48 research outputs found
Profile control chart based on maximum entropy
Monitoring a process over time is so important in manufacturing processes to
reduce the wastage of money and time. The purpose of this article is to monitor
profile coefficients instead of a process mean. In this paper, two methods are
proposed for monitoring the intercept and slope of the simple linear profile,
simultaneously. The first one is linear regression, and another one is the
maximum entropy principle. A simulation study is applied to compare the two
methods in terms of the second type of error and average run length. Finally,
two real examples are presented to demonstrate the ability of the proposed
chart
Reliability-Based Optimum Inspection Planning for Components Subjected to Fatigue Induced Damage
The degradation of metallic systems under cyclic loading is prone to significant uncertainty. This uncertainty in turn affects the reliability in the prediction of residual lifetime and the subsequent decision regarding the optimum inspection and maintenance schedules. In particular, the experimental data on the evolution of fatigue-induced cracks shows significant scatter stemming from initial flaws, metallurgical heterogeneities, and randomness in material properties like yield stress and fracture toughness. The objective of this research is to improve the reliability-based optimal inspection planning of metallic systems subjected to fatigue, taking into account the associated uncertainty. To that end, this research aims to address the two main challenges faced in developing a credible reliability-based framework for lifecycle management of fatigue-critical components. The first challenge is to construct a stochastic model that can adequately capture the nonlinearity and uncertainty observed in the crack growth histories. The second one involves presenting a computationally efficient strategy for solving the stochastic optimization associated with optimum maintenance scheduling. In order to fulfill these objectives, a Polynomial Chaos (PC) representation is constructed of fatigue-induced crack growth process using a database from a constant amplitude loading experiment. The PC representation relies on expanding the crack growth stochastic process on a set of random basis functions whose coefficients are estimated from the experimental database. The probabilistic model obtained is then integrated into a reliability framework that minimizes the total expected life-cycle cost of the system subjected to constraints in terms of time to inspections, and the maximum probability of failure defined by the limit state function. Lastly, an efficient and accurate optimization strategy that uses surrogate models is suggested to solve the stochastic optimization problem. The sensitivity of the optimum solution to the level of risk is also examined. This research aims to provide a decision support tool for informed decision-making under uncertainty in the life-cycle planning of systems subjected to fatigue failure
Advances in Theoretical and Computational Energy Optimization Processes
The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes
Automatic differentiation algorithms in model analysis
Title: Automatic differentiation algorithms in model analysisAuthor: M.J. HuiskesDate: 19 March, 2002In this thesis automatic differentiation algorithms and derivative-based methods are combined to develop efficient tools for model analysis. Automatic differentiation algorithms comprise a class of algorithms aimed at the derivative computation of functions that are represented as computer code. Derivative-based methods that may be implemented using these algorithms are presented for sensitivity analysis and statistical inference, particularly in the context of nonlinear parameter estimation.Local methods of sensitivity analysis are discussed for both explicit and implicit relations between variables. Particular attention is paid to propagation of uncertainty, and to the subsequent uncertainty decomposition of output uncertainty in the various sources of input uncertainty.Statistical methods are presented for the computation of accurate inferential information for nonlinear parameter estimation problems by means of higher-order derivatives of the model functions. Methods are also discussed for the assessment of the appropriateness of model structure complexity in relation to quality of data.To realize and demonstrate the potential of routines for model analysis based on automatic differentiation a software library is developed: a C++ library for the analysis of nonlinear models that can be represented by differentiable functions in which the methods for parameter estimation, statistical inference, model selection and sensitivity analysis are implemented. Several experiments are performed to assess the performance of the library. The application of the derivative-based methods and the routines of the library is further demonstrated by means of a number of case studies in ecological assessment. In two studies, large parameter estimation procedures for fish stock assessment are analyzed: for the Pacific halibut and North Sea herring species. The derivative-based methods of sensitivity analysis are applied in a study on the contribution of Russian forests to the global carbon cycle
Bayesiaanse geïntegreerde bepaling van de effectieve ionaire lading via remstralings- en ladingsuitwisselingsspectroscopie in tokamakplasma's
Dit doctoraatswerk is gekaderd in de ontwikkeling van gecontroleerde thermonucleaire fusie als een schone, veilige en nagenoeg onuitputtelijke energiebron. Het is geconcentreerd op magnetische opsluiting in de tokamakconfiguratie. In een eerste, experimenteel gedeelte werd een nieuwe diagnostiek ontwikkeld voor remstralingsspectroscopie in het zichtbare aan de TEXTOR-tokamak (Institut fuer Plasmaphysik, Forschungszentrum Juelich, Duitsland). De diagnostiek voorziet 24 zichtlijnen gekoppeld aan een gekoelde CCD-camera, waardoor de voordelen van zowel een relatief hoge tijdsresolutie als ruimtelijke resolutie worden gecombineerd. Emissiviteitsprofielen van remstraling kunnen gereconstrueerd worden door een Abel-inversie. De betrouwbaarheid van de gereconstrueerde profielen werd vergroot door Tikhonov- en Maximum Entropie-regularisatie. Op die manier kunnen samen met profielen van de elektrondichtheid en de elektrontemperatuur, profielen voor de effectieve ionaire lading Zeff afgeleid worden. Een nieuwe methode voor de relatieve calibratie van het systeem werd bedacht en getest, gebaseerd op de consistentievereiste van profielen onder een verandering van zichtgeometrie. In een tweede deel van het doctoraatswerk werd Bayesiaanse waarschijnlijkheidsrekening gebruikt met het oog op de oplossing van het aloude probleem van de incompatibiliteit van Zeff-schattingen afgeleid uit remstralingsspectroscopie enerzijds en uit de gewogen sommatie van individuele onzuiverheidsconcentraties verkregen door ladingsuitwisselingsspectroscopie (CXS) anderzijds. Een probabilistisch model werd opgezet dat metingen van zowel remstralingsspectroscopie als CXS integreert. Inherente statistische en systematische onzekerheden in de metingen werden op een behoorlijke manier in rekening gebracht. Hierdoor werd het mogelijk een meest waarschijnlijke waarde voor Zeff op de magnetische as af te leiden, die consistent is met beide sets metingen en met kleinere foutenmarges dan voordien. Het uiteindelijke doel is de betrouwbaarheid en robuustheid van Zeff-profielen te verbeteren over de volledige plasmadoorsnede, terwijl consistentheid met alle beschikbare ruwe metingen behouden blijft. Een gelijkaardige Bayesiaanse analyse kan toegepast worden op vele (sets van) fusiediagnostieken en dit heeft een aanzienlijk potentieel om in het fusieonderzoek de algemene consistentie en nauwkeurigheid van data te verbeteren
Timely Classification of Encrypted or ProtocolObfuscated Internet Traffic Using Statistical Methods
Internet traffic classification aims to identify the type of application or protocol that generated
a particular packet or stream of packets on the network. Through traffic classification,
Internet Service Providers (ISPs), governments, and network administrators can
access basic functions and several solutions, including network management, advanced
network monitoring, network auditing, and anomaly detection. Traffic classification is
essential as it ensures the Quality of Service (QoS) of the network, as well as allowing
efficient resource planning.
With the increase of encrypted or obfuscated protocol traffic on the Internet and multilayer
data encapsulation, some classical classification methods have lost interest from the
scientific community. The limitations of traditional classification methods based on port
numbers and payload inspection to classify encrypted or obfuscated Internet traffic have
led to significant research efforts focused on Machine Learning (ML) based classification
approaches using statistical features from the transport layer. In an attempt to increase
classification performance, Machine Learning strategies have gained interest from the scientific
community and have shown promise in the future of traffic classification, specially
to recognize encrypted traffic.
However, ML approach also has its own limitations, as some of these methods have a
high computational resource consumption, which limits their application when classifying
large traffic or realtime
flows. Limitations of ML application have led to the investigation
of alternative approaches, including featurebased
procedures and statistical methods. In
this sense, statistical analysis methods, such as distances and divergences, have been used
to classify traffic in large flows and in realtime.
The main objective of statistical distance is to differentiate flows and find a pattern in
traffic characteristics through statistical properties, which enable classification. Divergences
are functional expressions often related to information theory, which measure the
degree of discrepancy between any two distributions.
This thesis focuses on proposing a new methodological approach to classify encrypted
or obfuscated Internet traffic based on statistical methods that enable the evaluation of
network traffic classification performance, including the use of computational resources
in terms of CPU and memory. A set of traffic classifiers based on KullbackLeibler
and
JensenShannon
divergences, and Euclidean, Hellinger, Bhattacharyya, and Wootters distances
were proposed. The following are the four main contributions to the advancement
of scientific knowledge reported in this thesis.
First, an extensive literature review on the classification of encrypted and obfuscated Internet traffic was conducted. The results suggest that portbased
and payloadbased
methods are becoming obsolete due to the increasing use of traffic encryption and multilayer
data encapsulation. MLbased
methods are also becoming limited due to their computational
complexity. As an alternative, Support Vector Machine (SVM), which is also
an ML method, and the KolmogorovSmirnov
and Chisquared
tests can be used as reference
for statistical classification. In parallel, the possibility of using statistical methods
for Internet traffic classification has emerged in the literature, with the potential of good
results in classification without the need of large computational resources. The potential
statistical methods are Euclidean Distance, Hellinger Distance, Bhattacharyya Distance,
Wootters Distance, as well as KullbackLeibler
(KL) and JensenShannon
divergences.
Second, we present a proposal and implementation of a classifier based on SVM for P2P
multimedia traffic, comparing the results with KolmogorovSmirnov
(KS) and Chisquare
tests. The results suggest that SVM classification with Linear kernel leads to a better classification
performance than KS and Chisquare
tests, depending on the value assigned to
the Self C parameter. The SVM method with Linear kernel and suitable values for the Self
C parameter may be a good choice to identify encrypted P2P multimedia traffic on the
Internet.
Third, we present a proposal and implementation of two classifiers based on KL Divergence
and Euclidean Distance, which are compared to SVM with Linear kernel, configured
with the standard Self C parameter, showing a reduced ability to classify flows based
solely on packet sizes compared to KL and Euclidean Distance methods. KL and Euclidean
methods were able to classify all tested applications, particularly streaming and P2P,
where for almost all cases they efficiently identified them with high accuracy, with reduced
consumption of computational resources. Based on the obtained results, it can be
concluded that KL and Euclidean Distance methods are an alternative to SVM, as these
statistical approaches can operate in realtime
and do not require retraining every time a
new type of traffic emerges.
Fourth, we present a proposal and implementation of a set of classifiers for encrypted
Internet traffic, based on JensenShannon
Divergence and Hellinger, Bhattacharyya, and
Wootters Distances, with their respective results compared to those obtained with methods
based on Euclidean Distance, KL, KS, and ChiSquare.
Additionally, we present a comparative
qualitative analysis of the tested methods based on Kappa values and Receiver
Operating Characteristic (ROC) curves. The results suggest average accuracy values above
90% for all statistical methods, classified as ”almost perfect reliability” in terms of Kappa
values, with the exception of KS. This result indicates that these methods are viable options
to classify encrypted Internet traffic, especially Hellinger Distance, which showed
the best Kappa values compared to other classifiers. We conclude that the considered
statistical methods can be accurate and costeffective
in terms of computational resource
consumption to classify network traffic. Our approach was based on the classification of Internet network traffic, focusing on statistical
distances and divergences. We have shown that it is possible to classify and obtain
good results with statistical methods, balancing classification performance and the
use of computational resources in terms of CPU and memory. The validation of the proposal
supports the argument of this thesis, which proposes the implementation of statistical
methods as a viable alternative to Internet traffic classification compared to methods
based on port numbers, payload inspection, and ML.A classificação de tráfego Internet visa identificar o tipo de aplicação ou protocolo que
gerou um determinado pacote ou fluxo de pacotes na rede. Através da classificação de
tráfego, Fornecedores de Serviços de Internet (ISP), governos e administradores de rede
podem ter acesso às funções básicas e várias soluções, incluindo gestão da rede, monitoramento
avançado de rede, auditoria de rede e deteção de anomalias. Classificar o tráfego é
essencial, pois assegura a Qualidade de Serviço (QoS) da rede, além de permitir planear
com eficiência o uso de recursos.
Com o aumento de tráfego cifrado ou protocolo ofuscado na Internet e do encapsulamento
de dados multicamadas, alguns métodos clássicos da classificação perderam interesse de
investigação da comunidade científica. As limitações dos métodos tradicionais da classificação
com base no número da porta e na inspeção de carga útil payload para classificar
o tráfego de Internet cifrado ou ofuscado levaram a esforços significativos de investigação
com foco em abordagens da classificação baseadas em técnicas de Aprendizagem
Automática (ML) usando recursos estatísticos da camada de transporte. Na tentativa
de aumentar o desempenho da classificação, as estratégias de Aprendizagem Automática
ganharam o interesse da comunidade científica e se mostraram promissoras no futuro da
classificação de tráfego, principalmente no reconhecimento de tráfego cifrado.
No entanto, a abordagem em ML também têm as suas próprias limitações,
pois alguns
desses métodos possuem um elevado consumo de recursos computacionais, o que limita
a sua aplicação para classificação de grandes fluxos de tráfego ou em tempo real. As limitações
no âmbito da aplicação de ML levaram à investigação de abordagens alternativas,
incluindo procedimentos baseados em características e métodos estatísticos. Neste sentido,
os métodos de análise estatística, tais como distâncias e divergências, têm sido utilizados
para classificar tráfego em grandes fluxos e em tempo real.
A distância estatística possui como objetivo principal diferenciar os fluxos e permite encontrar
um padrão nas características de tráfego através de propriedades estatísticas, que
possibilitam a classificação. As divergências são expressões funcionais frequentemente
relacionadas com a teoria da informação, que mede o grau de discrepância entre duas
distribuições quaisquer.
Esta tese focase
na proposta de uma nova abordagem metodológica para classificação de
tráfego cifrado ou ofuscado da Internet com base em métodos estatísticos que possibilite
avaliar o desempenho da classificação de tráfego de rede, incluindo a utilização de recursos
computacionais, em termos de CPU e memória. Foi proposto um conjunto de classificadores
de tráfego baseados nas Divergências de KullbackLeibler
e JensenShannon
e Distâncias Euclidiana, Hellinger, Bhattacharyya e Wootters. A seguir resumemse
os tese.
Primeiro, realizámos uma ampla revisão de literatura sobre classificação de tráfego cifrado
e ofuscado de Internet. Os resultados sugerem que os métodos baseados em porta e
baseados em carga útil estão se tornando obsoletos em função do crescimento da utilização
de cifragem de tráfego e encapsulamento de dados multicamada. O tipo de métodos
baseados em ML também está se tornando limitado em função da complexidade computacional.
Como alternativa, podese
utilizar a Máquina de Vetor de Suporte (SVM),
que também é um método de ML, e os testes de KolmogorovSmirnov
e Quiquadrado
como referência de comparação da classificação estatística. Em paralelo, surgiu na literatura
a possibilidade de utilização de métodos estatísticos para classificação de tráfego
de Internet, com potencial de bons resultados na classificação sem aporte de grandes recursos
computacionais. Os métodos estatísticos potenciais são as Distâncias Euclidiana,
Hellinger, Bhattacharyya e Wootters, além das Divergências de Kullback–Leibler (KL) e
JensenShannon.
Segundo, apresentamos uma proposta e implementação de um classificador baseado na
Máquina de Vetor de Suporte (SVM) para o tráfego multimédia P2P (PeertoPeer),
comparando
os resultados com os testes de KolmogorovSmirnov
(KS) e Quiquadrado.
Os
resultados sugerem que a classificação da SVM com kernel Linear conduz a um melhor
desempenho da classificação do que os testes KS e Quiquadrado,
dependente do valor
atribuído ao parâmetro Self C. O método SVM com kernel Linear e com valores adequados
para o parâmetro Self C pode ser uma boa escolha para identificar o tráfego Par a Par
(P2P) multimédia cifrado na Internet.
Terceiro, apresentamos uma proposta e implementação de dois classificadores baseados
na Divergência de KullbackLeibler (KL) e na Distância Euclidiana, sendo comparados
com a SVM com kernel Linear, configurado para o parâmestro Self C padrão, apresenta
reduzida
capacidade de classificar fluxos com base apenas nos tamanhos dos pacotes
em relação aos métodos KL e Distância Euclidiana. Os métodos KL e Euclidiano foram
capazes de classificar todas as aplicações testadas, destacandose
streaming e P2P, onde
para quase todos os casos foi eficiente identificálas
com alta precisão, com reduzido consumo
de recursos computacionais.Com base nos resultados obtidos, podese
concluir que
os métodos KL e Distância Euclidiana são uma alternativa à SVM, porque essas abordagens
estatísticas podem operar em tempo real e não precisam de retreinamento cada vez
que surge um novo tipo de tráfego.
Quarto, apresentamos uma proposta e implementação de um conjunto de classificadores
para o tráfego de Internet cifrado, baseados na Divergência de JensenShannon
e nas Distâncias
de Hellinger, Bhattacharyya e Wootters, sendo os respetivos resultados comparados
com os resultados obtidos com os métodos baseados na Distância Euclidiana, KL, KS e Quiquadrado.
Além disso, apresentamos uma análise qualitativa comparativa dos
métodos testados com base nos valores de Kappa e Curvas Característica de Operação do
Receptor (ROC). Os resultados sugerem valores médios de precisão acima de 90% para todos
os métodos estatísticos, classificados como “confiabilidade quase perfeita” em valores
de Kappa, com exceçãode KS. Esse resultado indica que esses métodos são opções viáveis
para a classificação de tráfego cifrado da Internet, em especial a Distância de Hellinger,
que apresentou os melhores resultados do valor de Kappa em comparaçãocom os demais
classificadores. Concluise
que os métodos estatísticos considerados podem ser precisos e
económicos em termos de consumo de recursos computacionais para classificar o tráfego
da rede.
A nossa abordagem baseouse
na classificação de tráfego de rede Internet, focando em
distâncias e divergências estatísticas. Nós mostramos que é possível classificar e obter
bons resultados com métodos estatísticos, equilibrando desempenho de classificação e
uso de recursos computacionais em termos de CPU e memória. A validação da proposta
sustenta o argumento desta tese, que propõe a implementação de métodos estatísticos
como alternativa viável à classificação de tráfego da Internet em relação aos métodos com
base no número da porta, na inspeção de carga útil e de ML.Thesis prepared at Instituto de Telecomunicações Delegação
da Covilhã and at the Department
of Computer Science of the University of Beira Interior, and submitted to the
University of Beira Interior for discussion in public session to obtain the Ph.D. Degree in
Computer Science and Engineering.
This work has been funded by Portuguese FCT/MCTES through national funds and, when
applicable, cofunded
by EU funds under the project UIDB/50008/2020, and by operation
Centro010145FEDER000019
C4
Centro
de Competências em Cloud Computing,
cofunded
by the European Regional Development Fund (ERDF/FEDER) through
the Programa Operacional Regional do Centro (Centro 2020). This work has also been
funded by CAPES (Brazilian Federal Agency for Support and Evaluation of Graduate Education)
within the Ministry of Education of Brazil under a scholarship supported by the
International Cooperation Program CAPES/COFECUB Project
9090134/
2013 at the
University of Beira Interior