Three-Dimensional Density Distribution and Seismic Activity along the Guxiang–Tongmai Segment of the Jiali Fault,Tibet

The Guxiang-Tongmai segment of the Jiali fault is situated northeast of the Namche Barwa Syntaxis in northeastern Tibet.It is one of the most active strike-slip faults near the syntaxis and plays a pivotal role in the examination of seismic activity within the eastern Himalayan Syntaxis.New study in the research region has yielded a 1:200000 gravity dataset covering an area 1500 km^(2).Using wavelet transform multiscale decomposition,scratch analysis techniques,and 3D gravity inversion methods,gravity anomalies,fault distributions,and density structures were determined across various scales.Through the integration of our new gravity data with other geophysical and geological information,our findings demonstrate substantial variations in the overall crustal density within the region,with the fault distribution closely linked to these density fluctuations.Disparities in stratigraphic density are important causes of variations in the capacity of geological formations to endure regional tectonic stress.Earthquakes are predominantly concentrated within the density transition zone and are primarily situated in regions of elevated density.The hanging wall stress within the Guxiang-Tongmai segment of the Jiali fault exhibits a notable concentration,marked by pronounced anisotropy,and is positioned within the density differential zone,which is prone to earthquakes.

Multiple description scalable video coding based on 3D lifted wavelet transform

In this work, a new method to deal with the unconnected pixels in motion compensated temporal filtering (MCTF) is presented, which is designed to improve the performance of 3D lifted wavelet coding. Furthermore, multiple description scalable coding (MDSC) is investigated, and novel MDSC schemes based on 3D wavelet coding are proposed, using the lifting imple- mentation of temporal filtering. The proposed MDSC schemes can avoid the mismatch problem in multiple description video coding, and have high scalability and robustness of video transmission. Experimental results showed that the proposed schemes are feasible and adequately effective.

3-D Wavelet Transform for Very Low Bit-Rate Video Coding

A new improved Goh's 3 D wavelet transform(WT) coding scheme is presented in this paper. The new scheme has great advantages including a simple code structure, low computation cost and good performance in PSNR, compression ratios and visual quality of reconstructions, when compared to the other existing 3 D WT coding methods and the 2 D WT based coding methods. The new 3 D WT coding scheme is suitable for very low bit rate video coding.

Very Low Bit Rate Video Coding Using Motion Compensated 3 D Wavelet Transform

A new motion compensated 3 D wavelet transform (MC 3DWT) video coding scheme is presented in this paper. The new coding scheme has a good performance in average PSNR, compression ratio and visual quality of reconstructions compared with the existing 3 D wavelet transform (3DWT) coding methods and motion compensated 2 D wavelet transform (MC WT) coding method. The new MC 3DWT coding scheme is suitable for very low bit rate video coding.

AN EFFICIENT 3-DIMENSIONAL DISCRETE WAVELET TRANSFORM ARCHITECTURE FOR VIDEO PROCESSING APPLICATION

This paper presents an optimized 3-D Discrete Wavelet Transform (3-DDWT) architecture. 1-DDWT employed for the design of 3-DDWT architecture uses reduced lifting scheme approach. Further the architecture is optimized by applying block enabling technique, scaling, and rounding of the filter coefficients. The proposed architecture uses biorthogonal (9/7) wavelet filter. The architecture is modeled using Verilog HDL, simulated using ModelSim, synthesized using Xilinx ISE and finally implemented on Virtex-5 FPGA. The proposed 3-DDWT architecture has slice register utilization of 5%, operating frequency of 396 MHz and a power consumption of 0.45 W.

Defects Recognition of 3D Braided Composite Based on Dual-Tree Complex Wavelet Packet Transform

Textile-reinforced composites,due to their excellent highstrength-to-low-mass ratio, provide promising alternatives to conventional structural materials in many high-tech sectors. 3D braided composites are a kind of advanced composites reinforced with 3D braided fabrics; the complex nature of 3D braided composites makes the evaluation of the quality of the product very difficult. In this investigation,a defect recognition platform for 3D braided composites evaluation was constructed based on dual-tree complex wavelet packet transform( DT-CWPT) and backpropagation( BP) neural networks. The defects in 3D braided composite materials were probed and detected by an ultrasonic sensing system. DT-CWPT method was used to analyze the ultrasonic scanning pulse signals,and the feature vectors of these signals were extracted into the BP neural networks as samples. The type of defects was identified and recognized with the characteristic ultrasonic wave spectra. The position of defects for the test samples can be determined at the same time. This method would have great potential to evaluate the quality of 3D braided composites.

AN ALGORITHM FOR CODING VIDEO SIGNAL BASED ON 3-D WAVELET TRANSFORMATION

This paper presents an algorithm for coding video signal based on 3-D wavelet transformation. When the frame order t of a video signal is replaced by order 2, the video signal can be looked as a block in 3-D space. After splitting the block into smaller sub-blocks, imitate the method of 2-D wavelet transformation for images, we can transform the sub-blocks with 3-D wavelet. Most of video signal energy is in the decomposed low-frequency sub-bands. These sub-bands affect the visual quality of the video signal most. Quantizing different sub-bands with different precision and then entropy encoding each sub-band, we can eliminate inter- and intra-frame redundancy of the video signal and compress data. Our simulation experiments show that this algorithm can achieve very good result.

Fluorometric Discrimination Technique of Phytoplankton Population Based on Wavelet Analysis

The discrete excitation-emission-matrix fluorescence spectra (EEMS) at 12 excitation wavelengths (400, 430, 450, 460, 470, 490, 500, 510, 525, 550, 570, and 590 nm) and emission wavelengths ranging from 600-750 nm were determined for 43 phytoplankton species. A two-rank fluorescence spectra database was established by wavelet analysis and a fluorometric discrimination technique for determining phytoplankton population was developed. For laboratory simulatively mixed samples, the samples mixed from 43 algal species (the algae of one division accounted for 25%, 50%, 75%, 85%, and 100% of the gross biomass, respectively), the average discrimination rates at the level of division were 65.0%, 87.5%, 98.6%, 99.0%, and 99.1%, with average relative contents of 18.9%, 44.5%, 68.9%, 73.4%, and 82.9%, respectively; the samples mixed from 32 red tide algal species (the dominant species accounted for 60%, 70%, 80%, 90%, and 100% of the gross biomass, respectively), the average correct discrimination rates of the dominant species at the level of genus were 63.3%, 74.2%, 78.8%, 83.4%, and 79.4%, respectively. For the 81 laboratory mixed samples with the dominant species accounting for 75% of the gross biomass (chlorophyll), the discrimination rates of the dominant species were 95.1% and 72.8% at the level of division and genus, respectively. For the 12 samples collected from the mesocosm experiment in Maidao Bay of Qingdao in August 2007, the dominant species of the 11 samples were recognized at the division level and the dominant species of four of the five samples in which the dominant species accounted for more than 80% of the gross biomass were discriminated at the genus level; for the 12 samples obtained from Jiaozhou Bay in August 2007, the dominant species of all the 12 samples were recognized at the division level. The technique can be directly applied to fluorescence spectrophotometers and to the developing of an in situ algae fluorescence auto-analyzer for phytoplankton population.

Wavelets and Continuous Wavelet Transform for Autostereoscopic Multiview Images

Recently, the reference functions for the synthesis and analysis of the autostereoscopic multiview and integral images in three-dimensional displays were introduced. In the current paper, we propose the wavelets to analyze such images. The wavelets are built on these reference functions as on the scaling functions of the wavelet analysis. The continuous wavelet transform was successfully applied to the testing wireframe binary objects. The restored locations correspond to the structure of the testing wireframe binary objects.

Study on Power Transformers Fault Diagnosis Based on Wavelet Neural Network and D-S Evidence Theory

>Transformer faults are quite complicated phenomena and can occur due to a variety of reasons.There have been several methods for transformer fault synthetic diagnosis,but each of them has its own limitations in real fault diagnosis applications.In order to overcome those shortcomings in the existing methods,a new transformer fault diagnosis method based on a wavelet neural network optimized by adaptive genetic algorithm(AGA)and an improved D-S evidence theory fusion technique is proposed in this paper.The proposed method combines the oil chromatogram data and the off-line electrical test data of transformers to carry out fault diagnosis.Based on the fusion mechanism of D-S evidence theory,the comprehensive reliability of evidence is constructed by considering the evidence importance,the outputs of the neural network and the expert experience.The new method increases the objectivity of the basic probability assignment(BPA)and reduces the basic probability assigned for uncertain and unimportant information.The case study results of using the proposed method show that it has a good performance of fault diagnosis for transformers.

基于EWT与改进D-S证据理论的燃气调压器故障诊断

针对传统单一燃气调压器故障诊断模型存在诊断精度较低和结果误判别率高等问题,提出一种经验小波变换(EWT)与改进D-S证据理论结合的故障诊断方法,对燃气调压器故障状态进行诊断。使用EWT对传感器采集数据进行预处理并计算各分量能量熵,将其作为以广义回归神经网络、Elman神经网络和灰关联熵分析3种模型为基础构建的混合诊断模型的输入变量。根据D-S证据理论建立3个模型的基本信度函数,实现故障信息的决策融合,并引入证据关联系数法对证据体决策重要度和冲突问题加权修正。实验结果表明:EWT与改进D-S证据理论模型的故障诊断准确率达95.0%,在平均误差、均方误差、最大误差百分比等方面均优于单一的广义回归神经网网络、Elman神经网络和灰关联熵分析模型。

机器人锅炉冷态空气动力场测量系统开发

针对锅炉冷态空气动力场试验自动化程度低、操作危险性大的问题,开发了机器人锅炉冷态空气动力场试验测量系统。系统下位机采用STM32芯片作为主控芯片,控制爬壁机器人的运动以及与上位机的信息交换,同时引入混沌线性惯性权重对粒子群优化模糊PID算法进行优化,并将改进后的算法作为机器人运动路径的控制策略,对于机械臂的控制引入D-H法。上位机为LabVIEW搭建的操作平台,通过嵌入双树复小波变换去噪算法,对采集到的风速信号进行降噪处理。结果表明:所提出的系统各个模块均可正常且稳定运行,与人工测试的误差保持在±10%,能够满足锅炉冷态试验的要求。

基于小波变换的显微图像清晰度评价函数及3-D自动调焦技术

提出了基于小波变换的图像清晰度评价函数。采用大NA(数值孔径)和小NA的显微图像序列,比较分析了本文提出的评价函数和经典的归一化方差函数、熵函数、能量拉普拉斯函数以及另外两种基于小波变换评价函数的清晰度评价性能。同时采用带有标准偏差为25的高斯噪声显微图像序列,比较了这五种评价函数的抗噪能力。实验结果表明:提出的评价函数具有最高的聚焦精度和聚焦分辨率,且具有与抗噪能力最强的归一化方差函数相当的抗噪能力。提出了基于区域选择的自动聚焦方法,实现了处于不同深度的微操作对象的3-D自动聚焦。该评价函数和区域选择聚焦技术可以用于高精度的自动微操作作业中。进一步说明自动调焦是实现自动化微操作的关键技术,而其核心是清晰度评价函数的选取或构建。

采用D-S证据推理的电机转子故障诊断

提出了采用D-S(Dempster-Shafer)证据理论对感应电机转子断条故障进行识别的故障诊断方法。基于小波包变换的频率划分特性,对定子三相电流信号进行小波包分解,利用节点系数的均方根值构建电机转子故障的特征矢量(证据体);利用明氏距离测度构造基本可信度分配函数,求取证据体对转子故障所赋予的基本概率分配函数值,然后根据D-S证据融合规则进行融合处理,实现了对电机转子故障的准确识别。试验结果表明,该方法可实现转子断条故障的可靠诊断。

一种针对LDoS攻击的分布式协同检测方法

针对近年来提出的一类新型攻击——低速率拒绝服务攻击(LDoS:Low-rate Denial of Service),提出一种位于中间网络的分布式协同检测方法DCLD(Distributed Collaborative Ldos Detection).该方法运用小波分析从多角度对LDoS攻击进行特征提取,并根据D-S证据理论,组合各种特征证据对攻击进行综合判决.各检测节点之间采用分布式协同算法实现信息交互.模拟实验结果表明,DCLD能够以较高精确度检测LDoS攻击及其分布式形式,并于靠近攻击源处对其进行响应,有效减小了攻击及防范机制本身对合法流量的影响.

D-S证据理论在多传感器目标识别中的应用

针对多传感器目标信号的特点 ,运用小波变换和FOBW编码对信号进行特征提取。在此基础上 ,运用D S证据理论对目标进行融合识别。与单一传感器的识别结果相比较 ,上述方法能明显提高识别能力 ,同时降低识别的不确定性 ,提高识别的实时性。

基于D-S证据理论的感应电动机转子故障诊断方法研究

提出运用D-S(Dempster-Shafer)证据理论对感应电动机转子断条故障进行识别的故障诊断方法。基于小波包变换的频率划分特性,对定子电流信号进行小波包分解,建立了转子断条故障的特征矢量,提取转子断条故障的特征信息,利用BP神经网络对其识别的结果形成彼此独立的证据,并根据D-S证据融合规则进行融合处理,以实现对电动机转子断条故障的准确识别。实验结果表明,该方法克服了传统基于FFT分析方法难以提取故障特征频率分量的难点,提高了故障诊断的判决精度,可实现转子断条故障的可靠诊断。

一种新的3－D小波变换图像编码方法

基于图像序列三维小波变换的视频编码技术是一种很有潜力的编码方法．本文针对目前实际中采用的三维小波变换只是一种准三维变换，不能提供对变换结果的统一描述等不足，推导了信号真正的三维小波变换的有关理论及其实现方法．在此基础上，我们将这种三维小波变换方法用于视频图像的编码，模拟结果表明这种编码结构具有良好性能，值得进一步的深入研究．

基于多频小波分析和D-S推理的电路故障诊断

对于模拟电路,提出了一种对多频响应信号特征进行信息融合的故障诊断方法。选取多种频率信号作为电路的输入,对响应进行小波分析,提取各频带信号的能量特征,经归一化处理得到与电路状态对应的故障特征矢量。待测样本作为未知故障模式与故障特征矢量进行灰色关联分析,得到待测样本与各故障模式的灰色关联度。以灰色关联度构造证据合成规则所需的基本概率赋值函数,并利用D-S证据推理进行融合诊断。实验证明,与现有方法相比,本方法测试成本低、诊断准确率高,对于轻微的软故障,也能进行有效的诊断。

基于改进3D SPIHT的MRI序列图像压缩方法

目的研究一种有效的MRI序列图像压缩方法。方法以2组不同数量、不同层厚的MRI序列图像为例,针对3D SPIHT算法运算复杂度,在对D型、L型表项重复判断的不足上,提出了一种改进的3DSPIHT方法;同时,根据MRI序列图像的相关性特点,提出了分组编/解码的方法,结合3D小波变换和应用改进的3D SPIHT方法,实现了MRI序列图像压缩。结果分组结合改进3D SPIHT方法与2DSPIHT,3D SPIHT相比,能够得到较好重构图像,同时,峰值信噪比(PSNR)提高了1～8 dB左右。结论在相同码率下,分组结合改进3D SPIHT的方法提高了PSNR和图像恢复质量,可以更好地解决大量MRI序列图像存储与传输问题。