scholarly journals Sample Size Dependence in Measures of Proportional Similarity

1982 ◽  
Vol 9 ◽  
pp. 147-151 ◽  
Author(s):  
AJ Kohn ◽  
AC Riggs
Keyword(s):  
Sample Size ◽  
Size Dependence

Sample-size dependence of the fluorescence of 2-(N-acetylamino)-6-methylpyridine and its Zinc(II) chloride complex

2017 ◽  
Vol 190 ◽  
pp. 261-266 ◽  
Author(s):  
Alexey S. Berezin ◽  
Olga V. Antonova ◽  
Elizaveta V. Lider ◽  
Anton I. Smolentsev ◽  
Vladimir A. Nadolinny ◽  
...  
Keyword(s):  
Sample Size ◽  
Size Dependence ◽  
Chloride Complex

scholarly journals The Prediction of Human Abdominal Adiposity Based on the Combination of a Particle Swarm Algorithm and Support Vector Machine

2020 ◽  
Vol 17 (3) ◽  
pp. 1117
Author(s):  
Xiue Gao ◽  
Wenxue Xie ◽  
Shifeng Chen ◽  
Junjie Yang ◽  
Bo Chen
Keyword(s):  
Support Vector Machine ◽  
Sample Size ◽  
Size Dependence ◽  
Prediction Models ◽  
Test Group ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
Abdominal Adiposity ◽  
Modeling Group ◽  
Svm Algorithm

Background: Abdominal adiposity is an important risk factor of chronic cardiovascular diseases, thus the prediction of abdominal adiposity and obesity can reduce the risks of contracting such diseases. However, the current prediction models display low accuracy and high sample size dependence. The purpose of this study is to put forward a new prediction method based on an improved support vector machine (SVM) to solve these problems. Methods: A total of 200 individuals participated in this study and were further divided into a modeling group and a test group. Their physiological parameters (height, weight, age, the four parameters of abdominal impedance and body fat mass) were measured using the body composition tester (the universal INBODY measurement device) based on BIA. Intelligent algorithms were used in the modeling group to build predictive models and the test group was used in model performance evaluation. Firstly, the optimal boundary C and parameter gamma were optimized by the particle swarm algorithm. We then developed an algorithm to classify human abdominal adiposity according to the parameter setup of the SVM algorithm and constructed the prediction model using this algorithm. Finally, we designed experiments to compare the performances of the proposed method and the other methods. Results: There are different abdominal obesity prediction models in the 1 KHz and 250 KHz frequency bands. The experimental data demonstrates that for the frequency band of 250 KHz, the proposed method can reduce the false classification rate by 10.7%, 15%, and 33% in relation to the sole SVM algorithm, the regression model, and the waistline measurement model, respectively. For the frequency band of 1 KHz, the proposed model is still more accurate. (4) Conclusions: The proposed method effectively improves the prediction accuracy and reduces the sample size dependence of the algorithm, which can provide a reference for abdominal obesity.

Hyperplastic Forming: Process Potential and Factors Affecting Formability

1999 ◽  
Vol 601 ◽  
Author(s):  
Glenn S. Daehn ◽  
Vincent J. Vohnout ◽  
Subrangshu Datta
Keyword(s):  
Boundary Conditions ◽  
Sample Size ◽  
Active Control ◽  
High Velocity ◽  
Size Dependence ◽  
Recent Model ◽  
The Body ◽  
Electromagnetic Forming ◽  
Forming Process ◽  
Factors Affecting

AbstractThis paper has two distinct goals. First, we argue in an extended introduction that high velocity forming, as can be implemented through electromagnetic forming, is a technology that should be developed. As a process used in conjunction with traditional stamping, it may offer dramatically improved formability, reduced wrinkling and active control of springback among other advantages. In the body of the paper we describe the important factors that lead to improved formability at high velocity. In particular, high sample velocity can inhibit neck growth. There is a sample size dependence where larger samples have better ductility than those of smaller dimensions. These aspects are at least partially described by the recent model of Freund and Shenoy. In addition to this, boundary conditions imposed by sample launch and die impact can have important effects on formability.

scholarly journals Sample-size dependence of the ground-state energy in a one-dimensional localization problem

1996 ◽  
Vol 54 (1) ◽  
pp. 231-242 ◽  
Author(s):  
C. Monthus ◽  
G. Oshanin ◽  
A. Comtet ◽  
S. F. Burlatsky
Keyword(s):  
Sample Size ◽  
Ground State Energy ◽  
Ground State ◽  
Size Dependence ◽  
State Energy ◽  
Localization Problem ◽  
One Dimensional

scholarly journals Sample-size dependence of the magnetic critical current density inMgB2superconductors

2004 ◽  
Vol 69 (1) ◽  
Author(s):  
M. J. Qin ◽  
S. Keshavarzi ◽  
S. Soltanian ◽  
X. L. Wang ◽  
H. K. Liu ◽  
...  
Keyword(s):  
Critical Current Density ◽  
Sample Size ◽  
Critical Current ◽  
Current Density ◽  
Size Dependence

Size and Speed Effects From Lubricant Breakdown in Plastic Compression

1963 ◽  
Vol 85 (4) ◽  
pp. 329-334 ◽  
Author(s):  
G. W. Pearsall ◽  
W. A. Backofen
Keyword(s):  
Shear Strength ◽  
Size Effect ◽  
Strain Rate ◽  
Sample Size ◽  
Size Dependence ◽  
Peripheral Region ◽  
Rate Dependence ◽  
Average Pressure ◽  
Plastic Compression ◽  
Interface Sliding

A size dependence of the average pressure for reducing thin, geometrically similar, lubricated disks of aluminum has been associated with the development of lubricant-film breakdown at the periphery. In general, the greater amount of interface sliding (a maximum at the periphery) of larger disks led to earlier breakdown and higher stresses. However, with certain liquid lubricants the size-effect reversed with increasing reduction, the larger disks requiring the smaller stress. This reversal has been related to the presence of a lubricant reservoir, entrapped across the interface, that increases with sample size, supplying the peripheral region and eventually controlling the progress of breakdown. The effects of speed on pressure requirements were attributed to such factors as strain-rate dependence of lubricant shear strength, junction growth across the interface, and changes in the size of lubricant reservoirs.

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