相位校正網路 的英文怎麼說

中文拼音 [xiāngwèijiàozhēngwǎng]
相位校正網路 英文
phase corrective network
  • : 相Ⅰ名詞1 (相貌; 外貌) looks; appearance 2 (坐、立等的姿態) bearing; posture 3 [物理學] (相位...
  • : Ⅰ名詞1 (所在或所佔的地方) place; location 2 (職位; 地位) position; post; status 3 (特指皇帝...
  • : 校名詞1. (學校) school 2. (校官) field officer3. (姓氏) a surname
  • : 正名詞(正月) the first month of the lunar year; the first moon
  • : Ⅰ名詞1 (捕魚捉鳥的器具) net 2 (像網的東西) thing which looks like a net 3 (像網一樣的組織或...
  • : 1 (道路) road; way; path 2 (路程) journey; distance 3 (途徑; 門路) way; means 4 (條理) se...
  • 相位 : phase position; phase
  • 校正 : check; correction; adjust; revise; proofread and correct; rectify; calibrate; make true; master c...
  • 網路 : 1. [電學] network; electric network2. (網) meshwork; system; graph (指一維復形); mesh
  1. This paper, based on normalizing well logging data while drilling and correcting depth into true vertical depth and calculating reservoir parameters and etc, combining the practical ease of mobei oilfield, extracted logging and geological pattern characteristic of target oil - gas formation and geosteering mark formation, and used bp neural network and regressive analysis to create predicting mode of geosteering parameter to build relevant contrast curve ; adopted geometry geosteering method to fix on die drilling direction of bit upper and declination, the position in reservoir, to judge the real drilling case. all finely solved the problem to follow the geological target while drilling for three horizontal well these methods improve the drilling horizontal well ability by using the techniques to follow the geological target while drilling, and then it is convenient and practicable

    本文在開展隨鉆測井資料的標準化和斜井及儲層參數解釋與含流體性質判釋等工作的基礎上,結合研究工區莫北油田的實際情況,提取了目標油(氣)層和導向標志層的測井地質模式特徵,並採用bp神經法和回歸分析法建立了地質導向參數的預測模型、構造了應的對比曲線;採用幾何導向法確定鉆頭上下傾鉆進方向及其在目標層的置,以判斷實際鉆進地層情況,很好地解決了研究工區三口水平井的隨鉆跟蹤地質目標的問題。
  2. Taking into account the characteristics of the nmr spectrum or the resonance peak, the proposed algorithm uses an artificial neural network to choose the most appropriate algorithm to calculate the phase angles of a given peak

    針對這一情況,本文在綜合研究多種現有自動演算法的基礎上,提出了一種基於神經的,可以根據譜圖的特徵來選取最合適的演算法進行自動的綜合演算法。
  3. Using dead reckoning would lead to frequent state correction updates, increasing both network traffic and visible jolts ( because a quick position jump is sometimes needed when the blended replicated path differs to much from the original path )

    使用導航預測演算法可能會導致非常頻繁地發送狀態更新包,流量和視覺上的震動都會大大增加(因為當混合后的復制體徑與原始差較大時,有時候不得不做一些迅速的置跳轉) 。
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