簡單本徵值 的英文怎麼說

中文拼音 [jiǎndānběnzhǐzhí]
簡單本徵值 英文
simple eigenvalue
  • 簡 : Ⅰ形容詞（簡單） simple; simplified; brief Ⅱ動詞1 （使簡單; 簡化） simplify 2 [書面語] （選擇人才...
  • 本 : i 名詞1 （草木的莖或根）stem or root of plants 2 （事物的根源）foundation; origin; basis 3 （本錢...
  • 徵 : 名詞[音樂] （古代五音之一 相當于簡譜的「5」） a note of the ancient chinese five tone scale corre...
  • 簡單 : 1 （不復雜） simple; uncomplicated; plain; simplicity 2 （平凡 多用否定式） commonplace; ordinary...
  1. We construct weak classifier by a haar feature ; then weak classifiers are combined to a strong classifier in a linear way. the final classifier is built in a cascade structure, which could reject most non - face samples in the early layer. also we use integral image to quickly calculate the feature and reduce the detection time

    本文以簡單的haar特徵結合閾值構造弱分類器，通過adaboost學習選擇和集成弱分類器，最後按照分層結構把集成的分類器組合在一起；同時，在檢測過程中採用積分圖的方法計算特徵，保證了檢測的速度。
  2. In addition, this paper also discusses eigenvalue inverse design methods of continuous model of pole and girder, these methods can solve some eigenvalue inverse design problems of simple structures

    此外，本文還就桿和梁的連續模型的特徵值逆設計方法進行了闡述，運用這些方法，可以解決很多簡單結構的特徵值逆設計問題。
  3. By choosing some appropriate exponential weight functions we prove that the essential spectra and the eigenvalues ( except the simple zero eigenvalue ) have negative real parts, thus we get the locally asymptotically exponential stability of travelling waves ( al ) and ( a2 ) in some weighted spaces

    通過選用合適的權函數，我們證得在加權空間線性化運算元的本質譜和除簡單特徵值零以外的特徵值均具有負實部。因此，我們得到行波（ a1 ） ， （ a2 ）在加權的l ~ 2 （ r ）空間的局部漸近指數穩定。
  4. For viscous shock waves c, by the spectral analysis we prove that in l2 ( r ) space the essential spectra and the eigenvalues ( except the simple zero eigenvalue ) of the linearized operator have negative real parts, thus we show that the viscous shock waves c is locally asymptotically exponentially stable in l2 ( r ) space

    對粘性沖擊波c利用類似方法，我們證得在l ~ 2 （ r ）空間線性化運算元的本質譜和除簡單特徵值零以外的特徵值均具有負實部。於是，我們得到粘性沖擊波c在l ~ 2 （ r ）空間的局部漸近指數穩定。
  5. The ids works by two way, misuse detection and anomaly detection, misuse detection flags an intrusion on intrusion signature, this kind of detecting technic can be realized much more easily, and much more accurate, but it can not find some intrusiones that have been disguised or new kinds of intrusion. the anomaly detection can detect in more wide field, anomaly detection can compare new statistic data with average record, then anomaly record will be found, but it ' s more difficult to set a threshold, if the threshold is too big, some intrusion may be put through, if the threshold is too small, the ids will give more false positive alarm, and the threshold will be different with different people or different period, so the ids just simply show us their suspicious record, the administrator or expert will be in duty to analyze this record and give conclusion, the ids give more alarm than it should, leave us more detection record to analyze, and this is a hard work, we can not distinguish an intrusion or not if we analyze only one record, but we can judge if we find the relation among mass detection evidence. in this article, we try distinguish an intrusion using d - s theory ( proof theory ) instead using manual work, the ids will be more helpful and efficient

    濫用檢測採用的是特徵檢測的方法，實現較為簡單，判斷的準確性較高，但是不能判斷一些經過偽裝的入侵或特徵庫中尚未包含的入侵，異常檢測能夠根據以往記錄的特徵平均值，判斷出異常情況，但是對于異常到什麼程度才視為入侵，這個閥值非常難以確定，閥值設定的太高，有可能漏過真正的入侵，如果設定的閥值太低，又會產生較高的誤警率，而且這個閥值因人而異，因時而異，因此現在的入侵檢測系統把這部分異常記錄以一定的形式顯示出來或通知管理人員，交給管理人員去判斷，而這些ids系統難以判斷的記錄，如果對每個證據單獨地進行觀察，可能是難以判斷是否是入侵，而把許多先後證據關聯起來，專家或管理人員根據經驗能夠判斷訪問的合法性，本文試圖引入人工智慧中證據理論的推理策略和示例學習方法，代替人工檢查分析，可以提高效率，降低誤警率，並可以對一個正在進行得可疑訪問實現實時檢測，通過搜索及時判斷，及時阻斷非法訪問，比事後得人工處理更有意義。
  6. Bringing forward an intelligent decision method of image segmentation based on roughset theory to make the system automatically select segmentation algorithm in simple scenes. firstly, it selects some representative segmentation algorithms to make up of an algorithm library, which is used to process kinds of sample images ; secondly, it makes the decision informationtable utilizing diversified numerical features extracted from the sample images and the optimalsegmentation algorithm of each sample image according to segmentation quality evaluationcriterion ; finally, it applies rough set theory on discretization and attribution reduction of

    為了使系統在簡單場景下能夠通過自動選取分割演算法來提取目標，提出了一種基於粗糙集理論的圖像分割智能決策方法。首先選取若干具代表性的分割演算法構成演算法庫，並用它們對各種樣本圖像進行分割;然後利用從樣本圖像中提取出來的各種數值特徵，並根據圖像分割質量評價標準評判出各樣本圖像的最優分割演算法，用其構成決策信息表;最後應用粗糙集理論來對決策信息表進行離散化處理和屬性約簡，以生成圖像分割演算法選取的決策規則。
  7. However, the exact eigenvalues can be obtained only for simple geometries, like as rectangular and cylindrical resonator. for arbitrarily shaped cavities and dielectric - loaded cavities. numerical techniques must be used

    但是，實際中僅僅對於一些簡單幾何形狀的諧振腔可以得到準確的本徵值和場分佈，比如矩形諧振腔、圓柱諧振腔等。
  8. Meanwhile, we also study the convergence behavior of iterative detection in different methods, which are the proposed average source entropy and the covariance matrix perturbation characterized by the variations of its dominant eigenvalue as well as the conventional approach based on the mutual information

    為了分析迭代譯碼的收斂特性，本文從不同角度出發，在研究了傳統的互信息方法的基礎上，本文首次提出並使用了平均信源熵和用協方差矩陣最大特徵值表徵其抖動這兩種方法，使迭代過程的收斂分析變得簡單方便。
  9. Next, edge detection using differential operator, edge thinning and connecting are introduced. model and methods of image segmentation are discussed subsequently. stereo matching methods, such as correlative matching algorithm and least squares matching algorithm are given and realized

    本文針對圖像的灰度和邊緣等特徵，研究了微分運算元邊緣檢測方法以及邊緣細化和連接的演算法，給出了圖像分割的模型和灰度閾值的簡單分割及自動分割方法。
  10. A semi - discrete form of our scheme is also presented. this new reconstruction is a third order accuracy in smooth regions and non - oscillations at cell interfaces. our scheme enjoys the main advantage of the central schemes - simplicity, namely it does not employ riemann solvers and hence the intricate and time - consuming characteristic decomposition are avoided

    本文針對一維雙曲型守恆律的初值問題，研究了二階和三階中心差分格式，提出了一種改進的三階中心差分格式及其半離散形式，主要是引入了一種新的重構，並證明了這種重構在光滑區域具有三階精度且在網格邊界無振蕩，所提的格式保持了中心差分格式簡單的優點，不需要求解黎曼問題，避免了復雜且耗時的特徵分解過程。
分享友人
分享到 Line

分享到臉書