Water loss management strategies for developing countries

Diese Arbeit beschäftigt sich mit nicht in Rechnung stellbaren Wasserverlusten in städtischen Versorgungsnetzen in Entwicklungsländern. Es soll das Wissen über diese Verluste erweitert und aufgezeigt werden, ob diese auf ein ökonomisch vertretbares Maß reduziert werden können. Die vorliegende Doktorarbeit untersucht solche unberechneten Wasserverluste und versucht, neben der Quantifizierung von Leckagen auch Entscheidungswerkzeuge für ein verbessertes Management der Versorgungsnetze in Entwicklungsländern zu erarbeiten. Als Fallstudie dient Harare, die Hauptstadt von Simbabwe. Wasserverluste in Verteilungsnetzen sind unvermeidbar, sollten aber auf ein ökonomisch tragbares Niveau reduziert werden, wenn ein nachhaltiger Betrieb erreicht werden soll. Wasserverluste können sowohl durch illegale und ungenehmigte Anschlüsse oder durch Undichtigkeiten im Verteilnetz, als auch durch mangelhafte Mess- und Berechnungssysteme entstehen. Es sind bereits viele Ansätze zur Verringerung von Verlusten in Wasserverteilsystemen bekannt geworden, entsprechend existieren dazu auch zahlreiche Methoden und Werkzeuge. Diese reichen von computergestützten Verfahren über gesetzliche und politische Vorgaben sowie ökonomische Berechnungen bis hin zu Maßnahmen der Modernisierung der Infrastruktur. Der Erfolg dieser Anstrengungen ist abhängig von der Umsetzbarkeit und dem Umfeld, in dem diese Maßnahmen durchgeführt werden. Die Bewertung der Arbeitsgüte einer jeden Wasserversorgungseinheit basiert auf der Effektivität des jeweiligen Verteilungssystems. Leistungs- und Bewertungszahlen sind die meist genutzten Ansätze, um Wasserverteilsysteme und ihre Effizienz einzustufen. Weltweit haben sich zur Bewertung als Indikatoren die finanzielle und die technische Leistungsfähigkeit durchgesetzt. Die eigene Untersuchung zeigt, dass diese Indikatoren in vielen Wasserversorgungssystemen der Entwicklungsländer nicht zur Einführung von Verlust reduzierenden Managementstrategien geführt haben. Viele durchgeführte Studien über die Einführung von Maßnahmen zur Verlustreduzierung beachten nur das gesamte nicht in Rechnung stellbare Wasser, ohne aber den Anteil der Leckagen an der Gesamthöhe zu bestimmen. Damit ist keine Aussage über die tatsächliche Zuordnung der Verluste möglich. Aus diesem Grund ist ein Bewertungsinstrument notwendig, mit dem die Verluste den verschiedenen Ursachen zugeordnet werden können. Ein solches Rechenwerkzeug ist das South African Night Flow Analysis Model (SANFLOW) der südafrikanischen Wasser-Forschungskommission, das Untersuchungen von Wasserdurchfluss und Anlagendruck in einzelnen Verteilbezirken ermöglicht. In der vorliegenden Arbeit konnte nachgewiesen werden, dass das SANFLOW-Modell gut zur Bestimmung des Leckageanteiles verwendet werden kann. Daraus kann gefolgert werden, dass dieses Modell ein geeignetes und gut anpassbares Analysewerkzeug für Entwicklungsländer ist. Solche computergestützte Berechnungsansätze können zur Bestimmung von Leckagen in Wasserverteilungsnetzen eingesetzt werden. Eine weitere Möglichkeit ist der Einsatz von Künstlichen Neuronalen Netzen (Artificial Neural Network – ANN), die trainiert und dann zur Vorhersage der dynamischen Verhältnisse in Wasserversorgungssystemen genutzt werden können. Diese Werte können mit der Wassernachfrage eines definierten Bezirks verglichen werden. Zur Untersuchung wurde ein Mehrschichtiges Künstliches Neuronales Netz mit Fehlerrückführung zur Modellierung des Wasserflusses in einem überwachten Abschnitt eingesetzt. Zur Bestimmung des Wasserbedarfes wurde ein MATLAB Algorithmus entwickelt. Aus der Differenz der aktuellen und des simulierten Wassernachfrage konnte die Leckagerate des Wasserversorgungssystems ermittelt werden. Es konnte gezeigt werden, dass mit dem angelernten Neuronalen Netzwerk eine Vorhersage des Wasserflusses mit einer Genauigkeit von 99% möglich ist. Daraus lässt sich die Eignung von ANNs als flexibler und wirkungsvoller Ansatz zur Leckagedetektion in der Wasserversorgung ableiten. Die Untersuchung zeigte weiterhin, dass im Versorgungsnetz von Harare 36 % des eingespeisten Wassers verloren geht. Davon wiederum sind 33 % auf Leckagen zurückzuführen. Umgerechnet bedeutet dies einen finanziellen Verlust von monatlich 1 Millionen Dollar, was 20 % der Gesamteinnahmen der Stadt entspricht. Der Stadtverwaltung von Harare wird daher empfohlen, aktiv an der Beseitigung der Leckagen zu arbeiten, da diese hohen Verluste den Versorgungsbetrieb negativ beeinflussen. Abschließend wird in der Arbeit ein integriertes Leckage-Managementsystem vorgeschlagen, das den Wasserversorgern eine Entscheidungshilfe bei zu ergreifenden Maßnahmen zur Instandhaltung des Verteilnetzes geben soll.

Implementação de classificador de tarefas mentais baseado em EEG

BARBOSA, André F. ; SOUZA, Bryan C. ; PEREIRA JUNIOR, Antônio ; MEDEIROS, Adelardo A. D.de, . Implementação de Classificador de Tarefas Mentais Baseado em EEG. In: CONGRESSO BRASILEIRO DE REDES NEURAIS, 9., 2009, Ouro Preto, MG. Anais... Ouro Preto, MG, 2009

Applied web traffic analysis for numerical encoding of SQL Injection attack features.

SQL Injection Attack (SQLIA) remains a technique used by a computer network intruder to pilfer an organisation’s confidential data. This is done by an intruder re-crafting web form’s input and query strings used in web requests with malicious intent to compromise the security of an organisation’s confidential data stored at the back-end database. The database is the most valuable data source, and thus, intruders are unrelenting in constantly evolving new techniques to bypass the signature’s solutions currently provided in Web Application Firewalls (WAF) to mitigate SQLIA. There is therefore a need for an automated scalable methodology in the pre-processing of SQLIA features fit for a supervised learning model. However, obtaining a ready-made scalable dataset that is feature engineered with numerical attributes dataset items to train Artificial Neural Network (ANN) and Machine Leaning (ML) models is a known issue in applying artificial intelligence to effectively address ever evolving novel SQLIA signatures. This proposed approach applies numerical attributes encoding ontology to encode features (both legitimate web requests and SQLIA) to numerical data items as to extract scalable dataset for input to a supervised learning model in moving towards a ML SQLIA detection and prevention model. In numerical attributes encoding of features, the proposed model explores a hybrid of static and dynamic pattern matching by implementing a Non-Deterministic Finite Automaton (NFA). This combined with proxy and SQL parser Application Programming Interface (API) to intercept and parse web requests in transition to the back-end database. In developing a solution to address SQLIA, this model allows processed web requests at the proxy deemed to contain injected query string to be excluded from reaching the target back-end database. This paper is intended for evaluating the performance metrics of a dataset obtained by numerical encoding of features ontology in Microsoft Azure Machine Learning (MAML) studio using Two-Class Support Vector Machines (TCSVM) binary classifier. This methodology then forms the subject of the empirical evaluation.

Numerical encoding to tame SQL injection attacks.

Recent years have seen an astronomical rise in SQL Injection Attacks (SQLIAs) used to compromise the confidentiality, authentication and integrity of organisations’ databases. Intruders becoming smarter in obfuscating web requests to evade detection combined with increasing volumes of web traffic from the Internet of Things (IoT), cloud-hosted and on-premise business applications have made it evident that the existing approaches of mostly static signature lack the ability to cope with novel signatures. A SQLIA detection and prevention solution can be achieved through exploring an alternative bio-inspired supervised learning approach that uses input of labelled dataset of numerical attributes in classifying true positives and negatives. We present in this paper a Numerical Encoding to Tame SQLIA (NETSQLIA) that implements a proof of concept for scalable numerical encoding of features to a dataset attributes with labelled class obtained from deep web traffic analysis. In the numerical attributes encoding: the model leverages proxy in the interception and decryption of web traffic. The intercepted web requests are then assembled for front-end SQL parsing and pattern matching by applying traditional Non-Deterministic Finite Automaton (NFA). This paper is intended for a technique of numerical attributes extraction of any size primed as an input dataset to an Artificial Neural Network (ANN) and statistical Machine Learning (ML) algorithms implemented using Two-Class Averaged Perceptron (TCAP) and Two-Class Logistic Regression (TCLR) respectively. This methodology then forms the subject of the empirical evaluation of the suitability of this model in the accurate classification of both legitimate web requests and SQLIA payloads.

Detecção do mal-posicionamento rotacional de dedos em dispositivos de captura de impressões digitais multivista sem toque utilizando redes neurais artificiais

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2016.

Um sistema para a inferência de variáveis usando redes neurais artificiais

This work aims to obtain a low-cost virtual sensor to estimate the quality of LPG. For the acquisition of data from a distillation tower, software HYSYS ® was used to simulate chemical processes. These data will be used for training and validation of an Artificial Neural Network (ANN). This network will aim to estimate from available simulated variables such as temperature, pressure and discharge flow of a distillation tower, the mole fraction of pentane present in LPG. Thus, allowing a better control of product quality

Improving Resource Management in Virtualized Data Centers using Application Performance Models

The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important. Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements (SLAs) and multiple resource dimensions. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity. We specifically addressed two crucial data center operations. First, we precisely estimated capacity requirements of client virtual machines (VMs) while renting server space in cloud environment. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The benefits of accurate application performance modeling are multifold. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs. This thesis made the following contributions. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. Third, we presented an approach to optimal VM sizing by employing the performance models we created. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.

GA-ANN Short-Term Electricity Load Forecasting

This paper presents a methodology for short-term load forecasting based on genetic algorithm feature selection and artificial neural network modeling. A feed forward artificial neural network is used to model the 24-h ahead load based on past consumption, weather and stock index data. A genetic algorithm is used in order to find the best subset of variables for modeling. Three data sets of different geographical locations, encompassing areas of different dimensions with distinct load profiles are used in order to evaluate the methodology. The developed approach was found to generate models achieving a minimum mean average percentage error under 2 %. The feature selection algorithm was able to significantly reduce the number of used features and increase the accuracy of the models.

Deep Learning applicato alla diagnostica dei trasformatori

Il trasformatore è uno degli elementi più importanti di una rete di trasmissione; essendo il tramite fra reti di alta e media tensione, il suo corretto funzionamento garantisce l’alimentazione di tutti i dispositivi e carichi connessi alla linea. Oltre a questo, il trasformatore è anche l’elemento più costoso di tutta la linea elettrica; la sua manutenzione è di vitale importanza per evitare costi elevati per la sostituzione e disagi lungo la linea. Qui entra in gioco il ruolo della diagnostica; attraverso misure periodiche e mirate sul trasformatore è possibile agire tempestivamente ed evitare tutti i fenomeni precedentemente elencati. Nell’elaborato si tratterà l’analisi del trasformatore elettrico trifase durante il suo funzionamento, evidenziando i sottocomponenti e le rispettive criticità; inoltre, verranno mostrate le varie tecniche di diagnostica del trasformatore, in modo tale da poter estrarre un indice legato allo stato di vita, ossia l’Health Index. Ad oggi esistono diverse tecniche di approccio al calcolo dell’Health Index, quella che viene presentata è una tecnica del tutto innovativa, ossia sviluppare una rete neurale artificiale (Artificial Neural Network, ANN) in grado di prevedere lo stato del trasformatore basandosi su misure effettuate sullo stesso. Dunque, verranno presentante le basi per lo sviluppo di una rete neurale, partendo dall’analisi e formattazione dei dati, fino alla fase di ottimizzazione delle prestazioni. Infine, si attraverseranno tutte le fasi intermedie di realizzazione del progetto da cui l’elaborato prende il titolo; osservando l’evoluzione di una rete neurale che si trasforma da un programma scritto in ambiente Python a una applicazione pronta all’uso per gli operatori durante le operazioni di diagnostica.

Machine Learning algorithm on seat comfort level measurement, mapping pressure values with body regions

The comfort level of the seat has a major effect on the usage of a vehicle; thus, car manufacturers have been working on elevating car seat comfort as much as possible. However, still, the testing and evaluation of comfort are done using exhaustive trial and error testing and evaluation of data. In this thesis, we resort to machine learning and Artificial Neural Networks (ANN) to develop a fully automated approach. Even though this approach has its advantages in minimizing time and using a large set of data, it takes away the degree of freedom of the engineer on making decisions. The focus of this study is on filling the gap in a two-step comfort level evaluation which used pressure mapping with body regions to evaluate the average pressure supported by specific body parts and the Self-Assessment Exam (SAE) questions on evaluation of the person’s interest. This study has created a machine learning algorithm that works on giving a degree of freedom to the engineer in making a decision when mapping pressure values with body regions using ANN. The mapping is done with 92% accuracy and with the help of a Graphical User Interface (GUI) that facilitates the process during the testing time of comfort level evaluation of the car seat, which decreases the duration of the test analysis from days to hours.

Efficient learning of reactive robot behaviors with a Neural-Q_learning approach

The purpose of this paper is to propose a Neural-Q_learning approach designed for online learning of simple and reactive robot behaviors. In this approach, the Q_function is generalized by a multi-layer neural network allowing the use of continuous states and actions. The algorithm uses a database of the most recent learning samples to accelerate and guarantee the convergence. Each Neural-Q_learning function represents an independent, reactive and adaptive behavior which maps sensorial states to robot control actions. A group of these behaviors constitutes a reactive control scheme designed to fulfill simple missions. The paper centers on the description of the Neural-Q_learning based behaviors showing their performance with an underwater robot in a target following task. Real experiments demonstrate the convergence and stability of the learning system, pointing out its suitability for online robot learning. Advantages and limitations are discussed

Architecture for Living Neuronal Network Control of a mobile robot

The intelligent controlling mechanism of a typical mobile robot is usually a computer system. Research is however now ongoing in which biological neural networks are being cultured and trained to act as the brain of an interactive real world robot – thereby either completely replacing or operating in a cooperative fashion with a computer system. Studying such neural systems can give a distinct insight into biological neural structures and therefore such research has immediate medical implications. The principal aims of the present research are to assess the computational and learning capacity of dissociated cultured neuronal networks with a view to advancing network level processing of artificial neural networks. This will be approached by the creation of an artificial hybrid system (animat) involving closed loop control of a mobile robot by a dissociated culture of rat neurons. This paper details the components of the overall animat closed loop system architecture and reports on the evaluation of the results from preliminary real-life and simulated robot experiments.

Técnicas de inteligência artificial para a geração dinâmica de set points para uma coluna de destilação

Artificial Intelligence techniques are applied to improve performance of a simulated oil distillation system. The chosen system was a debutanizer column. At this process, the feed, which comes to the column, is segmented by heating. The lightest components become steams, by forming the LPG (Liquefied Petroleum Gas). The others components, C5+, continue liquid. In the composition of the LPG, ideally, we have only propane and butanes, but, in practice, there are contaminants, for example, pentanes. The objective of this work is to control pentane amount in LPG, by means of intelligent set points (SP s) determination for PID controllers that are present in original instrumentation (regulatory control) of the column. A fuzzy system will be responsible for adjusting the SP's, driven by the comparison between the molar fraction of the pentane present in the output of the plant (LPG) and the desired amount. However, the molar fraction of pentane is difficult to measure on-line, due to constraints such as: long intervals of measurement, high reliability and low cost. Therefore, an inference system was used, based on a multilayer neural network, to infer the pentane molar fraction through secondary variables of the column. Finally, the results shown that the proposed control system were able to control the value of pentane molar fraction under different operational situations

A neural system to robust Nonlinear optimization subject to disjoint and constrained sets

The ability of neural networks to realize some complex nonlinear function makes them attractive for system identification. This paper describes a novel method using artificial neural networks to solve robust parameter estimation problems for nonlinear models with unknown-but-bounded errors and uncertainties. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the network convergence to the equilibrium points. A solution for the robust estimation problem with unknown-but-bounded error corresponds to an equilibrium point of the network. Simulation results are presented as an illustration of the proposed approach.

Neural models for predicting hole diameters in drilling processes

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)