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This paper proves compactly the conclusion that the first datum of original data lists doesnt work in the course of building GM(1,1) model.Further, A zero addend GM(1,1) combining forecasting approach that takes into account the first datum is developed.

对GM(1,1)基本建模过程中第一个数据不起作用的结论作了全新的简洁的推导,提出了计及第一个数据的零加数GM(1,1)组合预测方法。

This design is divided into 6 part totally they respectively is:(1)General introduction;(2)The mineral bed endows with the natural general situation and the original data(3) mineral bed geologies of the regions;(4) Developmengt system;;(5)mining method;(6)Mineral mountain airiness;(7)The mineral mountain promotes and transports.

本设计共分为6个部分,它们分别是:(1)总论;(2)矿床赋存地区的自然概况及原始资料(3)矿床地质;(4)开拓系统;(5)采矿方法;(6)矿山通风;(7)矿山提升和运输。

Thus the original data is received. But anciently, it always takes hardwares to code.

但在以往采用这种方法时都是由专用的硬件来实现。

We develop a complete set of arithmetics which can fuse the original data.

给出了从临床原始数据到融合图像的一整套算法。

Though comparing Canny operator and center B spline dyadic wavelet, the following conclusion is proven in this dissertation: a Center B spline function has tight support and Canny operator hasn't. b Center B spline function asymptotic convergence to Gaussian function and the derivative of Center B spline function asymptotic convergence to Canny operator. c The derivative of fourth order center spline B function is more suitable as a optimal edge detector than Canny operator. d Center B spline function can balance the smoothing and approximation of original data, and the fourth center B spline function is the only optimal solution of two order smoothing problem. e The error between the valve of time-frequency uncertainty of the fourth center B spline function and the lower bound of time-frequency uncertainty does not exceed 0.143% of the lower bound. f The derivative of center spline B function can construct a stability dyadic wavelet and can give a fast algorithm for multiscale edge detection, but Canny operator can do neither.

作者给出了Canny算子与中心B样条二进小波严格的比较证明,得出如下结论:a中心B样条函数具有紧支集,Canny算子不具有紧支集。b中心B样条函数的极限收敛于高斯函数,中心B样条函数的导数收敛于Canny算子。c四阶中心B样条函数的导数比Canny算子更接近最佳边缘检测滤波器。d中心B样条函数比高斯函数更能兼顾对原函数平滑和逼近的折中要求,并且四阶中心B样条函数是二阶逼近问题的唯一最优解。e四阶中心B样条函数的时频测不准关系值与时频测不准关系下界的逼近误差不超过0.143%。f中心B样条函数的导数可以构成稳定的二进小波,存在快速的多尺度算法;而Canny算子不构成稳定的二进小波,无法给出快速的多尺度算法。

It is the method that a discrete model can be obtained basing on the original data.

该建模方法得到的是一个离散的数学模型。

In order to provide Undo capabilities, copies of large amount of original data are required to be stored prior to each destructive operation.

为了提供解开能力,很多原始的数据的拷贝要求被存放在各破坏性的操作之前。

Thereare two contributions in this paper. First, by regarding every base clustering as one attribute of the original data, it has found that the algorithm based on that is more extendable and flexible. Second, it designs a latent variable cluster ensemble model in this way and infers the algorithm of the model with Markov chain Monte Carlo approximation.

主要工作包括:第一,分析了把每一个基聚类器看成是原数据的一个属性这种处理方式的优越性,发现按此方法建立起来的聚类集成算法就具有良好的扩展性和灵活性;第二,在此基础之上,建立了latent variable cluster ensemble概率模型进行聚类集成,并且给出了LVCE 模型的Markovchain Monte Carlo算法。

The original data used were 5 in 12 RFLPs of a mouse F2 population including333 individuals presented in the software MAPMAKER.Classical and successive x2 testswere used to check segregation distortions of the codominant markers. And the resultexhibited that successive x2 tests was more efficient.

以MAPMAKER中提供的老鼠F2群体中333个体12个RFLP标记中的5个连锁标记为原始数据,用传统x2检测和连续x2检测两种方法处理共显性分子标记数据,比较它们对偏分离情况分析的能力,发现连续x2检测比传统的x2检测更加准确有效。

The enhanced configuration device then reconfigures the Stratix device with this new data, and you have an updated system.The original data that was shipped with the system is still safely stored in the factory application space. So in case there was an error during the update process, perhaps during the remote transmission of the configuration file, the Stratix device can signal用信号通知 the enhanced configuration device

单个ALM的输入能够灵活地分割到两个输出功能块中,使得宽输入的功能函数能更快地运行,因为这可以在一个ALM中完成,不像传统的4输入查找表架构,实现多于4个输入的功能函数时,需要至少两个查找表,因此还需要额外的布线资源(见书P43图1.49);而窄输入的功能函数能有效利用现有的资源,因为ALM能够灵活配置成不同的结构,来适应不同的输入数目。

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