美丽手工翻译成英语怎么说

Ⅰ 翻译成英文，要纯手工

It's my honor to work for such an excellent company.

Ⅱ 手工皮艺翻译成英语

RYZ手工皮艺坊
RYZ handcrafted leather Art Square
RYZ手工皮艺工作室
RYZ handmade leather arts studio

Ⅲ 英文翻译 要纯手工的

秋天的风一阵阵地吹来过自, The autumn wind blows
想起了去年的这个时候, And I thought of the same time last year
你的心到底在想些什么, What was your heart thinking
对你的思念是一天又一天, I miss you day after day
孤单的我还是没有改变, The lonely me has not changed
美丽的梦何时才能出现, When will the beautiful dream appear
看不穿是你美丽的眼神 Can't see through your beautiful eyes
猜不透是你迷人的微笑 Can't fathom your alluring smile
一阵风，一场梦，爱如生命般莫测 The blow of wind, a dream, love is as unpredictable as life.

Ⅳ 英语翻译 手工

这篇文章的风格我喜欢，来给你翻一下吧（我翻译都是意译的，并不是字句完全一对一的，你要看的话要理解再看）

快要到圣诞节了!这是我在一所新的学校的第一学期，相比我之前教过的任何集体，我更爱我的这个特殊的小班级。它们那么的渴望知识，而我也很享受教育他们。
其他老师已经告诉我说我们的孩子都是来自贫困家庭，所以不能期待他们会带来圣诞礼物。实际上，我并未期待任何礼物。
想象一下我的惊喜，当我假期到来的前一天每个孩子都送给我一份礼物。首先我收到了一个腼腆的小女孩送的玩具猴子，“他是我的最爱，但是我爱您，所以我想把它送给您，Taylor老师”她这样对我讲，我真的很激动!
接下来是一套圣诞树的装饰灯，它们都是从妈妈的橱柜中拿来的。
最后，我拿到一个小男孩的礼物要表达我的幸福，被他打断了，“看，他还是新的，连价格标签还在!”
当其他孩子笑这个孩子的时候，我停下来说，“如果我们没有读过，书中的故事永远都是新的，这样的书都是好的。现在让我们一起分享吧!”我在读我生命中最精彩的圣诞故事，每个孩子都在静静的听着。
到现在我还在保存着这些圣诞礼物。它们时常让我想起那群可爱的孩子。

这篇手稿文章应该是来自一位支教的老师，感情很真挚，很喜欢

Ⅳ 能帮我手工翻译几句话吗，翻译成英语

绝对口语化
i deeply crashed on you once we met. for me you stayed up so late, rose with the lark, stayin' by my side every single day, and sing for me（我觉得这句不要了吧，好奇内怪。）容 i can feet the real you, i knew it shall not be called cold, I can't help myself fallin' in love with you, its just not a thing that i can resist, but, i lied, which it cant be erased and icant, how could i be like dis?

Ⅵ 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。
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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 reprocing 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 rece 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 reced 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.
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翻译如下
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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，这张观测用的图像可以视作一个像素面积y=f(i.j); R^2→R^1的函数，这里的输入(i,j)是等于那个像素排和列的针数的二维向量，输出y是近似的发光量值。^([4]) 观测图像像素y(i,j)的拉格朗日乘数α_(i,j)可以用等式(3)轻松计算得到。
这里A=Ω^(-1), B=(I^T Ω^(-1))/(I^T Ω^(-1) I) ，而 O_α即是用A(I-IB)定义的N×N矩阵。
注意到，
观测图像像素y(i,j)的拉格朗日乘数α_(i,j)是由矩阵O_α和观测像Y的乘积决定的。也就是说，拉格朗日乘数是会被清晰图像S和随机噪音N共同影响。如等式4所示，观测像可以通过权值等于这些拉格朗日乘数值的核函数的线性组合来重建，同时一个合适的支持向量回归可以把信号能量集中到一些α_(i,j)为非零值的支持向量中去（SVs）。

支持向量本地化对成像应用特别合适，这对保护图像边缘和图像纹理十分重要。有正的拉格朗日乘数值的像素试着去提升自己和自己周围的灰阶，而同时有负的拉格朗日乘数的像素会试着减少灰阶，他们看起来会更暗。

因此，拉格朗日乘数能有效地给核函数加权来估算图像亮度值。另外，随机噪音能够被视作是试着使拉格朗日乘数在其标准值上下震荡的外力。噪音可以通过平滑化拉格朗日乘数来减少，然而锐边可能要靠一个合适的有非线性表述能力的核函数在一定的范围内进行保边。
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以上

Ⅶ 求手工翻译成英文

降低IT运营成本。减少数据丢失风险。
To cut down IT operating cost. To rece the risk of data loss.

1，SAP常见问题原因分回析。
2，如何正确答使用********
1. Cause analysis of FAQs on SAP.
2. The correct application of...

Ⅷ 求手工英文翻译

Abstract

The effective teachers' class questioning is an important part of the claa revolution, and it is also a good way to improve teachers' skills and develop students' new thinking styles. Effective class questioning builds a bridge between teachers and students, makes the class more active, however, there are still some wrong understanding of class questioning, only when we get out of the wrong understanding, can we make the class questiong more useful. So, authour include these strategies according the following five points: confirming the goal; enriching the contents; optimizing the questioning methods, keeping effective feedback, and the teacher's reflection. We are trying our best to learning about these strategies to increase the efficiency of class questioning, and make it better to serve for the ecation

Key words :effective questionging: get out of the wrong understanding ;questioning strategies

人工翻译，望采纳~如有不懂，请追问

Ⅸ 手工翻译英文

Just as we expected, when the jeans were tested again, they were announced to be qualified, so we don't worry about it. I will send you the test report tomorrow moring.

Ⅹ "做手工“用英语怎么说

“做手工”：makehandwork

handwork 英['hændwɜ:k] 美['hændˌwɜ:k]
n. 手工（尤指相对于机械）; 手工活;

[例句]Print is the combination of drawing and handwork.
版画是图画和手工的结合物。