelo completo. Finalmente se pretende utilizar el modelo multi-dominio como una herramienta para la prueba de nuevas estrategias de control y diagnóstico de fallas. The main objective of this project is the development of new multi-domain models of electric machines for control and fault diagnosis applications. The electromagnetic modeling of the induction motor (IM) will be done using the magnetic equivalent circuits approach. This model will be validated by simulation and by experimental results. As a second step of this project, new mechanical and thermal models for the IM will be developed, with the objective of coupling these models with the electromagnetic one. With this multi-domain model it will be possible to study the interaction between each others. After that, the complete model will be validated by simulation and experimental results. Finally, the model will be used as a tool for testing new control and fault diagnosis strategies.

Textural classification of land cover using support vector machines: an empirical comparison with parametric, non parametric and hybrid classifiers in the Bolivian Amazon

Land cover classification is a key research field in remote sensing and land change science as thematic maps derived from remotely sensed data have become the basis for analyzing many socio-ecological issues. However, land cover classification remains a difficult task and it is especially challenging in heterogeneous tropical landscapes where nonetheless such maps are of great importance. The present study aims to establish an efficient classification approach to accurately map all broad land cover classes in a large, heterogeneous tropical area of Bolivia, as a basis for further studies (e.g., land cover-land use change). Specifically, we compare the performance of parametric (maximum likelihood), non-parametric (k-nearest neighbour and four different support vector machines - SVM), and hybrid classifiers, using both hard and soft (fuzzy) accuracy assessments. In addition, we test whether the inclusion of a textural index (homogeneity) in the classifications improves their performance. We classified Landsat imagery for two dates corresponding to dry and wet seasons and found that non-parametric, and particularly SVM classifiers, outperformed both parametric and hybrid classifiers. We also found that the use of the homogeneity index along with reflectance bands significantly increased the overall accuracy of all the classifications, but particularly of SVM algorithms. We observed that improvements in producer’s and user’s accuracies through the inclusion of the homogeneity index were different depending on land cover classes. Earlygrowth/degraded forests, pastures, grasslands and savanna were the classes most improved, especially with the SVM radial basis function and SVM sigmoid classifiers, though with both classifiers all land cover classes were mapped with producer’s and user’s accuracies of around 90%. Our approach seems very well suited to accurately map land cover in tropical regions, thus having the potential to contribute to conservation initiatives, climate change mitigation schemes such as REDD+, and rural development policies.