Chan Chi Hang. / Thesis submitted in: August 2004. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 105-114). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Problem Statement --- p.3 / Chapter 1.2 --- Major Contributions --- p.5 / Chapter 1.3 --- Publication List --- p.6 / Chapter 1.4 --- Thesis Organization --- p.7 / Chapter 2 --- Background Survey --- p.9 / Chapter 2.1 --- Relevance Feedback Framework --- p.9 / Chapter 2.1.1 --- Relevance Feedback Types --- p.11 / Chapter 2.1.2 --- Data Distribution --- p.12 / Chapter 2.1.3 --- Training Set Size --- p.14 / Chapter 2.1.4 --- Inter-Query Learning and Intra-Query Learning --- p.15 / Chapter 2.2 --- History of Relevance Feedback Techniques --- p.16 / Chapter 2.3 --- Relevance Feedback Approaches --- p.19 / Chapter 2.3.1 --- Vector Space Model --- p.19 / Chapter 2.3.2 --- Ad-hoc Re-weighting --- p.26 / Chapter 2.3.3 --- Distance Optimization Approach --- p.29 / Chapter 2.3.4 --- Probabilistic Model --- p.33 / Chapter 2.3.5 --- Bayesian Approach --- p.39 / Chapter 2.3.6 --- Density Estimation Approach --- p.42 / Chapter 2.3.7 --- Support Vector Machine --- p.48 / Chapter 2.4 --- Presentation Set Selection --- p.52 / Chapter 2.4.1 --- Most-probable strategy --- p.52 / Chapter 2.4.2 --- Most-informative strategy --- p.52 / Chapter 3 --- Biased Support Vector Machine for Content-Based Image Retrieval --- p.57 / Chapter 3.1 --- Motivation --- p.57 / Chapter 3.2 --- Background --- p.58 / Chapter 3.2.1 --- Regular Support Vector Machine --- p.59 / Chapter 3.2.2 --- One-class Support Vector Machine --- p.61 / Chapter 3.3 --- Biased Support Vector Machine --- p.63 / Chapter 3.4 --- Interpretation of parameters in BSVM --- p.67 / Chapter 3.5 --- Soft Label Biased Support Vector Machine --- p.69 / Chapter 3.6 --- Interpretation of parameters in Soft Label BSVM --- p.73 / Chapter 3.7 --- Relevance Feedback Using Biased Support Vector Machine --- p.74 / Chapter 3.7.1 --- Advantages of BSVM in Relevance Feedback . . --- p.74 / Chapter 3.7.2 --- Relevance Feedback Algorithm By BSVM --- p.75 / Chapter 3.8 --- Experiments --- p.78 / Chapter 3.8.1 --- Synthetic Dataset --- p.80 / Chapter 3.8.2 --- Real-World Dataset --- p.81 / Chapter 3.8.3 --- Experimental Results --- p.83 / Chapter 3.9 --- Conclusion --- p.86 / Chapter 4 --- Self-Organizing Map-based Inter-Query Learning --- p.88 / Chapter 4.1 --- Motivation --- p.88 / Chapter 4.2 --- Algorithm --- p.89 / Chapter 4.2.1 --- Initialization and Replication of SOM --- p.89 / Chapter 4.2.2 --- SOM Training for Inter-Query Learning --- p.90 / Chapter 4.2.3 --- Incorporate with Intra-Query Learning --- p.92 / Chapter 4.3 --- Experiments --- p.93 / Chapter 4.3.1 --- Synthetic Dataset --- p.95 / Chapter 4.3.2 --- Real-World Dataset --- p.95 / Chapter 4.3.3 --- Experimental Results --- p.97 / Chapter 4.4 --- Conclusion --- p.98 / Chapter 5 --- Conclusion --- p.102 / Bibliography --- p.104

The enormous increase in the production of electronic data in today's information era has led to more challenges in analysing and understanding of the data. The rise in the innovations of technology devices, computers and the Internet has made it much easier to collect and store different kind of data ranging from personal, medical, financial, and scientific data. The growth in the amount of the generated data has introduced the term “Big Data” to describe this extremely high-dimensional and yet complex data. Making sense of the generated data sets is of great importance for the discovery of meaningful information that can be used to support decision making. Data mining techniques have been designed as a process for ex-ploring these data sets to extract meaning for decision making. An essential phase of the data mining procedure is the data transformation that involves the selection of input parameters. Selecting the right input parameters has a great impact on the performance of machine learning algorithms. Currently, there are existing manual statistical methods that are used for this task, but these are difficult to use, time consuming and require an expert. Automated data analysis is the initial step to relieve this burden from humans, through the provision of a systematic procedure of inspecting, transforming and modelling data for knowledge discovery. This project presents a novel method that exploits the power of self-organization for a sys-tematic procedure of conducting and inspecting data classification, with the identification of input parameters that are important for the process. The developed method can be used on different classification problems with practical application in various areas such as health con-dition monitoring in health care, machinery fault detection and analysis, and financial instrument analysis among others.

Yip King-Fung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 84-89). / Abstracts in English and Chinese. / Abstract --- p.i / 論文摘要 --- p.iii / Table of Contents --- p.iv / List of Abbreviations --- p.vi / Acknowledgements --- p.vii / Chapter 1. --- Introduction --- p.1 / Chapter 1.1. --- Background --- p.1 / Chapter 1.2. --- Motivation --- p.3 / Chapter 1.3. --- Thesis Organization --- p.4 / Chapter 2. --- A Survey of Content-based Image Retrieval --- p.5 / Chapter 2.1. --- Text-based Image Retrieval --- p.5 / Chapter 2.2. --- Content-Based Image Retrieval --- p.7 / Chapter 2.2.1. --- Content-Based Image Retrieval Systems --- p.7 / Chapter 2.2.2. --- Query Methods --- p.9 / Chapter 2.2.3. --- Image Features --- p.11 / Chapter 2.2.4. --- Summary --- p.16 / Chapter 3. --- Visual Thesaurus using SOM --- p.17 / Chapter 3.1. --- Algorithm --- p.17 / Chapter 3.1.1. --- Image Representation --- p.17 / Chapter 3.1.2. --- Self-Organizing Map --- p.21 / Chapter 3.2. --- Preliminary Experiment --- p.27 / Chapter 3.2.1. --- Feature differences --- p.27 / Chapter 3.2.2. --- Labeling differences --- p.30 / Chapter 4. --- Experiment --- p.33 / Chapter 4.1. --- Subjects --- p.33 / Chapter 4.2. --- Apparatus --- p.33 / Chapter 4.2.1. --- Systems --- p.33 / Chapter 4.2.2. --- Test Databases --- p.33 / Chapter 4.3. --- Procedure --- p.34 / Chapter 4.3.1. --- Description --- p.35 / Chapter 4.3.2. --- SOM (text) --- p.36 / Chapter 4.3.3. --- SOM (image) --- p.38 / Chapter 4.3.4. --- QBE (text) --- p.40 / Chapter 4.3.5. --- QBE (image) --- p.42 / Chapter 4.3.6. --- Questionnaire --- p.44 / Chapter 4.3.7. --- Experiment Flow --- p.45 / Chapter 4.4. --- Results --- p.46 / Chapter 4.5. --- Discussion --- p.51 / Chapter 5. --- Quantizing Color Histogram --- p.55 / Chapter 5.1. --- Algorithm --- p.56 / Chapter 5.1.1. --- Codebook Generation Phrase --- p.57 / Chapter 5.1.2. --- Histogram Generation Phrase --- p.66 / Chapter 5.2. --- Experiment --- p.67 / Chapter 5.2.1. --- Test Database --- p.67 / Chapter 5.2.2. --- Evaluation Methods --- p.67 / Chapter 5.2.3. --- Results and Discussion --- p.69 / Chapter 5.2.4. --- Summary --- p.74 / Chapter 6. --- Relevance Feedback --- p.75 / Chapter 6.1. --- Relevance Feedback in Text Information Retrieval --- p.75 / Chapter 6.2. --- Relevance Feedback in Multimedia Information Retrieval --- p.76 / Chapter 6.3. --- Relevance Feedback in Visual Thesaurus --- p.76 / Chapter 7. --- Conclusions --- p.80 / Chapter 7.1. --- Applications --- p.81 / Chapter 7.2. --- Future Directions --- p.81 / Chapter 7.2.1. --- SOM Generation --- p.81 / Chapter 7.2.2. --- Hybrid Architecture --- p.82 / References --- p.84

Genetic algorithms are some of the most flexible among optimization methods. Because of their low requirements on input data, they are able to solve a wide array of problems. The flexibility is balanced by their lower effectiveness. When compared to more specialized methods, their results are inferior. This thesis examines the possibility of increasing their effectiveness by means of controlling their run by an artificial neural network. Presented inside are means of controlling a run of a genetic algorithm by a self-organizing map. The thesis contains an algorithm proposal, a prototype implementation of such algorithm and a series of tests to assess its efficiency. While the results on benchmark functions show some positive properties, the problems of greater complexity yield less optimistic results.

Os seres vivos têm uma impressionante capacidade de lidar com ambientes complexos com grandes quantidades de informações de forma muito autônoma. Isto os torna um modelo ideal para o desenvolvimento de sistemas artificiais bioinspirados. A rede neural artificial auto-organizada de Kohonen é um excelente exemplo de um sistema baseado nos modelos biológicos. Esta tese discutirá ilustrativamente o reconhecimento e a generalização de padrões em alta dimensão nos sistemas biológicos e como eles lidam com redução de dimensionalidade para otimizar o armazenamento e o acesso às informações memorizadas para fins de reconhecimento e categorização de padrões, mas apenas para contextualizar o tema com as propostas desta tese. As novas propostas desenvolvidas nesta tese são úteis para aplicações de extração não supervisionada de conhecimento a partir dos mapas auto-organizados. Trabalha-se sobre o modelo da Rede Neural de Kohonen, mas algumas das metodologias propostas também são aplicáveis com outras abordagens de redes neurais auto-organizadas. Será apresentada uma técnica de reconstrução visual dos neurônios do Mapa de Kohonen gerado pelo método híbrido PCA+SOM. Essa técnica é útil quando se trabalha com banco de dados de imagens. Propõe-se também um método para melhorar a representação dos dados do mapa SOM e discute-se o resultado do mapeamento SOM como uma generalização das informações do espaço de dados. Finalmente, apresenta-se um método de exploração de espaço de dados em alta dimensão de maneira auto-organizada, baseado no manifold dos dados, cuja proposta foi denominada Self Organizing Manifold Mapping (SOMM). São apresentados os resultados computacionais de ensaios realizados com cada uma das propostas acima e eles são avaliados as com métricas de qualidade conhecidas, além de uma nova métrica que está sendo proposta neste trabalho. / Living beings have an amazing capacity to deal with complex environments with large amounts of information autonomously. They are the perfect model for bioinspired artificial system development. The artificial neural network developed by Kohonen is an excellent example of a system based on biological models. In this thesis, we will discuss illustratively pattern recognition and pattern generalization in high dimensional data space by biological system. Then, a brief discussion of how they manage dimensionality reduction to optimize memory space and speed up information access in order to categorize and recognize patterns. The new proposals developed in this thesis are useful for applications of unsupervised knowledge extraction using self-organizing maps. The proposals use Kohonens model. However, any self-organizing neural network in general can also use the proposed techniques. It will be presented a visual reconstruction technique for Kohonens neurons, which was generated by hybrid method PCA+SOM. This technique is useful when working with images database. It is also proposed a method for improving the representation of SOMs map and discussing the result of the SOMs mapping as a generalization of the information data space. Finally, it is proposed a method for exploring high dimension data space in a self-organized way on the data manifold. This new proposal was called Self Organizing Manifold Mapping (SOMM). We present the results of computational experiments on each of the above proposals and evaluate the results using known quality metrics, as well as a new metric that is being proposed in this thesis.

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.

No description available.

The planning and management of coastal and river structures such as breakwaters, groynes, jetties, bridges and tidal inlets require accurate predictions of equilibrium morphologies. Generally these types of situations are modelled numerically using process - based models, where wave, current and sediment transport modules are applied over a number of time - steps until a steady - state morphology is obtained. Two alternative methods have been developed and applied in this thesis, based on self - organisation and entropy approaches. The self - organisation - based method utilises a cellular automata model, where local rules produce a global stable pattern through positive and negative feedback. The entropy - based method is able to predict equilibrium morphologies directly. It compares different randomly generated morphologies using an objective function and optimisation, instead of moving to an equilibrium morphology through intermediate states. This avoids some potential problems associated with traditional models such as error propagation and reliance on accurate initial conditions. The models developed in this thesis have been applied to a number of case studies. It was found that the cellular automata model obtained a higher Brier Skill Score than a comparable process - based model when predicting the equilibrium morphology associated with a channel obstruction. The entropy - based method was able to predict a realistic erosional channel in a coastal lagoon, similar to field observations at the Murray River Mouth in South Australia. It had difficulties predicting the deposition pattern due to the bias of the objective function towards erosional environments. The entropy - based method outperformed a conventional model prediction of the equilibrium erosional channel associated with a laboratory - sized lagoon, but similar problems were observed with its deposition predictive ability. The modelling methods developed in this thesis are a first step into the use of non - traditional, entropy - and self - organisation - based models for the prediction of complex equilibrium morphologies. They have made use of non - conventional models in order to explore different objective function formulations or self - organisation rules and the sensitivity of these, and have compared the models to laboratory results. The work documented in this dissertation shows that it is possible to use self - organisation - and entropy - based modelling methods to predict stable, equilibrium morphologies in coastal and river environments. / Thesis (Ph.D.)--School of Civil and Environmental Engineering, 2006.

The understanding of the emergent behaviour of complex systems is probably one of the most intriguing challenges in modern theoretical physics. In the present Thesis we use novel data analysis techniques and numerical simulations in order to shed some light on the fundamental mechanisms involved in their dynamics. We divide the main core of the research into three parts, each of which address a specific, and formally well defined, issue. In the first part, we study the processes of self - organization and herding in the evolution of the stock market. The data analysis, carried out over the fluctuations of several international indices, shows an avalanche - like dynamics characterized by power laws and indicative of a critical state. Further evidence of criticality relates to the behaviour of the price index itself. In this case we observe a power law decline with superimposed embedded log - periodic oscillations which are possibly due to an intrinsic discrete scale invariance. A stochastic cellular automata, instead, is used to mimic an open stock market and reproduce the herding behaviour responsible for the large fluctuations observed in the price. The results underline the importance of the largest clusters of traders which, alone, can induce a large displacement between demand and supply and lead to a crash. The second part of the Thesis focuses on the role played by the complex network of interactions that is created among the elementary parts of the system itself. We consider, in particular, the influence of the so - called " scale - free " networks, where the distribution of connectivity follows a power law, on the antiferromagnetic Ising model and on a model of stochastic opinion formation. Novel features, not encountered on regular lattices, have been pointed out. In the former case a spin glass transition at low temperatures is present while, in the latter, the turbulent - like behaviour emerging from the model is found to be particularly robust against the indecision of the agents. The last part is left for a numerical investigation of an extremal dynamical model for evolution / extinction of species. We demonstrate how the mutual cooperation between them comes to play a fundamental role in the survival probability : a healthy environment can support even less fitted species. / Thesis (Ph.D.)--School of Chemistry and Physics, 2006.

<p>Many of the attacks on computing clusters and grids have been performed by using stolen authentication passwords and unprotected SSH keys, therefore there is a need for a system that can detect intruders masquerading as ordinary users. Our assumption is that an attacker behaves significantly different compared to an ordinary user. Previous work in this area is for example statistical analysis of process accounting using Support Vector Machines. We can formalize this into a classification problem that we will solve with Self-organizing maps. The proposed system will work in a tier model that uses process accounting and SSH log messages as data sources.</p>