Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

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Table Of Integrals Series And Products

Recently, to ensure the reliability and safety of modern large-scale industrial processes, data-driven methods have been receiving considerably increasing attention, particularly for the purpose of process monitoring. However, great challenges are also met under different real operating conditions by using the basic data-driven methods. In this paper, widely applied data-driven methodologies suggested in the literature for process monitoring and fault diagnosis are surveyed from the application point of view. The major task of this paper is to sketch a basic data-driven design framework with necessary modifications under various industrial operating conditions, aiming to offer a reference for industrial process monitoring on large-scale industrial processes.

A Review on Basic Data-Driven Approaches for Industrial Process Monitoring

This paper provides an overview of the recent developments in data-based techniques focused on modern industrial applications. As one of the hottest research topics for complicated processes, the data-based techniques have been rapidly developed over the past two decades and widely used in numerous industrial sectors nowadays. The core of data-based techniques is to take full advantage of the huge amounts of available process data, aiming to acquire the useful information within. Compared with the well-developed model-based approaches, data-based techniques provide efficient alternative solutions for different industrial issues under various operating conditions. The main objective of this paper is to review and summarize the recent achievements in data-based techniques, especially for complicated industrial applications, thus providing a referee for further study on the related topics both from academic and practical points of view. This paper begins with a brief evolutionary overview of data-based techniques in the last two decades. Then, the methodologies only based on process measurements and the model-data integrated techniques will be further introduced. The recent developments for modern industrial applications are, respectively, presented mainly from perspectives of monitoring and control. The new trends of data-based technique as well as potential application fields are finally discussed.

Data-Based Techniques Focused on Modern Industry: An Overview

Space-Time Block Coding for Wireless Communications

L'amelioration de la disponibilite et de la fiabilite des eoliennes offshores et des systemes de recuperation de l'energie des courants marins implique la necessite de minimiser et de prevoir les operations de maintenance. En fonctionnement a vitesse variable ou en regime transitoire, des techniques de traitement du signal avancees sont requises pour realiser la detection et le diagnostic des defaillances a partir des courants statoriques. Dans ce contexte, plusieurs etudes recentes ont proposees l'utilisation de techniques temps frequence et temps-echelle pour le diagnostic. Les techniques les plus utilisees sont : Le spectrogramme, la transformee en ondelettes, la representation de Wigner-Ville et la transformee de Hilbert-Huang. Cet article propose alors une etude comparative et une analyse de ces techniques pour la detection des defauts qui surviennent dans une generatrice asynchrone connectee a un reseau triphase fonctionnant en regime nominal.

https://hal.archives-ouvertes.fr/hal-00657623/document

Etude Comparative des Techniques de Traitement du Signal Non- Stationnaires Dédiées au Diagnostic des Génératrices Asynchrones dans les Eoliennes Offshores et les Hydroliennes

This paper presents a comparative study on global compensation methods for coherent optical communications. Specifically, it compares an approach based on statistical signal processing to a parametric network that combines the model knowledge with Machine/Deep Learning techniques. Simulation results show the effectiveness of both techniques in the presence of multiple linear effects like IQ imbalance, carrier frequency offset, and laser phase noise. Furthermore, these results show that the statistical signal processing approach benefits from a lower computational cost, while the parametric network is more flexible and can be easily adapted to system requirements.

A Comparative Study on Global Compensation Techniques for Coherent Optical Communications

We show here that the Huber Cost Function is more efficient than Mean Squared Error metric to train a multi-layer perceptron in strong fibre nonlinear regime for single-channel coherent 200-Gbps Dual-Polarization 16QAM transmission.

Optimized Cost Function of Multi-Layer Perceptron for Fibre Non-Linear Impairment Mitigation in Coherent 200-Gbps DP-16QAM Transmission System

In the last few years, Motor Current Signature Analysis (MCSA) has proven to be an effective method for electrical machines condition monitoring. Indeed, many mechanical and electrical faults manifest as side-band spectral components generated around the fundamental frequency component of the motor current. These components are called interharmonics and they are a major focus of fault detection using MCSA. However, the main drawback of this approach is that the interference of other more prevalent components can obstruct the effect of interharmonics in the spectrum and may therefore impede fault detection accuracy. Thus, we propose in this paper an alternative approach that decomposes the different current components based on the Vandermonde model and implements the tracking of each distinct component in time and spectral domains. This is achieved by estimating their respective relevant parameters using the Gradient Descent algorithm. The results of this work prove to be promising and establish the parametric tracking of the electrical current components using the Gradient Descent algorithm as a reliable monitoring approach.

Parametric Tracking of Electrical Currents Components Using Gradient Descent Algorithm

In this paper, we focus on the joint impairments compensation and symbol detection problem in communication systems. First, we introduce a new multi-layer channel model that represents the underlying physics of multiple impairments in communication systems. This model is composed of widely linear parametric layers that describe the input-output relationship of the front-end impairments and channel effects. Using this particular model, we show that the joint compensation and zero-forcing detection problem can be solved by a particular feedforward network called PhyCOM. Because of the small number of network parameters, a PhyCOM network can be trained efﬁciently using sophisticated optimization algorithms and a limited number of pilot symbols. Numerical examples are provided to demonstrate the effectiveness of PhyCOM networks with communication systems corrupted by transmitter and receiver IQ imbalances, carrier frequency offset, ﬁnite impulse response channels, and transmitter and receiver phase noise distortions. Compared to conventional digital signal processing approaches, simulation results show that the proposed technique is much more ﬂexible and offers better statistical performance both in terms of MSE and SER with a moderate increase of the computation complexity.

Vincent Choqueuse

Papers

Etude Comparative des Techniques de Traitement du Signal Non- Stationnaires Dédiées au Diagnostic des Génératrices Asynchrones dans les Eoliennes Offshores et les Hydroliennes

A Comparative Study on Global Compensation Techniques for Coherent Optical Communications

Optimized Cost Function of Multi-Layer Perceptron for Fibre Non-Linear Impairment Mitigation in Coherent 200-Gbps DP-16QAM Transmission System

Parametric Tracking of Electrical Currents Components Using Gradient Descent Algorithm

P hy com: a m ulti -l ayer p arametric n etwork for j oint l inear i mpairments c ompensation and s ymbol d etection