Indiana University-Purdue University Indianapolis (IUPUI) / In a wide-area power system, detecting dynamic events is critical to maintaining system stability. Large events, such as the loss of a generator or fault on a transmission line, can compromise the stability of the system by causing the generator rotor angles to diverge and lose synchronism with the rest of the system. If these events can be detected as they happen, controls can be applied to the system to prevent it from losing synchronous stability. In order to detect these events, pattern recognition tools can be applied to system measurements. In this thesis, the pattern recognition tool decision trees (DTs) were used for event detection. A single DT produced rules distinguishing between and the event and no event cases by learning on a training set of simulations of a power system model. The rules were then applied to test cases to determine the accuracy of the event detection. To use a DT to detect events, the variables used to produce the rules must be chosen. These variables can be direct system measurements, such as the phase angle of bus voltages, or indices created by a combination of system measurements. One index used in this thesis was the integral square bus angle (ISBA) index, which provided a measure of the overall activity of the bus angles in the system. Other indices used were the variance and rate of change of the ISBA. Fitting a polynomial curve to a sliding window of these indices and then taking the difference between the polynomial and the actual index was found to produce a new index that was non-zero during the event and zero all other times for most simulations. After the index to detect events was chosen to be the error between the curve and the ISBA indices, a set of power system cases were created to be used as the training data set for the DT. All of these cases contained one event, either a small or large power injection at a load bus in the system model. The DT was then trained to detect the large power injection but not the small one. This was done so that the rules produced would detect large events on the system that could potentially cause the system to lose synchronous stability but ignore small events that have no effect on the overall system. This DT was then combined with a second DT that predicted instability such that the second DT made the decision whether or not to apply controls only for a short time after the end of every event, when controls would be most effective in stabilizing the system.

In dieser Arbeit nutzen wir Effektive Feldtheorien, genauer gesagt die SMEFT, um BSM-Effekte modellunabhängig zu parametrisieren. Wir demonstrieren die Relevanz von Präzisionsmessungen sowohl an aktuellen als auch an zukünftigen Hadronenbeschleunigern durch die Untersuchung von Vh-Dibosonen-Prozessen. Diese Prozesse ermöglichen uns die Untersuchung einer Reihe von Dimension-6-Operatoren, die BSM-Effekte erzeugen, die mit der Schwerpunktsenergie wachsen. Im Besonderen betrachten wir die leptonischen Zerfallskanäle der Vektorbosonen und zwei verschiedene Zerfallsmodi des Higgs-Bosons, den Diphoton-Kanal und den h->bb-Kanal. Der Diphoton-Kanal zeichnet sich durch eine saubere Signatur aus, die mit relativ einfachen Mitteln sehr gut von den relevanten Hintergründen unterschieden werden kann. Aufgrund der geringen Rate dieses Higgs-Zerfallskanals werden diese Prozesse allerdings erst für die Untersuchung von BSM-Effekten am FCC-hh relevant. Dank des großen h->bb Verzweigungsverhältnisse liefert der Vh(->bb)-Kanal bereits eine kompetitive Sensitivität für BSM-Effekte am LHC. Jedoch leidet dieser Kanal unter großen QCD-induzierten Hintergründen, weswegen ausgefeiltere Analysetechniken nötig sind, um dieses Niveau an BSM-Sensitivität zu erreichen. Wir leiten die erwarteten Schranken für die zuvor erwähnten Operatoren für den Vh(->gamma gamma)-Kanal am FCC-hh und für den Vh(->bb)-Kanal am LHC Run 3, HL-LHC und FCC-hh her. Unsere Studie des Vh(->bb)-Kanals zeigt, dass die Extraktion von Schranken für BSM-Operatoren an Hadronenbeschleunigern eine höchst nicht-triviale Aufgabe sein kann. Algorithmen des Maschinellen Lernens können potenziell nützlich zur Analyse solch komplexer Event-Strukturen sein. Wir leiten Schranken her, indem wir Boosted Decision Trees zur Signal-Hintergrund Klassifizierung benutzen und und vergleichen sie mit den Schranken aus der zuvor diskutierten Cut-and-Count Analyse. Wir finden eine leichte Verbesserung von O(einige %) für die verschiedenen Operatoren. / In this thesis, we utilise the framework of Effective Field Theories, more specifically the Standard Model Effective Field Theory, to parameterise New-Physics effects in a model-independent way. We demonstrate the relevance of precision measurements both at current and future hadron colliders by studying Vh-diboson-production processes. These processes allow us to probe a set of dimension-6 operators that generate BSM effects growing with the center-of-mass energy. More specifically, we consider the leptonic decay channels of the vector bosons and two different decay modes of the Higgs boson, the diphoton channel and the hadronic h->bb channel. The diphoton channel is characterised by a clean signature that can be separated very well from the relevant backgrounds with relatively simple methods. However, due to the small rate of this Higgs-decay channel, these processes will only become viable to probe New-Physics effects at the FCC-hh. Thanks to the large h->bb branching ratio, the Vh(->bb) channel already provides competitive sensitivity to BSM effects at the LHC. However, it suffers from large QCD-induced backgrounds that require us to use more sophisticated analysis techniques to achieve this level of BSM sensitivity. We derive the expected bounds on the previously mentioned dimension-6 operators from the Vh(->gamma gamma) channel at the FCC-hh and from the Vh(->bb) channel at the LHC Run 3, HL-LHC and FCC-hh. Our study of the Vh(->bb) channel demonstrates that extracting bounds on BSM operators at hadron colliders can be a highly non-trivial task. Machine-Learning algorithms can potentially be useful for the analysis of such complex event structures. We derive bounds using Boosted Decision Trees for the signal-background classification and compare them with the ones from the previously discussed cut-and-count analysis. We find a mild improvement of O(few %) across the different operators.

Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: This thesis investigates various data-driven techniques by which pronunciation dictionaries can be automatically augmented. First, well-established grapheme-to-phoneme (G2P) conversion techniques are evaluated for Standard South African English (SSAE), British English (RP) and American English (GenAm) by means of four appropriate dictionaries: SAEDICT, BEEP, CMUDICT and PRONLEX. Next, the decision tree algorithm is extended to allow the conversion of pronunciations between different accents by means of phoneme-to-phoneme (P2P) and grapheme-andphoneme- to-phoneme (GP2P) conversion. P2P conversion uses the phonemes of the source accent as input to the decision trees. GP2P conversion further incorporates the graphemes into the decision tree input. Both P2P and GP2P conversion are evaluated using the four dictionaries. It is found that, when the pronunciation is needed for a word not present in the target accent, it is substantially more accurate to modify an existing pronunciation from a different accent, than to derive it from the word’s spelling using G2P conversion. When converting between accents, GP2P conversion provides a significant further increase in performance above P2P. Finally, experiments are performed to determine how large a training dictionary is required in a target accent for G2P, P2P and GP2P conversion. It is found that GP2P conversion requires less training data than P2P and substantially less than G2P conversion. Furthermore, it is found that very little training data is needed for GP2P to perform at almost maximum accuracy. The bulk of the accuracy is achieved within the initial 500 words, and after 3000 words there is almost no further improvement. Some specific approaches to compiling the best training set are also considered. By means of an iterative greedy algorithm an optimal ranking of words to be included in the training set is discovered. Using this set is shown to lead to substantially better GP2P performance for the same training set size in comparison with alternative approaches such as the use of phonetically rich words or random selections. A mere 25 words of training data from this optimal set already achieve an accuracy within 1% of that of the full training dictionary. / AFRIKAANSE OPSOMMING: Hierdie tesis ondersoek verskeie data-gedrewe tegnieke waarmee uitspraakwoordeboeke outomaties aangevul kan word. Eerstens word gevestigde grafeem-na-foneem (G2P) omskakelingstegnieke ge¨evalueer vir Standaard Suid-Afrikaanse Engels (SSAE), Britse Engels (RP) en Amerikaanse Engels (GenAm) deur middel van vier geskikte woordeboeke: SAEDICT, BEEP, CMUDICT en PRONLEX. Voorts word die beslissingsboomalgoritme uitgebrei om die omskakeling van uitsprake tussen verskillende aksente moontlik te maak, deur middel van foneem-na-foneem (P2P) en grafeem-en-foneem-na-foneem (GP2P) omskakeling. P2P omskakeling gebruik die foneme van die bronaksent as inset vir die beslissingsbome. GP2P omskakeling inkorporeer verder die grafeme by die inset. Beide P2P en GP2P omskakeling word evalueer deur middel van die vier woordeboeke. Daar word bevind dat wanneer die uitspraak benodig word vir ’n woord wat nie in die teikenaksent teenwoordig is nie, dit bepaald meer akkuraat is om ’n bestaande uitspraak van ’n ander aksent aan te pas, as om dit af te lei vanuit die woord se spelling met G2P omskakeling. Wanneer daar tussen aksente omgeskakel word, gee GP2P omskakeling ’n verdere beduidende verbetering in akkuraatheid bo P2P. Laastens word eksperimente uitgevoer om die grootte te bepaal van die afrigtingswoordeboek wat benodig word in ’n teikenaksent vir G2P, P2P en GP2P omskakeling. Daar word bevind dat GP2P omskakeling minder afrigtingsdata as P2P en substansieel minder as G2P benodig. Verder word dit bevind dat baie min afrigtingsdata benodig word vir GP2P om teen bykans maksimum akkuraatheid te funksioneer. Die oorwig van die akkuraatheid word binne die eerste 500 woorde bereik, en n´a 3000 woorde is daar amper geen verdere verbetering nie. ’n Aantal spesifieke benaderings word ook oorweeg om die beste afrigtingstel saam te stel. Deur middel van ’n iteratiewe, gulsige algoritme word ’n optimale rangskikking van woorde bepaal vir insluiting by die afrigtingstel. Daar word getoon dat deur hierdie stel te gebruik, substansieel beter GP2P gedrag verkry word vir dieselfde grootte afrigtingstel in vergelyking met alternatiewe benaderings soos die gebruik van foneties-ryke woorde of lukrake seleksies. ’n Skamele 25 woorde uit hierdie optimale stel gee reeds ’n akkuraatheid binne 1% van di´e van die volle afrigtingswoordeboek.

his thesis implements a multivariate analysis in the current cut- based search for the charged Higgs bosons, which are new scalar particles predicted by several extensions to the Standard Model. Heavy charged Higgs bosons (mH± mtop) produced in association with a top quark de- caying via H± → τν are considered. The final state contains a hadronic τ decay, missing transverse energy and a hadronically decaying top quark. This study is based on Monte Carlo samples simulated at CM-energy √ s = 13 TeV for signal and backgrounds. The figure of merit to measure the improvement of the new method with respect to the old analysis is the separation between the signal and background distributions. Four mass points (mH± = 200, 400, 600, 1000 GeV) are considered, and an increase of the separation ranging from 2.6% (1000 GeV) to 29.2% (200 GeV) com- pared to the current cut-based analysis is found. / Denna studie implementerar en flervariabel-analys till den befintliga snitt-baserade analysen av laddade Higgs-bosoner, nya skal ̈arpartiklar fo ̈rutsagda av flertalet fo ̈rl ̈angningar av Standardmodellen. Studien antar tunga lad- dade Higgs-bosoner (mH± mtop) producerade tillsammans med en top- kvark som fo ̈rfaller via H± → τν. Sluttillst ̊andet best ̊ar av ett hadroniskt τ-so ̈nderfall, f ̈orlorad transversell energi och en hadroniskt so ̈nderfallande √ toppkvark. Studien a ̈r baserad p ̊a data f ̈or signal och bakgrund. Fo ̈r att ma ̈ta fo ̈rba ̈ttringen av analysens ka ̈nslighet anva ̈nds avst ̊and mellan bakgrundens och signalens distribu- tioner som godhetstal. Fyra masspunkter (mH± = 200, 400, 600, 1000 GeV) anva ̈nds, och en o ̈kning av avst ̊and fr ̊an 2.6% (1000 GeV) till 29.2% (200 GeV) hittades.

A level set of a function is defined as the region, where the function gets over the specified level. A level set of the probability density function can be considered an alternative to the traditional confidence region because on certain conditions the level set covers the region with minimal volume over all regions with a given confidence level. The benefits of using level sets arise in situations where, for example, the given random variables are multimodal or the given random vectors have strongly correlated components. This thesis describes estimates of the level set by means of a so called plug-in method, which first estimates density from the data set and then specifies the level set from the estimated density. In addition, explicit direct methods are also studied, such as algorithms based on support vectors or dyadic decision trees. Special attention is paid to the nonparametric probability density estimates, which form an essential tool for plug-in estimates. Namely, the second chapter describes histograms, averaged shifted histograms, kernel density estimates and its generalization. A new technique transforming kernel supports is proposed to avoid the so called boundary effect in multidimensional data domains. Ultimately, all methods are implemented in Mathematica and compared on financial data sets.

Estudos de expressão gênica têm sido de extrema importância, permitindo desenvolver terapias, exames diagnósticos, medicamentos e desvendar uma infinidade de processos biológicos. No entanto, estes estudos envolvem uma série de dificuldades: grande quantidade de genes, sendo que geralmente apenas um pequeno número deles está envolvido no problema estudado; presença de ruído nos dados analisados; entre muitas outras. O projeto de pesquisa deste mestrado consiste no estudo de algoritmos de indução de árvores de decisão; na definição de uma metodologia capaz de tratar dados de expressão gênica usando árvores de decisão; e na implementação da metodologia proposta como algoritmos capazes de extrair conhecimento a partir desse tipo de dados. A indução de árvores de decisão procura por características relevantes nos dados que permitam modelar precisamente um conceito, mas tem também a preocupação com a compreensibilidade do modelo gerado, auxiliando os especialistas na descoberta de conhecimento, algo importante nas áreas médica e biológica. Por outro lado, tais indutores apresentam relativa instabilidade, podendo gerar modelos bem diferentes com pequenas mudanças nos dados de treinamento. Este é um dos problemas tratados neste mestrado. Mas o principal problema tratado se refere ao comportamento destes indutores em dados de alta dimensionalidade, mais especificamente dados de expressão gênica: atributos irrelevantes prejudicam o aprendizado e vários modelos com desempenho similar podem ser gerados. Diversas técnicas foram exploradas para atacar os problemas mencionados, mas este estudo se concentrou em duas delas: windowing, que foi a técnica mais explorada e para a qual este mestrado propôs uma série de alterações com vistas à melhoria de seu desempenho; e lookahead, que procura construir a árvore levando em considerações passos subsequentes do processo de indução. Quanto ao windowing, foram explorados aspectos relacionados ao procedimento de poda das árvores geradas durante a execução do algoritmo; uso do erro estimado em substituição ao erro de treinamento; uso de ponderação do erro calculado durante a indução de acordo com o tamanho da janela; e uso da confiança na classificação para decidir quais exemplos utilizar na atualização da janela corrente. Com relação ao lookahead, foi implementada uma versão de um passo à frente, ou seja, para tomar a decisão na iteração corrente, o indutor leva em consideração a razão de ganho de informação do passo seguinte. Os resultados obtidos, principalmente com relação às medidas de desempenho baseadas na compreensibilidade dos modelos induzidos, mostram que os algoritmos aqui propostos superaram algoritmos clássicos de indução de árvores. / Gene expression studies have been of great importance, allowing the development of new therapies, diagnostic exams, drugs and the understanding of a variety of biological processes. Nevertheless, those studies involve some obstacles: a huge number of genes, while only a very few of them are really relevant to the problem at hand; data with the presence of noise; among others. This research project consists of: the study of decision tree induction algorithms; the definition of a methodology capable of handling gene expression data using decision trees; and the implementation of that methodology as algorithms that can extract knowledge from that kind of data. The decision tree induction searches for relevant characteristics in the data which would allow it to precisely model a certain concept, but it also worries about the comprehensibility of the generated model, helping specialists to discover new knowledge, something very important in the medical and biological areas. On the other hand, such inducers present some instability, because small changes in the training data might produce great changes in the generated model. This is one of the problems being handled in this Master\'s project. But the main problem this project handles refers to the behavior of those inducers when it comes to high-dimensional data, more specifically to gene expression data: irrelevant attributes may harm the learning process and many models with similar performance may be generated. A variety of techniques have been explored to treat those problems, but this study focused on two of them: windowing, which was the most explored technique and to which this project has proposed some variations in order to improve its performance; and lookahead, which builds each node of a tree taking into consideration subsequent steps of the induction process. As for windowing, the study explored aspects related to the pruning of the trees generated during intermediary steps of the algorithm; the use of the estimated error instead of the training error; the use of the error weighted according to the size of the current window; and the use of the classification confidence as the window update criterion. As for lookahead, a 1-step version was implemented, i.e., in order to make the decision in the current iteration, the inducer takes into consideration the information gain ratio of the next iteration. The results show that the proposed algorithms outperform the classical ones, especially considering measures of complexity and comprehensibility of the induced models.

Embora existam soluções para sistemas de cobrança e telecomunicações que apresentem relatórios para auxílio à cobrança de clientes, ambas carecem de informações que apoiem a tomada de decisões, nas análises estratégicas e na propensão de pagamento. Desse modo, o objetivo deste projeto é implementar ferramentas e soluções inteligentes a fim de reduzir o desperdício de tempo e aumentar a produtividade do gestor, decorrentes da necessidade da análise e cruzamento de todos os dados para tomar qualquer ação durante os processos de cobrança e gestão de custos. / Although there are solutions for billing and telecommunications systems to present reports to support debt collection, both lack information to support decision making in strategic analysis and propensity to pay. Thus, the goal of this project is to implement intelligent tools and solutions taht are able to increase their productivity and reduce waste of managers time, due to the need of analyzing and crossing all the data to take action during the collection processes and cost management.

Embora existam soluções para sistemas de cobrança e telecomunicações que apresentem relatórios para auxílio à cobrança de clientes, ambas carecem de informações que apoiem a tomada de decisões, nas análises estratégicas e na propensão de pagamento. Desse modo, o objetivo deste projeto é implementar ferramentas e soluções inteligentes a fim de reduzir o desperdício de tempo e aumentar a produtividade do gestor, decorrentes da necessidade da análise e cruzamento de todos os dados para tomar qualquer ação durante os processos de cobrança e gestão de custos. / Although there are solutions for billing and telecommunications systems to present reports to support debt collection, both lack information to support decision making in strategic analysis and propensity to pay. Thus, the goal of this project is to implement intelligent tools and solutions taht are able to increase their productivity and reduce waste of managers time, due to the need of analyzing and crossing all the data to take action during the collection processes and cost management.

Este trabalho apresenta uma metodologia de detecção e classificação de distúrbios relacionados à qualidade da energia elétrica. A detecção é feita utilizando-se somente uma regra para inferir na presença ou não do distúrbio em uma janela analisada. Para a classificação é proposto um método baseado em árvore de decisão. A árvore recebe como entrada as características do sinal extraídas tanto no domínio do tempo como no domínio da frequência, sendo a última obtida pela Transformada de Fourier. Destaca-se que toda a metodologia de extração de características foi idealizada como tentativa de se reduzir ao máximo o esforço computacional das tarefas de detecção e classificação de distúrbios. Em suma, verifica-se que os resultados obtidos são satisfatórios para a proposta desta pesquisa. / This work presents a methodology for detection and classification of disturbance related to the electric power quality. The detection is performed using only one rule to infer in the presence or not of the disturbance in a window analyzed. For the classification is proposed a method based on decision tree. The tree receives as input features of the extracted signal both in time domain and in the frequency domain, being the last obtained by Fourier transform. It is emphasized that all the features extraction methodology was idealized as an attempt to reduce to the maximum the computational effort for the tasks of detection and classification of disturbances. In short, it is possible to verify that the results obtained are satisfactory for the purpose of this research.

Este trabalho apresenta uma metodologia de detecção e classificação de distúrbios relacionados à qualidade da energia elétrica. A detecção é feita utilizando-se somente uma regra para inferir na presença ou não do distúrbio em uma janela analisada. Para a classificação é proposto um método baseado em árvore de decisão. A árvore recebe como entrada as características do sinal extraídas tanto no domínio do tempo como no domínio da frequência, sendo a última obtida pela Transformada de Fourier. Destaca-se que toda a metodologia de extração de características foi idealizada como tentativa de se reduzir ao máximo o esforço computacional das tarefas de detecção e classificação de distúrbios. Em suma, verifica-se que os resultados obtidos são satisfatórios para a proposta desta pesquisa. / This work presents a methodology for detection and classification of disturbance related to the electric power quality. The detection is performed using only one rule to infer in the presence or not of the disturbance in a window analyzed. For the classification is proposed a method based on decision tree. The tree receives as input features of the extracted signal both in time domain and in the frequency domain, being the last obtained by Fourier transform. It is emphasized that all the features extraction methodology was idealized as an attempt to reduce to the maximum the computational effort for the tasks of detection and classification of disturbances. In short, it is possible to verify that the results obtained are satisfactory for the purpose of this research.