Recent emphasis by NASA on returning astronauts to the Moon has placed attention on the subject of lunar surface feature tracking. Although many algorithms have been proposed for lunar landmark tracking navigation, much less attention has been paid to the issue of navigational state initialization from lunar craters in a lost in low lunar orbit (LLO) scenario. A new crater detection and identification algorithm is developed in this dissertation that allows for navigation state initialization from as few as one image of the lunar surface with no a priori state knowledge. Craters are detected by a filter that is an extension of the Circular Hough Transform, after which verification is performed by a number of checks on the illuminated portion of the candidate crater interior. Detected craters are identified by matching them to entries in the USGS crater catalog via non-dimensional crater triangle parameters. False identifications are rejected based on a probability check. The algorithm was tested on Apollo 16 LLO images, and shown to perform well. / text

In this thesis, Genetic Granular Cognitive Fuzzy Neural Networks (GGCFNN), combining genetic algorithms (GA) and granular cognitive fuzzy neural networks (GCFNN), is proposed for pattern recognition problems. According to cognitive patterns, biological neural networks in the human brain can recognize different patterns. Since GA and neural networks represent two learning methods based on biological science, it is indispensable and valuable to investigate how biological neural networks and artificial neural networks recognize different patterns. The new GGCFNN, based on granular computing, soft computing and cognitive science, is used in the pattern recognition problems. The hybrid forward-wave-backward-wave learning algorithm, as a main learning technology in GCFNN, is used to enhance learning quality. GA optimizes parameters to make GGCFNN get better learning results. Both pattern recognition results generated by human persons and those by GGCFNN are analyzed in terms of computer science and cognitive science.

[pt] A análise técnica ou grafismo consiste na identificação visual de padrões geométricos em gráficos de séries de preços de mercado com o objetivo de antecipar tendências de preço. Esta Dissertação revisita a questão da validação estatística da análise técnica, que tem sido estudada na literatura sem os devidos cuidados com os problemas de heterogeneidade e de dependência estatística dos dados analisados - agrupamento de séries de retornos referentes a diversos ativos financeiros distintos. O objetivo central deste estudo consiste em resolver o primeiro problema citado, através de uma metodologia para homogeneizar os ativos no que concerne às distribuições de probabilidades de suas séries de retorno. Os passos gerais desta metodologia envolvem a identificação dos processos estocásticos geradores dos retornos dos ativos, o agrupamento de ativos semelhantes e, finalmente, a análise de presença, ou ausência, de informação advinda dos padrões de preços. Como ilustração, são analisadas séries de diversos ativos do mercado financeiro mundial. A nossa investigação verifica a existência de conteúdo informativo estatisticamente significante em dois dos três padrões usualmente identificados na análise técnica, a saber: triângulos retângulos e head & shoulders. / [en] Technical analysis or charting aims on visually identifying geometrical patterns in price charts in order to anticipate price trends. This dissertation revisits the issue of technical analysis statistical validation, which has been tackled in the literature without taking care of the presence of heterogeneity and statistical dependence in the analyzed data - agglutinated return time series from many distinct securities. The main purpose of this study is to address the first cited problem by suggesting a methodology to homogenize the securities according to the probability distributions of their return series. The general steps of the methodology go through the identification of the data generating stochastic processes for the security returns, the clustering of similar securities and, finally, the analysis of the presence, or absence, of informational content coming from those price patterns. We illustrate the proposed methodology with several financial securities of the global market. Our investigation shows that there is a statistically significant informational content in two out of the three common patterns usually found through technical analysis, namely: triangles, rectangle and head & shoulders.

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This work focuses on the development and testing of new driving data pattern recognition intelligent system techniques to support driver adaptive, real-time optimal power control and energy management of hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs). A novel, intelligent energy management approach that combines vehicle operation data acquisition, driving data clustering and pattern recognition, cluster prototype based power control and energy optimization, and real-time driving pattern recognition and optimal energy management has been introduced. The method integrates advanced machine learning techniques and global optimization methods form the driver adaptive optimal power control and energy management. Fuzzy C-Means clustering algorithm is used to identify the representative vehicle operation patterns from collected driving data. Dynamic Programming (DA) based off-line optimization is conducted to obtain the optimal control parameters for each of the identified driving patterns. Artificial Neural Networks (ANN) are trained to associate each of the identified operation patterns with the optimal energy management plan to support real-time optimal control. Implementation and advantages of the new method are demonstrated using the 2012 California household travel survey data, and driver-specific data collected from the city of Victoria, BC Canada. / Graduate

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