综合应用地震属性、测井数据反演储层参数

本文研究了样点地震属性的特征提取，利用BP神经网络建立测井声波时差和地震属性的非线性映射关系。采用距离加权平均对研究区所有井的反演结果进行外推。为了提高分辨率和计算结果的准确性，详细分析、研究了适合于地震信号分解和重构的小波，以及测井声波时差和地震波阻抗与层界面的关系。最后，对反演的声波时差资料进行界面影响校正，进而计算出较符合实际情况的孔隙率参数。经已知井和新钻井验证，计算结果和实际情况相一致，效果令人满意。     

小波分析及其在时变滤波和信号分离中的应用

利用小波变换具有的多时频分辨率特性，对地震勘探和测井信号这类非平衡信号进行分析，可以充分利用信号的时间－频率特性。文中介绍了小波变换的基本概念及其在地震勘探信号的时变滤波和声波全波列测井信号的信号分离中的应用，实例表明，小波变换在地球物理信号中具有广阔的应用前景。图５参７     

13SiMnNiCrMoV钢切口疲劳裂纹萌生门槛值

本文研究在13SiMnNiCrMoV 结构钢中用尖裂纹的应力强度因子来反映三点弯曲缺口试样的疲劳裂纹萌生规律。当 R=0.1,f=100Hz,试样尺寸 B×W×L=12.5×25×117mm 时,缺口名义应力幅的门槛值为Δσ_(th)=3315ρ~(0.352) MPa,0.5mm≤ρ≤5mmΔK_(th)=250ρ~(0.352) MPam~(1/2)门槛值与ρ有关。同时得到缺口裂纹萌生的循环次数 N_i 与名义应力幅Δσ、缺口曲率半径ρ的定量关系是N_i=3.98×10~(22)ρ~(1.62)/Δσ~(4.85),N_i≤10~(5.5)并讨论了(ΔK_1/ρ~(1/2))_(th)与ρ无关的结论。     

稀疏气泡流动的粒子跟踪测速技术研究

采用动态阈值技术实现了对气泡图像的正确分割，利用灰度加权方法提高了气泡颗粒的定位精度。根据稀疏气泡流动图像的特点，提出了一种新的基于相关的粒子跟踪测速算法（2-HPTV）。通过进行两时刻中围绕待研究气泡所取的两个小的诊断窗口的相关运算，成功实现了气泡颗粒的轨迹追踪。实验表明本文算法较传统的PIV技术在测速精度上有很大程度的提高，与4帧粒子跟踪测速算法相比，降低了对摄像机帧频的要求，具有较强的实用性。     

On denoising and compression of DNA microarray images

We address the problems of noise and huge data sizes in microarray images. First, we propose a mixture model for describing the statistical and structural properties of microarray images. Then, based on the microarray image model, we present methods for denoising and for compressing microarray images. The denoising method is based on a variant of the translation-invariant wavelet transform. The compression method introduces the notion of approximate contexts (rather than traditional exact contexts) in modeling the symbol probabilities in a microarray image. This inexact context modeling approach is important in dealing with the noisy nature of microarray images. Using the proposed denoising and compression methods, we describe a near-lossless compression scheme suitable for microarray images. Results on both denoising and compression are included, which show the performance of the proposed methods. Further experiments using the results of the proposed near-lossless compression scheme in gene clustering using cell-cycle microarray data for S. cerevisiae showed a general improvement in the clustering performance, when compared with using the original data. This provides an indirect validation of the effectiveness of the proposed denoising method.     

一种新的信号去噪方法及在激光信号中的应用

小波去噪是信号去噪的主要方法之一,目前的研究主要集中于如何选取阈值使去噪达到较好的效果,其中Donoho的阈值方法应用广泛.与传统双通道小波变换相比,三通道小波变换具有很多优势.基于这一理论,结合非参数自适应估计,推导出了一种适用于三通道小波域的自适应的信号去噪方法.实验证明,与三通道小波域中基于Donoho方法的去噪相比,该方法可以更好地保护细节以及提高重建信号的信噪比,在激光超声信号处理中也表现出良好的性能.     

多小波变换及其在信号滤波中的应用

多小波拥有对称性、正交性、有限支撑等这些信号处理中十分重要的特性,从而弥补了单小波的不足,具有更为广阔的应用前景.本文从滤波的角度,重点分析并设计了多小波预滤波与多小波分解系数处理方法.仿真表明多小波分析方法的优越性.     

A New Matlab De-noising Algorithm for Signal Extraction

The goal of a de-noising algorithm is to reconstruct a signal from its noise-corrupted observations. Perfect reconstruction is seldom possible and performance is measured under a given fidelity criterion. In a recent work, the authors addressed a new Matlab algorithm for de-noising. A key method of the algorithm is selecting an optimal basis from a library of wavelet bases for ideal de-noising. The algorithm with an optimal basis from a library of wavelet bases for de-noising was created through making use of Matlab＇s Wavelet Toolbox. The experimental results show that the new algorithm is efficient in signal de-nosing.     

Use of a neural network to link AFM etch patterns to plasma parameters

Plasmas play a critical role in depositing thin films or etching fine patterns while manufacturing integrated circuits. A new model for plasma diagnosis is presented. This was accomplished by linking atomic force microscopy (AFM) to plasma parameters using a neural network. Experimental AFM data were collected during the etching of silicon oxynitride films in C₂F₆ inductively coupled plasma. Surface roughness of etched patterns was characterized by means of discrete wavelet transformation. This led to the construction of three vertical (type I), diagonal (type II), and horizontal (type III) wavelet coefficient-based models. The performance of diagnosis models was evaluated in terms of the prediction and recognition accuracies. Both accuracies were optimized as a function of the number of hidden neurons. Comparisons revealed that the type I model yielded the largest recognition and the smallest prediction error. This was demonstrated even under stricter monitoring conditions. More improved diagnosis is expected by enhancing AFM resolution.     

A novel optical burst switching scheme—logical cascaded private subnet with start wavelength assignment policy

Many recent studies have convincingly demonstrated that network traffic exhibits a noticeable self-similar nature, which has a considerable impact on network performance, and most studies of optical burst switching (OBS) networks are under a fundamental assumption that full wavelength conversion is available throughout the network. In practice, however, economic and technical considerations are likely to dictate a more limited and sparse deployment of wavelength converters in the optical network. Therefore, we present a novel scheme for OBS networks, called logical cascaded private subnet (LCPN) with start wavelength assignment policy. We define the concept of canoe relative to cluster in self-similar traffic, and introduce a new device named payload segregator at the edge node as a gateway to the core node in OBS Networks. According to the changes in the edge node framework, we put forward the concept of cluster private subnet and canoe private subnet in the core node correspondingly. A new start wavelength assignment policy is proposed for the absence of (full) wavelength conversion capabilities in the core node of OBS Networks. The performance study indicates that, our new scheme is robust under self-similar traffic and wavelength continuity constraint.