序列空间

Stationary and nonstationary processes, including ARIMA processes. Estimation of process mean and autocovariance function. Fitting ARIMA models to data. Statistical tests for white noise. Forecasting. State space models and the Kalman filter. Robust time series analysis. Regression analysis with correlated errors. Statistical properties of long memory processes.

静态和非静态过程(包括 ARIMA 过程)，均值和时间序列分析：自协方差估计，用 ARIMA 模型拟和数据，白噪声序列的统计检验，预报，状态空间模型和 Kalman 过滤，时间序列分析的稳健性，残差具有相关性的回归分析。

In the first part, an ideal non-noise spatio-temporal series (33 modes Lorenz system) is used as a generator for chaotic spatio-temporal series in comparison and sensitive tests with the field time series analysis method.

在第一部分中，以一个"理想"的混沌时空序列即33模Lorenz系统，作为时空混沌序列的"发生器"，通过状态空间重构，利用场时间序列的局域近似方法建立预测模型，进行对比试验和敏感性试验。

Let C be a nonempty closed convex subset of a Hilbert space H, P:H→C be a nearest point projection, and T:C→H be a nonexpansive mapping satisfying the weakly inwardness condition, and :C→C be a fixed contractive mapping. Let the implicit iterative sequences {x},{y} and the explicit iterative sequences {x},{y}.

设C为实Hilbert空间H的非空闭凸子集，P：H→C为最近点投影映射，T：C→H为非扩张映象，且T满足弱内向条件，：C→C为压缩映象。t∈(0，l)，定义了2种隐式粘性迭代序列{x}，{y}和2种显式粘性迭代序列{x}，{y}，证明了T有不动点当且仅当序列{x}，{y}，{x}，{y}有界。

This paper analyzes irrational number series of square root of 2 and e in six aspects involving autocorrelation function,Power Spectral Density,phase space construction,correlation dimension,Principal Component Analysis and maximal Lyapunov exponent.

将已提出的π序列定义和分析方法推广到无理数领域，引出了无理数序列，并以常用的无理数根号2、e为例，对无理数序列的自相关、功率谱、相空间、关联维数、最大Lyapunov指数和主分量等6个方面进行深入分析。

It relies on the facts that the rows composed of all the image points span the same linear subspace as the rows composed of the 3D space points and that the basis of the subspace can consist of the two rows composed of the first image points and a row vector which is orthogonal to the former.

所有图像序列构成的行向量与3维空间点构成的行向量所生成的子空间是同一线性子空间，而且由第1幅图像点构成的2个行向量外加1个行向量就可以组成该子空间的一个基底。

An iterative factorization method based on linear subspace for projective reconstruction is presented in the paper. It relies on the facts that the rows in the matrix including all the image points span the same linear subspace as the rows in the matrix including space points and the fact that any basis of the subspace can be regarded as projective reconstruction. The projective reconstruction and the depth factors are obtained based on linear iteration.

摘要该文提出了一种基于子空间线性迭代的射影重建方法，该方法利用所有的图像序列构成的行向量生成的线性子空间之和与射影重建结构点构成的行向量生成的子空间是同一线性子空间及在该子空间中任何一个基底都可以作为射影重建的特性，线性迭代地求取射影重建及图像深度因子。

The method relies on the facts that the row vectors composed of all the image points span, the same linear subspace as the row vectors composed of 3D space points span and that a basis of the subspace can consist of two row vectors composed of all the image points and one row vector in 3D space that is orthogonal to the former. The row vector can be determined, and the 3D reconstruction is accomplished. The novelty lies in the fact that the method can treat all the image points uniformly.

利用所有图像序列构成的行向量生成的子空间之和与三维空间点构成的行向量生成的子空间是同一线性子空间、同时由所有图像点构成的2个行向量外加一个行向量就可以组成该子空间的一个基底的特性，线性地求取子空间中的行向量，最后完成三维重建。

Fourthly, it discusses the spectral image loss-less compression by applying thecorrelation and the entropy of the spectral image sequence. It gives and analyses three expressions of imaging spectrometer images, and tells the change in correlation coefficient and entropy for different expressions. At last, it puts forward D2PCM de-correlation method to reduce the spatial and spectral redundancy.

根据成像光谱图像的空间、谱间相关性和信息熵的特征，提出成像光谱图像序列的三种表示方法，分析了不同表示方法的相关系数和图像信息熵的变化，并研究出了成像光谱图像的无损压缩方法，即利用D~2PCM方法对光谱图像序列进行去相关处理，同时降低图像的空间和谱间冗余，在提高了无损压缩的压缩比和压缩效率的同时，算法具有简单快速的优点。

We drawn the predicted surface maps with the Ordinary Kriging method and illustrated in the time order to provide a visible presentation of the spatio-temporal distributions. By analyzing those maps, we found that the spatial distribution of spiders in the paddy field was similar to that of macropterous SBPH and larval SBPH, and was more similar to larval SBPH, and showed an obvious pursuing phenomenon between them, while the pursuing had a close relationship with the temporal dynamics of SBPH number. But the pursuing phenomenon was complex and influenced by factors such as the weather, the environmental conditions in paddy fields, other enemies of SBPH and other foods of spiders.

用普通克立格方法作出空间分布图，在时间序列上进行比较分析，得出稻田蜘蛛与灰飞虱成若虫的空间分布均有相似性，尤其与若虫相似性更强，表现出比较明显的空间跟随现象，且空间跟随现象与灰飞虱虫量的消长有密切的关系，但是由于天气、稻田环境、灰飞虱其他天敌以及蜘蛛其他猎物的存在等因素的影响使得其空间跟随又有着相当的复杂性。

According to study the tectonic facies map of Balkhash- Junggar region, the article establishment geology conceptual model from two aspects which are tectonic setting and tectonic evolution. Based on the idea of information science, the article using Geodatabase data model and discussing feature class, attribute table and their mutual relations to construct tectonic facies spatial data model of Balkhash - Junggar region that based on the idea of information science.

闭于巴人喀什-准噶人地域大地构造相图入行剖析，自卑地构造空间序列和大地构造时光序列两个角度来树立地量概念模型，基于信做科教的念惟，当用Geodatabase数据模型，谈论大地构造相图的要荤类、属性外及其之间的互相闭解，构建基于信做科教念惟的巴人喀什-准噶人地域大地构造相图空间数据模型。

Cynanchum Lingtai apricot production in the average weight 65 grams, the brightly-colored fruit, juicy rich, sweet-sour taste, sweet from the nucleolus, when the late Qing Dynasty famous Shaanxi, Gansu provinces, the Qing imperial court Tongzhi tribute for years.

灵台生产的牛心杏平均单果重65克，果实色泽鲜艳，汁多味浓，甜酸适口，离核仁甜，清末时就驰名陕、甘两省，清同治年间曾为朝廷贡品。

Chenopodium album,Solanum nigrum, and Amaranthus retroflexus were very susceptible to the herbicides. Polygonum persicaria and Abutilon theophrasti were relatively less susceptible to the herbicides, and Lycopersicon esculentum was not susceptible to it. The relationship between reduction rates of weed biomass and PPM values of weed leaves 2,4, and 6 days after treatment was established.

供试的6种杂草对该混剂的敏感性存在显著差异：红心藜Chenopodium album、龙葵Solanum nigrum和反枝苋Amaranthus retroflexus对该混剂最敏感，ED90值分别为47.65、71.67和29.17g/hm2；春蓼Polygonum persicaria和苘麻Abutilon theophrasti敏感，ED90值分别为96.91、114.20g/hm2；而番茄不敏感。