Application of Empirical Mode Decomposition and Extreme Learning Machine Algorithms on Prediction of the Surface Vibration Signal

Accurately predicting surface vibration signals of diesel engines is the key to evaluating the operation quality of diesel engines. Based on an improved empirical mode decomposition and extreme learning machine algorithm, the characteristics of diesel engine surface vibration signal were detected, predicted, and analyzed. First, the surface vibration signal was decomposed into a series of signal components by an improved empirical mode decomposition algorithm. Then, the extreme learning machine algorithm was applied to each signal component to obtain the predicted value of the corresponding signal component and determine the characteristics of the ground vibration signal. Compared with the empirical mode decomposition–extremum learning machine algorithm and the extremum learning machine algorithm, the results show that the improved empirical mode decomposition–extremum learning machine algorithm is feasible and effective.

Decomposing age effects in EEG alpha power

Increasing life expectancy is prompting the need to understand how the brain changes during healthy aging. Research utilizing Electroencephalography (EEG) has found that the power of alpha oscillations decrease from adulthood on. However, non-oscillatory (aperiodic) components in the data may confound results and thus require re-investigation of these findings. The present report aims at analyzing a pilot and two additional independent samples (total N = 533) of resting-state EEG from healthy young and elderly individuals. A newly developed algorithm will be utilized that allows the decomposition of the measured signal into aperiodic and aperiodic-adjusted signal components. By using multivariate sequential Bayesian updating of the age effect in each signal component, evidence across the datasets will be accumulated. It is hypothesized that previously reported age-related alpha power differences will disappear when absolute power is adjusted for the aperiodic signal component. Consequently, age-related differences in the intercept and slope of the aperiodic signal component are expected. Importantly, using a battery of neuropsychological tests, we will assess how the previously reported relationship between cognitive functions and alpha oscillations changes when taking the aperiodic signal into account; this will be done on data of the young and aged individuals separately. The aperiodic signal components and adjusted alpha parameters could potentially offer a promising biomarker for cognitive decline, thus finally the test–retest reliability of the aperiodic and aperiodic-adjusted signal components will be assessed.

Developing a Combined Method for Detection of Buried Metal Objects

This paper discusses the author-developed novel method for the detection of buried metal objects that combines two basic subsurface sensing methods: one based on changes in the electromagnetic field parameters as induced by the inner or surficial impedance of the medium when affected by a propagating magnetic field; and one based on changes in the input impedance of the receiver as induced by the electromagnetic properties of the probed medium. The proposed method utilizes three instrumentation channels: two primary channels come from the ferrite magnetic antenna (the receiver), where the first channel is used to measure the current voltage amplitude of the active input signal component, while the second channel measures the current voltage amplitude of the reactive input signal component; an additional (secondary) channel comes from the emitting frame antenna (the transmitter) to measure the current amplitude of the exciting current. This data redundancy proves to significantly improve the reliability and accuracy of detecting buried metal objects. Implementation of the computational procedures for the proposed method helped to detect and identify buried objects by their specific electrical conductance and magnetic permeability, while also locating them depth-wise. The research team has designed an induction probe that contains two mutually orthogonal antennas (a frame transmitter and ferrite receiver); the authors herein propose a metal detector design that implements the proposed induction sensing method. Experimental research proved the developed combined method for searching for buried metal objects efficient and well-performing.

Determination of Weak Knock Characteristics for Two-Stroke Spark Ignition UAV Engines Based on Mallat Decomposition Algorithm

Two-stroke spark ignition (SI) unmanned aerial vehicle (UAV) engines do not allow heavy knock and require a certain knock safety margin. However, weak knock can help the engine increase power output and reduce fuel consumption. To accurately extract the knock characteristics of engine vibration signals under the condition of weak knock, a signal feature extraction method based on the Mallat decomposition algorithm was proposed. Mallat decomposition algorithm can decompose the signal into two parts: a low-frequency signal and a high-frequency noise signal. The decomposed high-frequency noise is eliminated, and the low-frequency signal is retained as the characteristic domain signal. Simulation results show the effectiveness of the proposed algorithm. The engine vibration signal of a two-stroke SI UAV engine was decomposed into the low-frequency signal and the high-frequency signal by the Mallat decomposition algorithm. The low-frequency signal is taken as the knock characteristic domain signal component, and the wavelet packet energy method is used to verify the correctness of the obtained signal component. The relative energy parameter is calculated by using the knock characteristic domain signal component, which can be used as the determination index of knock intensity. This method provides a reference for the weak knock control of two-stroke SI UAV engines.

Algorithm for estimating energy characteristics at the output of the correlator

For the operation of the channels for tracking the carrier (phase and frequency) and the envelope of the signal of the primary processing of the navigation equipment, it is necessary to evaluate, in one way or another, the energy parameters at the output of the correlator: the amplitude of the signal component and the dispersion at the output of the correlator. In practice, to estimate the amplitude of the signal component, either the real part or the modulus of the complex correlator is used over the entire grouping interval. These approaches have a number of disadvantages. To estimate the variance of the correlator output, it is necessary to select separate channels in which there is obviously no useful signal, which takes away some part of the hardware resources. The article proposes an algorithm for estimating the energy parameters at the output of the correlator only from the data of the corresponding channel without the formation of special correlators, which is operable in both coherent and incoherent modes and is based on the use of correlation integrals over short and full intervals. Modeling and analysis of the accuracy characteristics of the proposed algorithm is carried out.

Decomposing the role of alpha oscillations during brain maturation

During childhood and adolescence, the human brain undergoes various micro- and macroscopic changes. Understanding the neurophysiological changes within this reorganizational process is crucial, as many major psychiatric disorders emerge during this critical phase of life. In electroencephalography (EEG), a widely studied signal component are alpha oscillations (~8-13 Hz), which have been linked to developmental changes throughout the lifespan. Previous neurophysiological studies have demonstrated an increase of the alpha peak frequency and a decrease of alpha power to be related to brain maturation. The latter results have been questioned by recent developments in EEG signal processing techniques, as it could be demonstrated that aperiodic (non-oscillatory) components in the EEG signal conflate findings on periodic (oscillatory) changes, and thus need to be decomposed accordingly. We therefore analyzed a large, openly available pediatric dataset of 1485 children and adolescents in the age range of 5 to 21 years, in order to clarify the role of alpha oscillations and aperiodic signal components in this period of life. We first replicated previous findings of an increase of alpha peak frequency with age. Our results further suggest that alpha oscillatory power decreases with increasing age, however, when controlling for the aperiodic signal component, this effect inverted such as the aperiodic adjusted alpha power parameters significantly increase with advanced brain maturation, while the aperiodic signal component flattens and its offset decreases. Thus, interpretations of these oscillatory changes should be done with caution and incorporate changes in the aperiodic signal. These findings highlight the importance of taking aperiodic signal components into account when investigating age related changes of EEG spectral power parameters.

The Waveform Inversion of Mainshock and Aftershock Data of the 2006 M6.3 Yogyakarta Earthquake

This research was examines the focal mechanism associated with the mainshock and three aftershocks of the magnitude 6.3 Yogyakarta earthquake on May 27, 2006. This study, therefore, aims to provide a cleareranswer on the source mechanism of the earthquake, which has been debated. Data were obtained from the mainshock and aftershock sources, on June 8, 9, and 16, 2006. The mainshock and three aftershocks were used to conduct waveform inversion by calculating the Green's functions through the extended reflectivity method of the near-field and the far-field signal component. The mainshock's focal mechanism has a strike, dip, and range angle of 243.40o, 77.50o, and -28.30o, respectively.Furthermore, the mainshock is not a pure strike-slip as previously hypothesized. The focal mechanism for the aftershock earthquake source on Mw 4.4, obtained on June 8, had a strike, dip, rake, and variance of 192.20o, 29.70o, -48.30o and 0.22, respectively. This aftershock had a different segment from the mainshock event and those obtained on the 9 and 16 of June with the same type of faulting as the mainshock with variance values of 0.195 and 0.243. These results showed that the mainshock of May 27, 2006, activated the aftershock on June 8, with a different type of fault.