連接類型屬性 的英文怎麼說

中文拼音 [liánjiēlèixíngzhǔxìng]
連接類型屬性 英文
gsm plmn connection type attribute
  • 連 : Ⅰ動詞1 （連接） link; join; connect 2 （連累） involve （in trouble）; implicate 3 [方言] （縫） ...
  • 接 : Ⅰ動詞1 （靠近;接觸） come into contact with; come close to 2 （連接; 使連接） connect; join; put ...
  • 類 : Ⅰ名1 （許多相似或相同的事物的綜合; 種類） class; category; kind; type 2 （姓氏） a surname Ⅱ動詞...
  • 屬 : 屬名詞1 （類別） category 2 [生物學] （生物分類系統上所用的等級之一） genus 3 （家屬; 親屬） fami...
  • 性 : Ⅰ名詞1 （性格） nature; character; disposition 2 （性能; 性質） property; quality 3 （性別） sex ...
  • 連接 : connect; fit together; link; marry; mate; joint; association trail; linkage; concatenate; concate...
  • 類型 : type; mold; form; cut類型論 theory of types; 類型語句 [計算機] type statements
  1. To implement advice dependent on both annotation type and properties, the system could include pointcut syntax capturing the annotation instances associated with the join points

    要實現取決于注釋類型和屬性的通知（ advice ） ，系統可以包括那些捕獲與連接點相關聯的注釋實例的切入點語法。
  2. I study mainly the storage of record and object, and bring forward the method of storage when the instance of object is embodied inside of record. 3 ) in the aspect of indexing, i presented a method that is generalized b - tree ( gbt ) and combining technique of founction template of c + + build the universal algorithm of queries, insert and delete. 4 ) finally, i analyze deeply and improve the query processing system of ormdbms, and confirm performance algorithm of linking and queries based on relational database. management system ( rdbms )

    在系統結構和特性中分析了系統所採用的兩層體系結構，並且詳細探討了在設計和實現數據庫時所涉及到的幾個主要問題：支持復雜對象、類型擴充、繼承機制和規則系統等；在存儲機制的討論中，主要研究了記錄和對象以及包含了對象實例記錄的存儲方法；在索引技術研究中，本文提出了類屬b樹的索引方法，通過c + +的函數模板技術給出了通用類屬b樹查詢、插入及其刪除的基本演算法；最後，文章以關系數據庫為基礎，深入分析並改進了在對象關系多媒體數據庫中的查詢處理系統，確定了選擇和連接操作的具體演算法。
  3. In the fourth section of the thesis, the method that constructing statistical features based - on time for connection records and using these features to construct classification models were studied in detail. in order to improve the accuracy of classification model and decrease the rate of false positive, some factors that may have bad influence on accuracy of classification model were analyzed and the method of selecting appropriate set of features was also provided

    論文的第四部分，詳細研究了使用網路連接記錄的基本特徵屬性構建基於時間的統計特徵屬性方法，通過選擇適當的特徵屬性集來提高異常檢測模型的分類精度，降低誤報率；同時分析了影響檢測模型分類精度的因素；對不同實驗條件下得到的實驗結果進行了比較和分析。
  4. Gsm plmn connection type attribute

    連接類型屬性
  5. The order of our discussions " about these tasks is as follows : firstly, we pay more attention to the characteristics and difficulties of its environment including the concept, typical system model, main challenges, mobile network connection and soft application. secondly, according to mobile specialties of the environment we make the sort of data into four kinds : general data, time series, spatial data and time - spatial data, and present general processing of data mining. lastly, we discuss the methods of data mining of these four kinds respectively : after the introduction of the actuality of data mining of every kind, an algorithm of rule updating based on rough set is given, then put forward the processing of data related to mobile users and flow chat according to characteristics of the other three kinds

    本文對以上任務的討論順序安排如下：首先是對移動計算環境的技術特點和難點進行討論，包括移動計算的概念和典型系統模型、主要挑戰、移動聯網以及軟體應用這幾個大的方面；其次根據移動環境的移動特性把移動計算環境中的數據分為普通數據，時間數據，空間數據以及時空數據，提出了在移動計算環境中數據挖掘的一般流程；接下來分別對這四類數據進行挖掘演算法的討論：每一部分都是先介紹該類數據的挖掘方法研究現狀，對于普通數據，針對我們已提出的一種挖掘演算法-粗糙集演算法（ rs ） ，提出了對應的規則更新演算法，對於後三種數據，本人根據其在移動計算環境中的特點分別提出了與移動用戶相關的該類數據的一種具體的處理方法和演算法流程圖，包括基於移位連接方法的多屬性時間序列的挖掘演算法，基於apriori演算法的空間關聯規則數據挖掘方法以及關于移動用戶移動模式的時空數據挖掘方法，並用matlab對其中的規則更新演算法和時間序列的挖掘演算法這兩方面進行了實例模擬。
  6. In order to calculate the semantic coupling effectively, the edge counting method is revisited for measuring basic semanticsimilarity by considering the weighting attributes from where theyaffect an edge s strength. the attributes of scaling depth effect, semantic relation type, and virtual connection for the edge counting areconsidered. furthermore, how the proposed edge counting method could bewell adapted for calculating context - based similarity is showed. thorough experimental results are provided for both edge counting andcontext - based similarity

    為有效地計算語義耦合值，我們對度量語義基本相似度的邊計算edge counting方法進行了修改，採用加權的屬性值來修正連接兩個概念之間的邊的強度所考慮的屬性包括：縮放深度效果scaling depth effect語義關系類型semantic relation type虛擬連接virtual connection等。
  7. First is the integration of back propagation, clustering and decision tree technology in a seamless hole. second is the improvement of three - layer architecture. and weights are detailed to the connection between different feature - value pairs and cases instead of to the connection between features and cases

    一是在同一模型中融合了後向傳播演算法、聚類技術、決策樹技術；二是把實例的三層結構進行了改進，並把實例與屬性之間的連接權重細化到實例與屬性取值間的聯接權重；三是在後向傳播演算法中引入差分hebb學習法，並在計算過程中充分考慮了歷史數據的影響。
  8. A join operation on two relations which share a common data item type produces a combined relation with attributes specified in the join operation

    對共享一個公共數據項類型的兩種關系，連接操作產生一種復合的關系，該關系具有連接操作中規定的屬性。
  9. Based on these parameters, the gensequence utility will connect to the library server database lsdb and verify that the given attribute is associated with the given item type

    基於上述參數， gensequence實用程序會與庫服務器數據庫（ lsdb ）連接，並驗證給定屬性與給定項目類型相關聯。
  10. Currently, the most common way to capture join points utilizes the implicit properties of program elements, including static properties such as signature which consists of type and method name, argument types, return type and exception type, etc. and lexical placement, as well as dynamic properties such as control flow

    目前，捕獲連接點的最常用方法是利用程序元素的隱式屬性，包括靜態屬性，如簽名（它包括類型和方法名、參數類型、返回類型和異常類型等）和詞匯排列（ lexical placement ） ，以及動態屬性（如控制流程） 。
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