Multiple moving sources passive location based on multiset canonical correlation analysis

To solve the problem of multiple moving sources passive location, a novel blind source separa- tion （BSS） algorithm based on the muhiset canonical correlation analysis （MCCA） is presented by exploiting the different temporal structure of uncorrelated source signals first, and then on the basis of this algorithm, a novel multiple moving sources passive location method is proposed using time difference of arrival （TDOA） and frequency difference of arrival （FDOA） measurements. The key technique of this location method is TDOA and FDOA joint estimation, which is based on BSS. By blindly separating mixed signals from multiple moving sources, the multiple sources location problem can be translated to each source location in turn, and the effect of interference and noise can also he removed. The simulation results illustrate that the performance of the MCCA algorithm is very good with relatively light computation burden, and the location algorithm is relatively simple and effective.

Study of engine noise based on independent component analysis

Independent component analysis was applied to analyze the acoustic signals from diesel engine. First the basic prin-ciple of independent component analysis (ICA) was reviewed. Diesel engine acoustic signal was decomposed into several inde-pendent components (ICs); Fourier transform and continuous wavelet transform (CWT) were applied to analyze the independent components. Different noise sources of the diesel engine were separated, based on the characteristics of different component in time-frequency domain.

Wavelet Analysis of the Stochastic System with Coular Stationary Noise

In this paper, wavelet transform is used to analyse the stochastic system with coular stationary noise as follows:dX(t)=F(t)X(t)dt+B(t)u(t)dt+G(t)N(t)dtFor wavelet transform, its properties are analysed, and its density degree, wavelet expansion, correlation degree of expansion coefficient and their properties are obtained. All these results are new and useful.

The power spectrum and correlation of flow noise for an axisymmetric body in water

Understanding the physical features of the flow noise for an axisymmetric body is important for improving the performance of a sonar mounted on an underwater platform. Analytical calculation and numerical analysis of the physical features of the flow noise for an axisymmetric body are presented and a simulation scheme for the noise correlation on the hydrophones is given. It is shown that the numerical values of the flow noise coincide well with the analytical values. The main physical features of flow noise are obtained. The flow noises of two different models are compared and a model with a rather optimal fore-body shape is given. The flow noise in horizontal symmetry profile of the axisymmetric body is non-uniform, but it is omni-directional and has little difference in the cross section of the body. The loss of noise diffraction has a great effect on the flow noise from boundary layer transition. Meanwhile, based on the simulation, the noise power level increases with velocity to approximately the fifth power at high frequencies, which is consistent with the experiment data reported in the literature. Furthermore, the flow noise received by the acoustic array has lower correlation at a designed central frequency, which is important for sonar system design.

A blind source separation algorithm based on negentropy and signal noise ratio

A novel blind source separation （BSS） algorithm based on the combination of negentropy and signal noise ratio （SNR） is presented to solve the deficiency of the traditional independent component analysis （ICA） algorithm after the introduction of the principle and algorithm of ICA. The main formulas in the novel algorithm are elaborated and the idiographic steps of the algorithm are given. Then the computer simulation is used to test the performance of this algorithm. Both the traditional FastlCA algorithm and the novel ICA algorithm are applied to separate mixed signal data. Experiment results show the novel method has a better performance in separating signals than the traditional FastlCA algorithm based on negentropy. The novel algorithm could estimate the source signals from the mixed signals more precisely.

Extraction of Buried Signals in Noise: Correlated Processes

In this paper, we propose extraction of signals correlated with noise in which they are buried. Proposed extraction method uses no a-priori information on the buried signal and works independently of the nature of noise, correlated or not with the signal, colored or white, Gaussian or not, and locations of its spectral extent. Extraction of buried correlated signals is achieved without averaging in the time or frequency domain.

Convergence Analysis of Splitting-Up Algorithm of the Zakai’s Equation with Correlated Noises

In the field of nonlinear filtering(NLF),it is well-known that the unnormalized conditional density of the states satisfies the Zakai’s equation.The splitting-up algorithm has been first studied in the independent noises case by Bensoussan,et al.(1990).In this paper,the authors extend this convergence analysis of the splitting-up algorithm to the correlated noises’case.Given a time discretization,one splits the solution of the Zakai’s equation into two interlacing processes(with possibly computational advantage).These two processes correspond respectively to the prediction and updating.Under certain conditions,the authors show that both processes tend to the solution of the Zakai’s equation,as the time step goes to zero.The authors specify the conditions imposed on the way of splitting-up to guarantee the convergence.The major technical difficulty in the correlated noises’case,compared with the independent case,is to control the gradient of the second process in some sense.To illustrate the potentially computational advantage of the schemes based on the splitting-up ways,the authors experiment on a toy NLF model using the feedback particle filter(FPF)developed based on the splitting-up method and the sampling importance and resampling(SIR)as comparison.The FPF outperforms in both accuracy and efficiency.

MODAL PARAMETERS EXTRACTION WITH CROSS CORRELATION FUNCTION AND CROSS POWER SPECTRUM UNDER UNKNOWN EXCITATION

In most of real operational conditions only response data are measurable while the actual excitations are unknown, so modal parameter must be extracted only from responses. This paper gives a theoretical formulation for the cross-correlation functions and cross-power spectra between the outputs under the assumption of white-noise excitation. It widens the field of modal analysis under ambient excitation because many classical methods by impulse response functions or frequency response functions can be used easily for modal analysis under unknown excitation. The Polyreference Complex Exponential method and Eigensystem Realization Algorithm using cross-correlation functions in time domain and Orthogonal Polynomial method using cross-power spectra in frequency domain are applied to a steel frame to extract modal parameters under operational conditions. The modal properties of the steel frame from these three methods are compared with those from frequency response functions analysis. The results show that the modal analysis method using cross-correlation functions or cross-power spectra presented in this paper can extract modal parameters efficiently under unknown excitation.

Extraction of Signals Buried in Noise: Non-Ergodic Processes

In this paper, we propose extraction of signals buried in non-ergodic processes. It is shown that the proposed method extracts signals defined in a non-ergodic framework without averaging or smoothing in the direct time or frequency domain. Extraction is achieved independently of the nature of noise, correlated or not with the signal, colored or white, Gaussian or not, and locations of its spectral extent. Performances of the pro-posed extraction method and comparative results with other methods are demonstrated via experimental Doppler velocimetry measurements.

Numerical Study on the Duct Noise Control of Single-Stage Axial Flow Fan with Wave Leading Edge Stator Blade Configurations

This study focuses on a single-stage axial flow fan, investigating the effect of three kinds of wave leading edge stator blades on its noise reduction. The DDES method and the duct acoustic analogy theory based on the penetrable data surface were used for noise prediction. The results showed that the three kinds of wave leading edge blades were effective in reducing the rotor-stator interaction tonal noise and also have a certain inhibitory effect on broadband noise. The A10W15 stator blade can effectively reduce broadband noise in the frequency range of 2200 - 4200 Hz. When the amplitude is increased to 20, the noise reduction effect is further enhanced. However, when the amplitude is increased to 30, the broadband noise reduction effect is no longer significant. Further research shows that the wave leading edge stator blades can significantly change the pressure fluctuation distribution on the leading edge and suction surface, which control the modal energy distribution. Finally, this paper analyzed multiple factors affecting the broadband noise reduction, such as the noise source cut-off and cut-on effect and correlation. The purpose of this paper is to explore the laws of the influence of wave leading edge blades on the duct noise of real fan, and to reveal its noise control mechanism. .

The Ultimate Noise Limit for Hall Plates in Voltage, Current, and Hybrid Operating Modes

If Hall plates are used as magnetic field sensors they are usually powered up by a current source connected to a pair of non-neighboring contacts. The output voltage is tapped at another pair of non-neighboring contacts. In this paper we study more general operating conditions of Hall plates with an arbitrary number of contacts. In such hybrid operating modes current sources are connected to a first set of contacts and voltage sources to a second set of contacts. Output voltages are tapped at the first set of contacts and output currents are measured at the second set of contacts. All these output signals are multiplied by coefficients and added up. The purpose of this work is to figure out which operating mode and which Hall plate achieve maximum signal at minimum thermal noise and power dissipation. To this end we develop a theory, which gives the ratio of signal over noise and power as a function of the resistance matrix of Hall plates, of the supply voltages and currents, and of the coefficients. Optimization is done analytically in closed form and numerically for specific examples. The results are: 1) all operating modes have identical noise performance if their parameters are optimized;2) for any Hall plate one can measure its resistance matrix and insert its values into our formulae to obtain the optimum supply currents and coefficients for optimum noise performance.

The study of the correlated X-ray scattering(CXS)and its data analysis

Background At present,it is insufficient to understand the basic data characteristics of the correlated X-ray scattering.And there is a great challenge about how to master the nature of the data.So it is difficult to use and analyze the experimental data more effectively.In addition,there are many reasons,for the experimental artifacts such as whether the shutter is on or off,whether there is the beam line or not,the swaying of the nozzle and the shadow of the detector.So it is rather challenging to analyze the scattering patterns.Purpose The purpose of this paper was to develop a method to filter the invalid scattering data and provide the theoretical and experiment fundamentals for studying the X-ray scattering data of the complex biological sample further.Methods The heliummolecules were scattered by the X-ray free-electron laser in Spring8 in Japan.Andmillions of scattering patterns were obtained from the X-ray free-electron laser experiment.Through the analysis of the scattering data,the sum,mean,median and variance of the scattering intensity were obtained.Then different clusters were obtained with the densitybased spatial clustering of applications with noise(DBSCAN)algorithm.Results Based on the DBSCAN,some of the scattering patterns with high artifacts were removed and different clusters were clarified.So the experimental scattering data could be analyzed more effectively.Conclusion The theoretical and experiment fundamentals for comprehensively studying the X-ray scattering data of the complex biological sample were provided.After the data filtering,the angular autocorrelation of different clusters with Kam’s method will be computed and analyzed effectively.

Adaptive cubature Kalman filter with the estimation of correlation between multiplicative noise and additive measurement noise

Mobile robots are often subject to multiplicative noise in the target tracking tasks,where the multiplicative measurement noise is correlated with additive measurement noise.In this paper,first,a correlation multiplicative measurement noise model is established.It is able to more accurately represent the measurement error caused by the distance sensor dependence state.Then,the estimated performance mismatch problem of Cubature Kalman Filter(CKF)under multiplicative noise is analyzed.An improved Gaussian filter algorithm is introduced to help obtain the CKF algorithm with correlated multiplicative noise.In practice,the model parameters are unknown or inaccurate,especially the correlation of noise is difficult to obtain,which can lead to a decrease in filtering accuracy or even divergence.To address this,an adaptive CKF algorithm is further provided to achieve reliable state estimation for the unknown noise correlation coefficient and thus the application of the CKF algorithm is extended.Finally,the estimated performance is analyzed theoretically,and the simulation study is conducted to validate the effectiveness of the proposed algorithm.

Passive localization of 3-D near-field sources in coexisted multiplicative noise and additive noise

By exploiting thvorable characteristics of a uniIbrm cross-array, a passive localization algorithm of narrowband sources in the spherical coordinates （azimuth, elevation and range） is proposed. Based on the properly chosen sensor outputs, we compute the third-order cyclic moment matrices, and exploit a pre-calibration technique to eliminate multiplicative noise. Then, we construct a parallel factor （PARAFAC） model, and adopt trilinear altemating least squares regression （TALS） to estimate three-dimensional （3-D） near-field parameters. The investigated algorithm is efficient in the sense that it can eliminate multiplicative noise and additive noise, provide the improved estimation accuracy, as well as avoid the parameter-pairing procedure. Simulation results are carried out to demonstrate the performance of the proposed algorithm.

西藏易贡湖流域地表水水化学特征及其控制因素

为探究易贡湖流域地表水水化学特征及其控制因素,以藏东南易贡湖流域为研究对象,采集了26组地表水样,绘制了水样点主要离子线性变化趋势图、Gibbs图及Piper三线图等,运用相关分析、线性趋势分析、数理统计和离子比等方法。分析了研究区地表水主要离子特征及其控制因素,揭示了研究区地表水水中的主要物质来源。结果显示:易贡湖流域地表水阳离子主要为Ca^(2+)、Mg^(2+),K^(+)和Na^(+)含量较低,4种阳离子的含量关系为:Ca^(2+)>Mg^(2+)>Na^(+)>K^(+),阴离子主要为HCO_(3)^(-)、SO_(4)^(2-)和Cl-,NO_(3)^(-)和F^(-)含量较低,阴离子含量关系为:HCO_(3)^(-)>SO_(4)^(2-)>Cl->NO_(3)^(-)>F^(-),H_(2)SiO_(3)浓度值变化平稳,围绕6.9 mg·L^(-1)上下波动变化,pH值沿程变化平稳,在7.84~8.37之间,水体属于弱碱性。Ca^(2+)、Mg^(2+)和HCO_(3)^(-)是构成易贡湖流域地表水中的3种主要离子,水化学类型以HCO_(3)^(-)Ca·Mg或HCO_(3)^(-)Ca型为主。岩石溶滤作用是主要控制因素,阳离子交替吸附作用和蒸发浓缩作用均相当微弱,人为活动影响十分有限。

天津某环境背景噪声分析

连续波形检查中发现,天津尔王庄(EWZ)等台明显受到某未知噪声的干扰,且EWZ台受影响程度最大,此噪声常年如此,不随昼夜时间发生变化,且已存在多年。长期观测还发现,此噪声每年会有几天观测不到,但时间并不确定。与通常的环境背景噪声不同,此噪声具有一些固定特征。本文对其从本身特征、影响范围、频谱特性、对地震的影响等几个方面进行了研究。研究表明,噪声不受天气、季节等自然因素影响,具有随距EWZ台距离增大而振幅变小的变化特征,其主要频率范围在1~2 Hz之间。噪声的影响范围较广泛,可被周围12个台站记录到,最远到达YGZ台,距离EWZ台约58 km。通过偏振分析,发现噪声的传播具有特定的方向性,初步判断此噪声具有固定源噪声的特点。此噪声会对地震记录造成影响,使震相拾取率降低,甚至完全淹没在噪声中,通过带阻频率滤波会有所改善。

CEEMD-FastICA-CWT联合瞬态响应阶次的电驱总成噪声源识别

以某增程式电驱动总成为研究对象,提出基于联合算法的噪声分离识别模型。首先,采用互补集合经验模态分解(complementary ensemble empirical mode decomposition,CEEMD)联合快速独立分量分析(fast independent component analysis,FastICA)方法提取纯电模式稳态工况下单一通道噪声信号特征,利用复Morlet小波变换及FFT对各分量信号时频特性进行识别。其次,采用阶次分析法和声能叠加法对稳态分量信号对应的各瞬态响应阶次能量进行对比分析,并结合皮尔逊积矩相关系数(Pearson product moment correlation coefficient,PPMCC)相似性识别确定不同噪声激励源贡献度。结果表明:减速齿副啮合噪声对该增程式电驱总成纯电模式运行噪声整体贡献度最大。

医学院校高被引论文多源数据评价指标相关性研究

目的/意义了解医学院校高被引论文在不同数据库中多源评价指标相关性,为文献资源发现、利用、评估提供参考。方法/过程以2013—2023年首都医科大学高被引论文为研究对象,分析其在H1 Connect、Dimensions、ESI、Web of Science、InCites、SciVal数据库中的38个评价指标,对代表性指标,专利、政策引用指标,浏览使用指标,相对指标,期刊指标,被引频次,Altmetric指标分别进行相关性分析。结果/结论各组相似指标具有相对较高相关性,个别指标如加权星级和微博提及数与大多数不同平台指标没有显著相关性。

基于无相位谐波监测数据的谐振源辨识方法

针对电网中大量谐波监测数据因缺乏相位信息难以实现电网谐振监测的技术难题,提出了一套计及主导系数、相关系数及敏感度指标的电网谐振源辨识方法。首先,基于多谐波源谐振分析模型,分析了谐振状态下谐波参数的相关特性与回归特征,提出了计及主导系数的强主导谐振状态判别方法。其次,论证了强主导谐振状态下的谐波阻抗特征及数据特征,构建了融合机理模型和谐振数据特征的电网谐振状态及谐振源辨识方法。最后,基于建模仿真和工程案例验证,在背景谐波对母线谐波电压贡献率小于20%的工况下,敏感谐振支路和敏感谐波源的耦合谐波阻抗测量偏差小于2%,所提方法能够准确辨识谐振状态及谐振源。

鄂尔多斯盆地延长组多油源贡献比例与分布规律:基于机器学习与可解释性研究

鄂尔多斯盆地延长组发育多套潜在的烃源岩,但不同烃源岩之间生物标志物特征相似,常规油源对比方法效果不佳,相关认识长期存在争议。基于这样的问题,本文提出了一种基于深度学习的油源对比方案,将人工智能方法应用于油源对比研究,所开展的工作和认识有:(1)以延长组不同层位大量泥岩、页岩样品的42种生物标志物参数作为学习数据,构建了一种识别未知样品油源类别的深度神经网络模型,对长7泥页岩、长8—长10泥页岩的判别正确率分别达到了79.6%和83.0%,实现了延长组主要烃源岩生烃产物的有效区分;(2)通过模型分析了大量砂岩、原油样品的油源分类,统计了不同烃源岩对于延长组各个油层组原油的贡献比例,总结了它们的分布规律;(3)基于目前较为先进的置换特征重要性(PFI)算法,对所得模型进行了敏感性分析,初步揭示了延长组两类主要烃源岩的生物标志物差异。本文对于人工智能方法、技术在石油分子地球化学领域的发展具有积极的参考价值。