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three dimensional space相关的网络例句

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This is my last OpenGL for a project to use the "three-dimensional vector space / point" and related functions, they can use a definition of space vector / points, for the vector translation, zooming and rotation, the use of correlation function can also achieve two vector by the fork, get a Vectors absolute length of a vector unit, received a 3:00 decision vector perpendicular to the plane, a space to be the distance between two points, two vector to be the plot points, to be the angle between two vectors, such as operating for the use of user-friendly, each function is detailed Notes, it is particularly important if the calculation vector plot points, fork also took note, we hope to have useful.

这是我过去进行一个OpenGL项目时使用到的"三维空间向量/点"类及相关函数,使用它们可以定义一个空间向量/点,实现向量的平移,缩放和旋转等,使用相关函数还可以实现两向量的叉乘,得到一个向量的绝对长度,将一个向量单位化,得到一个三点决定的平面的垂直向量,得到空间中两点的距离,得到两向量的点积,得到两向量的夹角等操作,为方便用户的使用,每个函数都有详细的注释,特别重要的向量计算如点积,叉乘还有相关说明,希望对大家有所用处。

Vector is an important mathematical tool for the study, the method is the use of vector algebra vector solution to the issue of three-dimensional geometry of a convenient method, space vector as the new content, in dealing with space issues have considerable advantages space than the original problem to deal with more flexibility.

向量是数学研究的一种重要工具,向量的方法是使用向量的代数方法去解决立体几何问题的一种便捷的方法,空间向量作为新加入的内容,在处理空间问题中具有相当的优越性,比原来处理空间问题的方法更有灵活性。

Reen: The vertical three-dimensional space garden and space hall bring color, texture and life to the office space, introducing the vitality of nature into the building in which one works or lives.

REEN:纵向式立体空中花园、空中大厅,为办公空间带来色彩、质感和生气,将大自然的生机与活力引入工作与生活的建筑当中。

As face space is always believed to be a low-dimensional submanifold embedded in high-dimensional ambient space, many methods of subspace feature extraction adopt manifold learning to explore intrinsic structure of face patterns.

在人脸识别中,提取有效的鉴别特征是实现人脸准确识别的一个关键因素。

Its Principal component analysis is used to establish a contact high-dimensional data space and movement style annihilator low-dimensional space mapping.

本方法基于子空间技术。主成分分析方法被用来建立一个联系高维运动数据空间和低维风格化子空间的映射。

And then, we prove the coincidence relation between one skyline point of high dimensional space and the most mild points of one subset, thus enabling it to transform skyline computing in high dimensional space to figuring out the most mild points in all subsets.

为此,本文首先提出了高维数据集上"平和点集"的概念,它由各属性值中的最小值比数据集中其他点对应最小值"好"的那些点组成;然后,论证了"平和点集"中最大平和点和skyline点的对应关系,从而转化高维数据集上skyline计算为其任意子空间上的最大平和点计算。

On the other hand, Let M~n be an z-dimensional complete noncompactoriented submanifold with finite total curvature in an-dimensional simplyconnected space form F~ of constant curvature c. In this thesis, we provethat if M satisfies one of the following: n≥3, c=0 and integral from n=M H~n<∞;n≥5, c=-1 and H<1-2/n~(1/2); n≥3, c=1 and H is bounded, where Hdenotes the mean curvature of M. then the dimension of the space of L~2 harmonic1-forms on M is finite.

本文还证明:若具常曲率c的完备单连通空间型F~中具有有限全曲率的n维完备非紧可定向子流形M满足下面条件之一:n≥3,c=0且integral from n=M H~n<∞;n≥5,c=-1且H<1-2/n~(1/2;n≥3,c=1且H有界的,则M上L~2调和1-形式空间的维数是有限的,这里H为M的平均曲率。

Fourier analysis consists of Fourier series which can solve limited one-dimensional space Heat-conduction question and Fourier integral which can solve infinite one-dimensional space Heat-conduction question. This is more systematic compared with the other methods, and embodies corresponding beauty of Mathematics to Physics.

傅里叶分析包括傅里叶级数和傅里叶积分,用傅里叶级数法解决有界细杆的热传导问题,用含参数的傅里叶变换法解决无界细杆的热传导问题,比其它方法更系统,体现出一种数学与物理对应的美感。

Infinite-dimensional optimization studies the case when the set of feasible solutions is a subset of an infinite- dimensional space, such as a space of functions.

组合优化问题是有关的一套可行的解决办法是离散或可归结为一个独立的一个。

First, the reason behind the negative data is analyzed and a nonnegative constraint is imposed on the classic PCA. Then a set of nonnegative linear independence weight vectors of principal components is obtained, by which a low dimensional space is constructed. Finally, a nonlinear optimization technique is used to determine the projection vectors of the high-dimensional multi-spectral data in the constructed space.

首先分析主成分分析法产生非光谱数据的原因,据此对经典主成分分析模型增加非负约束;然后求出一组线性无关的非负主成分权向量,用该组向量构造低维空间;最后用非线性优化技术确定高维数据在该低维空间中的投影值,实现了高维空间与低维空间的相互转换。

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