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Discovering frequent and significant episodes. Application to sequences of events recorded in power distribution networksQuiroga Quiroga, Oscar Arnulfo 18 December 2012 (has links)
This thesis proposes a formalism to analyse and automatically exploit sequences of events, which are related with faults occurred in power distribution networks and are recorded by power quality monitors at substations. This formalism allows to find dependencies or relationships among events, looking for meaningful patterns. Once those patterns are found, they can be used to better describe fault situations and their temporal evolution or can be also useful to predict future failures by recognising the events that match the early stages of a pattern. / En aquesta tesi es proposa un formalisme per analitzar conjunts de dades d'esdeveniments relacionats amb les fallades que es produeixen en les xarxes de distribució elèctrica, i explotar automàticament seqüències d'esdeveniments registrats pels monitors de qualitat d'ona instal•lats en substacions. Aquest formalisme permet cercar dependencies o relacions entre esdeveniments per trobar patrons significatius. Quan els patrons es troben, es poden utilitzar per descriure millor les situacions de fallada i la seva evolució. Els patrons també poden ser útils per a predir fallades futures mitjançant el reconeixement dels successos que coincideixin amb les primeres etapes d'un patró.
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Uma nova abordagem de aprendizagem de máquina combinando elicitação automática de casos, aprendizagem por reforço e mineração de padrões sequenciais para agentes jogadores de damasCastro Neto, Henrique de 21 November 2016 (has links)
Fundação de Amparo a Pesquisa do Estado de Minas Gerais / Agentes que operam em ambientes onde as tomadas de decisão precisam levar em
conta, além do ambiente, a atuação minimizadora de um oponente (tal como nos jogos),
é fundamental que o agente seja dotado da habilidade de, progressivamente, traçar um
perĄl de seu adversário que o auxilie em seu processo de seleção de ações apropriadas.
Entretanto, seria improdutivo construir um agente com um sistema de tomada de decisão
baseado apenas na elaboração desse perĄl, pois isso impediria o agente de ter uma Şidentidade
própriaŤ, o que o deixaria a mercê de seu adversário. Nesta direção, este trabalho
propõe um sistema automático jogador de Damas híbrido, chamado ACE-RL-Checkers,
dotado de um mecanismo dinâmico de tomada de decisões que se adapta ao perĄl de seu
oponente no decorrer de um jogo. Em tal sistema, o processo de seleção de ações (movimentos)
é conduzido por uma composição de Rede Neural de Perceptron Multicamadas e
biblioteca de casos. No caso, a Rede Neural representa a ŞidentidadeŤ do agente, ou seja,
é um módulo tomador de decisões estático já treinado e que faz uso da técnica de Aprendizagem
por Reforço TD( ). Por outro lado, a biblioteca de casos representa o módulo
tomador de decisões dinâmico do agente que é gerada pela técnica de Elicitação Automática
de Casos (um tipo particular de Raciocínio Baseado em Casos). Essa técnica possui
um comportamento exploratório pseudo-aleatório que faz com que a tomada de decisão
dinâmica do agente seja guiada, ora pelo perĄl de jogo do adversário, ora aleatoriamente.
Contudo, ao conceber tal arquitetura, é necessário evitar o seguinte problema: devido às
características inerentes à técnica de Elicitação Automática de Casos, nas fases iniciais do
jogo Ű em que a quantidade de casos disponíveis na biblioteca é extremamente baixa em
função do exíguo conhecimento do perĄl do adversário Ű a frequência de tomadas de decisão
aleatórias seria muito elevada, o que comprometeria o desempenho do agente. Para
atacar tal problema, este trabalho também propõe incorporar à arquitetura do ACE-RLCheckers
um terceiro módulo, composto por uma base de regras de experiência extraída
a partir de jogos de especialistas humanos, utilizando uma técnica de Mineração de Padrões
Sequenciais. O objetivo de utilizar tal base é reĄnar e acelerar a adaptação do
agente ao perĄl de seu adversário nas fases iniciais dos confrontos entre eles. Resultados
experimentais conduzidos em torneio envolvendo ACE-RL-Checkers e outros agentes correlacionados
com este trabalho, conĄrmam a superioridade da arquitetura dinâmica aqui
proposta. / ake into account, in addition to the environment, the minimizing action of an opponent
(such as in games), it is fundamental that the agent has the ability to progressively trace
a proĄle of its adversary that aids it in the process of selecting appropriate actions. However,
it would be unsuitable to construct an agent with a decision-making system based
on only the elaboration of this proĄle, as this would prevent the agent from having its
Şown identityŤ, which would leave it at the mercy of its opponent. Following this direction,
this work proposes an automatic hybrid Checkers player, called ACE-RL-Checkers,
equipped with a dynamic decision-making mechanism, which adapts to the proĄle of its
opponent over the course of the game. In such a system, the action selection process
(moves) is conducted through a composition of Multi-Layer Perceptron Neural Network
and case library. In the case, Neural Network represents the ŞidentityŤ of the agent, i.e.,
it is an already trained static decision-making module and makes use of the Reinforcement
Learning TD( ) techniques. On the other hand, the case library represents the
dynamic decision-making module of the agent, which is generated by the Automatic Case
Elicitation technique (a particular type of Case-Based Reasoning). This technique has a
pseudo-random exploratory behavior, which makes the dynamic decision-making on the
part of the agent to be directed, either by the game proĄle of the opponent or randomly.
However, when devising such an architecture, it is necessary to avoid the following problem:
due to the inherent characteristics of the Automatic Case Elicitation technique, in
the game initial phases, in which the quantity of available cases in the library is extremely
low due to low knowledge content concerning the proĄle of the adversary, the decisionmaking
frequency for random decisions is extremely high, which would be detrimental
to the performance of the agent. In order to attack this problem, this work also proposes
to incorporate onto the ACE-RL-Checkers architecture a third module composed
of a base of experience rules, extracted from games played by human experts, using a
Sequential Pattern Mining technique. The objective behind using such a base is to reĄne
and accelerate the adaptation of the agent to the proĄle of its opponent in the initial
phases of their confrontations. Experimental results conducted in tournaments involving
ACE-RL-Checkers and other agents correlated with this work, conĄrm the superiority of
the dynamic architecture proposed herein. / Tese (Doutorado)
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Méthodes hybrides parallèles pour la résolution de problèmes d'optimisation combinatoire : application au clustering sous contraintes / Parallel hybrid methods for solving combinatorial optimization problems : application to clustering under constraintsOuali, Abdelkader 03 July 2017 (has links)
Les problèmes d’optimisation combinatoire sont devenus la cible de nombreuses recherches scientifiques pour leur importance dans la résolution de problèmes académiques et de problèmes réels rencontrés dans le domaine de l’ingénierie et dans l’industrie. La résolution de ces problèmes par des méthodes exactes ne peut être envisagée à cause des délais de traitement souvent exorbitants que nécessiteraient ces méthodes pour atteindre la (les) solution(s) optimale(s). Dans cette thèse, nous nous sommes intéressés au contexte algorithmique de résolution des problèmes combinatoires, et au contexte de modélisation de ces problèmes. Au niveau algorithmique, nous avons appréhendé les méthodes hybrides qui excellent par leur capacité à faire coopérer les méthodes exactes et les méthodes approchées afin de produire rapidement des solutions. Au niveau modélisation, nous avons travaillé sur la spécification et la résolution exacte des problématiques complexes de fouille des ensembles de motifs en étudiant tout particulièrement le passage à l’échelle sur des bases de données de grande taille. D'une part, nous avons proposé une première parallélisation de l'algorithme DGVNS, appelée CPDGVNS, qui explore en parallèle les différents clusters fournis par la décomposition arborescente en partageant la meilleure solution trouvée sur un modèle maître-travailleur. Deux autres stratégies, appelées RADGVNS et RSDGVNS, ont été proposées qui améliorent la fréquence d'échange des solutions intermédiaires entre les différents processus. Les expérimentations effectuées sur des problèmes combinatoires difficiles montrent l'adéquation et l'efficacité de nos méthodes parallèles. D'autre part, nous avons proposé une approche hybride combinant à la fois les techniques de programmation linéaire en nombres entiers (PLNE) et la fouille de motifs. Notre approche est complète et tire profit du cadre général de la PLNE (en procurant un haut niveau de flexibilité et d’expressivité) et des heuristiques spécialisées pour l’exploration et l’extraction de données (pour améliorer les temps de calcul). Outre le cadre général de l’extraction des ensembles de motifs, nous avons étudié plus particulièrement deux problèmes : le clustering conceptuel et le problème de tuilage (tiling). Les expérimentations menées ont montré l’apport de notre proposition par rapport aux approches à base de contraintes et aux heuristiques spécialisées. / Combinatorial optimization problems have become the target of many scientific researches for their importance in solving academic problems and real problems encountered in the field of engineering and industry. Solving these problems by exact methods is often intractable because of the exorbitant time processing that these methods would require to reach the optimal solution(s). In this thesis, we were interested in the algorithmic context of solving combinatorial problems, and the modeling context of these problems. At the algorithmic level, we have explored the hybrid methods which excel in their ability to cooperate exact methods and approximate methods in order to produce rapidly solutions of best quality. At the modeling level, we worked on the specification and the exact resolution of complex problems in pattern set mining, in particular, by studying scaling issues in large databases. On the one hand, we proposed a first parallelization of the DGVNS algorithm, called CPDGVNS, which explores in parallel the different clusters of the tree decomposition by sharing the best overall solution on a master-worker model. Two other strategies, called RADGVNS and RSDGVNS, have been proposed which improve the frequency of exchanging intermediate solutions between the different processes. Experiments carried out on difficult combinatorial problems show the effectiveness of our parallel methods. On the other hand, we proposed a hybrid approach combining techniques of both Integer Linear Programming (ILP) and pattern mining. Our approach is comprehensive and takes advantage of the general ILP framework (by providing a high level of flexibility and expressiveness) and specialized heuristics for data mining (to improve computing time). In addition to the general framework for the pattern set mining, two problems were studied: conceptual clustering and the tiling problem. The experiments carried out showed the contribution of our proposition in relation to constraint-based approaches and specialized heuristics.
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