高考数学答题应注意的问题

学习能力是指学生自己发现问题，自己研究问题，自己解决问题的能力，如何提高学生的学习能力呢?古人云：“学贵自疑”、“学起于思，思源于疑”，疑是思之源，质疑是探求新知识的开始，也是创新的动力和源泉．在信息社会的今天，教会学生学习是一个重要内容，而教会学生问问题正是激励他们创造性学习的一个关键。     

A High Revolution Speed Noncontact Ultrasonic Motor Driven by a Non-Symmetrical Electrode

A noncontact ultrasonic motor based on a non-symmetrical electrode is proposed. This motor has the advantages of using a simple driving electrode and having a high revolution speed. The revolution speed of its three-blade rotor can reach 5100rpm under a driving voltage of 20 V. A method operated easily is proposed to measure the output torque,     

Entropy of Nonstatic Black Hole with the Internal Global Monopole and the Generalized Uncertainty Relation

The new equation of state density is obtained by the utilization of the generalized uncertainty relation. With the help of coordinates and the Wentzel-Kramers-Brillouin approximation, direct calculation of the scalar field entropy of the non-state black hole with an internal global monopole is performed. The entropy obtained from the calculation is proportional to the horizon area. The calculation can be free from convergence if without any cutoff, which is different from the brick-wall method. However, the pertinent result is limited.     

Some Key Concepts in Nonstandard Analysis Theory of Turbulence

Some important concepts in the nonstandard analysis theory of turbulence are presented. The point structure, on which differential equations are defined, is analysed. The distinction between the uniform point and the non-uniform point, as well as between the standard point and the nonstandard point, is shown. A new kind of equations, which differ essentially from those in existent theory, is emphasized. These new equations can hold at non-uniform points. The applicability of the Navier-Stokes equations to turbulence is discussed. Some illustrations of the nonstandard analysis theory of turbulence are also presented.     

外代数上复杂度为2的Koszul模

本文对外代数上复杂度为2的不可分解循环Koszul模M的极小投射分解进行了分析，构造出了基映射对应的矩阵的一种标准形式，进而刻划出了其合冲ΩM的滤链结构．     

完全二部图的P4k-1-因子分解

如果完全二部图K_m,n的边集可以划分为K_m,n的P_v-因子,则称K_m,n存在P_v-因子分解. 当v是偶数时, Ushio和Wang 给出了K_m,n存在P_v-因子分解的充分必要条件. Ushio同时提出了当v是奇数时K_m,n存在P_v-因子分解的猜想, 但是至今为止仅知当v=3时Ushio猜想成立. 对于正整数k,本文证明K_m,n存在P_4k−1-因子分解的充分必要条件是: (1) (2k−1)m ≤2kn, (2) (2k−1)n ≤ 2 km, (3) m+n ≡ 0 (mod 4k−1), (4) (4k−1)mn/[2(2k−1)(m+n)]是整数. 即证明了对于任意正整数k, 当v=4k−1时Ushio猜想成立.     

Marino-Vafa公式的一个注记

曲线模空间上的Hodge积分自然地出现在使用局部化方法计算Gromov-Witten不变量的过程中.Marino-Vafa的一个惊人的公式将Hodge积分的一个生成函数表示为一些看起来和原本是代数几何对象的Hodge积分无关的组合和代数的量. 本文将公式直接展开并仔细估计所涉及的项, 最终证明除了一个特定类型,其余至多包含3个Hodge类的Hodge积分都可以由 Marino-Vafa公式计算. 这意味着大量有关模空间和Gromov-Witten不变量的信息包含在这一复杂的公式中. 给出了一些低亏格的例子, 这些例子与文献中的已有结果吻合. 在承认关于Hodge类的约化的Mumford关系的前提下证明和计算都是初等的.     

图K(r,2)的邻强边色数

本文给出了每部有2个点的完全r-部图(r≥2)的邻强边色数.     

均衡信贷配给的期权定价分析

本文基于期权定价的视角分析信贷配给，以一条全新的思路阐释均衡信贷配给的成因及特征，并试图得出些许启示。     

Speech recognition based on a combination of acousticfeatures with articulatory information

The contributions of the static and dynamic articulatory information to speech recognition were evaluated, and the recognition approaches by combining the articulatory information with acoustic features were discussed. Articulatory movements were observed by the Electromagnetic Articulographic System for reading speech, and the speech signals were recorded simultaneously. First, we conducted several speech recognition experiments by using articulatory features alone, consisting of a number of specific articulatory channels, to evaluate the contribution of each observation point on articulators. Then, the displacement information of articulatory data were combined with acoustic features directly and adopted in speech recognition. The results show that articulatory information provides with additional information for speech recognition which is not encoded in acoustic features. Furthermore, the contribution of the dynamic information of the articulatory data was evaluated by combining them in speech recognition. It is found that the second derivative of articulatory information provided quite larger contribution to speech recognition comparing with the second derivative of acoustical information. At last, the combination methods of articulatory features and acoustic ones were investigated for speech recognition. The basic approach is that the Bayesian Network (BN) is added to each state of HMM, where the articulatory information is represented by the BN as a factor of observed signals during training the model and is marginalized as a hidden variable in recognition stage. Results based on this HMM/BN framework show a better performance than the traditional method.