Impact of observational MJO forcing on ENSO predictability in the Zebiak-Cane model: PartⅠ.Effect on the maximum prediction error

With the observational wind data and the Zebiak-Cane model, the impact of Madden-Iulian Oscillation （MJO） as external forcing on El Nino-Southern Oscillation （ENSO） predictability is studied. The observational data are analyzed with Continuous Wavelet Transform （CWT） and then used to extract MJO signals, which are added into the model to get a new model. After the Conditional Nonlinear Optimal Perturbation （CNOP） method has been used, the initial errors which can evolve into maximum prediction error, model errors and their join errors are gained and then the Nifio 3 indices and spatial structures of three kinds of errors are investigated. The results mainly show that the observational MJO has little impact on the maximum prediction error of ENSO events and the initial error affects much greater than model error caused by MJO forcing. These demonstrate that the initial error might be the main error source that produces uncertainty in ENSO prediction, which could provide a theoretical foundation for the adaptive data assimilation of the ENSO forecast and contribute to the ENSO target observation.

Distributional expansion of maximum from logarithmic general error distribution

Logarithmic general error distribution is an extension of lognormal distribution. In this paper, with optimal norming constants the higher-order expansion of distribution of partial maximum of logarithmic general error distribution is derived.

Analysis of Sampling Error Uncertainties and Trends in Maximum and Minimum Temperatures in China

In this paper we report an analysis of sampling error uncertainties in mean maximum and minimum temperatures （Tmax and Tmin） carried out on monthly,seasonal and annual scales,including an examination of homogenized and original data collected at 731 meteorological stations across China for the period 1951-2004.Uncertainties of the gridded data and national average,linear trends and their uncertainties,as well as the homogenization effect on uncertainties are assessed.It is shown that the sampling error variances of homogenized Tmax and Tmin,which are larger in winter than in summer,have a marked northwest-southeast gradient distribution,while the sampling error variances of the original data are found to be larger and irregular.Tmax and Tmin increase in all months of the year in the study period 1951-2004,with the largest warming and uncertainties being 0.400℃ （10 yr）-1 ＋ 0.269℃ （10 yr）-1 and 0.578℃ （10 yr）-1 ＋ 0.211℃ （10 yr）-1 in February,and the least being 0.022℃ （10 yr）-1 ＋ 0.085℃ （10 yr）-1 and 0.104℃ （10 yr）-1 ＋0.070℃ （10 yr）-1 in August.Homogenization can remove large uncertainties in the original records resulting from various non-natural changes in China.

Lower bound estimation of the maximum allowable initial error and its numerical calculation

In the numerical prediction of weather or climate events,the uncertainty of the initial values and/or prediction models can bring the forecast result’s uncertainty.Due to the absence of true states,studies on this problem mainly focus on the three subproblems of predictability,i.e.,the lower bound of the maximum predictable time,the upper bound of the prediction error,and the lower bound of the maximum allowable initial error.Aimed at the problem of the lower bound estimation of the maximum allowable initial error,this study first illustrates the shortcoming of the existing estimation,and then presents a new estimation based on the initial observation precision and proves it theoretically.Furthermore,the new lower bound estimations of both the two-dimensional ikeda model and lorenz96 model are obtained by using the cnop(conditional nonlinear optimal perturbation)method and a pso(particle swarm optimization)algorithm,and the estimated precisions are also analyzed.Besides,the estimations yielded by the existing and new formulas are compared;the results show that the estimations produced by the existing formula are often incorrect.

基于常循环码的纠缠辅助量子Maximum-Distance-Separable码的构造

纠缠辅助量子纠错码可以看作是经典量子纠错码的引申,其与经典量子纠错码的差别在于,如果发送者和接收者双方提前共享纠缠态,在不满足对偶包含的的情况下也可以由任意经典线性码构造出来.本文通过研究分圆陪集的结构性质,利用常循环码构造出几类新的具有较小预先共享纠缠态的纠缠辅助量子Maximum-Distance-Separable(MDS)码.

Real time remaining useful life prediction based on nonlinear Wiener based degradation processes with measurement errors

Real time remaining useful life(RUL) prediction based on condition monitoring is an essential part in condition based maintenance(CBM). In the current methods about the real time RUL prediction of the nonlinear degradation process, the measurement error is not considered and forecasting uncertainty is large. Therefore, an approximate analytical RUL distribution in a closed-form of a nonlinear Wiener based degradation process with measurement errors was proposed. The maximum likelihood estimation approach was used to estimate the unknown fixed parameters in the proposed model. When the newly observed data are available, the random parameter is updated by the Bayesian method to make the estimation adapt to the item's individual characteristic and reduce the uncertainty of the estimation. The simulation results show that considering measurement errors in the degradation process can significantly improve the accuracy of real time RUL prediction.

Stochastic Restricted Maximum Likelihood Estimator in Logistic Regression Model

In the presence of multicollinearity in logistic regression, the variance of the Maximum Likelihood Estimator (MLE) becomes inflated. Siray et al. (2015) [1] proposed a restricted Liu estimator in logistic regression model with exact linear restrictions. However, there are some situations, where the linear restrictions are stochastic. In this paper, we propose a Stochastic Restricted Maximum Likelihood Estimator (SRMLE) for the logistic regression model with stochastic linear restrictions to overcome this issue. Moreover, a Monte Carlo simulation is conducted for comparing the performances of the MLE, Restricted Maximum Likelihood Estimator (RMLE), Ridge Type Logistic Estimator(LRE), Liu Type Logistic Estimator(LLE), and SRMLE for the logistic regression model by using Scalar Mean Squared Error (SMSE).

Using Analysis State to Construct a Forecast Error Covariance Matrix in Ensemble Kalman Filter Assimilation

Correctly estimating the forecast error covariance matrix is a key step in any data assimilation scheme. If it is not correctly estimated, the assimilated states could be far from the true states. A popular method to address this problem is error covariance matrix inflation. That is, to multiply the forecast error covariance matrix by an appropriate factor. In this paper, analysis states are used to construct the forecast error covariance matrix and an adaptive estimation procedure associated with the error covariance matrix inflation technique is developed. The proposed assimilation scheme was tested on the Lorenz-96 model and 2D Shallow Water Equation model, both of which are associated with spatially correlated observational systems. The experiments showed that by introducing the proposed structure of the forecast error eovariance matrix and applying its adaptive estimation procedure, the assimilation results were further improved.

基于最大均值差异的能量侧信道泄露量化评估

能量侧信道分析是通过对密码设备运行时的能量消耗进行分析,推导出运行时的操作及操作涉及的敏感中间值.对密码设备进行能量泄露量化评估是分析密码设备信息泄露程度的重要手段,目前主流的评估方案主要关注于能量迹上单个样本点的泄露,并未充分考虑高阶攻击模型下的泄露评估问题,对于采用掩码防御措施的密码芯片来说,一旦发生泄露,通常表现为多变量联合泄露,因此采用传统的单样本点方法进行泄露评估会存在假阴性的问题.本文研究多点联合泄露评估问题,引入最大均值差异方法,提取能量迹的多变量联合特征,构建基于最大均值差异的能量泄露量化评估模型,提供了一种有效的能量侧信道泄露量化评估方法.通过实现无防御对策和有防御对策的AES算法,使用DPA contest v2、ASCAD v1和自采能量迹数据集进行实验,结果表明,基于最大均值差异的泄露量化评估方法能够有效降低单样本点检测方法出现假阴性的风险,HAC、MTD和Bartlett-F检验的对照结果也进一步验证了该方法的有效性.

Estimation under a Finite Mixture of Exponentiated Exponential Components Model and Balanced Square Error Loss

By exponentiating each of the components of a finite mixture of two exponential components model by a positive parameter, several shapes of hazard rate functions are obtained. Maximum likelihood and Bayes methods, based on square error loss function and objective prior, are used to obtain estimators based on balanced square error loss function for the parameters, survival and hazard rate functions of a mixture of two exponentiated exponential components model. Approximate interval estimators of the parameters of the model are obtained.

Estimation of the Mean of the Exponential Distribution Using Maximum Ranked Set Sampling with Unequal Samples

In this paper maximum ranked set sampling procedure with unequal samples (MRSSU) is proposed. Maximum likelihood estimator and modified maximum likelihood estimator are obtained and their properties are studied under exponential distribution. These methods are studied under both perfect and imperfect ranking (with errors in ranking). These estimators are then compared with estimators based on simple random sampling (SRS) and ranked set sampling (RSS) procedures. It is shown that relative efficiencies of the estimators based on MRSSU are better than those of the estimator based on SRS. Simulation results show that efficiency of proposed estimator is better than estimator based on RSS under ranking error.

Statistical Classification Using the Maximum Function

The maximum of k numerical functions defined on , , by , ??is used here in Statistical classification. Previously, it has been used in Statistical Discrimination [1] and in Clustering [2]. We present first some theoretical results on this function, and then its application in classification using a computer program we have developed. This approach leads to clear decisions, even in cases where the extension to several classes of Fisher’s linear discriminant function fails to be effective.

Efficient Mean Estimation in Log-normal Linear Models with First-order Correlated Errors

In this paper, we propose a log-normal linear model whose errors are first-order correlated, and suggest a two-stage method for the efficient estimation of the conditional mean of the response variable at the original scale. We obtain two estimators which minimize the asymptotic mean squared error （MM） and the asymptotic bias （MB）, respectively. Both the estimators are very easy to implement, and simulation studies show that they are perform better.

联合RIS子块与接收天线的索引调制方案性能分析

利用大量无源反射器件设计的可重构智能表面(RIS),具有拓展无线通信覆盖范围、降低系统成本/功耗和提升未来通信系统性能的优势;而索引调制(IM),通过激活通信资源的子集可实现信息的隐性传输,有望为无线通信带来更高的频谱效率和能量效率,因此,为进一步提升未来无线通信系统的性能,该论文融合RIS与IM技术,提出一种联合RIS子块与接收天线的IM方案。在该方案中,按照相邻原则,将RIS反射元件分割为若干子块,进而利用RIS子块索引与接收天线索引同时实现附加信息传递,即对待传输比特序列,将其分割为三部分,其中第一部比特被调制为星座符号,第二部分用于指示RIS子块划分,第三部分用于指示接收天线索引;在此基础上,根据最大似然(ML)检测的成对错误概率(PEP)解析形式,分四种情况,从理论上推导出对应的矩母函数(MGF),得到无条件PEP的解析表达式,最后得到该方案ML检测的平均误码率(ABER)性能上界。仿真结果表明,与现有RIS辅助的IM方案相比,无须增加额外频谱资源,所提方案的系统可达速率和系统差错性能方面均具有一定优势;同时,验证了ML检测的ABER上界具备严格一致性。

基于最大对比度的联合相位误差校正方法

针对高分辨率逆合成孔径雷达(ISAR)成像过程中,因目标转动所引入的转动相位误差无法被忽略,以及由于转动相位误差使得传统平动相位误差校正方法性能严重下降的问题,提出了一种基于最大对比度的联合相位误差校正的方法,可同时校正残余的平动相位误差和转动相位误差,进一步提高成像的质量。首先利用传统的运动补偿方法对回波信号进行处理,其次根据最大对比度准则,将相位误差校正问题转化为最大对比度优化问题,最后利用改进的拟牛顿算法进行求解。该方法能使高分辨率ISAR进行精准成像,且所得图像聚焦效果良好。通过对仿真数据和实测数据的处理,验证了文中方法的有效性。

基于fminimax优化函数的圆度误差评定

依据圆度误差标注代号和附加符号的含义,基于最小外接法、最大内切法和最小区域法分别建立了参考圆的圆心坐标优化目标函数和圆度误差评定模型,采用MATLAB软件编写了参考圆的圆心坐标优化目标函数子程序和圆度误差评定子程序,给出了圆度误差评定中fminimax优化函数调用方法。用Talyrond 585LT圆柱度测量仪对三个试样的圆周轮廓进行了提取,基于最小二乘法、最小外接法、最大内切法和最小区域法对提取的圆周轮廓分别进行了圆度误差评定。研究了优化搜索范围对圆度误差评定结果的影响,并将fminimax优化函数圆度误差评定的优化符合度与其他优化方法圆度误差评定的优化符合度进行了比较。fminimax优化函数的应用结果表明,评定结果精度高于或等于其他优化方法评定结果的精度,可满足圆度误差评定的需要。

一种最优化的轨迹数据L_(∞)-PLA压缩算法

随着全球定位系统的发展和应用,巨量的轨迹数据被实时收集,给数据的传输、存储和分析带来挑战.基于分段线性近似(piecewise linear approximation,PLA)的数据压缩技术因具有简单直观、压缩存储低和传输快的特点被广泛应用和研究.针对现有轨迹PLA压缩方法不能最优化地在线压缩多维数据的现状,在最大误差限定(maximum error bound,记为L_(∞))下提出多维轨迹数据的最优化PLA压缩问题(记为m DisPLA_(∞)),并给出一种在线MDisPLA算法予以解决.该算法利用“分治-融合”的策略扩展一维最优化PLA算法,以最优化地压缩多维轨迹数据.MDisPLA算法具有线性时间复杂性,可以生成最少的不连续分割,且可以保证生成直线表示的质量,即原始数据点和对应解压缩点之间的同步误差具有上界.通过与基于同步距离锥交(cone intersection using the synchronous Euclidean distance,CISED)的轨迹压缩算法进行理论和实验比较,验证了MDisPLA算法是稳健的,可生成具有保质性的直线表示.MDisPLA算法以更低的内存消耗,较CISED算法提高了14倍左右的处理速度,降低了约48%的分割个数和10.5%的存储个数.MDisPLA算法在保证压缩质量的同时,显著提高了处理速度和降低了存储空间,整体上优于CISED算法.

最大时间间隔误差与Allan方差关系研究

最大时间间隔误差和Allan方差都是常用于对时间频率信号生成与传递系统进行分析或评价的统计量。在有些应用中,测量最大时间间隔误差较测量Allan方差更为方便。为了确定一个信号的最大时间间隔误差和Allan方差之间是否存在一定的统计关系以及是否可以通过最大时间间隔误差对Allan方差做出估计,介绍了最大时间间隔误差和Allan方差的数学定义,通过数学分析推导,得到了最大时间间隔误差与Allan方差之间不能直接换算,而最大时间间隔误差一定时Allan方差存在上限。该关系对不同取样时间都成立。对幂律谱模型中常见的5种噪声类型,使用计算机程序生成每种噪声条件下的数值数据,对这一关系做了验证。数学推导计算和数值数据验证都表明:根据数据的最大时间间隔误差可以确定其Allan方差的上限。

基于极大熵法的三峡水库洪水预报误差分布规律

深入研究洪水预报误差的分布规律具有很大的实用价值,可为水库调度决策的拟定提供辅助参考。通常的研究方法为正态分布法与对数正态分布法,但这类研究方法受人为主观性的影响较大。使用三峡水库2020-2022年入库洪水数据将洪水分为4个量级,建立三峡水库洪水洪量预报误差分布的极大熵模型,得出不同预见期下4个量级的洪水洪量预报相对误差的分布规律,结果表明:采用极大熵模型可以定量地获得三峡水库的洪水预报误差分布规律,具有较强的适用性且可进一步推广使用。

考虑制造误差的摆线轮减速器转臂轴承承载分析

摆线轮减速器转臂轴承的承载性能和磨损状况与减速器的回转精度和寿命有密切关系。针对某150BX型摆线轮减速器轴承孔实际磨损状况,首先,建立转臂轴承的承载模型,明确了最大载荷及其方向的变化规律;然后,建立含制造误差的摆线轮和曲柄轴的运动模型,分析制造误差对转臂轴承运动过程中的干涉情况,获得了转臂轴承上最大干涉位置和误差选配方法。研究为摆线轮减速器转臂轴承的磨损计算和关键零部件的误差选配提供了依据。