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invariant property in Chinese

Pronunciation:
How to pronounce "invariant property""invariant property" in a sentence

Translationmobile phoneMobile

  • 不變性

Examples

  • One could consider invariant properties of figures under each of the above types of transformation .
    可以在上述的每一類型變換下考慮圖形的不變性質(zhì)。
  • Homotopic invariant property of mapping degrees on banach manifolds
    流形上映射度的同倫不變性
  • Some invariant properties of linkage classes
    連鎖類的幾個不變性質(zhì)
  • Frequency using wavelet theory , another is multi - channels based on gray . the first method is based multi - scale decompression using wavelet theory , it extract gray feature from each channel to form feature vector , it can distinguish objects , but this method not have invariant property when object rotate
    基于小波的頻率多通道是從小波多分辨分解的角度，提取各頻率通道的灰度特征形成特征向量，該方法可以很好的區(qū)分目標(biāo)，但不具有旋轉(zhuǎn)不變性。
  • Based on the previous work and current research , we have made the following work : ( 1 ) for robustness against jpeg compression , we analyze the invariant properties theorem during jpeg compression which was proposed in previous work , then proposed an improved scheme . based on those work , we design a watermarking algorithm , which has robustness against jpeg compression with common quality factor . in addition , the proposed algorithm can perfectly detect the watermarks without degrading the visual quality of original image
    根據(jù)不同的應(yīng)用需求設(shè)計(jì)相應(yīng)的水印算法是魯棒性水印的研究方向，根據(jù)這些特點(diǎn)，本文在前人研究的基礎(chǔ)上，完成了以下工作： ( 1 )本文就抗jpeg壓縮的水印設(shè)計(jì)進(jìn)行了研究，通過分析前人提出的jpeg壓縮不變性原理，發(fā)現(xiàn)了其在實(shí)際應(yīng)用中會出現(xiàn)的問題，并對之提出了改進(jìn)的方案，在此基礎(chǔ)上，設(shè)計(jì)了一種水印算法，可以對所有常用質(zhì)量因子的jpeg壓縮都免疫。
  • In the first part , the concepts of the completely normal spaces and strong completely normal spaces in l - topological spaces are defined , which are the generalization of the completely normal spaces in general topological spaces . they are some good properties such as hereditary , weakly homeomorphism invariant properties , good l - extension , but they are n ' t producible in general . moreover , their several sufficient and necessary conditions in induced spaces are presented
    第一部分的主要內(nèi)容如下：第一部分這一部分是將一般拓?fù)鋵W(xué)的完全正規(guī)分離性的概念推廣到了l -拓?fù)淇臻g，給出了l -拓?fù)淇臻g的完全正規(guī)分離性和強(qiáng)完全正規(guī)分離性的定義并討論了它們的若干性質(zhì)，比如，它們都是可遺傳的，弱同胚不變的， “ lowen意義下好的推廣”等。
  • First , using the scale - invariant property of multiscale model , i . e . markovian among scales , a method of qth - order tree - based for multiscale representation of a class of 1 - d stochastic process is presented . the multiscale stochastic model is established . the representation forms of parameter matrices , such as , the state transition matrix , the disturbance matrix , the initial state and the corresponding covariance matrix are deduced in detail
    本文在已有工作的基礎(chǔ)上，開展了以下幾個方面的研究工作： 1 、根據(jù)多尺度模型尺度不變性，即利用尺度間的markov性，給出了一類1 - d隨機(jī)過程基于一般q階樹的多尺度表示方法，建立了相應(yīng)的多尺度動態(tài)模型，詳細(xì)推導(dǎo)了多尺度模型中的狀態(tài)轉(zhuǎn)移陣、擾動陣、初始狀態(tài)和相應(yīng)的協(xié)方差陣，并通過計(jì)算機(jī)仿真給出了不同階樹的多尺度采樣路徑。
  • Hi the aspect of symmetry analyzing to the hopfield model neural network with hebbian learning , we study on the dynamical behavior of the state space under the action of isometric transformation group g = z2 ? n , and prove the invariant property of the energy orientation ? / / " ) of the state space under the action of g . we find that the symmetry relationship of the network is sx - sw = sh when the active function of the neuron is odd , where sx is the symmetry of the patterns set x under hebbian learning rule , sh is the symmetry of the network and sw is the symmetry of the weight matrix w of the network
    ) s _ n為手段，研究了網(wǎng)絡(luò)狀態(tài)空間在群g作用下各點(diǎn)的運(yùn)動情況，證明了群g作用下的不變性。證明了當(dāng)神經(jīng)元的激活函數(shù)f為奇函數(shù)時， hebb法則下存儲樣本集x的對稱性s _ x 、網(wǎng)絡(luò)對稱性s _ h以及連接矩陣對稱性s _ w三者之間滿足s _ x = s _ w = s _ h的關(guān)系；同時，我們還證明了：網(wǎng)絡(luò)穩(wěn)定態(tài)集vf同一s _ h軌道中的兩個穩(wěn)定態(tài)的動力學(xué)行為(能量和吸引域大小)相同；兩個等距網(wǎng)絡(luò)h和h 1 = g ? h ， ( ? ) g (

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