Self-tuning routine alarm analysis of vibration signals in steam turbine generators

This paper presents a self-tuning framework for knowledge-based diagnosis of routine alarms in steam turbine generators. The techniques provide a novel basis for initialising and updating time series feature extraction parameters used in the automated decision support of vibration events due to operational transients. The data-driven nature of the algorithms allows for machine specific characteristics of individual turbines to be learned and reasoned about. The paper provides a case study illustrating the routine alarm paradigm and the applicability of systems using such techniques

Empirical mode decomposition-based facial pose estimation inside video sequences

We describe a new pose-estimation algorithm via integration of the strength in both empirical mode decomposition (EMD) and mutual information. While mutual information is exploited to measure the similarity between facial images to estimate poses, EMD is exploited to decompose input facial images into a number of intrinsic mode function (IMF) components, which redistribute the effect of noise, expression changes, and illumination variations as such that, when the input facial image is described by the selected IMF components, all the negative effects can be minimized. Extensive experiments were carried out in comparisons to existing representative techniques, and the results show that the proposed algorithm achieves better pose-estimation performances with robustness to noise corruption, illumination variation, and facial expressions

Rehaussement du signal de parole par EMD et opérateur de Teager-Kaiser

The authors would like to thank Professor Mohamed Bahoura from Universite de Quebec a Rimouski for fruitful discussions on time adaptive thresholdingIn this paper a speech denoising strategy based on time adaptive thresholding of intrinsic modes functions (IMFs) of the signal, extracted by empirical mode decomposition (EMD), is introduced. The denoised signal is reconstructed by the superposition of its adaptive thresholded IMFs. Adaptive thresholds are estimated using the Teager–Kaiser energy operator (TKEO) of signal IMFs. More precisely, TKEO identifies the type of frame by expanding differences between speech and non-speech frames in each IMF. Based on the EMD, the proposed speech denoising scheme is a fully data-driven approach. The method is tested on speech signals with different noise levels and the results are compared to EMD-shrinkage and wavelet transform (WT) coupled with TKEO. Speech enhancement performance is evaluated using output signal to noise ratio (SNR) and perceptual evaluation of speech quality (PESQ) measure. Based on the analyzed speech signals, the proposed enhancement scheme performs better than WT-TKEO and EMD-shrinkage approaches in terms of output SNR and PESQ. The noise is greatly reduced using time-adaptive thresholding than universal thresholding. The study is limited to signals corrupted by additive white Gaussian noise

A novel approach for breast ultrasound classification using two-dimensional empirical mode decomposition and multiple features

Aim: Breast cancer stands as a prominent cause of female mortality on a global scale, underscoring the critical need for precise and efficient diagnostic techniques. This research significantly enriches the body of knowledge pertaining to breast cancer classification, especially when employing breast ultrasound images, by introducing a novel method rooted in the two dimensional empirical mode decomposition (biEMD) method. In this study, an evaluation of the classification performance is proposed based on various texture features of breast ultrasound images and their corresponding biEMD subbands. Methods: A total of 437 benign and 210 malignant breast ultrasound images were analyzed, preprocessed, and decomposed into three biEMD sub-bands. A variety of features, including the Gray Level Co-occurrence Matrix (GLCM), Local Binary Patterns (LBP), and Histogram of Oriented Gradient (HOG), were extracted, and a feature selection process was performed using the least absolute shrinkage and selection operator method. The study employed GLCM, LBP and HOG, and machine learning techniques, including artificial neural networks (ANN), k-nearest neighbors (kNN), the ensemble method, and statistical discriminant analysis, to classify benign and malignant cases. The classification performance, measured through Area Under the Curve (AUC), accuracy, and F1 score, was evaluated using a 10-fold cross-validation approach. Results: The study showed that using the ANN method and hybrid features (GLCM+LBP+HOG) from BUS images' biEMD sub-bands led to excellent performance, with an AUC of 0.9945, an accuracy of 0.9644, and an F1 score of 0.9668. This has revealed the effectiveness of the biEMD method for classifying breast tumor types from ultrasound images. Conclusion: The obtained results have revealed the effectiveness of the biEMD method for classifying breast tumor types from ultrasound images, demonstrating high-performance classification using the proposed approach