scholarly journals The regional features of precipitation variation trends over Sichuan in China by the ESMD method

MAUSAM ◽  
2021 ◽  
Vol 67 (4) ◽  
pp. 849-860
Author(s):  
J. R. LEI ◽  
Z. H. LIU ◽  
L. BAI ◽  
Z. S. CHEN ◽  
J. H. XU ◽  
...  
Keyword(s):  
Regional Differences ◽  
Sichuan Province ◽  
Change Points ◽  
Variation Trend ◽  
Mode Decomposition ◽  
Non Linear ◽  
Variance Contribution ◽  
Precipitation Variation ◽  
Non Stationary Signal ◽  
Variation Trends

Based on a precipitation time series from 49 meteorological stations in Sichuan Province during the period from 1961 to 2011, the multi-scale characteristics of precipitation variability are analyzed using the extreme-point symmetric mode decomposition method (ESMD). Regional differences in variation trends and change-points were also preliminarily discussed. The results indicated that in the last 50+ years, the overall precipitation in Sichuan Province has exhibited a significant non-linear downward trend, and its changes have clearly exhibited an inter-annual scale (quasi-3 and quasi-8-year) and interdecadal scale (quasi-13-year). The variance contribution rates of each component demonstrated that the inter-annual change had a strong influence on the overall precipitation change in Sichuan Province, and the reconstructed inter-annual variation trend could describe the fluctuation state of the original precipitation during the study period. The reconstructed interdecadal variability revealed that the climate mode in Sichuan Province had divided into three distinct variation periods with 1973 and 1998 as the boundaries. Furthermore, there were regional differences in the non-linear changes and change-points of precipitation. In addition, in order to study the relations between the changing more or less of rising or decrease and meteorological station’s geographical position (latitude, longitude and elevation) i.e., the Cokriging interpolation technique is applied directly to precipitation variation trend components through ESMD decomposition. At the same time, the results also suggested that the ESMD method can effectively reveal variations in long-term precipitation sequences at different time scales and can be used for the complex diagnosis of non-linear and non-stationary signal changes.  

A new fault diagnosis method based on adaptive spectrum mode extraction

2021 ◽  
pp. 147592172098694
Author(s):  
Zhijian Wang ◽  
Ningning Yang ◽  
Naipeng Li ◽  
Wenhua Du ◽  
Junyuan Wang
Keyword(s):  
Scale Space ◽  
Variational Mode Decomposition ◽  
Rolling Bearings ◽  
Mode Decomposition ◽  
Penalty Factor ◽  
Diagnosis Method ◽  
Mode Extraction ◽  
Non Stationary Signal ◽  
Envelope Spectrum ◽  
Fault Feature Extraction

Variational mode decomposition provides a feasible method for non-stationary signal analysis, but the method is still not adaptive, which greatly limits the wide application of the method. Therefore, an adaptive spectrum mode extraction method is proposed in this article. The proposed method is mainly composed of spectral segmentation, mode extraction, and feedback adjustment. In the spectral segmentation part, considering the lack of robustness of classical scale space in a strong noise environment, this article proposes a method of fault feature mapping, which solves over-decomposition of variational mode decomposition guided by classical scale space. In the mode extraction part, for insufficient self-adaptability of the single penalty factor in the variational mode decomposition method, this article proposes a method of spectral aggregation factor, which solves multiple penalty factors that conform to different intrinsic modal functions. In the feedback adjustment part, this article proposes the method of transboundary criterion, which makes the result of variational mode decomposition within a preset range. Finally, using dispersion entropy and kurtosis indicators, intrinsic modal function components containing fault frequencies are extracted for envelope spectrum analysis to extract fault characteristic frequencies. In order to verify the effectiveness of the proposed method, the proposed method is applied to simulation signals and bearing fault signals. Comparing the decomposition results of different methods, the conclusion shows that the proposed method is more advantageous for the fault feature extraction of rolling bearings.

scholarly journals Correction to: Emotion classification from speech signal based on empirical mode decomposition and non-linear features

2021 ◽  
Author(s):  
Palani Thanaraj Krishnan ◽  
Alex Noel Joseph Raj ◽  
Vijayarajan Rajangam
Keyword(s):  
Empirical Mode Decomposition ◽  
Speech Signal ◽  
Emotion Classification ◽  
Linear Features ◽  
Mode Decomposition ◽  
Non Linear

A correction to this paper has been published: https://doi.org/10.1007/s40747-021-00377-y

EEG Classification of Imaginary Left and Right Leg Movements based on Multivariate Empirical Mode Decomposition and Non-Linear Support Vector Machine

2021 ◽  
Vol 39 (11) ◽  
Author(s):  
Sahar Zolfaghari ◽  
Mohammad Hamiruce Marhaban ◽  
Siti Anom Ahmad ◽  
Asnor Juraiza Ishak ◽  
Pegah Khosropanah ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Empirical Mode Decomposition ◽  
Motor Imagery ◽  
Classification Model ◽  
Support Vector ◽  
Linear Support Vector Machine ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Non Linear ◽  
Left And Right

Motor-imagery brain-computer interfaces, as rehabilitation tools for motor-disabled individuals, could inherently enrich neuroplasticity and subsequently restore mobility. However, this endeavour's significant challenge is classifying left and right leg motor imagery tasks from non-stationary EEG signals. A subject-independent feature extraction method is essential in a BCI system, and this work involves developing a subject-independent algorithm to classify left/right leg motion intention. The Multivariate Empirical Mode Decomposition was used to decompose EEG during left and right foot movements during imagery tasks. We validated our proposed algorithm using open-access motor imagery data to detect the user's mental intention from EEG. Five subjects of various performance categories with almost 150 trials for each left/right leg MI of hand/leg/tongue, HaLT Paradigm, utilizing C3, C4, and Cz channels were examined to generalize this study to all subjects. A set of statistical features were extracted from the intrinsic mode functions, and the most relevant features were selected for classification using Sequential Floating Feature Selection. Different classifiers were trained using extracted features, and their performances' were evaluated. The findings suggest that the non-linear support vector machine is the best classification model, resulting in the mean classification sensitivity, specificity, precision, negative predictive value, F-measure, 98.15%, 90.74%, 91.97%, 98.33%, 94.72%, 94.44%, respectively. The proposed subject-independent signal processing method significantly improved the offline calibration mode by eliminating the frequency selection step, making it the common-used method for different types of MI-based BCI participants. Offline evaluations suggest that it can lead to significant increases in classification accuracy in comparison to current approaches.

System Analysis of the Circadian Rhythm of Euglena gracilis, I. Linearities and Non-Linearities in the Response to Temperature Signals

1982 ◽  
Vol 37 (11-12) ◽  
pp. 1240-1252
Author(s):  
Wolfram Lork ◽  
Til Kreuels ◽  
Wolfgang Martin ◽  
Klaus Brinkmann
Keyword(s):  
Euglena Gracilis ◽  
System Analysis ◽  
Superposition Principle ◽  
Reference Signal ◽  
Circadian System ◽  
Masking Effect ◽  
Cellular Motility ◽  
Apparent Lack ◽  
Non Linear ◽  
Non Stationary Signal

Abstract The approach of control theory is used to describe the structure of the circadian system of Euglena gracilis. As a first step we discriminate linear and non linear properties of the dynamics. The cellular motility as measured via long time records of sedimentation parameters in cultures is defined as the system output; sinusoidal temperature signals are used as input. By means of non stationary signal processing procedures we estimate gain and phase of the output signal. The problem of defining an appropiate gain of a cell suspension with an undefinite number of cells is solved by using the superimposition of two different input signals and by keeping one of them fixed as a reference signal. Linear properties are shown with a linear frequency transfer and with the validity of the superposition principle at least within distinct regions of amplitude and frequency. Non linear properties are the signal distortion, the restriction of linear amplification to a distinct range of input temperature and the ambiguity of phase coupling near the circadian eigenfrequency. The apparent lack of a limit of entrainment -an unexpected linear property - is explained by the masking effect of thermokinesis. A model is proposed describing the simultaneous control of motility by thermokinesis and the circadian system. On the base of that model further experiments are outlined.

COMPREHENSIVE ANALYSIS OF NORMAL AND DIABETIC HEART RATE SIGNALS: A REVIEW

2012 ◽  
Vol 12 (05) ◽  
pp. 1240033 ◽  
Author(s):  
OLIVER FAUST ◽  
V. RAMANAN PRASAD ◽  
G. SWAPNA ◽  
SUBHAGATA CHATTOPADHYAY ◽  
TEIK-CHENG LIM
Keyword(s):  
Heart Rate ◽  
Organ Damage ◽  
Extraction Methods ◽  
Time Frequency ◽  
Ecg Signals ◽  
Cardiac Health ◽  
Non Linear ◽  
Non Stationary Signal ◽  
Early Diagnosis Of Diabetes ◽  
Typical Range

A large section of the world's population is affected by diabetes mellitus (DM), commonly referred to as "diabetes." Every year, the number of cases of DM is increasing. Diabetes has a strong genetic basis, hence it is very difficult to cure, but can be controlled with medications to prevent subsequent organ damage. Therefore, early diagnosis of diabetes is very important. In this paper, we examine how diabetes affects cardiac health, which is reflected through heart rate variability (HRV), as observed in electrocardiography (ECG) signals. Such signals provide clues for both the presence and severity of diabetes as well as diabetes-induced cardiac impairments. Heart rate (HR) is a non-linear and non-stationary signal. Thus, extracting useful information from HRV signals is a difficult task. We review several sophisticated signal processing and information extraction methods in order to establish measurable relationships between the presence and the extent of diabetes as well as the changes in the HRV signals. Furthermore, we discuss a typical range of values for several statistical, geometric, time domain, frequency domain, time–frequency, and non-linear features for HR signals from 15 normal and 15 diabetic subjects. We found that non-linear analysis is the most suitable approach to capture and analyze the subtle changes in HRV signals caused by diabetes.

scholarly journals Application of variational mode decomposition to seismic random noise reduction

2017 ◽  
Vol 14 (4) ◽  
pp. 888-898 ◽  
Author(s):  
Wei Liu ◽  
Siyuan Cao ◽  
Zhiming Wang
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Random Noise ◽  
Computational Cost ◽  
Superior Performance ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Reconstruction Performance ◽  
Non Stationary Signal

Abstract We have proposed a new denoising method for the simultaneous noise reduction and preservation of seismic signals based on variational mode decomposition (VMD). VMD is a recently developed adaptive signal decomposition method and an advance in non-stationary signal analysis. It solves the mode-mixing and non-optimal reconstruction performance problems of empirical mode decomposition that have existed for a long time. By using VMD, a multi-component signal can be non-recursively decomposed into a series of quasi-orthogonal intrinsic mode functions (IMFs), each of which has a relatively local frequency range. Meanwhile, the signal will focus on a smaller number of obtained IMFs after decomposition, and thus the denoised result is able to be obtained by reconstructing these signal-dominant IMFs. Synthetic examples are given to demonstrate the effectiveness of the proposed approach and comparison is made with the complete ensemble empirical mode decomposition, which demonstrates that the VMD algorithm has lower computational cost and better random noise elimination performance. The application of on field seismic data further illustrates the superior performance of our method in both random noise attenuation and the recovery of seismic events.

Quantifying the complexity of human colonic pressure signals using an entropy measure

2016 ◽  
Vol 61 (1) ◽  
pp. 127-132 ◽  
Author(s):  
Fei Xu ◽  
Guozheng Yan ◽  
Kai Zhao ◽  
Li Lu ◽  
Zhiwu Wang ◽  
...  
Keyword(s):  
Signal Analysis ◽  
Colonic Motility ◽  
Approximate Entropy ◽  
Entropy Measure ◽  
Intrinsic Mode Functions ◽  
Spectral Entropy ◽  
Mode Decomposition ◽  
Power Spectral ◽  
Reconstructed Signal ◽  
Non Stationary Signal

Abstract Studying the complexity of human colonic pressure signals is important in understanding this intricate, evolved, dynamic system. This article presents a method for quantifying the complexity of colonic pressure signals using an entropy measure. As a self-adaptive non-stationary signal analysis algorithm, empirical mode decomposition can decompose a complex pressure signal into a set of intrinsic mode functions (IMFs). Considering that IMF2, IMF3, and IMF4 represent crucial characteristics of colonic motility, a new signal was reconstructed with these three signals. Then, the time entropy (TE), power spectral entropy (PSE), and approximate entropy (AE) of the reconstructed signal were calculated. For subjects with constipation and healthy individuals, experimental results showed that the entropies of reconstructed signals between these two classes were distinguishable. Moreover, the TE, PSE, and AE can be extracted as features for further subject classification.

scholarly journals Air temperature and precipitation variation trends of the Lancang river upstream from 1957 to 2011

2013 ◽  
Vol 17 (5) ◽  
pp. 1383-1388
Author(s):  
Ya-Nan Guo ◽  
Xiao-Hua Yang ◽  
Xiao-Juan Chen ◽  
Ying Mei ◽  
Chong-Li Di
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Practical Significance ◽  
Resources Allocation ◽  
Precipitation Time Series ◽  
Lancang River ◽  
Temperature And Precipitation ◽  
Precipitation Variation ◽  
Variation Trends ◽  
Kendall Method

Air temperature and precipitation variation trends of the upstream of Lancang river using the time series from 1957 to 2011 are evaluated. The Mann-Kendall method is applied to study the trend and climatic jump of the air temperature and precipitation time series. It shows that the temperature has an obvious uptrend with an increase of 0.023?C per year. The annual precipitation of the upstream of Lancang river is 954.96 mm without any change, however, the precipitation is gradually increased from upstream to downstream. This paper is significant for understanding the climate change over the years, and it has practical significance for water resources allocation and management in the future.

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