Modeling MOSFET Drain Current Non-Gaussian Distribution With Power-Normal Probability Density Function

2014 ◽  
Vol 35 (2) ◽  
pp. 154-156 ◽  
Author(s):  
Bo Yu ◽  
Yu Yuan ◽  
Kasim Mahmood ◽  
Joseph Wang ◽  
Ping Liu ◽  
...  
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Gaussian Distribution ◽  
Drain Current ◽  
Normal Probability ◽  
Non Gaussian ◽  
Normal Probability Density

A New Microcontact Model Developed for Variable Fractal Dimension, Topothesy, Density of Asperity, and Probability Density Function of Asperity Heights

2006 ◽  
Vol 74 (4) ◽  
pp. 603-613 ◽  
Author(s):  
Jeng Luen Liou ◽  
Jen Fin Lin
Keyword(s):  
Fractal Dimension ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Gaussian Distribution ◽  
Fractal Theory ◽  
Radius Of Curvature ◽  
The Mean ◽  
Contact Surfaces ◽  
Non Gaussian

In the present study, the fractal theory is applied to modify the conventional model (the Greenwood and Williamson model) established in the statistical form for the microcontacts of two contact surfaces. The mean radius of curvature (R) and the density of asperities (η) are no longer taken as constants, but taken as variables as functions of the related parameters including the fractal dimension (D), the topothesy (G), and the mean separation of two contact surfaces. The fractal dimension and the topothesy varied by differing the mean separation of two contact surfaces are completely obtained from the theoretical model. Then the mean radius of curvature and the density of asperities are also varied by differing the mean separation. A numerical scheme is thus developed to determine the convergent values of the fractal dimension and topothesy corresponding to a given mean separation. The topographies of a surface obtained from the theoretical prediction of different separations show the probability density function of asperity heights to be no longer the Gaussian distribution. Both the fractal dimension and the topothesy are elevated by increasing the mean separation. The density of asperities is reduced by decreasing the mean separation. The contact load and the total contact area results predicted by variable D, G*, and η as well as non-Gaussian distribution are always higher than those forecast with constant D, G*, η, and Gaussian distribution.

Probably a Distribution

2019 ◽  
pp. 143-162
Author(s):  
Steven J. Osterlind
Keyword(s):  
Probability Density Function ◽  
Least Squares ◽  
Probability Density ◽  
Density Function ◽  
Normal Curve ◽  
Method Of Least Squares ◽  
Modern Times ◽  
Normal Probability ◽  
Standard Normal ◽  
Normal Probability Density

This chapter is all about Carl Gauss, his life, and his accomplishments, including his work in plotting the orbits for Ceres, which he did while still a teenager and which set his reputation. The chapter tells, too, how and when he invented and used his method of least squares and of his dispute with Legendre on who invented it first. One of his most significant accomplishments is his devising (and proof) of the normal probability density function, or, more familiarly, the standard normal curve. This is described and its import and application to modern times is discussed. Also, there is a brief discussion of biographical events and details of his life, such as his reclusive nature in his hometown of Göttingen, and his caring for his ailing mother and then his first and second wives. Some details of his impact today and lasting accomplishments are also provided.

scholarly journals PENENTUAN THRESHOLD CITRA MULUT DENGAN METODE NORMAL PROBABILITY DENSITY FUNCTION (NPDF) GUNA MENDETEKSI MULUT PEMELAJAR

2017 ◽  
Vol 9 (2) ◽  
pp. 137-144
Author(s):  
Anas Anas
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Normal Probability ◽  
Normal Probability Density

Identifikasi wajah merupakan masalah sulit terutama ketika informasi dari fitur wajah tidak cukup atau terbatas. Misalnya dalam segmentasi mulut pemelajar, dimana objek yang diamati tergolong rumit, terutama ciri utama wajah yaitu, mata, mulut, hidung. Pada penelitian ini mengusulkan Normal Probability Density Function (Normal PDF ) dalam melakukan segmentasi dan pemisahan background dan foreground. Dari hasil eksperimen segmentasi mulut pemelajar metode Normal Probability Density Function (Normal PDF) dapat memberikan hasil segmentasi lebih baik. Proses pengukuran nilai rata-rata MSE menggunakan metode Normal Probability Density Function (NPDF) sebesar 275.3953475 piksel. Untuk Proses pengukuran nilai rata-rata PSNR menggunakan metode Normal Probability Density Function (NPDF) sebesar 24.39017959 piksel. Dari nilai rata-rata PSNR terbukti metode Normal Probability Density Function (NPDF) baik dan layak digunakan untuk melakukan segmentasi citra pada mulut pemelajar.

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