Efficient Data Gathering in Cloud Computing Using Clustering Schemes, Cloud Agent-Based Data Schemes and Efficient Path Planning Techniques

Cloud computing is a rapidly evolving field with a wide range of applications, including data storage, processing, and analysis. Clustering schemes, cloud agent-based data schemes, and efficient path planning techniques are all important aspects of cloud computing. Clustering schemes are used to group similar data items together, which can improve the efficiency of data processing and analysis. Cloud agent-based data schemes allow for the collection and management of data from a variety of sources, including cloud and edge devices. Efficient path planning techniques are used to optimize the routing of data packets in cloud networks. This research paper provides a comprehensive overview of clustering schemes, cloud agent-based data schemes, and efficient path planning techniques for cloud computing. The paper discusses the different types of clustering schemes and cloud agent-based data schemes, as well as their advantages and disadvantages. The paper also presents an overview of efficient path planning techniques for cloud computing. The paper concludes by discussing the challenges and future research directions in cloud computing. The authors believe that cloud computing is a rapidly evolving field with a bright future, and they encourage researchers to continue to develop new and innovative solutions for cloud computing

Evidential Evolving Gustafson-Kessel Algorithm For Online Data Streams Partitioning Using Belief Function Theory.

International audienceA new online clustering method called E2GK (Evidential Evolving Gustafson-Kessel) is introduced. This partitional clustering algorithm is based on the concept of credal partition defined in the theoretical framework of belief functions. A credal partition is derived online by applying an algorithm resulting from the adaptation of the Evolving Gustafson-Kessel (EGK) algorithm. Online partitioning of data streams is then possible with a meaningful interpretation of the data structure. A comparative study with the original online procedure shows that E2GK outperforms EGK on different entry data sets. To show the performance of E2GK, several experiments have been conducted on synthetic data sets as well as on data collected from a real application problem. A study of parameters' sensitivity is also carried out and solutions are proposed to limit complexity issues

Robust approach to object recognition through fuzzy clustering and hough transform based methods

Object detection from two dimensional intensity images as well as three dimensional range images is considered. The emphasis is on the robust detection of shapes such as cylinders, spheres, cones, and planar surfaces, typically found in mechanical and manufacturing engineering applications. Based on the analyses of different HT methods, a novel method, called the Fast Randomized Hough Transform (FRHT) is proposed. The key idea of FRHT is to divide the original image into multiple regions and apply random sampling method to map data points in the image space into the parameter space or feature space, then obtain the parameters of true clusters. This results in the following characteristics, which are highly desirable in any method: high computation speed, low memory requirement, high result resolution and infinite parameter space. This project also considers use of fuzzy clustering techniques, such as Fuzzy C Quadric Shells (FCQS) clustering algorithm but combines the concept of noise prototype to form the Noise FCQS clustering algorithm that is robust against noise. Then a novel integrated clustering algorithm combining the advantages of FRHT and NFCQS methods is proposed. It is shown to be a robust clustering algorithm having the distinct advantages such as: the number of clusters need not be known in advance, the results are initialization independent, the detection accuracy is greatly improved, and the computation speed is very fast. Recent concepts from robust statistics, such as least trimmed squares estimation (LTS), minimum volume ellipsoid estimator (MVE) and the generalized MVE are also utilized to form a new robust algorithm called the generalized LTS for Quadric Surfaces (GLTS-QS) algorithm is developed. The experimental results indicate that the clustering method combining the FRHT and the GLTS-QS can improve clustering performance. Moreover, a new cluster validity method for circular clusters is proposed by considering the distribution of the points on the circular edge. Different methods for the computation of distance of a point from a cluster boundary, a common issue in all the range image clustering algorithms, are also discussed. The performance of all these algorithms is tested using various real and synthetic range and intensity images. The application of the robust clustering methods to the experimental granular flow research is also included

An efficient polynomial chaos-based proxy model for history matching and uncertainty quantification of complex geological structures

A novel polynomial chaos proxy-based history matching and uncertainty quantification method is presented that can be employed for complex geological structures in inverse problems. For complex geological structures, when there are many unknown geological parameters with highly nonlinear correlations, typically more than 106 full reservoir simulation runs might be required to accurately probe the posterior probability space given the production history of reservoir. This is not practical for high-resolution geological models. One solution is to use a "proxy model" that replicates the simulation model for selected input parameters. The main advantage of the polynomial chaos proxy compared to other proxy models and response surfaces is that it is generally applicable and converges systematically as the order of the expansion increases. The Cameron and Martin theorem 2.24 states that the convergence rate of the standard polynomial chaos expansions is exponential for Gaussian random variables. To improve the convergence rate for non-Gaussian random variables, the generalized polynomial chaos is implemented that uses an Askey-scheme to choose the optimal basis for polynomial chaos expansions [199]. Additionally, for the non-Gaussian distributions that can be effectively approximated by a mixture of Gaussian distributions, we use the mixture-modeling based clustering approach where under each cluster the polynomial chaos proxy converges exponentially fast and the overall posterior distribution can be estimated more efficiently using different polynomial chaos proxies. The main disadvantage of the polynomial chaos proxy is that for high-dimensional problems, the number of the polynomial chaos terms increases drastically as the order of the polynomial chaos expansions increases. Although different non-intrusive methods have been developed in the literature to address this issue, still a large number of simulation runs is required to compute high-order terms of the polynomial chaos expansions. This work resolves this issue by proposing the reduced-terms polynomial chaos expansion which preserves only the relevant terms in the polynomial chaos representation. We demonstrated that the sparsity pattern in the polynomial chaos expansion, when used with the Karhunen-Loéve decomposition method or kernel PCA, can be systematically captured. A probabilistic framework based on the polynomial chaos proxy is also suggested in the context of the Bayesian model selection to study the plausibility of different geological interpretations of the sedimentary environments. The proposed surrogate-accelerated Bayesian inverse analysis can be coherently used in practical reservoir optimization workflows and uncertainty assessments

Applications of machine learning in diagnostics and prognostics of wind turbine high speed generator failure

The cost of wind energy has decreased over the last decade as technology has matured and the industry has benefited greatly from economies of scale. That being said, operations and maintenance still make up a significant proportion of the overall costs and needs to be reduced over the coming years as sites, particularly offshore, get larger and more remote. One of the key tools to achieve this is through enhancements of both SCADA and condition monitoring system analytics, leading to more informed and optimised operational decisions. Specifically examining the wind turbine generator and highspeed assembly, this thesis aims to showcase how machine learning techniques can be utilised to enhance vibration spectral analysis and SCADA analysis for early and more automated fault detection. First this will be performed separately based on features extracted from the vibration spectra and performance data in isolation before a framework will be presented to combine data sources to create a single anomaly detection model for early fault diagnosis. Additionally by further utilising vibration based analysis, machine learning techniques and a synchronised database of failures, remaining useful life prediction will also be explored for generator bearing faults, a key component when it comes to increasing wind turbine generator reliability. It will be shown that through early diagnosis and accurate prognosis, component replacements can be planned and optimised before catastrophic failures and large downtimes occur. Moreover, results also indicate that this can have a significant impact on the costs of operation and maintenance over the lifetime of an offshore development.The cost of wind energy has decreased over the last decade as technology has matured and the industry has benefited greatly from economies of scale. That being said, operations and maintenance still make up a significant proportion of the overall costs and needs to be reduced over the coming years as sites, particularly offshore, get larger and more remote. One of the key tools to achieve this is through enhancements of both SCADA and condition monitoring system analytics, leading to more informed and optimised operational decisions. Specifically examining the wind turbine generator and highspeed assembly, this thesis aims to showcase how machine learning techniques can be utilised to enhance vibration spectral analysis and SCADA analysis for early and more automated fault detection. First this will be performed separately based on features extracted from the vibration spectra and performance data in isolation before a framework will be presented to combine data sources to create a single anomaly detection model for early fault diagnosis. Additionally by further utilising vibration based analysis, machine learning techniques and a synchronised database of failures, remaining useful life prediction will also be explored for generator bearing faults, a key component when it comes to increasing wind turbine generator reliability. It will be shown that through early diagnosis and accurate prognosis, component replacements can be planned and optimised before catastrophic failures and large downtimes occur. Moreover, results also indicate that this can have a significant impact on the costs of operation and maintenance over the lifetime of an offshore development

Análise de padrões biométricos para otimização do desempenho académico

Tese de mestrado integrado em Engenharia InformáticaNowadays the demand for better results both academically and professionally has been increasing, causing people to have to deal with this tension daily. The fact that they leave the zone of tranquility does not mean that it is harmful, but when they are exposed to this type of situations for an extended time usually leads to health degradation. In this way, it can compromise the performance in the accomplishment of the tasks and influence in a negative way the desired productivity. Although there are already some techniques to help control stress, it is difficult to have an exact idea of when it appears and its influence on the final results. With the study focus on the academic environment, the goal is to collect the most information from the students during the exams. After the collection process, it is necessary to treat the data to later apply machine learning, using the most appropriate algorithm, to establish a pattern that allows to perceive the impact of the stress in the students. However, linking the different stages of the process can be an adversity to the progress of the study of stress, compromising the quality of the results and the time to achieve them. In order to solve this problem, in this dissertation project was developed a platform, Learning Management System (LMS), where the students can carry out the exams and in parallel the respective biometric data is collected to be analyzed afterwards. After the process is completed, the platform presents the results obtained through simple and intuitive interfaces allowing the user to visualize and draw conclusions.Hoje em dia a exigência de melhores resultados quer a nível académico como profissional tem vindo a aumentar, levando as pessoas a ter que lidar com essa tensão diariamente. O facto de saírem da zona de tranquilidade, não significa que seja prejudicial, mas quando ficam expostas a este tipo de situações por tempo prolongado normalmente leva a um agravamento a nível do estado de saúde. Deste modo, poderá comprometer o desempenho na realização das tarefas e influenciar de forma negativa o rendimento desejado. Apesar de já existirem algumas técnicas para ajudar a controlar o stress, não se consegue ter uma ideia exata do momento em que este aparece e a sua influência nos resultados finais. Tendo como foco de estudo o meio académico, o objetivo é recolher o maior número de informações dos estudantes durante a realização dos exames. Após o processo de recolha é necessário tratar os dados para posteriormente aplicar algoritmos de machine learning, utilizando o algoritmo mais adequado, de forma a estabelecer um padrão que permita perceber o impacto do stress nos estudantes. Contudo, a ligação das diferentes fases do processo pode ser uma adversidade para o progresso do estudo do stress, comprometendo a qualidade dos resultados e o tempo para a sua realização. De forma a resolver este problema, neste projeto de dissertação propõe-se o desenvolvimento de uma plataforma, Sistema de Gestão da Aprendizagem (SGA), onde os alunos possam realizar as provas e em simultâneo sejam recolhidos os respetivos dados biométricos a analisar posteriormente. Após o processo estar concluído, a plataforma apresenta os resultados obtidos através de interfaces simples e intuitivas permitindo ao utilizador visualizar e tirar conclusões

Models to Combat Email Spam Botnets and Unwanted Phone Calls

With the amount of email spam received these days it is hard to imagine that spammers act individually. Nowadays, most of the spam emails have been sent from a collection of compromised machines controlled by some spammers. These compromised computers are often called bots, using which the spammers can send massive volume of spam within a short period of time. The motivation of this work is to understand and analyze the behavior of spammers through a large collection of spam mails. My research examined a the data set collected over a 2.5-year period and developed an algorithm which would give the botnet features and then classify them into various groups. Principal component analysis was used to study the association patterns of group of spammers and the individual behavior of a spammer in a given domain. This is based on the features which capture maximum variance of information we have clustered. Presence information is a growing tool towards more efficient communication and providing new services and features within a business setting and much more. The main contribution in my thesis is to propose the willingness estimator that can estimate the callee's willingness without his/her involvement, the model estimates willingness level based on call history. Finally, the accuracy of the proposed willingness estimator is validated with the actual call logs