Seismic signal analysis based on the dual-tree complex wavelet packet transform

We tried to apply the dual-tree complex wavelet packet transform in seismic signal analysis. The complex wavelet packet transform (CWPT) combine the merits of real wavelet packet transform with that of complex continuous wavelet transform (CCWT). It can not only pick up the phase information of signal, but also produce better ″focal- izing″ function if it matches the phase spectrum of signals analyzed. We here described the dual-tree CWPT algo- rithm, and gave the examples of simulation and actual seismic signals analysis. As shown by our results, the dual-tree CWPT is a very effective method in analyzing seismic signals with non-linear phase.

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.

Quantization-Based Robust Image Watermarking Using the Dual Tree Complex Wavelet Transform

Conventional quantization index modulation (QIM) watermarking uses the fixed quantization step size for the host signal.This scheme is not robust against geometric distortions and may lead to poor fidelity in some areas of content.Thus,we proposed a quantization-based image watermarking in the dual tree complex wavelet domain.We took advantages of the dual tree complex wavelets (perfect reconstruction,approximate shift invariance,and directional selectivity).For the case of watermark detecting,the probability of false alarm and probability of false negative were exploited and verified by simulation.Experimental results demonstrate that the proposed method is robust against JPEG compression,additive white Gaussian noise (AWGN),and some kinds of geometric attacks such as scaling,rotation,etc.

A New Construction Method for the Dual Tree Complex Wavelet Based on Direction Sensitivity

The conception of 'main direction' of multi-dimensional wavelet is established in this paper, and the capabilities of several classical complex wavelets for representing directional singularities are investigated based on their main directions. It is proved to be impossible to represent directional singularities optimally by a multi-resolution analysis (MRA) of L2(R2). Based on the above results, a new algorithm to construct Q-shift dual tree complex wavelet is proposed. By optimizing the main direction of parameterized wavelet filters, the difficulty in choosing stop-band frequency is overcome and the performances of the designed wavelet are improved too. Furthermore, results of image enhancement by various multi-scale methods are given, which show that the new designed Q-shift complex wavelet do offer significant improvement over the conventionally used wavelets. Direction sensitivity is an important index to the performance of 2D wavelets.

A Dual Tree Complex Discrete Cosine Harmonic Wavelet Transform (ADCHWT) and Its Application to Signal/Image Denoising

A new simple and efficient dual tree analytic wavelet transform based on Discrete Cosine Harmonic Wavelet Transform DCHWT (ADCHWT) has been proposed and is applied for signal and image denoising. The analytic DCHWT has been realized by applying DCHWT to the original signal and its Hilbert transform. The shift invariance and the envelope extraction properties of the ADCHWT have been found to be very effective in denoising speech and image signals, compared to that of DCHWT.

Insect recognition based on integrated region matching and dual tree complex wavelet transform

To provide pest technicians with a convenient way to recognize insects,a novel method is proposed to classify insect images by integrated region matching (IRM) and dual tree complex wavelet transform (DTCWT).The wing image of the lepidopteran insect is preprocessed to obtain the region of interest (ROI) whose position is then calibrated.The ROI is first segmented with the k-means algorithm into regions according to the color features,properties of all the segmented regions being used as a coarse level feature.The color image is then converted to a grayscale image,where DTCWT features are extracted as a fine level feature.The IRM scheme is undertaken to find K nearest neighbors (KNNs),out of which the nearest neighbor is searched by computing the Canberra distance of DTCWT features.The method was tested with a database including 100 lepidopteran insect species from 18 families and the recognition accuracy was 84.47%.For the forewing subset,a recognition accuracy of 92.38% was achieved.The results showed that the proposed method can effectively solve the problem of automatic species identification of lepidopteran specimens.

Image inpainting using complex 2-D dual-tree wavelet transform

The dual-tree complex wavelet transform is a useful tool in signal and image process- ing. In this paper, we propose a dual-tree complex wavelet transform （CWT） based algorithm for image inpalnting problem. Our approach is based on Cai, Chan, Shen and Shen＇s framelet-based algorithm. The complex wavelet transform outperforms the standard real wavelet transform in the sense of shift-invariance, directionality and anti-aliasing. Numerical results illustrate the good performance of our algorithm.

Decision Tree Complexity of Graph Propertieswith Dimension at Most5

A graph property is a set of graphs such that if the set contains some graph G then it also contains each isomorphic copy of G (with the same vertex set). A graph property P on n ventices is said to be elusive, if every decision tree algorithm recognizing P must examine all n(n - 1)/2 pairs of ventices in the worst case. Karp conjectured that every nontrivial monotone graph property is elusive. In this paper, this conjecture is proved for some cases. Especially,it is shown that if the abstract simplicial complex of a nontrivial monotone graph property P has dimension not exceeding 5, then P is elusive.

Complex tree: the basic framework of protein-protein interaction networks

In living cells, proteins are dynamically connec ted through biochemical reactions, so its functi onal features are properly encoded into protein protein interaction networks (PINs). Up to pres ent, many efforts have been devoted to exploring the basic feature of PINs. However, it is still a challenging problem to explore a universal pr operty of PINs. Here we employed the complex networks theory to analyze the proteinprotein interactions from Database of Interacting Prot ein. Complex tree: the unique framework of PINs was revealed by three topological properties of the giant component of PINs (GCOP), including rightskewed degree distributions, relatively sm all clustering coefficients and short characteristic path lengths. Furthermore, we proposed a no nlinearly growth model: complex tree model to reflect the tree framework, the simulation resu lts of this model showed that GCOPs were well represented by our model, which could be help ful for understanding the treestructure: basic framework of PINs. Source code and binaries freely available for download at http://cic.scu. edu.cn/bioinformatics/STM/STM_code.rar.

Automatic Sleep Spindle Detection with EEG Based on Complex Demodulation Method and Decision Tree Model

Sleep spindle is the characteristic waveform of electroencephalogram (EEG) which is important for clinical diagnosis. In this study, an automatic sleep spindle detection method was developed. The EEG signals were recorded based on the standard polysomnogram (PSG) measurement. A preprocessing procedure is introduced to exclude the unnecessary data segments and normalized the necessary data segments. Complex demodulation method is adopted to detect the candidate sleep spindle waveforms and calculate the features. The sleep spindles are recognized based on a decision tree model. Finally, the detected sleep spindles were utilized to amend the sleep stage recognition results. The sleep EEG data from 3 patients with sleep disorders were analyzed. The obtained results showed that the detected sleep spindles in EEG signal improved the accuracy of sleep stage recognition.

基于视觉传达技术的激光光斑图像超分辨率重建方法

激光光斑图像在成像过程中易受到成像条件和成像方式的限制,导致激光光斑图像的分辨率比较低,难以满足实际需求。为此,提出基于视觉传达技术的激光光斑图像超分辨率重建方法。采用视觉传达技术采集激光光斑图像,并使用双树复小波阈值方法对激光光斑图像去噪处理,通过改进稠密神经网络提取激光光斑图像特征,基于奇异值分解方法降低字典中原子的数目,改进稀疏表达正则化方法,实现激光光斑图像的超分辨率重建。实验结果表明,所提方法的低分辨率图像重建结果与原始图像更加接近,重建图像的结构相似度均在0.9以上,证明该方法的重建效果好、更适合实际应用。

Exploration and Practice of Rubber Based Agroforestry Complex Systems in China

Agroforestry ecosystems are constructed by simulating natural ecosystems, applying the principles of symbiosis in nature, and organizing multiple plant populations to coexist, while conducting targeted cultivation and structural control scientifically. Rubber agroforestry complex ecosystems aim for sustainable development in terms of industry, ecology, resource utilization, and the livelihoods of producers. Rubber agroforestry complex ecosystems create a complex production structure system that integrates biology, society, and the economy through species combinations. Rubber trees and associated biological components coordinate with each other, mutually promote growth, and yield a variety of products for producers. Cultivation techniques and patterns of rubber agroforestry are essential components of these ecosystems. This study analyzes the production practices of rubber agroforestry complex cultivation, with a focus on the development and characteristics (complexity, systematicity, intensity, and hierarchy) of rubber agroforestry systems using a literature analysis and a survey approach. It explores the types and scales of complex planting, specifications and forms, and major effects of complex cultivation. This study identifies successful rubber agroforestry cultivation patterns and practical techniques, as well as the potential benefits of developing rubber agroforestry cultivation. It also points out the shortcomings in the development of complex planting, including an emphasis on production practices but insufficient theoretical research, a focus on production but inadequate attention to the market, and an emphasis on yield while overlooking the improvement of standards, brands, and added value. There are various complex patterns for young rubber plantations, but relatively fewer for mature plantations. Based on this analysis, this study suggests that future efforts should focus on in-depth research on interspecies and environmental interactions in rubber agroforestry ecosystems, clearly define key roles, accelerate the innovation of development patterns, and strengthen the foundation for development. It recommends promoting and demonstrating successful rubber agroforestry complex patterns and providing technical training, developing product branding for rubber agroforestry patterns, enhancing product value, expanding the application functions of rubber-forest mixed crop products, and establishing a stable and sustainable industry chain. This study provide practical experience and theoretical insights in rubber agroforestry complex systems from China the potential to enrich the knowledge of rubber agroforestry composite systems, provide practical experience to improve the operating income of smallholders, and even promote the sustainable development of rubber plantations.

故障树自动建树及分析软件的实现

故障树分析(Fault Tree Analysis)在系统可靠性评估中起着至关重要的作用。然而,作为故障树分析的核心故障树(FT)的构建,传统构建故障树的方法存在耗时长且容易出错等缺点。为了解决这些问题并应对复杂系统自动建造故障树的困难,该文提出一种基于元部件模型及系统结构模型的规范化描述方法,并以此为基础,通过建立元部件模型库、复杂标识符库,设计出故障树自动建树及分析软件,实现系统结构模型搭建、自动建树、复杂结构识别处理及可靠性分析过程的全自动化。详细阐述了故障树自动建树软件自动建树及分析的基本步骤,并通过一个简化的汽车ABS系统应用实例验证了该软件的有效性和可行性。实例应用结果表明,该故障树自动建树及分析软件不仅能够实现自动构建和分析故障树,提高工作效率,而且能够识别处理具有复杂结构的故障树,对复杂系统的自动建树及可靠性分析的推广具有重要意义。

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。

基于光响应非均匀性的WhatsApp压缩视频来源识别

光响应非均匀噪声(photo response nonuniformity,PRNU)是光学成像传感器成像时引入的一种独特噪声,可有效识别压缩视频的来源。针对现有算法提取压缩视频的PRNU效果并不显著的问题,论文提出了一种改进PRNU提取算法。首先,去除视频编解码的环路滤波器,对视频帧使用双密度双树复小波变换进行分解;然后对高频子带使用基于贝叶斯阈值估计的双变量收缩算法进行估计,再使用自适应加窗维纳滤波进行二次估计,得到噪声残差;最后用基于量化参数值加权的最大似然估计法聚合噪声残差,再与视频帧估计得到PRNU。实验结果表明:该文提出的方法在20 s时WhatsApp视频的识别率为75%。

基于摇振收获设备的核桃树复杂分枝特性研究

为确定大型果树树干摇振收获设备的激振参数,探究大型果树复杂分枝的共振特性,以南疆核桃树为研究对象,应用自主研发的树干摇振收获设备开展核桃树扫频共振检测试验,通过试验得到样本树检测点时间-加速度响应幅值曲线与激振频率-加速度响应幅值曲线,并进行分析。结果表明:核桃树分枝生长方向受扫频激振影响较小;核桃树复杂分枝上的主枝与其上分枝共振频率一致,样本树复杂分枝检测点的共振频率方差最大值为0.038Hz,小于0.05Hz;核桃树复杂分枝的分枝长径比与分枝共振响应幅值呈正相关,样本树复杂分枝的分枝长径比与Z轴共振响应幅值的相关系数为0.455,呈密切正相关。研究可为大型果树树干摇振收获设备激振频率的确定奠定基础。

基于逆强化学习与行为树的机械臂复杂操作技能学习方法

现有分层强化学习方法不仅在学习过程中存在样本效率低、奖励稀疏以及学习时间过长等问题,而且大多基于仿真环境,导致学习策略在机器人真实操作环境中部署困难。针对上述问题,提出一种基于逆强化学习与行为树的机械臂复杂操作技能学习方法。在对复杂操作任务进行分割的基础上,首先,根据专家演示轨迹确定分割后每个子任务的强化学习参数及其对应的专家策略;其次,根据子任务专家策略并使用生成对抗模仿学习算法进行预训练,得到每个子任务的奖励函数;再次,运用每个子任务的奖励函数并基于SAC算法做进一步训练,获取到每个子任务的最优策略;最后,将子任务最优策略从仿真环境部署到真实环境,并将部署后的每个子任务作为叶节点构建行为树,实现机械臂复杂任务规划。实验结果表明,所提方法能较好地学习机械臂复杂操作技能,并在性能上优于其他传统强化学习算法。

基于改进Prim算法的路径规划研究

文中提出一种基于聚类分析改进Prim(普里姆)最小生成树的路径规划算法,采用二分法将站网中的站点先聚类,分成多个微小的站点聚类中心,再以各聚类中心进行Prim最小生成树的路径规划,量化站网空间分布特征,通过聚类增强最小生成树,达到路径优化的目的。实践结果证明,改进的Prim算法适用于大型稠密的站网,在稠密的连通图中,只要调整指数进而控制聚类中心的数量,就能简化站网布局,降低算法的空间复杂度,达到更好的实际应用。

NEW METHOD OF EXTRACTING WEAK FAILURE INFORMATION IN GEARBOX BY COMPLEX WAVELET DENOISING

Because the extract of the weak failure information is always the difficulty and focus of fault detection. Aiming for specific statistical properties of complex wavelet coefficients of gearbox vibration signals, a new signal-denoising method which uses local adaptive algorithm based on dual-tree complex wavelet transform （DT-CWT） is introduced to extract weak failure information in gear, especially to extract impulse components. By taking into account the non-Gaussian probability distribution and the statistical dependencies among wavelet coefficients of some signals, and by taking the advantage of near shift-invariance of DT-CWT, the higher signal-to-noise ratio （SNR） than common wavelet denoising methods can be obtained. Experiments of extracting periodic impulses in gearbox vibration signals indicate that the method can extract incipient fault feature and hidden information from heavy noise, and it has an excellent effect on identifying weak feature signals in gearbox vibration signals.

基于数据挖掘的新旧动能转换成效监测体系研究

采用目前方法构建新旧动能转换成效监测体系时,没有对监测数据进行去噪处理,导致方法的监测精度和敏感度较低。为此,提出基于数据挖掘的新旧动能转换成效监测体系研究方法。首先确定构建新旧动能转换成效监测体系的具体步骤,其次在双树复小波原理的基础上对监测数据进行去噪处理,利用数据挖掘方法构建新旧动能转换成效监测指标体系,并对监测指标进行标准化处理,最后结合熵权法和非线性加权模型完成新旧动能转换成效的监测。实验结果表明所提方法的监测精度和敏感度较高。