Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction

This paper reports the results of the NN3 competition, which is a replication of the M3 competition with an extension of the competition towards neural network (NN) and computational intelligence (CI) methods, in order to assess what progress has been made in the 10 years since the M3 competition. Two masked subsets of the M3 monthly industry data, containing 111 and 11 empirical time series respectively, were chosen, controlling for multiple data conditions of time series length (short/long), data patterns (seasonal/non-seasonal) and forecasting horizons (short/medium/long). The relative forecasting accuracy was assessed using the metrics from the M3, together with later extensions of scaled measures, and non-parametric statistical tests. The NN3 competition attracted 59 submissions from NN, CI and statistics, making it the largest CI competition on time series data. Its main findings include: (a) only one NN outperformed the damped trend using the sMAPE, but more contenders outperformed the AutomatANN of the M3; (b) ensembles of CI approaches performed very well, better than combinations of statistical methods; (c) a novel, complex statistical method outperformed all statistical and Cl benchmarks; and (d) for the most difficult subset of short and seasonal series, a methodology employing echo state neural networks outperformed all others. The NN3 results highlight the ability of NN to handle complex data, including short and seasonal time series, beyond prior expectations, and thus identify multiple avenues for future research. (C) 2011 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved

Wearable video monitoring of people with age Dementia : Video indexing at the service of helthcare

International audienceExploration of video surveillance material for healthcare becomes a reality in medical research. In this paper we propose a video monitoring system with wearable cameras for early diagnostics of Dementia. A video acquisition set-up is designed and the methods are developed for indexing the recorded video. The noisiness of audio-visual material and its particularity yield challenging problems for automatic indexing of this content

Identification of expressive descriptors for style extraction in music analysis using linear and nonlinear models

La formalización de las interpretaciones expresivas aún se considera relevante debido a la complejidad de la música. La interpretación expresiva forma un aspecto importante de la música, teniendo en cuenta diferentes convenciones como géneros o estilos que una interpretación puede desarrollar con el tiempo. Modelar la relación entre las expresiones musicales y los aspectos estructurales de la información acústica requiere una base probabilística y estadística mínima para la robustez, validación y reproducibilidad de aplicaciones computacionales. Por lo tanto, es necesaria una relación cohesiva y una justificación sobre los resultados. Esta tesis se sustenta en la teoría y aplicaciones de modelos discriminativos y generativos en el marco del aprendizaje de maquina y la relación de procedimientos sistemáticos con los conceptos de la musicología utilizando técnicas de procesamiento de señales y minería de datos. Los resultados se validaron mediante pruebas estadísticas y una experimentación no paramétrica con la implementación de un conjunto de métricas para medir aspectos acústicos y temporales de archivos de audio para entrenar un modelo discriminativo y mejorar el proceso de síntesis de un modelo neuronal profundo. Adicionalmente, el modelo implementado presenta la oportunidad para la aplicación de procedimientos sistemáticos, automatización de transcripciones usando notación musical, entrenamiento de habilidades auditivas para estudiantes de música y mejorar la implementación de redes neuronales profundas usando CPU en lugar de GPU debido a las ventajas de las redes convolucionales para el procesamiento de archivos de audio como vectores o matriz con una secuencia de notas.MaestríaMagister en Ingeniería Electrónic

A flexible design strategy for three-element non-uniform linear arrays

This paper illustrates a flexible design strategy for a three-element non-uniform linear array (NULA) aimed at estimating the direction of arrival (DoA) of a source of interest. Thanks to the spatial diversity resulting from non-uniform sensor spacings, satisfactory DoA estimation accuracies can be achieved by employing a very limited number of receiving elements. This makes NULA configurations particularly attractive for low-cost passive location applications. To estimate the DoA of the source of interest, we resort to the maximum likelihood estimator, and the proposed design strategy is obtained by constraining the maximum pairwise error probability to control the errors occurring due to outliers. In fact, it is well known that the accuracy of the maximum likelihood estimator is often degraded by outliers, especially when the signal-to-noise power ratio does not belong to the so-called asymptotic region. The imposed constraint allows for the defining of an admissible region in which the array should be selected. This region can be further modified to incorporate practical design constraints concerning the antenna element size and the positioning accuracy. The best admissible array is then compared to the one obtained with a conventional NULA design approach, where only antenna spacings multiple of λ/2 are considered, showing improved performance, which is also confirmed by the experimental results

Vibration Monitoring: Gearbox identification and faults detection

L'abstract è presente nell'allegato / the abstract is in the attachmen

A review on Machine Learning Techniques

Machine learning is the essence of artificial intelligence. Machine Learning learns from past experiences to improve the performances of intelligent programs. Machine learning system builds the learning model that effectively “learns” how to estimate from training data of given example. IT refers to a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. In this new era, Machine learning is mostly in use to demonstrate the promise of producing consistently accurate estimates. The main goal and contribution of this review paper is to present the overview of machine learning and provides machine-learning techniques. Also paper reviews the merits and demerits of various machine learning algorithms in different approaches