LOCALIZED RADON-WIGNER TRANSFORM AND GENERALIZED-MARGINAL TIME-FREQUENCY DISTRIBUTIONS

This paper introduces the localized Radon transform (LRT) into time-frequency distributions and presents the localized Radon-Wigner transform (LRWT). The definition of LRWT and a fast algorithm is derived, the properties of LRWT and its relationship with Radon-Wigner transform, Wigner distribution (WD), ambiguity function (AF), and generalized-marginal time-frequency distributions are analyzed.

Time-Frequency Entropy Analysis of Arc Signal in Non-Stationary Submerged Arc Welding

The use of time-frequency entropy to quantitatively assess the stability of submerged arc welding process considering the distribution features of arc energy is reported in this paper. Time-frequency entropy is employed to calculate and analyze the stationary current signals, non-stationary current and voltage signals in the submerged arc welding process. It is obtained that time-frequency entropy of arc signal can be used as arc stability judgment criteria of submerged arc welding. Experimental results are provided to confirm the effectiveness of this approach.

Application of Wavelet Packet De-noising in Time-Frequency Analysis of the Local Wave Method

The local wave method is a very good time-frequency method for nonstationaryvibration signal analysis. But the interfering noise has a big influence on the accuracy oftime-frequency analysis. The wavelet packet de-noising method can eliminate the interference ofnoise and improve the signal-noise-ratio. This paper uses the local wave method to decompose thede-noising signal and perform a time-frequency analysis. We can get better characteristics. Finally,an example of wavelet packet de-noising and a local wave time-frequency spectrum application ofdiesel engine surface vibration signal is put forward.

Application of Local Wave Time-Frequency Spectrum and Neural Networks to Fault Classification in Rotating Machine

A new method of fault analysis and detection by signal classification inrotating machines is presented. The Local Wave time-frequency spectrum which is a new method forprocessing a non-stationary signal is used to produce the representation of the signal. This methodallows the decomposition of one-dimensional signals into intrinsic mode functions (IMFs) usingempirical mode decomposition and the calculation of a meaningful multi-component instantaneousfrequency. Applied to fault signals , it provides new time-frequency attributes. Then the momentsand margins of the time-frequency spectrum are calculated as the feature vectors. The probabilisticneural network is used to classify different fault modes. The accuracy and robustness of theproposed methods is investigated on signals obtained during the different fault modes (early rub,loose, misalignment of the rotor).

Preventing Pressure Oscillations Does Not Fix Local Linear Stability Issues of Entropy-Based Split-Form High-Order Schemes

Recently,it was discovered that the entropy-conserving/dissipative high-order split-form discontinuous Galerkin discretizations have robustness issues when trying to solve the sim-ple density wave propagation example for the compressible Euler equations.The issue is related to missing local linear stability,i.e.,the stability of the discretization towards per-turbations added to a stable base flow.This is strongly related to an anti-diffusion mech-anism,that is inherent in entropy-conserving two-point fluxes,which are a key ingredi-ent for the high-order discontinuous Galerkin extension.In this paper,we investigate if pressure equilibrium preservation is a remedy to these recently found local linear stability issues of entropy-conservative/dissipative high-order split-form discontinuous Galerkin methods for the compressible Euler equations.Pressure equilibrium preservation describes the property of a discretization to keep pressure and velocity constant for pure density wave propagation.We present the full theoretical derivation,analysis,and show corresponding numerical results to underline our findings.In addition,we characterize numerical fluxes for the Euler equations that are entropy-conservative,kinetic-energy-preserving,pressure-equilibrium-preserving,and have a density flux that does not depend on the pressure.The source code to reproduce all numerical experiments presented in this article is available online(https://doi.org/10.5281/zenodo.4054366).

Continuous time-varying Q-factor estimation method in the time-frequency domain

The Q-factor is an important physical parameter for characterizing the absorption and attenuation of seismic waves propagating in underground media,which is of great signifi cance for improving the resolution of seismic data,oil and gas detection,and reservoir description.In this paper,the local centroid frequency is defi ned using shaping regularization and used to estimate the Q values of the formation.We propose a continuous time-varying Q-estimation method in the time-frequency domain according to the local centroid frequency,namely,the local centroid frequency shift(LCFS)method.This method can reasonably reduce the calculation error caused by the low accuracy of the time picking of the target formation in the traditional methods.The theoretical and real seismic data processing results show that the time-varying Q values can be accurately estimated using the LCFS method.Compared with the traditional Q-estimation methods,this method does not need to extract the top and bottom interfaces of the target formation;it can also obtain relatively reasonable Q values when there is no eff ective frequency spectrum information.Simultaneously,a reasonable inverse Q fi ltering result can be obtained using the continuous time-varying Q values.

Detection method of forward-scatter signal based on Rényi entropy

The application scope of the forward scatter radar(FSR)based on the Global Navigation Satellite System(GNSS)can be expanded by improving the detection capability.Firstly,the forward-scatter signal model when the target crosses the baseline is constructed.Then,the detection method of the for-ward-scatter signal based on the Rényi entropy of time-fre-quency distribution is proposed and the detection performance with different time-frequency distributions is compared.Simula-tion results show that the method based on the smooth pseudo Wigner-Ville distribution(SPWVD)can achieve the best perfor-mance.Next,combined with the geometry of FSR,the influence on detection performance of the relative distance between the target and the baseline is analyzed.Finally,the proposed method is validated by the anechoic chamber measurements and the results show that the detection ability has a 10 dB improvement compared with the common constant false alarm rate(CFAR)detection.

A variation pixels identification method based on kernel spatial attraction model and local entropy for robust endmember extraction

A variation pixels identification method was proposed aiming at depressing the effect of variation pixels, which dilates the theoretical hyperspectral data simplex and misguides volume evaluation of the simplex. With integration of both spatial and spectral information, this method quantitatively defines a variation index for every pixel. The variation index is proportional to pixels local entropy but inversely proportional to pixels kernel spatial attraction. The number of pixels removed was modulated by an artificial threshold factor α. Two real hyperspectral data sets were employed to examine the endmember extraction results. The reconstruction errors of preprocessing data as opposed to the result of original data were compared. The experimental results show that the number of distinct endmembers extracted has increased and the reconstruction error is greatly reduced. 100% is an optional value for the threshold factor α when dealing with no prior knowledge hyperspectral data.

Novel detection method for infrared small targets using weighted information entropy

This paper presents a method for detecting the small infrared target under complex background. An algorithm, named local mutation weighted information entropy （LMWIE）, is proposed to suppress background. Then, the grey value of targets is enhanced by calculating the local energy. Image segmentation based on the adaptive threshold is used to solve the problems that the grey value of noise is enhanced with the grey value improvement of targets. Experimental results show that compared with the adaptive Butterworth high-pass filter method, the proposed algorithm is more effective and faster for the infrared small target detection.

Application of local wave ti me-frequency method in reciprocating mechanical fault diagnosis

To diagnosethe reciprocating mechanical fault.We utilizedlocal waveti me-frequency approach.Firstly,we gave the principle.Secondly,the application of local wave ti me-frequency was given.Finally,we discusseditsvirtue in reciprocating mechanical fault diagnosis.

Adaptive Bearing Fault Diagnosis based on Wavelet Packet Decomposition and LMD Permutation Entropy

Bearing fault signal is nonlinear and non-stationary, therefore proposed a fault feature extraction method based on wavelet packet decomposition （WPD） and local mean decomposition （LMD） permutation entropy, which is based on the support vector machine （SVM） as the feature vector pattern recognition device Firstly, the wavelet packet analysis method is used to denoise the original vibration signal, and the frequency band division and signal reconstruction are carried out according to the characteristic frequency. Then the decomposition of the reconstructed signal is decomposed into a number of product functions （PE） by the local mean decomposition （LMD） , and the permutation entropy of the PF component which contains the main fault information is calculated to realize the feature quantization of the PF component. Finally, the entropy feature vector input multi-classification SVM, which is used to determine the type of fault and fault degree of bearing The experimental results show that the recognition rate of rolling bearing fault diagnosis is 95%. Comparing with other methods, the present this method can effectively extract the features of bearing fault and has a higher recognition accuracy

Short-Term Prediction of Photovoltaic Power Generation Based on LMD Permutation Entropy and Singular Spectrum Analysis

The power output state of photovoltaic power generation is affected by the earth’s rotation and solar radiation intensity.On the one hand,its output sequence has daily periodicity;on the other hand,it has discrete randomness.With the development of new energy economy,the proportion of photovoltaic energy increased accordingly.In order to solve the problem of improving the energy conversion efficiency in the grid-connected optical network and ensure the stability of photovoltaic power generation,this paper proposes the short-termprediction of photovoltaic power generation based on the improvedmulti-scale permutation entropy,localmean decomposition and singular spectrum analysis algorithm.Firstly,taking the power output per unit day as the research object,the multi-scale permutation entropy is used to calculate the eigenvectors under different weather conditions,and the cluster analysis is used to reconstruct the historical power generation under typical weather rainy and snowy,sunny,abrupt,cloudy.Then,local mean decomposition(LMD)is used to decompose the output sequence,so as to extract more detail components of the reconstructed output sequence.Finally,combined with the weather forecast of the Meteorological Bureau for the next day,the singular spectrumanalysis algorithm is used to predict the photovoltaic classification of the recombination decomposition sequence under typical weather.Through the verification and analysis of examples,the hierarchical prediction experiments of reconstructed and non-reconstructed output sequences are compared.The results show that the algorithm proposed in this paper is effective in realizing the short-term prediction of photovoltaic generator,and has the advantages of simple structure and high prediction accuracy.

On the Superconductivity in High-Entropy Alloy (NbTa)1-X(HfZrTi)X

The superconductivity in (NbTa)1-X(HfZrTi)X high-entropy alloy is analyzed using the theory of strong-coupled superconductor. It is concluded that (NbTa)1-X(HfZrTi)X is a strong coupled superconductor. The variation in the superconducting transition temperature from 7.9 K to 4.6 K as x increases from 0.2 to 0.84 arises because of the decrease in electronic band width due to localization and broadening of the band. It is suggested that the decrease in electronic band width is due to crystalline randomness which gives rise to the mobility edge.

基于局部信息熵的计算机网络高维数据离群点检测系统

通过离群点检测可以及时发现计算机网络中的异常,从而为风险预警和控制提供重要线索。为此,设计一种基于局部信息熵的计算机网络高维数据离群点检测系统。在高维数据采集模块中,利用Wireshark工具采集计算机网络原始高维数据包;并在高维数据存储模块中建立MySQL数据库、Zooleeper数据库与Redis数据库,用于存储采集的高维数据包。在高维数据离群点检测模块中,通过微聚类划分算法划分存储的高维数据包,得到数个微聚类;然后计算各微聚类的局部信息熵,确定各微聚类内是否存在离群点;再依据偏离度挖掘微聚类内的离群点;最后,利用高维数据可视化模块呈现离群点检测结果。实验证明:所设计系统不仅可以有效采集计算机网络高维数据并划分计算机网络高维数据,还能够有效检测高维数据离群点,且离群点检测效率较快。

入侵意图分析下的软件定义网络DDoS攻击检测方法

为在数据样本回溯期内解决因本地信息熵值增大而造成的服务攻击问题,维护软件定义网络的运行安全性,提出入侵意图分析下的软件定义网络分布式拒绝服务(DDoS)攻击检测方法。按照软件定义网络场景重构原则,确定因果网转换标准,实现对识别参数的更新处理,完成攻击性行为的入侵意图分析,再定义DDoS数据集,根据攻击行为的时空特性,求解模型参数的取值范围,完成入侵意图分析下软件定义网络DDoS攻击检测方法的设计。实验结果表明,在该算法控制下数据样本回溯期为10 min,低于传统算法,能够较好维护软件定义网络的运行安全性。

基于广义柔度曲率信息熵的板式轨道脱空损伤识别

板式轨道填充层作为轨道结构关键部位,在高频列车荷载和环境共同作用下出现脱空损伤,引起脱空位置轨道结构刚度改变。为有效检测板式轨道的轨道板脱空情况,采用数值仿真分析得到无砟轨道模态信息,利用轨道脱空区域广义柔度曲率局部峰值进行轨道脱空损伤识别。结合广义柔度、均匀荷载面(Uniform load surface, ULS)、曲率和局部信息熵,提出可定位损伤的ULS曲率信息熵,并在CRTS III板式轨道上进行验证。研究结果表明:广义柔度曲率利用轨道脱空前后模态信息计算轨道脱空损伤曲率差,能够有效定位脱空位置;ULS曲率信息熵表征值只需要轨道的一阶模态信息便能够有效地反映轨道脱空位置及面积,且克服了广义柔度曲率需要健康模态信息的不足;轨道对称位置上相同面积脱空的ULS曲率信息熵值相同;ULS曲率信息熵值与脱空面积和厚度成正相关关系;ULS曲率信息熵表征值具有较好的损伤识别敏感性,能够识别小于单个测点布置面积的0.1 m×0.1 m小面积脱空,并且对轨道板边脱空识别敏感性高于轨道板中脱空识别敏感性。

基于Copula传递熵的设备级和网络级宽频振荡传播路径分析及振荡源定位方法

电力电子化电力系统的宽频振荡问题严重危害电网的安全稳定运行,及时确定宽频振荡的传播路径和振荡源位置,对振荡的抑制至关重要。该文从因果关系的角度提出一种基于Copula传递熵的设备级和网络级宽频振荡传播路径分析及振荡源定位方法,通过分析多变量之间的因果传递方向和因果强度系数,利用有向加权图构建宽频振荡因果网络,在此基础上确定振荡源位置,并通过保留因果强度系数最大的支路,确定振荡的主要传播路径。采用所提方法一方面能够从设备级分析控制器内部各状态变量的因果关联性;另一方面能够从网络级分析电网中各节点振荡数据的因果传递性,从而确定宽频振荡在控制器内部和电网中的传播路径及振荡源位置。最后,在强迫振荡、风机与弱电网交互、发电机轴系振荡中的仿真算例表明,所提方法适用于多种机理的宽频振荡,能够同时实现设备级和网络级宽频振荡传播路径分析和振荡源定位。

弧形绝缘子金属连接环局部机械损坏识别仿真

弧形绝缘子金属连接环通常安装在电力设备上,由于其所处的环境复杂,且存在各种环境干扰因素的影响,使得其局部出现机械损坏时,识别结果不准确。为了精准识别连接环局部机械损坏,提出一种弧形绝缘子金属连接环局部机械损坏识别方法。通过形态学开—闭重构运算对弧形绝缘子金属连接环图像展开建模,使用差分运算获取差分图像,并结合最大熵值法对其分割。根据弧形绝缘子金属连接环的特点,排除非损伤部分对图像识别的干扰。采用基于分形理论的差分盒维法,将图像划分为多个子块并计算其分形维数值,并判断图像是否在数值范围内,从而实现对连接环局部机械损坏的识别。实验结果表明,所提方法可以精准识别弧形绝缘子金属连接环局部机械损坏,具有比较强的适用性。

地方高校高质量发展水平测度、区域差异及分布动态演进

在构建地方高校高质量发展评价指标体系的基础上,利用全国31个省份的2004—2021年面板数据,使用熵权TOPSIS法测度地方高校高质量发展水平并分析我国地方高校高质量发展的时空特征、区域差异以及分布动态演进,研究结果表明:地方高校高质量发展水平整体不高但呈波动上升趋势;我国区域地方高校高质量发展不平衡,呈现东部-东北部-中部-西部依次递减态势;地方高校高质量发展水平存在显著的区域差异,主要来源于区域间差异,但有缩小的趋势,且西部和东北地区伴有极化情况出现。

减税降费规模与地方政府债务风险治理——机制与证据

减税降费是中国为积极应对经济下行压力、维护全局经济稳定的重要政策手段,但其规模的不断扩大会加剧地方政府债务风险。本文通过构建包含“借”、“用”、“还”3个环节的风险指标体系,采用熵权-TOPSIS法测算2015~2022年中国省级地方政府债务风险指数,使用双向固定效应模型、面板门槛模型实证分析减税降费规模对地方政府债务风险的影响。研究发现,减税降费规模与地方政府债务风险总体上呈正相关关系,并且存在经济发展水平的门槛效应,当经济发展水平跨过门槛值时,地方政府债务风险增长速度将显著降低。同时,减税降费规模与地方政府债务风险间存在非线性关系,随着减税降费规模不断扩大,地方政府债务风险增速也将被遏制。