Structural plane recognition from three-dimensional laser scanning points using an improved region-growing algorithm based on the robust randomized Hough transform

The staggered distribution of joints and fissures in space constitutes the weak part of any rock mass.The identification of rock mass structural planes and the extraction of characteristic parameters are the basis of rock-mass integrity evaluation,which is very important for analysis of slope stability.The laser scanning technique can be used to acquire the coordinate information pertaining to each point of the structural plane,but large amount of point cloud data,uneven density distribution,and noise point interference make the identification efficiency and accuracy of different types of structural planes limited by point cloud data analysis technology.A new point cloud identification and segmentation algorithm for rock mass structural surfaces is proposed.Based on the distribution states of the original point cloud in different neighborhoods in space,the point clouds are characterized by multi-dimensional eigenvalues and calculated by the robust randomized Hough transform(RRHT).The normal vector difference and the final eigenvalue are proposed for characteristic distinction,and the identification of rock mass structural surfaces is completed through regional growth,which strengthens the difference expression of point clouds.In addition,nearest Voxel downsampling is also introduced in the RRHT calculation,which further reduces the number of sources of neighborhood noises,thereby improving the accuracy and stability of the calculation.The advantages of the method have been verified by laboratory models.The results showed that the proposed method can better achieve the segmentation and statistics of structural planes with interfaces and sharp boundaries.The method works well in the identification of joints,fissures,and other structural planes on Mangshezhai slope in the Three Gorges Reservoir area,China.It can provide a stable and effective technique for the identification and segmentation of rock mass structural planes,which is beneficial in engineering practice.

利用Transformer的组合聚类算法在蛋白质数据分析中的应用

该研究将Transformer模型适配于蛋白质特征降维场景,通过其特有的自注意力机制,赋予模型对长程依赖关系的较好建模性能,同时,多头注意力设计使得模型能够从不同角度捕获特征间的相互作用,进一步提升降维结果的表达力和鲁棒性。文章提出了一种新型的GRKM组合聚类算法,在原始K-means算法中引入了灰狼优化算法(Grey Wolf Optimization Algorithm)确定聚类的K值,以随机游走算法(Random Walk)确定初始聚类中心,以马氏距离(Markov Distance)来衡量样本间的相似性。研究中,对5种具有代表性的蛋白质数据集进行了实验验证,得到了改进后算法在轮廓系数以及DB指数等方面相较于改进前都有较大提升的结论。最终的结果分析选取APP蛋白质数据,将蛋白质聚为8类,探讨了各类别的生物功能,在解释性方面也取得了较为明显的效果。所提算法为深入理解蛋白质功能、发现潜在生物标志物以及指导药物设计等实际应用提供了参考工具。

基于局部Transformer的泰语分词和词性标注联合模型

泰语分词和词性标注任务二者之间存在高关联性,已有研究表明将分词和词性标注任务进行联合学习可以有效提升模型性能,为此,提出了一种针对泰语拼写和构词特点的分词和词性标注联合模型。针对泰语中字符构成音节,音节组成词语的特点,采用局部Transformer网络从音节序列中学习分词特征;考虑到词根和词缀等音节与词性的关联,将用于分词的音节特征融入词语序列特征,缓解未知词的词性标注特征缺失问题。在此基础上,模型采用线性分类层预测分词标签,采用线性条件随机场建模词性序列的依赖关系。在泰语数据集LST20上的试验结果表明,模型分词F1、词性标注微平均F1和宏平均F1分别达到96.33%、97.06%和85.98%,相较基线模型分别提升了0.33%、0.44%和0.12%。

A novel image encryption scheme based on Keplers third law and random Hadamard transform

In this paper, a novel image encryption scheme based on Keplers third law and random Hadamard transform is proposed to ensure the security of a digital image. First, a set of Kepler periodic sequences is generated to permutate image data, which is characteristic of the plain-image and the Keplers third law. Then, a random Hadamard matrix is constructed by combining the standard Hadamard matrix with the hyper-Chen chaotic system, which is used to further scramble the image coefficients when the image is transformed through random Hadamard transform. In the end, the permuted image presents interweaving diffusion based on two special matrices, which are constructed by Kepler periodic sequence and chaos system. The experimental results and performance analysis show that the proposed encrypted scheme is highly sensitive to the plain-image and external keys, and has a high security and speed, which are very suitable for secure real-time communication of image data.

Laplace Transformation and Ergodic Potential Kernel for q-Process in Random Environment

This paper introduces some concepts such as q- process in random environment, Laplace transformation, ergodic potential kernel, error function and some basic lemmas.We study the continuity and Laplace transformation of random transition function. Finally, we give the sufficient condition for the existence of ergodic potential kernel for homogeneous q- processes in random environments.

Lyapunov exponents for continuous random transformations

In this paper, the concept of Lyapunov exponent is generalized to random transformations that are not necessarily differentiable. For a class of random repellers and of random hyperbolic sets obtained via small perturbations of deterministic ones respectively, the new exponents are shown to coincide with the classical ones.

Markov Chains Based on Random Generalized 1-Flipper Operations for Connected Regular Multi-digraphs

The properties of generalized flip Markov chains on connected regular digraphs are discussed.The 1-Flipper operation on Markov chains for undirected graphs is generalized to that for multi-digraphs.The generalized 1-Flipper operation preserves the regularity and weak connectivity of multi-digraphs.The generalized 1-Flipper operation is proved to be symmetric.Moreover,it is presented that a series of random generalized 1-Flipper operations eventually lead to a uniform probability distribution over all connected d-regular multi-digraphs without loops.

AFFINE TRANSFORMATION IN RANDOM ITERATED FUNCTION SYSTEMS

Random iterated function systems (IFSs) is discussed, which is one of the methods for fractal drawing. A certain figure can be reconstructed by a random IFS. One approach is presented to determine a new random IFS, that the figure reconstructed by the new random IFS is the image of the origin figure reconstructed by old IFS under a given affine transformation. Two particular examples are used to show this approach.

Definition of Laplace Transforms for Distribution of the First Passage of Zero Level of the Semi-Markov Random Process with Positive Tendency and Negative Jump

One of the important problems of stochastic process theory is to define the Laplace transforms for the distribution of semi-markov random processes. With this purpose, we will investigate the semimarkov random processes with positive tendency and negative jump in this article. The first passage of the zero level of the process will be included as a random variable. The Laplace transforms for the distribution of this random variable is defined. The parameters of the distribution will be calculated on the basis of the final results.

Improving the Autoregressive Modeling Method in Random Noise Suppression of GPR Data Using Undecimated Discrete Wavelet Transform

Geophysics has played a significant and efficient role in studying geological structures over the past decades as the goal of geophysical data acquisition is to investigate underground phenomena with the highest possible level of accuracy. The ground penetrating radar (GPR) method is used as a nondestructive method to reveal shallow structures by beaming electromagnetic waves through the Earth and recording the received reflections, albeit inevitably, along with random noise. Various types of noise affect GPR data, among the most important of which are random noise resulting from arbitrary motions of particles during data acquisition. Random noise which exists always and at all frequencies, along with coherent noise, reduces the quality of GPR data and must be reduced as much as possible. Over the recent years, discrete wavelet transform has proved to be an efficient tool in signal processing, especially in image and signal compressing and noise suppression. It also allows for obtaining an accurate understanding of the signal properties. In this study, we have used the autoregression in both wavelet and f-x domains to suppress random noise in synthetic and real GPR data. Finally, we compare noise suppression in the two domains. Our results reveal that noise suppression is conducted more efficiently in the wavelet domain due to decomposing the signal into separate subbands and exclusively applying the method parameters in autoregression modeling for each subband.

基于改进随机Hough变换的圆形目标检测算法

圆形目标检测在各个领域得到了广泛应用.为了提高规则圆的检出效率、降低不规则圆的漏检率,文章提出了一种改进随机Hough变换的圆形目标检测算法.算法首先用区域增长和RGB二值分割等算法对不同背景的图像进行二值分割和去噪处理,接着对连通域分割的特征区域用具有圆形性特征的边缘点进行候选圆的检测和验证.实验对比结果表明,本文算法可以显著提高规则圆的检出效率、降低不规则圆的漏检率,算法计算量小、运行速度快、鲁棒性强.

基于IHHT‑RF的配电网单相接地故障选线方法

小电流系统发生单相接地故障时故障特征易受高接地过渡电阻、小初相角等弱故障条件影响而导致选线准确率低。为此,提出一种基于改进希尔伯特黄变换—随机森林(improved Hilbert⁃Huang transform⁃random forest,IHHT⁃RF)的配电网单相接地故障选线方法。首先,提取每条线路在故障发生时的电流暂态信号,通过IHHT提取纯净的暂态电气量,构造标准差、能量熵和幅值畸变度3类特征向量;然后,将特征向量输入RF分类器建立故障选线模型,把故障选线问题转化为二分类问题;最后,将测量数据输入RF分类器中得出分类结果,实现故障线路的自动识别。仿真结果表明,该选线方法综合利用暂态信号的幅值、频率和能量等特征信息,不受弱故障条件、馈线结构等因素的影响,能有效提高故障选线的准确率,具有较强的适应性和可靠性。

基于二维散射变换的湖相碳酸盐岩储层厚度预测方法研究

济阳坳陷沙四段湖相碳酸盐岩受湖盆内沉积环境和构造运动等因素控制,储层厚度变化大,非均质性强,油气开发难度大.为了准确预测湖相碳酸盐岩储层的厚度,本文提出一种基于二维散射变换和随机森林的储层厚度预测方法.首先,引入二维散射变换提取地震时频属性,该变换是在二维小波变换的基础上,通过迭代小波分解和非线性操作来实现的.与传统的二维小波变换对比,散射变换提取的时频属性具有局部形变稳定性以及对噪声鲁棒性的优点,有助于提高储层厚度预测的准确率.在此基础上,在有限测井数据的条件下,利用随机森林算法建立多尺度时频属性与测井解释厚度之间的非线性关系,实现湖相碳酸盐岩储层预测.模型数据的预测结果表明,与基于传统地震振幅属性的厚度预测和基于二维小波变换的储层厚度预测对比,本文所提的厚度预测方法具有最优的性能.叠后三维地震数据的预测结果表明,与基于传统地震振幅属性的厚度预测和基于二维小波变换的储层厚度预测对比,本文所提方法的厚度预测结果与实际钻井数据误差更小,提高了储层厚度预测的精度,清晰刻画了灰礁、灰滩与灰泥等三种沉积亚相的空间展布,有利于后续井位部署和优化.

Mean Square Solutions of Second-Order Random Differential Equations by Using the Differential Transformation Method

The differential transformation method (DTM) is applied to solve the second-order random differential equations. Several examples are represented to demonstrate the effectiveness of the proposed method. The results show that DTM is an efficient and accurate technique for finding exact and approximate solutions.

傅里叶变换法在电梯结构振动测试的应用

针对电梯结构的振动,对牵引比为1:1的电梯振动特性进行了实验测试,在此基础上采用傅里叶变换(STFT)方法对电梯振动特性的测试结果进行了功率谱密度(PSD)检验,提出了电梯结构横向纵向耦合振动检测方法,研究电梯部件振动的主频率分布及其影响因素。结果表明轿厢架横向和纵向振动均为低频振动,主要是由于车架与车架之间的弹性相互作用所致,轿厢架纵向振动形式和幅值与平台架基本一致,但横向振动的PSD在高频区域有明显的峰值,而提升绳的横向和纵向振动频率较高。本研究为电梯监控传感器的设计和相关数据处理提供了参考依据。

卡斯卡迪亚慢滑移信息的GNSS时空探测

针对断层慢滑移时空分布信息探测困难的问题,基于全球卫星导航系统(GNSS)坐标序列,提出一种利用多通道奇异谱分析(MSSA)探测卡斯卡迪亚消减带慢滑移事件时空分布的方法:根据慢滑移分量的振荡特点确定窗口长度,采用时间迟滞矩阵对协方差阵进行增广,以提高对异常信息的识别能力;对振幅归一化获取空间响应,通过快速傅里叶变换分析慢滑移频谱特征。结果表明,卡斯卡迪亚消减带2007-01-05—2007-02-23和2008-04-22—2008-06-14发生了2次慢滑移事件,2次事件呈东南、西北区域的反向运动特征,且东南部响应程度明显高于西北地区;频谱特征显示,慢滑移信息表现为低频特征,且主要为随机游走噪声。研究结果可为板内地震前兆性信息探测提供参考。

基于长度可变密钥的光学图像加密方法

为提升光学图像加密效果,设计了一种基于长度可变密钥的光学图像加密方法。基于混沌方程构成混沌序列均匀化普适算法,运用该算法生成均匀化伪随机序列,以此作为长度可变密钥,经空间光调制器调制后,转换此类密钥为伪随机相位掩模(PRPM),运用两个转换后PRPM双重随机相位编码初始光学图像,并结合变形分数傅里叶变换(AFrFT),相位转换编码后光学图像,获得加密光学图像,完成光学图像加密。实验结果显示,该方法可针对不同光学图像生成敏感性与唯一性密钥,实现不同光学图像的加密处理,加密效果显著,加密光学图像的像素频数分布均匀,且解密后光学图像与初始光学图像几乎一致,整体加解密效果理想,可有效保障光学图像的安全性,而且本方法可以在最短3 s的时间内完成光学图像的加密和解密,加解密效率更高,综合效果更佳。

基于改进Swin Transformer的滑坡分割算法

随着遥感技术的逐步成熟,滑坡目标分割技术在灾害防控、城市建设等领域发挥着越来越关键的作用。然而,面临着卷积神经网络在理解上下文语义信息上存在困难以及滑坡数据集中正负样本分布不均的问题,需要寻找一种更优的解决方案。为此,提出了一种改进Swin Transformer的滑坡分割算法。首先,将Swin Transformer引入到滑坡目标分割任务中,并构建了Swin-Unet网络。其次,为了提高网络的表征能力,在Swin-Unet中加入通道注意力模块;为了平衡小样本正负样本不均衡,使用在线困难样例挖掘;为了进一步提高模型性能,引入并优化了条件随机场。实验结果表明,相较于基线算法,改进方法在滑坡目标分割任务上取得了更优的性能,Dice提高了1.9%,交并比(IoU)提高了1.2%,研究为滑坡目标分割提供了一种有效的方法,并为相关领域的研究提供了新的思路。

基于可伸缩型注意力机制的神经网络地震数据去噪方法

地震资料中的随机噪声会影响地震数据的质量,从而影响后续处理与解释的准确性。传统去噪方法受先验条件的约束,效率低下,神经网络具有强大的特征提取能力,能够弥补这些缺点。然而,由于传统神经网络卷积核的局限性,可能会导致全局信息丢失。为了克服这个缺点,本文在卷积神经网络(CNN)的基础上,添加了可伸缩型注意力机制。该机制在网络中同时呈现密集和稀疏两种类型的自注意力模块,这两种注意力模块交替使用可以显著增强神经网络的表现能力,扩大接受场。通过卷积层和注意力模块提取地震数据浅层特征和深层特征,结合CNN的局部建模能力和Transformer的全局建模能力,有利于提升网络的全局交互作用,增强其去除噪声和处理细节的能力。最后,合成和实际地震数据实验结果均表明,该方法相较于Unet和DnCNN,具有更好的噪声压制与保留地震数据有效信息的能力,可以大幅提高信噪比,为地震数据的处理和解释提供帮助。

滨海海底边界层对底栖微体化石仿真模拟的影响

计算流体力学CFD(computational fluid dynamics)近年来在古生物学研究中已有大量应用。该方法在研究生物化石的个体形态、器官功能,以及其与生存环境之间的关系方面具有重要意义。目前古生物领域的CFD仿真对象多为厘米级别体型的生物,而对于毫米级别大小的微体生物的仿真则相对较少。区别于厘米级别的宏体生物,底栖型微体生物的生活环境局限于海床上方垂直高度较小的区域,受海底黏性边界层低流速区域的影响更为明显,因而在设置这类化石仿真的水流环境时应当考虑对于边界层流域水体流速以及海床表面地理环境的还原。本文基于随机表面生成的方法,构建了不平坦的滨海海床表面模型,并模拟了水流在海底边界层附近受海床表面地形影响的流动状态;基于获得的仿真流速数据,我们仿真了寒武纪微型底栖刺细胞动物化石Quadrapyrgite在起伏的海床表面环境中四个不同位置的受水流阻力情况。仿真结果显示,近海床表面处的水体流动可形成明显的低流速区域,且低流速区域的厚度随着入口速度增大而变薄。在起伏表面的凹凸区域中,凸起地带的迎水面形成的低流速区域较薄,流速变化较快;而在凸起地带背水面以及凹陷地带形成的低流速区域较厚,流速较缓且易形成涡旋。流速的差异体现在不同位置的Quadrapyrgite的受阻力大小不同上,其差值可达数倍到数十倍。这种量级的受阻力差值可以在毫米级到厘米级尺度上塑造、影响底栖型生物的分布甚至其自身的摄食行为。本文的研究为CFD方法模拟底栖微型化石的生存环境提供了更加深入的思路和方法。