high-density data system 中文意思是什麼

high-density data system 解釋
高密度數據系統
  • high : adj 1 高的〈指物,形容人的身高用 tall〉;高處的;高地的。2 高級的,高等的,高位的,重要的。3 高尚...
  • density : n. 1. 稠密;濃厚。2. 【物理學】濃度;密度;比重。3. 愚鈍,昏庸。
  • data : n 1 資料,材料〈此詞系 datum 的復數。但 datum 罕用,一般即以 data 作為集合詞,在口語中往往用單數...
  • system : n 1 體系,系統;分類法;組織;設備,裝置。2 方式;方法;作業方法。3 制度;主義。4 次序,規律。5 ...
  1. In this article, the fundamental principle and current research of optical data storage including cd and dvd, three - dimensional optical data storage and near - field high - density optical data storage are summarized. a two - dimensional finite difference time domain program for analysis of solid immersion lens system is compiled

    本論文概述了已產業化的以cd和dvd為代表的光盤存儲技術和在開發中的高密度光存儲技術(主要包括三維光數據存儲技術及近場光學超高密度光存儲技術)的原理及研究現狀。
  2. Analysis of experimental data indicates that there exist several following problems during the whole operation : first, the emission is serious and the density of hc and co smoke is very high at on and off period. second, there is oil dripping from the dripping vitta as a result of the fault of ignition system and combustion structure. the last, there might be accidents of disabled ignition resulted from the improper operation at the beginning

    通過對樣機的試驗分析和研究表明,汽車空氣燃油加熱器在整個工作過程中,其排放存在以下三個問題:一、開機和關機兩個階段的排放問題嚴重,廢氣中hc和co的濃度值及煙度測量值都很高;二、因點火系統和燃燒結構的缺陷而導致開機后一段時間內有油滴從滴油管滴落;三、在點火過程中,會出現因操作不當而點火失效的現象。
  3. Three - dimensional high - density space / time soundings serve as the main body of data consisting of 3 - hr interval doppler probings, 10 - 30 min rainfall intensity, surface raindrop sizes sampling and gps - guided flight cloud physics detections with output taken at 2 - sec and 200 - m intervals. and specific systems associated therewith are developed for multiple - way communication and data collection and storage, a platform for analysis, retrieval softwares for dominant items and multi - scale cloud models - all constitute a system of techniques for meso to microscale observations and analysis. 2 ) atmospheric water resource and macroscopic rainfall properties in dry periods of spring and autumn of the target region

    以加密觀測的多普勒雷達、 3小時探空、 10 30分鐘雨強、地面雨滴譜等間隔取樣及在gps引導下的飛機雲物理探測等獲取的三維高時空密度的綜合探測為主體;配合專項設計開發的多路通訊採集存貯系統、多類信息的分析處理平臺、主要觀測項目的分析反演軟體,結合多尺度雲系模式,綜合構成層狀雲系中微尺度探測和分析處理技術方法。
  4. The research interests in this team are nonlinear optics at low light intensities, including photorefractive nonlinear optics ( photorefractive materials, effects and their applications ), optical storage materials, nonvolatile optical recording technology and mechanics with high recording rates, high data density and high data transfer rates, nonlinear dispersive system and the dynamic propagation properties of photons in such dispersive system, and nonlinear optical properties of phasonian systems such as electromegnetically induced transperancy

    本實驗室目前從事弱光光學非線性方面的研究:主要包括光折變非線性材料、效應以及應用;超快超高密度光存儲材料、非揮發存儲技術及其機理;非線性色散系統以及光子在非線性色散系統中的傳播動力學;量子相干系綜中的非線性光學性質如電磁感應透明等幾個方面的研究。
  5. Firstly, in this paper, we review the histories of xml and xml database, and all kinds of data models used widely now. then we review traditional access control policies and policies specially used on xml, and present a new expression method of access set on xml based on child tree of w3c data model. and we bring rbac model into xml database and present a new xml access model x - rbac. because the usage of data in databases has high frequency, we present an ids model based on access set density to logging the potential security threats. and then we introduce the design and implementation of a prototype of our native xml database nhx and its access control system. finally, we draw a conclusion for this paper and the future work

    本文首先回顧了xml及xml數據庫的歷史,並總結了目前廣泛使用的xml數據模型,在分析了各種傳統數據庫訪問控制策略,以及xml的專有訪問控制策略的優缺點后,基於w3c的xml數據模型,提出了一種基於子樹的具有高彈性的便於實現的的訪問區域表示法,並將rbac模型引入進來,提出了一種適用於xml數據庫的訪問控制模型x - rbac ,同時為了區分xml數據庫訪問中的合法訪問和合理訪問,我們根據數據庫訪問具有頻繁性這一特徵,提出了一種基於區域訪問密度的入侵檢測模型( ids ) ,以對隱藏在正常訪問中的潛在安全威脅進行報警和日誌。
  6. The holographic disc storage technology, which has the properties of simplicity in optical read - write head and the compatibility with existing disc system, is more suitable for the application of high - density and mass - capacity data storage

    三維盤式全息存儲方案以其相對簡單的光路讀寫機構以及與現有光盤系統的兼容性,更適合大容量數據存儲的應用,因而也更具實用意義。
  7. In next mobile communication system to suffice more and more high - speed data service and demand of qos ( quality of service ) many new wireless link layer transport technologies are going to be used such as mimo ( multiple input multiple output ), ofdm ( orthogonal frequency division multiplexing ), channel coding and acm ( adaptive coding modulation ) etc. low density parity check ( ldpc ) codes were first discovered in 1960 ’ s which belong to linear block codes with their parity matrix being sparse

    下一代移動通信系統為了滿足移動用戶對高速、寬帶數據傳輸業務不斷增長和更高服務質量的要求,採用了許多新的無線鏈路傳輸技術,包括多天線發射和接收技術、正交頻分復用技術、通道糾錯編碼技術和自適應編碼調制技術等。上世紀60年代提出的低密度校驗碼,是一種校驗矩陣為稀疏矩陣的線性分組碼。
  8. 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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