間距不準確度 的英文怎麼說

中文拼音 [jiānjùbùzhǔnquèdù]
間距不準確度 英文
spacing inaccuracy
  • 間 : 間Ⅰ名詞1 （中間） between; among 2 （一定的空間或時間里） with a definite time or space 3 （一間...
  • 距 : Ⅰ名詞1 （距離） distance 2 （雄雞、雉等的腿的後面突出像腳趾的部分） spur （of a cock etc ）Ⅱ動詞...
  • 不 : 名詞[書面語] （剁物所用的木墩） a block of wood
  • 準 : Ⅰ名詞1 （標準） standard; guideline; criterion; norm 2 （目標） aim; target Ⅱ動詞1 （依據; 依照）...
  • 確 : 形容詞1. （符合事實; 真實） true; reliable; authentic 2. （堅固; 堅定） firm
  • 度 : 度動詞[書面語] （推測; 估計） surmise; estimate
  • 間距 : interval; separation; spacing; espacement; space; spacing; space length; range; unpack; step
  1. To solve the inaccuracy problem caused by the two existing methods ( average end - area method and prismoidal method ) used for the calculation of roadway earthwork volume, this paper puts forward a new concept of the 3 - dimensional algorithm that takes all the roadway geometric design procedures as a kind of geometrical operation between the ground model ( original terrain model ) and the roadway model ( designed model ) under certain constraints, and then presents a complete 3 - dimensional algorithm of roadway earthwork volume as well as its executable computer program. the algorithm benefits from the re - triangulation technique of constrained delaunay triangulation ( cdt ), which can yield a true volume value theoretically. through a number of practical tests covering varied intervals between adjacent cross sections, it is proven to possess a higher accuracy compared with that of traditional methods. all the work involved in this paper indicates that the 3 - dimensional calculation of roadway earthwork volume is feasible, more accurate and should have further application in practice

    針對目前廣泛使用的道路土方量計算方法平均斷面法和稜柱體法計算不準確的缺點，提出了三維土方量計算演算法的概念.該演算法以帶約束的狄羅尼三角化（ cdt ）為技術核心，認為所有道路幾何設計過程都是地面模型和道路（設計）模型進行幾何運算的結果.基於此，本文設計出相應的演算法步驟，同時完成了相應的軟體開發，使得該三維演算法能和傳統的方法進行對比.此外，結合工程實例，採用了不同的道路橫斷面間距對三維計算方法和傳統方法的誤差進行比較、分析.結果證明三維演算法具有更好的精度，該演算法可用於道路、場地平整等工程土方量計算
  2. According to the numbers of segmentations, dts has multi scale feature and can reflect different trend similarity of time series under various analyzing frequency. 2 ) an enhanced algorithm, based on dual threshold value, and the conception of sub - series linear are proposed. relative point average error is used to measure the linear degree of sub series, which produced by bottom _ up algorithm

    對應時間序列線性分段數目的不同，序列趨勢距離具有基於時間的多尺度分析特性，可以有效反應不同分析頻率下時間序列的相似程度； 2 ）採用相對點平均殘差衡量bottom _ up演算法劃分的子序列線性度，提齣子序列線性度概念和一種雙誤差閥值改進演算法，大大提高了趨勢序列模型的準確性。
  3. It has been a long time on blood velocity measurement [ 9 ] of this type. there are many methods to obtain the windows. but the intensity signal obtained from the windows is n ' t accuray of some methods or the real time property is not very good of others

    用相關演算法時，血液流速是血管上兩窗口間的距離除以血液流過兩窗口所用時間，時間用兩窗口血液灰度信號最大相關計算得到，微循環血液流速測量的研究已經有很長的時間[ 9 ] ，其中血管窗口的選取現在有多種方法，但現有的方法要麼窗口取得不準確[ 9 ] ，要麼實時性較差。
  4. However, the hubble s law is based on the big bang theory which is still being developed, the accuracy of the distances obtained is questionable

    然而，由於天體飛逝速度跟距離間的關系是建基於宇宙膨脹的理論，故此量度出來的結果一般都不太準確。
  5. This paper introduces the development of data mining and the concepts and techniques about clustering will be discussed, and also mainly discusses the algorithm of cluster based on grid - density, then the algorithm will be applied to the system of insurance ? among the various algorithms of cluster put forward, they are usually based on the concepts of distance cluster o whether it is in the sense of traditional eculid distance such as " k - means " or others o these algorithms are usually inefficient when dealing with large data sets and data sets of high dimension and different kinds of attribute o further more, the number of clusters they can find usually depends on users " input 0 but this task is often a very tough one for the user0 at the same time, different inputs will have great effect on the veracity of the cluster ' s result 0 in this paper the algorithm of cluster based on grid - density will be discussed o it gives up the concepts of distance <, it can automatically find out all clusters in that subspaceo at the same time, it performs well when dealing with high dimensional data and has good scalability when the size of the data sets increases o

    在以往提出的聚類演算法中，一般都是基於「距離（ distance ） 」聚類的概念。無論是傳統的歐氏幾何距離（ k - means ）演算法，還是其它意義上的距離演算法，這類演算法的缺點在於處理大數據集、高維數據集和不同類型屬性時往往不能奏效，而且，發現的聚類個數常常依賴于用戶指定的參數，但是，這往往對用戶來說是很難的，同時，不同參數往往會影響聚類結果的準確性。在本文里要討論的基於網格密度的聚類演算法，它拋棄了距離的概念，它的優點在於能夠自動發現存在聚類的最高維子空間；同時具有很好的處理高維數據和大數據集的數據表格的能力。
分享友人
分享到 Line

分享到臉書