Nonlinear Feature Analysis Research Articles

Dynamic analysis of nonlinear features for high-precision fault diagnosis of large pumps and compressors in oil and gas fields

Dynamic analysis of nonlinear features for high-precision fault diagnosis of large pumps and compressors in oil and gas fields

A novel finite spectral entropy: Gated term memory unit recursive network integrated with Ladybug Beetle Optimization algorithm for epileptic seizure detection.

Professional medical experts use a visual electroencephalography (EEG) signal for epileptic seizure detection, although this method is time-consuming and highly subject to bias. The majority of previous epileptic detection techniques have poor efficiency, detection performance and also which are unsuited to handle large datasets. In order to solve the aforementioned issues and to assist medical professionals with an advanced technology, a computerized epileptic seizure detection system is essential. Therefore, the proposed work intends to design an automated detection tool for predicting an epileptic seizure from EEG signals. For this purpose, a novel non-linear feature analysis and deep learning algorithms are deployed in this work. Initially, the signal decomposition, filtering and artifacts removal operations are carried out with the use of finite Haar wavelet transformation technique. After that, the finite spectral entropy (FSE) based feature extraction model has been used to extract the time, frequency, and time-frequency features from the normalized signal. Consequently, the novel gated term memory unit recursive network (GTRN) model is employed to predict the given EEG signal as whether healthy or seizure affected including the class with high accuracy. During this process, the recently developed Ladybug Beetle Optimization (LBO) algorithm is used to compute the logistic sigmoid function based on the solution. The purpose of using this algorithm is to simplify the process of classification with increased seizure prediction accuracy and performance. Moreover, the standard and popular benchmark EEG datasets are used to validate and test the results of the proposed FSE-GTRN-LBO mechanism. By leveraging the finite Haar wavelet transformation and FSE-based feature extraction, we can efficiently process EEG signals. The utilization of the GTRN model enables accurate classification of healthy and seizure-affected EEG data. To optimize the classification process further, we integrate the LBO algorithm, streamlining the computation of the logistic sigmoid function. Through comprehensive validation on standard EEG datasets, our proposed FSE-GTRN-LBO mechanism achieves outstanding seizure prediction accuracy and performance, surpassing existing state-of-the-art techniques.

Analysis of nonlinear features associated with radio-frequency sheaths

An analytic study is conducted of nonlinear features that arise when radio-frequency (RF) sheath boundary conditions are solved self-consistently with the electromagnetic fields in a magnetized plasma. An instability associated with RF sheath–plasma interactions is identified and shown to be closely linked with sheath–plasma resonance and the occurrence of multiple solutions. It is proposed that this instability serves as a physical mechanism that enables jumping from one solution branch to another, providing a physical basis for the prediction of an RF sheath hysteresis in the time domain. It is further shown that this instability can lead to non-intuitive features such as spontaneous symmetry breaking in an otherwise completely symmetric problem. In plane-stratified geometry, two analytically solvable examples are considered for the cases of an unbounded half-space and a bounded domain.

Non-linear feature analysis of public emotion evolution for online teaching during the COVID-19 pandemic

PurposeNowadays, the breakout of the COVID-19 pandemic has caused an important change in teaching models. The emotional experience of this change has an important impact on online teaching. This paper aims to explore its time evolution characteristics and provide reference for the development of online teaching in the post epidemic era.Design/methodology/approachThe article firstly crawls the online teaching-related comment text data on Zhihu platform and performs emotional calculation to obtain a one-dimensional time series of daily average emotional values. Then, by using non-linear time-series analysis, this paper reconstructs the daily average emotion value time series in high-dimensional phase space, calculates the maximum Lyapunov exponent and correlation dimension and finally, explores the feature patterns through recurrence plot and recurrence quantification analysis.FindingsIt was found that the sequence has typical non-linear chaotic characteristics; its correlation dimension indicates that it contains obvious fractal characteristics; the public emotional evolution shows a cyclical rise and fall. By text mining and temporal evolution analysis, this paper explores the evolution law over chronically of the daily average emotion value time series, provides feasible strategies to improve students' online learning experience and quality and continuously optimizes this new teaching model in the era of pandemic.Originality/valueBased on social knowledge sharing platform of Q&A, this paper models and analyzes users interaction data under online teaching-related topics. This paper explores the evolution law over a long time period of the daily average emotion value time series using text mining and temporal evolution analysis. It then offers workable solutions to enhance the quality and experience of students' online learning, and it continuously improves this new teaching model in the age of pandemics.

Efficient batch process monitoring based on random nonlinear feature analysis

AbstractEfficient batch process monitoring is a challenging task because of the large data scale, the strong process nonlinearity, and the complicated local behaviours. To handle this issue, this paper presents a random nonlinear feature analysis method, called two‐level localized ensemble random Fourier feature analysis (TLERFFA), for effective batch process fault detection. Considering the large data scale of the nonlinear batch process, the random Fourier feature analysis (RFFA) is introduced to develop the statistical model, which is more efficient than the traditional kernel modelling method in terms of computation loads. Furthermore, for overcoming the model uncertainty resulting from the random mapping weights, the ensemble learning based on Bayesian inference is used to integrate multiply RFFA models with different weight settings. To further mine the rich local information of batch process data, a two‐level localization strategy is designed from the perspectives of the variables and the statistics. At the first level, the monitored variables are divided into several sub‐groups according to their maximal information coefficients, while at the second level, the local monitoring statistics are constructed based on the temporal–spatial neighbour analysis. Lastly, one case study on the benchmark process is given to demonstrate the effectiveness of the proposed methods.

Prediction of titanium alloy Ti-6A1-4V lifetime under consecutive shock-wave and gigacycle fatigue loads

The influence of consecutive shock-wave and gigacycle fatigue loads on the lifetime of titanium alloy VT6 (Ti-6A1-4V) was investigated. An experimental methodology for estimating the ultra-high cycle lifetime was developed with reference to accidental high-speed collision of solid particles with fan blades and subsequent fatigue failure under the flight cycle conditions normal for operating aircraft engines. The program of ultra-high cycle loading (the number of cycles is 107-109) of specially made specimens was implemented experimentally. In these experiments, massive flat targets made of titanium alloy VT6 after shock-wave loading were impacted by a flat striker (explosive generator method), which initiated controlled damage in the material. Ultra-high cycle loading tests were carried out on the Shimadzu USF-2000 ultrasonic testing machine, which made it possible to conduct fatigue loading tests up to 108-1010 cycles and frequency of 20 kHz.The "in situ" method of determining the accumulation of irreversible fatigue damage was applied. It is based on the analysis of nonlinear features of the feedback signal in a closed system of an ultrasonic fatigue installation, which allowed us to relate the microscopic mechanisms of fatigue with model representations, as well as to consider the stages of damage evolution based on the nonlinear kinetics of defect accumulation during cyclic loading in high - and gigacycle fatigue modes. In the mode of ultra-high cycle (gigacycle) fatigue, the material exhibits abnormalities of elastic properties, which lead to the effect of non-linearity in the oscillation amplitude.

Experiment Study of the Failure Mechanism and Evolution Characteristics of Water–Sand Inrush Geo-Hazards

Water–sand inrush disasters are frequently encountered during underground engineering construction in karst terrain. The objective of this paper is to study the failure mechanism and evolution characteristics of water–sand inrush caused by the instability of filling medium in karst cavity, as well as the impacts of soil compactness, hydraulic pressure and confining pressure on the instability process. In response to this purpose, a stress-controlled seepage test apparatus in consideration of particle loss was designed, and a series of seepage tests were performed correspondingly. The test results indicate that: (1) Based on the nonlinear feature analysis of water-outflow pattern, the water–sand inrush process can be divided into the “slow flow” stage, “transition flow” stage and “pipe flow” stage by Transition Point I, II. (2) The decreasing soil compactness and increasing hydraulic pressure both exponentially facilitate the seepage-erosion process by increasing the particle-erosion ability; the increasing confining pressure extends the “slow flow” stage and shortens the duration of the “transition flow” stage, ultimately advancing the occurrence of the “pipe flow” stage; the existence of critical hydraulic pressure for the seepage-erosion progress is confirmed, the occurrence of the “pipe flow” stage is significantly advanced once the hydraulic pressure over the critical value. (3) The particle loss caused by the seepage-erosion process is the internal mechanism of water–sand inrush, the variation characteristics of water-outflow pattern are crucial external manifestations correspondingly. Therefore, with the monitoring of water-outflow pattern variation tendency as indicators, the critical status of water–sand inrush can be near-real-time identified, which offers experimental foundation for the early warning and forecast of the occurrence of water–sand inrush.

Heart Rate Assessment for Posttraumatic Stress Disorder Symptoms among Military Veterans: A Narrative Literature Review

Post-Traumatic Stress Disorder (PTSD) is a psychiatric condition affecting as much as 20% of the returning veterans from the current wars in Afghanistan and Iraq (Ramchand et al., 2010). Due to its prevalence, assessment and intervention methods for PTSD symptoms among veterans are timely to ensure veterans’ faster recovery and their reintegration into society. A narrative review of literature was conducted to examine the literature on techniques and methods for detection and measurement of PTSD symptoms. Relevant reviews and seminal papers associated with psychophysiological measurements with emphasis on heart rate, the prevalence of PTSD in military veterans, and detection of PTSD were included. Psychophysiology involves the non-invasive recording of biological processes simultaneously (Pole, 2007). The measures that were found to be reliably related to PTSD are: (1) higher resting heart rate; (2) larger heart rate responses to standardized trauma cues; and (3) for idiographic cues facial muscle electromyography (EMG) and heart rate responses (Pole, 2007). Studies based on heart rate found that there were five beats per minute (bpm) increase in resting heart rates for combat veterans who suffer from PTSD compared to those who were not diagnosed (Beckham et al., 2000, Woodward et al., 2009). Buckley et al. (2004) found that for a mixed population (veterans and civilians) PTSD patients had a resting heart rate increase of 6.6 bpm compared to non-PTSD patients while adjusting for covariates. The exact mechanisms of how stress affects heart rate are not well-known; however, it is possible to observe stressful events and relate these to physiological changes in heart rate that can be measured using modern sensors (Andreoli et al., 2010). In the literature, heart rate is analyzed using statistical, geometric, frequency domain, time-frequency and non-linear feature analysis (Acharya et al., 2006). Non-linear features have been developed to quantify the dynamics of heart rate fluctuations. These include approximate entropy, Detrended Fluctuation Analysis (DFA), Lyapunov exponent, Recurrence Plots (RPs) and Correlation Dimension (CD) (Pincus, 1991; Huikuri et al., 2000; Acharya et al., 2004; Acharya et al., 2006). Our synthesis resulted in identification of three research gaps. Heart rate analysis has shown promise to link PTSD symptoms to differences in PTSD and non-PTSD subjects (Pole, 2007) but a characterization of PTSD symptoms from heart rate data seems to remain a research gap. While continuous monitoring of physiological parameters is gaining momentum, this particular method has not been studied to infer key characteristics associated with PTSD among military veterans. Continuous monitoring might be able to highlight muted response to startle or treatment. There is also a research gap in the assessment of heart rate accelerations and decelerations in response to specific PTSD symptoms (Khanade et al., 2017). The investigation into accelerations/decelerations associated with PTSD hyper-arousal triggers might have a potential to detect hyper-arousal instantaneously and would shed light on trigger-specific interventions to reduce harmful effects of PTSD triggers in a timely manner. In addition, most studies were conducted to observe differences among PTSD and non-PTSD populations. More work is warranted to focus on PTSD patients in isolation to explain variety of triggers and their specific physiological reactions.

MAPPING OF CORAL REEF ENVIRONMENT IN THE ARABIAN GULF USING MULTISPECTRAL REMOTE SENSING

Coral reefs of the Arabian Gulf are subject to several pressures, thus requiring conservation actions. Well-designed conservation plans involve efficient mapping and monitoring systems. Satellite remote sensing is a cost-effective tool for seafloor mapping at large scales. Multispectral remote sensing of coastal habitats, like those of the Arabian Gulf, presents a special challenge due to their complexity and heterogeneity. The present study evaluates the potential of multispectral sensor DubaiSat-2 in mapping benthic communities of United Arab Emirates. We propose to use a spectral-spatial method that includes multilevel segmentation, nonlinear feature analysis and ensemble learning methods. Support Vector Machine (SVM) is used for comparison of classification performances. Comparative data were derived from the habitat maps published by the Environment Agency-Abu Dhabi. The spectral-spatial method produced 96.41% mapping accuracy. SVM classification is assessed to be 94.17% accurate. The adaptation of these methods can help achieving well-designed coastal management plans in the region.

MAPPING OF CORAL REEF ENVIRONMENT IN THE ARABIAN GULF USING MULTISPECTRAL REMOTE SENSING

Abstract. Coral reefs of the Arabian Gulf are subject to several pressures, thus requiring conservation actions. Well-designed conservation plans involve efficient mapping and monitoring systems. Satellite remote sensing is a cost-effective tool for seafloor mapping at large scales. Multispectral remote sensing of coastal habitats, like those of the Arabian Gulf, presents a special challenge due to their complexity and heterogeneity. The present study evaluates the potential of multispectral sensor DubaiSat-2 in mapping benthic communities of United Arab Emirates. We propose to use a spectral-spatial method that includes multilevel segmentation, nonlinear feature analysis and ensemble learning methods. Support Vector Machine (SVM) is used for comparison of classification performances. Comparative data were derived from the habitat maps published by the Environment Agency-Abu Dhabi. The spectral-spatial method produced 96.41% mapping accuracy. SVM classification is assessed to be 94.17% accurate. The adaptation of these methods can help achieving well-designed coastal management plans in the region.

Corals & benthic habitat mapping using DubaiSat-2: a spectral-spatial approach applied to Dalma Island, UAE (Arabian Gulf)

ABSTRACTSatellite remote sensing-based monitoring of coastal habitats, like those in the Arabian Gulf, presents a special challenge due to the attenuation of light through the turbid atmosphere and water, as well as the spectral similarity of many benthic habitats. The present study aims to evaluate the potential of DubaiSat-2 imagery in mapping corals and benthic habitat in the vicinity of Dalma Island, United Arab Emirates (UAE). To do so, this study proposes a spectral-spatial method that uses a combination of different remote sensing techniques in order to discern benthic communities with reference to their spatial distribution. This method involves multi-level segmentation, non-linear feature analysis and ensemble learning methods for the characterization of corals, benthic classes and underlying seabeds. Maximum Likelihood and Random Forest, pixel-based methods, were also used for comparison of classification performances. Comparative data are derived from published marine habitat maps. The spectral-spatial method achieves 96.60% overall accuracy. Maximum Likelihood and Random Forest classification methods are assessed to be 92.59% and 94.52% accurate, respectively. The application of the proposed method and the use of DubaiSat-2 imagery can help in achieving well-designed coastal management plans in the region.

Autocorrelation functions in the generalized Boltzmann-Enskog model

The purpose of this paper is to study the asymptotic properties of time autocorrelation functions for the generalized nonlinear Boltzmann-Enskog model, which contains a long-range component of the interaction between the particles. On the basis of the analysis of non-linear features of the Boltzmann-Enskog kinetic equation, the role of nonlinear effects is directly revealed at the approach to an equilibrium state. It is shown that autocorrelation functions have power asymptotics t−3/2, and the effects that are related to the inclusion of the long-range component lead to a change in the coefficient at t−3/2. These results establish a closed expression for the determination of coefficients in the asymptotic expansion of the autocorrelation functions of rate and thermal diffusion.

The new method of Extraction and Analysis of Non-linear Features for face recognition

In this paper, we introduce the new method of Extraction and Analysis of Non-linear Features (EANF) for face recognition based on extraction and analysis of nonlinear features i.e. Locality Preserving Analysis. In our proposed algorithm, EANF removes disadvantages such as the length of search space, different sizes and qualities of imagees due to various conditions of imaging time that has led to problems in the previous algorithms and removes the disadvantages of ELPDA methods (local neighborhood separator analysis) using the Scatter matrix in the form of a between-class scatter that this matrix introduces and displayes the nearest neighbors to K of the outer class by the samples. In addition, another advantage of EANF is high-speed in the face recognition through miniaturizing the size of feature matrix by NLPCA (Non-Linear Locality Preserving Analysis). Finally, the results of tests on FERET Dataset show the impact of the proposed method on the face recognition. DOI: http://dx.doi.org/10.11591/ijece.v2i6.1773