間距密集度 的英文怎麼說

中文拼音 [jiānjùmìjídù]
間距密集度 英文
closeness of spacing
  • 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...
  • 密 : Ⅰ名詞1 （秘密） secret 2 [紡織] （密度） density 3 （姓氏） a surname Ⅱ形容詞1 （距離近; 空隙小）...
  • 集 : gatherassemblecollect
  • 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. Previous researchers have always determined the sp atial distribution patterns ( sdp ) of castanopsis kawakamii with a sample - dis tance method. however, the distribution patterns may be affected by the quadrat si ze and, in the course of analysis, the density differences among the cluster plots are not considered ; therefore, differences of cluster plot size and the dispersi on degree among individuals of cluster plots can not be known. authers of this pa per have determined the spatial distribution patterns of castanopsis kawakamii population in different habitats by means of non - quadrat distance method and a nalysed the pattern intensity and grain of the sdp. the pattern intensity is defi ned with the relative density differences and the pattern grain can embody the d ispersion degree of the individuals in the plots, and the dispersion degree among the plots. the determined results are as follows. the intensities of the species range in order from strong to week : litsea mollifolia p. kawakamii i. purpure a r. cochinchinensis c. kawakamii c. carlessii d. oldphamii s. superba. the gra ins of the species queue in order from coarse to close : s. superba = litsea mollif olia r. cohinchinensis c. kawakamii = i. purpurea c. carlessii p. racemosam d. oldp hamii. these determined results tally basiclly with the results authers of this paper have got in determining the same plots by means of aggregate index access ing method. in view of this, it is held that the sdp of c. kawakamii is closely related to the habitats and biological features

    前人都是採用樣方方法對格氏栲種群數量的空間格局進行測定，而格局分佈有可能受樣方大小的影響，且分析過程中沒有涉及聚塊間密度差的問題，因而無法掌握種群的聚塊大小差別及聚塊內個體間的離散程度.本研究採用無樣方距離法，測定不同生境的格氏栲種群空間格局，分析格氏栲種群格局的強度和紋理.強度以聚塊和間隙的密度差來定義，紋理則是體現聚塊內個體間的離散程度與諸聚塊間的分離程度.測定結果表明，格氏栲種群格局強度從高到低排列次序為：木姜子蚊母樹冬青茜草樹格氏栲米櫧虎皮楠木荷；格局紋理從粗到細的順序是：木荷=木姜子茜草樹格氏栲=冬青米櫧蚊母樹虎皮楠.這一測定結果與作者採用聚集度指標測定相同樣地格氏栲種群空間格局的結果基本相符.因此，格氏栲空間格局類型及分佈與格氏栲生物學特性及生境的關系密切
  2. The analysis of microstructure of samples showed that the grain of tio2 were very small under 700, the distance of grain became small with temperature increasing, the rate and size of pore was decreasing. the relative density of sample at 900 was 97 % and the grain size of sintered body was about 200nm. when the temperature exceeded 1100, the grain size of body grew up several times ( > 2 m )

    Tio _ 2燒結體sem顯微形貌分析表明：低溫（ 700 ）時坯體內顆粒無明顯長大，燒結體緻密度不高（ 80 ）晶粒間距隨溫度升高而變小，氣孔率也隨之降低，氣孔尺寸變小；當溫度超過900時，晶粒間連接緊密，燒結體內出現大量絮狀物質，緻密度大幅度提高，達97以上，小氣孔已聚集成大孔洞且分佈均勻，晶粒長大不明顯（ 200nm左右） ；當溫度超過1100時，燒結體緻密度有所提高，但晶粒尺寸出現異常長大，長大了十幾倍（達2 m以上） 。
  3. Research manifests that : ( 1 ) allocation in the year of precipitation is very uneven, and yearly precipitation is different in great scope, negative anomaly of precipitation appear concentratly in the 1990s, the precipitation in the 1990s decreased in different degree ; ( 2 ) close positive correlation exists between runoff and precipitation, runoff is abundant in the year with prolific precipitation, and generally in the year with scarce rain the volume of runoff is not enough ; ( 3 ) there is 20 % margin in their changes amplitude, this mainly resulted from high frequent human activities

    結果表明： （ 1 ）流域內降水年內分配極其不均；年際變化劇烈，進入90年代后降水負距平集中出現， 20世紀90年代降水較多年均值有較大程度的減少； （ 2 ）流域內河川徑流與降水之間存在密切的正相關關系，降水量多的年份，河川徑流豐富，反之較枯； （ 3 ）河川徑流積極響應降水的變化，然而河川徑流變化幅度卻比降水變化幅度大20 % ，這個偏差主要是由於頻繁的人類活動的干擾造成的。
  4. With the development of microelectronic products ( integrated circuit, printed circuit board, etc ) directing to high density, thin separation and low defect ratio, its inspection requirement is higher on aspects of precision, efficiency, universal, and intelligence etc. therefore, this paper researched on the general key techniques in the field of microelectronic products vision inspection, covered the shortage of traditional inspection on aspects of fast and precision locating, image mosaic, and fine defect test, completed theory study on physical dimension and defect inspection of microelectronic products based on machine vision, developed the prototype and used lots of experiments to prove its correctness and feasibility

    隨著微電子產品（集成電路晶元、印刷電路板等）向著高密度、細間距和低缺陷方向發展，對其檢測技術在精密、高效、通用和智能化等方面提出了更高要求。由此，本文對微電子產品視覺檢測中的關鍵技術進行研究，彌補了傳統檢測在精確快速定位、圖像全景組合和精細缺陷檢測等方面的不足，最終完成基於機器視覺的微電子產品外形尺寸和缺陷檢測的理論研究和樣機研製，並進行了大量實驗證明其正確性和可行性，力圖為我國自主創新的微電子產品視覺檢測技術提供理論和實際借鑒。
  5. Except that, many problem can " t be solved, such as the conflict of ccd " s high resolving power and big vision field, how to control the automatic gathering of pcb " s image using master and slave computer parallel structure, how to inspect the defect of pcb such as width of circuit, distance of circuit, losing circuit and so on. the research aim at how to combine computer vision, precise machine, automatic control with image process, at how to resolve the contradiction between high resolving power of image gathering and wide vision field, at how to realize automatic mosaic of image, at how to realize precise orientation of two dimension worktable, at how to realize communication between master computer and slave computer, and at how to inspect the defect of line width, line distance and losing

    除此以外，還有ccd高解析度和大視場之間的矛盾，上下位微機并行系統如何控制印刷電路板圖像自動採集，印刷電路板的線寬、線距和丟失線條等缺陷如何檢測等問題還懸而未決，本課題將就如何結合計算機視覺技術、精密機械技術、自動控制技術和圖像處理技術，如何解決圖像採集高解析度與大視場之間的矛盾，如何實現圖像的自動拼接，如何實現兩維工作臺的精確定位，如何實現上下位機的準確通訊，如何檢測線寬、線距缺陷和丟失線條等問題展開重點研究。
  6. 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。
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