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

The blind recognition of communication parameters is a key research issue for commercial and military communication systems. In this paper, we investigate the problem of the blind recognition of linear space-time block codes (STBC). To characterize the space time coding, we propose to compute a time-lag correlation of the received samples. Provided the number of transmitters, the noise variance and the symbol timing are well estimated, we show that the theoretical values of the correlation norm only depend on the STBC and are affected by neither the channel nor the symbol modulation. The automatic recognition of the STBC is realized by selecting the STBC which minimizes the distance between the theoretical values and the experimental ones. Simulations show that our method performs well even for low signal to noise ratio (0 dB).

/pdf/blind-recognition-of-linear-space-time-block-codes-32vajj5n0m.pdf

Blind recognition of linear space time block codes

https://hal.archives-ouvertes.fr/hal-00874031/document

EEMD-based wind turbine bearing failure detection using the generator stator current homopolar component

The blind recognition of communication parameters is a key research issue for commercial and military communication systems. The results of numerous investigations about symbol timing estimation, modulation recognition as well as identification of the number of transmitters have been reported in the literature. But, to our knowledge, none of them have dealt with the recognition of the Space-Time Block Codes (STBC) used in multiple transmitter communications. In order to blindly recognize the STBC of a wireless communication, this paper proposes a method based on the space-time correlations of the received signals. Under perfect timing synchronization and under ideal conditions (full rank channel and a number of receivers greater or equal to the number of transmitters), it shows that the Frobenius norms of these statistics present non-null values whose positions only depend on the STBC at the transmitter side. A classifier for the space-time code recognition of 5 linear STBC (Spatial Multiplexing, Alamouti Coding, and 3 Orthogonal STBC using 3 antennas) is presented. Simulations show that the proposed method performs well even at low signal-to-noise ratios.

/pdf/hierarchical-space-time-block-code-recognition-using-2xcn4qe89o.pdf

Hierarchical Space-Time Block Code Recognition Using Correlation Matrices

The blind recognition of communication parameters is an important research topic in both commercial and civilian systems. In this paper, we investigate the blind recognition of the modulation. Currently most part of the existing algorithms assumes that the transmitter uses a single-antenna. This study extends the problem for multiple-antennas (MIMO) systems. We adopt a Maximum Likelihood approach for the blind recognition of the modulation and we consider two different situations. First, we assume the channel knowledge at the receiver side and we expose the optimal solution which is called Average Likelihood Ratio Test (ALRT). Then, we relax this assumption and we propose a second method based on a Hybrid Likelihood Ratio Test (HLRT).

/pdf/blind-modulation-recognition-for-mimo-systems-2wapjfrj1z.pdf

Blind modulation recognition for mimo systems

The main objective of this paper is to detect faults in induction machines using a condition monitoring architecture based on stator current measurements. Two types of fault are considered: bearing and broken rotor bars faults. The proposed architecture is based on high-resolution spectral analysis techniques also known as subspace techniques. These frequency estimation techniques allow to separate frequency components including frequencies close to the fundamental one. These frequencies correspond to fault sensitive frequencies. Once frequencies are estimated, their corresponding amplitudes are obtained by using the least squares estimator. Then, a fault severity criterion is derived from the amplitude estimates. The proposed methods were tested using experimental stator current signals issued from two induction motors with the considered faults. The experimental results show that the proposed architecture has the ability to efficiently and cost-effectively detect faults and identify their severity.

/pdf/induction-machines-fault-detection-based-on-subspace-4xp7bgwezz.pdf

Vincent Choqueuse

Papers

Blind recognition of linear space time block codes

EEMD-based wind turbine bearing failure detection using the generator stator current homopolar component

Hierarchical Space-Time Block Code Recognition Using Correlation Matrices

Blind modulation recognition for mimo systems

Induction Machines Fault Detection Based on Subspace Spectral Estimation