Reliability Function Evaluation for Binary Switching System

Author(s):  
Akshay Kumar ◽  
Divya Prajapati ◽  
Sweta Sahu ◽  
Nidhi Thapa ◽  
Ashok Singh Bhandari ◽  
...  
2014 ◽  
Vol 709 ◽  
pp. 530-533 ◽  
Author(s):  
Aleksandr Vasilyevich Pitukhin ◽  
Igor Skobtsov

The purpose of this paper is to present the catastrophe theory method for the optimal design of machine components. A brief description of the cusp catastrophe is presented in the introduction. The statement of optimal design problem is given in the second part of the paper. A single criterion design is presented; the reliability function is used as the objective function. The last part is devoted to probability approach. Manage variables are viewed as stochastic quantities, analytical and statistical linearization methods are used for the reliability function evaluation.


2015 ◽  
Vol 741 ◽  
pp. 283-286 ◽  
Author(s):  
Aleksandr Vasilyevich Pitukhin ◽  
Igor Skobtsov

This paper deals with the statistical catastrophe theory method for the optimal design of machine components. A short introduction to the catastrophe theory is presented, the statement of optimal design problem is given in the first part of the paper. A single criterion design is presented; the reliability function is used as the objective function. The last part is focused on probability approach. Manage variables are viewed as random stationary processes, statistical linearization method and Pearson moment method are used for the reliability function evaluation.


1972 ◽  
Author(s):  
Lee Gurel ◽  
Margaret W. Linn ◽  
Bernard S. Linn

1997 ◽  
Vol 473 ◽  
Author(s):  
J. A. Davis ◽  
J. D. Meindl

ABSTRACTOpportunities for Gigascale Integration (GSI) are governed by a hierarchy of physical limits. The levels of this hierarchy have been codified as: 1) fundamental, 2) material, 3) device, 4) circuit and 5) system. Many key limits at all levels of the hierarchy can be displayed in the power, P, versus delay, td, plane and the reciprocal length squared, L-2, versus response time, τ, plane. Power, P, is the average power transfer during a binary switching transition and delay, td, is the time required for the transition. Length, L, is the distance traversed by an interconnect that joins two nodes on a chip and response time, τ, characterizes the corresponding interconnect circuit. At the system level of the hierarchy, quantitative definition of both the P versus td and the L-2 versus τ displays requires an estimate of the complete stochastic wiring distribution of a chip.Based on Rent's Rule, a well known empirical relationship between the number of signal input/output terminals on a block of logic and the number of gate circuits with the block, a rigorous derivation of a new complete stochastic wire length distribution for an on-chip random logic network is described. This distribution is compared to actual data for modern microprocessors and to previously described distributions. A methodology for estimating the complete wire length distribution for future GSI products is proposed. The new distribution is then used to enhance the critical path model that determines the maximum clock frequency of a chip; to derive a preliminary power dissipation model for a random logic network; and, to define an optimal architecture of a multilevel interconnect network that minimizes overall chip size. In essence, a new complete stochastic wiring distribution provides a generic basis for maximizing the value obtained from a multilevel interconnect technology.


Author(s):  
Hazim Mansour Gorgees ◽  
Bushra Abdualrasool Ali ◽  
Raghad Ibrahim Kathum

     In this paper, the maximum likelihood estimator and the Bayes estimator of the reliability function for negative exponential distribution has been derived, then a Monte –Carlo simulation technique was employed to compare the performance of such estimators. The integral mean square error (IMSE) was used as a criterion for this comparison. The simulation results displayed that the Bayes estimator performed better than the maximum likelihood estimator for different samples sizes.


Author(s):  
Nadia Hashim Al-Noor ◽  
Shurooq A.K. Al-Sultany

        In real situations all observations and measurements are not exact numbers but more or less non-exact, also called fuzzy. So, in this paper, we use approximate non-Bayesian computational methods to estimate inverse Weibull parameters and reliability function with fuzzy data. The maximum likelihood and moment estimations are obtained as non-Bayesian estimation. The maximum likelihood estimators have been derived numerically based on two iterative techniques namely “Newton-Raphson” and the “Expectation-Maximization” techniques. In addition, we provide compared numerically through Monte-Carlo simulation study to obtained estimates of the parameters and reliability function in terms of their mean squared error values and integrated mean squared error values respectively.


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