Wind speed forecasting based on second order blind identification and autoregressive model

Second order blind identification (SOBI) technique is a promising independent component analysis (ICA) method to extract somatosensory evoked potential (SEP). This simulation study focused on SEP extraction from EEG and power-line noise contaminated SEP signals at signal to noise ratio (SNR) of -10dB and -20dB. The correlation coefficients between template SEP and SOBI extracted SEP showed significant high similarity (r>0.76) at -10dB and mild acceptable similarity (r>0.6) at -20dB EEG contaminated SEP. However, SOBI extracted SEP showed good performance in power-line noise situation to achieve high correlation coefficients with template SEP (r=0.96). The fast extracted SEP showed stable amplitude and latency, which are almost identical with the SEP template. The results suggested that SOBI is an appropriate method to extract SEP from noisy background.

A robust method for removal of artifacts such as eye blinks and electrocardiogram (ECG) from the electroencephalograms (EEGs) has been developed in this paper. The proposed hybrid method fuses support vector machines (SVMs) based classification and blind source separation (BSS) based on independent component analysis (ICA). The carefully chosen features for the classifier mainly represent the data higher order statistics. We use the second order blind identification (SOBI) algorithm to separate the EEG into statistically independent sources and SVMs to identify the artifact components and thereby to remove such signals. The remaining independent components are remixed to reproduce the artifact free EEGs. Objective and subjective results from the simulation studies show that the algorithm outperforms previously proposed algorithms

A new output only modal analysis method is developed in this paper. This method uses continuous wavelet transform to modify a popular blind source separation algorithm, second order blind identification (SOBI). The wavelet modified SOBI (WMSOBI) method replaces original time domain signal with selected time-frequency domain wavelet coefficients, which overcomes the shortcomings of SOBI. Both numerical and experimental studies on bridge models are carried out when there are limited number of sensors. Identified modal properties from WMSOBI are analyzed and compared with fast Fourier transform (FFT), SOBI and eigensystem realization algorithm (ERA). The comparison shows WMSOBI can identify as many results as FFT and ERA. Further case study of structural health monitoring (SHM) on an arch bridge verifies the capability to detect damages by combining WMSOBI with incomplete flexibility difference method.

Recently, blind source separation (BSS) methods have gained significant attention in the area of signal processing. Independent component analysis (ICA) and second-order blind identification (SOBI) are two popular BSS methods that have been applied to modal identification of mechanical and structural systems. Published results by several researchers have shown that ICA performs satisfactorily for systems with very low levels of structural damping, for example, for damping ratios of the order of 1% critical. For practical structural applications with higher levels of damping, methods based on SOBI have shown significant improvement over ICA methods. However, traditional SOBI methods suffer when nonstationary sources are present, such as those that occur during earthquakes and other transient excitations. In this paper, a new technique based on SOBI, called the modified cross-correlation method, is proposed to address these shortcomings. The conditions in which the problem of structural system identification can be posed as a BSS problem is also discussed. The results of simulation described in terms of identified natural frequencies, mode shapes, and damping ratios are presented for the cases of synthetic wind and recorded earthquake excitations. The results of identification show that the proposed method achieves better performance over traditional ICA and SOBI methods. Both experimental and large-scale structural simulation results are included to demonstrate the applicability of the newly proposed method to structural identification problems.

Independent Components of Magnetoencephalography: Single-Trial Response Onset Times

The modal identification framework known as Blind Modal Identification (BMID) has recently been developed, drawing on techniques from Blind Source Separation (BSS). Therein, a BSS algorithm known as Second Order Blind Identification (SOBI) was adapted to solve the Modal IDentification (MID) problem. One of the drawbacks of the technique is that the number of modes identified must be less than the number of sensors used to measure the vibration of the equipment or structure. In this paper, an extension of the BMID method is presented for the underdetermined case, where the number of sensors is less than the number of modes to be identified. The analytic signal formed from measured vibration data is formed and the Second Order Blind Identification of Underdetermined Mixtures (SOBIUM) algorithm is applied to estimate the complex-valued modes and modal response autocorrelation functions. The natural frequencies and modal damping ratios are then estimated from the corresponding modal auto spectral density functions using a simple Single Degree Of Freedom (SDOF), frequency-domain method. Theoretical limitations on the number of modes identified given the number of sensors are provided. The method is demonstrated using a simulated six DOF mass-spring-dashpot system excited by white noise, where displacement at four of the six DOF is measured. All six modes are successfully identified using data from only four sensors. The method is also applied to a more realistic simulation of ambient building vibration. Seven modes in the bandwidth of interest are successfully identified using acceleration data from only five DOF. In both examples, the identified modal parameters (natural frequencies, mode shapes, modal damping ratios) are compared to the analytical parameters and are demonstrated to be of good quality.

The motivating vision behind Sandia's MENTOR/PAL LDRD project has been that of systems which use real-time psychophysiological data to support and enhance human performance, both individually and of groups. Relevant and significant psychophysiological data being a necessary prerequisite to such systems, this LDRD has focused on identifying and refining such signals. The project has focused in particular on EEG (electroencephalogram) data as a promising candidate signal because it (potentially) provides a broad window on brain activity with relatively low cost and logistical constraints. We report here on two analyses performed on EEG data collected in this project using the SOBI (Second Order Blind Identification) algorithm to identify two independent sources of brain activity: one in the frontal lobe and one in the occipital. The first study looks at directional influences between the two components, while the second study looks at inferring gender based upon the frontal component.

We present an efficient and new solution to the recovery of independent source signals from their convolutive mixtures without any a priori knowledge on the mixtures. In the case of instantaneous mixtures, a robust solution referred to as second order blind identification (SOBI) has been proposed previously. We extend this technique to the convolutive mixture case and we propose an algorithm that performs a joint block diagonalization of spatio-temporal correlation matrices instead of joint diagonalization as in SOBI. The proposed method has been successfully applied to the separation of speech signals.

Signal-to-noise ratio of the MEG signal after preprocessing

Short-term wind speed forecasting with Markov-switching model

Ultra-short-term wind speed forecasting based on EMD-VAR model and spatial correlation

Fault recognition method for speed-up and speed-down process of rotating machinery based on independent component analysis and Factorial Hidden Markov Model

Su1451 Dynamic Antral Scintigraphy Identifies Patterns of Gastric Contractility in Patients With Upper GI Motility Disorders: Comparison to Conventional Gastric Emptying Scintigraphy Data

In inter-molecular multiple quantum coherences (iMQC) experiments, the iMQC signals is easily contaminated by the conventional single-quantum coherence (SQC) signals. How to get the pure iMQC signal is still an open problem for the nuclear magnetic resonance (NMR) scientists. A soft-separation method using blind source separation (BSS) technique was presented to attempt to resolve the problem for the first time. The performance of the three kinds of the different BSS techniques, algorithm for multiple unknown signals extraction (AMUSE), second order nonstationary source separation (SONS) and second order blind identification (SOBI), is evaluated. The numerical experiments show that they can separate intermolecular zero-quantum coherence (iZQC) signal very well.

The paper presents an approach able to separate the vibrations induced by underground traffic from the vibrations induced by other sources, based on second order blind identification (SOBI) algorithm. The signals recorded in different locations of an instrumented building are mixed signals from different internal and external vibration sources. The blind source separation algorithm will estimate the independent vibration sources, as well as the mixing model of them. This model can be used to determine the contribution of each source in different measurement points, to evaluate the effect of vibration sources and their potential for building damage. The approach has been tested in simulation and on a building subject to different traffic forms.