Prognostics in switching systems: Evidential markovian classification of real-time neuro-fuzzy predictions.

International audienceCondition-based maintenance is nowadays considered as a key-process in maintenance strategies and prognostics appears to be a very promising activity as it should permit to not engage inopportune spending. Various approaches have been developed and data-driven methods are increasingly applied. The training step of these methods generally requires huge datasets since a lot of methods rely on probability theory and/or on artificial neural networks. This step is thus time-consuming and generally made in batch mode which can be restrictive in practical application when few data are available. A method for prognostics is proposed to face up this problem of lack of information and missing prior knowledge. The approach is based on the integration of three complementary modules and aims at predicting the failure mode early while the system can switch between several functioning modes. The three modules are: 1) observation selection based on information theory and Choquet Integral, 2) prediction relying on an evolving real-time neuro-fuzzy system and 3) classification into one of the possible functioning modes using an evidential Markovian classifier based on Dempster-Shafer theory. Experiments concern the prediction of an engine health based on more than twenty observations

Keypoint descriptor fusion with Dempster-Shafer Theory

Keypoint matching is the task of accurately nding the location of a scene point in two images. Many keypoint descriptors have been proposed in the literature aiming at providing robustness against scale, translation and rotation transformations, each having advantages and disadvantages. This paper proposes a novel approach to fuse the information from multiple keypoint descriptors using Dempster-Shafer Theory of evidence [1], which has proven particularly e cient in combining sources of information providing incomplete, imprecise, biased, and con ictive knowledge. The matching results of each descriptor are transformed into an evidence distribution on which a con dence factor is computed making use of its entropy. Then, the evidence distributions are fused using Dempster-Shafer Theory (DST), considering its con dence. As result of the fusion, a new evidence distribution that improves the result of the best descriptor is obtained. Our method has been tested with SIFT, SURF, ORB, BRISK and FREAK descriptors using all possible combinations of them. Results on the Oxford keypoint dataset [2] shows that the proposed approach obtains an improvement of up to 10% compared to the best one (FREAK)

Neuroverkot osakekurssihyppyjen ennustamisessa

This thesis proposes a new convolutional long short-term memory network with a feature-dimension attention model for predicting the occurence of stock price jumps by studying several popular neural network types for time series prediction and examining stock price jumps with data from NASDAQ limit order books for five different stocks. The proposed convolutional long short-term memory attention model network (CNN-LSTM-Attention) is further compared to a convolutional and a long-short term memory network from existing stock price prediction literature as well as a multi-layer perceptron. Normalized limit order book data with additional features is used to predict whether a jump will occur within the following minute. Testing the models yields very promising results for the predictability of jumps, which is especially significant as there is very little existing research on predicting stock price jumps with machine learning methods. Additionally, the proposed CNN-LSTM-Attention method is found the best from the tested ones, with the average F1 of 0.72. Furthermore, predicting existing jumps is found significantly easier than their size or direction, supporting the existence of a jump counting process which is separate from both the regular stock price process and the jump sizes, and which is not fully unpredictable

Contribuciones a la estimación de pose de cámara

El problema cuya resolución tiene como objetivo determinar la orientación y localización de una cámara respecto a un sistema de coordenadas se denomina Estimación de la pose de la cámara. Las soluciones basadas en imágenes para la resolución de este problema son una opción interesante debido a su bajo coste. El inconveniente fundamental de esta opción es que su precisión puede verse afectada debido a la presencia de ruido en la imagen. Trabajar con imágenes para estimar la pose de cámara está muy relacionado con dos problemas denominados Perspective-n-Point (PnP) y Bundle Adjustment (ajuste del haz). Dado un conjunto de n correspondencias entre puntos del espacio 3D y sus proyecciones 2D en una imagen, los métodos PnP tratan de obtener la pose de la cámara. Cuando la información acerca de la posición 3D de los puntos es desconocida, pero sí se tiene conocimiento de una serie de proyecciones 2D tomadas desde diferentes puntos de vista del mismo punto 3D, el ajuste del haz trata de estimar simultáneamente la posición tridimensional de los puntos y la pose de la cámara. Debido a esto la tarea de buscar correspondencias, ya sea entre puntos de la escena 3D y su proyección 2D en la imagen, o entre varias proyecciones 2D de imágenes diferentes no es trivial y resulta fundamental para la resolución de los problemas mencionados anteriormente. En esta Tesis Doctoral se han propuesto dos métodos novedosos para el problema de búsqueda de correspondencias usando marcas naturales y artificiales. En nuestra primera contribución, basada en el uso de marcas naturales, proponemos un método para encontrar correspondencias entre puntos 2D de diferentes imágenes, utilizando un nuevo enfoque de fusión que combina la información proporcionada por varios descriptores haciendo uso de la Teoría de Dempster-Shafer. El método propuesto es capaz de fusionar diferentes fuentes de información teniendo en cuenta además su confianza relativa con el fin de obtener una mejor solución. La segunda contribución se centra en el problema de búsqueda de proyecciones 2D de puntos 3D conocidos. Proponemos un enfoque novedoso para identificar marcadores artificiales, que son una alternativa muy popular cuando se requiere robustez y velocidad. En concreto, proponemos abordar el problema de identificación de marcadores artificiales como un problema de clasificación. Como consecuencia, hemos entrenado métodos capaces de detectar marcadores en imágenes afectadas por situaciones complejas como el desenfoque o la luz no uniforme. Ambas propuestas realizadas en esta Tesis han sido comparadas con métodos del estado del arte mostrando mejoras que son estadísticamente significativas.Camera pose estimation is the problem of finding the orientation and localization of a camera with respect to an arbitrary coordinate system. Image-based solutions for this problem are an interesting option because its reduced cost. However, their main drawback is that the accuracy of the results is afected by the presence of noise in the images. The use of images for the camera pose estimation task is strongly related to the Perspective-n-Point (PnP) and Bundle Adjustment problem. Given a set of n correspondences between 3D points and its 2D projections on the image, PnP methods provide estimations of the camera pose. In addition, when the information about the 3D positions is unknow but a set of 2D projections taken from diferent viewpoints of the same 3D point are known, Bundle Adjustment methods are capable of finding simultaneously the 3D position of the points and the camera pose. Then the task of finding correspondences between 3D points and its 2D projections, and between 2D projections of diferent images is a fundamental step for the above mentioned problems. This PhD Thesis proposes two novel approaches to solve the problem of finding correspondeces using both natural and artificial features. In our first contribution, based on natural features, we propose a novel approach to find 2D correspondeces between images by a novel fusion approach combining information provided by several descriptors using the Dempster-Shafer Theory. The proposed method is able to fuse diferent sources of information considering their relative confidence in order to provide a better solution. Our second contribution focuses on the problem of nding the 2D projections of 3D points. We propose a novel approach for identification of artificial landmarks, which are a very popular method when robustness and speed are required. In particular, we propose to tackle the marker identi cation problem as a classi cation one. As a consequence, we develop methods able to detect such markers in complex real situations such as blurring and non-uniform lightning. The two contributions made in this Thesis have been compared with the state-of-art methods showing statistically significant improvements

Contributions to Robust Multi-view 3D Action Recognition

This thesis focus on human action recognition using volumetric reconstructions obtained from multiple monocular cameras. The problem of action recognition has been addressed using di erent approaches, both in the 2D and 3D domains, and using one or multiple views. However, the development of robust recognition methods, independent from the view employed, remains an open problem. Multi-view approaches allow to exploit 3D information to improve the recognition performance. Nevertheless, manipulating the large amount of information of 3D representations poses a major problem. As a consequence, standard dimensionality reduction techniques must be applied prior to the use of machine learning approaches. The rst contribution of this work is a new descriptor of volumetric information that can be further reduced using standard Dimensionality Reduction techniques in both holistic and sequential recognition approaches. However, the descriptor itself reduces the amount of data up to an order of magnitude (compared to previous descriptors) without a ecting to the classi cation performance. The descriptor represents the volumetric information obtained by SfS techniques. However, this family of techniques are highly in uenced by errors in the segmentation process (e.g., undersegmentation causes false negatives in the reconstructed volumes) so that the recognition performance is highly a ected by this rst step. The second contribution of this work is a new SfS technique (named SfSDS) that employs the Dempster-Shafer theory to fuse evidences provided by multiple cameras. The central idea is to consider the relative position between cameras so as to deal with inconsistent silhouettes and obtain robust volumetric reconstructions. The basic SfS technique still have a main drawback, it requires the whole volume to be analized in order to obtain the reconstruction. On the other hand, octree-based representations allows to save memory and time employing a dynamic tree structure where only occupied nodes are stored. Nevertheless, applying the SfS method to octreebased representations is not straightforward. The nal contribution of this work is a method for generating octrees using our proposed SfSDS technique so as to obtain robust and compact volumetric representations.Esta tesis se centra en el reconocimiento de acciones humanas usando reconstrucciones volum etricas obtenidas a partir de m ultiples c amaras monoculares. El problema del reconocimiento de acciones ha sido tratado usando diferentes enfoques, en los dominios 2D y 3D, y usando una o varias vistas. No obstante, el desarrollo de m etodos de reconocimiento robustos, independientes de la vista empleada, sigue siendo un problema abierto. Los enfoques multi-vista permiten explotar la informaci on 3D para mejorar el rendimiento del reconocimiento. Sin embargo, manipular las grandes cantidades de informaci on de las representaciones 3D plantea un importante problema. Como consecuencia, deben ser aplicadas t ecnicas est andar de reducci on de dimensionalidad con anterioridad al uso de propuestas de aprendizaje. La primera contribuci on de este trabajo es un nuevo descriptor de informaci on volum etrica que puede ser posteriormente reducido mediante t ecnicas est andar de reducci on de dimensionalidad en los enfoques de reconocimiento hol sticos y secuenciales. El descriptor, por si mismo, reduce la cantidad de datos hasta en un orden de magnitud (en comparaci on con descriptores previos) sin afectar al rendimiento de clasi caci on. El descriptor representa la informaci on volum etrica obtenida en t ecnicas SfS. Sin embargo, esta familia de t ecnicas est a altamente in uenciada por los errores en el proceso de segmentaci on (p.e., una sub-segmentaci on causa falsos negativos en los vol umenes reconstruidos) de forma que el rendimiento del reconocimiento est a signi cativamente afectado por este primer paso. La segunda contribuci on de este trabajo es una nueva t ecnica SfS (denominada SfSDS) que emplea la teor a de Dempster-Shafer para fusionar evidencias proporcionadas por m ultiples c amaras. La idea central consiste en considerar la posici on relativa entre c amaras de forma que se traten las inconsistencias en las siluetas y se obtenga reconstrucciones volum etricas robustas. La t ecnica SfS b asica sigue teniendo un inconveniente principal; requiere que el volumen completo sea analizado para obtener la reconstrucci on. Por otro lado, las representaciones basadas en octrees permiten salvar memoria y tiempo empleando una estructura de arbol din amica donde s olo se almacenan los nodos ocupados. No obstante, la aplicaci on del m etodo SfS a representaciones basadas en octrees no es directa. La contribuci on nal de este trabajo es un m etodo para la generaci on de octrees usando nuestra t ecnica SfSDS propuesta de forma que se obtengan representaciones volum etricas robustas y compactas

Avances en modelos espacio-estado para el análisis de movimiento y comportamiento animal

La forma en que se mueven los animales es de gran interés en ecología, ya que afecta a la mayoría de los procesos ecológicos y evolutivos. Analizar estos procesos, implica estudiar sistemas que varían en el espacio y el tiempo a distintas escalas temporales y con distintos niveles de dependencia. Nuevas tecnologías han revolucionado la forma de estudiar y monitorear el movimiento de animales, su comportamiento, y su relación con el medio ambiente, llevando a la necesidad de desarrollar nuevas metodologías estadísticas. Bajo el contexto de modelos espacio-estado (MEE) y utilizando perspectiva Bayesiana, en esta tesis buscamos dar respuestas a este problema. Presentamos un MEE que permite describir trayectorias formulando el proceso de movimiento a tiempo continuo y la observación a tiempo discreto. Usando Modelos Ocultos de Markov, clasificamos datos temporales de aceleración en distintos comportamientos. Por ´ultimo, consideramos el error de las observaciones ambientales para describir trayectorias según la selección de recursos disponibles. Los resultados obtenidos resaltan la importancia de contar con modelos adecuados que permitan describir e interpretar correctamente estos sistemas y diagramar practicas de manejo apropiadas. Evidenciamos cómo la escala a la que los animales toman las decisiones de movimiento debe tenerse en cuenta a la hora de diseñar protocolos de colecta de datos y, que no siempre es necesario contar con datos de alta frecuencia para tener buenas estimaciones de ciertos procesos de movimiento. Además mostramos cómo el efecto del error en las observaciones ambientales puede sesgar las estimaciones de los análisis llevando a conclusiones erróneasRuiz Suarez, Sofia. Universidad Nacional de Rosario. Facultad de Ciencias Económicas y Estadística; Argentin

Multisensor triplet Markov chains and theory of evidence

Hidden Markov chains (HMC) are widely applied in various problems occurring in different areas like Biosciences, Climatology, Communications, Ecology, Econometrics and Finances, Image or Signal processing. In such models, the hidden process of interest X is a Markov chain, which must be estimated from an observable Y, interpretable as being a noisy version of X. The success of HMC is mainly due to the fact that the conditional probability distribution of the hidden process with respect to the observed process remains Markov, which makes possible different processing strategies such as Bayesian restoration. HMC have been recently generalized to ‘‘Pairwise’ ’ Markov chains (PMC) and ‘‘Triplet’ ’ Markov chains (TMC), which offer similar processing advantages and superior modeling capabilities. In PMC, one directly assumes the Markovianity of the pair (X, Y) and in TMC, the distribution of the pair (X, Y) is the marginal distribution of a Markov process (X, U, Y), where U is an auxiliary process, possibly contrived. Otherwise, the Dempster–Shafer fusion can offer interesting extensions of the calculation of the ‘‘a posteriori’ ’ distribution of the hidden data. The aim of this paper is to present different possibilities of using the Dempster–Shafer fusion in the context of different multisensor Markov models. We show that the posterior distribution remains calculable in different general situations and present some examples of their applications in remote sensing area