HTM approach to image classification, sound recognition and time series forecasting

Dissertação de mestrado em Biomedical EngineeringThe introduction of Machine Learning (ML) on the orbit of the resolution of problems typically associated within the human behaviour has brought great expectations to the future. In fact, the possible development of machines capable of learning, in a similar way as of the humans, could bring grand perspectives to diverse areas like healthcare, the banking sector, retail, and any other area in which we could avoid the constant attention of a person dedicated to the solving of a problem; furthermore, there are those problems that are still not at the hands of humans to solve - these are now at the disposal of intelligent machines, bringing new possibilities to the humankind development. ML algorithms, specifically Deep Learning (DL) methods, lack a bigger acceptance by part of the community, even though they are present in various systems in our daily basis. This lack of confidence, mandatory to let systems make big, important decisions with great impact in the everyday life is due to the difficulty on understanding the learning mechanisms and previsions that result by the same - some algorithms represent themselves as ”black boxes”, translating an input into an output, while not being totally transparent to the outside. Another complication rises, when it is taken into account that the same algorithms are trained to a specific task and in accordance to the training cases found on their development, being more susceptible to error in a real environment - one can argue that they do not constitute a true Artificial Intelligence (AI). Following this line of thought, this dissertation aims at studying a new theory, Hierarchical Temporal Memory (HTM), that can be placed in the area of Machine Intelligence (MI), an area that studies the capacity of how the software systems can learn, in an identical way to the learning of a human being. The HTM is still a fresh theory, that lays on the present perception of the functioning of the human neocortex and assumes itself as under constant development; at the moment, the theory dictates that the neocortex zones are organized in an hierarchical structure, being a memory system, capable of recognizing spatial and temporal patterns. In the course of this project, an analysis was made to the functioning of the theory and its applicability to the various tasks typically solved with ML algorithms, like image classification, sound recognition and time series forecasting. At the end of this dissertation, after the evaluation of the different results obtained in various approaches, it was possible to conclude that even though these results were positive, the theory still needs to mature, not only in its theoretical basis but also in the development of libraries and frameworks of software, to capture the attention of the AI community.A introdução de ML na órbita da resolução de problemas tipicamente dedicados ao foro humano trouxe grandes expectativas para o futuro. De facto, o possível desenvolvimento de máquinas capazes de aprender, de forma semelhante aos humanos, poderia trazer grandes perspetivas para diversas áreas como a saúde, o setor bancário, retalho, e qualquer outra área em que se poderia evitar o constante alerta de uma pessoa dedicada a um problema; para além disso, problemas sem resolução humana passavam a estar a mercê destas máquinas, levando a novas possibilidades no desenvolvimento da humanidade. Apesar de se encontrar em vários sistemas no nosso dia-a-dia, estes algoritmos de ML, especificamente de DL, carecem ainda de maior aceitação por parte da comunidade, devido a dificuldade de perceber as aprendizagens e previsões resultantes, feitas pelos mesmos - alguns algoritmos apresentam-se como ”caixas negras”, traduzindo um input num output, não sendo totalmente transparente para o exterior - é necessária confiança nos sistemas que possam tomar decisões importantes e com grandes impactos no quotidiano; por outro lado, os mesmos algoritmos encontram-se treinados para uma tarefa específica e de acordo com os casos encontrados no desenvolvimento do seu treino, sendo mais suscetíveis a erros em ambientes reais, podendo se discutir que não constituem, por isso, uma verdadeira Inteligência Artificial. Seguindo este segmento, a presente dissertação procura estudar uma nova teoria, HTM, inserida na área de MI, que pretende dar a capacidade aos sistemas de software de aprenderem de uma forma idêntica a do ser humano. Esta recente teoria, assenta na atual perceção do funcionamento do neocórtex, estando por isso em constante desenvolvimento; no momento, e assumida como uma teoria que dita a hierarquização estrutural das zonas do neocórtex, sendo um sistema de memória, reconhecedor de padrões espaciais e temporais. Ao longo deste projeto, foi feita uma análise ao funcionamento da teoria, e a sua aplicabilidade a várias tarefas tipicamente resolvidas com algoritmos de ML, como classificação de imagem, reconhecimento de som e previsão de series temporais. No final desta dissertação, após uma avaliação dos diferentes resultados obtidos em várias abordagens, foi possível concluir que apesar dos resultadospositivos, a teoria precisa ainda de maturar, não só a nível teórico como a nível prático, no desenvolvimento de bibliotecas e frameworks de software, de forma a capturar a atenção da comunidade de Inteligência Artificial

A State-of-the-art Integrated Transportation Simulation Platform

Nowadays, universities and companies have a huge need for simulation and modelling methodologies. In the particular case of traffic and transportation, making physical modifications to the real traffic networks could be highly expensive, dependent on political decisions and could be highly disruptive to the environment. However, while studying a specific domain or problem, analysing a problem through simulation may not be trivial and may need several simulation tools, hence raising interoperability issues. To overcome these problems, we propose an agent-directed transportation simulation platform, through the cloud, by means of services. We intend to use the IEEE standard HLA (High Level Architecture) for simulators interoperability and agents for controlling and coordination. Our motivations are to allow multiresolution analysis of complex domains, to allow experts to collaborate on the analysis of a common problem and to allow co-simulation and synergy of different application domains. This paper will start by presenting some preliminary background concepts to help better understand the scope of this work. After that, the results of a literature review is shown. Finally, the general architecture of a transportation simulation platform is proposed

Densifying the sparse cloud SimSaaS: The need of a synergy among agent-directed simulation, SimSaaS and HLA

Modelling & Simulation (M&S) is broadly used in real scenarios where making physical modifications could be highly expensive. With the so-called Simulation Software-as-a-Service (SimSaaS), researchers could take advantage of the huge amount of resource that cloud computing provides. Even so, studying and analysing a problem through simulation may need several simulation tools, hence raising interoperability issues. Having this in mind, IEEE developed a standard for interoperability among simulators named High Level Architecture (HLA). Moreover, the multi-agent system approach has become recognised as a convenient approach for modelling and simulating complex systems. Despite all the recent works and acceptance of these technologies, there is still a great lack of work regarding synergies among them. This paper shows by means of a literature review this lack of work or, in other words, the sparse Cloud SimSaaS. The literature review and the resulting taxonomy are the main contributions of this paper, as they provide a research agenda illustrating future research opportunities and trends

Liquid effluent treatment resorting to microalgae cultivation

Dissertação de mestrado em Biologia Molucular, Biotecnologia e Bioempreendedorismo em PlantasBioreactors from raceway category for microalgae production installed in domestic wastewater treatment facility and in fertilizers industrial plant were followed for approximately 11 months. Microalgae natural growth, mainly Scenedesmus obliquus and Scenedesmus quadricauda, was determined to evaluate their participation in domestic wastewater treatment and effluent from fertilizer plant. At WWTF, two raceways were installed, respectively after primary treatment and secondary treatment. At FIP, the installation of one raceway bioreactor had as main goal microalgae development, aiming nutrient removal from the effluent with potential reduction costs associated to the treatment. Microalgae growth evaluation in raceways was accomplished through determination of biomass dry weight from 107 samples. To evaluate microalgae activity, routine analysis were made for nitrate determination and external laboratories analysis for nutrient content determination (total nitrogen, ammonia, Kjeldahl nitrogen and total phosphorus). The obtained results from analysis performed by external laboratories demonstrated that collected sample from secondary treatment raceway presented 95 % ammonia removal, 39 % Kjeldahl nitrogen and 73 % total nitrogen. Only ammonia concentration of 3.36 mg/ L was under discharged limit value of 10 mg/ L. To evaluate effluent quality after microalgae activity biochemical oxygen demand and chemical oxygen demand were also evaluated. However, only one sample from the different systems were evaluated and, therefore, they cannot be considered representative. One of the following steps after effluent treatment is biomass recovery. It was necessary to reach their dehydration using flocculation processes. Although the results, this approach was not optimized unlike the harvest performed using centrifuge where 76% yield was achieved.Bioreatores do tipo raceway para produção de microalgas instalados numa estação de tratamento de águas residuais domésticas (ETAR) e numa unidade industrial de fertilizantes foram acompanhados e avaliados por um período de aproximadamente 11 meses. O crescimento natural de microalgas, maioritariamente Scenedesmus obliquus e Scenedesmus quadricauda, foi determinado para avaliar a sua eventual participação no tratamento de águas residuais domésticas e do efluente proveniente de uma fábrica de fertilizantes. Na ETAR foram instalados dois raceways, respetivamente após o tratamento primário e o tratamento secundário. Na Unidade industrial, a instalação de um bioreator raceway teve por objetivo o desenvolvimento das microalgas visando a redução de nutrientes do efluente com a eventual redução de custos associados ao seu tratamento. A avaliação do crescimento das microalgas nos raceways foi feita por determinação de peso seco (g / m2) de 107 amostras. Para avaliar a atividade das microalgas foram feitas análises rotineiras para determinação de nitratos e análises em laboratórios externos para determinação de outro tipo de nutrientes (azoto total, amónia, azoto Kjeldahl, fósforo total). Os resultados obtidos das análises feitas pelos laboratórios externos demonstraram que na amostra recolhida do raceway após o tratamento secundário uma remoção de amónia de 95 %, 39 % azoto Kjeldahl e 73% de azoto total. Apenas o valor de amónia de 3.36 mg / L esteve abaixo do limite de descarga de 10 mg/ L. Para avaliar a qualidade do efluente após a atividade das microalgas foram também avaliados a carência bioquímica de oxigénio (CBO5) e carência química de oxigénio (CQO), no entanto foram feitas análises apenas a uma amostra dos diferentes sistemas e por isso não pode ser considerada representativa. Um dos passos após o tratamento de efluentes é o aproveitamento da biomassa. Para isso foi necessário recorrer à desidratação da mesma usando processos de floculação. Apesar dos resultados, esta abordagem ficou por otimizar ao contrário da colheita feita por centrifugação onde foi obtido um rendimento de 76%

THE USE OF QFD AS A TOOL FOR QUALITY IMPROVEMENT IN THE RETAIL SALES AREA

The objective of this work is to use the QFD - Quality Function Deployment methodology to identify the desired and expected quality of the products and services in the sales area of a retail trade. The results will be obtained through VoC (Voice of Customer), which consists of data collection by interviewing and then a closed questionnaire, translating the real needs of the consumer into requirements of products and services. With the application of the QFD in the company it was possible to identify the target audience as well as its requirements, being necessary to carry out a planning to put in practice the opportunities raised. In view of these improvements, the company has had positive results both in product development and improvement in the production process and people, presenting to the company a growth of 7.7% in its sales.The QFD proved to be an excellent tool to aid in the quality management of the company, because it was possible to identify the main requirements and needs, thus allowing for more assertive information, eliminating assumptions