有源降噪声耳罩的发展现状和前景

阐述了有源消声耳罩的特点、历史和现状;探讨了有源耳罩实现技术及产品应用;分析了有源耳罩工程应用中的存在问题和有源消声耳罩的发展前景。     

前馈结构自适应有源降噪耳罩误差通道建模方法

稳定性是设计自适应有源降噪声罩过程中所必须考虑的首要问题，而误差通道模型的建模精度直接影响着系统的稳定。本文根据前馈结构有源降噪耳罩的特点，提出了一种简单有效的建模模型，与经典的建模模型相比。该模型能在强干扰噪声环境下获得较高的建模精度。     

纺织丝线张力信号实时消噪技术的研究

提出了基于提升框架的小波变换算法在丝线张力信号实时消噪过程中的应用,给出了该算法的流程图,通过对仿真信号与实测信号的消噪试验,验证了该算法的有效性,该算法可以应用于对实际信号的消噪处理,能够满足对张力信号处理的实时性要求,且易于实现。     

一种综合小波变换的心电信号消噪算法

针对心电信号中混有的基线漂移、工频干扰、肌电干扰等噪声,比较了适于心电信号的4种基于小波变换的心电信号消噪算法,结合消噪后的信噪比和信号失真度,提出一种综合小波变换的心电信号消噪算法.该算法先使用小波分解法消除心电信号中的基线漂移,再利用模极大值法消除工频干扰、肌电干扰等噪声.并且运用该算法对MIT-BIH心律失常数据库中的含有多种噪声的心电数据进行了仿真与实验,结果表明噪声被有效地消除并且失真度较小,可满足临床分析与诊断对心电波形的要求.     

基于蚁群算法优化选取阈值的EMD消噪方法

为了改进经验模式分解(EMD)算法的消噪性能,在传统EMD消噪分解的基础上,参照小波阈值的消噪方法,提出了一种基于自适应阈值的EMD消噪方法。首先,建立去噪阈值和均方误差之间的对应函数,在所选阈值保证均方误差最小的前提下,利用具有较好全局搜索性的蚁群算法,根据建立的函数搜索阈值,克服了传统方法中硬阈值和软阈值固定选取的缺陷,实现了最优阈值的选取。仿真信号分析和实际轴承故障信号分析表明,该方法与传统的EMD消噪方法、软硬阈值分析方法相比,消噪效果更加明显。     

基于数学形态学的汽车轮毂单元振动信号消噪算法研究

针对轮毂单元故障信号中的脉冲信号往往被背景噪声淹没的问题,将数学形态学滤波技术应用到一维振动信号的消噪中,并与3σ规则结合,提出了基于数学形态学运算和软阈值的振动信号消噪方法.首先,采用形态滤波对染噪的轮毂单元信号进行了过滤,并提取了峰谷信号;然后,采用3σ规则对峰谷信号进行了阈值处理,并将形态滤波结果与阈值处理后的峰谷信号相加,作为最终消噪结果;最后,对其进行了频谱分析以提取特征,并在Matlab中对该算法仿真试验进行了有效性评价以及轮毂单元振动信号的消噪试验.研究结果表明,该算法不仅运算简便,并且在最大限度抑制噪声的同时保留了绝大部分的有用信号,取得了较好的消噪效果,故障信号识别率提高了20％左右.     

基于LabVIEW的虚拟小波消噪仪设计

介绍了利用LabVIEW和MATLAB混合编程来实现虚拟小波消噪的方法,利用计算机图形化编程语言LabVIEW设计了一个虚拟小波消噪仪,并给出了仿真结果.该设计具有较好的人机界面功能.     

基于粒子群优化的形态学滤波器消噪方法

为了有效地消除信号的噪声,提出了基于粒子群优化的数学形态滤波器构造方法。首先,根据数学形态学算法的特性构造了形态学滤波器;然后,对于形态学滤波运算中的重要参数形态结构算子,采用具有全局优化性能的粒子群算法自适应选取,以最大信噪比作为整个优化过程的判定标准,从而实现了最优滤波器的构造;最后,通过仿真实验和轴承故障信号的分析表明,该形态学滤波器能够实现较好的滤波效果,可以有效地对机械设备的故障信号进行消噪。     

小波消噪在振动信号处理中的应用

信噪分离是小波分析中的一个有效的信号检测方法。本文在阐述了小波分析及消噪的小波理论的基础上,给出了从噪声污染信号中恢复原信号的实例,并与傅立叶分析消噪进行了比较,结果表明对于非平稳振动信号小波消噪的效果明显优于傅立叶变换。     

基于小波包变换和小波阈值消噪的语音特征提取

为了实现强噪声背景下语音信号的特征提取,根据小波变换的多分辨率特性,以及与人耳耳蜗滤渡相一致的特性,利用小波包变换,在各语音特征频率段上,提取出包含丰富的非平稳信息的语音特征;并在小波包分解去噪的基础上,构造了模糊阈值函数,利用小波模糊阈值去噪,得到了信噪比较高的语音信号.研究结果表明,小波包变换和小波阈值去噪,较好地消除了强噪声背景下的噪声,并有效地提取出了语音信号特征.     

基于归一化LMS算法的光纤陀螺降噪技术研究

惯导系统对高精度光纤陀螺的输出随机噪声水平要求很高,尤其是角度随机游走将直接影响惯性导航系统的精度.为能有效降低光纤陀螺角度随机游走噪声,分析角度随机游走产生的机理,提出了基于NLMS算法的前向线性预测滤波器(FLP)的降噪方法,该方法比标准LMS算法有更快的收敛速度和良好的动态跟踪特性.用Allan方差法对真实数据滤波前后的噪声水平进行分析比较,结果证明该方法使得光纤陀螺的性能得到了明显的提高,而且该算法简单,工程上易实现,能够有效的提高陀螺的信噪比和惯导系统的对准精度.     

A genetic algorithm for active noise control actuator positioning

Active noise control systems (ANCS) have been used extensively in several diverse applications in order to reduce the sound levels within a confined space. Several control schemes have been proposed, all of which rely on the same basic idea, namely that of introducing an artificial secondary sound field in order to partially cancel the primary sound field. This secondary field is created by using a number of actuators (usually speakers), placed in several locations within the space of interest. In our particular case of interest, the ANCS under consideration is an interior one. The overall ANCS comprises a set of sensors, a control system, and the actuators. Clearly, the optimal design of all three components needs to be addressed when designing an ANCS. In this paper, the problem of deciding to which specific locations (out of several possible ones) the actuators will be located for optimal performance of the ANCS is addressed. Specifically, a genetic algorithm is designed for this problem. The algorithm was implemented and tested against a simple greedy approach. Results indicate the efficiency of the Genetic Algorithm for the problem at hand.     

LMS algorithm for active noise control with improved gradient estimate

Most existed LMS algorithms have noisy gradient estimate since they use instantaneous square error to estimate ensemble average of the square errors. In this paper, a new type of LMS algorithm is developed for active noise control. The gradient estimate in the proposed algorithm is improved by using the weighted time average instead of the instantaneous value to approximate the ensemble average. Compared to the filtered-x LMS algorithm, the proposed algorithm is shown to have many improved convergence properties. Computer simulations are given to support the theoretical analysis.     

Adaptive RSOV filter using the FELMS algorithm for nonlinear active noise control systems

This paper presents a recursive second-order Volterra (RSOV) filter to solve the problems of signal saturation and other nonlinear distortions that occur in nonlinear active noise control systems (NANC) used for actual applications. Since this nonlinear filter based on an infinite impulse response (IIR) filter structure can model higher than second-order and third-order nonlinearities for systems where the nonlinearities are harmonically related, the RSOV filter is more effective in NANC systems with either a linear secondary path (LSP) or a nonlinear secondary path (NSP). Simulation results clearly show that the RSOV adaptive filter using the multichannel structure filtered-error least mean square (FELMS) algorithm can further greatly reduce the computational burdens and is more suitable to eliminate nonlinear distortions in NANC systems than a SOV filter, a bilinear filter and a third-order Volterra (TOV) filter.     

Modified-filtered-u LMS algorithm for active noise control and its application to a short acoustic duct

This paper presents a new adaptive algorithm for active noise control (ANC) that can be effectively applicable to a short acoustic duct, such as the intake system of an automobile engine, where the stability and fast convergence of the ANC system is particularly important. The new algorithm, called the modified-filtered-u LMS algorithm (MFU-LMS), is developed based on the recursive filtered-u LMS algorithm (FU-LMS) incorporating the simple hyper-stable adaptive recursive filter (SHARF) to ensure the control stability and the variable step size to enhance the convergence rate. The MFU-LMS algorithm is implemented by purely experimental ways, and is applied to active control of noise in a short acoustic duct, and is validated using two experimental cases of which the primary noise sources are a sinusoidal signal embedded in white noise and a chirp signal. The experimental results demonstrate that the proposed MFU-LMS algorithm gives a considerably better performance than other conventional algorithms, such as the filtered-x LMS (FX-LMS) and the FU-LMS algorithms.     

Active vibration control of flexible cantilever beam using piezo actuator and Filtered-X LMS algorithm

This paper presents the active vibration control of a flexible cantilever beam. The cantilever beam was excited by steady-state sinusoidal and white noise point forces. The vibrational control system was implemented using one piezo ceramic actuator bonded on the beam and the adaptive controller based on the Filtered-X LMS algorithm. Control results indicated that a considerable vibrational reduction could be achieved in a few seconds. Experimental results, demonstrate the feasibility of active vibration control of the flexible cantilever beam based on piezo ceramic actuator and the Filtered-X LMS algorithm.     

New analytical model for the filtered-x least mean squares algorithm verified through active noise control experiment

This paper presents a new analytical model for the behavior of the Filtered-x LMS adaptive algorithm when applied to active noise control (ANC) in a finite duct. The new model is derived using a stochastic differential equation (SDE) approach. The theoretical predictions are verified through experimental results. The real-time system has been implemented using only I/O cards and a general purpose computer. Automation of the experimental setup allows the practical verification of the theoretical statistical model using Monte Carlo techniques. It is verified that the behavior of the algorithm is accurately predicted by the theoretical model.     

Adaptive frequency tuner for active narrowband noise control systems

The reference frequency error can significantly degrade the performance of the narrowband active noise control (ANC) system. This paper investigates the relationship between the reference frequency error and the performance of the narrowband ANC system, and presents an adaptive frequency tuner which can reduce the reference frequency error and improve the performance of the system. Effectiveness of the adaptive frequency tuner is validated by simulation results using experimental transfer functions.     

一种稳健的窄带反馈型主动噪声控制系统

传统窄带反馈型主动噪声控制系统采用并型自适应滤波器结构,用于合成参考信号时,仍存在参考信号合成质量较差、 滤波器权值的初值设置难度高、应对非平稳窄带噪声的能力不足等问题。 为解决以上问题,通过引入高效的串并型自适应滤波 器结构来合成参考信号,提出新型的窄带反馈型主动噪声控制系统,改善参考信号合成质量,进而提升系统的整体降噪性能。 新型系统不仅有效解决了传统系统存在的参考信号合成质量较差的问题,还降低了滤波器权值的初值设置复杂度,同时还提升 了系统应对非平稳窄带噪声的能力。 实验结果表明,新型系统较传统系统的前后两部分的降噪量分别提升了 7. 89 和 9. 18 dB。 仿真和实验结果共同验证了提出的系统较传统系统具有更强的稳健性和降噪性能。     

Immune algorithm based active PID control for structure systems

An immune algorithm is a kind of evolutional computation strategies, which is developed in the basis of a real immune mechanism in the human body. Recently, scientific or engineering applications using this scheme are remarkably increased due to its significant ability in terms of adaptation and robustness for external disturbances. Particularly, this algorithm is efficient to search optimal parameters against complicated dynamic systems with uncertainty and perturbation. In this paper, we investigate an immune algorithm embedded Proportional Integral Derivate (called I-PID) control, in which an optimal parameter vector of the controller is determined offline by using a cell-mediated immune response of the immunized mechanism. For evaluation, we apply the proposed control to mitigation of vibrations for nonlinear structural systems, cased by external environment load such as winds and earthquakes. Comparing to traditional controls under same simulation scenarios, we demonstrate the innovation control is superior especially in robustness aspect.