Application of a New Method to Estimate Free Fluid Level and Recognition of Compositional Grading Using Wireline Formation Testing Data

10.2523/iptc-16565-ms ◽

2013 ◽

Author(s):

S. Khajooie ◽

M. Qassamipour ◽

M. Farhani

Keyword(s):

New Method ◽

Free Fluid ◽

Fluid Level ◽

Testing Data ◽

Compositional Grading

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Improving Pattern Recognition Rate by Gaussian Hopfield Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.189-193.2042 ◽

2011 ◽

Vol 189-193 ◽

pp. 2042-2045 ◽

Cited By ~ 1

Author(s):

Shang Jen Chuang ◽

Chiung Hsing Chen ◽

Chien Chih Kao ◽

Fang Tsung Liu

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Gaussian Distribution ◽

Test Pattern ◽

Recognition Rate ◽

Hopfield Neural Network ◽

Training Data ◽

New Method ◽

Gaussian Filter ◽

Testing Data

English letters cannot be recognized by the Hopfield Neural Network if it contains noise over 50%. This paper proposes a new method to improve recognition rate of the Hopfield Neural Network. To advance it, we add the Gaussian distribution feature to the Hopfield Neural Network. The Gaussian filter was added to eliminate noise and improve Hopfield Neural Network’s recognition rate. We use English letters from ‘A’ to ‘Z’ as training data. The noises from 0% to 100% were generated randomly for testing data. Initially, we use the Gaussian filter to eliminate noise and then to recognize test pattern by Hopfield Neural Network. The results are we found that if letters contain noise between 50% and 53% will become reverse phenomenon or unable recognition [6]. In this paper, we propose to uses multiple filters to improve recognition rate when letters contain noise between 50% and 53%.

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New Method for Fluid Level Depression Test Interpretation Based on Modern Multiphase Flow Calculation Techniques

10.2118/129562-ms ◽

2010 ◽

Cited By ~ 2

Author(s):

Mars Magnavievich Khasanov ◽

Vitaly Krasnov ◽

Rinat Khabibullin ◽

Alexander Pashali ◽

Andrey A. Semenov

Keyword(s):

Multiphase Flow ◽

New Method ◽

Test Interpretation ◽

Fluid Level ◽

Flow Calculation

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Selective Sampling and Orientation of Non-Homogeneous Tissues

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100100238 ◽

1968 ◽

Vol 26 ◽

pp. 98-99

Author(s):

C. C. Clawson ◽

L. W. Anderson ◽

R. A. Good

Keyword(s):

Electron Microscopy ◽

Electron Microscope ◽

Light Microscopy ◽

Random Sampling ◽

New Method ◽

Microscope Examination ◽

Small Areas ◽

Selective Sampling ◽

Large Numbers ◽

Specific Orientation

Investigations which require electron microscope examination of a few specific areas of non-homogeneous tissues make random sampling of small blocks an inefficient and unrewarding procedure. Therefore, several investigators have devised methods which allow obtaining sample blocks for electron microscopy from region of tissue previously identified by light microscopy of present here techniques which make possible: 1) sampling tissue for electron microscopy from selected areas previously identified by light microscopy of relatively large pieces of tissue; 2) dehydration and embedding large numbers of individually identified blocks while keeping each one separate; 3) a new method of maintaining specific orientation of blocks during embedding; 4) special light microscopic staining or fluorescent procedures and electron microscopy on immediately adjacent small areas of tissue.

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