Identification of denatured and normal biological tissues based on compressed sensing and refined composite multi-scale fuzzy entropy during high intensity focused ultrasound treatment

In high intensity focused ultrasound(HIFU)treatment,it is crucial to accurately identify denatured and normal biological tissues.In this paper,a novel method based on compressed sensing(CS)and refined composite multi-scale fuzzy entropy(RCMFE)is proposed.First,CS is used to denoise the HIFU echo signals.Then the multi-scale fuzzy entropy(MFE)and RCMFE of the denoised HIFU echo signals are calculated.This study analyzed 90 cases of HIFU echo signals,including 45 cases in normal status and 45 cases in denatured status,and the results show that although both MFE and RCMFE can be used to identify denatured tissues,the intra-class distance of RCMFE on each scale factor is smaller than MFE,and the inter-class distance is larger than MFE.Compared with MFE,RCMFE can calculate the complexity of the signal more accurately and improve the stability,compactness,and separability.When RCMFE is selected as the characteristic parameter,the RCMFE difference between denatured and normal biological tissues is more evident than that of MFE,which helps doctors evaluate the treatment effect more accurately.When the scale factor is selected as 16,the best distinguishing effect can be obtained.

Radar emitter signal recognition based on multi-scale wavelet entropy and feature weighting

In modern electromagnetic environment, radar emitter signal recognition is an important research topic. On the basis of multi-resolution wavelet analysis, an adaptive radar emitter signal recognition method based on multi-scale wavelet entropy feature extraction and feature weighting was proposed. With the only priori knowledge of signal to noise ratio(SNR), the method of extracting multi-scale wavelet entropy features of wavelet coefficients from different received signals were combined with calculating uneven weight factor and stability weight factor of the extracted multi-dimensional characteristics. Radar emitter signals of different modulation types and different parameters modulated were recognized through feature weighting and feature fusion. Theoretical analysis and simulation results show that the presented algorithm has a high recognition rate. Additionally, when the SNR is greater than-4 d B, the correct recognition rate is higher than 93%. Hence, the proposed algorithm has great application value.

The Study of Image Segmentation Based on the Combination of the Wavelet Multi-scale Edge Detection and the Entropy Iterative Threshold Selection

This paper proposes an image segmentation method based on the combination of the wavelet multi-scale edge detection and the entropy iterative threshold selection.Image for segmentation is divided into two parts by high- and low-frequency.In the high-frequency part the wavelet multiscale was used for the edge detection,and the low-frequency part conducted on segmentation using the entropy iterative threshold selection method.Through the consideration of the image edge and region,a CT image of the thorax was chosen to test the proposed method for the segmentation of the lungs.Experimental results show that the method is efficient to segment the interesting region of an image compared with conventional methods.

Multi-scale complexity entropy causality plane: An intrinsic measure for indicating two-phase flow structures

We extend the complexity entropy causality plane（CECP） to propose a multi-scale complexity entropy causality plane（MS-CECP） and further use the proposed method to discriminate the deterministic characteristics of different oil-in-water flows. We first take several typical time series for example to investigate the characteristic of the MS-CECP and find that the MS-CECP not only describes the continuous loss of dynamical structure with the increase of scale, but also reflects the determinacy of the system. Then we calculate the MS-CECP for the conductance fluctuating signals measured from oil–water two-phase flow loop test facility. The results indicate that the MS-CECP could be an intrinsic measure for indicating oil-in-water two-phase flow structures.

Wi-Wheat+:Contact-free wheat moisture sensing with commodity WiFi based on entropy

In this paper,we propose a contact-free wheat moisture monitoring system,termed Wi-Wheatþ,to address the several limitations of the existing grain moisture detection technologies,such as time-consuming process,expensive equipment,low accuracy,and difficulty in real-time monitoring.The proposed system is based on Commodity WiFi and is easy to deploy.Leveraging WiFi CSI data,this paper proposes a feature extraction method based on multi-scale and multi-channel entropy.The feasibility and stability of the system are validated through experiments in both Line-Of-Sight(LOS)and Non-Line-Of-Sight(NLOS)scenarios,where ten types of wheat moisture content are tested using multi-class Support Vector Machine(SVM).Compared with the Wi-Wheat system proposed in our prior work,Wi-Wheatþhas higher efficiency,requiring only a simple training process,and can sense more wheat moisture content levels.

基于VMD-MSE与SSA-SVM的往复式压缩机气阀故障诊断

往复压缩机气阀故障振动信号具有较强的非线性和非平稳性。为了从往复压缩机气阀振动信号中提取故障特征用于故障诊断,提出一种基于变分模态分解(variational mode decomposition,VMD)与多尺度熵(multi-scale entrope,MSE)的故障特征提取方法,并与采用麻雀寻优算法(soarrow search algorithm,SSA)优化的支持向量机(suppot vector mackine,SVM)相结合,用于往复压缩机气阀故障诊断;通过对往复压缩机气阀信号进行VMD分解,选取合适的内禀模态分量(intrinsic mode function,IMF)进行信号重构,基于MSE熵值分析构成特征向量集,最后将其输入SSA-SVM训练并识别故障类型。试验结果表明,基于VMD-MSE与SSA-SVM的故障诊断模型能有效并准确的识别往复压缩机气阀故障。

Modified multi-scale symbolic dynamic entropy and fuzzy broad learning-based fast fault diagnosis of railway point machines

Railway point machine(RPM)condition monitoring has attracted engineers’attention for safe train operation and accident prevention.To realize the fast and accurate fault diagnosis of RPMs,this paper proposes a method based on entropy measurement and broad learning system(BLS).Firstly,the modified multi-scale symbolic dynamic entropy(MMSDE)module extracts dynamic characteristics from the collected acoustic signals as entropy features.Then,the fuzzy BLS takes the above entropy features as input to complete model training.Fuzzy BLS introduces the Takagi-Sug eno fuzzy system into BLS,which improves the model’s classification performance while considering computational speed.Experimental results indicate that the proposed method significantly reduces the running time while maintaining high accuracy.

基于MSE-PCA的脑电睡眠分期方法研究

针对传统的自动睡眠分期准确率不足问题,提出一种将多尺度熵(MSE)和主成分分析(PCA)联合使用的自动睡眠分期方法。以8例受试者睡眠脑电(EEG)监测数据及专家人工分期结果作为样本,首先使用MSE表征受试者脑电信号不同睡眠期的非线性动力学特征;然后使用PCA的前两个主成分向量代替MSE特征进行降维,实现降低数据冗余的同时保留绝大多数EEG非线性特征;最终将新向量的特征参数输入到反馈神经网络(BPNN)分类器中实现MSE-PCA模型的脑电睡眠状态的自动识别分类。实验结果表明,自动分期准确率可达到87.9%,kappa系数0.77,该方法能提高脑电自动睡眠分期系统的准确率和稳定性。

Dense Fractal Networks, Trends, Noises and Switches in Homeostasis Regulation of Shannon Entropy for Chromosomes’ Activity in Living Cells for Medical Diagnostics

We analyze correlations and patterns of oxidative activity of 3D DNA at DNA fluorescence in complete sets of chromosomes in neutrophils of peripheral blood. Fluorescence of DNA is registered by method of flow cytometry with nanometer spatial resolution. Experimental data present fluorescence of many ten thousands of cells, from different parts of body in each population, in various blood samples. Data is presented in histograms as frequency distributions of flashes in the dependence on their intensity. Normalized frequency distribution of information in these histograms is used as probabilistic measure for definition of Shannon entropy. Data analysis shows that for this measure of Shannon entropy common sum of entropy, i.e. total entropy E, for any histogram is invariant and has identical trends of changes all values of E (r) = lnr at reduction of rank r of histogram. This invariance reflects informational homeostasis of chromosomes activity inside cells in multi-scale networks of entropy, for varied ranks r. Shannon entropy in multi-scale DNA networks has much more dense packing of correlations than in “small world” networks. As the rule, networks of entropy differ by the mix of normal D 2 and abnormal D > 2 fractal dimensions for varied ranks r, the new types of fractal patterns and hinges for various topology (fractal dimension) at different states of health. We show that all distributions of information entropy are divided on three classes, which associated in diagnostics with a good health or dominants of autoimmune or inflammatory diseases. This classification based on switching of stability at transcritical bifurcation in homeostasis regulation. We defined many ways for homeostasis regulation, coincidences and switching patterns in branching sequences, the averages of Hölder for deviations of entropy from homeostasis at different states of health, with various saturation levels the noises of entropy at activity of all chromosomes in support regulation of homeostasis.

Fermentation process modeling of exopolysaccharide using neural networks and fuzzy systems with entropy criterion

The prediction accuracy and generalization of fermentation process modeling on exopolysaccharide (EPS) production from Lactobacillus are often deteriorated by noise existing in the corresponding experimental data. In order to circumvent this problem, a novel entropy-based criterion is proposed as the objective function of several commonly used modeling methods, i.e. Multi-Layer Perceptron (MLP) network, Radial Basis Function (RBF) neural network, Takagi-Sugeno-Kang (TSK) fuzzy system, for fermentation process model in this study. Quite different from the traditional Mean Square Error (MSE) based criterion, the novel entropy-based criterion can be used to train the parameters of the adopted modeling methods from the whole distribution structure of the training data set, which results in the fact that the adopted modeling methods can have global approximation capability. Compared with the MSE- criterion, the advantage of this novel criterion exists in that the parameter learning can effectively avoid the over-fitting phenomenon, therefore the proposed criterion based modeling methods have much better generalization ability and robustness. Our experimental results confirm the above virtues of the proposed entropy-criterion based modeling methods.

基于AO-VMD的往复压缩机故障特征提取方法

采用原始VMD方法对往复压缩机故障进行诊断时,往复压缩机易损部件的振动信号存在非平稳、非线性这一问题,为此,提出了一种使用天鹰算法(AO),以各分量样本熵的最小值作为适应度函数,对变分模态分解(VMD)进行优化分解的往复压缩机故障特征提取方法。首先,对往复压缩机滑动轴承的故障进行了分析,对其不同状态下的振动信号进行了分析处理;然后,先使用小波消噪对振动信号进行了消噪处理,再分别使用原始VMD和AO-VMD新型分解方法对其进行了处理,并得到了BLIMF分量;最后,计算两种分解方法中各分量的多尺度样本熵(MSE)值,对不同状态的多尺度样本熵值进行了对比分析,从而实现了对往复压缩机各类故障的诊断。研究结果表明:AO-VMD方法利用AO强大的快速搜索和开发能力后,故障分类性能明显优于原始VMD分解方法,各类故障信号多尺度样本熵值区分明显;其省时方面效果显著,基于遗传算法优化VMD方法分解耗时427 s,而AO-VMD方法仅需165 s,满足故障诊断分解方法要求。

复合层次模糊熵及其在滚动轴承故障诊断中的应用

针对样本熵和多尺度熵中相似性度量函数的突变问题,及它们在分析时间序列复杂性时捕捉不到高频组分信息的局限,提出了一种新的时间序列的复杂性度量方法——复合层次模糊熵(CHFE)。为了有效地提取滚动轴承早期故障特征,提出了一种基于CHFE、拉普拉斯分值和支持向量机的滚动轴承故障诊断方法。首先,提取振动信号的CHFE值;其次,采用拉普拉斯分值对特征向量进行降维优化;再次,建立基于支持向量机的多故障分类器,实现滚动轴承的故障诊断;最后,将该方法应用于实验数据分析,结果验证了方法的有效性。

心电图的多尺度熵分析

我们使用Costa等人提出的算法,研究了心电图(ECG)的多尺度熵(MSE)的特性。发现健康人的样本熵要高于冠心病人和心梗病人且健康人的复杂度最高。而冠心病人的样本熵(SampEn)要高于心梗病人,但是已很接近心梗病人。说明冠心病人和心梗病人的复杂度明显低于健康人,而冠心病人很容易导致心梗发作,从而引起生命危险。

经颅直流电刺激对孤独症谱系障碍儿童脑电的影响研究

孤独症谱系障碍(ASD)是一种复杂的大脑神经发育障碍,其核心特征是社交障碍和刻板行为。针对孤独症儿童的脑发育异常,将新兴脑调控技术——经颅直流电刺激(tDCS)应用于孤独症儿童脑调控。共招募24名孤独症儿童参加试验,其中12名孤独症儿童接受每周2次共计10次脑调控干预,另外12名孤独症儿童接受每周2次共计10次的伪刺激,作为对照组。利用功率谱和多尺度熵算法,评估脑电的功率谱和复杂度变化。结果表明,经过调控干预后,实验组儿童干预前后4~8 Hz theta频段在全脑均有显著下降(P<0.05),其中,额叶从(1.13±0.07) dB/Hz下降到(0.96±0.06)dB/Hz,左颞叶从(1.18±0.05) dB/Hz下降到(1.03±0.07)dB/Hz,中央区从(1.43±0.06) dB/Hz下降到(1.16±0.03)dB/Hz,右颞叶从(1.14±0.09) dB/Hz下降到(0.96±0.04)dB/Hz,枕叶从(1.39±0.06) dB/Hz下降到(1.09±0.03)dB/Hz;通过计算15个尺度的熵值发现,顶叶(P3,Pz,C3,C4)、枕叶(O1)以及左侧背外侧前额叶(F3)均有显著增加。研究表明,颅直流电刺激能够以无损安全的方式实现对孤独症儿童的神经调控,改善异常脑功能,因此在孤独症的康复中有很大的应用潜力。

基于单通道脑电信号的自动睡眠分期

针对传统自动睡眠分期准确率不足的问题,提出一种基于单通道脑电(EEG)信号的新型睡眠分期方法。以8例受试者睡眠脑电监测数据及专家人工分期结果作为样本,首先使用两层滤波器实现对原始脑电信号的去噪,然后利用小波变换算法提取各睡眠阶段节律波的相对能量均值作为第一部分特征参数,并添加多尺度熵算法分析各睡眠阶段的复杂度特征,选取9~13尺度的多尺度熵值作为第二部分的特征参数。将所有的特征参数输入到反馈传播神经网络分类器中实现睡眠阶段的自动识别分类。通过实验结果的统计分析,该方法的平均分期准确率达到85.81%,相比传统的小波变换、样本熵和模糊熵方法,有更高的系统稳定性和准确率。

小波变换分析降水时间序列的多分辨率特性研究

利用小波变换的多分辨率特性分析降水时间序列,其小波分解级数应是确定的。为解决此问题,提出一种新的框架:首先利用àtrous小波变换对降水时间序列进行分解,然后对分解的序列进行多尺度熵(MSE)分析以获取原始降水时间序列的一些潜在特征。分析表明,小波分解级数可以由不同尺度下近似信号MSE曲线的Mann-Kendall(MK)检验确定。对环渤海湾地区包括大连、锦州、天津、潍坊四个站点的降水时间序列复杂度进行多尺度分析比较,结果表明,利用MSE分析和MK检验可以得到较理想的小波分解级数。不同地方的降水时间序列复杂度分析可以为水资源的规划和应用提供参考。

清醒和麻醉大鼠肾交感神经活动的多尺度熵分析

为了研究乌拉坦、氯醛糖混合麻醉对大鼠肾交感神经活动的影响,应用多尺度熵方法研究了麻醉药物对肾交感神经活动的影响。对清醒和麻醉大鼠的肾交感神经活动分析的结果表明:当尺度增大到12之后,清醒鼠的神经活动熵值显著大于麻醉鼠;两种状态下大鼠神经活动的熵值随尺度的变化趋势显著不同。对于肾交感神经活动这类貌似随机的时间序列,多尺度熵比样品熵等算法更能全面地反映出神经系统的非线性特征。

基于局部均值分解和多尺度熵的运动想象脑电信号特征提取方法

研究了脑电信号特征的提取。考虑到传统的脑电信号特征提取方法不能够很好地刻画脑电信号特征,因而会给不同意识任务下运动想象脑电信号的分类带来困难,该研究提出了一种基于局部均值分解(LMD)和多尺度熵(MSE)的脑电信号特征提取方法。该方法首先把脑电信号自适应地分解为一系列具有物理意义的乘积函数(PF)分量;然后选取有效的PF分量并计算多尺度熵,将多尺度熵组成特征向量;最后将其作为支持向量机(SVM)的输入来对脑电信号进行分类识别。实验表明该方法能够有效地提取脑电信号的特征,从而验证了该方法的有效性和可行性。

基于交叉熵算法的NC-OFDM系统导频设计

交叉熵算法是目前逐步优化而形成的的一种计算方法,在解决多种优化组合的问题上具有较好的性能。在基于信道均方误差的准则下,本文基于交叉熵算法,提出一种适用于非连续正交频分复用(Non-contiguous Orthogonal Frequency Division,NC-OFDM)系统上的导频设计方法。该方法先按照伯努利分布生成导频位置的随机样本,得出信道估计的最小均方误差(Minimum Mean Square Error,MSE)的样本值,然后通过更新规则对分布参数进行更新,经过几次迭代得到较优的导频位置。仿真结果表明,使用该方法得到的信道具有更好的MSE性能及误比特率(Bit Error Rate,BER)性能。

全封闭往复式压缩机制造缺陷诊断方法研究

全封闭往复式压缩机作为制冷系统的核心部件,其品质决定了整机系统的能效水平、静音效果和产品寿命。在生产线制造过程中,针对全封闭结构特点难以识别缺陷产品的短板问题,本文提出了一种基于壳体振动信号的压缩机制造缺陷诊断方法。首先通过集合经验模态分解(EEMD)对振动信号进行频谱分解,再利用多尺度样本熵(MSE)来表征不同尺度下各模态分量的复杂度并以此作为特征向量,最后利用支持向量机(SVM)完成制造缺陷的分类。实验结果表明,本文所提诊断方法能准确实现典型制造缺陷的识别与分类,为全封闭往复式压缩机制造缺陷的在线检测提供了相关理论与检测依据。