This work aims to improve the capability of accurate information extraction from high-dimensional data, with a specific neural learning paradigm, the Self-Organizing Map (SOM). The SOM is an unsupervised learning algorithm that can faithfully sense the manifold structure and support supervised learning of relevant information from the data. Yet open problems regarding SOM learning exist. We focus on the following two issues. 1. Evaluation of topology preservation. Topology preservation is essential for SOMs in faithful representation of manifold structure. However, in reality, topology violations are not unusual, especially when the data have complicated structure. Measures capable of accurately quantifying and informatively expressing topology violations are lacking. One contribution of this work is a new measure, the Weighted Differential Topographic Function (WDTF), which differentiates an existing measure, the Topographic Function (TF), and incorporates detailed data distribution as an importance weighting of violations to distinguish severe violations from insignificant ones. Another contribution is an interactive visual tool, TopoView, which facilitates the visual inspection of violations on the SOM lattice. We show the effectiveness of the combined use of the WDTF and TopoView through a simple two-dimensional data set and two hyperspectral images. 2. Learning multiple latent variables from high-dimensional data. We use an existing two-layer SOM-hybrid supervised architecture, which captures the manifold structure in its SOM hidden layer, and then, uses its output layer to perform the supervised learning of latent variables. In the customary way, the output layer only uses the strongest output of the SOM neurons. This severely limits the learning capability. We allow multiple, k, strongest responses of the SOM neurons for the supervised learning. Moreover, the fact that different latent variables can be best learned with different values of k motivates a new neural architecture, the Conjoined Twins, which extends the existing architecture with additional copies of the output layer, for preferential use of different values of k in the learning of different latent variables. We also automate the customization of k for different variables with the statistics derived from the SOM. The Conjoined Twins shows its effectiveness in the inference of two physical parameters from Near-Infrared spectra of planetary ices.

Magistriniame darbe nagrinėjamas vertybinių popierių portfelio optimizavimas panaudojant saviorganizuojančius neuroninius tinklus (SOM). Darbe supažindinta su SOM algoritmu, neuroninio tinklo struktūra. Aprašytas SOM panaudojimas finansinėse operacijose. Detaliai aprašyta vertybinių popierių analizės specifika. Supažindinama su technin��, bei finansine analizėmis, bei jų metodais ir indikatoriais. Atlikta teorinė finansinių rodiklių analizė, kurios metu, naudojant OMX Vilnius vertybinių popierių biržos istorinius duomenis, nustatyta finansinių rodiklių tinkamumas vertybinių popierių portfelio optimizavimo uždaviniams spręsti. Projektinių sprendimų dalyje aprašytos finansinių rodiklių modifikacijos, bei pritaikymas vertybinių popierių portfelio optimizavimui. Sukurtas VP portfelio optimizavimo modelis. Modelio teisingumas pagrįstas atliktais praktiniais eksperimentais naudojant istorinius duomenis. Parašytos praktinės VP portfelio optimizavimo modelio taikymo rekomendacijos. Suformuluotos magistrinio darbo išvados. / There is analyzed stock portfolio optimization by using self-organizing maps (SOM) in Master thesis. It introduce with SOM algorithm neuron network structure. Also there is described SOM usage in financial operations. Particularity of stock analysis is described. There are introduction with technical, financial analysis and their methods and indicators. It was performed theoretical financial indicators analysis using OMX Vilnius stock market historical data and find out financial indicators suitability for stock portfolio optimization problem solving. Financial indicators modifications and their usage in stock portfolio optimization are described in project decision chapter. VP portfolio optimization model was created and models correctness based on practical experiments using historical data. Practical VP portfolio optimization usage recommendations are given there. Master thesis conclusions are defined in this work.

Several content-based interfaces for music collection exploration rely on Self-Organizing Maps (SOMs) to produce 2D or 3D visualizations of music spaces. In these visualizations, perceptually similar songs are clustered together. The positions of clusters containing similar songs, however, cannot be determined in advance due to particularities of the traditional SOM algorithm. In this thesis, I propose a variation on the traditional algorithm named anchoredSOM. This variation avoids changes in the positions of the aforementioned clusters. Moreover, anchoredSOM allows users to personalize the music space by choosing the locations of clusters containing per- ceptually similar tracks. This thesis introduces SoundAnchoring, an interface for music collection exploration featuring anchoredSOM. SoundAnchoring is evaluated by means of a user study. Results show that SoundAnchoring offers engaging ways to explore music collections and build playlists. / Graduate / 0984 / 0413 / leandro.collares@gmail.com

Thesis (M.S.)--University of Nevada, Reno, 2007. / "May, 2007." Includes bibliographical references (leaves 62-64). Online version available on the World Wide Web. Library also has microfilm. Ann Arbor, Mich. : ProQuest Information and Learning Company, [2007]. 1 microfilm reel ; 35 mm.

O elevado custo de manutenção nos ambientes industriais motivou pesquisas de novas técnicas para melhorar as ações de reparos. Com a evolução tecnológica, principalmente da eletrônica, que proporcionou o uso de sistemas embarcados para melhorar as atividades de manutenção, estas agregaram inteligência e evoluíram para uma manutenção pró-ativa. Através de ferramentas de processamento de sinais, inteligência artificial e tolerância a falhas, surgiram novas abordagens para os sistemas de monitoramento a serviço da equipe de manutenção. Os ditos sistemas de manutenção inteligente, cuja tarefa é realizar testes em funcionamento (on-line) nos equipamentos industriais, promovem novos modelos de confiabilidade e disponibilidade. Tais sistemas são baseados nos conceitos de tolerância a falhas, e visam detectar, diagnosticar e predizer a ocorrência de falhas. Deste modo, fornece-se aos engenheiros de manutenção a informação antecipada do estado de comportamento do equipamento antes mesmo deste manifestar uma falha, reduzindo custos, aumentando a vida útil e tornando previsível o reparo. Para o desenvolvimento do sistema de manutenção inteligente objeto deste trabalho, foram estudadas técnicas de inteligência artificial (redes neurais artificiais), técnicas de projeto de sistemas embarcados e de prototipação em plataformas de hardware. No presente trabalho, a rede neural Mapas Auto-Organizáveis foi adotada como ferramenta base para detecção e diagnóstico de falhas. Esta foi prototipada numa plataforma de sistema embarcado baseada na tecnologia FPGA (Field Programmable Gate Array). Como estudo de caso, uma válvula elétrica utilizada em dutos para transporte de petróleo foi definida como aplicação alvo dos experimentos. Através de um modelo matemático, um conjunto de dados representativo do comportamento da válvula foi simulado e utilizado como entrada do sistema proposto. Estes dados visam o treinamento da rede neural e visam fornecer casos de teste para experimentação no sistema. Os experimentos executados em software validaram o uso da rede neural como técnica para detecção e diagnóstico de falhas em válvulas elétricas. Por fim, também realizou-se experimentos a fim de validar o projeto do sistema embarcado, comparando-se os resultado obtidos com este aos resultados obtidos a partir de testes em software. Os resultados revelam a escolha correta do uso da rede neural e o correto projeto do sistema embarcado para desempenhar as tarefas de detecção e diagnóstico de falhas em válvulas elétricas. / The high costs of maintenance in industrial environments have motivated research for new techniques to improve repair activities. The technological progress, especially in the electronics field, has provided for the use of embedded systems to improve repair, by adding intelligence to the system and turning the maintenance a proactive activity. Through tools like signal processing, artificial intelligence and fault-tolerance, new approaches to monitoring systems have emerged to serve the maintenance staff, leading to new models of reliability and availability. The main goal of these systems, also called intelligent maintenance systems, is to perform in-operation (on-line) test of industrial equipments. These systems are built based on fault-tolerance concepts, and used for the detection, the diagnosis and the prognosis of faults. They provide the maintenance engineers with information on the equipment behavior, prior to the occurrence of failures, reducing maintenance costs, increasing the system lifetime and making it possible to schedule repairing stops. To develop the intelligent maintenance system addressed in this dissertation, artificial intelligence (neural networks), embedded systems design and hardware prototyping techniques were studied. In this work, the neural network Self-Organizing Maps (SOM) was defined as the basic tool for the detection and the diagnosis of faults. The SOM was prototyped in an embedded system platform based on the FPGA technology (Field Programmable Gate Array). As a case study, the experiments were performed on an electric valve used in a pipe network for oil transportation. Through a mathematical model, a data set representative of the valve behavior was obtained and used as input to the proposed maintenance system. These data were used for neural network training and also provided test cases for system monitoring. The experiments were performed in software to validate the chosen neural network as the technique for the detection and diagnosis of faults in the electrical valve. Finally, experiments to validate the embedded system design were also performed, so as to compare the obtained results to those resulting from the software tests. The results show the correct choice of the neural network and the correct embedded systems design to perform the activities for the detection and diagnosis of faults in the electrical valve.

O avanço tecnológico atual está permitindo que as pessoas recebam cada vez mais informações visuais dos mais diferentes tipos, nas mais variadas mídias. Esse aumento fantástico está obrigando os pesquisadores e as indústrias a imaginar soluções para o armazenamento e recuperação deste tipo de informação, pois nossos computadores ainda utilizam, apesar dos grandes avanços nessa área, um sistema de arquivos imaginado há décadas, quando era natural trabalhar com informações meramente textuais. Agora, nos deparamos com novos problemas: Como encontrar uma paisagem específica em um banco de imagens, em que trecho de um filme aparece um cavalo sobre uma colina, em que parte da fotografia existe um gato, como fazer um robô localizar um objeto em uma cena, entre outras necessidades. O objetivo desse trabalho é propor uma arquitetura de rede neural artificial que permita o reconhecimento de objetos genéricos e de categorias em banco de imagens digitais, de forma que se possa recuperar imagens específicas a partir da descrição da cena fornecida pelo usuário. Para que esse objetivo fosse alcançado, foram utilizadas técnicas de Visão Computacional e Processamento de Imagens na etapa de extração de feições de baixo nível e de Redes Neurais(Mapas Auto-Organizáveis de Kohonen) na etapa de agrupamento de classes de objetos. O resultado final desse trabalho pretende ser um embrião para um sistema de reconhecimento de objetos mais genérico, que possa ser estendido para a criação de indices de forma automática ou semi-automática em grandes bancos de imagens. / The current technological progress allows people to receive more and more visual information of the most different types, in different medias. This huge augmentation of image availability forces researchers and industries to propose efficient solutions for image storage and recovery. Despite the extraordinary advances in computational power, the data files system remain the same for decades, when it was natural to deal only with textual information. Nowadays, new problems are in front of us in this field. For instance, how can we find an specific landscape in a image database, in which place of a movie there is a horse on a hill, in which part of a photographic picture there is a cat, how can a robot find an object in a scene, among other queries. The objective of this work is to propose an Artificial Neural Network (ANN) architecture that performs the recognition of generic objects and object’s categories in a digital image database. With this implementation, it becomes possible to do image retrieval through the user´s scene description. To achieve our goal, we have used Computer Vision and Image Processing techniques in low level features extraction and Neural Networks (namely Kohonen’s Self-Organizing Maps) in the phase of object classes clustering. The main result of this work aims to be a seed for a more generic object recognition system, which can be extended to the automatic or semi-automatic index creation in huge image databases.

Facebook is the largest and the most popular online social network that records the large amount of users’ behavior expressed in various activities such as Facebook Likes, status updates, posts, comments, photos, tags and shares. One of the major attractions of such a big data offered by Facebook relates to the predictability of individuals’ psychological traits from their digital footprints which helps researchers and service providers to improve personalized products and services. The goal of this research project is to investigate the predictability of Facebook users’ personality traits measured by BIG5 test as a function of their digital records of behavior such as Facebook Likes. This research is based on a dataset of 92,255 users who provided their Facebook Likes and the results of their BIG5 personality test. For preprocessing the Likes data including 600 attributes, the proposed model uses the R Package “fscaret” to automatically determine the importance level of the attributes as a function of the set of learning algorithms applied to this problem. Two supervised versions of the Multi-Layer Self-Organizing-Map (MLSOM) algorithm is used to visualize the data and predict the users’ personality profiles as a function of Facebook profiles. The model predicts Facebook users' BIG5 personality traits with mean squared error of at most 0.053 for neuroticism and correlation of at most 0.3 for openness.

O elevado custo de manutenção nos ambientes industriais motivou pesquisas de novas técnicas para melhorar as ações de reparos. Com a evolução tecnológica, principalmente da eletrônica, que proporcionou o uso de sistemas embarcados para melhorar as atividades de manutenção, estas agregaram inteligência e evoluíram para uma manutenção pró-ativa. Através de ferramentas de processamento de sinais, inteligência artificial e tolerância a falhas, surgiram novas abordagens para os sistemas de monitoramento a serviço da equipe de manutenção. Os ditos sistemas de manutenção inteligente, cuja tarefa é realizar testes em funcionamento (on-line) nos equipamentos industriais, promovem novos modelos de confiabilidade e disponibilidade. Tais sistemas são baseados nos conceitos de tolerância a falhas, e visam detectar, diagnosticar e predizer a ocorrência de falhas. Deste modo, fornece-se aos engenheiros de manutenção a informação antecipada do estado de comportamento do equipamento antes mesmo deste manifestar uma falha, reduzindo custos, aumentando a vida útil e tornando previsível o reparo. Para o desenvolvimento do sistema de manutenção inteligente objeto deste trabalho, foram estudadas técnicas de inteligência artificial (redes neurais artificiais), técnicas de projeto de sistemas embarcados e de prototipação em plataformas de hardware. No presente trabalho, a rede neural Mapas Auto-Organizáveis foi adotada como ferramenta base para detecção e diagnóstico de falhas. Esta foi prototipada numa plataforma de sistema embarcado baseada na tecnologia FPGA (Field Programmable Gate Array). Como estudo de caso, uma válvula elétrica utilizada em dutos para transporte de petróleo foi definida como aplicação alvo dos experimentos. Através de um modelo matemático, um conjunto de dados representativo do comportamento da válvula foi simulado e utilizado como entrada do sistema proposto. Estes dados visam o treinamento da rede neural e visam fornecer casos de teste para experimentação no sistema. Os experimentos executados em software validaram o uso da rede neural como técnica para detecção e diagnóstico de falhas em válvulas elétricas. Por fim, também realizou-se experimentos a fim de validar o projeto do sistema embarcado, comparando-se os resultado obtidos com este aos resultados obtidos a partir de testes em software. Os resultados revelam a escolha correta do uso da rede neural e o correto projeto do sistema embarcado para desempenhar as tarefas de detecção e diagnóstico de falhas em válvulas elétricas. / The high costs of maintenance in industrial environments have motivated research for new techniques to improve repair activities. The technological progress, especially in the electronics field, has provided for the use of embedded systems to improve repair, by adding intelligence to the system and turning the maintenance a proactive activity. Through tools like signal processing, artificial intelligence and fault-tolerance, new approaches to monitoring systems have emerged to serve the maintenance staff, leading to new models of reliability and availability. The main goal of these systems, also called intelligent maintenance systems, is to perform in-operation (on-line) test of industrial equipments. These systems are built based on fault-tolerance concepts, and used for the detection, the diagnosis and the prognosis of faults. They provide the maintenance engineers with information on the equipment behavior, prior to the occurrence of failures, reducing maintenance costs, increasing the system lifetime and making it possible to schedule repairing stops. To develop the intelligent maintenance system addressed in this dissertation, artificial intelligence (neural networks), embedded systems design and hardware prototyping techniques were studied. In this work, the neural network Self-Organizing Maps (SOM) was defined as the basic tool for the detection and the diagnosis of faults. The SOM was prototyped in an embedded system platform based on the FPGA technology (Field Programmable Gate Array). As a case study, the experiments were performed on an electric valve used in a pipe network for oil transportation. Through a mathematical model, a data set representative of the valve behavior was obtained and used as input to the proposed maintenance system. These data were used for neural network training and also provided test cases for system monitoring. The experiments were performed in software to validate the chosen neural network as the technique for the detection and diagnosis of faults in the electrical valve. Finally, experiments to validate the embedded system design were also performed, so as to compare the obtained results to those resulting from the software tests. The results show the correct choice of the neural network and the correct embedded systems design to perform the activities for the detection and diagnosis of faults in the electrical valve.

abstract: This dissertation proposes a new set of analytical methods for high dimensional physiological sensors. The methodologies developed in this work were motivated by problems in learning science, but also apply to numerous disciplines where high dimensional signals are present. In the education field, more data is now available from traditional sources and there is an important need for analytical methods to translate this data into improved learning. Affecting Computing which is the study of new techniques that develop systems to recognize and model human emotions is integrating different physiological signals such as electroencephalogram (EEG) and electromyogram (EMG) to detect and model emotions which later can be used to improve these learning systems. The first contribution proposes an event-crossover (ECO) methodology to analyze performance in learning environments. The methodology is relevant to studies where it is desired to evaluate the relationships between sentinel events in a learning environment and a physiological measurement which is provided in real time. The second contribution introduces analytical methods to study relationships between multi-dimensional physiological signals and sentinel events in a learning environment. The methodology proposed learns physiological patterns in the form of node activations near time of events using different statistical techniques. The third contribution addresses the challenge of performance prediction from physiological signals. Features from the sensors which could be computed early in the learning activity were developed for input to a machine learning model. The objective is to predict success or failure of the student in the learning environment early in the activity. EEG was used as the physiological signal to train a pattern recognition algorithm in order to derive meta affective states. The last contribution introduced a methodology to predict a learner's performance using Bayes Belief Networks (BBNs). Posterior probabilities of latent nodes were used as inputs to a predictive model in real-time as evidence was accumulated in the BBN. The methodology was applied to data streams from a video game and from a Damage Control Simulator which were used to predict and quantify performance. The proposed methods provide cognitive scientists with new tools to analyze subjects in learning environments. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2017

O avanço tecnológico atual está permitindo que as pessoas recebam cada vez mais informações visuais dos mais diferentes tipos, nas mais variadas mídias. Esse aumento fantástico está obrigando os pesquisadores e as indústrias a imaginar soluções para o armazenamento e recuperação deste tipo de informação, pois nossos computadores ainda utilizam, apesar dos grandes avanços nessa área, um sistema de arquivos imaginado há décadas, quando era natural trabalhar com informações meramente textuais. Agora, nos deparamos com novos problemas: Como encontrar uma paisagem específica em um banco de imagens, em que trecho de um filme aparece um cavalo sobre uma colina, em que parte da fotografia existe um gato, como fazer um robô localizar um objeto em uma cena, entre outras necessidades. O objetivo desse trabalho é propor uma arquitetura de rede neural artificial que permita o reconhecimento de objetos genéricos e de categorias em banco de imagens digitais, de forma que se possa recuperar imagens específicas a partir da descrição da cena fornecida pelo usuário. Para que esse objetivo fosse alcançado, foram utilizadas técnicas de Visão Computacional e Processamento de Imagens na etapa de extração de feições de baixo nível e de Redes Neurais(Mapas Auto-Organizáveis de Kohonen) na etapa de agrupamento de classes de objetos. O resultado final desse trabalho pretende ser um embrião para um sistema de reconhecimento de objetos mais genérico, que possa ser estendido para a criação de indices de forma automática ou semi-automática em grandes bancos de imagens. / The current technological progress allows people to receive more and more visual information of the most different types, in different medias. This huge augmentation of image availability forces researchers and industries to propose efficient solutions for image storage and recovery. Despite the extraordinary advances in computational power, the data files system remain the same for decades, when it was natural to deal only with textual information. Nowadays, new problems are in front of us in this field. For instance, how can we find an specific landscape in a image database, in which place of a movie there is a horse on a hill, in which part of a photographic picture there is a cat, how can a robot find an object in a scene, among other queries. The objective of this work is to propose an Artificial Neural Network (ANN) architecture that performs the recognition of generic objects and object’s categories in a digital image database. With this implementation, it becomes possible to do image retrieval through the user´s scene description. To achieve our goal, we have used Computer Vision and Image Processing techniques in low level features extraction and Neural Networks (namely Kohonen’s Self-Organizing Maps) in the phase of object classes clustering. The main result of this work aims to be a seed for a more generic object recognition system, which can be extended to the automatic or semi-automatic index creation in huge image databases.