Submitted by João Arthur Martins (joao.arthur@ufpe.br) on 2015-03-12T17:38:41Z No. of bitstreams: 2 Dissertaçao Tiago de Lima.pdf: 1469834 bytes, checksum: 95a0326778b3d0f98bd35a7449d8b92f (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) / Approved for entry into archive by Daniella Sodre (daniella.sodre@ufpe.br) on 2015-03-13T14:23:38Z (GMT) No. of bitstreams: 2 Dissertaçao Tiago de Lima.pdf: 1469834 bytes, checksum: 95a0326778b3d0f98bd35a7449d8b92f (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) / Made available in DSpace on 2015-03-13T14:23:38Z (GMT). No. of bitstreams: 2 Dissertaçao Tiago de Lima.pdf: 1469834 bytes, checksum: 95a0326778b3d0f98bd35a7449d8b92f (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Previous issue date: 2013-02-26 / In this dissertation, we present a methodology that aims the automatic construction of multi-classifiers systems based on the combination of selection and fusion. The presented method initially finds an optimum number of clusters for training data set and subsequently determines an ensemble for each cluster found. For model evaluation, the testing data set are submitted to clustering techniques and the nearest cluster to data input will emit a supervised response through its associated ensemble. Self-organizing maps were used in the clustering phase and multilayer perceptrons were used in the classification phase. Adaptive differential evolution has been used in this work in order to optimize the parameters and performance of the different techniques used in the classification and clustering phases. The proposed method, called SFJADE - Selection and Fusion (SF) via Adaptive Differential Evolution (JADE), has been tested on data compression of signals generated by artificial nose sensors and well-known classification problems, including cancer, card, diabetes, glass, heart, horse, soybean and thyroid. The experimental results have shown that the SFJADE method has a better performance than some literature methods while significantly outperforming most of the methods commonly used to construct Multi-Classifier Systems. / Nesta dissertação, nós apresentamos uma metodologia que almeja a construção automática de sistemas de múltiplos classificadores baseados em uma combinação de seleção e fusão. O método apresentado inicialmente encontra um número ótimo de grupos a partir do conjunto de treinamento e subsequentemente determina um comitê para cada grupo encontrado. Para avaliação do modelo, os dados de teste são submetidos à técnica de agrupamento e o grupo mais próximo do dado de entrada irá emitir uma resposta supervisionada por meio de seu comitê associado. Mapas Auto Organizáveis foi usado na fase de agrupamento e Perceptrons de múltiplas camadas na fase de classificação. Evolução Diferencial Adaptativa foi utilizada neste trabalho a fim de otimizar os parâmetros e desempenho das diferentes técnicas utilizadas nas fases de classificação e agrupamento de dados. O método proposto, chamado SFJADE – Selection and Fusion (SF) via Adaptive Differential Evolution (JADE), foi testado em dados gerados para sensores de um nariz artificial e problemas de referência em classificação de padrões, que são: cancer, card, diabetes, glass, heart, heartc e horse. Os resultados experimentais mostraram que SFJADE possui um melhor desempenho que alguns métodos da literatura, além de superar a maioria dos métodos geralmente usados para a construção de sistemas de múltiplos classificadores.

Abstract Global warming has set in motion a trend for cutting energy costs to reduce the carbon footprint. Reducing energy consumption, cutting greenhouse gas emissions and eliminating energy wastage are among the main goals of the European Union (EU). The buildings sector is the largest user of energy and CO2 emitter in the EU, estimated at approximately 40% of the total consumption. According to the International Panel on Climate Change, 30% of the energy used in buildings could be reduced with net economic benefits by 2030. At the same time, indoor air quality is recognized more and more as a distinct health hazard. Because of these two factors, energy efficiency and healthy housing have become active topics in international research. The main aims of this thesis were to study and develop a wireless building monitoring and control system that will produce valuable information and services for end-users using computational methods. In addition, the technology developed in this thesis relies heavily on building automation systems (BAS) and some parts of the concept termed the “Internet of Things” (IoT). The data refining process used is called knowledge discovery from data (KDD) and contains methods for data acquisition, pre-processing, modeling, visualization and interpreting the results and then sharing the new information with the end-users. In this thesis, four examples of data analysis and knowledge deployment are presented. The results of the case studies show that innovative use of computational methods provides a good basis for researching and developing new information services. In addition, the data mining methods used, such as regression and clustering completed with efficient data pre-processing methods, have a great potential to process a large amount of multivariate data effectively. The innovative and effective use of digital information is a key element in the creation of new information services. The service business in the building sector is significant, but plenty of new possibilities await capable and advanced companies or organizations. In addition, end-users, such as building maintenance personnel and residents, should be taken into account in the early stage of the data refining process. Furthermore, more advantages can be gained by courageous co-operation between companies and organizations, by utilizing computational methods for data processing to produce valuable information and by using the latest technologies in the research and development of new innovations. / Tiivistelmä Rakennus- ja kiinteistösektori on suurin fossiilisilla polttoaineilla tuotetun energian käyttäjä. Noin 40 prosenttia kaikesta energiankulutuksesta liittyy rakennuksiin, rakentamiseen, rakennusmateriaaleihin ja rakennuksien ylläpitoon. Ilmastonmuutoksen ehkäisyssä rakennusten energiankäytön vähentämisellä on suuri merkitys ja rakennuksissa energiansäästöpotentiaali on suurin. Tämän seurauksena yhä tiiviimpi ja energiatehokkaampi rakentaminen asettaa haasteita hyvän sisäilman laadun turvaamiselle. Näistä seikoista johtuen sisäilman laadun tutkiminen ja jatkuvatoiminen mittaaminen on tärkeää. Väitöskirjan päätavoitteena on kuvata kehitetty energiankulutuksen ja sisäilman laadun monitorointijärjestelmä. Järjestelmän tuottamaa mittaustietoa on jalostettu eri loppukäyttäjiä palvelevaan muotoon. Tiedonjalostusprosessi koostuu tiedon keräämisestä, esikäsittelystä, tiedonlouhinnasta, visualisoinnista, tulosten tulkitsemisesta ja oleellisen tiedon välittämisestä loppukäyttäjille. Aineiston analysointiin on käytetty tiedonlouhintamenetelmiä, kuten esimerkiksi klusterointia ja ennustavaa mallintamista. Väitöskirjan toisena tavoitteena on tuoda esille jatkuvatoimiseen mittaamiseen liittyviä haasteita sekä rohkaista yrityksiä ja organisaatioita käyttämään tietovarantoja monipuolisemmin ja tehokkaammin. Väitöskirja pohjautuu viiteen julkaisuun, joissa kuvataan kehitetty monitorointijärjestelmä, osoitetaan tiedonjalostusprosessin toimivuus erilaisissa tapauksissa ja esitetään esimerkkejä kuhunkin prosessivaiheeseen soveltuvista laskennallisista menetelmistä. Julkaisuissa on kuvattu energiankulutuksen ja sisäilman laadun informaatiopalvelu sekä sisäilman laatuun liittyviä data-analyysejä omakoti- ja kerrostaloissa sekä koulurakennuksissa. Innovatiivinen digitaalisen tiedon hyödyntäminen on avainasemassa kehitettäessä uusia informaatiopalveluita. Kiinteistöalalle on kehitetty lukuisia informaatioon pohjautuvia palveluita, mutta ala tarjoaa edelleen hyviä liiketoimintamahdollisuuksia kyvykkäille ja kehittyneille yrityksille sekä organisaatioille.

The aim of this thesis is comparison of selected classification methods which are logistic regression (binary and multinominal), multilayer perceptron and classification trees, CHAID and CRT. The first part is reminiscent of the theoretical basis of these methods and explains the nature of parameters of the models. The next section applies the above classification methods to the six data sets and then compares the outputs of these methods. Particular emphasis is placed on the discriminatory power rating models, which a separate chapter is devoted to. Rating discriminatory power of the model is based on the overall accuracy, F-measure and size of the area under the ROC curve. The benefit of this work is not only a comparison of selected classification methods based on statistical models evaluating discriminatory power, but also an overview of the strengths and weaknesses of each method.

L'iris est considérée comme une des modalités les plus robustes et les plus performantes en biométrie à cause de ses faibles taux d'erreurs. Ces performances ont été observées dans des situations contrôlées, qui imposent des contraintes lors de l'acquisition pour l'obtention d'images de bonne qualité. Relâcher ces contraintes, au moins partiellement, implique des dégradations de la qualité des images acquises et par conséquent une réduction des performances de ces systèmes. Une des principales solutions proposées dans la littérature pour remédier à ces limites est d'améliorer l'étape de segmentation de l'iris. L'objectif principal de ce travail de thèse a été de proposer des méthodes originales pour la segmentation des images dégradées de l'iris. Les chaînes de Markov ont été déjà proposées dans la littérature pour résoudre des problèmes de segmentation d'images. Dans ce cadre, une étude de faisabilité d'une segmentation non supervisée des images dégradées d'iris en régions par les chaînes de Markov a été réalisée, en vue d'une future application en temps réel. Différentes transformations de l'image et différentes méthodes de segmentation grossière pour l'initialisation des paramètres ont été étudiées et comparées. Les modélisations optimales ont été introduites dans un système de reconnaissance de l'iris (avec des images en niveaux de gris) afin de produire une comparaison avec les méthodes existantes. Finalement une extension de la modélisation basée sur les chaînes de Markov cachées, pour une segmentation non supervisée des images d'iris acquises en visible, a été mise en place / Iris is considered as one of the most robust and efficient modalities in biometrics because of its low error rates. These performances were observed in controlled situations, which impose constraints during the acquisition in order to have good quality images. The renouncement of these constraints, at least partially, implies degradations in the quality of the acquired images and it is therefore a degradation of these systems’ performances. One of the main proposed solutions in the literature to take into account these limits is to propose a robust approach for iris segmentation. The main objective of this thesis is to propose original methods for the segmentation of degraded images of the iris. Markov chains have been well solicited to solve image segmentation problems. In this context, a feasibility study of unsupervised segmentation into regions of degraded iris images by Markov chains was performed. Different image transformations and different segmentation methods for parameters initialization have been studied and compared. Optimal modeling has been inserted in iris recognition system (with grayscale images) to produce a comparison with the existing methods. Finally, an extension of the modeling based on the hidden Markov chains has been developed in order to realize an unsupervised segmentation of the iris images acquired in visible light

Ce travail de thèse propose des approches d’identification et de reconnaissance des harmoniques de courant qui sont basées sur des stratégies d’apprentissage automatique. Les approches proposées s’appliquent directement dans les dispositifs d’amélioration de la qualité de l’énergie électrique.Des structures neuronales complètes, dotées de capacités d’apprentissage automatique, ont été développées pour identifier les composantes harmoniques d’un signal sinusoïdal au sens large et plus spécifiquement d’un courant alternatif perturbé par des charges non linéaires. L’identification des harmoniques a été réalisée avec des réseaux de neurones de type Multi–Layer Perceptron (MLP). Plusieurs schémas d’identification ont été développés, ils sont basés sur un réseau MLP composé de neurones linéaire ou sur plusieurs réseaux MLP avec des apprentissages spécifiques. Les harmoniques d’un signal perturbé sont identifiées avec leur amplitude et leur phase, elles peuvent servir à générer des courants de compensation pour améliorer la forme du courant électrique. D’autres approches neuronales a été développées pour reconnaître les charges. Elles consistent en des réseaux MLP ou SVM (Support Vector Machine) et fonctionnent en tant que classificateurs. Leur apprentissage permet à partir des harmoniques de courant de reconnaître le type de charge non linéaire qui génère des perturbations dans le réseau électrique. Toutes les approches d’identification et de reconnaissance des harmoniques ont été validées par des tests de simulation à l’aide des données expérimentales. Des comparaisons avec d’autres méthodes ont démontré des performances supérieures et une meilleure robustesse. / This thesis proposes identifying approaches and recognition of current harmonics that are based on machine learning strategies. The approaches are applied directly in the quality improvement devices of electric energy and in energy management solutions. Complete neural structures, equipped with automatic learning capabilities have been developed to identify the harmonic components of a sinusoidal signal at large and more specifically an AC disturbed by non–linear loads. The harmonic identification is performed with multilayer perceptron neural networks (MLP). Several identification schemes have been developed. They are based on a MLP neural network composed of linear or multiple MLP networks with specific learning. Harmonics of a disturbed signal are identified with their amplitude and phases. They can be used to generate compensation currents fed back into the network to improve the waveform of the electric current. Neural approaches were developed to distinguish and to recognize the types of harmonics and is nonlinear load types that are at the origin. They consist of MLP or SVM (Support Vector Machine) acting as classifier that learns the harmonic profile of several types of predetermined signals and representative of non–linear loads. They entry are the parameters of current harmonics of the current wave. Learning can recognize the type of nonlinear load that generates disturbances in the power network. All harmonics identification and recognition approaches have been validated by simulation tests or using experimental data. The comparisons with other methods have demonstrated superior characteristics in terms of performance and robustness.

It is possible to simulate an airflow by additives to shoot images and records of such flowing. Additives can be in the form of particles or continuous filaments. A computer evaluation of such data differs depending on the kind of visualization method. This thesis deals with a number of different approaches to determine the airjet shape and airflow velocity in airflow images and records. Exact procedures area sed for these purposes as well as neural networks and genetic algorithms.

The project deals with artificial neural networks. After designing and debugging the test data set and the training sample set, we created a multilayer perceptron network in the Neural NetworkToolbox (NNT) of Matlab. When creating networks, we used different training algorithms and algorithms improving the generalization of the network. When creating a radial basis network, we did not use the NNT, but a specific source code in Matlab was written. Functionality of neural networks was tested on simple training and testing patterns. Realistic training data were obtained by the simulation of twelve monoconic antennas operating in the frequency range from 2 to 6 GHz. Antennas were located inside a mathematical model of Octavia II. Using CST simulations, electromagnetic fields in a car were obtained. Trained networks are described by regressive characteristics andthe mean square error of training. Algorithms improving generalization are applied on the created and trained networks. The performance of individual networks is mutually compared.

The thesis concentrates on using recurrent neural networks in computer vision. The theoretical part describes the basic knowledge about artificial neural networks with focus on a recurrent architecture. There are presented some of possible applications of the recurrent neural networks which could be used for a solution of real problems. The practical part concentrates on face recognition from an image sequence using the Elman simple recurrent network. For training there are used the backpropagation and backpropagation through time algorithms.

The need for a robust model for predicting the value of condominiums and houses are becoming more apparent as further evidence of systematic errors in existing models are presented. Traditional valuation methods fail to produce good predictions of condominium sales prices and systematic patterns in the errors linked to for example the repeat sales methodology and the hedonic pricing model have been pointed out by papers referenced in this thesis. This inability can lead to monetary problems for individuals and in worst-case economic crises for whole societies. In this master thesis paper we present how a predictive model constructed from a multilayer perceptron can predict the price of a condominium in the centre of Stockholm using objective data from sources publicly available. The value produced by the model is enriched with a predictive interval using the Inductive Conformal Prediction algorithm to give a clear view of the quality of the prediction. In addition, the Multilayer Perceptron is compared with the commonly used Support Vector Regression algorithm to underline the hallmark of neural networks handling of a broad spectrum of features. The features used to construct the Multilayer Perceptron model are gathered from multiple “Open Data” sources and includes data as: 5,990 apartment sales prices from 2011- 2013, interest rates for condominium loans from two major banks, national election results from 2010, geographic information and nineteen local features. Several well-known techniques of improving performance of Multilayer Perceptrons are applied and evaluated. A Genetic Algorithm is deployed to facilitate the process of determine appropriate parameters used by the backpropagation algorithm. Finally, we conclude that the model created as a Multilayer Perceptron using backpropagation can produce good predictions and outperforms the results from the Support Vector Regression models and the studies in the referenced papers. / Behovet av en robust modell för att förutsäga värdet på bostadsrättslägenheter och hus blir allt mer uppenbart alt eftersom ytterligare bevis på systematiska fel i befintliga modeller läggs fram. I artiklar refererade i denna avhandling påvisas systematiska fel i de estimat som görs av metoder som bygger på priser från repetitiv försäljning och hedoniska prismodeller. Detta tillkortakommandet kan leda till monetära problem för individer och i värsta fall ekonomisk kris för hela samhällen. I detta examensarbete påvisar vi att en prediktiv modell konstruerad utifrån en “Multilayer Perceptron” kan estimera priset på en bostadsrättslägenhet i centrala Stockholm baserad på allmänt tillgängligt data (“Öppen Data”). Modellens resultat har utökats med ett prediktivt intervall beräknat utifrån “Inductive Conformal Prediction”- algoritmen som ger en klar bild över estimatets tillförlitlighet. Utöver detta jämförs “Multilayer Perceptron”-algoritmen med en annan vanlig algoritm för maskinlärande, den så kallade “Support Vector Regression” för att påvisa neurala nätverks kvalité och förmåga att hantera dataset med många variabler. De variabler som används för att konstruera “Multilayer Perceptron”-modellen är sammanställda utifrån allmänt tillgängliga öppna datakällor och innehåller information så som: priser från 5990 sålda lägenheter under perioden 2011- 2013, ränteläget för bostadsrättslån från två av de stora bankerna, valresultat från riksdagsvalet 2010, geografisk information och nitton lokala särdrag. Ett flertal välkända förbättringar för “Multilayer Perceptron”-algoritmen har applicerats och evaluerats. En genetisk algoritm har använts för att stödja processen att hitta lämpliga parametrar till “Backpropagation”-algoritmen. I detta arbete drar vi slutsatsen att modellen kan producera goda förutsägelser med en modell konstruerad utifrån ett neuralt nätverk av typen “Multilayer Perceptron” beräknad med “backpropagation”, och därmed utklassar de resultat som levereras av Support Vector Regression modellen och de studier som refererats i denna avhandling

The development of machine learning research has provided statistical innovations and further developments within the field of time series analysis. This study seeks to investigate two different approaches on artificial neural network models based on different learning techniques, and answering how well the neural network approach compares with a basic autoregressive approach, as well as how the artificial neural network models compare to each other. The models were compared and analyzed in regards to the univariate forecast accuracy on 20 randomly drawn time series from two different time frequencies from the M4 competition dataset. Forecasting was made dependent on one time lag (t-1) and forecasted three and six steps ahead respectively. The artificial neural network models outperformed the baseline Autoregressive model, showing notably lower mean average percentage error overall. The Multilayered perceptron models performed better than the Long short-term memory model overall, whereas the Long short-term memory model showed improvement on longer prediction time dimensions. As the training were done univariately  on a limited set of time steps, it is believed that the one layered-approach gave a good enough approximation on the data, whereas the added layer couldn’t fully utilize its strengths of processing power. Likewise, the Long short-term memory model couldn’t fully demonstrate the advantagements of recurrent learning. Using the same dataset, further studies could be made with another approach to data processing. Implementing an unsupervised approach of clustering the data before analysis, the same models could be tested with multivariate analysis on models trained on multiple time series simultaneously.