一种改进的金相图像处理自适应加权均值滤波方法

金相组织图像分析是显微图像分析中的一个重要内容。由于噪声的存在会对图像分析带来很大的误差,因此进行图像分析前需要对金相图像进行滤噪处理,从而使图像的细节更加突出,便于目标识别。针对标准均值滤波方法存在的不足,提出自适应加权均值滤波方法。该方法通过检测确定图像中的脉冲噪声点,并用改进的均值滤波方法对检测出的噪声点进行滤波。实验结果表明,自适应加权均值滤波能在有效地去除噪声的同时,较好地保护图像细节,较标准均值滤波具有更优良的滤波效果,而且可与更大窗口的中值滤波效果相媲美,其处理速度比大窗口的中值滤波快。     

直流开关电源传导型噪声分析

传导型噪声是开关电源的主要骚扰源,它可以通过电源线、信号线、互连线、接地导体等进行耦合.其类型有输入端噪声和超高频输出噪声.其中输入端噪声有共模和差模2种噪声.由于传导型噪声会降低直流开关电源的性能,严重时会造成直流开关电源的损坏.因此,分析其传导型噪声,并采取相应防范措施,具有现实意义.详细分析了传导型噪声产生的原因,并分析了传导型噪声模型,给出了应采取的措施.     

Stiffness-Damping Simultaneous Identification under Limited Observation

A more reliable and stable method, compared to the previous one developed by the present writers, of stiffness-damping simultaneous identification of shear-type building structures is proposed using stationary random records under limited observation. It is shown that when stationary random horizontal accelerations are recorded at the floors just above and below a specific story in a shear building model, the story stiffness and the damping ratio can be identified uniquely. The viscous damping coefficient and the material damping ratio can also be identified simultaneously in a numerical model structure. It is also shown that unfavorable effects by extraneous noises, including measurement noises, due to wind excitation, building facilities, and the motion of the occupants can be eliminated partially by using the present method. The accuracy of the present identification method is investigated through the actual records under limited observation in a base-isolated building.     

基于多帧平均和小波分析的X射线影像图像去噪处理

分析了低强度X射线影像系统图像噪声来源及特点，以此提出了处理该图像噪声的“多帧平均滤波 小波变换滤波”的复合算法。通过对峰值信噪比的计算表明，该方法明显优于任何单一算法，取得较好效果。     

基于子波变换的一种信号除噪新算法研究

针对Donoho去噪算法中阈值难以确定，通过分析白噪声的能量分布在于波变换域随尺度变化的规律，提出了一种采用子波变换来消除信号噪声的新算法。通过对含噪信号的求导运算实现信号与噪声在于波变换域约分离，以达到消除噪声的目的，算法的特点在于能够自动给出信号中噪声的阈值。经过测试，该算法在消除信号中的白噪声时，能够取得比较理想的效果。     

Structurally noise resistant classifier for multi-modal person verification

In this letter we propose a piece-wise linear (PL) classifier for use as the decision stage in a two-modal verification system, comprised of a face and a speech expert. The classifier utilizes a fixed decision boundary that has been specifically designed to account for the effects of noisy audio conditions. Experimental results on the VidTIMIT database show that in clean conditions, the proposed classifier is outperformed by a traditional weighted summation decision stage (using both fixed and adaptive weights). Using white Gaussian noise to corrupt the audio data resulted in the PL classifier obtaining better performance than the fixed approach and similar performance to the adaptive approach. Using a more realistic noise type, namely “operations room” noise from the NOISEX-92 corpus, resulted in the PL classifier obtaining better performance than both the fixed and adaptive approaches. The better results in this case stem from the PL classifier not making a direct assumption about the type of noise that causes the mismatch between training and testing conditions (unlike the adaptive approach). Moreover, the PL classifier has the advantage of having a fixed (non-adaptive, thus simpler) structure.     

Coping with Uncertainty in Map Learning

In many applications in mobile robotics, it is important for a robot to explore its environment in order to construct a representation of space useful for guiding movement. We refer to such a representation as a map, and the process of constructing a map from a set of measurements as map learning. In this paper, we develop a framework for describing map-learning problems in which the measurements taken by the robot are subject to known errors. We investigate approaches to learning maps under such conditions based on Valiant's probably approximately correct learning model. We focus on the problem of coping with accumulated error in combining local measurements to make global inferences. In one approach, the effects of accumulated error are eliminated by the use of local sensing methods that never mislead but occasionally fail to produce an answer. In another approach, the effects of accumulated error are reduced to acceptable levels by repeated exploration of the area to be learned. We also suggest some insights into why certain existing techniques for map learning perform as well as they do. The learning problems explored in this paper are quite different from most of the classification and boolean-function learning problems appearing in the literature. The methods described, while specific to map learning, suggest directions to take in tackling other learning problems.     

汽轮发电机电磁振动与噪声分析

由电机的谐波磁场分析电磁振动与噪声的起因，危害及消除电磁噪声的有效措施。     

Physiological cost of energy-equivalent noise exposures with a rating level of 85 dB(A):: Hearing threshold shifts associated with energetically negligible continuous and impulse noise

In industry continuous or impulse noise does not occur exclusively; rather it is a combination of both. If low-level continuous noise or impulse noise (below 120 dB) is added to an already existing high-level continuous noise this often numerically causes no essential increase in the rating level. Yet, it cannot be expected that also aural strain of these exposures is always negligible. Therefore, in a cross-over test series, ten male subjects (Ss) were exposed to white noise of 94 dB(A) for 1 h (TS I), energy-equivalent to an 8 h-rating level L_Ard of 85 dB(A). In a second test series (TS II) the same exposure was combined with 900 energetically negligible 5-ms impulses with a noise level of 113 dB(A) which increased the rating level by only 0.4 dB. The noise exposure of TS I and TS II was followed by an idealized resting phase in a soundproof cabin. In a third test series (TS III) the continuous noise of 94 dB(A) / 1 h was followed by 3 h of white noise at 70 dB(A). Such an additional load increases the L_Ard by merely 0.1 dB to 85.1 dB(A). In all three test series, the noise-induced temporary threshold shift (TTS₂) and its restitution were measured. The continuous noise exposure of 94 dB(A) for 1 h was associated with a TTS₂ of around 20 dB which disappeared completely after about 2 h. The additional impulse noise caused a small increase in the TTS₂ and a prolongation of the restitution time. The maximum mean temporary threshold shift for the group increased only slightly (from 22.5 to 25.9 dB, which nevertheless can be statistically proven at a significance level of p 0.99). Yet, more importantly, the restitution time increased from 126 to 175 min, i. e. 3 h, which can be statistically proven at a significance level of p0.95. The TTS₂-values of TS III did not differ significantly from those resulting from TS I. That was expected as the conditions up to that point in time were identical. But due to the additional subsequent exposure, the mean restitution time increased considerably from 126 min up to 240 min (4 h). The mean total physiological cost represented by the integrated restitution temporary threshold shift (IRTTS) increased in TS II by approximately 40% and in TS III even by 140%.

Relevance to industry

The results of the study show that levels of noise which have no influence on the rating level which traditionally is calculated according to the energy-equivalence principle are often of great importance, as they can lead to considerably prolonged restitution times. Therefore, the purely energy-equivalent determination of the rating level of both impulse noise and low sound levels can lead to an underestimation of latent problems so that over time a reversible TTS can evolve into a permanent threshold shift. The results are also of importance for the acoustic design of break rooms for noise-exposed workers. There should be conditions that allow an undisturbed restitution of hearing.     

基于小波域的图像噪声估计新方法

提出一种基于小波域区域分割的估计图像噪声的新疗法。该方法利用图像的小波高频系数，在提出图像平滑区域的基础上，准确地估计图像高斯噪声的标准方差、由于考虑了图像的局部信息，因此该方法优于传统的估计方法。用于多幅实验图像的结果表明：在图像受噪声比较小或图像含高频信息较丰富时，该方法比传统疗法更准确。