Made available in DSpace on 2014-12-17T14:08:35Z (GMT). No. of bitstreams: 1 MariaVMMpdf.pdf: 1276052 bytes, checksum: 2a1c6384ed87c24c3ab5a2346947a35d (MD5) Previous issue date: 2009-07-24 / The main objective of this study is to apply recently developed methods of physical-statistic to time series analysis, particularly in electrical induction s profiles of oil wells data, to study the petrophysical similarity of those wells in a spatial distribution. For this, we used the DFA method in order to know if we can or not use this technique to characterize spatially the fields. After obtain the DFA values for all wells, we applied clustering analysis. To do these tests we used the non-hierarchical method called K-means. Usually based on the Euclidean distance, the K-means consists in dividing the elements of a data matrix N in k groups, so that the similarities among elements belonging to different groups are the smallest possible. In order to test if a dataset generated by the K-means method or randomly generated datasets form spatial patterns, we created the parameter Ω (index of neighborhood). High values of Ω reveals more aggregated data and low values of Ω show scattered data or data without spatial correlation. Thus we concluded that data from the DFA of 54 wells are grouped and can be used to characterize spatial fields. Applying contour level technique we confirm the results obtained by the K-means, confirming that DFA is effective to perform spatial analysis / O principal objetivo do presente trabalho foi aplicar m?todos recentemente desenvolvidos em f?sica-estat?stica ?s s?ries temporais, em especial a dados de perfis el?tricos de indu??o de 54 po?os de petr?leo localizados no Campo de Namorado Bacia de Campos - RJ, para estudar a similaridade petrof?sica dos po?os numa distribui??o espacial. Para isto, utilizamos o m?todo do DFA com o intuito de saber se podemos, ou n?o, utilizar esta t?cnica para caracterizar espacialmente o campo. Depois de obtidos os valores de DFA para todos os po?os, fizemos uma an?lise de agrupamento com rela??o a estas caracter?sticas; para tanto, utilizamos o m?todo de agrupamento n?o-hier?rquico chamado m?todo K-m?dia. Geralmente baseado na dist?ncia euclidiana, o K-m?dia consiste em dividir os elementos de uma matriz n de dados em k grupos bem definidos, de maneira que as semelhan?as existentes entre elementos pertencentes a grupos distintos sejam as menores poss?veis. Com o objetivo de verificar se um conjunto de dados gerados pelo m?todo do K-m?dia ou gerado aleatoriamente forma padr?es espaciais, criamos o par?metro Ω (?ndice de vizinhan?a). Altos valores de Ω implicam em dados mais agregados e baixos valores de Ω em dados dispersos ou sem correla??o espacial. Com aux?lio do m?todo de Monte Carlo observamos que dados agrupados aleatoriamente apresentam uma distribui??o de Ω inferior ao valor emp?rico. Desta forma conclu?mos que os dados de DFA obtidos nos 54 po?os est?o agrupados e podem ser usados na caracteriza??o espacial de campos. Ao cruzar os dados das curvas de n?vel com os resultados obtidos pelo K-m?dia, confirmamos a efici?ncia do mesmo para correlacionar po?os em distribui??o espacial

Made available in DSpace on 2016-06-02T20:06:03Z (GMT). No. of bitstreams: 1 2596.pdf: 1631367 bytes, checksum: 90f2d842a935f1dd50bf587a33f6a2cb (MD5) Previous issue date: 2009-02-16 / The clustering techniques have frequently been used in literature to the analyse data in several fields of application. The main objective of this work is to study such techniques. There is a large number of clustering techniques in literature. In this work we concentrate on Self Organizing Map (SOM), k-means, k-medoids and Expectation- Maximization (EM) algorithms. These algorithms are applied to gene expression data. The analisys of gene expression, among other possibilities, identifies which genes are differently expressed in synthesis of proteins associated to normal and sick tissues. The purpose is to do a comparing of these metods, sticking out advantages and disadvantages of such. The metods were tested for simulation and after we apply them to a real data set. / As técnicas de agrupamento (clustering) vêm sendo utilizadas com freqüência na literatura para a solução de vários problemas de aplicações práticas em diversas áreas do conhecimento. O principal objetivo deste trabalho é estudar tais técnicas. Mais especificamente, estudamos os algoritmos Self Organizing Map (SOM), k-means, k-medoids, Expectation-Maximization (EM). Estes algoritmos foram aplicados a dados de expressão gênica. A análise de expressão gênica visa, entre outras possibilidades, a identificação de quais genes estão diferentemente expressos na sintetização de proteínas associados a tecidos normais e doentes. O objetivo deste trabalho é comparar estes métodos no que se refere à eficiência dos mesmos na identificação de grupos de elementos similares, ressaltando vantagens e desvantagens de cada um. Os métodos foram testados por simulação e depois aplicamos as metodologias a um conjunto de dados reais.

For many companies today, it is highly valuable to collect and analyse data in order to support decision making and functions of various sorts. However, this kind of data-driven approach is seldomly applied to software testing and there is often a lack of verification that the testing performed is relevant to how the system under test is used. Therefore, the aim of this thesis is to investigate the possibility of introducing a data-driven approach to test automation by extracting user behaviour data and curating it to form input for testing. A prestudy was initially conducted in order to collect and assess different data sources for augmenting the testing. After suitable data sources were identified, the required data, including data about user activity in the system, was extracted. This data was then processed and three prototypes where built on top of this data. The first prototype augments the model-based testing by automatically creating models of the most common user behaviour by utilising data mining algorithms. The second prototype tests the most frequent occurring client actions. The last prototype visualises which features of the system are not covered by automated regression testing. The data extracted and analysed in this thesis facilitates the understanding of the behaviour of the users in the system under test. The three prototypes implemented with this data as their foundation can be used to assist other testing methods by visualising test coverage and executing regression tests.

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.

Lithological core logging is a subjective and time consuming endeavour which could possibly be automated, the question is if and to what extent this automation would affect the resulting core logs. This study presents a case from the Zinkgruvan Zn-Pb-Ag mine, Bergslagen, Sweden; in which Classification and Regression Trees and K-means Clustering on the Self Organising Map were applied to X-Ray Flourescence lithogeochemistry data derived from automated core scan technology. These two methods are assessed through comparison to manual core logging. It is found that the X-Ray Fluorescence data are not sufficiently accurate or precise for the purpose of automated full lithological classification since not all elements are successfully quantified. Furthermore, not all lithologies are possible to distinquish with lithogeochemsitry alone furter hindering the success of automated lithological classification. This study concludes that; 1) K-means on the Self Organising Map is the most successful approach, however; this may be influenced by the method of domain validation, 2) the choice of ground truth for learning is important for both supervised learning and the assessment of machine learning accuracy and 3) geology, data resolution and choice of elements are important parameters for machine learning. Both the supervised method of Classification and Regression Trees and the unsupervised method of K-means clustering applied to Self Organising Maps show potential to assist core logging procedures.

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The aim of this thesis is to explore clustering algorithms of machine unsupervised learning, which can be used for image database classification by similarity. For chosen clustering algorithms is written up a theoretical basis. For better classification of used database this thesis deals with different methods of image preprocessing. With these methods the features from image are extracted. Next the thesis solves of implementation of preprocessing methods and practical application of clustering algorithms. In practical part is programmed aplication in Python programming language, which classifies the database of images into classes by similarity. The thesis tests all of used methods and at the end of the thesis is processed searches of results.

This thesis introduces readers various methods that are currently used for detection of network-based applications. Further part deals with selection of appropriate detection method and implementation of proof-of-concept script, including testing its reliability and accuracy. Chosen detection algorithm is based on statistics data from network flows of tested network communication. Due to its final solution does not depend on whether communication is encrypted or not. Next part contains several possible variants of how to integrate proposed solution in the current architecture of the existing product Kernun UTM --- which is firewall produced by Trusted Network Solutions a.s. company. Most suitable variant is chosen and described furthermore in more details. Finally there is also mentioned plan for further developement and possible ways how to improve final solution.

This master"s thesis deals with the knowledge discovery in multimedia databases. It contains general principles of knowledge discovery in databases, especially methods of cluster analysis used for data mining in large and multidimensional databases are described here. The next chapter contains introduction to multimedia databases, focusing on the extraction of low level features from images and video data. The practical part is then an implementation of the methods BIRCH, DBSCAN and k-means for cluster analysis. Final part is dedicated to experiments above TRECVid 2008 dataset and description of achievements.

The master's thesis deals with biometric systems, especially these based on human recognition by finger veins. It describes some development principles of the new biometric system. It proposes some new approaches to the comparison of finger vein patterns and their fast identification in sizable databases. Verification is based on templates comparison by similarity and distance measures with proposed alignment approaches. The proposed method of identification is based on the combination of clustering and genetic algorithm. The second option is using the indexing tree structure and searching by range query.