Fault Current Identification of DC Traction Feeder Based on Optimized VMD and Sample Entropy

A current identification method based on optimized variational mode decomposition(VMD)and sample entropy(SampEn)is proposed in order to solve the problem that the main protection of the urban rail transit DC feeder cannot distinguish between train charging current and remote short circuit current.This method uses the principle of energy difference to optimize the optimal mode decomposition number k of VMD;the optimal VMD for DC feeder current is decomposed into the intrinsic modal function(IMF)of different frequency bands.The sample entropy algorithm is used to perform feature extraction of each IMF,and then the eigenvalues of the intrinsic modal function of each frequency band of the current signal can be obtained.The recognition feature vector is input into the support vector machine model based on Bayesian hyperparameter optimization for training.After a large number of experimental data are verified,it is found that the optimal VMD_SampEn algorithm to identify the train charging current and remote short circuit current is more accurate than other algorithms.Thus,the algorithm based on optimized VMD_SampEn has certain engineering application value in the fault current identification of the DC traction feeder.

Tool State Detection by Harmonic Wavelet and Sample Entropy

It is a fact that acoustic emission(AE) signals contain potentially valuable information for tool wear and breakage monitoring and detection.However,AE stress waves produced in the cutting zone are distorted by the transmission path and the measurement systems,it is difficult to obtain a reliable result by these raw AE data.It is generally known that the process of tool wear belongs to detect weak singularity signals in strong noise.The objective of this paper is to combine Newland Harmonic wavelet and Richman-Moorman(2000) sample entropy for detecting weak singularity signals embedded in strong signals.First,the raw AE signal is decomposed by harmonic wavelet and transformed into the three-dimensional time-frequency mesh map of the harmonic wavelet,at the same time,the contours of the mesh map with log space is induced.Second,the profile map of the three-dimensional time-frequency mesh map is offered,which corresponds to decomposed level on harmonic wavelets.Final,by computing sample entropy in each level,the weak singularity signal can be easily extracted from strong noise.Machining test was carried out on HL-32 NC turning center.This lathe does not have a tailstock.Tungsten carbide finishing tool was used to turn free machining mild steel.The work material was chosen for ease of machining,allowing for generation of surfaces of varying quality without the use of cutting fluids.In turning experiments,the feasibility for tool condition monitoring is demonstrated by 27 kinds of cutting conditions with the sharp tool and the worn tool,54 group data are sampled by AE.The sample entropy of each level of wavelet decomposed for each one of 54 AE datum is computed,wear tool and shaper tool can be distinguished obviously by the sample entropy value at the 12th level,this is a criterion.The proposed research provides a new theoretical basis and a new engineering application on the tool condition monitoring.

Research on natural language recognition algorithm based on sample entropy

Sample entropy can reflect the change of level of new information in signal sequence as well as the size of the new information. Based on the sample entropy as the features of speech classification, the paper firstly extract the sample entropy of mixed signal, mean and variance to calculate each signal sample entropy, finally uses the K mean clustering to recognize. The simulation results show that： the recognition rate can be increased to 89.2% based on sample entropy.

Aerodynamic system instability identification with sample entropy algorithm based on feature extraction

Based on the sample entropy algorithm in nonlinear dynamics,an improved sample entropy method is proposed in the aerodynamic system instability identification for the stall precursor detection based on the nonlinear feature extraction algorithm in an axial compressor.The sample entropy algorithm is an improved algorithm based on the approximate entropy algorithm,which quantifies the regularity and the predictability of data in time series.Combined with the spatial modes representing for the rotating stall in the circumferential direction,the recognition capacity of the sample entropy is displayed well on the detection of stall inception.The indications of rotating waves are extracted by the circumferential analysis from modal wave energy.The significant ascendant in the amplitude of the spatial mode is a pronounced feature well before the imminence of stall.Data processing with the spatial mode effectively avoids the problems of inaccurate identification of a single measuring point only depending on pressure.Due to the different selections of similarity tolerance,two kinds of sample entropy are obtained.The properties of the development process of the identification model show obvious mutation phenomena at the boundary of instability,which reveal the inherent characteristic in aerodynamic system.Then the dynamic difference quotient is computed according to the difference quotient criterion,after the smooth management by discrete wavelet.The rapid increase of difference quotient can be regarded as a significant feature of the system approaching the flow instability.It is proven that based on the principle of sample entropy algorithm,the nonlinear characteristic of rotating stall can be well described.The inception can be suggested by about 12-68 revolutions before the stall arrival.This prediction method presenting is accounted for the nonlinearity of the complex flow in stall,which is in a view of data fusion system of pressure for the spatial mode tracking.

Sample entropy analysis of laser speckle fluctuations to suppress motion artifact on blood flow monitoring

Laser speckle imaging is a common technique to monitor blood flow.The fluctuations in speckle intensity can be related to the blood flow by calculating the speckle contrast,the ratio between the standard deviation of speckle fluctuations and the average intensity.However,this simple statistic calculation is easily affected by motion artifacts.In this study,we applied sample entropy analysis instead of calculating standard deviations of the speckle fluctuations.Similar to the traditional method,sample entropy-based speckle contrast increases linearly with flow rate but was shown to be more immune to sudden movements during an upper arm occlusion test.

Short-Term Wind Power Prediction Based on ICEEMDAN-SE-LSTM Neural Network Model with Classifying Seasonal

Wind power prediction is very important for the economic dispatching of power systems containing wind power.In this work,a novel short-term wind power prediction method based on improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)and(long short-term memory)LSTM neural network is proposed and studied.First,the original data is prepossessed including removing outliers and filling in the gaps.Then,the random forest algorithm is used to sort the importance of each meteorological factor and determine the input climate characteristics of the forecast model.In addition,this study conducts seasonal classification of the annual data where ICEEMDAN is adopted to divide the original wind power sequence into numerous modal components according to different seasons.On this basis,sample entropy is used to calculate the complexity of each component and reconstruct them into trend components,oscillation components,and random components.Then,these three components are input into the LSTM neural network,respectively.Combined with the predicted values of the three components,the overall power prediction results are obtained.The simulation shows that ICEEMDAN-SE-LSTM achieves higher prediction accuracy ranging from 1.57%to 9.46%than other traditional models,which indicates the reliability and effectiveness of the proposed method for power prediction.

Analysis of the effect of repeated-pulse transcranial magnetic stimulation at the Guangming point on electroencephalograms

Here, we administered repeated-pulse transcranial magnetic stimulation to healthy people at the left Guangming （GB37） and a mock point, and calculated the sample entropy of electroencephalo- gram signals using nonlinear dynamics. Additionally, we compared electroencephalogram sample entropy of signals in response to visual stimulation before, during, and after repeated-pulse tran- scranial magnetic stimulation at the Guangming. Results showed that electroencephalogram sample entropy at left （F3） and right （FP2） frontal electrodes were significantly different depending on where the magnetic stimulation was administered. Additionally, compared with the mock point, electroencephalogram sample entropy was higher after stimulating the Guangming point. When visual stimulation at Guangming was given before repeated-pulse transcranial magnetic stimula- tion, significant differences in sample entropy were found at five electrodes （C3, Cz, C4, P3, T8） in parietal cortex, the central gyrus, and the right temporal region compared with when it was given after repeated-pulse transcranial magnetic stimulation, indicating that repeated-pulse transcranial magnetic stimulation at Guangming can affect visual function. Analysis of electroencephalogram revealed that when visual stimulation preceded repeated pulse transcranial magnetic stimulation, sample entropy values were higher at the C3, C4, and P3 electrodes and lower at the Cz and T8 electrodes than visual stimulation followed preceded repeated pulse transcranial magnetic stimula- tion. The findings indicate that repeated-pulse transcranial magnetic stimulation at the Guangming evokes different patterns of electroencephalogram signals than repeated-pulse transcranial mag- netic stimulation at other nearby points on the body surface, and that repeated-pulse transcranial magnetic stimulation at the Guangrning is associated with changes in the complexity of visually evoked electroencephalogram signals in parietal regions, central gyrus, and temporal regions.

Multi-scale morphology analysis of acoustic emission signal and quantitative diagnosis for bearing fault

Monitoring of potential bearing faults in operation is of critical importance to safe operation of high speed trains.One of the major challenges is how to differentiate relevant signals to operational conditions of bearings from noises emitted from the surrounding environment.In this work,we report a procedure for analyzing acoustic emission signals collected from rolling bearings for diagnosis of bearing health conditions by examining their morphological pattern spectrum（MPS） through a multi-scale morphology analysis procedure.The results show that acoustic emission signals resulted from a given type of bearing faults share rather similar MPS curves.Further examinations in terms of sample entropy and Lempel-Ziv complexity of MPS curves suggest that these two parameters can be utilized to determine damage modes.

Multi-mapping Fault Diagnosis of High Voltage Circuit Breaker Based on Mathematical Morphology and Wavelet Entropy

Mechanical faults of high voltage circuit breakers(CBs)seriously affect the reliability of their operation,which may cause severe damage to power systems.In order to monitor operational conditions and detect mechanical faults of CBs,a multi-parameter monitoring system is designed and a fault diagnosis method based on multi-mapping is proposed.The paper focuses on the trip/close circuits,the spring-charging mechanism and the transmission mechanism,and obtains four current signals and a vibration signal that can reflect CB conditions.For the current signals,a morphological filter is used to remove noise and then characteristics of the waveforms’shape information are extracted.For vibration signals,the wavelet packet transform is used to decompose the signal into various frequency bands,and the sample entropy of the low frequency bands and the wavelet energy of the high frequency bands are calculated,respectively.Based on these feature parameters,a multi-mapping strategy is proposed for CB fault diagnosis.Laboratory experiments have been conducted to obtain on-site signals under various conditions,and experiment results have verified that monitoring the aforementioned signals and using the corresponding feature extraction and fault diagnosis methods,the mechanical faults of high voltage CBs can be effectively diagnosed.

Dynamic multivariate multiscale entropy based analysis on brain death diagnosis

The recently introduced multivariate multiscale sample entropy(MMSE)well evaluates the long correlations in multiple channels,so that it can reveal the complexity of multivariate biological signals.The existing MMSE algorithm deals with short time series statically whereas long time series are common for real-time computation in practical use.As a solution,we novelly proposed our dynamic MMSE(DMMSE)as an extension of MMSE.This helps us gain greater insight into the complexity of each section of time series,producing multifaceted and more robust estimates than the standard MMSE.The simulation results illustrated the feasibility and well performance in the brain death diagnosis.

sEMG Pattern Recognition of Muscle Force of Upper Arm for Intelligent Bionic Limb Control

Two new feature extraction methods, window sample entropy and window kurtosis, were proposed, which mainly aims to complete the surface Elcctromyography （sEMG）-muscle force pattern recognition for intelligent bionic limb. The inspiration is drawn from physiological process of muscle force generation. Five hand movement tasks were implemented for sEMG-muscle force data record. With two classical features： Integrated Electromyography （IEMG） and Root Mean Square （RMS）, two new features were fed into the wavelet neural network to predict the muscle force. To solve the issues that amputates＇ residual limb couldn＇t provide full train data for pattern recognition, it is proposed that force was predicted by neural network which is trained by contralateral data in this paper. The feasibility of the proposed features extraction methods was demonstrated by both ipsi- lateral and contralateral experimental results. The ipsilateral experimental results give very promising pattern classification accuracy with normalized mean square 0.58 ± 0.05. In addition, unilateral transradial amputees will benefit from the proposed method in the contralateral experiment, which probably helps them to train the intelligent bionic limb by their own sEMG.

Multiband decomposition and spectral discriminative analysis for motor imagery BCI via deep neural network

Human limb movement imagery,which can be used in limb neural disorders rehabilitation and brain-controlled external devices,has become a significant control paradigm in the domain of brain-computer interface(BCI).Although numerous pioneering studies have been devoted to motor imagery classification based on electroencephalography(EEG)signal,their performance is somewhat limited due to insufficient analysis of key effective frequency bands of EEG signals.In this paper,we propose a model of multiband decomposition and spectral discriminative analysis for motor imagery classification,which is called variational sample-long short term memory(VS-LSTM)network.Specifically,we first use a channel fusion operator to reduce the signal channels of the raw EEG signal.Then,we use the variational mode decomposition(VMD)model to decompose the EEG signal into six band-limited intrinsic mode functions(BIMFs)for further signal noise reduction.In order to select discriminative frequency bands,we calculate the sample entropy(SampEn)value of each frequency band and select the maximum value.Finally,to predict the classification of motor imagery,a LSTM model is used to predict the class of frequency band with the largest SampEn value.An open-access public data is used to evaluated the effectiveness of the proposed model.In the data,15 subjects performed motor imagery tasks with elbow flexion/extension,forearm supination/pronation and hand open/close of right upper limb.The experiment results show that the average classification result of seven kinds of motor imagery was 76.2%,the average accuracy of motor imagery binary classification is 96.6%(imagery vs.rest),respectively,which outperforms the state-of-the-art deep learning-based models.This framework significantly improves the accuracy of motor imagery by selecting effective frequency bands.This research is very meaningful for BCIs,and it is inspiring for end-to-end learning research.

Cell Registration and Flickering Detection for the Complexity Analysis of Red Blood Cell Dynamics with GSM Exposure

Red blood cells(RBC)' flickering present the dynamic properties of the cytomembrane. Its complexity could be used for aging analysis or the evaluation for the storage quality. The flickering activity is a kind of reversible perpendicular motion of the specified pixel. Therefore, the complexity analysis depends on the reliable detection of temporal variation for the gray-scale values from each pixel of the cells. In this paper, we improved our previous work on the screening of the horizontal drifted cells with a surface based on cell registration method and the effect of GSM exposure to the dynamic properties of the RBCs in terms of multi-scale sample entropy was presented in the paper.

Comparison of Heart Rate Variability between Normoxia and Hypobaric Hypoxia Exposure

This study aimed to use the sample entropy(Samp En)method to compare the difference between normoxia and hypobaric hypoxia exposure in heart rate variability.Eight healthy male volunteers were included in this research.For each subject,electrocardiography and finger pulse signal were recorded to obtain the heart rate time series.The Sp O2 signal was collected at the same time.In normoxia or hypobaric hypoxia 4300 m section,the segment of RR time series from the second5 min episode(from the 6 th to the 10 th min)was chosen for the Samp En calculation.The differences between normoxia and hypobaric hypoxia states were analyzed.Results showed that a significant difference exists in Samp En between two states(normoxia 0.54±0.05 vs hypobaric hypoxia 4300 m exposure 0.46±0.05),indicating the regulatory mechanisms of autonomic nervous system from a nonlinear perspective.