基于改进Marching Cubes算法的雷达气象数据三维重建

在气象领域中,多普勒天气雷达探测到的气象数据采用以雷达站点为原点的三维极坐标系进行存储,具有形状不规则、数据量大等特征。Marching Cubes(MC)算法是三维重建中的经典算法,但应用于气象领域时存在重建效率低下和不能直接处理气象数据的缺点。为了实现气象数据的三维重建,基于MC算法提出雷达数据归一化处理与状态标记判别算法NBV-MC。该算法根据雷达基数据文件的特点对其进行归一化预处理,使用雷达基数据构建拟梯形六面体体素,对每一个六面体体素进行状态标记,在遍历六面体体素时动态判别其是否需要处理。实验结果表明,NBV-MC算法不仅解决了由于气象数据具有不规则性而不能直接用于MC算法的问题,而且可以在保证数据真实性和重建效果的情况下有效减少绘制等值面所需要的三角面片数量,提高重建速度。与MC算法相比,NBV-MC算法的重建效率提升了77.70%以上,有利于实时场景交互,便于气象研究人员直接分析雷达数据。

CUBE自动滤波在远海多波束测深中的应用

为提升远海多波束测深数据标准化处理能力,验证CUBE自动滤波在远海海域的实际应用效果,选取远海海域3个典型深度测区,使用优选的CUBE自动处理参数组,对多波束测深数据进行自动滤波处理,并从处理用时、逐点标记以及交叉点水深差值3个方面,将处理结果与多名作业人员的手工处理结果进行对比分析。实验结果表明,使用CUBE自动处理与人工处理结果基本相同,且少量处理不同之处产生的水深成果差值均在测量误差允许范围之内,且CUBE自动处理在处理效率、成果标准一致性方面均优于人工处理。本文成果对于提升远海多波束测深数据标准化处理能力具有较强的实践指导意义。

Multivariate Time Series Anomaly Detection Based on Spatial-Temporal Network and Transformer in Industrial Internet of Things

In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working state.When the systems are attacked,timely identification of outliers in time series is critical to ensure security.Although many anomaly detection methods have been proposed,the temporal correlation of the time series over the same sensor and the state(spatial)correlation between different sensors are rarely considered simultaneously in these methods.Owing to the superior capability of Transformer in learning time series features.This paper proposes a time series anomaly detection method based on a spatial-temporal network and an improved Transformer.Additionally,the methods based on graph neural networks typically include a graph structure learning module and an anomaly detection module,which are interdependent.However,in the initial phase of training,since neither of the modules has reached an optimal state,their performance may influence each other.This scenario makes the end-to-end training approach hard to effectively direct the learning trajectory of each module.This interdependence between the modules,coupled with the initial instability,may cause the model to find it hard to find the optimal solution during the training process,resulting in unsatisfactory results.We introduce an adaptive graph structure learning method to obtain the optimal model parameters and graph structure.Experiments on two publicly available datasets demonstrate that the proposed method attains higher anomaly detection results than other methods.

The Mechanism of Cube Texture Formation in Ni-7 at%W Substrate

In order to obtain the sharp cube texture,a new process,the intermediate annealing rolling technique,has been introduced to prepare the Ni7W substrate.In this paper,a cubic texture content up to 98.5%within 10°of the standard cubic orientation is obtained in the final substrate and the influence of this improved rolling technique on the cube texture formation has been discussed.The results show that the increased cube texture in the Ni7W substrate is caused by the optimized deformation texture and the increased nucleated fraction of the cube grains.

An Improved Framework of Massive Superpoly Recovery in Cube Attacks Against NFSR-Based Stream Ciphers

A critical problem in the cube attack is how to recover superpolies efficiently.As the targeting number of rounds of an iterative stream cipher increases,the scale of its superpolies becomes larger and larger.Recently,to recover massive superpolies,the nested monomial prediction technique,the algorithm based on the divide-and-conquer strategy,and stretching cube attacks were proposed,which have been used to recover a superpoly with over ten million monomials for the NFSR-based stream ciphers such as Trivium and Grain-128AEAD.Nevertheless,when these methods are used to recover superpolies,many invalid calculations are performed,which makes recovering superpolies more difficult.This study finds an interesting observation that can be used to improve the above methods.Based on the observation,a new method is proposed to avoid a part of invalid calculations during the process of recovering superpolies.Then,the new method is applied to the nested monomial prediction technique and an improved superpoly recovery framework is presented.To verify the effectiveness of the proposed scheme,the improved framework is applied to 844-and 846-round Trivium and the exact ANFs of the superpolies is obtained with over one hundred million monomials,showing the improved superpoly recovery technique is powerful.Besides,extensive experiments on other scaled-down variants of NFSR-based stream ciphers show that the proposed scheme indeed could be more efficient on the superpoly recovery against NFSR-based stream ciphers.

Quasi-static and dynamic compressive behaviour of additively manufactured Menger fractal cube structures

This paper presents the first-ever investigation of Menger fractal cubes'quasi-static compression and impact behaviour.Menger cubes with different void ratios were 3D printed using polylactic acid(PLA)with dimensions of 40 mm×40 mm×40 mm.Three different orders of Menger cubes with different void ratios were considered,namely M1 with a void ratio of 0.26,M2 with a void ratio of 0.45,and M3with a void ratio of 0.60.Quasi-static Compression tests were conducted using a universal testing machine,while the drop hammer was used to observe the behaviour under impact loading.The fracture mechanism,energy efficiency and force-time histories were studied.With the structured nature of the void formation and predictability of the failure modes,the Menger geometry showed some promise compared to other alternatives,such as foams and honeycombs.With the increasing void ratio,the Menger geometries show force-displacement behaviour similar to hyper-elastic materials such as rubber and polymers.The third-order Menger cubes showed the highest energy absorption efficiency compared to the other two geometries in this study.The findings of the present work reveal the possibility of using additively manufactured Menger geometries as an energy-efficient system capable of reducing the transmitting force in applications such as crash barriers.

Comprehensive evaluation and spatial-temporal evolution characteristics of urban resilience in Chengdu-Chongqing Economic Circle

To clarify the connotations and extensions of urban resilience,this study focuses on the Chengdu-Chongqing Economic Circle with 16 cities as research subjects.A comprehensive evaluation index system was constructed to measure the resilience of each city from 2003 to 2020.The spatial-temporal evolution characteristics were analyzed using Kernel density estimation,standard deviation ellipse,and spatial Markov chain analysis,and the spatial Tobit model was introduced to discover the influencing factors.The results indicate the following:①Urban resilience in the Chengdu-Chongqing Economic Circle displays an upward trend,with the center of gravity moving to the southwest,and the polarization phenomenon intensifying.②The urban resilience level in a region has certain spatial and geographical dependence,while the probability of urban resilience transfer differs in adjacent cities with different resilience levels.③Urban centrality,economic scale,openness level,and financial development promote urban resilience,whereas government scale significantly inhibits it.Finally,this paper proposes countermeasures and suggestions to improve the urban resilience of the Chengdu-Chongqing Economic Circle.

AFSTGCN:Prediction for multivariate time series using an adaptive fused spatial-temporal graph convolutional network

The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other fields.Furthermore,it is important to construct a digital twin system.However,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted accuracy.In this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)layer.Specifically,we fuse the spatial-temporal graph based on the interrelationship of spatial graphs.Simultaneously,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding methods.Subsequently,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)module.The module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like data.AFSTGCN dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted accuracy.Experiments on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.

Spatial-temporal distribution and geochemistry of highly evolved Mesozoic granites in Great Xing’an Range,NE China:Discriminant criteria and geological significance

Highly evolved granite is an important sign of the mature continent crust and closely associated with deposits of rare metals.In this work,the authors undertake systematically zircon U-Pb ages and whole rock elemental data for highly evolved granitic intrusions from the Great Xing’an Range(GXR),NE China,to elucidate their discriminant criteria,spatial-temporal distribution,differentiation and geodynamic mecha-nism.Geochemical data of these highly evolved granites suggest that high w(SiO_(2))(>70%)and differentiation index(DI>88)could be quantified indicators,while strong Eu depletion,high TE_(1,3),lowΣREE and low Zr/Hf,Nb/Ta,K/Rb could only be qualitative indicators.Zircon U-Pb ages suggest that the highly evolved gran-ites in the GXR were mainly formed in Late Mesozoic,which can be divided into two major stages:Late Ju-rassic-early Early Cretaceous(162-136 Ma,peak at 138 Ma),and late Early Cretaceous(136-106 Ma,peak at 126 Ma).The highly evolved granites are mainly distributed in the central-southern GXR,and display a weakly trend of getting younger from northwest to southeast,meanwhile indicating the metallogenic potential of rare metals within the central GXR.The spatial-temporal distribution,combined with regional geological data,indicates the highly evolved Mesozoic granites in the GXR were emplaced in an extensional environ-ment,of which the Late Jurassic-early Early Cretaceous extension was related to the closure of the Mongol-Okhotsk Ocean and roll-back of the Paleo-Pacific Plate,while the late Early Cretaceous extension was mainly related to the roll-back of the Paleo-Pacific Plate.

Spatial-temporal Divergence Characteristics and Driving Factors of Green Economic Efficiency in the Yangtze River Economic Belt of China

The spatial and temporal variation of green economic efficiency and its driving factors are of great significance for the construction of high-efficiency and low-consumption green development model and sustainable socio-economic development.The research focused on the Yangtze River Economic Belt(YREB)and employed the miniumum distance to strong efficient frontier DEA(MinDs)model to measure the green economic efficiency of the municipalities in the region between 2008 and 2020.Then,the spatial autocorrelation model was used to analyze the evolution characteristics of its spatial pattern.Finally,Geodetector was applied to reveal the drivers and their interactions on green economic efficiency.It is found that:1)the overall green economic efficiency of the YREB from 2008 to 2020 shows a W-shaped fluctuating upward trend,green economic efficiency is greater in the downstream and smallest in the upstream;2)the spatial distribution of green economic efficiency shows clustering characteristics,with multi-core clustering based on‘city clusters-central cities'becoming more obvious over time;the High-High agglomeration type is mainly clustered in Jiangsu and Zheji-ang,while the Low-Low agglomeration type is clustered in the western Sichuan Plateau area and southwestern Yunnan;3)from input-output factors,whether it is the YREB as a whole or the upper,middle and lower reaches regions,the economic development level,labor input,and capital investment are the leading factors in the spatial-temporal evolution of green economic efficiency,with the com-prehensive influence of economic development level and pollution index being the most important interactive driving factor;4)from so-cio-economic factors,information technology drivers such as government intervention,transportation accessibility,information infra-structure,and Internet penetration are always high impact influencers and dominant interaction factors for green economic efficiency in the YREB and the three major regions in the upper,middle and lower reaches.Accordingly,the article puts forward relevant policy re-commendations in terms of formulating differentiated green transformation strategies,strengthening network leadership and informa-tion technology construction and coordinating multi-factor integrated development,which could provide useful reference for promoting synergistic green economic efficiency in the YREB.

Adaptive spatial-temporal graph attention network for traffic speed prediction

Considering the nonlinear structure and spatial-temporal correlation of traffic network,and the influence of potential correlation between nodes of traffic network on the spatial features,this paper proposes a traffic speed prediction model based on the combination of graph attention network with self-adaptive adjacency matrix(SAdpGAT)and bidirectional gated recurrent unit(BiGRU).First-ly,the model introduces graph attention network(GAT)to extract the spatial features of real road network and potential road network respectively in spatial dimension.Secondly,the spatial features are input into BiGRU to extract the time series features.Finally,the prediction results of the real road network and the potential road network are connected to generate the final prediction results of the model.The experimental results show that the prediction accuracy of the proposed model is im-proved obviously on METR-LA and PEMS-BAY datasets,which proves the advantages of the pro-posed spatial-temporal model in traffic speed prediction.

Spatial-temporal Variation Characteristics of Water Quality in the Lower Reaches of the Nenjiang River

As an important river in the western part of Jilin Province,the lower reach of the Nenjiang River is an important wetland water source conservation area in Jilin Province.Within the watershed,it governs the Momoge Wetland,the Xianghai Wetland,and the Danjiang Wetland in Jilin Province.The main problem in the lower reaches of the Nenjiang River is the uneven distribution of water resources in time and space,and the intensification of land salinization.Zhenlai County and Da an City in the Nenjiang River Basin have sufficient surface water resources,with surface water as the drinking water source.Baicheng City and Tongyu County have scarce surface water resources,and both use groundwater as their domestic water source.The main polluted section in the basin is the Xianghai Reservoir,and the annual water quality evaluation is Class V.However,the water quality of the Tao er River,the main stream of the Nenjiang River,is significantly better than that of the Xianghai Reservoir.In order to better study the water environmental pollution situation in the Nenjiang River basin,monitoring data from five sections of non seasonal rivers in the basin from 2012 to 2021 were selected for studying water quality.This in-depth exploration of the water pollution status and river water quality change trends in the Nenjiang River basin is of great significance for future rural development,agricultural pattern transformation,and the promotion of water ecological civilization construction.

基于X-CUBE-AI的神经网络手语识别系统设计

手语交流是聋哑人与他人交流的主要方法,设计了一款基于STM32的神经网络手语识别系统,采用TensorFlow和Keras机器学习框架训练网络模型,由于训练数据的局限性,加入L2正则化以解决训练过拟合问题。使用意法半导体推出的STM32Cube.AI工具包在STM32嵌入式平台上部署神经网络,搭载OV2640摄像头采集图像并进行缩放处理,将图像数据传入网络进行推理可获得识别结果。经测试,网络模型获得了较好的鲁棒性和适应性,在STM32上运行平均每帧推理时间为15.8 ms,平均准确率达92.3%。使用STM32作为手语识别终端,极大地提高了设备的便携性,节约成本,反应迅速。为聋哑人与正常人交流提供了一种新的解决方案,具有较高的实际应用价值。

Marching Cubes算法研究现状

对现有的Marching Cubes改进算法从拓扑结构二义性、提高逼近精度、算法的时间和空间效率3个方面进行综述,对每一类改进算法进行新的分类,并对各类算法的实验结果进行比较.

利用CUBE算法剔除多波束测深粗差研究

针对国内基于不确定度信息处理多波束测深数据相对滞后现状,将CUBE算法应用于大批量多波束测深粗差剔除研究。通过与人工交互编辑结果对比,表明CUBE算法剔除粗差效率较高并能保证测深成果质量,具有较高的推广应用价值。该研究对进一步优化我国多波束测深粗差处理算法也具有重要意义。

Marching Cubes改进算法及其气象三维模拟

用三等分体元边界的方法取代了原始Marching Cubes算法中通过线性插值获得等值面与体元交点的方法,该方法不仅解决了线性插值不适用于气象数据模拟的问题,还有效地减少了等值面绘制过程中生成的三角面片的数量,在减少冗余提高绘制速度的同时也进一步提高了等值面图像绘制的质量。将改进之后的Marching Cubes算法应用在对气象模式数据即WRF数据的等值面模拟中,在图像绘制速度和绘制质量上均取得了不错的实验效果。

DHMC:一种有效的高维Cube并行分布式存储结构

在数据仓库系统中,数据立方体(Cube)及其预聚集处理在OLAP起到非常重要的作用.对于一个d维的data Cube可以生成2d个聚集Cuboids和multiply from i=1 to d(|Di|+1)个聚集数据单元,但对于一个高维Cube,要创建这些所有聚集Cuboids是不现实的.提出通过共享分段立方体Mini-Cube的高维Cube并行分布式存储结构(DHMC),将高维Cube划分成若干个低维共享分段立方体Mini-Cube,利用并行分布式处理技术来创建这些分割的分段共享Mini-Cube及其聚集Cuboids,来实现高维Cube的并行创建和增量更新维护,从而解决高维OLAP聚集海量数据的存储与查询问题.理论分析与实验结果均表明DHMC性能最佳.

基于Kriging和Marching cube算法的地学3维形态模拟

地质构造的复杂性和勘查数据的不完备性,决定了对地质要素(地质体和场)的空间形态进行3维模拟是一个面临众多技术难题的艰巨任务。针对此问题,探讨了一种利用空间插值和等值面勾绘来建立地质要素的接近真实的3维可视化模型的方法。Kriging算法,由于其在揭示空间数据非均匀、各向异性分布规律方面具有良好表现,因而被用于对不完备地学数据进行空间插值,即先将分散的不规则分布的数据转换成规则分布的网格数据;再通过Marching cube算法实现由网格离散点构建光滑3维等值曲面;最后通过OpenGL接口实现等值曲面的计算机3维重绘。整个过程采用VC++.net结合OpenGL编程完成。应用这种方法,可实现有限钻孔取样分析数据的矿体品位3维等值面模拟和电磁测深2维反演数据的电阻率3维等值面模拟,其模拟结果接近于传统勘探方法解释的结果,而且其效率高得多,也更加直观,证明此方法是有效的和实用的。

磁共振Bravo序列与Cube Flair序列对肺癌脑转移患者的应用比较

目的比较分析3D Bravo及3D Cube Flair两种MR增强扫描序列对肺癌脑转移检出能力的优劣,优化肺癌脑转移病例的MR检查方案。方法回顾分析25例临床确诊的肺癌患者的MR检查结果,患者MR检查时同时行3D Bravo及3D Cube Flair两种MR增强扫描序列,比较两者的病灶显示能力并作统计分析。结果 3D Cube flair共检出135个病灶,其中脑膜转移灶78个、脑内转移灶57个;3D Bravo共检出91个病灶,其中脑膜转移灶36、脑内转移灶55个。脑膜转移者两种扫描序列相比P<0.01,有明显差异;脑内转移者两种扫描序列相比P>0.05,无差异。结论 3D Cube Flair增强扫描序列在早期判断肺癌脑转移尤其是早期脑膜转移具有较高应用价值,与3D Bravo扫描序列联合应用对诊断价值更高。

CUBE算法及其在多波束数据处理中的应用

阐述了一种基于表面生成的多波束数据处理方法——CUBE(Combined Uncertainty and Bathymetry Estimator)算法,用该方法可以对观测区域网格节点"真实"水深及相关误差进行估计。与从测量水深中选择出"最佳"数据的手工交互方式的多波束数据编辑手段不同,CUBE算法具有很强的抗差性和较高的效率,适合于实时多波束数据处理。对南海某测区多波束数据处理结果表明,在没有人工干预的情况下,利用CUBE算法去噪生成的海底DTM图与手工编辑生成的相当吻合。CUBE算法和手工编辑方法综合对比得出,CUBE算法能够很好地保留水深地形细节,在计算效率、误差评估、实时处理等方面比手工编辑方法具有较大的优势。