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Reliability analysis of combinational logic circuits using error probabilities methods, such as PTM, has been widely developed and used in literature. However, using these methods for reliability analysis of sequential logic circuits will lead to inaccurate results, because of existence of loops in their architecture. In this paper a new method is proposed based on converting the sequential circuit to a secondary combinational circuit and applying an iterative reliability analysis to the resulting configuration. Experimental results demonstrate good accuracy levels for this method  

Motivated by numerous applications in Monte Carlo techniques and as of late, in deriving non dominated solutions in multi-objective optimization problems, this article addresses generating uniform random variables (λi, λi ≥ 0, i = 1,..., n) over a simplex in ℝ2 (n ≥ 2), i.e., Σi=1 n λi = 1. In this article, first, conditional distribution of λi where Σi=1 n λi = 1 is derived and then inverse method is applied to generate random variables  

Purpose: This work aims to study factors, such as driver characteristics, environmental conditions, and vehicle characteristics, that affect different crash types with a special focus on distraction parameters. For this purpose, distraction factors are divided into five groups: cellphone usage, cognitive distractions, passengers distracting the driver, outside events attracting the driver’s attention, and in-vehicle activities. Methods: Taking the crashes that occurred in the USA into account, the crash types are divided into two main groups, single-vehicle crashes and two-vehicle crashes. Since there were different crash types (alternatives) in the dataset and the probable correlation in... 

Several methods have been developed in the past for analyzing the porosity and other types of well logs for large-scale porous media, such as oil reservoirs, as well as their permeability distributions. We developed a method for analyzing the porosity logs φ(h) (where h is the depth) and similar data that are often nonstationary stochastic series. In this method one first generates a new stationary series based on the original data, and then analyzes the resulting series. It is shown that the series based on the successive increments of the log y(h)=φ(h+δh)-φ(h) is a stationary and Markov process, characterized by a Markov length scale hM. The coefficients of the Kramers-Moyal expansion for... 

In this paper, we present a novel framework for the coupled hidden Markov model (CHMM), based on the forward and backward recursions and conditional probabilities, given a multidimensional observation. In the proposed framework, the interdependencies of states networks are modeled with Markovian-like transition laws that influence the evolution of hidden states in all channels. Moreover, an offline inference approach by maximum likelihood estimation is proposed for the learning procedure of model parameters. To evaluate its performance, we first apply the CHMM model to classify and detect disturbances using synthetic data generated by the FitzHugh-Nagumo model. The average sensitivity and... 

This paper presents probabilistic models and methods for detailed seismic risk analysis of structures using structural reliability methods. This approach to risk analysis is an alternative to those that employ the theorem of total probability and conditional probability distributions. Detailed risk analysis entails probabilistic quantification of responses, the ensuing damage of individual structural and nonstructural components, and the resulting economic and social losses. Such an analysis requires a library of probabilistic models for hazards, responses, damage, repair cost, downtime, and casualty with a specific format as presented in this paper. Two analysis options are proposed: one... 

In this paper, the problem of statistical reconstruction and prediction of chaotic systems with unknown governing equations using stochastic Markov models is investigated. Using the time series of only one measurable state, an algorithm is proposed to design any orders of Markov models and the approach is state transition matrix extraction. Using this modeling, two goals are followed: first, using the time series, statistical reconstruction is performed through which the probability density and conditional probability density functions are reconstructed; and second, prediction is performed. For this problem, some estimators are required and here the maximum likelihood and the conditional...