15,864 research outputs found
Improved Signal Characterization via Empirical Mode Decomposition to Enhance in-line Quality Monitoring
The machine tool industry is facing the need to increase the sensorization of production systems to meet evolving market demands. This leads to the increasing interest for in-process monitoring tools that allow a fast detection of faults and unnatural process behaviours during the process itself. Nevertheless, the analysis of sensor signals implies several challenges. One major challenge consists of the complexity of signal patterns, which often exhibit a multiscale content, i.e., a superimposition of both stationary and non-stationary fluctuations on different time-frequency levels. Among time-frequency techniques, Empirical Mode Decomposition (EMD) is a powerful method to decompose any signal into its embedded oscillatory modes in a fully data-driven way, without any ex-ante basis selection. Because of this, it might be used effectively for automated monitoring and diagnosis of manufacturing processes. Unfortunately, it usually yields an over-decomposition, with single oscillation modes that can be split into more than one scale (this effect is also known as “mode mixing”). The literature lacks effective strategies to automatically synthetize the decomposition into a minimal number of physically relevant and interpretable components. This paper proposes a novel approach to achieve a synthetic decomposition of complex signals through the EMD procedure. A new criterion is proposed to group together multiple components associated to a common time-frequency pattern, aimed at summarizing the information content into a minimal number of modes, which may be easier to interpret. A real case study in waterjet cutting is presented, to demonstrate the benefits and the critical issues of the proposed approach
Astrophysically robust systematics removal using variational inference: application to the first month of Kepler data
Space-based transit search missions such as Kepler are collecting large
numbers of stellar light curves of unprecedented photometric precision and time
coverage. However, before this scientific goldmine can be exploited fully, the
data must be cleaned of instrumental artefacts. We present a new method to
correct common-mode systematics in large ensembles of very high precision light
curves. It is based on a Bayesian linear basis model and uses shrinkage priors
for robustness, variational inference for speed, and a de-noising step based on
empirical mode decomposition to prevent the introduction of spurious noise into
the corrected light curves. After demonstrating the performance of our method
on a synthetic dataset, we apply it to the first month of Kepler data. We
compare the results, which are publicly available, to the output of the Kepler
pipeline's pre-search data conditioning, and show that the two generally give
similar results, but the light curves corrected using our approach have lower
scatter, on average, on both long and short timescales. We finish by discussing
some limitations of our method and outlining some avenues for further
development. The trend-corrected data produced by our approach are publicly
available.Comment: 15 pages, 13 figures, accepted for publication in MNRA
Integration of Human Walking Gyroscopic Data Using Empirical Mode Decomposition
The present study was aimed at evaluating the Empirical Mode Decomposition (EMD) method to estimate the 3D orientation of the lower trunk during walking using the angular velocity signals generated by a wearable inertial measurement unit (IMU) and notably flawed by drift. The IMU was mounted on the lower trunk (L4-L5) with its active axes aligned with the relevant anatomical axes. The proposed method performs an offline analysis, but has the advantage of not requiring any parameter tuning. The method was validated in two groups of 15 subjects, one during overground walking, with 180° turns, and the other during treadmill walking, both for steady-state and transient speeds, using stereophotogrammetric data. Comparative analysis of the results showed that the IMU/EMD method is able to successfully detrend the integrated angular velocities and estimate lateral bending, flexion-extension as well as axial rotations of the lower trunk during walking with RMS errors of 1 deg for straight walking and lower than 2.5 deg for walking with turns
Data-driven multivariate and multiscale methods for brain computer interface
This thesis focuses on the development of data-driven multivariate and multiscale methods
for brain computer interface (BCI) systems. The electroencephalogram (EEG), the
most convenient means to measure neurophysiological activity due to its noninvasive nature,
is mainly considered. The nonlinearity and nonstationarity inherent in EEG and its
multichannel recording nature require a new set of data-driven multivariate techniques to
estimate more accurately features for enhanced BCI operation. Also, a long term goal
is to enable an alternative EEG recording strategy for achieving long-term and portable
monitoring.
Empirical mode decomposition (EMD) and local mean decomposition (LMD), fully
data-driven adaptive tools, are considered to decompose the nonlinear and nonstationary
EEG signal into a set of components which are highly localised in time and frequency. It
is shown that the complex and multivariate extensions of EMD, which can exploit common
oscillatory modes within multivariate (multichannel) data, can be used to accurately
estimate and compare the amplitude and phase information among multiple sources, a
key for the feature extraction of BCI system. A complex extension of local mean decomposition
is also introduced and its operation is illustrated on two channel neuronal
spike streams. Common spatial pattern (CSP), a standard feature extraction technique
for BCI application, is also extended to complex domain using the augmented complex
statistics. Depending on the circularity/noncircularity of a complex signal, one of the
complex CSP algorithms can be chosen to produce the best classification performance
between two different EEG classes.
Using these complex and multivariate algorithms, two cognitive brain studies are
investigated for more natural and intuitive design of advanced BCI systems. Firstly, a Yarbus-style auditory selective attention experiment is introduced to measure the user
attention to a sound source among a mixture of sound stimuli, which is aimed at improving
the usefulness of hearing instruments such as hearing aid. Secondly, emotion experiments
elicited by taste and taste recall are examined to determine the pleasure and displeasure
of a food for the implementation of affective computing. The separation between two
emotional responses is examined using real and complex-valued common spatial pattern
methods.
Finally, we introduce a novel approach to brain monitoring based on EEG recordings
from within the ear canal, embedded on a custom made hearing aid earplug. The new
platform promises the possibility of both short- and long-term continuous use for standard
brain monitoring and interfacing applications
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