SEA and contribution analysis for interior noise of a high speed train

This paper presents a detailed Statistical Energy Analysis (SEA) and contribution analysis of the interior noise of a high-speed train through extensive simulations and measurements. The SEA model was developed based on the actual geometrical parameters of a benchmark high-speed coach. Sound transmission loss levels of the structural components of the car body, which are required in the SEA model, were tested in a dedicated acoustic laboratory following international standard ISO 140-3:1995. Modal densities of these structural components were derived from measured frequency response functions using the modal counting method. Damping loss factors were obtained using the half-power bandwidth method and the vibration attenuation method. By considering the relationship between sound radiation and power transmission, coupling loss factors between structures and cavities were estimated. Source inputs to the SEA model were derived from field experiment data. Interior noise due to those sources was predicted using the SEA model and the prediction was generally in good agreement with measurement. Contribution analysis was then performed using this validated model through parametric study, and this analysis was further examined experimentally. In conclusion, for the coach that was investigated in this paper, the key factors for interior noise are sidewall vibration, bogie area noise, and floor sound transmission loss. Based on this and other engineering considerations, an interior noise control strategy can be defined.     

Towards a diffusion model of acoustic energy flow in large undamped structures

It is shown that energy transport parameters for vibrations in a complex structure may be extracted from the kind of data that might be obtained from limited and cost effective direct numerical simulations (DNS). A diffusion model is proposed which promises to avoid the requirement of statistical energy analysis (SEA)-like substructuring. The model is successfully fit to simulated data with an underlying nature which is unambiguously diffusive, as opposed to DNS data where the underlying nature is in question. The data is constructed with noise that mimics the sorts of fluctuations expected in practice. The algorithm's robustness depends heavily on the number of adjustable parameters.     

Corrected Statistical Energy Analysis Model in a Non-Reverberant Acoustic Space

Statistical Energy Analysis (SEA) is a well-known method to analyze the flow of acoustic and vibration energy in a complex structure. This study investigates the application of the corrected SEA model in a non-reverberant acoustic space where the direct field component from the sound source dominates the total sound field rather than a diffuse field in a reverberant space which the classical SEA model assumption is based on. A corrected SEA model is proposed where the direct field component in the energy is removed and the power injected in the subsystem considers only the remaining power after the loss at first reflection. Measurement was conducted in a box divided into two rooms separated by a partition with an opening where the condition of reverberant and non-reverberant can conveniently be controlled. In the case of a non-reverberant space where acoustic material was installed inside the wall of the experimental box, the signals are corrected by eliminating the direct field component in the measured impulse response. Using the corrected SEA model, comparison of the coupling loss factor (CLF) and damping loss factor (DLF) with the theory shows good agreement.     

中频声振耦合的波函数统计能量法

为了提高中频声振耦合的计算效率,提出了波函数-统计能量法的结构-声学耦合方法,该方法从波动理论的角度出发,将波函数法(WBM)和统计能量法(SEA)结合,通过在耦合面分别施加声压激励和速度边界条件,推导了耦合面参数理论计算公式。将该方法用到长方体声腔和钢板耦合的模型中,并对100~1000 Hz的计算结果进行了实验验证。WBM-SEA模型与参考FEM-SEA模型以及实验模型的频响曲线对比结果表明,WBM-SEA与FEM-SEA以及实验结果吻合很好,验证了混合WBM-SEA的有效性。通过收敛性分析发现混合WBM-SEA方法计算时间比混合FEM-SEA方法更少。从而可以得出结论:混合波函数-统计能量法方法对中频声振耦合预测是有效的,且比FEM-SEA更加高效。      

基于混合群智能优化算法的混沌系统参数估计

混沌系统的未知系统参数估计是实现混沌控制和同步的首要问题,通过构造一个合理的适应度函数,可将其转化为一个多维搜索空间的优化问题.提出一种融合改进骨干粒子群算法与改进差分进化算法的混合群智能优化方法来解决上述优化问题.对骨干粒子群算法中的粒子位置更新机制以及差分进化算法中的变异操作、交叉操作、交叉概率因子的设计等进行改进,有效兼顾了种群的多样性与算法的收敛性.在此基础上,讨论骨干粒子群优化算法与差分进化的融合优化策略,实现两个算法的协同进化,进一步提高算法的综合优化性能.用6个基准测试函数以及Lorenz混沌系统为例进行仿真实验,结果表明该方法具有全局寻优能力强、收敛速度快、搜索精度高、稳健性好等优点.     

Numerical and experimental validation of a hybrid finite element-statistical energy analysis method

The finite element (FE) and statistical energy analysis (SEA) methods have, respectively, high and low frequency limitations and there is therefore a broad class of "mid-frequency" vibro-acoustic problems that are not suited to either FE or SEA. A hybrid method combining FE and SEA was recently presented for predicting the steady-state response of vibro-acoustic systems with uncertain properties. The subsystems with long wavelength behavior are modeled deterministically with FE, while the subsystems with short wavelength behavior are modeled statistically with SEA. The method yields the ensemble average response of the system where the uncertainty is confined in the SEA subsystems. This paper briefly summarizes the theory behind the method and presents a number of detailed numerical and experimental validation examples for structure-borne noise transmission.     

Time-reversal multiple signal classification in case of noise: a phase-coherent approach

The problem of locating point-like targets beyond the classical resolution limit is revisited. Although time-reversal MUltiple SIgnal Classification (MUSIC) is known for its super-resolution ability in localization of point scatterers, in the presence of noise this super-resolution property will easily break down. In this paper a phase-coherent version of time-reversal MUSIC is proposed, which can overcome this fundamental limit. The algorithm has been tested employing synthetic multiple scattering data based on the Foldy-Lax model, as well as experimental ultrasound data acquired in a water tank. Using a limited frequency band, it was demonstrated that the phase-coherent MUSIC algorithm has the potential of giving significantly better resolved scatterer locations than standard time-reversal MUSIC.     

State and parameter estimation in stochastic dynamical models

This paper derives generalized maximum likelihood estimates of state and model parameters of a stochastic dynamical model. In contrast to previous studies, the change in background distribution due to changes in model parameters is taken into account. An ensemble approach to solving the maximum likelihood estimates is proposed. An exact solution for the ensemble update based on a square root Kalman Filter is derived. This solution involves a two step procedure in which an ensemble is first produced by a standard ensemble Kalman Filter, and then “corrected” to account for parameter estimation, thereby allowing a user to take advantage of an existing ensemble filter. The solution is illustrated with simple, low-dimensional stochastic dynamical models and shown to work well and outperform augmentation methods for estimating stochastic parameters.     

Improving robustness of filtered-x least mean p-power algorithm for active attenuation of standard symmetric-α-stable impulsive noise

The paper concerns active control of impulsive noise having peaky distribution with heavy tail. Such impulsive noise can be modeled using non-Gaussian stable process for which second order moments do not exist. The most famous filtered-x least mean square (FxLMS) algorithm for active noise control (ANC) systems is based on the minimization of variance (second order moment) of error signal, and hence, becomes unstable for the impulsive noise. In order to improve the robustness of adaptive algorithms for processes having distributions with heavy tails (i.e. signals with outliers), either (1) a robust optimization criterion may be used to derive the adaptive algorithm or (2) the large amplitude samples may be ignored or replaced by an appropriate threshold value. Among the existing algorithms for ANC of impulsive noise, one is based on the minimizing least mean p-power (LMP) of the error signal, resulting in FxLMP algorithm (approach 1). The other is based on modifying; on the basis of statistical properties; the reference signal in the update equation of the FxLMS algorithm (approach 2). In this paper we propose two solutions to improve the robustness of the FxLMP algorithm. In first proposed algorithm, the reference and the error signals are thresholded before being used in the update equation of FxLMP algorithm. As another solution to improve the performance of FxLMP algorithm, a modified normalized step size is proposed. The computer simulations are carried out, which demonstrate the effectiveness of the proposed algorithms.     

改进的噪声鲁棒语音稀疏线性预测算法

语音线性预测分析算法在噪声环境下性能会急剧恶化,针对这一问题,提出一种改进的噪声鲁棒稀疏线性预测算法。首先采用学生t分布对具有稀疏性的语音线性预测残差建模,并显式考虑加性噪声的影响以提高模型鲁棒性,从而构建完整的概率模型。然后采用变分贝叶斯方法推导模型参数的近似后验分布,最终实现噪声鲁棒的稀疏线性预测参数估计。实验结果表明,与传统算法以及近几年提出的基于l₁范数优化的稀疏线性预测算法相比,该算法在多项指标上具有优势,对环境噪声具有更好的鲁棒性,并且谱失真度更小,因而能够有效提高噪声环境下的语音质量。      

Evacuation of pedestrians from a hall by game strategy update

In this paper, a cellular automaton model considering game strategy update is proposed to study the pedestrian evacuation in a hall. Pedestrians are classified into two categories, i.e., cooperators and defectors, and they walk to an exit according to their own strategy change. The conflicts that two or three pedestrians try to occupy the same site at the same time are investigated in the Game theory model. Based on it, the relationship between the pedestrian flow rate and the evacuation time as well as the variation of cooperative proportion against evacuation time is investigated from the different initial cooperative proportions under the influence of noise. The critical value of the noise is found when there is a small number of defectors in the initial time. Moreover, the influences of the initial cooperative proportion and strength of noise on evacuation are discussed. The results show that the lower the initial cooperative proportion as well as the bigger the strength of noise, the longer the time it takes for evacuation.     

Finding the dominant energy transmission paths in statistical energy analysis

A key issue for noise, vibration and harshness purposes, when modelling the vibroacoustic behaviour of a system, is that of determining how energy is transmitted from a given source, where external energy is being input, to a target where energy is to be reduced. In many situations of practical interest, a high percentage of the transmitted energy is driven by a limited set of dominant paths. For instance, this is at the core of the existence of transmission loss regulations between dwellings. In this work, it is shown that in the case of a system modelled with statistical energy analysis (SEA), the problem of ranking dominant paths can be posed as a variation of the so-called K shortest path problem in graph theory. An algorithm for the latter is then modified and adapted to obtain the sorted set of K dominant energy transmission paths in a SEA model. A numerical example to show its potential for practical applications is included.     

Wide-aperture tomography of emissive objects

Classical tomohraphic methods for image reconstruction are constructed based on the assumption that projection data were obtained by integration over infinitely thin lines. However, a detector receiving a signal in a real experiment always has a certain width and, in a more general case, a nonzero angular aperture specified by a collimator. A modified iterative algorithm on the basis of the Neumann technique is proposed that makes it possible to take the actual system of signal recording in the process of reconstruction into account. The computational experiment is carried out on a model simulating local two-dimensional distributions of emission coefficients. Modification of the algorithm consists in the utilization of nonlinear relaxation and regularizing cubic splines. This makes it possible to stabilize the iteration process and to avoid a loss in accuracy when the noise level in projection data increases. The algorithm is also stable to certain parameters variations of the geometry of collection of projection data. The results of reconstruction by using the method proposed here are compared with the results of operation of the classical method of tomography—the method of inverse projection with filtering (Shepp-Logan filter).     

Scan-based near-field acoustical holography and partial field decomposition in the presence of noise and source level variation

Practical holography measurements of composite sources are usually performed using a multireference cross-spectral approach, and the measured sound field must be decomposed into spatially coherent partial fields before holographic projection. The formulations by which the latter approach have been implemented have not taken explicit account of the effect of additive noise on the reference signals and so have strictly been limited to the case in which noise superimposed on the reference signals is negligible. Further, when the sound field is measured by scanning a subarray over a number of patches in sequence, the decomposed partial fields can suffer from corruption in the form of a spatially distributed error resulting from source level variation from scan-to-scan. In the present work, the effects of both noise included in the reference signals, and source level variation during a scan-based measurement, on partial field decomposition are described, and an integrated procedure for simultaneously suppressing the two effects is provided. Also, the relative performance of two partial field decomposition formulations is compared, and a strategy for obtaining the best results is described. The proposed procedure has been verified by using numerical simulations and has been applied to holographic measurements of a subsonic jet.     

Monte-Carlo simulations of Ising-like materials: Multipath Metropolis vs cluster update approach

Multipath Metropolis simulation of classical Heisenberg model has been proposed in Physica A 441, 69 (2016). In contrast to the original single–path algorithm, multipath simulation approach is inherently parallel because it uses different random–walk paths that are mutually independent. To further test the reliability of multipath approach, single flip multipath simulation is tested against standard cluster update algorithm implemented with ALPS libraries in case of exactly solvable 2D Ising model.     

Inverse problem of a statistical estimation of scatterer characteristics and model examples

The inverse scattering problem involving experimental data with errors is considered in its statistical formulation. An algorithm for retrieving regularized estimates of the characteristics of spatially distributed scatterers (the phase velocity and absorption inhomogeneities) is developed. The algorithm takes into account the multiple scattering and generalizes the Wiener filtering to the nonlinear case. For weak scatterers, this nonlinear generalization is reduced to the linear Wiener filtering. The performance of the algorithm is verified by model examples simulating the operation of active acoustic tomography systems against various noise levels. The necessity of taking into account the multiple scattering and the fundamental role of the nonlinear filtering in the regularization procedure is demonstrated. It is remarkable that the noise robustness achieved in the reconstruction of strong scatterers with the optimal regularization algorithm is only slightly inferior to that achieved in the case of weak scatterers.     

An improved algorithm for noise-robust sparse linear prediction of speech

The performance of linear prediction analysis of speech deteriorates rapidly under noisy environments.To tackle this issue,an improved noise-robust sparse linear prediction algorithm is proposed.First,the linear prediction residual of speech is modeled as Student-t distribution,and the additive noise is incorporated explicitly to increase the robustness,thus a probabilistic model for sparse linear prediction of speech is built.Furthermore,variational Bayesian inference is utilized to approximate the intractable posterior distributions of the model parameters,and then the optimal linear prediction parameters are estimated robustly.The experimental results demonstrate the advantage of the developed algorithm in terms of several different metrics compared with the traditional algorithm and the l₁ norm minimization based sparse linear prediction algorithm proposed in recent years.Finally it draws to a conclusion that the proposed algorithm is more robust to noise and is able to increase the speech quality in applications.     

基于改进巴氏指标和模型更新的视觉跟踪算法

传统的Mean Shift算法采用巴氏系数度量模型与候选模型之间的统计特征相似性,但是由于背景特征的影响,有时应用巴氏指标进行匹配得到最优解的位置并不一定是目标的实际位置,在跟踪过程中可能导致目标定位出现偏差。该文提出一种改进的巴氏系数相似度指标,指标由于引入了前景/背景置信值,能够有效抑制待匹配区域中背景特征的影响,突出目标特征的权重,与原始的巴氏指标相比,明显提高了目标匹配的准确性。基于改进的巴氏指标,对目标与背景区域双模型相似度系数进行综合分析,合理地判断干扰目标匹配的原因,从而采取相应的模型更新策略。采用4段具有挑战性的视频序列对5种跟踪算法进行了测试,通过定量实验分析可知,文中算法处理1帧视频所需的平均时间为75.76 ms,实时性仅次于原始的Mean Shift跟踪算法,同时跟踪误差在5种跟踪算法中取得了最优结果。实验结果表明,该算法能够有效抑制背景干扰和避免模型漂移,在不同的复杂场景下都具有一定的鲁棒性。     

Bayesian force reconstruction with an uncertain model

This paper presents a Bayesian approach for force reconstruction which can deal with both measurement noise and model uncertainty. In particular, an uncertain model is considered for inversion in the form of a matrix of frequency response functions whose modal parameters originate from either measurements or a finite element model. The model uncertainty and the regularization parameter are jointly determined with the unknown force through Monte Carlo Markov chain methods. Bayesian credible intervals of the force are built from its posterior probability density function by taking into account the quantified model uncertainty and measurement noise. The proposed approach is illustrated and validated on numerical and experimental examples.     

Improved Quantum Evolutionary Computation Based on Particle SwarmOptimization and Two-Crossovers

A quantum evolutionary computation （QEC） algorithm with particle swarm optimization （PSO） and two-crossovers is proposed to overcome identified limitations. PSO is adopted to update the Q-bit automatically, and two-crossovers are applied to improve the convergence quality in the basic QEC model. This hybrid strategy can effectively employ both the ability to jump out of the local minima and the capacity of searching the global optimum. The performance of the proposed approach is compared with basic QEC on the standard unconstrained scalable benchmark problem that numerous hard combinatorial optimization problems can be formulated. The experimental results show that the proposed method outperforms the basic QEC quite significantly.