Controlo e optimização de sistemas AVAC recorrendo a técnicas de inteligência artificial

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

Multiple Linear Regression and Artificial Neural Networks to Predict Time and Efficiency of Soil Vapor Extraction

The prediction of the time and the efficiency of the remediation of contaminated soils using soil vapor extraction remain a difficult challenge to the scientific community and consultants. This work reports the development of multiple linear regression and artificial neural network models to predict the remediation time and efficiency of soil vapor extractions performed in soils contaminated separately with benzene, toluene, ethylbenzene, xylene, trichloroethylene, and perchloroethylene. The results demonstrated that the artificial neural network approach presents better performances when compared with multiple linear regression models. The artificial neural network model allowed an accurate prediction of remediation time and efficiency based on only soil and pollutants characteristics, and consequently allowing a simple and quick previous evaluation of the process viability.

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.

Aquisição por Visão Artificial e Medição Automática de Micro Dureza Vickers

O ensaio de dureza, e mais concretamente o ensaio de micro dureza Vickers, é no universo dos ensaios mecânicos um dos mais utilizados quer seja na indústria, no ensino ou na investigação e desenvolvimento de produto no âmbito das ciências dos materiais. Na grande maioria dos casos, a utilização deste ensaio tem como principal aplicação a caracterização ou controlo da qualidade de fabrico de materiais metálicos. Sendo um ensaio de relativa simplicidade de execução, rapidez e com resultados comparáveis e relacionáveis a outras grandezas físicas das propriedades dos materiais. Contudo, e tratando-se de um método de ensaio cuja intervenção humana é importante, na medição da indentação gerada por penetração mecânica através de um sistema ótico, não deixa de exibir algumas debilidades que daí advêm, como sendo o treino dos técnicos e respetivas acuidades visuais, fenómenos de fadiga visual que afetam os resultados ao longo de um turno de trabalho; ora estes fenómenos afetam a repetibilidade e reprodutibilidade dos resultados obtidos no ensaio. O CINFU possui um micro durómetro Vickers, cuja realização dos ensaios depende de um técnico treinado para a execução do mesmo, apresentando todas as debilidades já mencionadas e que o tornou elegível para o estudo e aplicação de uma solução alternativa. Assim, esta dissertação apresenta o desenvolvimento de uma solução alternativa ao método ótico convencional na medição de micro dureza Vickers. Utilizando programação em LabVIEW da National Instruments, juntamente com as ferramentas de visão computacional (NI Vision), o programa começa por solicitar ao técnico a seleção da câmara para aquisição da imagem digital acoplada ao micro durómetro, seleção do método de ensaio (Força de ensaio); posteriormente o programa efetua o tratamento da imagem (aplicação de filtros para eliminação do ruído de fundo da imagem original), segue-se, por indicação do operador, a zona de interesse (ROI) e por sua vez são identificadas automaticamente os vértices da calote e respetivas distâncias das diagonais geradas concluindo, após aceitação das mesmas, com o respetivo cálculo de micro dureza resultante. Para validação dos resultados foram utilizados blocos-padrão de dureza certificada (CRM), cujos resultados foram satisfatórios, tendo-se obtido um elevado nível de exatidão nas medições efetuadas. Por fim, desenvolveu-se uma folha de cálculo em Excel com a determinação da incerteza associada às medições de micro dureza Vickers. Foram então comparados os resultados nas duas metodologias possíveis, pelo método ótico convencional e pela utilização das ferramentas de visão computacional, tendo-se obtido bons resultados com a solução proposta.

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.

An integrated approach to Physcomitrella patens transcriptomics reveals importantclues of the evolutionary development of plant organs and sexual reproduction

Land plant evolution required the generation of a new body plan that could resist the harsher and fluctuating environmental conditions found outside of aquatic environments. Unraveling the genetic basis of plant developmental innovations is not only revealing in terms of an evolutionary point of view, but it is also important for understanding the emergence of agronomically important traits. Comparative genetic studies between basal and modern land plants, both at the genome and trancriptome levels, can help in the generation of hypotheses related to the genetic basis of plant evolutionary development.(...)

Predicting market movement with Artificial Neural Networks

This paper presents an application of an Artificial Neural Network (ANN) to the prediction of stock market direction in the US. Using a multilayer perceptron neural network and a backpropagation algorithm for the training process, the model aims at learning the hidden patterns in the daily movement of the S&P500 to correctly identify if the market will be in a Trend Following or Mean Reversion behavior. The ANN is able to produce a successful investment strategy which outperforms the buy and hold strategy, but presents instability in its overall results which compromises its practical application in real life investment decisions.

Using artificial neural networks to generate trading signals for crude oil, copper and gold futures

In this thesis, a feed-forward, back-propagating Artificial Neural Network using the gradient descent algorithm is developed to forecast the directional movement of daily returns for WTI, gold and copper futures. Out-of-sample back-test results vary, with some predictive abilities for copper futures but none for either WTI or gold. The best statistically significant hit rate achieved was 57% for copper with an absolute return Sharpe Ratio of 1.25 and a benchmarked Information Ratio of 2.11.

Development and preclinical assessment of a bioartificial pancreas.

Transplantation of insulin secreting cells is regarded as a possible treatment for type 1 diabetes. One major difficulty in this approach is, however, that the transplanted cells are exposed to the patient's inflammatory and autoimmune environment, which originally destroyed their own beta-cells. Therefore, even if a good source of insulin-secreting cells can be identified for transplantation therapy, these cells need to be protected against these destructive influences. The aim of this project was to evaluate, using a clonal mouse beta-cell line, whether genetic engineering of protective genes could be a viable option to allow these cells to survive when transplanted into autoimmune diabetic mice. We demonstrated that transfer of the Bcl-2 anti-apoptotic gene and of several genes specifically interfering with cytokines intracellular signalling pathways, greatly improved resistance of the cells to inflammatory stresses in vitro. We further showed that these modifications did not interfere with the capacity of these cells to correct hyperglycaemia for several months in syngeneic or allogeneic streptozocin-diabetic mice. However, these cells were not protected against autoimmune destruction when transplanted into type 1 diabetic NOD mice. This suggests that in addition to inflammatory attacks by cytokines, autoimmunity very efficiently kills the transplanted cells, indicating that multiple protective mechanisms are required for efficient transplantation of insulin-secreting cells to treat type 1 diabetes.

Caspase-3 Protects Stressed Organs against Cell Death.

The ability to generate appropriate defense responses is crucial for the survival of an organism exposed to pathogenesis-inducing insults. However, the mechanisms that allow tissues and organs to cope with such stresses are poorly understood. Here we show that caspase-3-knockout mice or caspase inhibitor-treated mice were defective in activating the antiapoptotic Akt kinase in response to various chemical and environmental stresses causing sunburns, cardiomyopathy, or colitis. Defective Akt activation in caspase-3-knockout mice was accompanied by increased cell death and impaired survival in some cases. Mice homozygous for a mutation in RasGAP that prevents its cleavage by caspase-3 exhibited a similar defect in Akt activation, leading to increased apoptosis in stressed organs, marked deterioration of their physiological functions, and stronger disease development. Our results provide evidence for the relevance of caspase-3 as a stress intensity sensor that controls cell fate by either initiating a RasGAP cleavage-dependent cell resistance program or a cell suicide response.

Tecnología del salado y secado artificial de la merluza (merluccius gayi peruanus)

Describe el desarrollo del proceso de salado y secado artificial de la merluza, como un método alternativo a la producción de filetes y bloques congelados, comprendiendo las experiencias comparativas de métodos de salado e igualmente las condiciones termodinámicas del aíre para el secado artificial, estudios de estabilidad de almacenamiento del producto experimental, calidad, rendimiento y aceptación por el consumidor. Además, se presenta sugerencias para las normas de procesamiento y calidad del producto.

The performance and strategies of adolescents in learning to read artificial words /

The present study explored processing strategies used by individuals when they begin to read c;l script. Stimuli were artificial words created from symbols and based on an alphabetic system. The words were.presented to Grade Nine and Ten students, with variations included in the difficulty of orthography and word familiarity, and then scores were recorded on the mean number of trials for defined learning variables. Qualitative findings revealed that subjects 1 earned parts of the visual a'nd auditory features of words prior to hooking up the visual stimulus to the word's name. Performance measures-which appear to affect the rate of learning were as follows: auditory short-term memory, auditory delayed short-term memory, visual delayed short- term memory, and word attack or decod~ng skills. Qualitative data emerging in verbal reports by the subjects revealed that strategies they pefceived to use were, graphic, phonetic decoding and word .reading.

Enabling and Measuring Complexity in Evolving Designs using Generative Representations for Artificial Architecture

As the complexity of evolutionary design problems grow, so too must the quality of solutions scale to that complexity. In this research, we develop a genetic programming system with individuals encoded as tree-based generative representations to address scalability. This system is capable of multi-objective evaluation using a ranked sum scoring strategy. We examine Hornby's features and measures of modularity, reuse and hierarchy in evolutionary design problems. Experiments are carried out, using the system to generate three-dimensional forms, and analyses of feature characteristics such as modularity, reuse and hierarchy were performed. This work expands on that of Hornby's, by examining a new and more difficult problem domain. The results from these experiments show that individuals encoded with those three features performed best overall. It is also seen, that the measures of complexity conform to the results of Hornby. Moving forward with only this best performing encoding, the system was applied to the generation of three-dimensional external building architecture. One objective considered was passive solar performance, in which the system was challenged with generating forms that optimize exposure to the Sun. The results from these and other experiments satisfied the requirements. The system was shown to scale well to the architectural problems studied.