New results on PWARX model identification based on clustering approach

This paper deals with the problem of piecewise auto regressive systems with exogenous input(PWARX) model identification based on clustering solution. This problem involves both the estimation of the parameters of the affine sub-models and the hyper planes defining the partitions of the state-input regression. The existing identification methods present three main drawbacks which limit its effectiveness. First, most of them may converge to local minima in the case of poor initializations because they are based on the optimization using nonlinear criteria. Second, they use simple and ineffective techniques to remove outliers. Third, most of them assume that the number of sub-models is known a priori. To overcome these drawbacks, we suggest the use of the density-based spatial clustering of applications with noise(DBSCAN) algorithm. The results presented in this paper illustrate the performance of our methods in comparison with the existing approach. An application of the developed approach to an olive oil esterification reactor is also proposed in order to validate the simulation results.     

A minimum variance distortionless response filter based on the bifrequency spectrum for single-channel noise reduction

This paper deals with the problem of single-channel noise reduction in the short-time Fourier transform (STFT) domain. Many algorithms have been developed to solve this important problem, most of which generally assume that the STFT coefficients in different frequency bands are uncorrelated, so the noise reduction is achieved by applying a gain function to the STFT of the noisy speech in each frequency band. However, this assumption is not accurate and the STFT coefficients of speech signals between neighboring frequency bands are correlated in practice due to the use of small lengths of the fast Fourier transform (FFT) and overlap add/save techniques in implementation. This paper formulates the noise reduction problem by taking into account the interband correlation using the so-called bifrequency spectrum. Based on this formulation, a single-channel minimum variance distortionless response (MVDR) filter is derived, which is shown to be able to significantly improve the signal-to-noise ratio (SNR) and meanwhile maintain the desired speech not much distorted. Simulations are presented to justify the claimed merits of the developed MVDR filter.     

机床噪声及降低噪声的途径

分析了机床噪声的起因，并提出了相应的降噪措施。     

哈尔滨市主要道路交通噪声污染评价

针对哈尔滨市的主要道路交通噪声进行监测,统计并分析整体污染情况、噪声分级状况及空间分布特征。为哈尔滨市今后的道路交通噪声治理工作提供有利的依据,具有较好的实际应用价值。     

基于图像噪声分析的计算机生成图像检测算法

计算机生成图像与自然图像在物理生成机理上的差异性导致其在噪声分布上具有明显的区别,据此提出一种基于图像噪声分析的计算机生成图像检测新方法。首先,用小波隐马尔可夫(MMT)模型对图像噪声进行预处理;接着,基于最大似然估计方法估测图像参考模式噪声;最后,在提取图像模式噪声统计距及其与参考模式噪声相关差值基础上,利用支持向量机(SVM)分类器进行鉴别。实验结果显示,较已有的典型算法,该算法具有更好的检测率。     

汽车车内行驶噪声测试的阶次跟踪法

应用PULSE测试分析系统中阶次跟踪法,测试分析某型乘用车车内四个位置的噪声水平随发动机转速变化的关系.揭示了该型汽车在测试工况下,不同的转速对车内噪声的贡献规律.为降低车内行驶噪声和振动响应提供了重要的分析依据和参考.     

局部同质性测度的平滑式图像噪声估计

提出一种基于局部同质性测度的静止图像平滑式加性噪声估计方法。为增强图像中各像元的同质性测度值差异,以柯西分布函数作为核函数,提出一种图像同质性测度的新方法。为提取图像的真实边缘,采用各向异性扩散平滑算法。在去除包含图像真实边缘的子图像的基础上,根据所设定的同质性测度阈值,选取具有较高同质性的子图像,并计算这些子图像的标准方差。最后。将这些各子图像标准方差的中值作为图像噪声的估计值。本文所提出的图像噪声估计算法可自动化实现,无需任何人工参与。试验结果表明,与其它滤波式噪声估计方法比较,本文所提出的方法具有更高的估计精度和更好的鲁棒性。     

高职高专噪声控制技术课程教学模式改革探析

高职高专噪声控制技术课程教学中不同程度地存在教材内容陈旧、教学方法单一、实践教学环节较少等方面的问题。文章从教材建设、教学方法和实践教学等几个方面提出了相应的解决措施,从而整体优化高职高专噪声控制技术课程教学模式,保证教学质量。     

数据挖掘中的预处理技术研究

分析了数据预处理提出的背景,归纳了常见的数据预处理方法,分析比较了不同方法的特点。     

基于图像邻域特性的高斯噪声去噪

针对传统高斯噪声去噪算法残余噪声较大的不足,根据噪声对图像视觉的影响,提出了基于像素邻域相关性的去噪算法.首先运用邻域像素的连续性判断像素点是否位于平滑区内;其次对非平滑区根据边缘和纹理的局部连续性运用形态学提取图像边缘和纹理进而定位噪声点;最后对平滑区内的噪声运用自适应邻域进行去噪处理,对非平滑区的噪声仅利用非平滑区的邻域进行平滑,实现了对高斯噪声先定位再去噪.经实验结果验证:与传统方法相比,该算法较好地抑制了图像平滑区内噪声,提高了去噪后图像的视觉效果.