Following the significant transition from the traditional production industry to an informationbased economy, the telecommunications industry was faced with an explosion of innovation, resulting in a continuous change in user behaviour. The industry has made efforts to adapt to a more datadriven future, which has given rise to larger and more complex systems. Therefore, troubleshooting systems such as anomaly detection and root cause analysis are essential features for maintaining service quality and facilitating daily operations. This study aims to explore the possibilities, benefits, and drawbacks of implementing cluster analysis for anomaly detection in hybrid fibercoaxial networks. Based on the literature review on unsupervised anomaly detection and an assumption regarding the anomalous behaviour in hybrid fibercoaxial network data, the kmeans, SelfOrganizing Map, and Gaussian Mixture Model were implemented both with and without Principal Component Analysis. Analysis of the results demonstrated an increase in performance for all models when the Principal Component Analysis was applied, with kmeans outperforming both SelfOrganizing Map and Gaussian Mixture Model. On this basis, it is recommended to apply Principal Component Analysis for clusteringbased anomaly detection. Further research is necessary to identify whether cluster analysis is the most appropriate unsupervised anomaly detection approach. / Följt av övergången från den traditionella tillverkningsindustrin till en informationsbaserad ekonomi stod telekommunikationsbranschen inför en explosion av innovation. Detta skifte resulterade i en kontinuerlig förändring av användarbeteende och branschen tvingades genomgå stora ansträngningar för att lyckas anpassa sig till den mer datadrivna framtiden. Större och mer komplexa system utvecklades och således blev felsökningsfunktioner såsom anomalidetektering och rotfelsanalys centrala för att upprätthålla servicekvalitet samt underlätta för den dagliga driftverksamheten. Syftet med studien är att utforska de möjligheterna, för- samt nackdelar med att använda klusteranalys för anomalidetektering inom HFC- nätverk. Baserat på litteraturstudien för oövervakad anomalidetektering samt antaganden för anomalibeteenden inom HFC- data valdes algritmerna k- means, Self- Organizing Map och Gaussian Mixture Model att implementeras, både med och utan Principal Component Analysis. Analys av resultaten påvisade en uppenbar ökning av prestanda för samtliga modeller vid användning av PCA. Vidare överträffade k- means, både Self- Organizing Maps och Gaussian Mixture Model. Utifrån resultatanalysen rekommenderas det således att PCA bör tillämpas vid klusterings- baserad anomalidetektering. Vidare är ytterligare forskning nödvändig för att avgöra huruvida klusteranalys är den mest lämpliga metoden för oövervakad anomalidetektering.

Financial reporting fraud leads to not only significant investment risks for external stockholders, but also financial crises for the capital market. Although the issue of fraudulent financial reporting has drawn much attention, relevant research is much less than issues of predicting financial distress or bankruptcy. Furthermore, one purpose of exploring the financial reporting fraud with various forms is to obtain a better understand of the corporate through investigating its financial and corporate governance indicators. This study addresses the challenge with proposing an approach with the following four phases: (1) to identify a set of financial and corporate governance indicators that are significantly correlated with the financial reporting fraud; (2) to use the Growing Hierarchical Self-Organizing Map (GHSOM) to cluster the normal and fraud listed corporate data; (3) to extract knowledge about the financial reporting fraud through observing the hierarchical relationship displayed in the trained GHSOM; and (4) to make the justification of the extracted knowledge. The proposed approach is feasible because researchers claim that the GHSOM can discover the hidden hierarchical relationship from data with high dimensionality.

Neste trabalho, um novo enfoque para detecção e diagnóstico de falhas (DDF) em robôs manipuladores é apresentado. Um robô com falhas pode causar sérios danos e pode colocar em risco o pessoal presente no ambiente de trabalho. Geralmente, os pesquisadores têm proposto esquemas de DDF baseados no modelo matemático do sistema. Contudo, erros de modelagem podem ocultar os efeitos das falhas e podem ser uma fonte de alarmes falsos. Aqui, duas redes neurais artificiais são utilizadas em um sistema de DDF para robôs manipuladores. Um perceptron multicamadas treinado por retropropagação do erro é usado para reproduzir o comportamento dinâmico do manipulador. As saídas do perceptron são comparadas com as variáveis medidas, gerando o vetor de resíduos. Em seguida, uma rede com função de base radial é usada para classificar os resíduos, gerando a isolação das falhas. Quatro algoritmos diferentes são empregados para treinar esta rede. O primeiro utiliza regularização para reduzir a flexibilidade do modelo. O segundo emprega regularização também, mas ao invés de um único termo de penalidade, cada unidade radial tem um regularização individual. O terceiro algoritmo emprega seleção de subconjuntos para selecionar as unidades radiais a partir dos padrões de treinamento. O quarto emprega o mapa auto-organizável de Kohonen para fixar os centros das unidades radiais próximos aos centros dos aglomerados de padrões. Simulações usando um manipulador com dois graus de liberdade e um Puma 560 são apresentadas, demostrando que o sistema consegue detectar e diagnosticar corretamente falhas que ocorrem em conjuntos de padrões não-treinados. / In this work, a new approach for fault detection and diagnosis in robotic manipulators is presented. A faulty robot could cause serious damages and put in risk the people involved. Usually, researchers have proposed fault detection and diagnosis schemes based on the mathematical model of the system. However, modeling errors could obscure the fault effects and could be a false alarm source. In this work, two artificial neural networks are employed in a fault detection and diagnosis system to robotic manipulators. A multilayer perceptron trained with backpropagation algorithm is employed to reproduce the robotic manipulator dynamical behavior. The perceptron outputs are compared with the real measurements, generating the residual vector. A radial basis function network is utilized to classify the residual vector, generating the fault isolation. Four different algorithms have been employed to train this network. The first utilizes regularization to reduce the flexibility of the model. The second employs regularization too, but instead of only one penalty term, each radial unit has a individual penalty term. The third employs subset selection to choose the radial units from the training patterns. The forth algorithm employs the Kohonen’s self-organizing map to fix the radial unit center near to the cluster centers. Simulations employing a two link manipulator and a Puma 560 manipulator are presented, demonstrating that the system can detect and isolate correctly faults that occur in nontrained pattern sets.

This research created a new theoretical Soft Computing (SC) hybridized network intrusion detection diagnostic system including complex hybridization of a 3D full color Self-Organizing Map (SOM), Artificial Immune System Danger Theory (AISDT), and a Fuzzy Inference System (FIS). This SOM+ diagnostic archetype includes newly defined intrusion types to facilitate diagnostic analysis, a descriptive computational model, and an Invisible Mobile Network Bridge (IMNB) to collect data, while maintaining compatibility with traditional packet analysis. This system is modular, multitaskable, scalable, intuitive, adaptable to quickly changing scenarios, and uses relatively few resources.

The strong variation in carbonate content of the geological formations in the basin of Sprimont, Belgium, implies different groundwater behaviours in the substratum. The existence of carbonated materials such as limestone is responsible for the existence of a well-developed karstic system restricted to the Carboniferous materials which has a strong impact on the local hydrology and hydrogeology. Surface streams lose through local sinkholes to resurface at the spring of Trou Bleu, the only outlet of the basin and the drainage point of local aquifers. The study focuses on the assessment of existing links between surface losing streams and the spring, and on the tracing of any anthropogenic contamination, by using hydrochemical parameters (major ions, nitrogen forms, organic carbon…), isotopic compositions (δ15N-NO3, δ18O-NO3 and δ11B), pharmaceutical substances (caffeine, paracetamol…) and natural fluorescence as tools. Results strongly support each other regarding evidences of sewage contamination and agriculture and livestock farming waste occurrence in the basin, while reflecting the strong heterogeneity of landuse as observed on-site. Previous knowledge on hydrological dynamics is supported by collected data and reinforced by stream-by-stream discussions and estimate of the relative contribution of each surface streams to the spring water composition. / Erasmus+ traineeship at University of Liège

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.

Neste trabalho, um novo enfoque para detecção e diagnóstico de falhas (DDF) em robôs manipuladores é apresentado. Um robô com falhas pode causar sérios danos e pode colocar em risco o pessoal presente no ambiente de trabalho. Geralmente, os pesquisadores têm proposto esquemas de DDF baseados no modelo matemático do sistema. Contudo, erros de modelagem podem ocultar os efeitos das falhas e podem ser uma fonte de alarmes falsos. Aqui, duas redes neurais artificiais são utilizadas em um sistema de DDF para robôs manipuladores. Um perceptron multicamadas treinado por retropropagação do erro é usado para reproduzir o comportamento dinâmico do manipulador. As saídas do perceptron são comparadas com as variáveis medidas, gerando o vetor de resíduos. Em seguida, uma rede com função de base radial é usada para classificar os resíduos, gerando a isolação das falhas. Quatro algoritmos diferentes são empregados para treinar esta rede. O primeiro utiliza regularização para reduzir a flexibilidade do modelo. O segundo emprega regularização também, mas ao invés de um único termo de penalidade, cada unidade radial tem um regularização individual. O terceiro algoritmo emprega seleção de subconjuntos para selecionar as unidades radiais a partir dos padrões de treinamento. O quarto emprega o mapa auto-organizável de Kohonen para fixar os centros das unidades radiais próximos aos centros dos aglomerados de padrões. Simulações usando um manipulador com dois graus de liberdade e um Puma 560 são apresentadas, demostrando que o sistema consegue detectar e diagnosticar corretamente falhas que ocorrem em conjuntos de padrões não-treinados. / In this work, a new approach for fault detection and diagnosis in robotic manipulators is presented. A faulty robot could cause serious damages and put in risk the people involved. Usually, researchers have proposed fault detection and diagnosis schemes based on the mathematical model of the system. However, modeling errors could obscure the fault effects and could be a false alarm source. In this work, two artificial neural networks are employed in a fault detection and diagnosis system to robotic manipulators. A multilayer perceptron trained with backpropagation algorithm is employed to reproduce the robotic manipulator dynamical behavior. The perceptron outputs are compared with the real measurements, generating the residual vector. A radial basis function network is utilized to classify the residual vector, generating the fault isolation. Four different algorithms have been employed to train this network. The first utilizes regularization to reduce the flexibility of the model. The second employs regularization too, but instead of only one penalty term, each radial unit has a individual penalty term. The third employs subset selection to choose the radial units from the training patterns. The forth algorithm employs the Kohonen’s self-organizing map to fix the radial unit center near to the cluster centers. Simulations employing a two link manipulator and a Puma 560 manipulator are presented, demonstrating that the system can detect and isolate correctly faults that occur in nontrained pattern sets.

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.

No description available.

This thesis discusses the design and the utilization of the artificial neural networks as ECG classifiers and the detectors of heart diseases in ECG signal especially myocardial ischaemia. The changes of ST-T complexes are the important indicator of ischaemia in ECG signal. Different types of ischaemia are expressed particularly by depression or elevation of ST segments and changes of T wave. The first part of this thesis is orientated towards the theoretical knowledges and describes changes in the ECG signal rising close to different types of ischaemia. The second part deals with to the ECG signal pre-processing for the classification by neural network, filtration, QRS detection, ST-T detection, principal component analysis. In the last part there is described design of detector of myocardial ischaemia based on artificial neural networks with utilisation of two types of neural networks back – propagation and self-organizing map and the results of used algorithms. The appendix contains detailed description of each neural networks, description of the programme for classification of ECG signals by ANN and description of functions of programme. The programme was developed in Matlab R2007b.