Yes / In this paper, a stochastic approach for the operation of active distribution networks within a joint active and reactive distribution market environment is proposed. The method maximizes the social welfare using market based active and reactive optimal power flow (OPF) subject to network constraints with integration of demand response (DR). Scenario-Tree technique is employed to model the uncertainties associated with solar irradiance, wind speed and load demands. It further investigates the impact of solar and wind power penetration on the active and reactive distribution locational prices (D-LMPs) within the distribution market environment. A mixed-integer linear programming (MILP) is used to recast the proposed model, which is solvable using efficient off-the shelf branch-and cut solvers. The 16-bus UK generic distribution system is demonstrated in this work to evaluate the effectiveness of the proposed method. Results show that DR integration leads to increase in the social welfare and total dispatched active and reactive power and consequently decrease in active and reactive D-LMPs. / Ministry of Higher Education and Scientific Research of Iraq

The Braess Paradox is a rare phenomenon that only occurs under specific scenarios. This project aims to study the probability of the Braess Paradox occurring in a Directed Weighted Graph while the number of edges increases. The graphs in the experiment are focused on studying the occurrence of the Braess Paradox in a directed weighted scale-free network while transforming it into a directed weighted complete graph. A simulation model is used to simulate the bots traveling through a network to detect the occurrence of the Braess Paradox, considering the increase of directed weighted edges. A Graph Neural Network (GNN) is later used to train on the data produced by the simulation model.

This thesis investigates the application of Hidden Markov Models (HMMs) to model financial volatility-regimes and presents a parameter learning approach using real-world data. Although HMMs as regime-switching models are established, empirical studies regarding the parameter estimation of such models remain limited. We address this issue by creating a systematic approach (algorithm) for parameter learning using Python programming and the hmmlearn library. The algorithm works by initializing a wide range of random parameter values for an HMM and maximizing the log-likelihood of an observation sequence, obtained from market data, using expectation-maximization; the optimal number of volatility regimes for the HMM is determined using information criterion. By training models on historical market and volatility index data, we found that a discrete model is favored for volatility modeling and option pricing due to its low complexity and high customizability, and a Gaussian model is favored for asset allocation and price simulation due to its ability to model market regimes. However, practical applications of these models were not researched, and thus, require further studies to test and calibrate.

Les réseaux de télécommunication doivent être fiables et robustes pour garantir la haute disponibilité des services. Les opérateurs cherchent actuellement à automatiser autant que possible les opérations complexes de gestion des réseaux, telles que le diagnostic de pannes.Dans cette thèse nous nous sommes intéressés au diagnostic automatique de pannes dans les réseaux d'accès optiques de l'opérateur Orange. L'outil de diagnostic utilisé jusqu'à présent, nommé DELC, est un système expert à base de règles de décision. Ce système est performant mais difficile à maintenir en raison, en particulier, du très grand volume d'informations à analyser. Il est également impossible de disposer d'une règle pour chaque configuration possible de panne, de sorte que certaines pannes ne sont actuellement pas diagnostiquées.Dans cette thèse nous avons proposé une nouvelle approche. Dans notre approche, le diagnostic des causes racines des anomalies et alarmes observées s'appuie sur une modélisation probabiliste, de type réseau bayésien, des relations de dépendance entre les différentes alarmes, compteurs, pannes intermédiaires et causes racines au niveau des différents équipements de réseau. Ce modèle probabiliste a été conçu de manière modulaire, de façon à pouvoir évoluer en cas de modification de l'architecture physique du réseau.Le diagnostic des causes racines des anomalies est effectué par inférence, dans le réseau bayésien, de l'état des noeuds non observés au vu des observations (compteurs, alarmes intermédiaires, etc...) récoltées sur le réseau de l'opérateur. La structure du réseau bayésien, ainsi que l'ordre de grandeur des paramètres probabilistes de ce modèle, ont été déterminés en intégrant dans le modèle les connaissances des experts spécialistes du diagnostic sur ce segment de réseau. L'analyse de milliers de cas de diagnostic de pannes a ensuite permis de calibrer finement les paramètres probabilistes du modèle grâce à un algorithme EM (Expectation Maximization).Les performances de l'outil développé, nommé PANDA, ont été évaluées sur deux mois de diagnostic de panne dans le réseau GPON-FTTH d'Orange en juillet-août 2015. Dans la plupart des cas, le nouveau système, PANDA, et le système en production, DELC, font un diagnostic identique. Cependant un certain nombre de cas sont non diagnostiqués par DELC mais ils sont correctement diagnostiqués par PANDA. Les cas pour lesquels les deux systèmes émettent des diagnostics différents ont été évalués manuellement, ce qui a permis de démontrer dans chacun de ces cas la pertinence des décisions prises par PANDA. / Telecommunication networks must be reliable and robust to ensure high availability of services. Operators are currently searching to automate as much as possible, complex network management operations such as fault diagnosis.In this thesis we are focused on self-diagnosis of failures in the optical access networks of the operator Orange. The diagnostic tool used up to now, called DELC, is an expert system based on decision rules. This system is efficient but difficult to maintain due in particular to the very large volume of information to analyze. It is also impossible to have a rule for each possible fault configuration, so that some faults are currently not diagnosed.We proposed in this thesis a new approach. In our approach, the diagnosis of the root causes of malfunctions and alarms is based on a Bayesian network probabilistic model of dependency relationships between the different alarms, counters, intermediate faults and root causes at the level of the various network component. This probabilistic model has been designed in a modular way, so as to be able to evolve in case of modification of the physical architecture of the network. Self-diagnosis of the root causes of malfunctions and alarms is made by inference in the Bayesian network model of the state of the nodes not observed in view of observations (counters, alarms, etc.) collected on the operator's network. The structure of the Bayesian network, as well as the order of magnitude of the probabilistic parameters of this model, were determined by integrating in the model the expert knowledge of the diagnostic experts on this segment of the network. The analysis of thousands of cases of fault diagnosis allowed to fine-tune the probabilistic parameters of the model thanks to an Expectation Maximization algorithm. The performance of the developed probabilistic tool, named PANDA, was evaluated over two months of fault diagnosis in Orange's GPON-FTTH network in July-August 2015. In most cases, the new system, PANDA, and the system in production, DELC, make an identical diagnosis. However, a number of cases are not diagnosed by DELC but are correctly diagnosed by PANDA. The cases for which self-diagnosis results of the two systems are different were evaluated manually, which made it possible to demonstrate in each of these cases the relevance of the decisions taken by PANDA.

In Natural Language Processing (NLP), speech and text are parsed and generated with language models and parser models, and translated with translation models. Each model contains a set of numerical parameters which are found by applying a suitable training algorithm to a set of training data. Many such training algorithms are instances of the Expectation-Maximization (EM) algorithm. In [BSV15], a generic EM algorithm for NLP is described. This work presents a particular speech model, the Hidden Markov model, and its standard training algorithm, the Baum-Welch algorithm. It is then shown that the Baum-Welch algorithm is an instance of the generic EM algorithm introduced by [BSV15], from which follows that all statements about the generic EM algorithm also apply to the Baum-Welch algorithm, especially its correctness and convergence properties.:1 Introduction 1.1 N-gram models 1.2 Hidden Markov model 2 Expectation-maximization algorithms 2.1 Preliminaries 2.2 Algorithmic skeleton 2.3 Corpus-based step mapping 2.4 Simple counting step mapping 2.5 Regular tree grammars 2.6 Inside-outside step mapping 2.7 Review 3 The Hidden Markov model 3.1 Forward and backward algorithms 3.2 The Baum-Welch algorithm 3.3 Deriving the Baum-Welch algorithm 3.3.1 Model parameter and countable events 3.3.2 Tree-shaped hidden information 3.3.3 Complete-data corpus 3.3.4 Inside weights 3.3.5 Outside weights 3.3.6 Complete-data corpus (cont.) 3.3.7 Step mapping 3.4 Review Appendix A Elided proofs from Chapter 3 A.1 Proof of Lemma 3.8 A.2 Proof of Lemma 3.9 B Formulary for Chapter 3 Bibliography

In recent years additional requirements have been imposed on financial institutions, including Central Counterparty clearing houses (CCPs), as an attempt to assess quantitative measures of their exposure to different types of risk. One of these requirements results in a need to perform stress tests to check the resilience in case of a stressed market/crisis. However, financial markets develop over time and this leads to a situation where some instruments traded today are not present at the chosen date because they were introduced after the considered historical event. Based on current routines, the main goal of this thesis is to provide a more sophisticated method to impute (fill in) historical missing data as a preparatory work in the context of stress testing. The models considered in this paper include two methods currently regarded as state-of-the-art techniques, based on maximum likelihood estimation (MLE) and multiple imputation (MI), together with a third alternative approach involving copulas. The different methods are applied on historical return data of commodity futures contracts from the Nordic energy market. By using conventional error metrics, and out-of-sample log-likelihood, the conclusion is that it is very hard (in general) to distinguish the performance of each method, or draw any conclusion about how good the models are in comparison to each other. Even if the Student’s t-distribution seems (in general) to be a more adequate assumption regarding the data compared to the normal distribution, all the models are showing quite poor performance. However, by analysing the conditional distributions more thoroughly, and evaluating how well each model performs by extracting certain quantile values, the performance of each method is increased significantly. By comparing the different models (when imputing more extreme quantile values) it can be concluded that all methods produce satisfying results, even if the g-copula and t-copula models seems to be more robust than the respective linear models. / På senare år har ytterligare krav införts för finansiella institut (t.ex. Clearinghus) i ett försök att fastställa kvantitativa mått på deras exponering mot olika typer av risker. Ett av dessa krav innebär att utföra stresstester för att uppskatta motståndskraften under stressade marknader/kriser. Dock förändras finansiella marknader över tiden vilket leder till att vissa instrument som handlas idag inte fanns under den dåvarande perioden, eftersom de introducerades vid ett senare tillfälle. Baserat på nuvarande rutiner så är målet med detta arbete att tillhandahålla en mer sofistikerad metod för imputation (ifyllnad) av historisk data som ett förberedande arbete i utförandet av stresstester. I denna rapport implementeras två modeller som betraktas som de bäst presterande metoderna idag, baserade på maximum likelihood estimering (MLE) och multiple imputation (MI), samt en tredje alternativ metod som involverar copulas. Modellerna tillämpas på historisk data förterminskontrakt från den nordiska energimarkanden. Genom att använda väl etablerade mätmetoder för att skatta noggrannheten förrespektive modell, är det väldigt svårt (generellt) att särskilja prestandan för varje metod, eller att dra några slutsatser om hur bra varje modell är i jämförelse med varandra. även om Students t-fördelningen verkar (generellt) vara ett mer adekvat antagande rörande datan i jämförelse med normalfördelningen, så visar alla modeller ganska svag prestanda vid en första anblick. Däremot, genom att undersöka de betingade fördelningarna mer noggrant, för att se hur väl varje modell presterar genom att extrahera specifika kvantilvärden, kan varje metod förbättras markant. Genom att jämföra de olika modellerna (vid imputering av mer extrema kvantilvärden) kan slutsatsen dras att alla metoder producerar tillfredställande resultat, även om g-copula och t-copula modellerna verkar vara mer robusta än de motsvarande linjära modellerna.

Recently, Udwani defined a new class of set functions under monotonicity and subadditivity, called submodular order functions, which is a subfamily of submodular functions. Informally, the submodular order function admits a very limited form of submodularity which is defined over a specific permutation of the ground set. His work pointed out the intriguing connection between streaming submodular maximization and submodular order maximization. Inspired by a 0.25-approximation streaming algorithm for maximizing a monotone submodular function subject to a matroid constraint, Udwani gave a 0.25-approximation algorithm for submodular order functions maximization subject to a matroid constraint. Based on the above results, we would like to explore further in which cases it is feasible to generalize from streaming submodular maximization algorithms to submodular order maximization algorithms. As a more general constraint than matroid, p-matchoid is a collection of p matroids with each matroid defined on some subsets of the ground set. Related work gave a 1/4p-approximation streaming algorithm for monotone submodular functions maximization under a p-matchoid constraint. Inspired by the above algorithms and the intriguing connection, we used some techniques to try to generalize several streaming algorithms for submodular functions to the offline algorithms for submodular order functions, including interleaved partitions and incremental values. Assuming that the objective function f is subadditive and non-negative, we gave a 1/4p-approximation algorithm for monotone submodular order maximization to a p-matchoid constraint. In addition, we summarize the failures of other cases. / Nyligen definierade Udwani en ny klass av mängdfunktioner under monotonicitet och subadditivitet, som kallas submodulära ordningsfunktioner och som är en underfamilj av submodulära funktioner. Informellt sett medger den submodulära ordningsfunktionen en mycket begränsad form av submodularitet som är definierad över en specifik permutation av grundmängden. Hans arbete pekade på det spännande sambandet mellan strömmande submodulär maximering och submodulär ordermaximering. Inspirerad av en strömningsalgoritm med 0.25-approximation för maximering av en monoton submodulär funktion som är föremål för en matroidbegränsning, gav Udwani en algoritm med 0.25-approximation för maximering av submodulära ordningsfunktioner som är föremål för en matroidbegränsning. Baserat på ovanstående resultat skulle vi vilja utforska ytterligare i vilka fall det är möjligt att generalisera från algoritmer för strömning av submodulära maximeringsfunktioner till algoritmer för maximering av submodulära orderfunktioner. Som en mer allmän begränsning än matroid är p-matchoid en samling av p matroider där varje matroid definieras på vissa delmängder av grundmängden. Relaterade arbeten gav en strömmingsalgoritm med 1/4p-tillnärmning för monoton submodulär funktionsmaximering under en p-matchoid-begränsning. Inspirerade av ovanstående algoritmer och det spännande sambandet använde vi vissa tekniker för att försöka generalisera flera strömningsalgoritmer för submodulära funktioner till offline-algoritmer för submodulära ordningsfunktioner, inklusive interleaved partitions och inkrementella värden. Under förutsättning att målfunktionen f är subadditiv och icke-negativ gav vi en algoritm för 1/4p-tillnärmning för monoton submodulär ordermaximering till ett p-matchoid-begränsningsvillkor. Dessutom sammanfattar vi misslyckanden i andra fall.

Doctor of Philosophy / Department of Electrical and Computer Engineering / Balasubramaniam Natarajan / In recent years, there has been a rapid expansion in the development and use of low-power, low-cost wireless modules with sensing, computing, and communication functionality. A wireless sensor network (WSN) is a group of these devices networked together wirelessly. Wireless sensor networks have found widespread application in infrastructure, environmental, and human health monitoring, surveillance, and disaster management. While there are many interesting problems within the WSN framework, we address the challenge of energy availability in a WSN tasked with a cooperative objective. We develop approximation algorithms and execute an analysis of concave utility maximization in resource constrained systems. Our analysis motivates a unique algorithm which we apply to resource management in WSNs. We also investigate energy harvesting as a way of improving system lifetime. We then analyze the effect of using these limited and stochastically available communication resources on the convergence of decentralized optimization techniques. The main contributions of this research are: (1) new optimization formulations which explicitly consider the energy states of a WSN executing a cooperative task; (2) several analytical insights regarding the distributed optimization of resource constrained systems; (3) a varied set of algorithmic solutions, some novel to this work and others based on extensions of existing techniques; and (4) an analysis of the effect of using stochastic resources (e.g., energy harvesting) on the performance of decentralized optimization methods. Throughout this work, we apply our developments to distribution estimation and rate maximization. The simulation results obtained help to provide verification of algorithm performance. This research provides valuable intuition concerning the trade-offs between energy-conservation and system performance in WSNs.

A phylogenetic tree is used to model gene evolution and species evolution using molecular sequence data. For artifactual and biological reasons, a gene tree may differ from a species tree, a phenomenon known as gene tree-species tree incongruence. Assuming the presence of one or more evolutionary events, e.g., gene duplication, gene loss, and lateral gene transfer (LGT), the incongruence may be explained using a reconciliation of a gene tree inside a species tree. Such information has biological utilities, e.g., inference of orthologous relationship between genes. In this thesis, we present probabilistic models and methods for orthology analysis and species tree inference, while accounting for evolutionary factors such as gene duplication, gene loss, and sequence evolution. Furthermore, we use a probabilistic LGT-aware model for inferring gene trees having temporal information for duplication and LGT events. In the first project, we present a Bayesian method, called DLRSOrthology, for estimating orthology probabilities using the DLRS model: a probabilistic model integrating gene evolution, a relaxed molecular clock for substitution rates, and sequence evolution. We devise a dynamic programming algorithm for efficiently summing orthology probabilities over all reconciliations of a gene tree inside a species tree. Furthermore, we present heuristics based on receiver operating characteristics (ROC) curve to estimate suitable thresholds for deciding orthology events. Our method, as demonstrated by synthetic and biological results, outperforms existing probabilistic approaches in accuracy and is robust to incomplete taxon sampling artifacts. In the second project, we present a probabilistic method, based on a mixture model, for species tree inference. The method employs a two-phase approach, where in the first phase, a structural expectation maximization algorithm, based on a mixture model, is used to reconstruct a maximum likelihood set of candidate species trees. In the second phase, in order to select the best species tree, each of the candidate species tree is evaluated using PrIME-DLRS: a method based on the DLRS model. The method is accurate, efficient, and scalable when compared to a recent probabilistic species tree inference method called PHYLDOG. We observe that, in most cases, the analysis constituted only by the first phase may also be used for selecting the target species tree, yielding a fast and accurate method for larger datasets. Finally, we devise a probabilistic method based on the DLTRS model: an extension of the DLRS model to include LGT events, for sampling reconciliations of a gene tree inside a species tree. The method enables us to estimate gene trees having temporal information for duplication and LGT events. To the best of our knowledge, this is the first probabilistic method that takes gene sequence data directly into account for sampling reconciliations that contains information about LGT events. Based on the synthetic data analysis, we believe that the method has the potential to identify LGT highways. / <p>QC 20150529</p>

Companies are faced with challenges constraining the achievement of set budgets, goals, profit and cost of product, to name a few, on a daily basis. These challenges influence value creation and sustainable value creation. Value-based management is an integrated management tool which may assist in achieving sustainable value creation within a company. Achieving sustainable value creation will result in benefits for both the shareholders and the various stakeholders. In 2008 and 2009 Eskom, South Africa’s sole electricity provider announced a major shortage of electricity and consequently major price increases. Since electricity consumption is a crucial part of the production process, this announcement had a devastating effect on mining companies. The primary objective of the current study is to investigate whether a local mining company is focusing on applicable endeavours to overcome the electricity constraint and price hikes in order to sustain value creation. This was done by studying the company’s financial & management reports, public announcements and media coverage, in conjunction with a quantitative study, collecting primary data by using standardised questionnaires distributed among the mining company’s employees. The results from this study indicate that the selected company is focusing on relevant projects to overcome the electricity constraints. Further, the conclusion made from the results of the questionnaires shows that the higher staff levels are more informed and aware of value-based management. It also points out that the lower levels and employees from the production and mining departments are less informed and aware of value-based management. / Thesis (MBA)--North-West University, Potchefstroom Campus, 2012