Modern power grids are some of the largest and most complex engineered systems. Due to economic competition and deregulation, the power systems are operated closer their security limit. When the system is operating under a heavy loading condition, the unstable voltage condition may cause a cascading outage. The voltage fluctuations are presently being further aggravated by the increasing integration of utility-scale renewable energy sources. In this regards, a fast response and reliable voltage control approach is indispensable. The continuing success of synchrophasor has ushered in new subdomains of power system applications for real-time situational awareness, online decision support, and offline system diagnostics. The primary objective of this dissertation is to develop a data analytic based framework for regional voltage control utilizing high-speed data streams delivered from synchronized phasor measurement units. The dissertation focuses on the following three studies: The first one is centered on the development of decision-tree based voltage security assessment and control. The second one proposes an adaptive decision tree scheme using online ensemble learning to update decision model in real time. A system network partition approach is introduced in the last study. The aim of this approach is to reduce the size of training sample database and the number of control candidates for each regional voltage controller. The methodologies proposed in this dissertation are evaluated based on an open source software framework. / Ph. D. / Modern power grids are some of the largest and most complex engineered systems. When the system is heavily loaded, a small contingency may cause a large system blackout. In this regard, a fast response and reliable control approach is indispensable. Voltage is one of the most important metrics to indicate the system condition. This dissertation develops a cost-effective control method to secure the power system based on the real-time voltage measurements. The proposed method is developed based on an open source framework.

Diabetic Retinopathy (DR) is one of the principal sources of blindness due to diabetes mellitus. It can be identified by lesions of the retina, namely microaneurysms, hemorrhages, and exudates. DR can be effectively prevented or delayed if discovered early enough and well-managed. Prior studies on diabetic retinopathy typically extract features manually but are time-consuming and not accurate. In this research, we propose a research framework using advanced retina image processing, deep learning, and a boosting algorithm for high-performance DR grading. First, we preprocess the retina image datasets to highlight signs of DR, then follow by a convolutional neural network to extract features of retina images, and finally apply a boosting tree algorithm to make a prediction based on extracted features. Experimental results show that our pipeline has excellent performance when grading diabetic retinopathy images, as evidenced by scores for both the Kaggle dataset and the IDRiD dataset. / Master of Science / Diabetes is a disease in which insulin can not work very well, that leads to long-term high blood sugar level. Diabetic Retinopathy (DR), a result of diabetes mellitus, is one of the leading causes of blindness. It can be identified by lesions on the surface of the retina. DR can be effectively prevented or delayed if discovered early enough and well-managed. Prior image processing studies of diabetic retinopathy typically detect features manually, like retinal lesions, but are time-consuming and not accurate. In this research, we propose a framework using advanced retina image processing, deep learning, and a boosting decision tree algorithm for high-performance DR grading. Deep learning is a method that can be used to extract features of the image. A boosting decision tree is a method widely used in classification tasks. We preprocess the retina image datasets to highlight signs of DR, followed by deep learning to extract features of retina images. Then, we apply a boosting decision tree algorithm to make a prediction based on extracted features. The results of experiments show that our pipeline has excellent performance when grading the diabetic retinopathy score for both Kaggle and IDRiD datasets.

Thesis (MSc)--Stellenbosch University, 2004. / ENGLISH ABSTRACT: It is possible to improve on the accuracy of a single neural network by using an ensemble of diverse and accurate networks. This thesis explores diversity in ensembles and looks at the underlying theory and mechanisms employed to generate and combine ensemble members. Bagging and boosting are studied in detail and I explain their success in terms of well-known theoretical instruments. An empirical evaluation of their performance is conducted and I compare them to a single classifier and to each other in terms of accuracy and diversity. / AFRIKAANSE OPSOMMING: Dit is moontlik om op die akkuraatheid van 'n enkele neurale netwerk te verbeter deur 'n ensemble van diverse en akkurate netwerke te gebruik. Hierdie tesis ondersoek diversiteit in ensembles, asook die meganismes waardeur lede van 'n ensemble geskep en gekombineer kan word. Die algoritmes "bagging" en "boosting" word in diepte bestudeer en hulle sukses word aan die hand van bekende teoretiese instrumente verduidelik. Die prestasie van hierdie twee algoritmes word eksperimenteel gemeet en hulle akkuraatheid en diversiteit word met 'n enkele netwerk vergelyk.

Ultra wide band (UWB) is an extremely promising wireless technology for researchers and industrials. One of the most interesting is its high data rate and fading robustness due to selective frequency fading. However, beside such advantages, UWB system performance is highly affected by existing narrowband interference (NBI), undesired UWB signals and tone/multi-tone noises. For this reason, research about NBI cancellation is still a challenge to improve the system performance vs. receiver complexity, power consumption, linearity, etc. In this work, the two major receiver sections, i.e., analog (radiofrequency or RF) and digital (digital signal processing or DSP), were considered and new techniques proposed to reduce circuit complexity and power consumption, while improving signal parameters. In the RF section, different multiband UWB low-noise amplifier key design parameters were investigated like circuit configuration, input matching and desired/undesired frequency band filtering, highlighting the most suitable filtering package for efficient UWB NBI cancellation. In the DSP section, due to pulse transmitter signals, different issues like modulation type and level, pulse variety, shape and color noise/tone noise assumptions, were addressed for efficient NBI cancelation. A comparison was performed in terms of bit-error rate, signal-to-interference ratio, signal-to-noise ratio, and channel capacity to highlight the most suitable parameters for efficient DSP design. The optimum number of filters that allows the filter bandwidth to be reduced by following the required low sampling rate and thus improving the system bit error rate was also investigated.

[pt] Um sistema de produção e escoamento de petróleo, quando equipado com manifold submarino, viabiliza a produção conjunta de poços de diferentes capacidades de produção. O equilíbrio conseguido na produção desses poços se faz à custa do estrangulamento (choking) dos poços de mais alta capacidade de produção, de forma a propiciar um equilíbrio com aqueles de mais baixa capacidade de produção. Naturalmente as vazões atingidas nessa forma sinérgica são menores do que aquelas que seriam atingidas com a produção em separado desses poços mas, tais menores vazões são economicamente mais atraentes devido às economias atingidas nos custos de investimento (e.g., redução do comprimento total de linhas de produção e do número de risers) e das esperadas e normalmente ocorrentes reduções de custo operacional nesses sistemas. Entretanto, a partir da disponibilidade da tecnologia de bombeamento multifásico submarino, na qual se torna possível diretamente transferir energia à misturas multifásicas (óleo, gás e água) em produção, viabiliza-se o estabelecimento de um novo e mais atraente tipo de equilíbrio nesses sistemas. Tal novo equilíbrio, possível pelo uso de sistemas de bombeamento multifásico instalados em manifold submarinos de produção, deve propiciar níveis de produção governados inclusive pelos limites impostos pela engenharia de reservatórios e não mais tão somente pelas características físicas dos sistemas (seções de escoamento, distâncias, lâminas d`água, propriedades dos fluidos etc.). A esta forma inédita de obtenção de um novo e mais alto patamar de equilíbrio da produção, a literatura vem utilizando a denominação Estrangulamento Positivo (Positive Choking). Assim, baseados no aumento das vazões de produção - antecipação de produção - e no potencial aumento dos fatores de recuperação - maiores volumes produzidos - acredita-se ser tal técnica economicamente atraente quando aplicada em sistemas de produção ainda em implementação, ou mesmo, na implantação da mesma em sistemas já instalados. O propósito desta tese é o de contribuir na análise técnica e econômica da inédita aplicação de um sistema de bombeamento multifásico submarino num sistema de produção equipado com manifold. / [en] A subsea oil production system allows simultaneous production of several wells with different flow rates when the system has a subsea manifold. In order to balance the different flow rates, the higher production wells have their flow rates reduced via a choking system. As a result, the total flow is lower than the summmation of all individual well flow rates. But, this combined and lower production has a very attractive economics, as it requires lower capital expenditures, mainly due to the shorter overall length of flowlines and lower number of risers and, also due to the expected lower operational costs. Nowadays, however, with the availabity of the technology of subsea multiphase pumping system, which enables the transfer of energy to multiphase mixtures (oil, gas and water) under production, becomes possible to achieve an even higher and more attractive plateau in these petroleum production systems. This new equilibrium plateau, made possible by the technology of subsea multiphase pumping, will lead to production levels that will attempt to take benefit of all reservoir allowance and then extending the primary production limit imposed by the production system characteris tics (e.g., flowline length, water depth, produced fluid properties etc.). This novel scheme that allows obtaining a now and higher production level is being called in the literature as Positive Choking. Therefore, based on the resultant aspects of production flow rate increase - production anticipation - and on the potentially higher recovery factors - larger produced volumes - is what drives the belief that such technology can be economically attractive to new production systems being installed or even in those already in operation. The purpose of this work is to contribute in the technical and economical evaluation of Multiphase Pumping System application on a subsea production system equipped with a manifold.

No abstract

Road extraction from satellite or aerial imagery is a popular topic in remote sensing, and there are many road extraction algorithms suggested by various researches. However, the need of reliable remotely sensed road information still persists as there is no sufficiently robust road extraction algorithm yet. In this study, we explore the road extraction problem taking advantage of the multi-resolution analysis and adaptive boosting based classifiers. That is, we propose a new road extraction algorithm exploiting both spectral and structural features of the high resolution multi-spectral satellite images. The proposed model is composed of three major components / feature extraction, classification and road detection. Well-known spectral band ratios are utilized to represent reflectance properties of the data whereas a segmentation operation followed by an elongatedness scoring technique renders structural evaluation of the road parts within the multi-resolution analysis framework. The extracted features are fed into Adaptive Boosting (Adaboost) learning procedure, and the learning method iteratively combines decision trees to acquire a classifier with a high accuracy. The road network is identified from the probability map constructed by the classifier suggested by Adaboost. The algorithm is designed to be modular in the sense of its extensibility, that is / new road descriptor features can be easily integrated into the existing model. The empirical evaluation of the proposed algorithm suggests that the algorithm is capable of extracting majority of the road network, and it poses promising performance results.

Ultra wide band (UWB) is an extremely promising wireless technology for researchers and industrials. One of the most interesting is its high data rate and fading robustness due to selective frequency fading. However, beside such advantages, UWB system performance is highly affected by existing narrowband interference (NBI), undesired UWB signals and tone/multi-tone noises. For this reason, research about NBI cancellation is still a challenge to improve the system performance vs. receiver complexity, power consumption, linearity, etc. In this work, the two major receiver sections, i.e., analog (radiofrequency or RF) and digital (digital signal processing or DSP), were considered and new techniques proposed to reduce circuit complexity and power consumption, while improving signal parameters. In the RF section, different multiband UWB low-noise amplifier key design parameters were investigated like circuit configuration, input matching and desired/undesired frequency band filtering, highlighting the most suitable filtering package for efficient UWB NBI cancellation. In the DSP section, due to pulse transmitter signals, different issues like modulation type and level, pulse variety, shape and color noise/tone noise assumptions, were addressed for efficient NBI cancelation. A comparison was performed in terms of bit-error rate, signal-to-interference ratio, signal-to-noise ratio, and channel capacity to highlight the most suitable parameters for efficient DSP design. The optimum number of filters that allows the filter bandwidth to be reduced by following the required low sampling rate and thus improving the system bit error rate was also investigated.

This thesis consists of four chapters. Chapter 1 focuses on theoretical results on high-order laplacian-based regularization in function estimation. We studied the iterated laplacian regularization in the context of supervised learning in order to achieve both nice theoretical properties (like thin-plate splines) and good performance over complex region (like soap film smoother). In Chapter 2, we propose an innovative static path-planning algorithm called m-A* within an environment full of obstacles. Theoretically we show that m-A* reduces the number of vertex. In the simulation study, our approach outperforms A* armed with standard L1 heuristic and stronger ones such as True-Distance heuristics (TDH), yielding faster query time, adequate usage of memory and reasonable preprocessing time. Chapter 3 proposes m-LPA* algorithm which extends the m-A* algorithm in the context of dynamic path-planning and achieves better performance compared to the benchmark: lifelong planning A* (LPA*) in terms of robustness and worst-case computational complexity. Employing the same beamlet graphical structure as m-A*, m-LPA* encodes the information of the environment in a hierarchical, multiscale fashion, and therefore it produces a more robust dynamic path-planning algorithm. Chapter 4 focuses on an approach for the prediction of spot electricity spikes via a combination of boosting and wavelet analysis. Extensive numerical experiments show that our approach improved the prediction accuracy compared to those results of support vector machine, thanks to the fact that the gradient boosting trees method inherits the good properties of decision trees such as robustness to the irrelevant covariates, fast computational capability and good interpretation.

Ziel dieser Diplomarbeit ist es, eine flexibel zu verwendende Plattform für Visual Servoing-Aufgaben zu Erstellen, mit der eine Vielzahl von verschiedenen Anwendungsfällen abgedeckt werden kann. Kernaufgabe der Arbeit ist es dabei, verschiedene Verfahren der Gesichtserkennung (face detection) am Beispiel der Haar-Kaskade und -wiedererkennung (face recognition) am Beispiel von Eigenfaces und Fisherfaces zu betrachten und an ausführlichen Beispielen vorzustellen. Dabei sollen allgemeine Grundbegriffe der Bildverarbeitung und bereits bekannte Verfahren vorgestellt und ihre Implementierung im Detail dargestellt werden. Aus den dadurch gewonnen Erkenntnissen und dem sich ergebenden Anforderungsprofil an die zu entwickelnde Plattform leitet sich anschließend die Realisierung als eigenständige Anwendung ab. Hierbei ist weiterhin zu untersuchen, wie die neu zu entwickelnde Software zukunftssicher und in Hinblick auf einen möglichen Einsatz in Praktika einfach zu verwenden realisiert werden kann. Sämtliche während der Arbeit entstandenen Programme und Quellcodes werden auf einem separaten Datenträger zur Verfügung gestellt. Eine komplett funktionsfähige Entwicklungsumgebung wird als virtuelle Maschine beigelegt.