Selection Constructive based Hyper-heuristic for Dynamic Scheduling

A função de escalonamento desempenha um papel importante nos sistemas de produção. Os sistemas de escalonamento têm como objetivo gerar um plano de escalonamento que permite gerir de uma forma eficiente um conjunto de tarefas que necessitam de ser executadas no mesmo período de tempo pelos mesmos recursos. Contudo, adaptação dinâmica e otimização é uma necessidade crítica em sistemas de escalonamento, uma vez que as organizações de produção têm uma natureza dinâmica. Nestas organizações ocorrem distúrbios nas condições requisitos de trabalho regularmente e de forma inesperada. Alguns exemplos destes distúrbios são: surgimento de uma nova tarefa, cancelamento de uma tarefa, alteração na data de entrega, entre outros. Estes eventos dinâmicos devem ser tidos em conta, uma vez que podem influenciar o plano criado, tornando-o ineficiente. Portanto, ambientes de produção necessitam de resposta imediata para estes eventos, usando um método de reescalonamento em tempo real, para minimizar o efeito destes eventos dinâmicos no sistema de produção. Deste modo, os sistemas de escalonamento devem de uma forma automática e inteligente, ser capazes de adaptar o plano de escalonamento que a organização está a seguir aos eventos inesperados em tempo real. Esta dissertação aborda o problema de incorporar novas tarefas num plano de escalonamento já existente. Deste modo, é proposta uma abordagem de otimização – Hiper-heurística baseada em Seleção Construtiva para Escalonamento Dinâmico- para lidar com eventos dinâmicos que podem ocorrer num ambiente de produção, a fim de manter o plano de escalonamento, o mais robusto possível. Esta abordagem é inspirada em computação evolutiva e hiper-heurísticas. Do estudo computacional realizado foi possível concluir que o uso da hiper-heurística de seleção construtiva pode ser vantajoso na resolução de problemas de otimização de adaptação dinâmica.

Distributed Linear Precoding and User Selection in Coordinated Multicell Systems

In this manuscript we tackle the problem of semidistributed user selection with distributed linear precoding for sum rate maximization in multiuser multicell systems. A set of adjacent base stations (BS) form a cluster in order to perform coordinated transmission to cell-edge users, and coordination is carried out through a central processing unit (CU). However, the message exchange between BSs and the CU is limited to scheduling control signaling and no user data or channel state information (CSI) exchange is allowed. In the considered multicell coordinated approach, each BS has its own set of cell-edge users and transmits only to one intended user while interference to non-intended users at other BSs is suppressed by signal steering (precoding). We use two distributed linear precoding schemes, Distributed Zero Forcing (DZF) and Distributed Virtual Signalto-Interference-plus-Noise Ratio (DVSINR). Considering multiple users per cell and the backhaul limitations, the BSs rely on local CSI to solve the user selection problem. First we investigate how the signal-to-noise-ratio (SNR) regime and the number of antennas at the BSs impact the effective channel gain (the magnitude of the channels after precoding) and its relationship with multiuser diversity. Considering that user selection must be based on the type of implemented precoding, we develop metrics of compatibility (estimations of the effective channel gains) that can be computed from local CSI at each BS and reported to the CU for scheduling decisions. Based on such metrics, we design user selection algorithms that can find a set of users that potentially maximizes the sum rate. Numerical results show the effectiveness of the proposed metrics and algorithms for different configurations of users and antennas at the base stations.

Artificial antibodies for troponin T by its imprinting on the surface of multiwalled carbon nanotubes: Its use as sensory surfaces

A novel artificial antibody for troponin T (TnT) was synthesized by molecular imprint (MI) on the surface of multiwalled carbon nanotubes (MWCNT). This was done by attaching TnT to the MWCNT surface, and filling the vacant spaces by polymerizing under mild conditions acrylamide (monomer) in N,N′-methylenebisacrylamide (cross-linker) and ammonium persulphate (initiator). After removing the template, the obtained biomaterial was able to rebind TnT and discriminate it among other interfering species. Stereochemical recognition of TnT was confirmed by the non-rebinding ability displayed by non-imprinted (NI) materials, obtained by imprinting without a template. SEM and FTIR analysis confirmed the surface modification of the MWCNT. The ability of this biomaterial to rebind TnT was confirmed by including it as electroactive compound in a PVC/plasticizer mixture coating a wire of silver, gold or titanium. Anionic slopes of 50 mV decade−1 were obtained for the gold wire coated with MI-based membranes dipped in HEPES buffer of pH 7. The limit of detection was 0.16 μg mL−1. Neither the NI-MWCNT nor the MWCNT showed the ability to recognize the template. Good selectivity was observed against creatinine, sucrose, fructose, myoglobin, sodium glutamate, thiamine and urea. The sensor was tested successfully on serum samples. It is expected that this work opens new horizons on the design of new artificial antibodies for complex protein structures.

Biomimetic Sensor Potentiometric System for Doxycycline Antibiotic Using a Molecularly Imprinted Polymer as an Artificial Recognition Element

Molecular imprinting is a useful technique for the preparation of functional materials with molecular recognition properties. A Biomimetic Sensor Potentiometric System was developed for assessment of doxycycline (DOX) antibiotic. The molecularly imprinted polymer (MIP) was synthesized by using doxycycline as a template molecule, methacrylic acid (MAA) and/or acrylamide (AA) as a functional monomer and ethylene glycol dimethacrylat (EGDMA) as a cross-linking agent. The sensing elements were fabricated by the inclusion of DOX imprinted polymers in polyvinyl chloride (PVC) matrix. The sensors showed a high selectivity and a sensitive response to the template in aqueous system. Electrochemical evaluation of these sensors under static (batch) mode of operation reveals near-Nernstian response. MIP/MAA membrane sensor was incorporated in flow-through cells and used as detectors for flow injection analysis (FIA) of DOX. The method has the requisite accuracy, sensitivity and precision to assay DOX in tablets and biological fluids.

BoaVista – Sensor Dedicado de Visão Artificial Baseado em Hardware (Re)configurável

4º Festival Nacional de Robótica - Actas do Encontro Científico - Proceedings of the Scientific Meeting, Biblioteca Almeida Garrett, Palácio de Cristal – Porto, 23-24 Abril 2004

Gait Selection for Quadruped Legged Robots

This paper studies periodic gaits of quadruped locomotion systems. The purpose is to determine the best set of gait and locomotion variables for different robot velocities based on the system dynamics during walking. In this perspective, several performance measures are formulated and a set of experiments reveals the influence of the gait and locomotion variables upon those proposed indices. The results show that the locomotion parameters (β, LS and HB) should be adapted to the walking velocity in order to optimize the robot performance. Furthermore, for the case of a quadruped robot, we concluded that the gait should be adapted to VF .

Gait Analysis of Natural and Artificial Walking Systems

This paper studies periodic gaits of multi-legged locomotion systems based on dynamic models. The purpose is to determine the system performance during walking and the best set of locomotion variables. For that objective the prescribed motion of the robot is completely characterized in terms of several locomotion variables such as gait, duty factor, body height, step length, stroke pitch, foot clearance, legs link lengths, foot-hip offset, body and legs mass and cycle time. In this perspective, we formulate three performance measures of the walking robot namely, the mean absolute energy, the mean power dispersion and the mean power lost in the joint actuators per walking distance. A set of model-based experiments reveals the influence of the locomotion variables in the proposed indices.

Importância da Inseminação Artificial na Terapêutica da Infertilidade

Introdução: Desde há vários anos que a inseminação intra-uterina intra-conjugal (IAC), com ou sem estimulação ovárica, vem sido usada no tratamento da infertilidade. No entanto, o seu uso permanece controverso. Material e métodos: Efectuou-se uma análise retrospectiva (1997-1999) de 114 ciclos de IAC com estimulação ovárica controlada em 66 casais, com o objectivo de determinar a eficácia da IAC e identificar variáveis significativas predictíveis do seu sucesso. Analisou-se o protocolo de estimulação, taxa de gravidez, resultado da gravidez e complicações da terapêutica. Resultados: A taxa de gravidez foi de 10,5% por ciclo e de 18% por casal, sendo a taxa de gravidez múltipla de 25% e a de aborto 0%. Metade de todas as gravidezes resultantes ocorreram no primeiro ciclo de IAC. A análise estatística identificou duas variáveis significativas: número de folículos e duração da infertilidade. Baixas doses de FSH parecem prevenir a gravidez múltipla e o síndrome de hiperestimulação ovárica. Conclusão: Concluímos que uma selecção criteriosa das pacientes associada a estimulação ovárica adequada é fundamental para o sucesso da IAC e que esta técnica constitui um tratamento eficaz para algumas formas de infertilidade.

Adhesive selection for single lap bonded joints: Experimentation and advanced techniques for strength prediction

The integrity of multi-component structures is usually determined by their unions. Adhesive-bonding is often used over traditional methods because of the reduction of stress concentrations, reduced weight penalty, and easy manufacturing. Commercial adhesives range from strong and brittle (e.g., Araldite® AV138) to less strong and ductile (e.g., Araldite® 2015). A new family of polyurethane adhesives combines high strength and ductility (e.g., Sikaforce® 7888). In this work, the performance of the three above-mentioned adhesives was tested in single lap joints with varying values of overlap length (LO). The experimental work carried out is accompanied by a detailed numerical analysis by finite elements, either based on cohesive zone models (CZM) or the extended finite element method (XFEM). This procedure enabled detailing the performance of these predictive techniques applied to bonded joints. Moreover, it was possible to evaluate which family of adhesives is more suited for each joint geometry. CZM revealed to be highly accurate, except for largely ductile adhesives, although this could be circumvented with a different cohesive law. XFEM is not the most suited technique for mixed-mode damage growth, but a rough prediction was achieved.

Analysis of Natural and Artificial Phenomena Using Signal Processing and Fractional Calculus

In this paper we study several natural and man-made complex phenomena in the perspective of dynamical systems. For each class of phenomena, the system outputs are time-series records obtained in identical conditions. The time-series are viewed as manifestations of the system behavior and are processed for analyzing the system dynamics. First, we use the Fourier transform to process the data and we approximate the amplitude spectra by means of power law functions. We interpret the power law parameters as a phenomenological signature of the system dynamics. Second, we adopt the techniques of non-hierarchical clustering and multidimensional scaling to visualize hidden relationships between the complex phenomena. Third, we propose a vector field based analogy to interpret the patterns unveiled by the PL parameters.

Gestão de Informação: Inteligência Artificial

A informação e a sua gestão é considerada nos nossos dias como o principal factor de sucesso ou insucesso para qualquer actividade económica ou social. O desenvolvimento de novas tecnologias força todos os agentes econcómicos a desenvolverem-se nestas áreas para conseguirem vantagens concorrenciais. Este trabalho visa fazer uma apresentação de uma “nova” área da ciência da computação a que se chamou Inteligência Artificial.

Stratigraphic interpretation of Well-Log data of the Athabasca Oil Sands of Alberta Canada through Pattern recognition and Artificial Intelligence

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Inteligência artificial aplicada na previsão de mercados financeiros

Neste documento, são investigados vários métodos usados na inteligência artificial, com o objetivo de obter previsões precisas da evolução dos mercados financeiros. O uso de ferramentas lineares como os modelos AR, MA, ARMA e GARCH têm muitas limitações, pois torna-se muito difícil adaptá-los às não linearidades dos fenómenos que ocorrem nos mercados. Pelas razões anteriormente referidas, os algoritmos como as redes neuronais dinâmicas (TDNN, NARX e ESN), mostram uma maior capacidade de adaptação a estas não linearidades, pois não fazem qualquer pressuposto sobre as distribuições de probabilidade que caracterizam estes mercados. O facto destas redes neuronais serem dinâmicas, faz com que estas exibam um desempenho superior em relação às redes neuronais estáticas, ou outros algoritmos que não possuem qualquer tipo de memória. Apesar das vantagens reveladas pelas redes neuronais, estas são um sistema do tipo black box, o que torna muito difícil extrair informação dos pesos da rede. Isto significa que estes algoritmos devem ser usados com precaução, pois podem tornar-se instáveis.

Biogenic amines in wine: gene transcription as a tool for selection of Oenococcus oeni starter strains

Dissertation presented to obtain the Ph.D degree in Biology