Comparison analysis of sampling methods to estimate regional precipitation based on the Kriging interpolation methods： A case of northwestern China

The accuracy of spatial interpolation of precipitation data is determined by the actual spatial variability of the precipitation, the interpolation method, and the distribution of observatories whose selections are particularly important. In this paper, three spatial sampling programs, including spatial random sampling, spatial stratified sampling, and spatial sandwich sampling, are used to analyze the data from meteorological stations of northwestern China. We compared the accuracy of ordinary Kriging interpolation methods on the basis of the sampling results. The error values of the regional annual pre-cipitation interpolation based on spatial sandwich sampling, including ME （0.1513）, RMSE （95.91）, ASE （101.84）, MSE （？0.0036）, and RMSSE （1.0397）, were optimal under the premise of abundant prior knowledge. The result of spatial stratified sampling was poor, and spatial random sampling was even worse. Spatial sandwich sampling was the best sampling method, which minimized the error of regional precipitation estimation. It had a higher degree of accuracy compared with the other two methods and a wider scope of application.

Interpolation of Images Using Discrete Wavelet Transform to Simulate Image Resizing as in Human Vision

This paper presents discrete wavelet transform （DWT） and its inverse （IDWT） with Haar wavelets as tools to compute the variable size interpolated versions of an image at optimum computational load. As a human observer moves closer to or farther from a scene, the retinal image of the scene zooms in or out, respectively. This zooming in or out can be modeled using variable scale interpolation. The paper proposes a novel way of applying DWT and IDWT in a piecewise manner by non-uniform down- or up-sampling of the images to achieve partially sampled versions of the images. The partially sampled versions are then aggregated to achieve the final variable scale interpolated images. The non-uniform down- or up-sampling here is a function of the required scale of interpolation. Appropriate zero padding is used to make the images suitable for the required non-uniform sampling and the subsequent interpolation to the required scale. The concept of zeroeth level DWT is introduced here, which works as the basis for interpolating the images to achieve bigger size than the original one. The main emphasis here is on the computation of variable size images at less computational load, without compromise of quality of images. The interpolated images to different sizes and the reconstructed images are benchmarked using the statistical parameters and visual comparison. It has been found that the proposed approach performs better as compared to bilinear and bicubic interpolation techniques.

Non-parametric machine learning methods for interpolation of spatially varying non-stationary and non-Gaussian geotechnical properties

Spatial interpolation has been frequently encountered in earth sciences and engineering.A reasonable appraisal of subsurface heterogeneity plays a significant role in planning,risk assessment and decision making for geotechnical practice.Geostatistics is commonly used to interpolate spatially varying properties at un-sampled locations from scatter measurements.However,successful application of classic geostatistical models requires prior characterization of spatial auto-correlation structures,which poses a great challenge for unexperienced engineers,particularly when only limited measurements are available.Data-driven machine learning methods,such as radial basis function network(RBFN),require minimal human intervention and provide effective alternatives for spatial interpolation of non-stationary and non-Gaussian data,particularly when measurements are sparse.Conventional RBFN,however,is direction independent(i.e.isotropic)and cannot quantify prediction uncertainty in spatial interpolation.In this study,an ensemble RBFN method is proposed that not only allows geotechnical anisotropy to be properly incorporated,but also quantifies uncertainty in spatial interpolation.The proposed method is illustrated using numerical examples of cone penetration test(CPT)data,which involve interpolation of a 2D CPT cross-section from limited continuous 1D CPT soundings in the vertical direction.In addition,a comparative study is performed to benchmark the proposed ensemble RBFN with two other non-parametric data-driven approaches,namely,Multiple Point Statistics(MPS)and Bayesian Compressive Sensing(BCS).The results reveal that the proposed ensemble RBFN provides a better estimation of spatial patterns and associated prediction uncertainty at un-sampled locations when a reasonable amount of data is available as input.Moreover,the prediction accuracy of all the three methods improves as the number of measurements increases,and vice versa.It is also found that BCS prediction is less sensitive to the number of measurement data and outperforms RBFN and MPS when only limited point observations are available.

A Blackman-Harris windowed triple-spectrum-line interpolation method for measuring SNR of ADCs

In the practical measurement of signal to noise ratio(SNR)of analog-to-digital converters(ADCs)by using fast Fourier transformation(FFT)method,the non-coherent sampling is inevitable,leading to spectral leakage which in turn affects the calculation accuracy and final measurement results.In this paper,a new method based on the Blackman-Harris windowed triple-spectrum-line interpolation is presented for the measurement of ADCs SNR by FFT.The simulation platform is built based on MATLAB and the behavioral dynamic models of the high-speed ADC products of Analog Devices Inc.(ADI)are simulated.The simulation results show that,even in the case of the maximum non-coherent degree,the SNR error is less than0.23dB and reaches the testing standards provided by ADI,confirming that the proposed method is effective for suppressing the spectral leakage effects and improving the SNR test accuracy.

Sample插值算法的余项分析

对 Sample插值算法的余项进行了分析 ,利用 L eibniz判别法及 Abel引理 ,证明了在一定条件下 ,余项 E2 l( x) =O( 1 / l)

Sampling Strategies for Soil Available K and P at Field Scale

Field nutrient distribution maps obtained from the study on soil variations within fields are the basis of precision agriculture. The quality of these maps for management depends on the accuracy of the predicted values, which depends on the initial sampling. To produce reliable predictions efficiently the minimal sampling size and combination should be decided firstly, which could avoid the misspent funds for field sampling work. A 7.9 hectare silage field close to the Agricultural Research institute at Hillsborough, Northern Ireland, was selected for the study. Soil samples were collected from the field at 25 m intervals in a rectangular grid to provide a database of selected soil properties. Different data combinations were subsequently abstracted from this database for comparison purposes, and ordinary kriging used to produce interpolated soil maps. These predicted data groups were compared using least significant difference (LSD) test method. The results showed that the 62 sampling sizes of triangle arrangement for soil available K were sufficient to reach the required accuracy. The triangular sample combination proved to be superior to a rectangular one of similar sample size.

Optimised Spatial Sampling Scheme for Soil Electriclal Conductivity Based on Variance Quad-Tree (VQT) Method

The acquisition of precise soil data representative of the entire survey area, is a critical issue for many treatments such as irrigation or fertilization in precision agriculture. The aim of this study was to investigate the spatial variability of soil bulk electrical conductivity （ECb） in a coastal saline field and design an optimized spatial sampling scheme of ECb based on a sampling design algorithm, the variance quad-tree （VQT） method. Soil ECb data were collected from the field at 20 m interval in a regular grid scheme. The smooth contour map of the whole field was obtained by ordinary kriging interpolation, VQT algorithm was then used to split the smooth contour map into strata of different number desired, the sampling locations can be selected within each stratum in subsequent sampling. The result indicated that the probability of choosing representative sampling sites was increased significantly by using VQT method with the sampling number being greatly reduced compared to grid sampling design while retaining the same prediction accuracy. The advantage of the VQT method is that this scheme samples sparsely in fields where the spatial variability is relatively uniform and more intensive where the variability is large. Thus the sampling efficiency can be improved, hence facilitate an assessment methodology that can be applied in a rapid, practical and cost-effective manner.

Brittleness index predictions from Lower Barnett Shale well-log data applying an optimized data matching algorithm at various sampling densities

The capability of accurately predicting mineralogical brittleness index (BI) from basic suites of well logs is desirable as it provides a useful indicator of the fracability of tight formations.Measuring mineralogical components in rocks is expensive and time consuming.However,the basic well log curves are not well correlated with BI so correlation-based,machine-learning methods are not able to derive highly accurate BI predictions using such data.A correlation-free,optimized data-matching algorithm is configured to predict BI on a supervised basis from well log and core data available from two published wells in the Lower Barnett Shale Formation (Texas).This transparent open box (TOB) algorithm matches data records by calculating the sum of squared errors between their variables and selecting the best matches as those with the minimum squared errors.It then applies optimizers to adjust weights applied to individual variable errors to minimize the root mean square error (RMSE)between calculated and predicted (BI).The prediction accuracy achieved by TOB using just five well logs (Gr,ρb,Ns,Rs,Dt) to predict BI is dependent on the density of data records sampled.At a sampling density of about one sample per 0.5 ft BI is predicted with RMSE~0.056 and R^(2)~0.790.At a sampling density of about one sample per0.1 ft BI is predicted with RMSE~0.008 and R^(2)~0.995.Adding a stratigraphic height index as an additional (sixth)input variable method improves BI prediction accuracy to RMSE~0.003 and R^(2)~0.999 for the two wells with only 1 record in 10,000 yielding a BI prediction error of>±0.1.The model has the potential to be applied in an unsupervised basis to predict BI from basic well log data in surrounding wells lacking mineralogical measurements but with similar lithofacies and burial histories.The method could also be extended to predict elastic rock properties in and seismic attributes from wells and seismic data to improve the precision of brittleness index and fracability mapping spatially.

A Universal Approach to Designing an Image Interpolator with an Image Smoothing Filter

A number of conventional interpolation techniques have been proposed. However, it seems that there do not exist good criteria for the design of optimal linear interpolators. Also, such an interpolator can hardly provide a satisfactory solution for interpolating noisy images. In this paper, the novelty of this research is that a universal approach is proposed to design an image interpolator with any one image smoothing filter, thereby not only interpolating a down-sampled image but also preserving the characteristics of the performing filtering.

Performance evaluation of fixed-station sampling design for a fishery-independent survey with multiple objectives

Fixed-station sampling design was widely used in fishery-independent surveys because of its characteristics of convenient sampling station setting,but the non-probabilistic(fixed)nature made it more uncertainty of drawing inferences on population.The performance of fixed-station sampling design for multispecies survey has not been evaluated,and we are uncertain if the design could detect the temporal trends of different populations in multispecies fishery-independent survey.In this study,spatial distribution of abundance indices for three species with different spatial distribution patterns including small yellow croaker(Larimichthys polyactis),whitespotted conger(Conger myriaster)and Fang’s blenny(Enedrias fangi)were simulated using ordinary kriging interpolation as the“true”population distribution.The performance of fixed-station sampling design was compared with simple random sampling design by resampling the simulated“true”populations in this simulation study.The results showed that the fixed-station sampling design had the power to detect the seasonal trends of species abundance.The effectiveness of fixed-station sampling design were different in different species distribution patterns.When the species had even distribution,fixed-station sampling design could get high quality abundance data;when the distribution was uneven with heterogeneity or patchiness,fixed-station sampling design tended to underestimate or overestimate the abundance.Evidently,the estimates of abundance index based on the fixedstation sampling design must be calibrated cautiously while applying them for fisheries stock assessment and management.This study suggested that fixed-station sampling design could catch the temporal dynamics of population abundance,but the abundance estimates from the fixed-station sampling design could not be treated as the absolute estimates of populations.

An effective indoor radio map construction scheme based on crowdsourced samples

The crowdsourcing-based WLAN indoor localization system has been widely promoted for the effective reduction of the workload from the offline phase data collection while constructing radio maps.Aiming at the problem of the inaccurate location annotation of the crowdsourced samples,the existing invalid access points(APs)in collected samples,and the uneven sample distribution,as well as the diverse terminal devices,which will result in the construction of the wrong radio map,an effective WLAN indoor radio map construction scheme(WRMCS)is proposed based on crowdsourced samples.The WRMCS consists of 4 main modules:outlier detection,key AP selection,fingerprint interpolation,and terminal device calibration.Moreover,an online localization algorithm is put forward to estimate the position of the online test fingerprint.The simulation results show that the proposed scheme can achieve higher localization accuracy than the peer schemes,and possesses good effectiveness and robustness at the same time.

Sampling and Reconstruction of Zero-Order Hold Signals by Parallel RC Filters

In this work we describe a reconstruction algorithm for zero-order hold (ZOH) waveforms measured by a parallel sam-pling scheme. In the method the ZOH signal is fed to a parallel network consisting of resistor-capacitor (RC) filters, whose outputs are sampled simultaneously. The algorithm reconstructs N previous samples of the input signal from output samples of N parallel RC circuits. The method is especially useful in sampling and reconstruction of the ZOH signals produced by the digital-to-analog converters. Using the parallel sampling method the sampling rate of the analog-to-digital converters can be increased by a factor of N. We discuss a variety of applications such as reconstruction of ZOH pulse sequences produced by ultra wide band (UWB) transmitters.

利用瑞利面波提高近地表横波速度精度的方法

二维油气地震勘探中,由于受野外施工条件、成本效益等诸多因素限制,地震记录的野外采集道间距往往较大,这种地震记录往往容易产生空间假频,降低其频率—速度谱信噪比。为此,提出利用小波变换良好的时频分析特性,对大时空采样记录进行基于小波基的道内插值,以提高P-SV转换波地震资料中的瑞利面波频散高频信息的精度,从而提高反演表层横波速度结构精度,提高P-SV转换波的横波静校正精度。对理论模型和实际数据进行的验证计算表明,基于小波基的道内插值方法具有抗假频性,瑞利面波基阶频散曲线拐点更清晰,高频段能量更加集中,反演的横波速度精度更高,取得了较好效果。

基于空间插值的不规则海洋地质样品测试分析数据聚类算法研究

海洋地质调查中获取大量海洋沉积物柱状样样品测试分析数据,样品测试分析目的不同导致柱状样数据采样深度不同,由此造成地质取样数据在三维空间上呈现不规则散点状分布。传统聚类算法无法在三维空间上对此类不规则散点数据进行聚类分析。对此,文章设计了一种基于空间插值的不规则地质样品测试分析数据聚类算法,有效地将三维样品测试分析散点数据降为二维数据后进行聚类分析,本算法较好地解决了地质体中试验测试数据的不均衡性问题,为海洋地质大数据分析提供了基础技术方法。

基于近红外光谱法的血液酒精浓度检测仪

针对解决呼气式酒精检测仪存在飞沫传播风险的问题,提出了一种基于近红外光谱法的便携式无污染血液酒精浓度检测方案。使用硬件完成信号采集、放大、滤波再放大,利用软件完成卡尔曼滤波、三样条插值法去基等算法获取标准的脉搏波信号在上位机中通过呼气式的测量结果与脉搏波信号的幅值建立数学模型,将数学模型写入单片机实现离线测量。通过实验测试验证数学模型精度。结果表明,其满足相对误差小于10%的精度要求,符合实际使用需求。

基于相控阵超声的汽车后桥螺旋锥齿轮畸变控制方法

由于汽车后桥螺旋锥齿轮畸变控制的过程中易受畸变回波的影响,存在大量的噪声信号,导致控制误差较大、控制精度较低及控制效果较差,为此,提出基于相控阵超声的汽车后桥螺旋锥齿轮畸变控制方法。首先采用相控阵超声技术采集汽车后桥螺旋锥齿轮的振动信号,并将采集后的齿轮振动信号通过异性扩散降噪法进行降噪处理;然后根据降噪处理结果,采用畸变散射算法计算散射波的散射幅值,获取畸变信号的回波特性,采用曲线矩阵回波算法对畸变信号进行定位,得到畸变信号位置点后,提取畸变信号的特征;最后根据多普勒效应,计算畸变信号特征的频偏曲率,采用重采样畸变控制技术根据不同段的频偏基准点重新采样,进行齿轮畸变控制。实验结果表明,所提方法的畸变控制精度高、效果好。

Sample Numbers and Optimal Lagrange Interpolation of Sobolev Spaces W_(1)^(r)

This paper investigates the optimal recovery of Sobolev spaces W_(1)^(r)[-1,1],r∈N in the space L_(1)[-1,1].They obtain the values of the sampling numbers of W_(1)^(r)[-1,1]in L_(1)[-1,1]and show that the Lagrange interpolation algorithms based on the extreme points of Chebyshev polynomials are optimal algorithms.Meanwhile,they prove that the extreme points of Chebyshev polynomials are optimal Lagrange interpolation nodes.

基于三次样条插值法的柱面近场测量欠采样修正算法研究

为解决暗室近场测试效率低的问题,本文针对天线近场测量中高效测量方法进行了研究,提出了一种柱面近场欠采样恢复算法。该算法利用高精度的三次样条插值(cubic spline interpolation,CSI)法对未采样的点进行恢复,可减少采样点数,提高测试速度。此外,在柱面近场欠采样恢复CSI求解过程中充分利用系数矩阵的正定、对称等优良特性,使线性方程组的计算量从O(n^(3))降为O(n),大大提高了CSI的计算效率。最后使用插值后的近场幅度和相位结合柱面近远场变换理论求得天线远场方向图。为验证所提出算法的有效性,试验测试了天线样机,试验结果与理论分析一致。

基于扩散概率模型的非均一地震数据插值方法

【目的】能源勘探领域长期面临非均一地震数据插值的难题。由于检波器无法精确放置在理论网格点,现有均一插值技术常导致结果存在偏差和细节失真。【方法】为解决此问题,提出一种基于扩散概率模型的非均一插值新方法。扩散概率模型是一种新兴的生成式深度学习模型,分为扩散和生成两个过程。在扩散过程中,对完整地震数据迭代施加噪声,训练神经网络的去噪能力;在生成过程中,使用神经网络对噪声数据迭代去噪,得到重构数据。利用插值算子计算迭代数据与采集数据的偏差,作为神经网络的额外输入,提高扩散概率模型的非均一插值能力。在数值实验部分,使用二维合成数据集与实际数据集对非均一采样进行了测试,并使用均一插值模型与所提模型进行比较。【结果和结论】结果表明:该方法显著提升了扩散概率模型对非均一采样的处理能力,在合成数据与实际数据测试中,信噪比大约提高了7 dB,有效提高了深度学习方法对非均一插值的精度,为非均一地震数据插值算法提供了新的思路。

交交变频调速系统中变周期均值采样的研究应用

针对交交变频调速系统中电压、电流纹波含量较大且纹波周期时变,无法直接反馈于调速控制系统的问题,提出一种变周期均值采样算法。首先,该算法以实时交交变频功率器件触发脉冲的间隔时间作为均值周期,对电压、电流实际值进行均值计算。其次,根据电压、电流实时频率及均值周期计算该均值周期所带来的相位滞后角度。最后,按照相位滞后角度进行滞后补偿及插补运算,得到更理想的电压、电流反馈值,用于调速控制系统。相较于一般的均值采样,该算法更适合交交变频纹波周期时变的情况,并能消除采样滞后,提升调速系统控制性能及稳定性。