基于双耳自相关函数的车辆噪声烦恼度研究

利用主成分分析法研究车辆噪声的双耳自相关函数(ACF)因子,确定了车辆噪声的原始听感与决定其代表性听觉感知属性的主导听感,并从各类听感描述因子与基本因子的变化规律角度,分析了车辆噪声主观烦恼度的形成机制.首先,依据车辆噪声的ACF特性,对既有ACF因子理论进行了补充和完善;提出平均起伏度(EMD)与包络衰减率(ESM)...     

一种基于自相关函数的幂律检测器

针对水下高斯噪声中非高斯瞬态信号的检测问题,在研究Nuttall提出的power-law(幂律)检测器的基础上,为了提高其检测性能,依据信号和噪声的自相关函数的差异,提出了基于自相关函数的power-law检测器,并对其参数的最佳选取及检测性能进行了仿真研究。仿真结果表明,基于自相关函数的检测器较基于DFT的power-law检测器性能有所提高,有一定的可行性。     

一种基于单水听器宽带信号自相关函数的水下目标定位稳健方法

在浅海波导中, 未知声源的本征声线相对到达时延估计是非常困难的. 声源位置在邻域内变化和声速剖面扰动对本征声线相对达到时延的影响是类似的. 基于这种思想, 提出了基于单水听器宽带信号自相关函数的水下目标定位稳健方法. 通过设计邻域位置约束, 构造加权函数, 将宽带信号自相关函数包含的交叉干扰峰值转化为能够改善目标定位性能的有用信息. 该方法不仅不需要估计本征声线的相对到达时延, 而且对搜索网格失配和环境参数失配具有相当的宽容性. 利用典型浅海环境下的仿真数据和海上实验数据(Shallow Water 2006 experiments)对所提出方法的有效性进行了验证.     

激光诱导击穿光谱的自组织特征映射结合相关判别对天然地质样品分类方法研究

激光诱导击穿光谱技术具有微损、原位、快速分析的特点,在样品分类识别、成分分析等领域有广阔的应用前景。为探索该技术在天然地质样品识别应用的可行性,提出了一种自组织特征映射神经网络结合相关判别对天然地质样品LIBS光谱分类识别的方法。为减小全谱中背景噪声等不相关数据干扰、降低计算量,在元素谱线归属的基础上进行了特征谱线提取,实现了高维光谱数据的降维。以特征谱数据为输入建立网络训练模型,得到具有输入样本特征的权向量,通过权向量与待测样本进行相关分析可以实现样品分类。对16种天然地质样品的分类算法实验证明,在全谱、主成分降维和特征谱段三种数据处理方法中,特征谱的降维和提取LIBS数据主特征效果最优。改进的SOM网络结合相关判别算法比支持向量机方法和直接应用SOM网络方法的分类准确度更高,初步证实了该方法的有效性。     

利用自相关函数与平稳小波变换的^(252)Cf源驱动核材料质量识别方法北大核心CSCD

以252Cf中子源驱动噪声分析测量法为依据,利用中子脉冲信号自相关函数与被测核材料(252 U)质量的关系,设计了一种基于神经网络的核材料质量识别方法,探索借助时域特征进行质量识别的有效性。利用平稳小波变换抑制中子统计涨落对自相关函数带来的影响,利用分布式Elman神经网络对不同质量核材料的自相关函数样本进行训练和识别,并研究了有限样本前提下不同子网个数对最终识别结果所造成的影响。对4种核材料质量共计120组样本进行的实验,结果表明:在理想实验条件下,平稳小波变换抑制了统计涨落对信号自相关函数的影响;分布式Elman神经网络能够较好地识别自相关函数的特征,分辨不同质量的核材料,平均识别误差小于0.1。     

利用自相关函数与平稳小波变换的252Cf源驱动核材料质量识别方法

 以²⁵²Cf中子源驱动噪声分析测量法为依据,利用中子脉冲信号自相关函数与被测核材料(²⁵²U)质量的关系,设计了一种基于神经网络的核材料质量识别方法,探索借助时域特征进行质量识别的有效性。利用平稳小波变换抑制中子统计涨落对自相关函数带来的影响,利用分布式Elman神经网络对不同质量核材料的自相关函数样本进行训练和识别,并研究了有限样本前提下不同子网个数对最终识别结果所造成的影响。对4种核材料质量共计120组样本进行的实验,结果表明：在理想实验条件下,平稳小波变换抑制了统计涨落对信号自相关函数的影响;分布式Elman神经网络能够较好地识别自相关函数的特征,分辨不同质量的核材料,平均识别误差小于0.1。     

Annoyance of noise stimuli in relation to the spatial factors extracted from the interaural cross-correlation function

While considering auditory-brain model for subjective responses, effects of spatial factors extracted from the interaural cross-correlation function (IACF) on annoyance of noise stimuli are examined. The previously developed indices to measure sound pressure levels (SPL) and frequency characteristics cannot fully explain the psychological effects of noise. In the first experiment, subjects judged their annoyance by changing fluctuations in the magnitude of interaural cross-correlation function (IACC) and the SPL. In the second, they judged their annoyance by changing fluctuations in the interaural time delay (τ_IACC) and the SPL. Results show that: (1) annoyance increased by increasing the fluctuations of IACC as well as the SPL, (2) annoyance increased by increasing the fluctuations of τ_IACC as well as the SPL.     

Multifractal Fourier detrended cross-correlation analysis of traffic signals

Multifractal detrended cross-correlation analysis (MF-DXA) has been developed to detect the long-range power-law cross-correlation of considered signals in the presence of non-stationarity. However, crossovers arising from extrinsic periodic trends make the scaling behavior difficult to analyze. We introduce a Fourier filtering method to eliminate the trend effects and systematically investigate the multifractal cross-correlation of simulated and real traffic signals. The crossover locations are found approximately corresponding to the periods of underlying trend. Traffic velocity on one road and flows on adjacent roads show strong cross-correlation. They also present weak multifractality after periodic trends are removed. The traffic velocity and flow are cross-correlated in opposite directions which is accordant to their actual evolution.     

Moments of the auto-correlation function and the KMS-condition

Inequalities between successive moments of the time dependent auto-correlation function are derived. Furthermore, we prove that they provide an infinite set of characterizations of an equilibrium state.     

利用船舶噪声的自相关与倒谱联合估计多径时延

对2007南海试验中多个航路船舶噪声的自相关和倒谱特征进行了分析研究,结果发现:近距离处倒谱图中的时延峰曲线消失、自相关相对倒谱有更高的时延峰检测信干比。类似现象此前未见报道,说明在一定条件下采用单一自相关法或倒谱法均难于连续检测时延峰。为解决这一问题,首先应用射线声学理论结合数值仿真,解释了该试验现象的产生与“随距离减小,倒谱和自相关的时延峰绝对幅度分别减小和增大”的性质有关,进而提出了一种利用船舶噪声的自相关和倒谱联合估计多径时延的方法。海试数据处理结果表明:本文方法能够稳健地跟踪多径时延峰,有效地弥补传统单一自相关法和倒谱法的不足,对解决多径时延的连续估计问题有明显效果。     

Phases of scaling and cross-correlation behavior in traffic

While many microscopic models of traffic flow describe transitions between different traffic phases, such transitions are difficult to quantify in measured traffic data. Here we study long-term traffic recordings consisting of ≈2900$\approx 2900$ days of flow, density, and velocity time series with minute resolution from a Spanish motorway. We calculate fluctuations, cross-correlations, and long-term persistence properties of these quantities in the flow–density diagram. This leads to a data-driven definition of (local) traffic states based on the dynamical properties of the data, which differ from those given in standard guidelines. We find that detrending techniques must be used for persistence analysis because of non-stationary daily and weekly traffic flow patterns. We compare our results for the measured data with analysis results for a microscopic traffic model, finding good agreement in most quantities. However, the simulations cannot easily reproduce the congested traffic states observed in the data. We show how fluctuations and cross-correlations in traffic data may be used for prediction, i.e., as indications of increasing or decreasing velocities.     

Continuous detrended cross-correlation analysis on generalized Weierstrass function

The paper mainly introduces a continuous detrended cross-correlation analysis method to detect the long-range fractal scaling behaviors of continuous and integrable functions. The effects of such method are tested by the generalized Weierstrass function with various seeds. It presents accurate scaling characterization as desired by comparing with that from the popular detrended cross-correlation analysis method of discrete case.     

Arrival-time structure of the time-averaged ambient noise cross-correlation function in an oceanic waveguide

Coherent deterministic arrival times can be extracted from the derivative of the time-averaged ambient noise cross-correlation function between two receivers. These coherent arrival times are related to those of the time-domain Green's function between these two receivers and have been observed experimentally in various environments and frequency range of interest (e.g., in ultrasonics, seismology, or underwater acoustics). This nonintuitive result can be demonstrated based on a simple time-domain image formulation of the noise cross-correlation function, for a uniform distribution of noise sources in a Pekeris waveguide. This image formulation determines the influence of the noise-source distribution (in range and depth) as well as the dependence on the receiver bandwidth for the arrival-time structure of the derivative of the cross-correlation function. These results are compared with previously derived formulations of the ambient noise cross-correlation function. Practical implications of these results for sea experiments are also discussed.     

Motorway noise modelling based on perpendicular propagation analysis of traffic noise

A model for motorway traffic noise has been obtained from measurements along the the Bangkok-Chonburi motorway. The model’s parameters include traffic volume and combination, the average spot speed of each type of vehicle and the physical conditions of the motorway in terms of right-of-way width, number of lanes, lane width, shoulder width, and median width for both of the main carriageways and frontage roads. The noise level that is generated by each type of vehicle has been analyzed according to the propagation in the direction perpendicular to the center line of motorway’s carriageway. The total traffic noise is then analyzed from traffic volume of all vehicle types on both sides of carriageways and frontage roads. The basic noise levels used in the motorway traffic noise model are modified according to the effective ground effect along the propagation path. The final result of this study is that a motorway traffic noise model based on the perpendicular propagation analysis technique performs well in a statistical goodness-of-fit test against the field data, and therefore, can be used effectively in traffic noise prediction for related or similar motorway projects.