Winter Wheat Yield Estimation Based on Sparrow Search Algorithm Combined with Random Forest:A Case Study in Henan Province,China

Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat.Therefore,there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield,making precise yield prediction increasingly important.This study was based on four type of indicators including meteorological,crop growth status,environmental,and drought index,from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield.Using the sparrow search al-gorithm combined with random forest(SSA-RF)under different input indicators,accuracy of winter wheat yield estimation was calcu-lated.The estimation accuracy of SSA-RF was compared with partial least squares regression(PLSR),extreme gradient boosting(XG-Boost),and random forest(RF)models.Finally,the determined optimal yield estimation method was used to predict winter wheat yield in three typical years.Following are the findings:1)the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms.The best yield estimation method is achieved by four types indicators’composition with SSA-RF)(R^(2)=0.805,RRMSE=9.9%.2)Crops growth status and environmental indicators play significant roles in wheat yield estimation,accounting for 46%and 22%of the yield importance among all indicators,respectively.3)Selecting indicators from October to April of the follow-ing year yielded the highest accuracy in winter wheat yield estimation,with an R^(2)of 0.826 and an RMSE of 9.0%.Yield estimates can be completed two months before the winter wheat harvest in June.4)The predicted performance will be slightly affected by severe drought.Compared with severe drought year(2011)(R^(2)=0.680)and normal year(2017)(R^(2)=0.790),the SSA-RF model has higher prediction accuracy for wet year(2018)(R^(2)=0.820).This study could provide an innovative approach for remote sensing estimation of winter wheat yield.yield.

Research on Methods of Parameter Estimation in Combining Forecasting Based on Harmonic Mean

Two kinds of parameter estimation methods (I) and (II) of combining forecasting based on harmontic mean are proposed and compared through a lot of simulation forecasting examples. A very helpful conclusion is obtained, which can lay solid foundations for correct application of the above methods.

Generalized weighted functional proportional mean combining forecasting model and its method of parameter estimation

A new kind of combining forecasting model based on the generalized weighted functional proportional mean is proposed and the parameter estimation method of its weighting coefficients by means of the algorithm of quadratic programming is given. This model has extensive representation. It is a new kind of aggregative method of group forecasting. By taking the suitable combining form of the forecasting models and seeking the optimal parameter, the optimal combining form can be obtained and the forecasting accuracy can be improved. The effectiveness of this model is demonstrated by an example.

Combined SAI-SHAO prediction of Earth Orientation Parameters since 2012 till 2017

As the participants of Earth Orientation Parameters Combination of Prediction Pilot Project(EOPC PPP),Sternberg Astronomical Institute of Moscow State University(SAI) and Shanghai Astronomical Observatory(SHAO) have accumulated ～1800 days of Earth Orientation Parameters(EOP) predictions since2012 till 2017, which were up to 90 days into the future, and made by four techniques: auto-regression(AR), least squares collocation(LSC), and neural network(NNET) forecasts from SAI, and least-squares plus auto-regression(LS+AR) forecast from SHAO. The predictions were finally combined into SAISHAO COMB EOP prediction. In this work we present five-year real-time statistics of the combined prediction and compare it with the uncertainties of IERS bulletin A predictions made by USNO.

A weighted averaging method for signal probability of logic circuit combined with reconvergent fan-out structures

By analyzing the structures of circuits,a novel approach for signal probability estimation of very large-scale integration(VLSI)based on the improved weighted averaging algorithm(IWAA)is proposed.Considering the failure probability of the gate,first,the first reconvergent fan-ins corresponding to the reconvergent fan-outs were identified to locate the important signal correlation nodes based on the principle of homologous signal convergence.Secondly,the reconvergent fan-in nodes of the multiple reconverging structure in the circuit were identified by the sensitization path to determine the interference sources to the signal probability calculation.Then,the weighted signal probability was calculated by combining the weighted average approach to correct the signal probability.Finally,the reconvergent fan-out was quantified by the mixed-calculation strategy of signal probability to reduce the impact of multiple reconvergent fan-outs on the accuracy.Simulation results on ISCAS85 benchmarks circuits show that the proposed method has approximate linear time-space consumption with the increase in the number of the gate,and its accuracy is 4.2%higher than that of the IWAA.

Improved Approach to Determine the Material Parameters for a Combined Hardening Model

For the simulation of isothermal mechanically loaded components, it is indispensable to have a material model, which describes the material behavior very accurately. In this case, a combined hardening model was chosen in order to reflect the prevalent deformation behavior. The combined hardening model enables simulation independent of the number of load cycles and the chosen strain amplitude. The main point is the declaration of the parameters from the chosen material model. This work deals with the estimation of the parameters. For validation and as input data of the here defined approach low cycle fatigue (LCF) tests were performed on cast aluminum and at 250°C. The comparison of the test results and the simulations indicated that σmax from the simulated hysteresis lies inside a range of ±5% referred to the test results.

State Estimation Method for GNSS/INS/Visual Multi-sensor Fusion Based on Factor Graph Optimization for Unmanned System

With the development of unmanned driving technology,intelligent robots and drones,high-precision localization,navigation and state estimation technologies have also made great progress.Traditional global navigation satellite system/inertial navigation system(GNSS/INS)integrated navigation systems can provide high-precision navigation information continuously.However,when this system is applied to indoor or GNSS-denied environments,such as outdoor substations with strong electromagnetic interference and complex dense spaces,it is often unable to obtain high-precision GNSS positioning data.The positioning and orientation errors will diverge and accumulate rapidly,which cannot meet the high-precision localization requirements in large-scale and long-distance navigation scenarios.This paper proposes a method of high-precision state estimation with fusion of GNSS/INS/Vision using a nonlinear optimizer factor graph optimization as the basis for multi-source optimization.Through the collected experimental data and simulation results,this system shows good performance in the indoor environment and the environment with partial GNSS signal loss.

Monte Carlo Simulation of a Combined-Cycle Power Plant Considering Ambient Temperature Fluctuations

A combined-cycle power plant (CCPP) is broadly utilized in many countries to cover energy demand due to its higher efficiency than other conventional power plants. The performance of a CCPP is highly sensitive to ambient air temperature (AAT) and the generated power varies widely during the year with temperature fluctuations. To have an accurate estimation of power generation, it is necessary to develop a model to predict the average monthly power of a CCPP considering ambient temperature changes. In the present work, the Monte Carlo (MC) method was used to obtain the average generated power of a CCPP. The case study was a combined-cycle power plant in Tehran, Iran. The region’s existing meteorological data shows significant fluctuations in the annual ambient temperature, which severely impact the performance of the mentioned plant, causing a stochastic behavior of the output power. To cope with this stochastic nature, the probability distribution of monthly outdoor temperature for 2020 was determined using the maximum likelihood estimation (MLE) method to specify the range of feasible inputs. Furthermore, the plant was accurately simulated in THERMOFLEX to capture the generated power at different temperatures. The MC method was used to couple the ambient temperature fluctuations to the output power of the plant, modeled by THERMOFLEX. Finally, the mean value of net power for each month and the average output power of the system were obtained. The results indicated that each unit of the system generates 436.3 MW in full load operation. The average deviation of the modeling results from the actual data provided by the power plant was an estimated 3.02%. Thus, it can be concluded that this method helps achieve an estimation of the monthly and annual power of a combined-cycle power plant, which are effective indexes in the economic analysis of the system.

Bayesian network model for traffic flow estimation using prior link flows

In order to estimate traffic flow a Bayesian network BN model using prior link flows is proposed.This model sets link flows as parents of the origin-destination OD flows. Under normal distribution assumptions the model considers the level of total traffic flow the variability of link flows and the violation of the conservation law.Using prior link flows the prior distribution of all the variables is determined. By updating some observed link flows the posterior distribution is given.The variances of the posterior distribution normally decrease with the progressive update of the link flows. Based on the posterior distribution point estimations and the corresponding probability intervals are provided. To remove inconsistencies in OD matrices estimation and traffic assignment a combined BN and stochastic user equilibrium model is proposed in which the equilibrium solution is obtained through iterations.Results of the numerical example demonstrate the efficiency of the proposed BN model and the combined method.

Lower bound on BER performance for maximal ratio combining with weighting errors

The theoretical lower bounds on mean squared channel estimation errors for typical fading channels are presented by the infinite-length and non-causal Wiener filter and the exact closed-form expressions of the lower bounds for different channel Doppler spectra are derived. Based on the obtained lower bounds on mean squared channel estimation errors, the limits on bit error rate （BER） for maximal ratio combining （MRC） with Gaussian distributed weighting errors on independent and identically distributed （i. i. d） fading channels are presented. Numerical results show that the BER performances of ideal MRC are the lower bounds on the BER performances of non-ideal MRC and deteriorate as the maximum Doppler frequency increases or the SNR of channel estimate decreases.

露天矿区环境下GPS/INS组合导航算法研究

针对露天矿区道路窄、弯道多,沿途布满碎石、路面颠簸不平,导致运输车辆在定位时出现系统模型异常及量测噪声不确定性的问题,提出一种带有噪声估计器的SVD-AUKF算法。该算法通过对协方差矩阵进行奇异值分解,抑制滤波过程中出现的病态矩阵,利用Sage-Husa估计器对量测噪声进行估计,减少系统状态误差,引入自适应因子实时修正系统模型误差以抑制滤波发散。将提出的SVD-AUKF算法应用于GPS/INS组合导航系统模型,并在矿山环境下进行车载实验,结果表明,新算法适用性好,相较AUKF算法,每个定位点在东、北、天方向上的位置精度分别提高50.69%、51.20%、48.54%,速度精度分别提高31.24%、33.83%、31.30%,能够有效改善滤波性能,提高系统定位精度。

Rake接收中信号幅度估计算法及误差影响分析

针对直接扩频通信系统Rake接收中信号幅度估计的问题,分析了幅度估计误差对信号信噪比恶化的影响,得出归一化幅度估计方差与两路信号合并之后的信号信噪比恶化的关系公式;对典型的信号幅度估计算法进行分析,提出了一种基于辅助通道的幅度估计算法,可以在低信噪比且无数据辅助的情况下获得更好的估计性能;对于QPSK调制,两路信号信噪比分别为snr1和snr2,且snr1≥snr2,snr2≥-10 dB,的条件下,取观测长度为2048,采用基于辅助通道的幅度估计算法进行信号幅度估计,实验结果表明,幅度估计误差导致的性能恶化小于0.01 dB,达到了抑制多径干扰、提高通信系统性能的效果。

Winter wheat yield estimation based on assimilated Sentinel-2 images with the CERES-Wheat model

Assimilating Sentinel-2 images with the CERES-Wheat model can improve the precision of winter wheat yield estimates at a regional scale. To verify this method, we applied the ensemble Kalman filter(EnKF) to assimilate the leaf area index(LAI) derived from Sentinel-2 data and simulated by the CERES-Wheat model. From this, we obtained the assimilated daily LAI during the growth stage of winter wheat across three counties located in the southeast of the Loess Plateau in China: Xiangfen, Xinjiang, and Wenxi. We assigned LAI weights at different growth stages by comparing the improved analytic hierarchy method, the entropy method, and the normalized combination weighting method, and constructed a yield estimation model with the measurements to accurately estimate the yield of winter wheat. We found that the changes of assimilated LAI during the growth stage of winter wheat strongly agreed with the simulated LAI. With the correction of the derived LAI from the Sentinel-2 images, the LAI from the green-up stage to the heading–filling stage was enhanced, while the LAI decrease from the milking stage was slowed down, which was more in line with the actual changes of LAI for winter wheat. We also compared the simulated and derived LAI and found the assimilated LAI had reduced the root mean square error(RMSE) by 0.43 and 0.29 m^(2) m^(–2), respectively, based on the measured LAI. The assimilation improved the estimation accuracy of the LAI time series. The highest determination coefficient(R2) was 0.8627 and the lowest RMSE was 472.92 kg ha^(–1) in the regression of the yields estimated by the normalized weighted assimilated LAI method and measurements. The relative error of the estimated yield of winter wheat in the study counties was less than 1%, suggesting that Sentinel-2 data with high spatial-temporal resolution can be assimilated with the CERES-Wheat model to obtain more accurate regional yield estimates.

Estimation of dynamic stress spectrum distribution in structural fatigue test

The determination of structural dynamic stress spectrum distribution is of great signifi- cance in the structural fatigue strength evaluation as well as reliability design. In previous empirical data processing methods, the data grouping and distribution fitting were excessively coarse and contained distinctive defects. This paper proposed an effective approach to statistically group actual measured dynamic stress data and validly extrapolate the combined distribution to fit the dynamic stress spectrum distribution. This approach has been verified its effectiveness through chi-square test, stress spectrum extrapolation and damage calculation in dynamic stress study.

The Combining Confidence Distribution Method to the Behrens-Fisher Problem

Based on the Confidence Distribution method to the Behrens-Fisher problem, we consider two approaches of combining Confidence Distributions: P Combination and AN Combination to solve the Behrens-Fisher problem. Firstly, we provide some Confidence Distributions to the Behrens-Fisher problem, and then we give the Confidence Distribution method to the Behrens-Fisher problem. Finally, we compare the “combination” and the “single” through the numerical simulation.

Estimation of Population Ratio in Post-Stratified Sampling Using Variable Transformation

Extending the work carried out by [1], this paper proposes six combined-type estimators of population ratio of two variables in post-stratified sampling scheme, using variable transformation. Properties of the proposed estimators were obtained up to first order approximations,(on–1), both for achieved sample configurations (conditional argument) and over repeated samples of fixed size n (unconditional argument). Efficiency conditions were obtained. Under these conditions the proposed combined-type estimators would perform better than the associated customary combined-type estimator. Furthermore, optimum estimators among the proposed combined-type estimators were obtained both under the conditional and unconditional arguments. An empirical work confirmed the theoretical results.

人口普查遗漏的组合式估计方法

以人口普查标准时点上的人口总体(全国、全国以下各行政区)为研究对象,研究如何利用对总体登记的普查人口名单和覆盖调查人口名单构造的组合式遗漏估计量估计人口普查遗漏人口数。目前各国政府统计部门在人口普查遗漏估计中主要使用单重遗漏估计量,但该方法未包括同时遗漏于这两份调查人口名单的双重遗漏人口,低估了总体普查遗漏人口数。组合式遗漏估计量除了包括单重遗漏人口外,还包括了双重遗漏人口,避免了单重遗漏估计量的缺陷。采取抽样估计和数理模型相结合的方法研究单重遗漏估计量、双重遗漏估计量和组合式遗漏估计量,理论和数据分析结果表明:组合式遗漏估计量在抽样估计精度和遗漏人口数估计上优于单重遗漏估计量,尤其是在分析双重遗漏人口特征和改进未来人口普查登记工作等方面;在使用刀切法近似计算总体的组合式遗漏估计量的抽样方差时,不能忽视等概率人口层之间的协方差,否则可能低估或高估其抽样方差;单重遗漏估计量和组合式遗漏估计量属于有偏估计量,要使用均方误差比较其估计精度。本文创新之处在于,利用原始数据全面演示了单重遗漏估计量、双重遗漏估计量和组合式遗漏估计量及其抽样方差估计量等的详细计算过程,同时讨论了其统计性质,尤其是利用设置的数据模拟了有偏性,有助于中国人口普查遗漏估计方案的科学制定及估计精度的提高。

Cahn-Hilliard方程的一个超紧致有限差分格式

研究四阶Cahn-Hilliard方程的数值求解方法。给出组合型超紧致差分格式,将其用于四阶Cahn-Hilliard方程的空间导数离散,采用四阶Runge-Kutta格式离散时间导数,将二者结合得到四阶Cahn-Hilliard方程的离散格式,并给出了该格式的误差估计。通过编程计算得到其数值解,并与精确解进行对比,结果表明本文的数值方法误差小,验证了所提方法的有效性和可行性。

基于时变稳健加权最小二乘法的股市收益率预测

提出一类时变参数稳健加权最小二乘法(TRWLS),并将其用于预测标准普尔500指数收益率.该方法将时间相关权重与稳健估计权重相结合,既能捕捉参数时变性,又能降低数据噪声的影响.TRWLS模型组合揭示了经济和统计意义上均显著的收益率可预测性,而且预测能力明显高于普通最小二乘法.TRWLS模型的预测表现也好于传统时变参数模型和稳健回归模型.其预测能力主要来源于时间权重和稳健性权重的互补性以及超参数的学习能力.预测结果在调整多元信息组合方法、权重核函数和验证集长度的情况下均具有稳健性.

最优组合赋权在航空发动机性能评估中的应用

为了提高航空发动机机队性能评估的可信度和科学性,提出一种结合改进TOPSIS和最优组合赋权的综合性能评估方法。首先利用单一主、客观赋权法计算各评价指标的权重,再通过基于矩估计理论的最优组合赋权模型得到评价指标的最优组合权重。基于此权重值,利用余弦相似理论和虚拟负理想解对TOPSIS方法进行改进,得到更为合理的相对贴近度对发动机性能进行评估。以某公司PW4056发动机机队为例进行验证,结果表明所提出的新方法能够有效地评估发动机的性能水平,具有很好的工程应用价值。