Classification and Visualization of Surrounding Rock Mass Stability Based on Multi-Dimensional Cloud Model

The classification of the stability of surrounding rock is an uncertain system with multiple indices.The Multidimensional Cloud Model provides an advanced solution through the use of an improved model of One-dimensional Cloud Model.Setting each index as a one-dimensional attribute,the Multi-dimensional Cloud Model can set the digital characteristics of each index according to the cloud theory.The Multi-dimensional cloud generator can calculate the certainty of each grade,and then determine the stability levels of the surrounding rock according to the principle of maximum certainty.Using this model to 5 coal mine roadway surrounding rock examples and comparing the results with those of One-dimensional and Two-dimensional Cloud Models,we find that the Multi-dimensional Cloud Model can provide a more accurate solution.Since the classification results of the Multidimensional Cloud Model are difficult to be presented intuitively and visually,we reduce the Multi-dimensional Cloud Model to One-dimensional and Two-dimensional Cloud Models in order to visualize the results achieved by the Multi-dimensional Cloud Model.This approach provides a more accurate and intuitive method for the classification of the surrounding rock stability,and it can also be applied to other types of classification problems.

Study on the Universality of the Normal Cloud Model

The distribution function is an important tool in the study of the stochastic variances. The normal distribution is very popular in the nature and our society. The idea of membership functions is the foundation of the fuzzy sets theory. While the fuzzy theory is widely used, the completely certain membership function that has no any fuzziness at all has been the bottleneck of the applications of this theory. Cloud models are the effective tools in transforming between qualitative concepts and their quantitative expressions. It can represent the fuzziness and randomness and their relations of uncertain concepts. Also cloud models can show the concept granularity in multi-scale spaces by the digital characteristic Entropy (En). The normal cloud model not only broadens the form conditions of the normal distribution but also makes the normal membership function be the expectation of the random membership degree. In this paper, the universality of the normal cloud model is proved, which is more superior and easier, and can fit the fuzziness and gentleness of human cognitive processing. It would be more applicable and universal in the representation of uncertain notions.

Road Surface Modeling and Representation from Point Cloud Based on Fuzzy Clustering

A scheme for an automatic road surface modeling from a noisy point cloud is presented. The normal vectors of the point cloud are estimated by distance-weighted fitting of local plane. Then, an automatic recognition of the road surface from noise is performed based on the fuzzy clustering of normal vectors, with which the mean value is calculated and the projecting plane of point cloud is created to obtain the geometric model accordingly. Based on fuzzy clustering of the intensity attributed to each point, different objects on the road surface are assigned different colors for representing abundant appearances. This unsupervised method is demonstrated in the experiment and shows great effectiveness in reconstructing and rendering better road surface.

Comprehensive evaluation of 5G+smart distribution network based on combined weighting method-cloud model

With the large-scale application of 5G technology in smart distribution networks,the operation effects of distribution networks are not clear.Herein,we propose a comprehensive evaluation model of a 5G+smart distribution network based on the combination weighting and cloud model of the improved Fuzzy Analytic Hierarchy-Entropy Weight Method(FAHP-EWM).First,we establish comprehensive evaluation indexes of a 5G+smart distribution network from five dimensions:reliable operation,economic operation,efficient interaction,technological intelligence,and green emission reduction.Second,by introducing the principle of variance minimization,we propose a combined weighting method based on the improved FAHP-EWM to calculate the comprehensive weight,so as to reduce the defects of subjective arbitrariness and promote objectivity.Finally,a comprehensive evaluation model of 5G+smart distribution network based on cloud model is proposed by considering the uncertainty of distribution network node information and equipment status information.The example analysis indicates that the overall operation of the 5G+smart distribution network project is decent,and the weight value calculated by the combined weighting method is more reasonable and accurate than that calculated by the single weighting method,which verifies the effectiveness and rationality of the proposed evaluation method.Moreover,the proposed evaluation method has a certain guiding role for the large-scale application of 5G communication technology in smart distribution networks.

Risk transmission evaluation for parallel construction of warships based on IFCM and the cloud model

To cope with multi-directional transmission coupling,spreading, amplification, and chain reaction of risks during multiproject parallel construction of warships, a risk transmission evaluation method is proposed, which integrates an intuitionistic cloud model with a fuzzy cognitive map. By virtue of expectancy Ex, entropy En, and hyper entropy He, the risk fuzziness and randomness of the knowledge of experts are organically combined to develop a method for converting bi-linguistic variable decision-making information into the quantitative information of the intuitionistic normal cloud(INC) model. Subsequently, the threshold function and weighted summation operation in the traditional fuzzy cognitive map is converted into the INC ordered weighted averaging operator to create the risk transmission model based on the intuitionistic fuzzy cognitive map(IFCM) and the algorithm for solving it. Subsequently, the risk influence sequencing method based on INC and the risk rating method based on nearness are proposed on the basis of Monte Carlo simulation in order to realize the mutual conversion of the qualitative and quantitative information in the risk evaluation results.Example analysis is presented to verify the effectiveness and practicality of the methods.

基于组合赋权与灰云模型的综合能源系统需求响应效益评价

考虑到当前综合能源系统研究缺乏完善的需求响应量化评估方案,难以在优化运行层面综合评估需求响应效益这一问题,提出了一种基于改进AHP群决策—CRITIC组合赋权与灰云模型的综合能源系统需求响应效益评价方法。首先,从综合能效性、社会经济性和需求侧互动性三个维度构建了需求响应效益评价指标体系。其次,采用一致性和权重拟合性更优的指数标度法构造判断矩阵,降低赋值误差,并通过AHP群决策法确定主观权重,从而削弱主观极值偏差对权重的影响;在由CRITIC法确定客观权重后,基于最小欧氏距离建立组合权重模型,并通过非线性规划求取最优组合权重;针对评价等级信息的模糊性与隶属等级的随机性,采用正态灰云白化权模型确定指标分类等级与场景评分。最后,以北方某综合能源系统为例,根据用户参与需求响应方式设置了4种运行场景,分析了不同需求响应对系统运行的影响与作用,算例结果表明所提指标体系与评价方法科学有效。

基于熵权-正态云模型的小净距隧道施工风险评估模型

随着我国基础设施建设的大力发展,小净距隧道在我国高速公路建设过程中的占比越来越高。由于地质条件的原因,部分小净距隧道可能会穿越岩堆体,而影响其施工安全的风险因素繁多、关系模糊复杂、风险因子不确定性大,导致风险发生概率存在各种不确定性。针对穿越岩堆体的小净距隧道,提出一种基于熵权-正态云模型的小净距隧道施工风险评估模型,选取工程地质条件、隧道设计、外部因素和工程管控等4个方面的15个评估指标,并以某实际隧道工程为例,论证了该模型在小净距隧道施工风险定量评估中应用的可行性和独特优势。该研究对其他类似工程施工的风险评估有一定的借鉴意义。

基于特征点动态选择的三维人脸点云模型重建

针对典型的点云配准方法中伪特征点过多导致配准效率低和配准结果不精确的问题,提出一种基于特征点动态选择的三维人脸点云模型重建方法。该方法在粗配准阶段,采用动态特征矩阵求解法获取粗匹配特征变换矩阵以避免伪特征点的干扰。在精配准过程中,采用二次加权法向量垂直距离法在人脸流形表面选择更有效的特征点以减少伪特征点的数量,并采用基于特征融合与局部特征一致性的迭代最近点方法进行精配准。经过对比实验验证了算法的可行性,实验结果表明,该算法能够实现高精度且快速的三维人脸点云模型重建,且均方根误差达到1.8165 mm,相较其他算法,其在模型重建精度和效率方面都有所提升,具有良好的应用前景。

基于不完全退火的正态云禁忌搜索算法及应用

针对RC电路参数优化应用时的局限性,提出一种基于不完全退火的正态云禁忌搜索算法。该算法对原始禁忌搜索算法在寻优过程中出现的对寻优起点过度依赖、早熟、灵敏度不一致等问题进行了改进,首先通过引入改进的不完全模拟退火Metropolis准则作为算子,使得原始禁忌搜索不再依赖初始可行解,具备了一定逃离局部最优解能力;然后采用正态云模型,提出了一种自适应禁忌表记忆策略及遗忘机制,使得禁忌搜索算法具备更强随机性与模糊性,进一步增强其全局搜索性能;最后针对灵敏度不一致问题,提出串行多维多灵敏度混合编、解码策略,实现在同一算法框架下对不同寻优对象定义不同搜索范围,使得低灵敏度解受忽略程度大大降低。采用8个基准函数及TSP-Oliver30标准测试算例进行性能评估,并与其他6种同类型算法对比,证明该算法对原始禁忌搜索算法优化效果良好。将该算法应用于SiMOSFET加速器磁铁开关电源参数优化后,经仿真与实验,结果表明:所提改进算法能够对工程应用进行实际优化,具备优越性与可行性。

基于自适应分段云模型的电力异构数据聚类研究

云空间中的电力数据种类繁多、数据维度大、结构复杂,容易引发电力系统运行安全问题。有效的数据聚类可以消除云空间中电力异构的无关、冗余数据。引入自适应分段云模型,提出一种全新的电力异构数据聚类方法。通过Tent映射预处理电力异构数据,使数据空间分布更加均匀,提高了数据聚类精度。利用自适应正态云模型建立云期望曲线方程,获取正态云滴,以确定数据聚类中心。根据熵值定义分段聚合条件,实现异构数据有序聚类。测试结果表明,所提方法在聚类中心空间中的异构数据分布较为均匀,分类聚类面积较大,聚类收敛较快,轮廓系数为0.4。所提方法的电力异构数据聚类效果更优,具有较好的适用性和有效性。

基于机载LiDAR数据的单木分割研究

基于机载LiDAR数据进行单木分割,分别讨论了基于冠层高度模型的单木提取方法(FCHM)以及基于归一化点云的单木提取算法(FNPC)。FCHM的分离单木的总正确率的均方根值为0.93,F_Score的均方根值为0.62,FNPC算法分别为0.90和0.56,FCHM算法的精度优于FNPC算法。通过从不同的林相、林型、林分密度的分析得出,在林分密度小于400棵/hm^(2)的单层针叶林中提取效果最好。

基于熵权-正态云模型的水资源承载力数据可视化评价分析

水资源承载力(CCWR)是衡量社会经济发展支撑力的表现方式之一,科学地描述CCWR综合评价过程的模糊性、随机性与离散性的问题尤为重要。文章以水资源量、用水量与经济生活为准则构建CCWR评价指标体系,基于熵权-正态云模型评价2020年全国各省CCWR。结果表明:各省用水与排污量均与区域经济发展高度关联,用水类型以农业用水为主;中国从西到东、从南到北CCWR呈递减趋势,要形成优势互补的高质量区域经济发展布局应综合地理位置、经济生活、技术革新等多方面因素进行水资源的优化配置。

基于高斯分布和多元正态分布结合模型的点云异常点检测与去噪方法研究

针对异常点检测在点云采集中,由于复杂数据分布导致去噪准确率和效率下降的问题,文章提出了一种结合高斯分布模型和多元正态分布模型的异常点检测方法。实验结果表明,该方法能够快速有效地去除大量外部噪声,并且去噪后的点云数据较好地保留了原始点云特征。

改进蚁狮优化算法的永磁同步电机多参数辨识

为提高永磁同步电机参数辨识的精度,提出一种基于Tent映射和正态云模型的改进蚁狮优化算法辨识永磁同步电机参数。首先,在蚂蚁和蚁狮初始化阶段引入Tent映射使初始种群更加均匀地分布于搜素空间;其次,在蚂蚁位置更新阶段以全局最优个体为目标,使用正态云模型更新精英蚁狮的位置;最后,根据迭代次数的增长自适应缩小云滴的生成范围,提升算法的全局收敛能力和精度。仿真实验结果表明,改进蚁狮优化算法能更加精确、迅速地辨识永磁同步电机参数。

基于NCM-FPN的古建筑修缮阶段施工安全综合评价

为有效预防古建筑修缮阶段施工安全事故,提出了基于正态云模型(Normal Cloud Model, NCM)与模糊Petri网(Fuzzy Petri Net, FPN)的施工安全综合评价方法。首先,分析古建筑修缮阶段施工特点及风险特性,建立多因素耦合作用下的三脚架事故致因模型(Tripod-Delta),构建指标体系。然后,将指标体系转换为施工安全多因素耦合FPN网络结构,采用NCM确定FPN指标初始状态,通过逆向搜索策略约简FPN冗余指标节点,并运用模糊推理算法与障碍因子诊断模型得出评价结果。结果表明,实例评价结果与现场情况基本一致,协同管理与材料设备是影响古建筑修缮阶段施工安全的关键因素。所提方法能充分表达古建筑修缮阶段施工安全风险的耦合特性,并确定安全管理的关键因素,评价结果客观准确。

基于ISSO-PP组合赋权的双向区间约束正态云模型的换流站阀冷系统冷却能力研究

阀冷系统是保证换流站安全运行的关键一环,其作用在于排出阀体中元件形成的热量,因此其冷却能力的准确评估至关重要。针对上述问题,提出基于ISSO-PP组合赋权的双向区间约束正态云模型的换流阀冷却能力评价模型。首先,对采集数据预处理并确定阀冷系统冷却能力评价指标体系;其次,利用层次分析法和改进CRITIC法计算评价指标主、客观权重,并由改进鲨鱼优化投影寻踪法(ISSO-PP)得到综合权重;最后,由数据等级评价标准为中间型这一特性,提出双向区间约束的正向云数字特征计算方法,同时对逆向云数字特征计算方法进行改进,结合综合权重计算得到最终隶属度,依据最大隶属度原则确定最终评价结果。

集成正态云和动态扰动的哈里斯鹰优化算法

针对基本哈里斯鹰算法收敛速度慢和易陷入局部寻优等问题,提出一种集成正态云模型和动态扰动策略的改进哈里斯鹰算法.在全局搜索阶段,利用正态云模型的随机性和模糊性和随机反向学习思想对哈里斯鹰位置进行更新,从而丰富种群多样性和提高算法全局搜索能力.在局部开发阶段,引入动态扰动策略更新不同捕食策略下的哈里斯鹰位置,改善算法局部开发能力.选取不同维度的基准测试函数进行仿真,同时利用Wilcoxon秩和检验、Friedman检验、三杆桁架设计问题对改进哈里斯鹰算法和其它优化算法进行对比分析.实验结果表明:提出的改进哈里斯鹰算法收敛速度更快,寻优精度更高,验证了改进策略的有效性.

基于地形认知的布料模拟滤波算法

数字高程模型(Digital Elevation Model,DEM)可以反映一个地区的地形特征,具有广泛的科研应用。对激光雷达点云数据进行点云滤波以提取地面点,并对地面点进行插值是构建DEM的常用步骤,其中在点云滤波过程中使用的滤波算法直接影响到最终构建的DEM的精度。布料模拟滤波(Cloth Simulation Filtering,CSF)算法作为一种点云滤波算法,具有模型简单、滤波效率高等优点,其针对平坦地区的滤波精度较高,但在处理复杂地形时会因布料模型的内部弹力以及重力惯性等因素,导致滤波结果的精度较差。为了提升CSF算法在处理复杂地形时的滤波精度和地形适应性,提高其构建DEM的精度,提出了基于地形认知的布料模拟滤波算法(Cloth Simulation Filtering Algorithm with Topography Cognition,CSFTC)。该算法提出了地形认知模型,基于点云数据点的局部分布特征构建认知模型,并将其扩展为粗精度数字高程模型(Rough Digital Elevation Mo-del,R-DEM);通过点云地形归一化实现宏观地形趋势与微观地形细节的分离;最终使用经典CSF算法结合R-DEM实现了点云滤波。文中设计了CSFTC算法与经典CSF算法的对比实验,CSFTC算法的平均总误差率从9.30%下降到5.10%,平均Ⅱ类误差率从30.02%下降到8.46%。实验结果表明,与经典CSF算法相比,CSFTC算法在平坦地区的滤波精度小幅上升,对复杂地形的滤波精度明显上升,提升了算法的地形适应性;Ⅱ类误差显著下降有助于提高构建的DEM的精度。

基于组合赋权—云模型的珠江三角洲水资源可持续性动态评价

为综合评价区域水资源可持续性状况,构建基于水资源-社会经济-生态环境复合系统的区域水资源可持续性评价体系,采用投影寻踪模型和云模型合理确定各指标的综合权重,引入正态云模型确定指标隶属度,建立基于组合赋权-云模型的区域水资源可持续性评价模型对珠江三角洲9座城市的水资源可持续性状况进行动态评价。结果表明:2007-2020年珠江三角洲城市群水资源可持续性状况整体呈上升趋势,到2020年已处于较好水平;区域内部水资源可持续状况呈现东部城市优于西部城市、南部近海岸城市优于远海岸城市的空间分布格局;各子系统之间可持续性等级不和谐,生态环境子系统发展水平较好,水资源子系统、社会经济子系统是造成珠江三角洲城市群水资源可持续性状况存在空间差异的主要因素。

基于变权重−云模型的岩溶隧道涌突水灾害风险评估——以中梁山隧道为例

针对岩溶隧道涌突水的致险因素的不确定性、复杂性和隧道涌突水风险评价的主观性,以成渝中线中梁山岩溶隧道工程为背景,建立基于正态云模型的隧道涌突水风险评价方法。通过选取地层岩性、地质构造、地表汇水条件、隧道空间位置、地下水循环交替条件作为风险影响因素,构建涌突水风险评估体系;基于正态云模型确定的各影响因子数字特征及变权向量计算综合隶属度,最终判定岩溶隧道涌突水灾害风险等级。结果表明:成渝中线中梁山隧道涌突水灾害为“Ⅱ级”与“Ⅴ级”之间,涌突水灾害发生可能性大且危害高,与实际开挖结果一致。文章构建的岩溶隧道涌突水灾害风险评估方法,实现了多元决策下的隧道涌突水灾害风险分级客观性,适合岩溶隧道的风险评估,为日后隧道质量控制和寿命评估提供参考。