Formula for calculating spatial similarity degrees between point clouds on multi-scale maps taking map scale change as the only independent variable

The degree of spatial similarity plays an important role in map generalization, yet there has been no quantitative research into it. To fill this gap, this study first defines map scale change and spatial similarity degree/relation in multi-scale map spaces and then proposes a model for calculating the degree of spatial similarity between a point cloud at one scale and its gener- alized counterpart at another scale. After validation, the new model features 16 points with map scale change as the x coordinate and the degree of spatial similarity as the y coordinate. Finally, using an application for curve fitting, the model achieves an empirical formula that can calculate the degree of spatial similarity using map scale change as the sole independent variable, and vice versa. This formula can be used to automate algorithms for point feature generalization and to determine when to terminate them during the generalization.

Scale Effects and a Method for Similarity Evaluation in Micro Electrical Discharge Machining

Electrical discharge machining（EDM） is a promising non-traditional micro machining technology that offers a vast array of applications in the manufacturing industry. However, scale effects occur when machining at the micro-scale, which can make it difficult to predict and optimize the machining performances of micro EDM. A new concept of ＂scale effects＂ in micro EDM is proposed, the scale effects can reveal the difference in machining performances between micro EDM and conventional macro EDM. Similarity theory is presented to evaluate the scale effects in micro EDM. Single factor experiments are conducted and the experimental results are analyzed by discussing the similarity difference and similarity precision. The results show that the output results of scale effects in micro EDM do not change linearly with discharge parameters. The values of similarity precision of machining time significantly increase when scaling-down the capacitance or open-circuit voltage. It is indicated that the lower the scale of the discharge parameter, the greater the deviation of non-geometrical similarity degree over geometrical similarity degree, which means that the micro EDM system with lower discharge energy experiences more scale effects. The largest similarity difference is 5.34 while the largest similarity precision can be as high as 114.03. It is suggested that the similarity precision is more effective in reflecting the scale effects and their fluctuation than similarity difference. Consequently, similarity theory is suitable for evaluating the scale effects in micro EDM. This proposed research offers engineering values for optimizing the machining parameters and improving the machining performances of micro EDM.

Morphology Similarity Distance for Bearing Fault Diagnosis Based on Multi-Scale Permutation Entropy

Bearings are crucial components in rotating machines,which have direct effects on industrial productivity and safety.To fast and accurately identify the operating condition of bearings,a novel method based on multi⁃scale permutation entropy(MPE)and morphology similarity distance(MSD)is proposed in this paper.Firstly,the MPE values of the original signals were calculated to characterize the complexity in different scales and they constructed feature vectors after normalization.Then,the MSD was employed to measure the distance among test samples from different fault types and the reference samples,and achieved classification with the minimum MSD.Finally,the proposed method was verified with two experiments concerning artificially seeded damage bearings and run⁃to⁃failure bearings,respectively.Different categories were considered for the two experiments and high classification accuracies were obtained.The experimental results indicate that the proposed method is effective and feasible in bearing fault diagnosis.

A Closure for Isotropic Turbulence Based on Extended Scale Similarity Theory in Physical Space

The closure of a turbulence field is a longstanding fundamental problem, while most closure models are introduced in spectral space. Inspired by Chou＇s quasi-normal closure method in spectral space, we propose an analytical closure model for isotropic turbulence based on the extended scale similarity theory of the velocity structure function in physical space. The assumptions and certain approximations are justified with direct numerical simulation. The asymptotic scaling properties are reproduced by this new closure method, in comparison to the classical Batchelor model.

Investigation of scale efect for the computation of turbulent flow around a circular cylinder

In order to investigate the scale effect of turbulent flow around a circular cylinder, two similarity numbers （criteria） based on turbulent kinetic and dissipation rates associ- ated with the fluctuation characteristics of turbulence wake are deduced by analyzing the Reynolds averaged NavierStokes equations （RANS）. The RNG k-s models and finite volume method are used to solve the governing equations and the second-order implicit time and upwind space discretization algorithms are used to discrete the governing equations. A numerical computation of flow parameters around a two-dimensional circular cylinder with Reynolds numbers ranging from 102 to l07 is accomplished and the result indicates that the fluctuation of turbulence flow along the center line in the wake of circular cylinder can never be changed with increasing Reynolds numbers when Re ≥ 3 × 10^6. This conclusion is useful for controlling the scale of numerical calculations and for applying model test data to engineering practice.

EXTENDED SELF SIMILARITY OF PASSIVE SCALAR IN RAYLEIGH-BNARD CONVECTION FLOW BASED ON WAVELET TRANSFORM

Wavetet transform was used to analyze the scaling law of temperature data (passive scalar) in Rayleigh-Bénard convection flow from two aspects. The first one was to utilize the method of extended self similarity, presented first by Benzi et al., to study the scaling exponent of temperature data. The obtained results show that the inertial range is much wider than that one determined directly from the conventional structure function, and find the obtained scaling exponent agrees well with the one obtained from the temperature data in an experiment of wind tunnel. The second one was that, by extending the formula which was proposed by A. Arneodo et al. for extracting the scaling exponent ζ(q) of velocity data to temperature data, a newly defined formula which is also based on wavelet transform, and can determine the scaling exponent ξ(q) of temperature data was proposed. The obtained results demonstrate that by using the method which is named as WTMM (wavelet transform maximum modulus) ξ(q) correctly can be extracted.

Einstein’s Dark Energy via Similarity Equivalence, ‘tHooft Dimensional Regularization and Lie Symmetry Groups

Realizing the physical reality of ‘tHooft’s self similar and dimensionaly regularized fractal-like spacetime as well as being inspired by a note worthy anecdote involving the great mathematician of Alexandria, Pythagoras and the larger than life man of theoretical physics Einstein, we utilize some deep mathematical connections between equivalence classes of equivalence relations and E-infinity theory quotient space. We started from the basic principles of self similarity which came to prominence in science with the advent of the modern theory of nonlinear dynamical systems, deterministic chaos and fractals. This fundamental logico-mathematical thread related to partially ordered sets is then applied to show how the classical Newton’s kinetic energy E = 1/2mv2 leads to Einstein’s celebrated maximal energy equation E = mc2 and how in turn this can be dissected into the ordinary energy density E(O) = mc2/22 and the dark energy density E(D) = mc2(21/22) of the cosmos where m is the mass;v is the velocity and c is the speed of light. The important role of the exceptional Lie symmetry groups and ‘tHooft-Veltman-Wilson dimensional regularization in fractal spacetime played in the above is also highlighted. The author hopes that the unusual character of the analysis and presentation of the present work may be taken in a positive vein as seriously attempting to propose a different and new way of doing theoretical physics by treating number theory, set theory, group theory, experimental physics as well as conventional theoretical physics on the same footing and letting all these diverse tools lead us to the answer of fundamental questions without fear of being labelled in one way or another.

Holographic Principle and Large Scale Structure in the Universe

A reasonable representation of large scale structure, in a closed universe so large it’s nearly flat, can be developed by extending the holographic principle and assuming the bits of information describing the distribution of matter density in the universe remain in thermal equilibrium with the cosmic microwave background radiation. The analysis identifies three levels of self-similar large scale structure, corresponding to superclusters, galaxies, and star clusters, between today’s observable universe and stellar systems. The self-similarity arises because, according to the virial theorem, the average gravitational potential energy per unit volume in each structural level is the same and depends only on the gravitational constant. The analysis indicates stellar systems first formed at z ≈ 62, consistent with the findings of Naoz et al., and self-similar large scale structures began to appear at redshift z ≈ 4. It outlines general features of development of self-similar large scale structures at redshift z < 4. The analysis is consistent with observations for angular momentum of large scale structures as a function of mass, and average speed of substructures within large scale structures. The analysis also indicates relaxation times for star clusters are generally less than the age of the universe and relaxation times for more massive structures are greater than the age of the universe.

基于跨尺度相似先验的遥感图像时空融合算法

遥感卫星图像在空间分辨率和时间分辨率之间权衡导致图像序列的时空矛盾.时空图像融合提供了一个生成高空间分辨率和高时间分辨率图像的解决方案,以满足各种地球观测应用.基于稀疏表示的时空融合算法通过联合训练字典和稀疏编码表示建立高低空间分辨率图像之间的关系,为物候变化、类型变化等各种情况提供了统一的融合框架.然而,多源遥感图像来自于不同的传感器,高低空间分辨率图像之间关系模型暗含有传感器映射关系,导致模型设备依赖.针对该问题,本文提出将多源遥感图像时空融合过程分解为传感器偏差校正和时空融合两个子问题,即设备依赖部分和设备无关部分.传感器偏差校正部分可以作为时空融合预处理模块,提高融合精度,并且使得后续的融合模型更加具有普适性.当高低空间分辨率图像空间分辨率差异较大时,“高低空间分辨率图像稀疏系数一致”的假设带来的融合误差非常突出.针对该问题,本文提出基于跨尺度相似先验的遥感图像时空融合算法,利用跨尺度相似块构建稀疏结构先验的正则项,优化稀疏表示的目标函数,并构建中间尺度图像,降低跨尺度相似块的二义性.本文分别使用3组典型场景的实验数据集与其他算法进行对比,实验结果表明,在BOREAS数据集上,与次优的指标相比,本文算法的结构相似度(Structural SIMilarity,SSIM)提高了4.2%,光谱角(Spectral Angle Mapper,SAM)提高了4.6%;在CIA数据集上,与次优的指标相比,本文算法的SSIM提高了2.7%,SAM提高了12.8%;在LGC数据集上,与次优的指标相比,本文算法的SSIM提高了7.1%,SAM提高了16.3%;证明本文算法在空间和光谱特性上表现出优秀的特性.

自动驾驶场景类间相似特征自适应分类网络

解决类间相似度问题是自动驾驶场景分类研究中一项充满挑战的任务,主要研究在相似度较高的真实复杂交通场景中,利用网络学习目标特征的差异性,并构建特征之间整体关联性进行场景分类。提出一种多尺度自适应特征筛选的自动驾驶场景分类网络。采用双重多尺度特征提取模块预处理,初步提取不同尺度下的类间相似特征;设计了特征分化筛选模块完成场景相似特征筛选,使网络更关注不同场景类别的典型易区分特征;将特征筛选结果和多尺度特征图共同传递至特征融合分类模块进行场景分类,捕捉场景特征之间的关联性;由自适应学习算法通过输出结果动态调整训练参数,加快网络收敛速度并提升精度。所提方法在三种数据集BDD100k、BDD100k+和自制数据集上与现有网络方法进行比较,相较Top2网络在精度上分别领先了3.29%、5.59%、12.65%(相对),实验结果表明了所提方法的有效性,并展现了很好的泛化能力。提出的场景分类方法旨在学习不同复杂场景类别下的典型易区分的特征及其关联性,降低多目标类间相似的影响,使得在真实交通场景数据集中场景分类结果更加准确。

隧道突涌水灾害缩尺物理模型试验研究综述

随着我国经济的快速发展,隧道工程建设越来越多地面临着大埋深、高地应力、高地温、高渗透压力等复杂地质环境,运用物理模型试验开展复杂环境下隧道突涌水灾变机理与安全防控研究已成为岩石力学的热点问题。结合近年来国内外针对隧道突涌水灾变缩尺物理模型试验开展的研究工作,重点对该类物理模型的流固耦合相似理论、相似材料研制、渗透压加载方法等内容进行了系统梳理,并对下一步的可能发展方向进行展望。参考部分文献和突水典型案例后,总结了流固耦合相似准则和高地应力下的相似准则、相似材料选用方案及材料配比、试验中的水压加载方案,认为未来关注的重点包括考虑温度场的物理模型试验相似准则的研究和涌水涌泥与突水突泥间渐变过程的研究2个方面。

螺杆挤出机相似放大理论的研究进展

总结了近些年来国内外提出的螺杆挤出机相似放大准则,并对比分析了常用的放大系数和适用范围,同时,介绍了相似放大理论在螺杆挤出机中的优化挤出机的结构参数、工艺条件的实际应用,最后指出了螺杆挤出机相似放大理论进一步探索和优化,为螺杆挤出机行业的可持续发展提供更加有效的理论支持。

跨尺度混合注意力的遥感图像超分辨率重建

为了解决现有遥感图像超分辨率重建模型对长期特征相似性和多尺度特征相关性关注不足的问题,提出了一种基于跨尺度混合注意力机制的遥感图像超分辨率重建算法.首先提出了一个全局层注意力机制(global layer attention,GLA),利用层注意力机制加权融合不同层级的全局特征,建模低分辨率与高分辨率图像特征间的长期依赖关系.同时,设计了跨尺度局部注意力机制(cross-scale local attention,CSLA),在多尺度的低分辨率特征图中寻找与高分辨率图像匹配的局部信息补丁,并融合不同尺度的补丁特征,以优化模型对图像细节信息的恢复能力.最后,提出一种局部信息感知损失函数来指导图像的重建过程,进一步提高了重建图像的视觉质量和细节保留能力.在UC-Merced数据集上的实验结果表明,本文方法在3种放大倍数下的平均PSNR/SSIM优于大多数主流方法,并在视觉效果方面展现出更高的质量和更好的细节保留能力.

基于CFD方法的民用飞机缩比飞行试验尺度效应研究

缩比飞行试验在民用飞机的研制过程中日益受到重视,其尺度效应影响评估是设计缩比飞行试验工况的基础,也是确认飞行数据适用性的关键。基于几何和动力学相似准则,针对某民用飞机及其缩比验证机设计分析工况组,采用计算流体力学方法研究相似准则参数(马赫数Ma、雷诺数Re、弗劳德数Fr)对气动载荷、典型气动导数及弹性变形的影响。研究结果表明,在动力学相似时,验证机与原型机间的气动特性差异主要来源于马赫数,雷诺数影响较小;当原型机以中低马赫数(Ma<0.5)飞行时,此时马赫数压缩效应不明显,验证机与原型机具有近似的展向与弦向载荷分布特征,典型稳定导数及操纵导数差异约10%,在控制律设计鲁棒性范围内;进一步保持质量与刚度分布相似时,机翼翼尖扭转变形差异不超过0.5°,挠度差异不超过0.5%,在工程上属于可接受的范围。

Large Scale Fundamental Interactions in the Universe

The author of this paper once attempted to propose a unified framework for gauge fields based on the mathematical and physical picture of the principal fiber bundle: that is, to believe that our universe may have more fundamental interactions than the four, and these fundamental gauge fields are only components on the bottom manifold (i.e. our universe) projected by a unified gauge potential of the principal fiber bundle manifold;these components can satisfy the transformation of gauge potential, or even be transformed from one basic interaction gauge potential to another basic interaction gauge potential, and can be summarized into a unified equation, namely the generalized gauge equation expression, corresponding to gauge transformation invariance;so the invariance of gauge transformation is a necessary condition for unified field theory, and the four (or more) fundamental interaction fields of the universe are unified in a unified gauge field defined by the connection on the principal fiber bundle. In this paper, the author continues to propose a model of large-scale (gravitational) fundamental interactions in the universe based on the mathematical and physical picture of the principal fiber bundle, attempting to explain that dark matter and dark energy are merely reflections of these gravitational fundamental interactions that deviate in intensity from the gravitational fundamental interactions of the solar system at galaxy scales or some cosmic scales which are much larger than the solar system. All these “gravitational” fundamental interactions originate from the unified gauge field of the universe, namely the connection or curvature on the principal fiber bundle. These interactions are their projected representations on the bottom manifold (i.e. our universe) by different cross-sections (gauge transformations). These projection representations of the universe certainly are described by the generalized gauge equation or curvature similarity equation, and under the guidance of curvature gauge transformation factors, oscillate and evolve between the curvatures 1→0→-1→0→1 of the universe.

考虑材料应变率效应的冲击响应模型相似律研究

目的研究考虑材料应变率效应的冲击响应模型相似律,获得一个能够综合材料弹塑性和应变率效应的精确相似解,支撑工程领域的模型试验。方法根据BuckinghamП定理,综合考虑材料的弹性、应变硬化和应变率效应,进一步推导了工程结构在冲击响应的模型相似律表达式。在此基础上,提出改变结构质量和冲击器速度来满足模型相似律的方法。结果相比最近冲击模型相似律的研究中存在近似相似、迭代求解的缺陷,该方法是一个显式、完全精确的相似解,并且能够同时考虑材料的弹性、应变硬化和应变率效应。采用圆板撞击的有限元数值模型进行了验证。结论推导出的模型相似律能够准确地预测原型的动态冲击的响应。相比而言,使用以往的相似方法具有明显的误差。

基于AHP与TOPSIS的水电站能效管理水平评价方法研究

针对现有水电站能效管理水平评价方法的不足,将层次分析法(AHP)和优劣解距离法(TOPSIS)引入评价过程中,综合考虑经济、安全和管理3个方面的因素,构建了水电站能效管理评价指标体系;针对九分位标度法的不足,采用指数标度法确定各指标权重;分别利用TOPSIS方法和专家打分法对定量指标、定性指标进行评分,并进一步实现水电站能效管理水平的综合评价;以某实际水电站为例,分析和计算2018-2022年的水电站能效管理水平得分,结果表明该电站能效管理水平总体上呈上升趋势。

强化位置感知的光学与SAR图像一体化配准方法

图像配准是光学与SAR图像信息融合的基础。现有典型的配准方法大多依赖于特征点检测与匹配来实现,对不同场景区域的适用性较差,容易出现误匹配点多或有效同名点不足以致配准失效的情况。针对该问题,本文提出了一种强化位置感知的光学与SAR图像一体化配准方法,利用深度网络直接回归图像间的几何变换关系,在不依赖特征点检测与匹配的情况下,实现端到端的高精度配准。具体地,首先在骨干网络中利用融合坐标注意力的特征提取模块,捕获输入图像对中具有位置敏感性的细粒度特征;其次,融合骨干网络输出的多尺度特征,兼顾浅层特征的定位信息与高层特征的语义信息;最后提出联合位置偏差与图像相似性的损失函数优化配准结果。基于高分辨率光学与SAR图像配准公开数据集OS-Dataset的实验结果表明,与现有典型的OS-SIFT、RIFT2、DHN及DLKFM四种算法相比,所提方法对于城市、农田、河流、重复纹理及弱纹理等不同场景区域均具有良好的稳健性,在配准的目视效果以及定量的精度指标上均优于现有算法。其中平均角点误差小于3个像素的百分比与四种算法中精度最高的DLKFM相比提高了25%以上;配准速度与四种算法中最快的DHN基本相当,可实现高精度、高效率的光学与SAR图像配准。

复杂城市交通场景下的自动驾驶语义分割方法

多尺度特征金字塔可以缓解语义分割在复杂交通场景下通常存在漏分割、错误分割、边界分割不清晰等问题,但现有的多尺度特征金字塔在获取丰富的语义信息时,不得不下采样特征图,牺牲空间细节信息,而这导致了最终的分割结果仍然精度受限。针对该问题,本文提出了特征强化模块,使得有利于正确分类像素的相似特征在下采样过程之前得到基于不同矢量间余弦相似度的进一步加强,降低下采样带来的负面影响。另外,结合空洞卷积和条带卷积原理,本文对大卷积核进行了改造,并构建新的多尺度特征金字塔模块,以获取尺度不同且具备更大感受野的语义信息。该分割方法实时高效,能够满足自动驾驶语义分割要求,在VOC2012数据集上的实验表明,所提出的方法分割结果mIoU达到了74.36%,FPS达到了43,优于目前的主流语义分割方法。

基于交互式特征与多尺度特征的文本相似度研究

针对文本相似度分析过程中缺乏信息传递和忽略多元语义信息而导致相似度计算结果准确率低的问题,结合双向长短期记忆网络(BiLSTM),提出一种新颖的交互式特征与多尺度特征的文本相似度模型(IF-MSF)。首先,利用BiLSTM对句子进行编码提取全局特征矩阵,分别用软注意力机制和余弦相似度对特征矩阵进行交互,以相互传递两组特征矩阵内部的语义信息。其次,加权两组交互式特征以综合所有交互信息,并利用BiLSTM对加权交互式特征和初始编码特征再编码以捕获特征之间的差异信息。再次,使用多尺度卷积提取差异信息的多元语义特征并结合通道注意力机制增强重要特征信息。最后,融合两组增强特征判断文本对是否相似。实验选取2个数据集来验证该方法,该模型F1值分别取得最高值88.15%和85.03%,优于其他方法。