Unified Data Model of Urban Air Pollution Dispersion and 3D Spatial City Model:Groundwork Assessment towards Sustainable Urban Development for Malaysia

Understanding the behavior of urban air pollution is important en route for sustainable urban development (SUD). Malaysia is on its mission to be a developed country by year 2020 comprehends dealing with air pollution is one of the indicators headed towards it. At present monitoring and managing air pollution in urban areas encompasses sophisticated air quality modeling and data acquisition. However, rapid developments in major cities cause difficulties in acquiring the city geometries. The existing method in acquiring city geometries data via ground or space measurement inspection such as field survey, photogrammetry, laser scanning, remote sensing or using architectural plans appears not to be practical because of its cost and efforts. Moreover, air monitoring stations deployed are intended for regional to global scale model whereby it is not accurate for urban areas with typical resolution of less than 2 km. Furthermore in urban areas, the pollutant dispersion movements are trapped between buildings initiating it to move vertically causing visualization complications which imply the limitations of existing visualization scheme that is based on two-dimensional (2D) framework. Therefore this paper aims is to perform groundwork assessment and discuss on the current scenario in Malaysia in the aspect of current policies towards SUD, air quality monitoring stations, scale model and detail discussion on air pollution dispersion model used called the Operational Street Pollution Model (OSPM). This research proposed the implementation of three-dimensional (3D) spatial city model as a new physical data input for OSPM. The five Level of Details (LOD) of 3D spatial city model shows the scale applicability for the dispersion model implementtation. Subsequently 3D spatial city model data commonly available on the web, by having a unified data model shows the advantages in easy data acquisition, 3D visualization of air pollution dispersion and improves visual analysis of air quality monitoring in urban areas.

Combining Environmental Factors and Lab VNIR Spectral Data to Predict SOM by Geospatial Techniques

Soil organic matter(SOM) is an important parameter related to soil nutrient and miscellaneous ecosystem services. This paper attempts to improve the performance of traditional partial least square regression(PLSR) model by considering the spatial autocorrelation and soil forming factors. Surface soil samples(n = 180) were collected from Honghu City located in the middle of Jianghan Plain, China. The visible and near infrared(VNIR) spectra and six environmental factors(elevation, land use types, roughness, relief amplitude, enhanced vegetation index, and land surface water index) were used as the auxiliary variables to construct the multiple linear regression(MLR), PLSR and geographically weighted regression(GWR) models. Results showed that: 1) the VNIR spectra can increase about 39.62% prediction accuracy than the environmental factors in predicting SOM; 2) the comprehensive variables of VNIR spectra and the environmental factors can improve about 5.78% and 44.90% relative to soil spectral models and soil environmental models, respectively; 3) the spatial model(GWR) can improve about 3.28% accuracy than MLR and PLSR. Our results suggest that the combination of spectral reflectance and the environmental variables can be used as the suitable auxiliary variables in predicting SOM, and GWR is a promising model for predicting soil properties.

Far-zone behaviors of scattering-induced statistical properties of partially polarized spatially and spectrally partially coherent electromagnetic pulsed beam

In this study, we explore the far-zero behaviors of a scattered partially polarized spatially and spectrally partially coherent electromagnetic pulsed beam irradiating on a deterministic medium. The analytical formula for the cross-spectral density matrix elements of this beam in the spherical coordinate system is derived. Within the framework of the first-order Born approximation, the effects of the scattering angle θ, the source parameters (i.e., the pulse duration T0 and the temporal coherence length Tcxx), and the scatterer parameter (i.e., the effective width of the medium σR) on the spectral density, the spectral shift, the spectral degree of polarization, and the degree of spectral coherence of the scattered source in the far-zero field are studied numerically and comparatively. Our work improves the scattering theory of stochastic electromagnetic beams and it may be useful for the applications involving the interaction between incident light waves and scattering media.

Sanxingdui Cultural Relics Recognition Algorithm Based on Hyperspectral Multi-Network Fusion

Sanxingdui cultural relics are the precious cultural heritage of humanity with high values of history,science,culture,art and research.However,mainstream analytical methods are contacting and detrimental,which is unfavorable to the protection of cultural relics.This paper improves the accuracy of the extraction,location,and analysis of artifacts using hyperspectral methods.To improve the accuracy of cultural relic mining,positioning,and analysis,the segmentation algorithm of Sanxingdui cultural relics based on the spatial spectrum integrated network is proposed with the support of hyperspectral techniques.Firstly,region stitching algorithm based on the relative position of hyper spectrally collected data is proposed to improve stitching efficiency.Secondly,given the prominence of traditional HRNet(High-Resolution Net)models in high-resolution data processing,the spatial attention mechanism is put forward to obtain spatial dimension information.Thirdly,in view of the prominence of 3D networks in spectral information acquisition,the pyramid 3D residual network model is proposed to obtain internal spectral dimensional information.Fourthly,four kinds of fusion methods at the level of data and decision are presented to achieve cultural relic labeling.As shown by the experiment results,the proposed network adopts an integrated method of data-level and decision-level,which achieves the optimal average accuracy of identification 0.84,realizes shallow coverage of cultural relics labeling,and effectively supports the mining and protection of cultural relics.

Generation of endurance time excitation functions using spectral representation method

In this study,application of the spectral representation method for generation of endurance time excitation functions is introduced.Using this method,the intensifying acceleration time series is generated so that its acceleration response spectrum in any desired time duration is compatible with a time-scaled predefined acceleration response spectrum.For this purpose,simulated stationary acceleration time series is multiplied by the time dependent linear modulation function,then using a simple iterative scheme,it is forced to match a target acceleration response spectrum.It is shown that the generated samples have excellent conformity in low frequency,which is useful for nonlinear endurance time analysis.In the second part of this study,it is shown that this procedure can be extended to generate a set of spatially correlated endurance time excitation functions.This makes it possible to assess the performance of long structures under multi-support seismic excitation using endurance time analysis.

Spatial-Aware Supervised Learning for Hyper-Spectral Image Classification Comprehensive Assessment

A comprehensive assessment of the spatial-aware supervised learning algorithms for hyper-spectral image(HSI)classification was presented.For this purpose,standard support vector machines(SVMs),multinomial logistic regression(MLR)and sparse representation(SR) based supervised learning algorithm were compared both theoretically and experimentally.Performance of the discussed techniques was evaluated in terms of overall accuracy,average accuracy,kappa statistic coefficients,and sparsity of the solutions.Execution time,the computational burden,and the capability of the methods were investigated by using probabilistic analysis.For validating the accuracy a classical benchmark AVIRIS Indian pines data set was used.Experiments show that integrating spectral-spatial context can further improve the accuracy,reduce the misclassification error although the cost of computational time will be increased.

空间外差遥感数据光谱-干涉图双向校正算法研究

空间外差光谱技术(SHS)作为新型高光谱分析技术,被广泛应用于大气检测、卫星遥感等领域,但由于空间外差光谱仪的制作不理想或工作环境的变化都会改变仪器参数而引入误差,使干涉图数据不准确,需要进行误差校正。在卫星平台下,由于与地面环境的巨大差异,导致地面测定的校正参数对空间外差干涉图数据不再适用,特别是调制项误差(相位误差和非均匀误差)的改变,极大影响了光谱的准确性。针对空间外差遥感数据的调制项误差,从光谱与干涉图两个方面入手,进行误差分离与原因分析,认为空间外差遥感数据误差主要来源于CCD尺寸伸缩导致的光谱频率变化和CCD响应改变导致的干涉图强度变化,提出了光谱-干涉图双向校正方法。校正测试数据选取高分五号搭载检测仪(GMI)实测的12条O 2吸收光谱。取其中一条作为标定光谱,以SCIATRAN仿真的无误差光谱为基准,与标定光谱进行对比,分析两者在光谱维的频率偏差,通过特征峰频率确定两者的频率变换关系。然后根据变换关系将仿真光谱进行频率拉伸,使拉伸后的仿真光谱与实测光谱谱峰重合,计算拉伸仿真光谱与实测光谱干涉图,将两者干涉图相比得到干涉图强度的变化关系。最后将干涉图强度变化关系用于其余11条光谱的校正,得到校正后光谱。为了衡量校正效果,以校正前后光谱的标准差、均方误差和信噪比作为评价指标。结果显示标准差、均方误差都明显降低,同时信噪比显著增大,且标准差基本都在0.07以下,信噪比基本能达到20以上。其中校正效果最好的实测光谱,标准差减少了0.3767,信噪比提高了25.1016,均方误差降低了0.1587,校正效果较差的实测光谱,标准差减少了0.2296,信噪比提高了9.6328,均方误差降低了0.1049。因此所提出的光谱-干涉图双向校正方法对空间外差遥感数据的误差校正起到较好的效果,且处理过程简单,为同类数据的处理提供了一种新思路。

对外开放对全国统一大市场建设的影响研究:基于我国市场分割的视角

双循环新发展格局下,进一步扩大对外开放将成为我国构建全国统一大市场的关键。在分析对外开放影响全国统一大市场建设的理论机制基础上,选取2005—2020年中国省级面板数据,构建空间计量模型,检验对外开放对我国市场分割产生的空间溢出效应,并通过中介效应模型对理论机制进行检验。研究表明:对外开放能显著降低本地区的市场分割程度,具有正向的空间外溢效应;理论机制检验进一步发现,对外开放通过国企股份制改革、基础设施建设以及提高地区经济发展水平三条路径打破市场分割局面,对全国统一大市场建设产生积极效应。

图卷积神经网络综述

图卷积神经网络是图论与深度学习的交叉,已成为机器学习领域的研究热点。基于此,介绍了图卷积神经网络的形成,梳理了两大类经典的图卷积神经网络:谱方法和空间方法,详细介绍了这两类图卷积神经网络模型,分析了图卷积操作的核心理论基础,介绍了图卷积神经网络在各领域的应用,总结了图卷积神经网络面临的主要挑战,展望了图卷积神经网络的发展趋势,并分析了图卷积神经网络在野外环境下蝴蝶识别任务中的潜在应用。

全国统一大市场背景下中国工业天然气价格市场空间关联研究

天然气价格市场空间关联是反映天然气市场运行效率的关键指标,探究天然气价格的空间传导机制,对于建设和完善天然气统一大市场具有重要的现实意义。基于中国2012年1月—2023年2月各省会城市的工业天然气面板数据,验证工业天然气价格市场空间关联情况,并运用社会网络分析法描绘工业天然气价格市场空间关联网络。结果表明:工业天然气价格市场空间网络整体上结构稳定,并呈现复杂性和多线程性,省份之间存在空间溢出。具体而言,山东、广西、河北等省份中心度较高;中国工业天然气价格网络分为经纪人、净受益、主受益和净溢出四个板块,板块之间存在“溢出效应”和“梯度效应”。加强天然气市场内部的协作机制,特别是以政商合作促进信息交流,对推动天然气行业向市场化转型至关重要。

感应读出延时线阳极光子计数探测器研究

针对空间天文、生物荧光、光谱测量等领域对高灵敏、大面阵探测器的应用需求,研制出基于感应读出方式的延迟线阳极光子计数成像探测器。该探测器由微通道板、延时线阳极以及读出电路组成,其中延时线阳极的性能直接影响探测器的成像质量。作为一种电荷感应读出延时线阳极,该阳极利用信号到达的时间差解码入射光子的位置,可获得高的探测灵敏度和大的成像面积,同时简化了工艺难度并提升了可靠性。设计了感应读出延时线阳极,理论分析了探测器不同设计参数及材料对感应电荷量的影响,提出了解决异层电极间感应电荷量难以平衡的方法。据此研制出了收集面积为40 mm×40 mm的位敏阳极。测试结果表明该阳极信号传输衰减小于10%,极间串扰小于3%。利用该阳极进行了光子计数成像实验,获得了优于150μm的空间分辨率,为研制可应用于天文紫外光谱测量的大面阵、高灵敏探测器提供了理论依据及实验指导。

基于高光谱图像波段融合的猕猴桃软腐病早期分类检测

软腐病是猕猴桃采后贮藏和销售过程中危害最严重的真菌病害,其潜伏期长,在染病早期还未表现出明显病状时,依靠人工筛选很难将其分类。为此应用高光谱成像技术(470~900 nm)对软腐病的早期分类检测展开研究。采集了健康猕猴桃以及感染软腐病的早期和晚期猕猴桃共295个高光谱图像,并采用Kennard-Stone算法将样本按照7∶3划分为训练集和测试集样本。首先对样本进行感兴趣区域的选择,然后取该区域的平均光谱作为样本的原始光谱曲线。对原始光谱曲线采用主成分分析(PCA)、连续投影算法(SPA)和竞争性自适应重加权算法(CARS)进行光谱特征的提取。与此同时,对SPA求解过程中的8个特征波段使用非下采样轮廓波变换(NSCT)进行波段融合获得融合图像,然后使用灰度共生矩阵法(GLCM)提取融合图像的纹理特征。最后将光谱特征和纹理特征进行融合并分别建立最近邻算法(KNN)、随机森林(RF)以及支持向量机(SVM)分类模型进行猕猴桃软腐病的早期分类检测。此外,还与其他文献中使用主成分图像或特征波段提取的纹理特征进行了对比。该研究主要创新点为:使用NSCT对特征波段图像进行融合后再提取其纹理特征,既降低了特征维度,减少了特征冗余,又融合了不同波段图像的互补信息,提高了分类准确率。实验结果表明,SVM是最适合该研究的分类器,单独使用光谱特征或纹理特征进行分类的结果都不够理想,但两种特征融合后分类准确率最高可达到92.05%,多数猕猴桃软腐病早期样本得到了正确识别,这说明两种特征的融合获得了高光谱图像中光谱和图像的差异性信息,体现了高光谱图像的“空谱合一”。该研究对软腐病早期猕猴桃进行了快速、准确的无损检测,可为猕猴桃的采后品质分级提供一定的参考和指导意义。

苏州湿地鸟类多样性热点时空分布变化及其影响因素研究

湿地鸟类在湿地生态系统中具有重要的指示作用,其多样性监测有益于开展湿地保护工作。以苏州市为研究区,基于最大熵模型模拟分析2018、2019和2020年41种水鸟多样性热点空间分布格局及时空变化。结果表明,对鸟类空间分布影响较大的环境因子为年均降水量、兴趣点(POI)核密度、土地利用类型和距离水源距离。3年间主要影响因子贡献度出现较明显变动,人类活动干扰因素相对稳定。水鸟多样性热点地区面积逐年增加,主要聚集在苏州北部沿长江地带的沿江滩涂湿地和西南部环太湖的大型湖泊湿地,集中程度将会进一步上升。与迁徙水鸟的热点分布情况相比,以普通翠鸟、白鹭、白胸苦恶鸟和小为主的留鸟热点地区面积更大。由于热点地区面积扩大,2019年的监测点已无法满足苏州水鸟全域监测的需求,建议增设双山岛区域、通沙码头区域和望虞河沿线等15处监测区域。

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。

Selective sampling with Gromov–Hausdorff metric:Efficient dense-shape correspondence via Confidence-based sample consensus

Background Functional mapping, despite its proven efficiency, suffers from a “chicken or egg” scenario, in that, poor spatial features lead to inadequate spectral alignment and vice versa during training, often resulting in slow convergence, high computational costs, and learning failures, particularly when small datasets are used. Methods A novel method is presented for dense-shape correspondence, whereby the spatial information transformed by neural networks is combined with the projections onto spectral maps to overcome the “chicken or egg” challenge by selectively sampling only points with high confidence in their alignment. These points then contribute to the alignment and spectral loss terms, boosting training, and accelerating convergence by a factor of five. To ensure full unsupervised learning, the Gromov–Hausdorff distance metric was used to select the points with the maximal alignment score displaying most confidence. Results The effectiveness of the proposed approach was demonstrated on several benchmark datasets, whereby results were reported as superior to those of spectral and spatial-based methods. Conclusions The proposed method provides a promising new approach to dense-shape correspondence, addressing the key challenges in the field and offering significant advantages over the current methods, including faster convergence, improved accuracy, and reduced computational costs.

基于空谱融合和随机多图的高光谱遥感图像农作物分类

针对高光谱遥感图像复杂农作物分类问题,提出了一种基于空谱融合和随机多图的高光谱遥感图像农作物分类方法。通过使用一种潜在特征融合和最优聚类(Latent Features Fusion and Optimal Clustering Framework,LFFOCF)的波段选择方法和分段主成分分析(Segmented Principal Component Analysis,SPCA)进行光谱降维,采用多尺度二维奇异谱分析(2-D-Singular Spectrum Analysis,2-D-SSA)应用于降维图像,以提取不同尺度的空间特征。将多尺度空间特征与主成分分析(Principal Component Analysis,PCA)得到的全局光谱特征融合送到随机多图(Random Multi-Graphs,RMG)中进行分类。在印度松树、萨利纳斯和龙口数据集上,所提出的方法与一些现有的方法进行了对比实验。实验结果表明,该方法的类别精度(Class Accuracy,CA)、总体分类精度(Overall Accuracy,OA)、平均分类精度(Average Accuracy,AA)和Kappa系数优于这些方法。

强干扰与通道误差条件下弱信号DOA估计的实验研究

在实际测向系统中,当弱信号和强干扰空间临近时,空间谱测向系统仅能对强干扰进行波达方向(Direction of Arrival,DOA)估计,弱信号DOA估计性能下降甚至失效。针对这一问题,研究了空间谱扩展噪声子空间算法结合通道幅相误差校正,在强干扰抑制条件下对弱信号进行DOA估计的方法。该方法对采样信号的噪声协方差进行去加权处理,并对空间谱扩展噪声子空间算法的空间谱导向矢量进行修正。基于通用软件无线电外设(Universal SoftwareRadioPeripheral,USRP)和印刷偶极子线形天线阵构建实验平台,实验结果证明空间谱扩展噪声子空间算法结合改进的通道幅相误差校正方法,能对临近干扰源进行空间谱抑制的同时,实现对弱信号的DOA估计。

基于谱元法的近场地震动空间相干性分析

由于专门用于地震动空间相关性研究的台阵强震记录偏少,基于强震记录统计回归的经验模型发展受限,采用谱元程序SPECFEM2D合成了不同震源机制引起的近场强地震动。通过对地震动场模拟结果进行地震动空间相关性分析,着重比较了不同的震中距对地震动空间相关性的影响,给出了不同震中距对应的相干函数,并得到如下结论:基岩地震动的相干性随震中距的增加而降低;震中距小于或等于15 km时的相干系数大于倾滑断层结果,震中距大于15 km时的相干系数小于倾滑断层结果,但均大于走滑断层结果;基于地震动模拟结果得到的相干性高于基于实测记录的统计结果。

基于DSP的快速MUSIC测角算法

基于阵列信号处理的MUSIC算法,由于其超高的角分辨率和角精度,受到广泛的关注。但是其高运算复杂度,较大地限制了算法的硬件实现。为此,基于MUSIC算法的基本原理,文中分析了该算法在DSP(TI1843)硬件系统实现中关键子步骤的运行时长。针对最为耗时和占用内存最大的空间谱构建和谱峰搜索子步骤,分别提出了便于工程实现的简化方法,并通过硬件平台移植验证了算法的可行性,对比发现该方法大大减少了运算时间和内存空间,具有较高实际应用价值。