識閾效應 的英文怎麼說

中文拼音 [zhìyùxiàoyīng]
識閾效應 英文
threshold effect
  • 識 : 識Ⅰ動詞[書面語] （記） remember; commit to memory Ⅱ名詞1. [書面語] （記號） mark; sign 2. （姓氏） a surname
  • 閾 : 名詞1. [書面語] （門坎兒） threshold; doorsill2. （界限; 范圍） threshold
  • 效 : Ⅰ名詞（效果; 功用） effect; efficiency; result Ⅱ動詞1 （仿效） imitate; follow the example of 2 ...
  • 應 : 應動詞1 （回答） answer; respond to; echo 2 （滿足要求） comply with; grant 3 （順應; 適應） suit...
  • 效應 : [物理學] effect; action; influence
  1. The characteristic and essentiality of structural damage alarming are discussed. a method which applied two different wavelet bases to decompose and recompose measure signals is proposed to detect structural damage. by a coefficient of damage alarming which is defined as k and a threshold which is specified as u, information of emergence and damage time are obtained

    探討了結構損傷預警的特點和重要性，提出了一種應用兩種小波進行分解和重構的損傷預警方法，並定義了一個多尺度信號損傷奇異點搜尋的指標_ k ，以預警指標閾值能有效識別結構是否發生損傷。
  2. If we could make use of the technology of computer seeing and realize automatic recognition of identifying id card numbers, we should greatly improve the efficiency of id card administration. presently, most recognition systems of id card numbers are based on scanner, but this systems cannot meet the demand of long and real time catching image. this text lucubrates theoretic about image manipulation and pattern recognition and sets up a integrated regnition system of identifying id card numbers. on ccd vedic. concretery consist : ( 1 ) firstly investigating the segmentation about image of illumination asymmetry. due to a dotal lamp - house, the illumination of id card numbers image is asymmetrical

    本文應用了圖像處理和模式識別技術，建立了基於攝像頭的身份證號碼的識別系統。主要研究內容具體包括如下： （ 1 ）研究了亮度不均勻的身份證號碼圖像的分割。由於採用點光源進行局部照射，導致身份證號碼圖像的亮度不均勻，因此直接對圖像採用全局閾值進行分割不能有效地分離號碼體。
  3. This paper analyzes the percolation mechanism and characteristics of gas under low - velocity percolation state in terms of slippage effect and threshold pressure effect by using molecular dynamics, thermodynamics and percolation mechanics in order to understand the practical reason of low - velocity non - darcy percolation

    摘要為明確氣體在低速滲流狀態下的滲流規律以及產生低速非達西滲流的實質性原因，運用分子動力學、熱力學和滲流力學等相關知識，基於滑脫效應和閾壓效應兩方面分析了氣體在低速滲流狀態下的滲流機理及滲流特徵。
  4. This paper illustrates detailedly the thin groupware auto - adaptive recognition system ; it also illlustrates the procession of capture image and take indispensable foreclose to wipe off noise in order to get boundary easilyer. the recognition system uses " hough " transform method to make the recognition area orientation, and according to the unstable environment such as lights which leads to the change of the image ' s brightness, thresholds picture using an iterative selection method and then growing process for cell image segmentation based on local color similarity and global shape criteria, adaptively gets the best threshold to divide the washer off the background. the recognition system uses the classifier based on minimal - error - ratio bayes method to make decision after getting image characteristic

    本文詳細介紹了薄形組合件自適應識別系統；闡明了圖像的分通道自動採集過程，以及對採集到的原始圖像所進行的預處理方法。通過採用哈夫變換去除偽邊緣點的方法，有效地解決了識別區域的定位問題。針對裝配零件（主要是墊片）薄、小導致圖像信息少、識別難度大，以及材質不一導致採集到的組合件圖像亮度波動等問題，提出了使用最佳閾值迭代法和使用種子填充的圖像串列分割技術，自適應地找出最佳閡值，使墊片和背景分離，從而提取墊片數目信息。
  5. The paper recounts implementing thoughts of this system and advances some improved algorithm in pretreating image, for example image enforce, segmentation of image and so on. we get rather satisfying effect by using these algorithms. in initial phase of the system, in order to get the information of field and automobile " s edge, the paper introduces detecting algorithm to confirm end - points of field, improved hough algorithm, and worm - following algorithm to pick up edge. in real - time checking phase, the most importance aim is to identify sign circles, so we advance a qiuck searching algorithm based on threshold

    文中詳敘了系統的實現思路，對于預處理階段採用的圖像處理技術，如圖像增強、圖像分割等，提出了一些相應的改進演算法，取得了較滿意的處理效果；在系統初始化階段，為了獲取場地的端點信息和汽車邊緣信息，分別介紹了自動確定場地端點位置的檢測演算法、改進的hough變換演算法和提取區域邊緣的「蟲隨法」 ；在系統的實時檢測階段，最主要的目的是識別檢測標志圓，為此，文中提出了基於閾值的快速搜索演算法，有效的提高了目標物體區域的提取和識別速度。
  6. Later on, after elaborating the disadvantages of the old methods in detecting and recognizing moving objects, a series of corresponding approaches are proposed, such as grid scan, local tracking bug and dynamic window in object tracing to reduce the huge data needed to be processed, maximum and minimum for selecting a proper segmentation threshold and improved conversion from rgb model to hsv and so on to decrease the influence of inhomogeneous lighting and the color noise, a bilinear interpolation in each quadrant to eliminate the bad effect on the recognition precise because of the distortions of the camera. after that, much emphasis is given on application study in pattern recognition with a feed - forward neural network. both the basic bp algorithm and improved bp algorithm in the study process are described in detail, and the later is used to quicken convergence speed and improve validity of the network

    然後，分析和闡明了傳統的運動目標檢測方法的不足，並在此基礎上結合研究中的實際實驗環境，提出了一系列解決方法，包括針對降低龐大數據量而提出的網格掃描、局部「跟蟲」追蹤和動態窗口掃描等目標檢測方法，針對實驗環境中光照不均和顏色干擾提出基於人機交互的最大最小值閾值選取方法和引入改進的rgb模型到hsv模型的轉換方法，為消除圖像畸變對識別精度的惡劣影響而採用的通過控制點進行雙線性插值進行畸變校正的方法；緊接著，概述了神經網路的發展歷史和幾種常用神經網路模型的特點，重點研究了前饋型神經網路在模式識別中的應用問題，詳細闡述了基本的bp演算法和學習過程中bp演算法的改進，從而使網路收斂速度更快，解決問題更有效，並在此基礎上，設計了一個基於bp神經網路的運動目標識別系統，給出了實驗結果。
  7. First of all, the emd - based wavelet threshold denoising algorithm is apllied to denoise noisy structural response data to reduce the effect resulting from noise. during the process of the empirical mode decomposition ( emd ), the two boundaries of the response signal are processed with semi - periodical ? semi - symmetrical method. subsequently, hilbert - huang transform ( hht ) is used in identifying structural intrinsic frequency

    這套技術用以解決實際工程應用中遇到的在信噪比較低情況下通過結構的響應信號來進行結構損傷識別問題，即先用基於emd的小波閾值去噪演算法對含噪結構響應進行去噪處理，以有效降低噪聲影響（在去噪的emd處理過程中，對信號的邊界採用「半周期半對稱」延括演算法來抑制邊界誤差） ，然後再用希爾伯特?黃變換（ hht ）進行結構的固有頻率識別，最後計算出結構剛度。
  8. During image recognition, it reduces the yawp and improves the quality of the image with the technology of the median filter, it uses the method of maximum between - class variance to segment the object, then gets better effect. the algorithm is simple, and the adapted capacity is great. it doesn ’ t influence by the change between the contrast and lighteness of the image. so it applies on the real time system

    在圖像識別過程中，本文運用了中值濾波技術降低了噪聲，提高了圖像的質量，並採用了最大類間閾值分割法分割目標，得到了較好的效果，該方法計算簡單，自適應性強，在一定條件下不受圖像對比度與亮度變化的影響，因而在一些實時圖像處理系統中得到了廣泛的應用。
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