A Synchrosqueezed Wavelet Transform assisted machine learning framework for time series forecasting Contributions to KDWEB poster session, a.d. 2016

Abstract. The attention of researchers in the field of signal analysis has recently been captured by several kinds of reassignment techniques. Among the reallocation methods the Synchrosqueezing approach maps a continuous wavelet transform from the time-scale to the time-frequency plane, allowing a much more definite and consistent representation of the frequency content of a signal. Its mathematical foundations prove that the Synchrosqueezing Wavelet Transform is directly related to the Empirical Mode Decomposition -EMD, since both methods allow the decomposition of a signal having finite energy in its intrinsic mode functions, each having time-varying frequency and amplitude. We develop a fast 1 C++ implementation of the Synchrosqueezed Wavelet Transform -SST suitable for the synchronic extrusion of instantaneous frequency information from univariate time series. Such module, totally configurable and adaptable to input datasets having different statistical properties, can be coupled with a predictor system to the purpose of real value forecasting. We project such a composite application and plan to research the forecasting accuracy achievable, using different types of non parametric statistical estimators or neural regressors

A Sparse Random Feature Model for Signal Decomposition

Signal decomposition and multiscale signal analysis provide useful tools for time-frequency analysis. In this thesis, an overview of the signal decomposition problem is given and popular methods are discussed. A novel signal decomposition algorithm is presented: Sparse Random Mode Decomposition (SRMD). This method sparsely represents a signal as a sum of random windowed-sinusoidal features before clustering the time-frequency localized features into the constituent modes. SRMD outperforms state-of-the-art methods on a variety of mathematical signals, and is applied to real-world astronomical and musical examples. Finally, we discuss a neural network approach to tackle challenging musical signals

State of the art in structural health monitoring of offshore and marine structures

This paper deals with state of the art in structural health monitoring (SHM) methods in offshore and marine structures. Most SHM methods have been developed for onshore infrastructures. Few studies are available to implement SHM technologies in offshore and marine structures. This paper aims to fill this gap and highlight the challenges in implementing SHM methods in offshore and marine structures. The present work categorises the available techniques for establishing SHM models in oil rigs, offshore wind turbine structures, subsea systems, vessels, pipelines and so on. Additionally, the capabilities of proposed ideas in recent publications are classified into three main categories: model-based methods, vibration-based methods and digital twin methods. Recently developed novel signal processing and machine learning algorithms are reviewed and their abilities are discussed. Developed methods in vision-based and population-based approaches are also presented and discussed. The aim of this paper is to provide guidelines for selecting and establishing SHM in offshore and marine structures.publishedVersio

Multivariate time-frequency analysis

Recent advances in time-frequency theory have led to the development of high resolution time-frequency algorithms, such as the empirical mode decomposition (EMD) and the synchrosqueezing transform (SST). These algorithms provide enhanced localization in representing time varying oscillatory components over conventional linear and quadratic time-frequency algorithms. However, with the emergence of low cost multichannel sensor technology, multivariate extensions of time-frequency algorithms are needed in order to exploit the inter-channel dependencies that may arise for multivariate data. Applications of this framework range from filtering to the analysis of oscillatory components. To this end, this thesis first seeks to introduce a multivariate extension of the synchrosqueezing transform, so as to identify a set of oscillations common to the multivariate data. Furthermore, a new framework for multivariate time-frequency representations is developed using the proposed multivariate extension of the SST. The performance of the proposed algorithms are demonstrated on a wide variety of both simulated and real world data sets, such as in phase synchrony spectrograms and multivariate signal denoising. Finally, multivariate extensions of the EMD have been developed that capture the inter-channel dependencies in multivariate data. This is achieved by processing such data directly in higher dimensional spaces where they reside, and by accounting for the power imbalance across multivariate data channels that are recorded from real world sensors, thereby preserving the multivariate structure of the data. These optimized performance of such data driven algorithms when processing multivariate data with power imbalances and inter-channel correlations, and is demonstrated on the real world examples of Doppler radar processing.Open Acces

A Statistical Perspective of the Empirical Mode Decomposition

This research focuses on non-stationary basis decompositions methods in time-frequency analysis. Classical methodologies in this field such as Fourier Analysis and Wavelet Transforms rely on strong assumptions of the underlying moment generating process, which, may not be valid in real data scenarios or modern applications of machine learning. The literature on non-stationary methods is still in its infancy, and the research contained in this thesis aims to address challenges arising in this area. Among several alternatives, this work is based on the method known as the Empirical Mode Decomposition (EMD). The EMD is a non-parametric time-series decomposition technique that produces a set of time-series functions denoted as Intrinsic Mode Functions (IMFs), which carry specific statistical properties. The main focus is providing a general and flexible family of basis extraction methods with minimal requirements compared to those within the Fourier or Wavelet techniques. This is highly important for two main reasons: first, more universal applications can be taken into account; secondly, the EMD has very little a priori knowledge of the process required to apply it, and as such, it can have greater generalisation properties in statistical applications across a wide array of applications and data types. The contributions of this work deal with several aspects of the decomposition. The first set regards the construction of an IMF from several perspectives: (1) achieving a semi-parametric representation of each basis; (2) extracting such semi-parametric functional forms in a computationally efficient and statistically robust framework. The EMD belongs to the class of path-based decompositions and, therefore, they are often not treated as a stochastic representation. (3) A major contribution involves the embedding of the deterministic pathwise decomposition framework into a formal stochastic process setting. One of the assumptions proper of the EMD construction is the requirement for a continuous function to apply the decomposition. In general, this may not be the case within many applications. (4) Various multi-kernel Gaussian Process formulations of the EMD will be proposed through the introduced stochastic embedding. Particularly, two different models will be proposed: one modelling the temporal mode of oscillations of the EMD and the other one capturing instantaneous frequencies location in specific frequency regions or bandwidths. (5) The construction of the second stochastic embedding will be achieved with an optimisation method called the cross-entropy method. Two formulations will be provided and explored in this regard. Application on speech time-series are explored to study such methodological extensions given that they are non-stationary

No-estacionariedad, multifractalidad y limpieza de ruido en señales reales

Las señales biomédicas, como el electrocardiograma, el electroencefalograma, o la señal de voz, tienen en común características de no estacionariedad y no linealidad. Aunque enmuchas aplicaciones se considera que se trata de señales estacionarias procedentes de sistemas lineales, ésta simplificación constituye una hipótesis de trabajo válida sólo como una aproximación que permite la aplicación de técnicas clásicas deanálisis de señales. Muchos trastornos que afectan a uno o varios órganos pueden ser detectados a través de un correcto análisis de las señales en cuya producción están involucrados. Sin embargo, debe atenderse al hecho de que una señal procedente de un sistema patológico se aleja aún más de las condiciones hipotéticas de estacionariedad y linealidad. Se desprende de esta circunstancia la necesidad de abordar el análisis de las señales biomédicas mediante técnicas no convencionales que permitan su tratamiento en un marco que tenga en cuenta sus características de no estacionariedad y no linealidad. Sobre la base de la experiencia del grupo de trabajo en las áreas del análisis tiempo-frecuencia/escala, análisis y modelado estadístico, análisis multifractal, complejidad y métodos guiados por los datos (adaptativos), a partir de problemas reales se han propuesto y estudiado nuevas técnicas que posibiliten su solución

Machine learning in sustainable ship design and operation: a review

The shipping industry faces a large challenge as it needs to significantly lower the amounts of Green House Gas emissions. Traditionally, reducing the fuel consumption for ships has been achieved during the design stage and, after building a ship, through optimisation of ship operations. In recent years, ship efficiency improvements using Machine Learning (ML) methods are quickly progressing, facilitated by available data from remote sensing, experiments and high-fidelity simulations. The data have been successfully applied to extract intricate empirical rules that can reduce emissions thereby helping achieve green shipping. This article presents an overview of applying ML techniques to enhance ships’ sustainability. The work covers the ML fundamentals and applications in relevant areas: ship design, operational performance, and voyage planning. Suitable ML approaches are analysed and compared on a scenario basis, with their space for improvements also discussed. Meanwhile, a reminder is given that ML has many inherent uncertainties and hence should be used with caution

Improved techniques to estimate mean sea level, velocity and acceleration from long ocean water level time series to augment sea level (and climate change) research

The prominence of the climate change issue has placed more emphasis on examination of the extensive global repository of mean sea level records, which along with temperature and carbon dioxide, remain the key proxy data sets used to monitor and quantify changes in the global climate system. Over the past decade, the scientific literature is replete with conflicting conclusions regarding the detection (or otherwise) of a measurable acceleration in mean sea level attributable to climate change. The key aim of the research program is to develop improved techniques (and associated tools) for estimating mean sea level and associated velocity and acceleration from long individual ocean water level time series to augment climate change research. This task involved extensive time series analysis which identified Singular Spectrum Analysis (SSA) as an optimal analytic for resolving estimates of mean sea level from long tide gauge records with improved accuracy and temporal resolution. SSA was further tested and optimised, then integrated into an open source analytical software package ( msltrend ), specifically customised for sea level research, providing enhanced estimates of mean sea level and associated velocity and acceleration on a year to year basis. The application of this package to the data rich margins of USA and Europe have provided more insight into the temporal and regional characteristics of mean sea level than previously available. In general, key findings across both margins suggest that not only are more recent accelerations no different to other periods elsewhere in the historical records, but, they are neither sufficiently strong nor sustained enough (yet) to accord with those associated with climate change projection models. Further, until such time as the velocities and accelerations are sufficiently large not to be obscured by complex influences inducing decadal to multi-decadal variability and other background noise, the search for accelerations in ocean water level records require more intuitive, diagnostic considerations which are explored in the research. The techniques espoused in this research form an integral part of the evolutionary process by which to measure kinematic properties of mean sea level with improving robustness and consistency

Advanced Techniques for Ground Penetrating Radar Imaging

Ground penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in non-destructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology, glaciology, civil engineering and civil works, archaeology, and security and defense. In recent decades, improvements in georeferencing and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPR–SAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others. This Special Issue provides an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives