Assessment and Localization of Active Discontinuities Using Energy Distribution Between Intrinsic Modes

A method for localization and severity assessment of structural damages is proposed. The algorithm works based on nonlinear behavior of certain type of damages such as breathing cracks which are called active discontinuities in this paper. Generally, nonlinear features are more sensitive to such damages although their extraction is sometimes controversial. A major controversy is the imposition of spurious modes on the expansion of the signal which needs to be addressed for an effective application and robustness of the method. The energy content of Intrinsic Mode Functions (IMFs), which are the resultants of Empirical Mode Decomposition (EMD), and also the shape of energy distribution between these modes before and after damage, are used for localization and severity assessment of the damages. By using EMD, we preserve the nonlinear aspects of the signal while avoiding imposition of spurious harmonics on its expansion without any assumption of stationarity. The developed algorithms are used to localize and assess the damage in a steel cantilever beam. The results show that the method can be used effectively for detecting active structural discontinuities due to damage

Supporting the page-hinkley test with empirical mode decomposition for change detection

In the dynamic scenarios faced nowadays, when handling non stationary data streams it is of utmost importance to perform change detection tests. In this work, we propose the Intrinsic Page Hinkley Test (iPHT), which enhances the Page Hinkley Test (PHT) eliminating the user-defined parameter (the allowed magnitude of change of the data that are not considered real distribution change of the data stream) by using the second order intrinsic mode function (IMF) which is a data dependent value reflecting the intrinsic data variation. In such way, the PHT change detection method is expected to be more robust and require less tunes. Furthermore, we extend the proposed iPHT to a blockwise approach. Computing the IMF over sliding windows, which is shown to be more responsive to changes and suitable for online settings. The iPHT is evaluated using artificial and real data, outperforming the PHT. © Springer International Publishing AG 2017

Epigenetics in breast cancer: What's new?

Epigenetic changes are critical for development and progression of cancers, including breast cancer. Significant progress has been made in the basic understanding of how various epigenetic changes such as DNA methylation, histone modification, miRNA expression, and higher order chromatin structure affect gene expression. The present review will focus on methylation and demethylation of histones. While the acetylation of histones has been at the forefront of well-characterized post-translational modifications of histones, including the development of inhibitors targeting de-acetylating enzymes, the past few years have witnessed a dramatic increase in knowledge regarding the role of histone methylation/demethylation. This is an exciting and rapidly evolving area of research, with much promise for potential clinical intervention in several cancers including breast cancer. We also summarize efforts to identity DNA methylation signatures that could be prognostic and/or predictive markers in breast cancer, focusing on recent studies using genome-wide approaches. Finally, we briefly review the efforts made by both the National Institutes of Health Epigenome Project and The Cancer Genome Atlas, especially highlighting the study of breast cancer epigenetics, exciting technological advances, potential roadblocks, and future directions. © 2010 BioMed Central Ltd

Separation between coherent and turbulent fluctuations. What can we learn from the Empirical Mode Decomposition?

The performances of a new data processing technique, namely the Empirical Mode Decomposition, are evaluated on a fully developed turbulent velocity signal perturbed by a numerical forcing which mimics a long-period flapping. First, we introduce a "resemblance" criterion to discriminate between the polluted and the unpolluted modes extracted from the perturbed velocity signal by means of the Empirical Mode Decomposition algorithm. A rejection procedure, playing, somehow, the role of a high-pass filter, is then designed in order to infer the original velocity signal from the perturbed one. The quality of this recovering procedure is extensively evaluated in the case of a "mono-component" perturbation (sine wave) by varying both the amplitude and the frequency of the perturbation. An excellent agreement between the recovered and the reference velocity signals is found, even though some discrepancies are observed when the perturbation frequency overlaps the frequency range corresponding to the energy-containing eddies as emphasized by both the energy spectrum and the structure functions. Finally, our recovering procedure is successfully performed on a time-dependent perturbation (linear chirp) covering a broad range of frequencies.Comment: 23 pages, 13 figures, submitted to Experiments in Fluid

UAV Downwash-Based Terrain Classification Using Wiener-Khinchin and EMD Filters

This work was partially funded by FCT Strategic Program UID/EEA/00066/203 of the Center of Technologies and System (CTS) of UNINOVA - Institute for the Development of new Technologies.Knowing how to identify terrain types is especially important in the autonomous navigation, mapping, decision making and detect landings areas. A recent area is in cooperation and improvement of autonomous behavior between robots. For example, an unmanned aerial vehicle (UAV) is used to identify a possible landing area or used in cooperation with other robots to navigate in unknown terrains. This paper presents a computer vision algorithm capable of identifying the terrain type where the UAV is flying, using its rotors’ downwash effect. The algorithm is a fusion between the frequency Wiener-Khinchin adapted and spatial Empirical Mode Decomposition (EMD) domains. In order to increase certainty in terrain identification, machine learning is also used. The system is validated using videos acquired onboard of a UAV with an RGB camera.authorsversionpublishe

Aspects of structural health and condition monitoring of offshore wind turbines

Wind power has expanded significantly over the past years, although reliability of wind turbine systems, especially of offshore wind turbines, has been many times unsatisfactory in the past. Wind turbine failures are equivalent to crucial financial losses. Therefore, creating and applying strategies that improve the reliability of their components is important for a successful implementation of such systems. Structural health monitoring (SHM) addresses these problems through the monitoring of parameters indicative of the state of the structure examined. Condition monitoring (CM), on the other hand, can be seen as a specialized area of the SHM community that aims at damage detection of, particularly, rotating machinery. The paper is divided into two parts: in the first part, advanced signal processing and machine learning methods are discussed for SHM and CM on wind turbine gearbox and blade damage detection examples. In the second part, an initial exploration of supervisor control and data acquisition systems data of an offshore wind farm is presented, and data-driven approaches are proposed for detecting abnormal behaviour of wind turbines. It is shown that the advanced signal processing methods discussed are effective and that it is important to adopt these SHM strategies in the wind energy sector

Finding motif pairs in the interactions between heterogeneous proteins via bootstrapping and boosting

<p>Abstract</p> <p>Background</p> <p>Supervised learning and many stochastic methods for predicting protein-protein interactions require both negative and positive interactions in the training data set. Unlike positive interactions, negative interactions cannot be readily obtained from interaction data, so these must be generated. In protein-protein interactions and other molecular interactions as well, taking all non-positive interactions as negative interactions produces too many negative interactions for the positive interactions. Random selection from non-positive interactions is unsuitable, since the selected data may not reflect the original distribution of data.</p> <p>Results</p> <p>We developed a bootstrapping algorithm for generating a negative data set of arbitrary size from protein-protein interaction data. We also developed an efficient boosting algorithm for finding interacting motif pairs in human and virus proteins. The boosting algorithm showed the best performance (84.4% sensitivity and 75.9% specificity) with balanced positive and negative data sets. The boosting algorithm was also used to find potential motif pairs in complexes of human and virus proteins, for which structural data was not used to train the algorithm. Interacting motif pairs common to multiple folds of structural data for the complexes were proven to be statistically significant. The data set for interactions between human and virus proteins was extracted from BOND and is available at <url>http://virus.hpid.org/interactions.aspx</url>. The complexes of human and virus proteins were extracted from PDB and their identifiers are available at <url>http://virus.hpid.org/PDB_IDs.html</url>.</p> <p>Conclusion</p> <p>When the positive and negative training data sets are unbalanced, the result via the prediction model tends to be biased. Bootstrapping is effective for generating a negative data set, for which the size and distribution are easily controlled. Our boosting algorithm could efficiently predict interacting motif pairs from protein interaction and sequence data, which was trained with the balanced data sets generated via the bootstrapping method.</p