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component extraction in Chinese

Pronunciation:
How to pronounce "component extraction""component extraction" in a sentence

Translationmobile phoneMobile

  • 元素提取

Examples

  • Harmonic component extraction from a chaotic signal based on emd method
    方法的混沌信號(hào)中周期分量的提取
  • Applications of adaptive principal components extraction algorithm in the feature extraction of human face
    自適應(yīng)主元提取算法在人臉圖像特征提取中的應(yīng)用
  • Existing temporal component extraction method uses multiple regression analysis ( mra ) after the application of temporal pca to estimate the amplitude of resultant components and thus made it up
    現(xiàn)有的時(shí)間成分提取法在進(jìn)行時(shí)間pca之后，利用重回歸分析來(lái)估計(jì)成分的波形和振幅，從而彌補(bǔ)了這一缺陷。
  • Foreground area is extracted by adaptive threshold method , the object is searched by extending the connected components extraction method , the object is recognized based on the geometrical character
    以自適應(yīng)門(mén)限值提取前景區(qū)域,通過(guò)擴(kuò)展的連通分量提取算法實(shí)現(xiàn)了目標(biāo)的快速搜索,最后基于幾何特征對(duì)目標(biāo)加以識(shí)別。
  • To solve this problem , it ' s necessary to develop a spatial component extraction method so that the spatial component topography mapping sequence along the time course could be easily got into hand , even in a dense electrode array study
    為了解決這一問(wèn)題，在采用密電極陣進(jìn)行的研究中也能夠輕易得到隨時(shí)間變化的空間成分拓?fù)鋱D序列，我們有必要設(shè)計(jì)開(kāi)發(fā)新的空間成分提取法。
  • It mainly includes following aspects : in this paper , firstly implication of wavelet analysis in signal harmonic component extraction is presented in detail , and on this foundation signal given frequency band component extraction method is proposed . then we investigate frequency aliasing phenomenon , reason why frequency aliasing phenomenon is easy happened when wavelet is implied in field of motor fault diagnosis , and frequency bands where frequency aliasing phenomenon is easer happened , and put forward approved morlet wavelet analysis method which can effectively alleviates frequency aliasing phenomenon . third we set up vibration signal detected system , design experiments to obtain three groups of fault signals : eccentricity fault , mixed fault of eccentricity plus mild rub , mixed fault of eccentricity plus moderate rub , and process these data using analysis method proposed in this paper
    首先對(duì)論文的選題以及相關(guān)背景進(jìn)行了探討,并對(duì)相關(guān)的小波基礎(chǔ)理論進(jìn)行了闡述,主要針對(duì)感應(yīng)電動(dòng)機(jī)的偏心故障和偏心+碰摩復(fù)合故障形式及相關(guān)的應(yīng)用算法進(jìn)行了詳細(xì)探討,主要工作內(nèi)容包括下面幾個(gè)方面:在論文中,首先對(duì)小波分析在信號(hào)的諧波分量提取中的應(yīng)用進(jìn)行了研究,在此基礎(chǔ)上,提出了信號(hào)的特定頻帶成分的提取方法;研究了小波分析的頻域混疊現(xiàn)象,探討了小波分析應(yīng)用于電機(jī)故障診斷領(lǐng)域時(shí)易發(fā)生頻域混疊現(xiàn)象的原因,以及混疊現(xiàn)象較易發(fā)生的頻帶,提出了可以有效減輕頻域混疊現(xiàn)象的改進(jìn)的morlet小波分析方法;搭建了振動(dòng)信號(hào)檢測(cè)系統(tǒng),設(shè)計(jì)實(shí)驗(yàn)采集得到三組故障數(shù)據(jù):偏心故障,偏心+輕微碰摩復(fù)合故障,偏心+中度碰摩復(fù)合故障,并對(duì)這些數(shù)據(jù)應(yīng)用本文提出的分析方法進(jìn)行信號(hào)處理,提取出的特征參數(shù)可有效區(qū)分這兩種故障形式;研究了基于matlab和labview的混合編程方法,完成了電機(jī)故障檢測(cè)系統(tǒng)的軟件開(kāi)發(fā)。
  • This study is backed by the research project of stimulus onset asynchrony ( soa ) detection , a newly devised multisensory erp experimental method . on the basis of spatiotemporal pca and existing temporal component extraction method , a spatial component extraction method was designed and materialized . detailed algorithm description , software development introduction and application example discussion were all presented
    本研究以一種新型的多感知erp實(shí)驗(yàn)方法?異步刺激檢測(cè)法( soadetection )的研究項(xiàng)目為背景，以時(shí)空pca與現(xiàn)有的時(shí)間成分提取法為基礎(chǔ)，進(jìn)行了空間成分提取法的算法設(shè)計(jì)，并做了軟件開(kāi)發(fā)及應(yīng)用分析。
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