混合序列部分和的若干收敛性质

本文研究了混合序列部分和的若干收敛性质.利用Serfling不等式推广情形,证明了一类随机变量序列部分和的一个收敛性结果,获得了混合序列部分和的收敛性,并进一步得到了混合序列加权和的强收敛性和完全收敛性,推广并改进了文[2]中有关结果.

混合序列加权和的Marcinkiewicz强大数定律(英文)

通过建立混合序列加权和的Bernstein不等式,研究了混合序列加权和线性统计量的Marcinkiewicz-Zygmund强大数定律,实质性的改进和推广了Z.D.Bai与P.E.Cheng的结果。最后讨论了一个实际例子,预测城市物价增长。

混合序列的完全收敛性和部分和的几乎处处收敛性

讨论了不同分布混合序列的完全收敛性和部分和的几乎处处收敛性,利用矩不等式和截尾方法,得到了和独立情形完全一样的结果.

混合序列加权和的若干收敛性质

讨论了混合序列加权和的弱收敛性、Lp收敛性和完全收敛性定理,推广了前人的一系列结果。

混合序列的大数定律和完全收敛性探讨

文章主要讨论了混合序列的大数定律和完全收敛性,引入了随机变量阵列的Cesáro一致可积性和它的等价条件;接着在Cesáro一致可积的若干条件下,讨论了行为混合序列的随机变量阵列加权和的弱大数定律及收敛性.作为特例,得到了混合序列的弱大数定律及收敛性。从讨论中可以看出,Cesáro一致可积性也是研究混合序列弱大数定律的非常有效的工具。

混合序列产生的移动平均过程的矩完全收敛性(英文)

设{Yi;-∞<i<∞}是同分布的混合序列,{ai;-∞<i<∞}是一个绝对可求和的实数列,我们证明了{∑ from k=1 to n∑ from i=-∞ to ∞(ai+kYi/n1/p;n≥1}的矩完全收敛性。从而把李和张(2004)在负相协下的结果扩展到混合序列。

混合序列加权和的强收敛

本文讨论了混合序列的广义Jamison型加权和的强收敛性,推广了著名的Jamison定理。

混合随机变量序列加权和最大值的几乎处处收敛性

利用混合随机变量的矩不等式获得了以ank为加权系数的ρ槇混合随机变量序列加权和最大值的强收敛性,所得结论概括并推广了独立情形的相应结果。

缺失数据下混合误差线性模型的参数估计

研究了响应变量随机缺失下的线性回归模型,在模型误差为混合序列的情形下,获得了回归参数估计的强相合性,推广了已有的结果.

一类混合误差下半参数模型估计的r阶平均相合性

对半参数回归模型yi=xiβ+g(ti)+ei,i=1,2,…,n,运用最小二乘法和非参数核方法给出了β和g(·)的估计的定义,将误差序列推广为的混合误差序列下证明了参数β和非参数函数g(·)的估计量的r阶平均相合性,丰富了相依误差下半参数回归模型估计的理论研究.

Prediction of Gas Emission Based on Infor-mation Fusion and Chaotic Time Series

In order to make more exact predictions of gas emissions, information fusion and chaos time series are com- bined to predict the amount of gas emission in pits. First, a multi-sensor information fusion frame is established. The frame includes a data level, a character level and a decision level. Functions at every level are interpreted in detail in this paper. Then, the process of information fusion for gas emission is introduced. On the basis of those data processed at the data and character levels, the chaos time series and neural network are combined to predict the amount of gas emission at the decision level. The weights of the neural network are gained by training not by manual setting, in order to avoid subjectivity introduced by human intervention. Finally, the experimental results were analyzed in Matlab 6.0 and prove that the method is more accurate in the prediction of the amount of gas emission than the traditional method.

A new hybrid algorithm for global optimization and slope stability evaluation

A new hybrid optimization algorithm was presented by integrating the gravitational search algorithm （GSA） with the sequential quadratic programming （SQP）, namely GSA-SQP, for solving global optimization problems and minimization of factor of safety in slope stability analysis. The new algorithm combines the global exploration ability of the GSA to converge rapidly to a near optimum solution. In addition, it uses the accurate local exploitation ability of the SQP to accelerate the search process and find an accurate solution. A set of five well-known benchmark optimization problems was used to validate the performance of the GSA-SQP as a global optimization algorithm and facilitate comparison with the classical GSA. In addition, the effectiveness of the proposed method for slope stability analysis was investigated using three ease studies of slope stability problems from the literature. The factor of safety of earth slopes was evaluated using the Morgenstern-Price method. The numerical experiments demonstrate that the hybrid algorithm converges faster to a significantly more accurate final solution for a variety of benchmark test functions and slope stability problems.

Extreme value distributions of mixing two sequences with different MDA＇s

Suppose {Xi, i≥1} and {Yi, i≥1} are two independent sequences with distribution functions FX(x) and FY(x), respectively. Zi is the combination of Xi and Yi with a probability pn for each i with 1≤i≤n. The extreme value distribution ,n GZ(x) of this particular triangular array of the i.i.d. random variables Z1, , Z2, ,…, Zn n n ,nis discussed. We found a new form of the extreme value distribution ΛA(ρx)Λ(x)(0<ρ <1), which is not max-stable. It occurs if FX(x) and FY(x) belong to the same MDA(Λ). GZ(x) does not exist as mixture forms of the different types of extreme value distributions.

Moving object detection method based on complementary multi resolution background models

A novel moving object detection method was proposed in order to adapt the difficulties caused by intermittent object motion,thermal and dynamic background sequences.Two groups of complementary Gaussian mixture models were used.The ghost and real static object could be classified by comparing the similarity of the edge images further.In each group,the multi resolution Gaussian mixture models were used and dual thresholds were applied in every resolution in order to get a complete object mask without much noise.The computational color model was also used to depress illustration variations and light shadows.The proposed method was verified by the public test sequences provided by the IEEE Change Detection Workshop and compared with three state-of-the-art methods.Experimental results demonstrate that the proposed method is better than others for all of the evaluation parameters in intermittent object motion sequences.Four and two in the seven evaluation parameters are better than the others in thermal and dynamic background sequences,respectively.The proposed method shows a relatively good performance,especially for the intermittent object motion sequences.

On the Second Borel-Cantelli Lemma for α-mixing Sequences of Events

In this paper, we give some conditions on diverging rate of series of the probabilities and converging rate of series of the α-mixing coefficients for sequences of events, under which the conclusion of the Second Borel-Cantelli Lemma holds. As corollaries, some moment conditions are obtained, under which the strong law of large numbers holds for sequences of identically distributed random variables.

Charaterization of Citrus Hybrid ＂Huangguogan＂ through the Combination of Morphological and Molecular Markers

Huangguogan, an obvious Citrus hybrid, is suitable for transportation and export and ripens in March or April. Because of late season, it may playa significant role in fruit market. However, its origin is still unconfirmed. The aim of this study was to clarify the possible parentage of Huangguogan via the combination of morphological and molecular markers including simple sequence repeat （SSR） and chloroplast simple sequence repeat （cpSSR）. Analysis of morphological traits including leaf stalk length, phylliform index and fruit shape index indicated that Huangguogan had similarities in morphology with Sweet orange. The SSR Cluster Analysis showed that Huangguogan was clustered together with Hongju tangerine and revealed -80% genetic similarity. They illustrated a close genetic distance between Huangguogan and Hongju tangerine. In addition, the bands of2 polymorphic cpSSR were identical in Huangguoggan and Sweet orange. Consequently, it is likely that its female parentage was the sweet orange （Citrus sinensis （L.） Osbeck） and its male parentage was the tangerine （Citrus reticulata Blanco）.

Introducing semantic information into motion graph

To improve motion graph based motion synthesis,semantic control was introduced.Hybrid motion features including both numerical and user-defined semantic relational features were extracted to encode the characteristic aspects contained in the character's poses of the given motion sequences.Motion templates were then automatically derived from the training motions for capturing the spatio-temporal characteristics of an entire given class of semantically related motions.The data streams of motion documents were automatically annotated with semantic motion class labels by matching their respective motion class templates.Finally,the semantic control was introduced into motion graph based human motion synthesis.Experiments of motion synthesis demonstrate the effectiveness of the approach which enables users higher level of semantically intuitive control and high quality in human motion synthesis from motion capture database.

Fast garment simulation with aid of hybrid bones

A data-driven method was proposed to realistically animate garments on human poses in reduced space. Firstly, a gradient based method was extended to generate motion sequences and garments were simulated on the sequences as our training data. Based on the examples, the proposed method can fast output realistic garments on new poses. Our framework can be mainly divided into offline phase and online phase. During the offline phase, based on linear blend skinning(LBS), rigid bones and flex bones were estimated for human bodies and garments, respectively. Then, rigid bone weight maps on garment vertices were learned from examples. In the online phase, new human poses were treated as input to estimate rigid bone transformations. Then, both rigid bones and flex bones were used to drive garments to fit the new poses. Finally, a novel formulation was also proposed to efficiently deal with garment-body penetration. Experiments manifest that our method is fast and accurate. The intersection artifacts are fast removed and final garment results are quite realistic.

Linear Growth of Continuous-Wave Four-Wave Mixing with Dual Induced Transparency

Using Schroedinger-Maxwell formalism, we propose and analyze an opticalfour-wave mixing (FWM) scheme for the generation of coherent light in a coherent six-level atomicmedium based on dual electromagnetically induced transparency (EIT). We show that the significantlyenhanced conversion efficiency enabled by ultraslow propagation of pump waves has no directrelationship with the single-photon detuning, which is different from the FWM with a single EIT. Themost important feature is that our scheme is also capable of inhibiting and delaying the onset ofthe detrimental three-photon destructive interference that looks like a recent scheme [Phys. Rev.Lett. 91 (2003) 243902] and may be used for generating short-wave-length coherent radiation.

Research on natural language recognition algorithm based on sample entropy

Sample entropy can reflect the change of level of new information in signal sequence as well as the size of the new information. Based on the sample entropy as the features of speech classification, the paper firstly extract the sample entropy of mixed signal, mean and variance to calculate each signal sample entropy, finally uses the K mean clustering to recognize. The simulation results show that： the recognition rate can be increased to 89.2% based on sample entropy.