擬并行性 的英文怎麼說

中文拼音 [bīnghángxìng]
擬并行性 英文
quasi-parallelism
  • : 動詞1. (設計; 起草) draw up; draft 2. (打算; 想要) intend; plan 3. (模仿) imitate
  • : 行Ⅰ名詞1 (行列) line; row 2 (排行) seniority among brothers and sisters:你行幾? 我行三。where...
  • : Ⅰ名詞1 (性格) nature; character; disposition 2 (性能; 性質) property; quality 3 (性別) sex ...
  1. Compared with traditional numerical methods such as the fem, fdm, etc, the lb method has several important features, including : simplicity in algorithm, easily programming, direct calculation of pressure from a state equation and amenability to simulate all kinds of flow field with complex boundaries, it also has much advantage in the respect of parallel computation because of its regional evolution

    與傳統的計算方法如fem 、 fdm等相比, lb方法具有演算法簡單、編程容易、壓力可以通過狀態方程直接求解、能夠模各種復雜邊界的流場等優點,並且計算的局域使其在計算方面也具有很大的優勢。
  2. Two block time - recursive algorithms are developed for the efficient and fast computation of the 1 - d rdgt coefficients and for the fast reconstruction of the original signal from the coefficients in both the critical sampling case and the oversampling case. the two algorithms are implemented respectively by a unified parallel lattice structure. and the computational complexity analysis and comparison show that the proposed algorithms provide a more efficient and faster method for the computation of the discrete gabor transforms

    首先論證了一維rdgt系數求解演算法和由變換系數重建原信號演算法,不論是在臨界抽樣條件下還是在過抽樣條件下,都同樣具有塊時間遞歸特,並提出了相應的塊時間遞歸演算法及其格型結構實現方法,計算機模驗證了格型結構實現的可,計算復雜分析與比較也說明了rdgt塊時間遞歸演算法的格型結構在計算時間方面所具有的高速和高效能。
  3. Aiming at higher computational accuracy, the unstructured hybrid mesh and the distributed parallel computation are used in 3d complex flow field simulation, the combination of these two techniques often causes difficulties in data structure and sub - domain definition because of the non - unification of mesh elements

    在三維復雜外形的流場計算中,為了得到較高的計算效率及保證數值模的準確,經常要採用混合網格及計算技術。
  4. In this text, we first do some research on the genetic algorithm about clustering, discuss about the way of coding and the construction of fitness function, analyze the influence that different genetic manipulation do to the effect of cluster algorithm. then analyze and research on the way that select the initial value in the k - means algorithm, we propose a mix clustering algorithm to improve the k - means algorithm by using genetic algorithm. first we use k - learning genetic algorithm to identify the number of the clusters, then use the clustering result of the genetic clustering algorithm as the initial cluster center of k - means clustering. these two steps are finished based on small database which equably sampling from the whole database, now we have known the number of the clusters and initial cluster center, finally we use k - means algorithm to finish the clustering on the whole database. because genetic algorithm search for the best solution by simulating the process of evolution, the most distinct trait of the algorithm is connotative parallelism and the ability to take advantage of the global information, so the algorithm take on strong steadiness, avoid getting into the local

    本文首先對聚類分析的遺傳演算法進了研究,討論了聚類問題的編碼方式和適應度函數的構造方案與計算方法,分析了不同遺傳操作對聚類演算法的能和聚類效果的影響意義。然後對k - means演算法中初值的選取方法進了分析和研究,提出了一種基於遺傳演算法的k - means聚類改進(混合聚類演算法) ,在基於均勻采樣的小樣本集上用k值學習遺傳演算法確定聚類數k ,用遺傳聚類演算法的聚類結果作為k - means聚類的初始聚類中心,最後在已知初始聚類數和初始聚類中心的情況下用k - means演算法對完整數據集進聚類。由於遺傳演算法是一種通過模自然進化過程搜索最優解的方法,其顯著特點是隱含和對全局信息的有效利用的能力,所以新的改進演算法具有較強的穩健,可避免陷入局部最優,大大提高聚類效果。
  5. One of the main problems in pdes is how to partition the network simulation workload to decrease the time needed to complete the simulation and improve performance of simulation. here a new optimized partition algorithm was put forward, which first analyses the performance factors of parallel simulation and then constructs a performance estimation model for partition ; based on this model, it mends the graph partition algorithm to consider all factors, including simulation applications and simulation environments. optimized factors are workload balance 、 communication cost and time window of lookhead

    鑒于基於傳統圖劃分演算法的任務劃分工具存在諸多不足,本文提出了網路模任務的優化劃分方法,其思想是:首先從pdes機制出發,分析影響網路模能的各種因素並建立一個能夠綜合考慮各種因素的網路模能估計模型;其次,改進多級圖劃分演算法,使得演算法具有綜合考慮模應用與模環境,同時在優化過程中使用能估計模型指導劃分,實現對影響網路模能的三個因素(包括負載均衡、通信開銷、安全時間窗口長度)的優化能力;最後,結合併網路模能估計模型與改進的多極圖劃分演算法,實現了網路模任務的優化劃分。
  6. To analyze some key technologies of optical network relative with rwa in detail, such as transmission, switching and internetworking ; to emphasize on the research of function, fabric and performance of optical cross - connection ; to carry out numerical simulations for crosstalk introduced by optical cross connect and to present measurements for suppressing it such as doubly filtering, fixing optimum decision threshold and appropriately choosing the number of multiplexed wavelengths ; 3. to research the fundamental principle and some problems relative with rwa, including the type of optical network, the type of traffic, the type of service, the survivability of optical network ; to classify and compare rwa algorithms and particularly research some dynamic rwa algorithms ; 4. to present reserved light - path and classify network resource such as used, unused and reserved status, to emulate establishment of all - optical connection in optical network through modified rwa algorithm and show effectively reducing setup time of all - optical connection utilizing reserved light - path ; to research rwa algorithms of multi - fiber network, to present new link weight functions dependent on node degree, unused fiber ( s ) per wavelength - layer and routing policies, to perform emulation of rwa based wavelength layer graph applying new link weight functions and show them make algorithms better performance and network lower blocking rate ; 5

    詳細分析了與rwa相關的光網路關鍵技術,包括傳輸、交換、組網等,重點研究了光交叉連接的功能和結構、能,對其引入的串擾進了詳細分析,選擇恰當的器件參數進了數值模,並提出了抑制措施(如雙重濾波、優化判決門限、選擇恰當的復用波長數) ; 3 .研究了光網路的r認叭的基本原理、與r認叭的幾個相關問題(光網路類型、業務類型、流量類型、光網路生存) 、 r認人演算法的分類和比較,具體研究了幾種動態r場人演算法; 4 .研究了以全光連接建立時間為優化目標的r認認演算法,提出預置光路的概念,對網路資源進狀態分類(佔用、未佔用、預置) ,利用改進的r認叭演算法模,預置光路可為部分新到的連接請求快速建立連接,從而提高網路能;研究了以多光纖網路連接阻塞率為優化目標的r認城演算法,提出了以節點度數、每個波長分層的空閑光纖數以及路由策略決定的幾種鏈路權重函數,利用基於波長分層圖模型的r場人演算法模,利用新的鏈路權重函數使得演算法具有更優的能,使網路具有更低的連接阻塞率。
  7. This overview highlights selected algorithmic solver code advances in the used simulation tools, the use and the modelling of new materials for crash energy absorption, concept car design techniques, massive parallel programming and performance gains, side impact barrier modelling, mechanical occupant surrogate modelling ( dummies ), biomechanical models of human parts, as well as extensions of crash simulation techniques to the simulation of drop tests for appliances, shock absorption of a mars lander, etc

    內容包括作為模手段的計算程序的最新進展,新的緩沖材料的應用和模,大規模程序的編制和能的增加,側撞障礙物模,乘客模型(假人) ,生物人體部件模型,碰撞模技術擴展應用到設備跌落試驗和火星著陸艙的緩沖等。
  8. Genetic algorithm, as a computational model simulating the biological evolution process of the genetic selection theory of dar - win, is a whole new global optimization algorithm and is widely used in many fields with its remarkable characteristic of simplicity, commonability, stability, suitability for parallel processing, high - efficiency, and practibility. on the other hand, there are many op - timization problems in the field of digital image processing, such as image compression, pattern - recognition, image rectification, image segmentation, 3d image recovery, image inquiry, and or so. in fact all these problems can be generalized as the problem of searching for a global optimal solution in a large solution space, which is the classic application field of genetic algorithm

    遺傳演算法是模達爾文的遺傳選擇和自然淘汰的生物進化過程的計算模型,是一種新的全局優化搜索演算法,具有簡單通用、穩定強、適于處理以及高效、實用等顯著特點,在很多領域得到了廣泛應用,另一方面,在圖像處理領域有很多優化問題如圖像壓縮,模式識別,圖像校準,圖像分割,三維重建,圖像檢索等等,實際上都等同於一個大范圍搜索尋優問題,而最優化問題是遺傳演算法經典應用領域,因此遺傳演算法完全勝任在圖像處理中優化方面的計算。
  9. At first, in order to prevent the premature convergence of genetic algorithm effectively, the author brings forward a novel dyadic floating - point supplementary mutation operator. then, simulating the natural evolution, the author presents a novel topology, unoriented - connected topology, for parallel genetic algorithm. in the end, an interval decomposed optimization method is brought forward for ipga, which can improve the optimization performance of the algorithm

    為提高演算法的能,作者對遺傳演算法進三種改進:首先,為克服遺傳演算法早熟收斂,作者提出一種新的二元浮點補碼變異運算元;其次,模生物自然進化模式,為遺傳演算法提出了一個新的拓撲結構- - - -無定向拓撲連接;最後,作者提出一種區間分解優化思想,來提高對最優解的搜索能力。
  10. Thirdly, genetic algorithm is a kind of search and optimization method simulating the life evolution mechanism, which has the advantages of global optimization and implicit concurrency

    遺傳演算法是一種模生命進化機制的搜索和優化方法,與常規優化演算法相比,具有隱含和全局搜索特,因此選擇遺傳演算法進尋優計算。
  11. We focused our discussions on the mechanism of saturation of the wakefield and the electron parametric instabilities which affect the process of the wakefield generation and electrons acceleration. we developed a 2 - dimension distributed parallel pic code under mpi parallel environment and got a good speedup ratio tested on yh - iv and pc computer groups

    為了解決研究激光和稀薄等離子體相互作用所需的大量計算,我們研製了21 2d分散式粒子模程序,在微機群和巨型機yh ? iv對程序能進了測試得到了較高的加速比。
  12. Considering the np - complete problem, how to get the approximate optimized scheme of job - shop scheduling, and aimed at improving the efficiency of products and taking good advantage of concurrence, asynchronism, distributing and juxtaposition in multi - products and devices processing, we could divide the working procedures into the attached one which has the only precursor and subsequence and unattached one by analyzing working flow chart of job - shop, that is the working procedures are divided into two types, then the bf and the ff methods about memory scheduling in os are applied, therefore a new approximate optimized scheme is presented in the paper which could solve the common job - shop scheduling. namely, the acpm and the bfsm are applied to the classified and grouped working procedures considering the compact of the procedures and practical examples approved it. the results we analyzing and tested show that it is better than the heuristic algorithm common used, for less restriction terms, more satisfying algorithm complexity and better optimized results

    針對job - shop調度問題求最優解演算法這一npc問題,本文以充分發揮多產品、多設備加工所具有並發、異步、分佈的加工優勢,從而提高產品的加工效率為目標,對job - shop調度問題的工藝圖進適當分解,使工序在一定時間段或是為具有唯一緊前、緊后相關工序或是為獨立工序,即將工序分兩類,再結合操作系統中內存調度的最佳適應( bf )調度方法和首次適應( ff )調度方法的先進思想,通過分析提出了一種解決一般job - shop調度問題的全新近優解方案:在考慮關鍵設備上工序盡量緊湊的前提下,將工序分類、對這兩類工序分批採用關鍵路徑法( acpm )和最佳適應調度方法( bfsm )安排工序的演算法,用實例加以驗證,並給出結果甘特圖。
  13. Ga is a computational models of the human evolution, with implicit parallelism and capacity of using effectively global information

    遺傳演算法( ga )是一種通過模自然進化過程搜索最優解的方法,其顯著特點是隱含和對全局信息的有效利用能力。
  14. Genetic algorithms are search algorithms based on the mechanics of natural selection and natural genetics. it is widely used in many kinds of fields because of its less - dependency of optimization problem, simplicity, robustness and implicit parallelism

    遺傳演算法是模遺傳學和自然選擇機理構造的一種搜索演算法,因其對優化問題的弱依賴、求解的簡單和魯棒、隱含等特點被廣泛應用於當前的各個領域。
  15. Genetic algorithm is an fresh subject in recent years, it is a search algorithm based on the mechanics of natural selection and natural genetics. it is widely used in many kinds of fields because of its less - dependency of optimization problem, simplicity robustness and implicit parallelism

    遺傳演算法是近年來新興的一門學科,是模遺傳學和自然選擇機理構造的一種搜索演算法,因其對優化問題的弱依賴、求解的非線和魯棒、隱含等特點被廣泛應用於當前的各個領域。
  16. Storing information in molecules of dna could allow for an information density of approximately 1 bit per cubic nanometer. the energy consumed by dna molecular biology computing is billionth of that consumed by one conventional computer. the characteristics of dna molecular biology computing mentioned above which are high parallelism, huge capability and low consumption are incomparable and irreplaceable to the existing computers and parallel ones

    Dna分子生物演算法具有高度的,運算速度快; dna作為信息的載體其貯存的容量非常之大, 1立方米的dna溶液可存儲1萬億億的二進制數據; dna分子生物計算所消耗的能量只有一臺電子計算機完成同樣計算所消耗的能量的十億分之一; dan計算的上述特,即運算的高度、大容量、低消耗是目前計算機和計算機所無法比和替代的。
  17. Unlike most other partitioning algorithms, the proposed algorithm preserves circuit concurrency by balancing the workload on processors at real time

    本文提出的演算法很好的解決了這個問題,保證了模過程中各個處理器上的實時負載平衡,實現了高度的
  18. Gasa is characteristic of many advantages, such as the calculating robustness, implied inherent parallelism, global searching and local convergence. these advantages are integrated in gcs in our method and make the constraint problems solved robustly and efficiently. based on such approach, the ecological niche ideal is further integrated with gasa

    由於遺傳模退火演算法本身具有很多優點:很強的計算魯棒、隱含的內在、全局搜索與局部快速收斂能力,因此將遺傳模退火演算法與約束求解相結合大大提高了約束求解的魯棒和效率。
  19. Control systems in modern automatic engineering are nonlinear, time - changed and indefinite. lt is difficult to model by traditional method, even sometime impossible. under these circumstances we should apply model identification to gain the approximate model of object for effective control, there are many models to be chosen, fuzzy model is one of them, it is put forward with the development of fuzzy control. fuzzy model has characteristics of general approximation and strong nonlinear, it is fit for describing complex, nonlinear systems. to avoid rules expansion when the number of input values are very big. in this paper we apply hierarchical fuzzy model to resolve this problem, we also illustrate it has general approximation to any nonlinear systems. genetic algorithm is a algorithm to help find the best parameters of process. lt has abilities of global optimizing and implicit parallel, it can be generally used for all applications. in our paper we use fuzzy model as predictive model and apply ga to identify fuzzy model ( including hierarchical fuzzy model ), we made experiments to nonlinear predictive systems and got very good results. the paper contains chapters as below : chapter 1 preface

    現代控制工程中的系統多表現為非線、時變和不確定,採用傳統的建模方法比較困難,或者根本無法實現,在這種情況下,要實現有效的控制,必須採用模型辨識的方法來獲取對象的近似模型,並加以控制,目前用於系統辨識的模型種類很多,模糊模型是其中的一種,它隨著模糊控制的發展而被人提出,模糊模型具有萬能逼近和強非線的特點,比較適合於描述復雜非線系統,為了解決模糊模型在輸入變量較多時規則數膨脹的問題,文中引入遞階型模糊模型,並引證這種結構的通用逼近特。遺傳演算法是模自然界生物進化「優勝劣汰」原理的一種參數尋優演算法,它具有隱含和全局最優化的能力,並且對尋優對象的要求比較低,在工程應用和科學研究中,得到了廣泛的應用,本文將遺傳演算法引入模糊模型的辨識,取得了很好的效果。
  20. The algorithm mainly adopted the parallel performance of neural network and the character that can simulate arbitrary non - circular function, and cut the time of interpolation greatly. so interpolate for any curve or for space disperse become possible

    該演算法主要利用神經網路的能和可模任意非線函數的特,使得插補運算的時間大幅度縮短,並且可以對任意曲線或空間離散點進直接插補。
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