Dynamic Modeling and Analysis of 4UPS-UPU Spatial Parallel Mechanism with Spherical Clearance Joint

Clearance between the moving joints is unavoidable in real working process. At present, many researches are mainly focused on dynamics of plane revolute joint in plane mechanism, but few on dynamics of spatial spherical joint clearance in spatial parallel mechanism. In this paper, a general method is proposed for establishing dynamic equations of spatial parallel mechanism with spatial spherical clearance by Lagrange multiplier method. The kinematic model and contact force model of the spherical joint clearance were established successively. Lagrange multiplier method was used to deduce the dynamics equation of 4 UPS-UPU mechanism with spherical clearance joint systematically. The influence of friction coefficient on dynamics response of 4 UPS-UPU mechanism with spherical clearance joint was analyzed. Non-linear characteristics of clearance joint and moving platform were analyzed by Poincare map, phase diagram, and bifurcation diagram. The results show that variation of friction coefficient and clearance value had little effect on stability of the mechanism, but the chaotic phenomenon was found at spherical clearance joint. The research has theoretical guiding significance for improving the dynamic performance and avoiding of chaos of parallel mechanisms including spherical joint clearance.

Stress Analysis of Spatial Non-Concurrent Tubular Joints

The formulations of analytic-numerical method for the stress analysis of non-concurrent spatial tubular joints are introduced in the paper. The spatial DT joints with different eccentricity in the vertical diametrical plane of chord are computed. Finally the influence of eccentricity on the stress at possible hot spots is discussed.

THE SPATIAL DISTRIBUTION AND MOMENTS OF CIRCULAR FREELY JOINTED CHAIN

The spatial distribution function and second moments of circular freely jointed chain are derived based on an analytical method. The circular Gauss chain, which is simple for long chains, is compared with the circular freely jointed chain, which is exact for short chains. It is shown that the Gauss chain model predicts a more compact configurational distribution than the exact freely jointed chain. The two chain models, however, become closer to each other when the chain length increases. It is found that the difference of the mean square radius of gyration calculated with these two chain models is a constant, independent of the chain length.

Next Generation Semantic and Spatial Joint Perception——Neural Metric-Semantic Understanding

Efficient perception of the real world is a long-standing effort of computer vision.Mod⁃ern visual computing techniques have succeeded in attaching semantic labels to thousands of daily objects and reconstructing dense depth maps of complex scenes.However,simultaneous se⁃mantic and spatial joint perception,so-called dense 3D semantic mapping,estimating the 3D ge⁃ometry of a scene and attaching semantic labels to the geometry,remains a challenging problem that,if solved,would make structured vision understanding and editing more widely accessible.Concurrently,progress in computer vision and machine learning has motivated us to pursue the capability of understanding and digitally reconstructing the surrounding world.Neural metric-se⁃mantic understanding is a new and rapidly emerging field that combines differentiable machine learning techniques with physical knowledge from computer vision,e.g.,the integration of visualinertial simultaneous localization and mapping(SLAM),mesh reconstruction,and semantic un⁃derstanding.In this paper,we attempt to summarize the recent trends and applications of neural metric-semantic understanding.Starting with an overview of the underlying computer vision and machine learning concepts,we discuss critical aspects of such perception approaches.Specifical⁃ly,our emphasis is on fully leveraging the joint semantic and 3D information.Later on,many im⁃portant applications of the perception capability such as novel view synthesis and semantic aug⁃mented reality(AR)contents manipulation are also presented.Finally,we conclude with a dis⁃cussion of the technical implications of the technology under a 5G edge computing scenario.

SPATIAL-TEMPORAL JOINT FILTERP ROCESSING APPROACH FOR PHASED ARRAY AIRBORNE EARLY WARNING RADARS

To suppress the ground clutter for airborne early warning (AEW) radars is the key technique in radar signal processing. In this paper, a spatial-temporal nonadaptive joint filter processing approach is proposed to suppress the clutter for AEW radars, which can significantly reduce the computation compared with other optimal or suboptimal methods. The performance of this approach is better than that of the conventional cascaded nonadaptive processing, especially.

Spatial Modulation-Based Ambient Backscatter: Bringing Energy Self-Sustainability to Massive Internet of Everything in 6G

Ambient backscatter communications(AmBC)is a new ultra-low-power communication paradigm,which holds great promise for enabling energy self-sustainability(ESS)to massive data-intensive Internet of Everything(IoE)devices in 6G.Recent advances improve throughput and reliability by adopting multiple-antenna techniques in conventional backscatter communications(CoBC),but they cannot be directly applied to AmBC devices for high spectral and energy efficiency due to the unknown RF source and minimalist design in backscatter tag.To fill this gap,we propose SM-backscatter,an AmBC-compatible system that greatly improves spectral efficiency while maintaining ultra-low-power consumption.Specifically,the SM-backscatter consists of two novel components:i)a multiple-antenna backscatter tag that adopts spatial modulation(SM),and ii)a joint detection algorithm that detects both backscatter and source signals.To this end,we theoretically obtain an optimal detector and propose two suboptimal detectors with low complexity.Subsequently,we derive the BERs of both the backscatter and source signals to analyze the communication performance by introducing a two-step algorithm.Our simulation results verify the correctness of the theoretical analysis and indicate that our system can significantly outperform existing solutions.

PROBABILISTIC ANALYSIS AND MONTE CARLO SIMULATION OF THE KINEMATIC ERROR IN A SPATIAL LINKAGE

The stochastic models of the usual joints are first established through intro-ducing the concepts of“clearance characteristic element”and“clearance space”.After de-riving the probability density function of the joint clearance and making the probabilisticanalysis of the resulted kinematic errors,the sampling formulas of the independent varia-bles of the joint clearances are further deduced.Through Monte Carlo simulation,the sta-tistical characteristics and frequency histograms of the kinematic errors are then analysedon computer.

Spatial Modeling of Soil Lime Requirements with Uncertainty Assessment Using Geostatistical Sequential Indicator Simulation

This work presents and analyses a geostatistical methodology for spatial modelling of Soil Lime Requirements (SLR) considering punctual samples of Cation Exchange Capacity (CEC) and Base Saturation (BS) soil properties. Geostatistical Sequential Indicator Simulation is used to draw realizations from the joint uncertainty distributions of the CEC and the BS input variables. The joint distributions are accomplished applying the Principal Component Analyses (PCA) approach. The Monte Carlo method for handling error propagations is used to obtain realization values of the SLR model which are considered to compute and store statistics from the output uncertainty model. From these statistics, it is obtained predictions and uncertainty maps that represent the spatial variation of the output variable and the propagated uncertainty respectively. Therefore, the prediction map of the output model is qualified with uncertainty information that should be used on decision making activities related to the planning and management of environmental phenomena. The proposed methodology for SLR modelling presented in this article is illustrated using CEC and BS input sample sets obtained in a farm located in Ponta Grossa city, Paraná state, Brazil.

Skeleton Split Strategies for Spatial Temporal Graph Convolution Networks

Action recognition has been recognized as an activity in which individuals’behaviour can be observed.Assembling profiles of regular activities such as activities of daily living can support identifying trends in the data during critical events.A skeleton representation of the human body has been proven to be effective for this task.The skeletons are presented in graphs form-like.However,the topology of a graph is not structured like Euclideanbased data.Therefore,a new set of methods to perform the convolution operation upon the skeleton graph is proposed.Our proposal is based on the Spatial Temporal-Graph Convolutional Network(ST-GCN)framework.In this study,we proposed an improved set of label mapping methods for the ST-GCN framework.We introduce three split techniques(full distance split,connection split,and index split)as an alternative approach for the convolution operation.The experiments presented in this study have been trained using two benchmark datasets:NTU-RGB+D and Kinetics to evaluate the performance.Our results indicate that our split techniques outperform the previous partition strategies and aremore stable during training without using the edge importance weighting additional training parameter.Therefore,our proposal can provide a more realistic solution for real-time applications centred on daily living recognition systems activities for indoor environments.

Joint spatial modelling of disease risk using multiple sources:an application on HIV prevalence from antenatal sentinel and demographic and health surveys in Namibia

Background:In disease mapping field,researchers often encounter data from multiple sources.Such data are fraught with challenges such as lack of a representative sample,often incomplete and most of which may have measurement errors,and may be spatially and temporally misaligned.This paper presents a joint model in the effort to deal with the sampling bias and misalignment.Methods:A joint(bivariate)spatial model was applied to estimate HIV prevalence using two sources:2014 National HIV Sentinel survey(NHSS)among pregnant women aged 15-49 years attending antenatal care(ANC)and the 2013 Namibia Demographic and Health Surveys(NDHS).Results:Findings revealed that health districts and constituencies in the northern part of Namibia were found to be highly associated with HIV infection.Also,the study showed that place of residence,gender,gravida,marital status,number of kids dead,wealth index,education,and condom use were significantly associated with HIV infection in Namibia.Conclusion:This study had shown determinants of HIV infection in Namibia and had revealed areas at high risk through HIV prevalence mapping.Moreover,a joint modelling approach was used in order to deal with spatially misaligned data.Finally,it was shown that prediction of HIV prevalence using the NDHS data source can be enhanced by jointly modelling other HIV data such as NHSS data.These findings would help Namibia to tailor national intervention strategies for specific regions and groups of population.

焊接空心球连接H型钢Z字形螺栓节点静力性能研究

提出了一种新型焊接空心球连接H型钢Z字形螺栓节点(新型螺栓节点),该节点适用于全装配大跨度弦支穹顶结构体系上部网壳连接。该节点的弦杆采用热轧H型钢,方便栓接、施工便捷、制造能耗低,可以实现部品部件到体系的标准化。为了研究新型螺栓节点的静力性能,对该节点进行了轴压、轴拉、纯弯和压弯荷载工况下的有限元分析,研究了该节点在不同荷载工况下的破坏模式、承载力、刚度、延性等力学性能指标,对比分析了新型螺栓节点与焊接空心球连接H型钢焊接节点(焊接节点)的破坏模式和静力性能。研究表明,在轴压和压弯荷载下新型螺栓节点和焊接节点的极限破坏模式均为H型钢弦杆局部屈曲和屈服,在轴拉荷载下新型螺栓节点和焊接节点的极限破坏模式均为H型钢弦杆全截面屈服,在纯弯荷载下新型螺栓节点和焊接节点的极限破坏模式均为H型钢弦杆局部屈曲。新型螺栓节点可以实现塑性铰远离焊缝和拼接区,符合“强连接,弱杆件”的设计要求。

高光谱图像去噪的稀疏空谱Transformer模型

现阶段Transformer模型的应用提升了高光谱图像去噪的性能,但原始Transformer模型对图像空间-光谱耦合关联性的利用仍存在不足;对空间特征的处理存在过于平滑,容易丢失小尺度结构的现象;同时在光谱维度上也过于关注全部通道特征,缺乏对不同光谱波段间差异性的利用;为了应对这些问题,本文提出了一种新的稀疏空谱Transformer模型,提升了对空谱耦合关联性的利用。在空间维度,引入局部增强模块增强空间特征细节,应对过平滑问题;同时在光谱维度上提出了Top-k稀疏自注意力机制,自适应选择前K个最相关的光谱通道特征进行特征交互,从而能够有效捕获空谱特征。最终通过稀疏空谱Transformer的层级残差连接实现高光谱图像的去噪。在ICVL数据集上分别对高斯噪声和复杂噪声进行去噪处理,峰值信噪比分别达到40.56 dB和40.19 dB,证明了本文提出的稀疏空谱Transformer模型优越的性能。

BYOL框架下的自监督高光谱图像分类

高光谱图像可以获取波段连续的图谱合一的立体数据,其具有丰富的图谱信息,能区分不同物质的类别,被广泛应用于各种遥感勘测领域。但在实际中高光谱图像的标注需要耗费大量的人力、财力和时间,可用的标注样本数量较少,难以通过训练来获得准确的分类结果,所以针对于只有少量标记样本的高光谱图像分类是一个挑战。近年来,自监督学习(Self-supervised Learning,SSL)已成为一种有效的方法,可以减少高光谱图像分类对昂贵的数据标注的依赖。SSL方法通过学习在同一图像的不同视图之间产生的潜在特征,在自然图像分类中取得了较高的分类精度。为了探索SSL方法在高光谱图像分类中的潜力,一种Bootstrap Your Own Latent(BYOL)框架下的自监督高光谱图像分类方法(BSSL)被提出。该方法通过引用自监督的图像特征学习框架BYOL,可以不需要负样本对,利用空间光谱相似的同类样本对进行网络训练及参数微调,提取到更具判别性特征。具体来说,该方法主要包括四个部分:BYOL的预训练、超像素聚类、基于“相似对”的BYOL的再训练和最终分类。为了验证该方法的有效性,在三个公开数据集上进行测试,并与五种先进的无监督、自监督分类方法SuperPCA、S3PCA、ContrastNet、SSCL和N2SSL进行对比,在Indian Pines和Salinas数据集上,BSSL方法的总体分类精度(OA)、平均分类精度(AA)、Kappa系数、召回率(recall)和f1分数(f1-score)都取得了更优值。其中在Indian Pines数据集上,OA分别比SuperPCA,S3PCA,ContrastNet,SSCL和N2SSL提高了1.32%,1.05%,5.68%,3.12%和1.27%。而在University of Pavia数据集上,BSSL方法表现没有那么出色,但在综合分类性能上也表现最优。这表明BSSL方法更适用于地物区域面积较大且分布较集中的场景,因为这对于超像素聚类来说更友好。

抬梁式空间木结构古建筑有限元建模方法

基于空间抬梁式木结构古建筑拟静力试验结果,以榫卯缝隙、摩擦系数及屋顶存在与否为变量,构建六组有限元数值模型,探讨这些变量对模型精度的影响.研究结果表明:所建立的数值模型能够准确模拟结构在加载过程中的变形特性;结构滞回曲线为反“S”形,具有明显的“捏缩效应”,与木结构的实际耗能特征相符;在受力性能方面,燕尾榫节点之间的缝隙会降低结构的承载力;结构的承载力和刚度随摩擦系数的增大而增大;提出的两种屋顶简化方法使结构的承载力分别增加20%和60%.通过对比明确了抬梁式空间木结构数值模拟中影响其精度的关键因素,为后续木结构数值模型的建立与修正提供了重要的参考依据.

面向大规模MIMO的分块空间调制技术研究

广义空间调制(Generalized Spatial Modulation,GSM)是一种基于多输入多输出(Multiple-Input Multiple-Output,MIMO)系统的高效的数字调制技术,在传统调制符号之外同时通过激活天线模式组合发送信息比特。针对大规模MIMO系统中发送天线数量过多导致的激活天线模式组合的数量过多的问题,提出了一种分块空间调制算法,将每个发送天线子块的激活模式组合构成完整的激活天线模式组合,可有效降低大规模MIMO系统中发送端的调制复杂度。在接收端使用球形译码方法实现逐个子块的天线激活模式组合的解调,可以大幅降低接收端的计算复杂度。仿真分析表明,提出的球形译码算法可以在大幅降低计算复杂度的情况下,实现接近最大似然(Maximum Likelihood,ML)接收机算法的误比特率(Bit Error Rate,BER)性能,且可实现接收端检测性能和计算复杂度之间的最佳折衷。

基于深度特征提取残差网络的高光谱图像分类

深度学习由于其模块化设计和强大的特征提取能力,已成为高光谱图像分类的重要手段之一。然而,如何有效地提取更深层次的特征以及同时提高分析空间和光谱联合特征的能力仍是亟待解决的问题。针对这些问题,本文提出了一种深度特征提取的残差网络,该网络由两个关键部分组成:多级传递融合残差网络和空间-光谱多分辨率融合注意力残差网络。多级传递融合残差网络可以有效促进特征信息之间的相互作用,获得更深层次的特征。接着利用空间-光谱多分辨率融合注意力残差网络可以确保从高光谱数据中全面提取空间-光谱联合特征和多分辨率特征。为了验证其有效性,本文在Indian Pines,Pavia University和Salinas Valley三个高光谱数据集上对所提出方法的性能进行了评估,分类精度分别达到了98.10%,99.81%和99.94%。实验结果表明,与其他方法相比,该网络具有更好的泛化能力和分类性能。

一种联合空谱特征的沥青路面裂缝检测方法

文章提出了一种基于高光谱图像的沥青路面裂缝检测算法,旨在解决传统方法下裂缝检测精度低、误检较多的问题。本方法利用高光谱成像技术获取道路的空间-光谱信息,并通过裂缝与路面的光谱信息差异提取裂缝,同时利用裂缝的空间特征信息对目标进行阈值分割、形态学运算等以此来提取裂缝主干,进而联合空间-光谱特征检测结果进行数学运算以实现对沥青路面裂缝的精确检测。实验结果表明,与ACI、VIS2算法相比,本文算法的F1-score分别提升了14.11%和29.03%,证明了该方法的有效性,同时针对不同类型和尺寸的裂缝目标表现出了高检测精度、强抗干扰性和显著的鲁棒性。

一种机载组网雷达协同目标检测算法

多机协同的机载雷达组网联合目标探测可有效提高复杂电磁干扰环境下对隐身弱目标的探测能力。文中针对机载雷达组网探测时空间配准误差大、协同探测难以实现的难题,提出了一种基于轨迹空间配准的协同目标检测算法,通过雷达间少量距离-多普勒域数据及低检测门限下目标轨迹域数据的交互,采用极大似然估计广义似然比检测器对目标进行联合恒虚警检测(CFAR),并通过轨迹域空间配准与CFAR的迭代计算,实现配准精度和目标联合检测性能的双提升。数值仿真实验的结果表明,在四部雷达组网工作时,在相参积累后信噪比9 dB、虚警概率10-4的典型场景下,经过迭代处理,空间配准精度可达到一个距离-多普勒分辨单元;对目标的检测概率由单部雷达的28.5%提高到四部雷达协同下的83.67%。

分布式间歇采样转发信号对机载干涉仪参数测量影响分析

针对反机载干涉仪侦察场景,该文提出一种基于间歇采样转发技术生成分布式信号对干涉仪参数测量施加干扰的方法。辐射源与转发干扰机分布部署构成干扰系统,转发干扰机对辐射源脉冲信号进行间歇采样并向干涉仪转发,转发信号与辐射源信号准同步到达干涉仪,实现对干涉仪空域参数和时域参数的同时干扰。对于机载干涉仪运动过程中与干扰系统发生的位置关系变化,给出信号准同步约束并构建分布式信号叠加模型。然后针对干涉仪体制对脉冲空域和时域参数的测量机理,分析分布式信号实施干扰的原理,并分析信号参数对干扰效果的影响,进而提出分布式信号设计原则。仿真实验和暗室实验结果表明,该文所提出分布式波形可有效扰乱干涉仪对信号到达方位角、脉宽和脉冲重复周期等空时域参数的正确测量。

联合注意力与混合卷积的高光谱地物识别研究

针对高光谱地物识别模型在提取空谱联合特征时,缺乏对空间特征有效关注的问题,提出了一种基于注意力机制和混合卷积神经网络的高光谱地物识别方法.该方法用三维CNN(3DCNN)以及二维CNN(2DCNN)对高光谱图像的空谱联合特征进行提取,并在二维卷积阶段引入了注意力机制,构建AFCNet地物识别模型,使得其在提取空谱联合特征的同时,实现对空间特征的有效关注和激活.所提模型使用带批归一化层(batch normalization,BN)的3D卷积核和2D卷积核,加快了模型的收敛速度,防止了过拟合现象的发生.相对于传统的卷积网络模型,所提模型提高了噪声抑制能力,得到了较好的地物识别效果,在Salinas和Pavia University&Center数据集上,取得了99.96%和99.87%的地物识别精度,验证了所提方法的有效性.