Made available in DSpace on 2016-06-02T19:06:05Z (GMT). No. of bitstreams: 1 5287.pdf: 3924606 bytes, checksum: 935b5a09df26eb1b41df901a189a6e2a (MD5) Previous issue date: 2013-05-29 / Universidade Federal de Sao Carlos / Geographic information systems with the aid of spatial databases store and manage crisp spatial data (or exact spatial data), whose shapes (boundaries) are well defined and have a precise location in space. However, several spatial data do not have precisely known boundaries or have an uncertain location in space, which are called vague spatial data. The boundaries of a given vague spatial data may shrink or extend, therefore, may have a minimum and maximum extension. Clouds of pollution, deforestation, fire outbreaks, route of an airplane, habitats of plants and animals are examples of vague spatial data. In the literature, there are currently vague spatial data models, such as Egg-Yolk, QMM and VASA. However, according to our knowledge, they focus only on the formal aspect of the model definition. Thus, real or synthetic vague spatial data is not available for use. The main objective of this master thesis is the development of algorithms for the generation of synthetic vague spatial data based on the crisp models of spatial data vague Egg-Yolk, QMM and VASA. It was also implemented a tool, called VagueDataGeneration, to assist in the process of generation such data. For both the algorithms and the tool, the user is able to set the properties related to the data type of model, such as size, shape, volume, complexity, location and spatial distribution. By using the proposed algorithms and the VagueDataGeneration tool, researchers can generate large samples of vague spatial data, enabling new research, such as testing indexes for vague spatial data or evaluating query processing over data warehouses that store vague spatial data. The validation of the vague spatial data generation was conducted using a case study with data from vague rural phenomena. / Sistemas de informação geográfica com o auxílio de bancos de dados espaciais armazenam e gerenciam dados espaciais exatos, cujas formas (fronteiras) são bem definidas e que possuem uma localização exata no espaço. Entretanto, vários dados espaciais reais não possuem os seus limites precisamente conhecidos ou possuem uma localização incerta no espaço, os quais são denominados dados espaciais vagos. Os limites de um dado espacial vago podem encolher ou estender, portanto, podem ter uma extensão mínima e máxima. Nuvens de poluição, desmatamentos, focos de incêndios, rota de um avião, habitats de plantas e de animais são exemplos de dados espaciais vagos. Na literatura, atualmente existem modelos de dados espaciais vagos, tais como Egg-Yolk, QMM e VASA. No entanto, segundo o nosso conhecimento, estes enfocam apenas no aspecto formal da definição do modelo. Com isso, dados espaciais vagos reais ou sintéticos não estão disponíveis para uso. O principal objetivo deste trabalho de mestrado consiste no desenvolvimento de algoritmos para a geração de dados espaciais vagos sintéticos baseados nos modelos exatos de dados espaciais vagos Egg-Yolk, QMM e VASA. Também foi implementada uma ferramenta, chamada VagueDataGeneration, para auxiliar no processo de geração desses dados. Nos algoritmos propostos e na ferramenta desenvolvida, o usuário define as propriedades referentes ao tipo de dado de um modelo, tais como tamanho, formato, volume, complexidade, localização e distribuição espacial dos dados espaciais vagos a serem gerados. Por meio do uso dos algoritmos propostos e da ferramenta VagueDataGeneration, os pesquisadores podem gerar grandes amostras de dados espaciais vagos, possibilitando novas pesquisas, como exemplo, testar índices para dados espaciais vagos ou testar técnicas de processamento de consultas em Data Warehouses que armazenam dados espaciais vagos. A validação da geração de dados espaciais vagos foi efetuada usando um estudo de caso com dados de fenômenos rurais vagos.

In electronic warfare, one of the key technologies is radar. Radar is used to detect and identify unknown aerial, nautical or land-based objects. An attribute of of a pulsed radar signal is the Pulse Repetition Interval (PRI) which is the time interval between pulses in a pulse train. In a passive radar receiver system, the PRI can be used to recognize the emitter system. Correct classification of emitter systems is a crucial part of Electronic Support Measures (ESM) and Radar Warning Receivers (RWR) in order to deploy appropriate measures depending on the emitter system. Inaccurate predictions of emitter systems can have lethal consequences and variables such as time and confidence in the predictions are essential for an effective predictive method. Due to the classified nature of military systems and techniques, there are no industry standard systems or techniques that perform quick and accurate classifications of emitter systems based on PRI. Therefore, methods that allows for fast and accurate predictions based on PRI is highly desirable and worthy of research. This thesis explores and compares the capabilities of two machine learning methods for the task of classifying emitters based on received PRI. The first method is an attention based model which performs well throughout all levels of realistic noise and is quick to learn and even quicker to give accurate predictions. The second method is a K-Nearest Neighbor (KNN) implementation that, while performing well for noise-free PRI, finds its performance degrading as the amount of noise increases. An additional outcome of this thesis is the development of a system to generate samples in an automated fashion. The attention based model performs well, achieving a macro avarage F1-score of 63% in the 59-class recognition task whereas the performance of the KNN is lower, achieving a macro avarage F1-score of 43%. Future research could be conducted with the purpose of designing a better attention based model for producing higher and more confident predictions and designing algorithms to reduce the time complexity of the KNN implementation. / En av de viktigaste teknikerna inom telektrig är radarn. Radar används för att upptäcka och identifiera okända, luftburna, sjögående eller landbaserade förmål. En komponent av radar är Pulsrepetitionsinterval (Pulse Repetition Intervall, PRI) som beskrivs som tidsintervallet mellan två inkommande pulser. I ett radarvarnar system (Radar Warning Receiver, RWR) kan PRI användas för att identifiera radarsystem. Korrekt identifiering av radarsystem är en viktig uppgift för elektroniska understödsmedel (Electronic Support Measures, ESM) med syfte att tillsätta lämpliga medel beroende på radarsystemet i fråga. Icke tillförlitlig identifiering av radarsystem kan ha dödliga konsekvenser och variabler som tid och säkerhet i identifieringen är avgörande för ett effektivt system. Då dokumentation och specifikationer för militära system i regel är hemligstämplade är det svårt att utröna någon typ av industristandard för att utföra snabb och säker klassificering av radarsystem baserat på PRI. Därför är det av stort intresse detta område och möjligheterna för sådana lösningar utforskas. Detta examensarbete utforskar och jämför förmågorna hos två maskininlärningsmetoder i avseende att korrekt identifiera radarsändare baserat på genererat PRI. Den första metoden är ett djupt neuralt nätverk som använder sig av tekniken ”attention”. Det djupa nätverket presterar bra för alla brusnivåer och lär sig snabbt att känna igen attributen hos PRI som kännetecknar vilken radarsändare och som efter träning dessutom är snabb på att korrekt identifiera PRI. Den andra metoden är en K-Nearest Neighbor implementation som förvisso presterar bra på icke brusig data men vars förmåga försämras allt eftersom brusnivåerna ökar. Ett ytterligare resultat av arbetet är utvecklingen och implementationen av en metod för att specificera PRI och sedan generera PRI efter specifikation. Attention modellen genererar bra prediktioner för data bestående av 59 klasser, med ett F1-score snitt om 63% medan KNN-implementationen för samma uppgift har en lägre träffsäkerhet med ett F1-score snitt om 43%. Vidare forskning kan innefatta utökad utveckling av det djupa, neurala nätverket i syfte att förbättra dess förmåga för identifiering och metoder för att minimera tidsåtgången för KNN implementationen.

ISAGENis a tool for the automatic generation of structurally complex test inputs that imitate real communication in the context of modern information systems . Complex, typically tree-structured data currently represents the standard means of transmitting information between nodes in distributed information systems. Automatic generator ISAGENis founded on the methodology of data-driven testing and uses concrete data from the production environment as the primary characteristic and specification that guides the generation of new similar data for test cases satisfying given combinatorial adequacy criteria. The main contribution of this thesis is a comprehensive proposal of automated data generation techniques together with an implementation, which demonstrates their usage. The created solution enables testers to create more relevant testing data, representing production-like communication in information systems.

<div> This thesis proposes an artificial neural network (NN) model to predict fuel consumption in heavy vehicles. The model uses predictors derived from vehicle speed, mass, and road grade. These variables are readily available from telematics devices that are becoming an integral part of connected vehicles. The model predictors are aggregated over a fixed distance traveled (i.e., window) instead of fixed time interval. It was found that 1km windows is most appropriate for the vocations studied in this thesis. Two vocations were studied, refuse and delivery trucks.</div><div><br></div><div> The proposed NN model was compared to two traditional models. The first is a parametric model similar to one found in the literature. The second is a linear regression model that uses the same features developed for the NN model.</div><div><br></div><div> The confidence level of the models using these three methods were calculated in order to evaluate the models variances. It was found that the NN models produce lower point-wise error. However, the stability of the models are not as high as regression models. In order to improve the variance of the NN models, an ensemble based on the average of 5-fold models was created. </div><div><br></div><div> Finally, the confidence level of each model is analyzed in order to understand how much error is expected from each model. The mean training error was used to correct the ensemble predictions for five K-Fold models. The ensemble K-fold model predictions are more reliable than the single NN and has lower confidence interval than both the parametric and regression models.</div>