Evaluation of Spectral Scale Effects in Estimation of Vegetation Leaf Area Index Using Spectral Indices Methods

Spectral index methodology has been widely used in Leaf Area Index(LAI) retrieval at different spatial scales. There are differences in the spectral response of different remote sensors and thus spectral scale effect generated during the use of spectral indices to retrieve LAI. In this study, PROSPECT, leaf optical properties model and Scattering by Arbitrarily Inclined Layers(SAIL) model, were used to simulate canopy spectral reflectance with a bandwidth of 5 nm and a Gaussian spectral response function was employed to simulate the spectral data at six bandwidths ranging from 10 to 35 nm. Additionally, for bandwidths from 5 to 35 nm, the correlation between the spectral index and LAI, and the sensitivities of the spectral index to changes in LAI and bandwidth were analyzed. Finally, the reflectance data at six bandwidths ranging from 40 to 65 nm were used to verify the spectral scale effect generated during the use of the spectral index to retrieve LAI. Results indicate that Vegetation Index of the Universal Pattern Decomposition(VIUPD) had the highest accuracy during LAI retrieval. Followed by Normalized Difference Vegetation Index(NDVI), Modified Simple Ratio Indices(MSRI) and Triangle Vegetation Index(TVI), although the coefficient of determination R^2 was higher than 0.96, the retrieved LAI values were less than the actual value and thus lacked validity. Other spectral indices were significantly affected by the spectral scale effect with poor retrieval results. In this study, VIUPD, which exhibited a relatively good correlation and sensitivity to LAI, was less affected by the spectral scale effect and had a relatively good retrieval capability. This conclusion supports a purported feature independent of the sensor of this model and also confirms the great potential of VIUPD for retrieval of physicochemical parameters of vegetation using multi-source remote sensing data.

Wave-Number Spectral Characteristics of Drift Wave Micro-Turbulence with Large-Scale Structures

Wave-number spectral characteristics of drift wave micro-turbulence with large-scale structures （LSSs） including zonal flows （ZFs） and Kelvin-Holmheltz （KH） mode are investigated based on three dimensional gyrofluid simulations in a slab geometry. The focus is on the property of the wave-number spectral scaling law of the ambient turbulence under the back reaction of the self-generated LSSs. A comparison of the spectral scaling laws between ion/electron temperature gradient （ITG/ETG） driven turbulences is presented. It is shown that the spectral scaling of the ITG turbulence with robust ZFs is fitted well by an exponential-law function （Φ^2/2）E∝e^-λkx in kx and a power-law one in （Φ^2/2）p∝ky^-β in ky. However, the ETG turbulence is characterized by a mixing Kolmogorov-like power-law and exponential-law （Φ^2/2）E∝e-λkx＇yk^-3x,y/（1 ＋ k^2x,y）^2 scaling for both kx and ky spectra due to the ZFs and KH mode dynamics, with λ and β the slope index factors. The underlying physical mechanism is understood as the spectral scattering caused by the back-reaction of the LSSs on the ambient turbulence. These findings may provide helpful guideline to diagnose the plasma fluctuations and flow structures in experiments.

Spectral matching algorithm based on nonsubsampled contourlet transform and scale-invariant feature transform

A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency im- age. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity matrix, a matching matrix （or matching result） reflecting the match- ing degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy.

Single-Channel Speech Enhancement Using Critical-Band Rate Scale Based Improved Multi-Band Spectral Subtraction

This paper addresses the problem of single-channel speech enhancement in the adverse environment. The critical-band rate scale based on improved multi-band spectral subtraction is investigated in this study for enhancement of single-channel speech. In this work, the whole speech spectrum is divided into different non-uniformly spaced frequency bands in accordance with the critical-band rate scale of the psycho-acoustic model and the spectral over-subtraction is carried-out separately in each band. In addition, for the estimation of the noise from each band, the adaptive noise estimation approach is used and does not require explicit speech silence detection. The noise is estimated and updated by adaptively smoothing the noisy signal power in each band. The smoothing parameter is controlled by a-posteriori signal-to-noise ratio (SNR). For the performance analysis of the proposed algorithm, the objective measures, such as, SNR, segmental SNR, and perceptual evaluations of the speech quality are conducted for the variety of noises at different levels of SNRs. The speech spectrogram and objective evaluations of the proposed algorithm are compared with other standard speech enhancement algorithms and proved that the musical structure of the remnant noise and background noise is better suppressed by the proposed algorithm.

Seasonal Characteristics and Interannual Variability of Monthly Scale Low-Frequency Oscillation in a Low-Order Global Spectral Model

Analysis is done of five-year low-pass filtered data by a five-layer low-order global spectral model, indicating that although any non-seasonal external forcing is not considered in the model atmosphere,monthly-scale anomaly takes place which is of remarkable seasonality and interannual variability.Analysis also shows that for the same seasonal external forcing the model atmosphere can exhibit two climatic states,similar in the departure pattern but opposite in sign, indicating that the anomaly is but the manifestation of the adverse states, which supports the theory of multi-equilibria proposed by Charney and Devore(1979) once again.Finally, the source for the low-frequency oscillation of the global atmosphere is found to be the convective heat source / sink inside the tropical atmosphere as discussed before in our study.Therefore, the key approach to the exploration of atmospheric steady low-frequency oscillation and the associated climatic effect lies in the examination of the distribution of convective heat sources / sinks and the variation in the tropical atmosphere.

瑞马唑仑用于重症监护病房俯卧位通气深镇静诱导的半数有效剂量

目的测定瑞马唑仑用于重症监护病房(ICU)患者俯卧位通气深镇静诱导的半数有效剂量(ED50)。方法选择2022年11月至2023年7月达州市中心医院收入ICU的早期中、重度急性呼吸窘迫综合征(ARDS)行俯卧位通气深镇静患者45例,给药舒芬太尼0.4μg/kg使重症监护疼痛观察工具(CPOT)评分达0~1分后,使用瑞马唑仑诱导镇静,镇静达标后开始俯卧位通气。瑞马唑仑诱导镇静的剂量由改良Dixon序贯试验法确定,根据预试验确定瑞马唑仑的起始剂量为0.2 mg/kg,剂量梯度为0.025 mg/kg。镇静达标定义为给药后3 min内Richmond躁动-镇静评分(RASS)≤-4分且光谱熵(SE)≤50。但如俯卧位通气3 min之内出现明显的体动、皱眉、流泪、呛咳和吞咽等反应且RASS>-4分或SE>50,仍表示镇静不理想。镇静达标且理想则下一例患者在上一例的给药剂量基础上降低一个梯度,若镇静未达标或不理想,下一例给药剂量升高一个梯度。连续出现10次交叉后终止研究。运用Probit回归分析法,计算出瑞马唑仑的ED50和95%有效剂量(ED95)。记录给药前后心率(HR)、呼吸频率(RR)、平均动脉压(MAP)、血氧饱和度(SpO_(2))及俯卧位通气中心动过缓和恶心呕吐等不良反应的发生情况。结果瑞马唑仑用于ICU患者俯卧位通气深镇静诱导的ED50为0.228 mg/kg(95%CI 0.208~0.248),ED95为0.365 mg/kg(95%CI 0.299-0.518)。与舒芬太尼镇痛达标后(T_(1))比较,瑞马唑仑诱导镇静后2 min(T_(2))和俯卧位通气后2 min(T_(3))各时间点的血氧饱和度(SpO_(2))差异无统计学意义(P>0.05);T_(3)时HR、MAP及RR较T_(1)和T_(2)时均有下降(P<0.05),但下降幅度在20%以内;T_(2)和T_(3)时镇静深度指标RASS及SE比T_(1)时显著下降(P<0.01)。诱导期间3例患者出现低血压,给予麻黄碱有效;2例患者出现心动过缓,给予阿托品有效,无其他不良反应。结论瑞马唑仑用于ICU患者俯卧位通气深镇静诱导的ED50为0.228 mg/kg,ED95为0.365 mg/kg,镇静效果确切,呼吸循环影响较小,不良反应少。

电力系统小干扰稳定性评估的多重谱变换方法

针对电力系统小干扰稳定性分析过程中遗漏不稳定特征值的问题,提出了一种求解大规模系统不稳定特征值的多重谱变换方法。该方法首先利用所提出的多重谱变换将全部不稳定特征值映射为主导特征值;在利用Krylov-Schur方法收敛得到全部主导特征值之后,通过逆变换得到相应的不稳定特征值;该方法还揭示了指数变换与位移逆变换以及凯莱变换之间的内在联系。相较于位移逆变换和凯莱变换,多重谱变换方法由于具有类似于指数变换的良好谱特性,从而有利于特征值收敛;同时其又避免了指数变换引起的指数矩阵与向量的乘积,从而具有更高的计算效率和计算精度。在Xingo6u和Xingo3012这两个源于巴西互联电网上的算例分析结果表明,所提出的多重谱变换方法不仅保证了不遗漏不稳定特征值,而且相较于指数变换方法,计算精度提升了约3个数量级,计算耗时减少了90%以上。

草甸草原植物beta多样性高光谱遥感估算方法

目前,全球生物多样性的状态非常堪忧,因此,利用光谱技术估算生物多样性成为了生态学家和遥感学家共同关注的热点。目前关于植物alpha多样性的研究很多,而关于beta多样性的研究较少,仍存在一些问题值得去探索。为了探究利用遥感技术估算植物beta多样性的最佳光谱指数以及最佳影像空间分辨率,以草甸草原为研究区,基于无人机高光谱遥感影像,从光谱距离、光谱角度、生物多样性概念三个方面计算了6种beta多样性估算指数(4种为我们构建的新指数,2种为已有的指数),并采用Mantel tests和相关系数筛选最佳的光谱指数。然后将筛选出来的指数应用于不同空间分辨率的影像,以期得到最佳观测尺度。另外,为了提高指数的估算能力,对比了一阶导数变换和Savitzky-Golay滤波两种光谱变换方法,以及相关系数法、连续投影法、竞争性自适应重加权法三种特征波段选择方法。结果表明,不论是采用亚尺度观测(像元大小<样方大小)还是等尺度观测(像元大小=样方大小),最佳的光谱指数均为光谱距离指数,且光谱距离指数在不同影像空间分辨率下均表现良好。在草原地区,当影像空间分辨率约为0.25 m时,该指数可以取得最佳的估算结果。经一阶导数变换并用相关系数法提取特征波段后构建的光谱距离指数与beta多样性拥有最强的相关性,今后可利用该指数构建估算模型或者直接表征beta多样性。该研究对于科学的选取光谱指数和影像空间分辨率去估算植物beta多样性具有一定的指导意义。

基于三阶张量的大规模数据谱聚类集成算法

为了降低大规模数据谱聚类计算负担,进一步提高聚类的准确性和鲁棒性,提出了一种基于三阶张量的大规模数据谱聚类集成算法。首先,提出一种混合代表最近邻近似方法构造数据间的稀疏亲和子矩阵;然后将稀疏亲和子矩阵表示为二部图,通过图分割的方法得到初步聚类结果;最后,提出三阶张量集成方法,将多个聚类结果进行融合,得到最终的聚类结果。在大规模的真实数据集和合成数据集上验证,相较经典的谱聚类算法、聚类集成算法以及近年来对其改进的算法,该算法表现出更优异的性能。

新疆达坂城风区顺风向脉动风功率谱

为研究新疆乌鲁木齐所处达坂城风区的复杂脉动风环境,基于实测风场风速数据分析并拟合了该风区的顺风向脉动风功率谱。结果表明:以反比例函数拟合山区峡谷地形平均风速与湍流强度的关系误差较小;山区峡谷地形湍流积分尺度分布较离散,但整体随平均风速的增大而增大;考虑地形系数的Kaimal修正谱在含能区和惯性子区间能较好地表征实测谱,在耗能区选用三参数拟合描述实测谱效果良好;选用过渡函数来描述惯性子区间与耗能区接触区域的功率谱,拟合效果较为理想。可见达坂城风区具有特殊顺风向脉动风特性,以修正谱和拟合谱分段构成的函数模型可用于表征该风区顺风向脉动风功率谱。

高频域多深度空洞网络的遥感图像全色锐化算法

遥感图像全色锐化是提取多光谱图像的光谱信息和全色图像的结构信息,将其融合成高分辨率多光谱遥感图像的过程。然而,高分辨率多光谱图像会存在光谱或结构信息的缺失问题。为了优化这一问题,该文提出一种基于多深度神经网络的遥感图像全色锐化算法,该算法有结构保护和光谱保护2个模块。结构保护模块使用滤波操作,提取全色图像和多光谱图像的高频信息,然后采用多深度神经网络提取图像的多尺度信息,从而提高模型的空间信息提取能力,减小过拟合的风险;光谱保护模块通过跳跃连接将上采样的多光谱图像与结构保护模块相连接,以保护图像的光谱信息。为了验证新模型的有效性,在相同实验条件下,将所提方法与多种遥感图像全色锐化算法进行比较,并从主观视觉效果和客观评价2个方面进行评估。实验结果表明,所提方法能够改善当前算法存在的结构信息缺失现象,更好地保护多光谱图像的光谱信息以及全色图像的结构信息。

高聚物管道流超大尺度结构特征实验研究

高聚物减阻在管道输送中发挥着重要作用。管道湍流中的相干结构与高聚物减阻机理密切相关。实际工程中的管道流动大多为高雷诺数流动,高雷诺数管道流中最主要的含能相干结构为10倍管径量级的超大尺度结构。本文对高雷诺数高聚物管道流中的超大尺度结构进行实验研究。开展了4种高聚物浓度(以质量分数表征)、3种雷诺数共12组TR-PIV(Time-Resolved Particle Image Velocimetry)实验,使用预乘谱对超大尺度结构的尺度和强度特征进行了分析。研究结果表明:在相同雷诺数下,随着高聚物浓度增大,中心流区超大尺度结构的尺度和强度均明显增大,且与大尺度结构的强度之比也显著增大;在较高浓度下,超大尺度结构取代大尺度结构,成为了外区的主导含能结构。

Study on Multi-Scale Blending Initial Condition Perturbations for a Regional Ensemble Prediction System

An initial conditions （ICs） perturbation method was developed with the aim to improve an operational regional ensemble prediction system （REPS）. Three issues were identified and investigated： （1） the impacts of perturbation scale on the ensemble spread and forecast skill of the REPS; （2） the scale characteristic of the IC perturbations of the REPS; and （3） whether the REPS＇s skill could be improved by adding large-scale information to the IC perturbations. Numerical experiments were conducted to reveal the impact of perturbation scale on the ensemble spread and forecast skill. The scales of IC perturbations from the REPS and an operational global ensemble prediction system （GEPS） were analyzed. A ＂multi-scale blending＂ （MSB） IC perturbation scheme was developed, and the main findings can be summarized as follows： The growth rates of the ensemble spread of the REPS are sensitive to the scale of the IC perturbations; the ensemble forecast skills can benefit from large-scale perturbations; the global ensemble IC perturbations exhibit more power at larger scales, while the regional ensemble IC perturbations contain more power at smaller scales; the MSB method can generate IC perturbations by combining the small-scale component from the REPS and the large-scale component from the GEPS; the energy norm growth of the MSB-generated perturbations can be appropriate at all forecast lead times; and the MSB-based REPS shows higher skill than the original system, as determined by ensemble forecast verification.

新疆卡姆斯特地区星载AHSI矿物填图

近年来国产卫星高光谱遥感技术获得了迅速发展,其搭载的AHSI高光谱相机为地质填图提供了不同光谱尺度的数据源。为验证不同星源AHSI的矿物填图效果,以新疆卡姆斯特地区为研究区,采用空间分辨率为30 m的GF-5B卫星、GF-5卫星和ZY1-02D卫星载AHIS高光谱数据,通过大气校正以及混合调谐匹配滤波(MTMF)等方法开展了数据处理和矿物填图工作。光谱真实性验证结果表明:星载AHSI高光谱数据大气校正后光谱特征吸收峰位置偏差均小于光谱分辨率;星载AHSI高光谱遥感数据可以提取与成岩和成矿紧密相关的多种蚀变矿物信息,且空间分布状况与航空高光谱数据的蚀变矿物填图结果一致;GF-5B星载AHIS高光谱数据由于信噪比较高,相对于ZY1-02D与GF-5数据提取效果更好;星载AHSI空间分辨率较低,不能对矿物亚系进行有效区分。因此得出结论,星载AHSI高光谱数据矿物填图结果在空间分布上基本可以满足地质普查需求,结合航空等高空间分辨率高光谱遥感数据,在地质勘查领域具有广阔的应用前景。

Prediction of Vibration Characteristics in Beam Structure Using Sub-Scale Modeling with Experimental Validation

Geometric or sub-scale modeling techniques are used for the evaluation of large and complex dynamic structures to ensure accurate reproduction of load path and thus leading to true dynamic characteristics of such structures. The sub-scale modeling technique is very effective in the prediction of vibration characteristics of original large structure when the experimental testing is not feasible due to the absence of a large testing facility. Previous researches were more focused on free and harmonic vibration case with little or no consideration for readily encountered random vibration. A sub-scale modeling technique is proposed for estimating the vibration characteristics of any large scale structure such as Launch vehicles, Mega structures, etc., under various vibration load cases by utilizing precise scaled-down model of that dynamic structure. In order to establish an analytical correlation between the original structure and its scaled models, different scale models of isotropic cantilever beam are selected and analyzed under various vibration conditions（ i.e. free, harmonic and random） using finite element package ANSYS. The developed correlations are also validated through experimental testing The prediction made from the vibratory response of the scaled-down beam through the established sets of correlation are found similar to the response measured from the testing of original beam structure. The established correlations are equally applicable in the prediction of dynamic characteristics of any complex structure through its scaled-down models. This paper presents modified sub-scale modeling technique that enables accurate prediction of vibration characteristics of large and complex structure under not only sinusoidal but also for random vibrations.

Scaling of weighted spectral distribution in weighted small-world networks

Many real-world systems can be modeled by weighted small-world networks with high clustering coefficients. Recent studies for rigorously analyzing the weighted spectral distribution（W SD） have focused on unweighted networks with low clustering coefficients. In this paper, we rigorously analyze the W SD in a deterministic weighted scale-free small-world network model and find that the W SD grows sublinearly with increasing network order（i.e., the number of nodes） and provides a sensitive discrimination for each input of this model. This study demonstrates that the scaling feature of the W SD exists in the weighted network model which has high and order-independent clustering coefficients and reasonable power-law exponents.

CMA全球集合预报系统误差增长及预报性能的尺度依赖特征诊断分析

利用CMA全球集合预报(CMA-GEPS)业务系统2020年6月1日至2021年5月31日一整年的500 hPa位势高度场(H_(500))预报数据,诊断评估了CMA-GEPS在北半球地区误差增长及预报性能的尺度依赖特征。使用谱滤波方法实现H_(500)不同尺度(包括行星尺度、天气尺度与次天气尺度)分量的分离。从集合平均均方根误差(简称集合平均误差)-离散度关系来看,在预报前期(108 h之前),CMA-GEPS集合平均误差小于集合离散度,存在过度发散的问题,主要是由天气尺度分量离散度过大导致;在预报后期(108 h之后),CMA-GEPS集合平均误差大于集合离散度,离散度偏小,是由行星尺度与天气尺度分量离散度不足共同引起。采用Dalcher等1987年修订的误差增长模型对H_(500)集合平均预报误差增长特征进行诊断分析,发现CMA-GEPS误差增长过程合理,初始误差在次天气尺度上增长最快,行星尺度上增长最慢;就绝对(相对)误差而言,模式误差对预报误差的影响随空间尺度的增大而增大(减小)。此外,将使用1989至2018年共计30 a的ERA-Interim再分析逐日数据得到的气候态分布作为参考预报,通过连续分级概率预报技巧评分(Continuously Ranked Probability Skill Score,CRPSS)检验了CMA-GEPS H_(500)及其不同尺度分量的概率预报技巧。结果表明,行星尺度分量概率预报技巧最高,次天气尺度分量最小,未经滤波的H_(500)预报技巧位于行星尺度与天气尺度分量预报技巧之间。上述诊断结果可为CMA-GEPS改进方向提供一定的客观依据。

Time-Spectral Solution of Initial-Value Problems—Subdomain Approach

Temporal and spatial subdomain techniques are proposed for a time-spectral method for solution of initial-value problems. The spectral method, called the generalised weighted residual method (GWRM), is a generalisation of weighted residual methods to the time and parameter domains [1]. A semi-analytical Chebyshev polynomial ansatz is employed, and the problem reduces to determine the coefficients of the ansatz from linear or nonlinear algebraic systems of equations. In order to avoid large memory storage and computational cost, it is preferable to subdivide the temporal and spatial domains into subdomains. Methods and examples of this article demonstrate how this can be achieved.

Stability of weighted spectral distribution in a pseudo tree-like network model

The comparison of networks with different orders strongly depends on the stability analysis of graph features in evolving systems. In this paper, we rigorously investigate the stability of the weighted spectral distribution（i.e., a spectral graph feature） as the network order increases. First, we use deterministic scale-free networks generated by a pseudo treelike model to derive the precise formula of the spectral feature, and then analyze the stability of the spectral feature based on the precise formula. Except for the scale-free feature, the pseudo tree-like model exhibits the hierarchical and small-world structures of complex networks. The stability analysis is useful for the classification of networks with different orders and the similarity analysis of networks that may belong to the same evolving system.

错列布局风电场尾流演变实验研究

在规模化风电场中,多机组尾流相互干扰对机组能量转换、区域大气边界层结构有着显著的影响。为研究湍流来流条件下错列布局风电场尾流演变统计特性,在边界层型风洞中,错列布置6排15台水平轴风力机阵列,利用热线风速仪对尾流场进行高频率数据采集。结果表明:相比串列布局,错列布局能够显著提高尾流主流向速度恢复水平,具有提高输出功率的潜力;同时,降低风电场内部流动相关性,风力机迎风面的湍流积分尺度变小;风电场采用紧凑布局时,需要考虑上游两侧风力机后方的局部高湍流强度区对下游风力机叶尖载荷的影响。基于速度谱分析,进一步量化在流场能量输运过程中,各尺度涡旋运动对尾流湍动能贡献度的变化,在近尾流区,流场受到风力机旋转的显著影响,而在远尾流场,低频、大尺度的尾流蜿蜒成为主导。