High-frequency compensation for seismic data based on adaptive generalized S transform

The low-pass fi ltering eff ect of the Earth results in the absorption and attenuation of the high-frequency components of seismic signals by the stratum during propagation.Hence,seismic data have low resolution.Considering the limitations of traditional high-frequency compensation methods,this paper presents a new method based on adaptive generalized S transform.This method is based on the study of frequency spectrum attenuation law of seismic signals,and the Gauss window function of adaptive generalized S transform is used to fi t the attenuation trend of seismic signals to seek the optimal Gauss window function.The amplitude spectrum compensation function constructed using the optimal Gauss window function is used to modify the time-frequency spectrum of the adaptive generalized S transform of seismic signals and reconstruct seismic signals to compensate for high-frequency attenuation.Practical data processing results show that the method can compensate for the high-frequency components that are absorbed and attenuated by the stratum,thereby eff ectively improving the resolution and quality of seismic data.

Enhancing the resolution of seismic data based on the generalized S-transform

In this paper, we analyze the seismic signal in the time-frequency domain using the generalized S-transform combined with spectrum modeling. Without assuming that the reflection coefficients are random white noise as in the conventional resolution-enhanced techniques, the wavelet which changes with time and frequency was simulated and eliminated. After using the inverse S-transform for the processed instantaneous spectrum, the signal in the time domain was obtained again with a more balanced spectrum and broader frequency band. The quality of seismic data was improved without additional noise.

A Protection Method of VSC-HVDC Cables Based on Generalized S-Transform

Generalized S-transform is a time-frequency analysis method which has higher resolution than S-transform. It can precisely extract the time-amplitude characteristics of different frequency components in the signal. In this paper, a novel protection method for VSC-HVDC (Voltage source converter based high voltage DC) based on Generalized S-transform is proposed. Firstly, extracting frequency component of fault current by Generalized S-transform and using mutation point of high frequency to determine the fault time. Secondly, using the zero-frequency component of fault current to eliminate disturbances. Finally, the polarity of sudden change currents in the two terminals is employed to discriminate the internal and external faults. Simulations in PSCAD/EMTDC and MATLAB show that the proposed method can distinguish faults accurately and effectively.

Amplitude spectrum compensation and phase spectrum correction of seismic data based on the generalized S transform

We propose a method for the compensation and phase correction of the amplitude spectrum based on the generalized S transform. The compensation of the amplitude spectrum within a reliable frequency range of the seismic record is performed in the S domain to restore the amplitude spectrum of reflection. We use spectral simulation methods to fit the time-dependent amplitude spectrum and compensate for the amplitude attenuation owing to absorption. We use phase scanning to select the time-, space-, and frequencydependent phases correction based on the parsimony criterion and eliminate the residual phase effect of the wavelet in the S domain. The method does not directly calculate the Q value; thus, it can be applied to the case of variable Q. The comparison of the theory model and field data verify that the proposed method can recover the amplitude spectrum of the strata reflectivity, while eliminating the effect of the residual phase of the wavelet. Thus, the wavelet approaches the zero-phase wavelet and, the seismic resolution is improved.

Symmetry Transformations and Exact Solutions of a Generalized Hyperelastic Rod Equation

In this paper,a nonlinear wave equation with variable coefficients is studied,interestingly,this equation can be used to describe the travelling waves propagating along the circular rod composed of a general compressible hyperelastic material with variable cross-sections and variable material densities.With the aid of Lou’s direct method1,the nonlinear wave equation with variable coefficients is reduced and two sets of symmetry transformations and exact solutions of the nonlinear wave equation are obtained.The corresponding numerical examples of exact solutions are presented by using different coefficients.Particularly,while the variable coefficients are taken as some special constants,the nonlinear wave equation with variable coefficients reduces to the one with constant coefficients,which can be used to describe the propagation of the travelling waves in general cylindrical rods composed of generally hyperelastic materials.Using the same method to solve the nonlinear wave equation,the validity and rationality of this method are verified.

Uncertainty Principles for the Generalized Fourier Transform Associated with Spherical Mean Operator

The aim of this paper is to establish an extension of qualitative and quantitative uncertainty principles for the Fourier transform connected with the spherical mean operator.

广义S变换和阈值分割联合抗频谱扩展-压缩移频干扰

根据频谱扩展-压缩(spectrum spread and compression,SSC)移频干扰信号和回波信号时频分布特性的差异,提出一种基于广义S变换和Tsallis交叉熵阈值分割的干扰抑制方法。分析了SSC移频干扰的干扰原理和干扰信号经过解线调后的信号形式,并利用时频聚焦性较好的广义S变换获取接收信号经过解线调后的时频图像,根据时频图像对应的灰度图像,以Tsallis交叉熵最小化作为目标函数,求出灰度图像的最佳分割阈值,并根据分割阈值构建时频滤波器,实现干扰抑制。仿真结果表明:该方法对于SSC移频干扰产生的假目标具有较好的抑制效果,干扰抑制比可达30 dB以上。

基于VMD-GST和AMCNN相结合的滚动轴承故障诊断方法

针对传统滚动轴承故障诊断方法故障特征提取困难、诊断准确率低等问题,提出了一种变分模态分解(VMD)、广义S变换(GST)和注意力卷积神经网络(AMCNN)相结合的智能诊断方法。首先,基于VMD算法分解振动信号,以互信息指标筛选真实分量并进行重构,通过GST将重构信号转化为时频图;然后,以得到的二维特征图像为输入,通过AMCNN自适应学习其时频特征;最后,通过分类器输出滚动轴承的故障诊断结果。以NJ208EM圆柱滚子轴承为例进行试验验证,结果表明:VMD-GST方法能有效提取故障特征,AMCNN模型具有更强的特征提取能力和识别能力,平均故障识别准确率达到99.76%,优于其他方法。

基于LR-GST变换的稳定层间Q值提取方法

为了解决常规谱比法不稳定及等效Q值储层识别精度低的问题,在基于Lucy-Richardson算法的广义S变换(LR-GST)的基础上,改进了谱比法计算Q值的公式,同时构建反演目标函数将等效Q值转换为层间Q值。结果表明:Q值求取精度与噪声比率、求取方法等因素有关,含噪比率不同每种方法的准确性也差异较大。通过合成数据及实际应用表明,本文方法的分辨率及聚集性更高,方法的稳定性更好,得到的Q值在含油气区域异常明显。

频率矫正广义S变换及在储层含油气性预测中的应用

随着我国油气勘探程度的不断加深,陆相页岩油气等非常规油气逐渐成为我国勘探开发的重点,陆相页岩非均质性强、储层薄且纵横向变化剧烈等典型特征导致含油气性预测精度差,影响后续井位部署和生产等.时频变换方法是储层含油气性预测最有效的工具.为了提高储层含油气性预测的精度,本文提出了一种频率矫正广义S变换(Frequency-Corrected Generalized S-Transform, FCGST),该变换既具有S变换多尺度多分辨率的特性,又可以通过自适应选取窗函数参数以控制窗函数的带宽和主频等,这一特性可以适用于不同工区地震数据的处理与解释.与传统的S变换和广义S变换不同,该变换修正了传统S变换和广义S变换中的尺度因子,改善了S变换和广义S变换等传统方法中时频谱主频向高频方向偏移的问题,有利于精确描述复杂储层含油气性.合成数据算例表明,频率矫正广义S变换提升了地震信号局部时频特征的表征能力;将频率矫正广义S变换应用于鄂尔多斯盆地某工区储层含油气性预测中,实钻结果数据进一步验证了该时频分析方法在储层含油气性解释中的有效性和潜力.

基于同步提取广义S变换的机械故障诊断方法研究

现有的同步提取变换(synchroextracting transform, SET)窗函数固定缺乏灵活性,在进行故障诊断时很难有效获取到高时频精度和高抗干扰性能的瞬时频率,针对此问题,结合广义S变换可以自适应调节窗函数宽度的优点,提出一种基于同步提取广义S变换(synchroextracting generalized Stransform, SEGST)的机械故障诊断方法。SEGST方法的特点在于将Rényi熵作为度量时频聚集性的标准,通过在高斯窗函数中引入2个尺度调节因子来选择参数的最佳值,对得到的广义S变换二维时频谱构造出同步提取算子来提取时频脊线处的时频系数,该算子能保留与信号的时变特征最相关的TF信息,剔除多余的模糊时频能量,从而得到高时频分辨率的时频能量特征。仿真结果表明,所提方法不论在时频分辨率方面,还是在噪声鲁棒性方面,都优于传统时频分析方法,并且保持了良好的重构性。最后,将所提方法应用于航空发动机高速滚动轴承故障诊断中,结果表明,该方法能够准确识别故障信号中的特征频率。

May ChatGPT be a tool producing medical information for common inflammatory bowel disease patients’questions?An evidencecontrolled analysis

Artificial intelligence is increasingly entering everyday healthcare.Large language model(LLM)systems such as Chat Generative Pre-trained Transformer(ChatGPT)have become potentially accessible to everyone,including patients with inflammatory bowel diseases(IBD).However,significant ethical issues and pitfalls exist in innovative LLM tools.The hype generated by such systems may lead to unweighted patient trust in these systems.Therefore,it is necessary to understand whether LLMs(trendy ones,such as ChatGPT)can produce plausible medical information(MI)for patients.This review examined ChatGPT’s potential to provide MI regarding questions commonly addressed by patients with IBD to their gastroenterologists.From the review of the outputs provided by ChatGPT,this tool showed some attractive potential while having significant limitations in updating and detailing information and providing inaccurate information in some cases.Further studies and refinement of the ChatGPT,possibly aligning the outputs with the leading medical evidence provided by reliable databases,are needed.

基于同步提取增强广义S变换的柴油机气门性能退化状态评估

柴油机在运行过程中气门间隙逐渐增大,其状态会随气门性能退化而发生改变。针对传统状态评估方法难以对其气门性能退化状态进行准确评估的问题,提出基于同步提取增强广义S变换(Synchro Extracting Enhanced Generalized S-Transform,SEEGST)的柴油机气门性能退化状态评估方法。通过传感器采集反映柴油机状态的振动信号;为解决传统信号时频分析方法存在时频分辨率低、能量聚集性弱等问题,基于同步提取算法与广义S变换提出SEEGST时频分析方法,将振动信号转换为二维时频图;利用MLP-Mixer模型提取时频图像特征进行训练,实现柴油机状态评估。通过柴油机状态监测实验台开展气门性能退化实验,将所提方法与SSGST-MLPMixer、GST-MLPMixer、SEEGST-ViT、SEEGST-2DCNN、FFT spectrum-1DCNN 5种传统方法对比。实验结果表明:所提方法的整体评估准确率达到98.96%,可有效应用于柴油机气门性能退化状态评估领域,为开展柴油机气门性能退化状态评估提供一种新的思路。

基于广义S变换的光伏电站谐波和间谐波分析方法

针对S变换高斯窗函数形式固定不变的问题,引入高斯窗调节因子改进S变换,提出一种基于广义S变换的光伏电站谐波和间谐波分析方法。首先通过MATLAB/Simulink搭建光伏电站的仿真模型,在自然采样双极性SPWM调制方式下采集逆变器的三相输出电压;然后以A相电压为例,采用广义S变换对电压信号处理得到一个模时频矩阵;最后对该矩阵分析实现光伏电站谐波和间谐波参数的准确计算。仿真结果表明,本文方法的幅值计算误差最大仅为3.30×10^(-4)%,频率的平均计算误差为0%,远小于S变换幅值误差35.19%和频率误差2.39%;能满足光伏电站谐波和间谐波检测精度的需要。

基于深度学习和广义S变换协同的风速预测

针对实测风速的非平稳性特点,提出一种基于深度学习和时频分析的风速混合预测方法。首先,采用经验模态分解(EMD)将风速分解为若干子层,由此得到趋势分量和脉动分量以降低风速的非线性。根据2个分量的时频特性,采用长短时记忆(LSTM)处理趋势分量,极限学习机(ELM)处理脉动分量。其次,引入广义S变换(GST)来获得预测过程中的时频特性。同时,采用改进的灰狼算法(IGWO)对GST、LSTM和ELM的参数进行优化。最后,以内蒙古某风场实测风速对所提模型进行验证,结果表明该模型具有较高的精度。

s-parameterized Weyl transformation and the corresponding quantization scheme

By extending the usual Weyl transformation to the s-parameterized Weyl transformation with s being a real parameter,we obtain the s-parameterized quantization scheme which includes P–Q quantization, Q–P quantization, and Weyl ordering as its three special cases. Some operator identities can be derived directly by virtue of the s-parameterized quantization scheme.

基于广义S变换和并联神经网络的结构损伤识别研究

目前在利用CNN网络提取特征的结构损伤识别研究中,仅仅利用1D-CNN和2D-CNN提取的特征进行损伤识别存在准确率低、识别效率不高等问题。提出了一种基于广义S变换和并联神经网络的结构损伤识别方法。为了丰富输入信号的特征维度,利用广义S变换将滤波后的信号转化成时频图,并同时将一维加速度响应信号和二维时频图分别输入1D-CNN和2D-CNN中进行时域和时频域特征提取,并在汇聚层进行特征拼接,然后通过FC层和Softmax层对损伤识别结果进行分类。利用IASC-ASCE SHM Benchmark结构第二阶段试验数据对所提出的并联网络模型进行验证,结果表明,所提出的网络模型与其他同类方法相比具有更高的识别精度和识别效率。

On the generalized Cauchy function and new Conjecture on its exterior singularities

This article studies on Cauchy’s function f （z） and its integral, （2πi）J[ f （z）] ≡ ■C f （t）dt/（t z） taken along a closed simple contour C, in regard to their comprehensive properties over the entire z = x ＋ iy plane consisted of the simply connected open domain D ＋ bounded by C and the open domain D outside C. （1） With f （z） assumed to be C n （n ∞-times continuously differentiable） z ∈ D ＋ and in a neighborhood of C, f （z） and its derivatives f （n） （z） are proved uniformly continuous in the closed domain D ＋ = [D ＋ ＋ C]. （2） Cauchy’s integral formulas and their derivatives z ∈ D ＋ （or z ∈ D ） are proved to converge uniformly in D ＋ （or in D = [D ＋C]）, respectively, thereby rendering the integral formulas valid over the entire z-plane. （3） The same claims （as for f （z） and J[ f （z）]） are shown extended to hold for the complement function F（z）, defined to be C n z ∈ D and about C. （4） The uniform convergence theorems for f （z） and F（z） shown for arbitrary contour C are adapted to find special domains in the upper or lower half z-planes and those inside and outside the unit circle ｜z｜ = 1 such that the four general- ized Hilbert-type integral transforms are proved. （5） Further, the singularity distribution of f （z） in D is elucidated by considering the direct problem exemplified with several typ- ical singularities prescribed in D . （6） A comparative study is made between generalized integral formulas and Plemelj’s formulas on their differing basic properties. （7） Physical sig- nificances of these formulas are illustrated with applicationsto nonlinear airfoil theory. （8） Finally, an unsolved inverse problem to determine all the singularities of Cauchy function f （z） in domain D , based on the continuous numerical value of f （z） z ∈ D ＋ = [D ＋ ＋ C], is presented for resolution as a conjecture.

Evaluating the role of large language models in inflammatory bowel disease patient information

This letter evaluates the article by Gravina et al on ChatGPT’s potential in providing medical information for inflammatory bowel disease patients.While promising,it highlights the need for advanced techniques like reasoning+action and retrieval-augmented generation to improve accuracy and reliability.Emphasizing that simple question and answer testing is insufficient,it calls for more nuanced evaluation methods to truly gauge large language models’capabilities in clinical applications.

广义S变换下串联故障电弧的时频分析及识别研究

在当前用户侧负载日益复杂的情形下,故障电弧信号难以有效识别,阻碍了线路隐患监测及预警技术的发展。该文基于广义S变换进行了故障串联电弧的时频分析及识别研究。首先,比较了短时傅里叶变换、小波变换、广义S变换3种电弧时频特征提取方法的区别,阐明广义S变换在处理非线性负载高频特征方面的优势。然后,利用双曲高斯窗的广义S变换对负载信号进行时频特征提取,构建图像特征样本。最后,利用二维卷积神经网络对样本进行训练及分类,通过准确率、识别结果聚类进一步分析和验证识别算法的有效性。该文算法总体识别准确率在96.81%,涉及负载广泛,为后续电弧故障的监测识别研究提供参考。