手工用英语怎么说

[尴尬用英语怎么说]“尴尬”用英语说法：embarrassed 读法：英 [ɪm'bærəst] 美 [ɪm'bærəst] 释义： 1、adj. 尴尬的；窘迫的 2、v. 使...困窘；使...局促不安（embarrass的过去分词形式） feel embarra...+阅读

手工

1. handwork 2.handcraft

网络释义

手工WORKMANSHIP

手工修润hand finishing

手工竹纸china paper

手工 [shǒu gōng]

（手做的工作） handwork:

do handwork

做手工

（用手操作） by hand; manual; handmade:

hand-knitted;

手工编织的

be made by hand

手工做的

（手工劳动的报酬） charge for a piece of handwork:

How much did you pay for the tailoring of this coat?

你这件上衣手工多少？

手工的英文是什么

nhk:manualstudio或handmadestudio...都是不可以用滴~...这2个只是中文的直译...外国人会看不懂的...顶多让他们...猜中你想说的意思而已...manual或handmade也是形容词...指人手操作的...手工制作的意思...跟中文手工坊的手工（=名词）...意思不完全1样...manualstudio会让人误会...以为你的工作坊...是以人手搭建出来的（临时）工作坊...即人手制作的工作坊...或是...以人手去操作的工作坊...意义会完全変质...再者...studio是用于摄影...录音...设计等的工作室...要嘛就是比较艺术性的专业工作室...这个也跟手工坊的坊...意思不完全吻合...其实...小型的工作坊...采用workshop...或workroom...都是最适切的选择...workshop本身...己经是工作坊...小型工作室的意思...如果1定要注明是...手作的工作坊...可以写成handcraftworkshop...但atelier或loft等工作室字眼...在这里不太适用...另外...找乐趣来手作...就是从自制手作的过程中...找到许多乐趣的意思...可以既简单...又直接地翻译成...byhandcraft,havinglotoffun！！（=做手作，满乐趣！！）...只可惜...不知道你说的手作...是那1类型的手作...不然...句子的翻译...可以调整到更准确...现在只能暂时以handcraft...取代所有...各类型的手工艺/手作.........

求手工的英语翻译

With the rapid development of computer software, computer software is increasingly integrated into our life. Improved the work efficiency of people, but hard to get out a pen and paper records and maintenance and high cost. Computer software for science and technology in modern times, modern management plays an important role.

Training management system is according to the training of a system, in today's society, with a skill is very important, mainly is the study of theoretical school thing, many people still need to go to the training institutions or training organized by the school meet the needs of the social enterprise. Training management system can make the training more system, including training, the students' information records and periods of management.

This system USES a MYSQL relational database, the front-end + JSP development using the bootstrap framework, the back-end using JAVA language + SSH development (Spring + struts 2 + hibernate) framework, friendly interface beautiful, more modern aesthetic, to optimize the user experience from the senses

手工英语翻译

这篇更专业了，我尽力翻译了，希望对你有点帮助。================================================================ To overcome this problem, the Wiener filter has been extended to multiple-bases representations for noise removal. Mihcak and Kozintsev^([1]) approached the signal estimation problem from the perspective of designing the Wiener filter in the wavelet domain. The technology indirectly yields an estimate of the signal subspace that is leveraged into the design of the filter. This paper studies the problem of nonlinear Wiener filtering in reproducing kernel Hilbert spaces via least square support vector regression, The method reflected new perspectives within the framework of kernel methods for denoising problem. Experimental results confirm a significant improvement in image denoising.Least support squares vector regression is a new universal learning machine proposed by Suykens etal.^([2]) Let x∈R^d, y∈R, R^d represent input space, d is the dimension. By some nonlinear mapping ∅， x is mapping into some a prior chosen Hilbert space spanned by the linear combination of a set of functions.with ∅（x）: R^d→R. Such that the following regularized risk function J is minimized:The parameter γ is a positive regularization constant. After elimination of w,e one obtains the solution:Where Y=[y_1…y_N]， ρ_1=[1…1]， α=[α_1…α_N] and Ω=K+γ^（-1）I . The resulting least support squares vector regression model for function estimation becomes:where K(x,x_i)=∅（x） ∅（x_i）(i=1，…，N) is the kernel function and must satisfy the Mercer condition,^([3]) α are Lagrange multipliers and b almost equals the mean of y.Consider a 2D image consisting of a matrix of M=N*N pixels, the observation image can be regarded as a function in pixel areas y=f(i.j); R^2→R^1, where input (i,j) is 2D vector equals to the row and column indices of that pixel, where output y is the approximated intensity value.^([4]) The Lagrange multipliers α_（i,j） of the observed image pixel y(i,j) can be easily calculated using Eq.(3).where A=Ω^（-1）, B=(I^T Ω^（-1）)/(I^T Ω^（-1） I) and O_α is a N*N matrix defined by A(I-IB). Notice that, the Lagrange multipliers α_（i,j） of the observed image pixel y(i,j) is determined by the multiplication of the matrix O_α and the observed image Y. That is, the Lagrange multipliers are influenced by the clean image S and random noise N. As in Eq.(4), the observed image can be reconstructed by a linear combination of kernels with weights equal to the values of Lagrange multipliers and an appropriate support vector regression can concentrate the signal energy into a number of support vectors(SVs) that α_（i,j） is nonzero.The localization of SVs is particularly appropriate for imaging applications, where it is crucial to preserve fine details like edges and textures. Pixels with positive Lagrange values try to raise the grey levels of themselves and their neighbors, while those with negative Lagrange multipliers will try to reduce the grey levels and they appear darker. Therefore, the Lagrange multipliers effectively weigh the kernel functions to estimate intensity value of image. Furthermore, random noise can be considered as forces that try to make Lagrange multipliers to oscillate above and below the standard value. The noise can be reduced by smoothing the value of Lagrange multipliers, whereas sharp edges may be preserved within certain ranges which rely on a suitable kernel function possessing the capability of nonlinear representation.---------------------------------------------------------------- 翻译如下---------------------------------------------------------------- 为了解决这个问题，我们把维纳滤波法扩展到多基表述来除噪音。

Mihcak和Kozintsev^([1]) ，从在小波域里设计维纳滤波器的角度来解决信号估算问题。这种技术间接地给出了一个可以补充到滤波器的设计中的信号子空间的估值。这篇论文研究非线性维纳滤波法通过最小二乘支持向量回归在再生成核希尔伯特空间中的问题，以及在降噪问题核心算法的构架内新角度下的方法。实验的结果确认了图像降噪中的显著优化。最小二乘向量回归是一种由Suykens etal.^([2])提出的新的通用学习机器。让x∈R^d, y∈R, R^d代表输入空间，d代表维度。通过一些非线性映射∅ ，x会映射到一些事先选好的由一组函数的线性组合扩展开来的希尔伯特空间。由∅（x）: R^d→R.于是下面的调整风险函数被最小化：系数γ是正的调整常数。消去w,e后，解得：这里Y=[y_1…y_N]， ρ_1=[1…1]， α=[α_1…α_N] ，Ω=K+γ^（-1）I 得出的用来函数估值的最小二乘向量回归模型变成了：这里K(x,x_i)=∅（x） ∅（x_i）(i=1，…，N)成为核函数，而且必须符合Mercer条件^（[3]）， α是拉格朗日乘数同时b近乎等于y的平均值。

考虑一张由一个N*N像素的矩阵M组成的二维图像...