Communication failure analysis for a fleet formation flight of drones based on absorbing Markov chain

2018 ◽  
pp. 2595-2601 ◽  
Author(s):  
R. Abdallah ◽  
C. Sarraf ◽  
R. Kouta ◽  
J. Gaber ◽  
M. Wack
Keyword(s):  
Markov Chain ◽  
Failure Analysis ◽  
Formation Flight ◽  
Communication Failure ◽  
Absorbing Markov Chain

Localization of a Complex Failure by Combining ATPG and Customer-Oriented Application Testing

2013 ◽  
Author(s):  
Kai Wang ◽  
Rhys Weaver ◽  
David Johnson
Keyword(s):  
Failure Analysis ◽  
Semiconductor Device ◽  
Digital Design ◽  
Radio Communication ◽  
Communication Failure ◽  
Systemic Analysis ◽  
Rf Communication ◽  
System On A Chip ◽  
Electrical Diagnostics ◽  
Failure Location

Abstract A systemic analysis was chosen to evaluate a real case Bluetooth (BT) radio failure in the aspects of RF communication, digital design, firmware, application software, semiconductor device physics and processing, and failure analysis. This paper explores the range of testing, including customer application testing, required to confirm and localize a BT RF communication failure. It shows that the radio communication failure was not, as expected, caused by faulty radio hardware; it was rather linked to problematic encryption hardware at the assistance of the Synergy BT to mobile application. The paper also explores that the digital fault can only be detected by the timing sensitive transition fault scan patterns and how to obtain the physical failure location. Thus, the combination of ATPG and application testing provides a consistency between electrical diagnostics which yields a higher success rate at subsequent physical failure analysis of complex modern RF System on a Chip.

On the age distribution of a Markov chain

1978 ◽  
Vol 15 (1) ◽  
pp. 65-77 ◽  
Author(s):  
Anthony G. Pakes
Keyword(s):  
Markov Chain ◽  
Random Walk ◽  
Limit Theorems ◽  
Age Distribution ◽  
Weak Limit ◽  
Absorbing State ◽  
Absorbing Markov Chain ◽  
Continuous Random Walk ◽  
A Chain ◽  
Watson Process

This paper develops the notion of the limiting age of an absorbing Markov chain, conditional on the present state. Chains with a single absorbing state {0} are considered and with such a chain can be associated a return chain, obtained by restarting the original chain at a fixed state after each absorption. The limiting age, A(j), is the weak limit of the time given Xn = j (n → ∞).A criterion for the existence of this limit is given and this is shown to be fulfilled in the case of the return chains constructed from the Galton–Watson process and the left-continuous random walk. Limit theorems for A (J) (J → ∞) are given for these examples.

The Study on Convergence and Convergence Rate of Genetic Algorithm Based on an Absorbing Markov Chain

2012 ◽  
Vol 239-240 ◽  
pp. 1511-1515 ◽  
Author(s):  
Jing Jiang ◽  
Li Dong Meng ◽  
Xiu Mei Xu
Keyword(s):  
Genetic Algorithm ◽  
Markov Chain ◽  
Convergence Rate ◽  
Hitting Time ◽  
Sufficient Condition ◽  
First Hitting Time ◽  
Absorbing Markov Chain ◽  
Expected Hitting Time ◽  
Optimization Efficiency ◽  
Theoretical Issues

The study on convergence of GA is always one of the most important theoretical issues. This paper analyses the sufficient condition which guarantees the convergence of GA. Via analyzing the convergence rate of GA, the average computational complexity can be implied and the optimization efficiency of GA can be judged. This paper proposes the approach to calculating the first expected hitting time and analyzes the bounds of the first hitting time of concrete GA using the proposed approach.

On the age distribution of a Markov chain

1978 ◽  
Vol 15 (01) ◽  
pp. 65-77 ◽  
Author(s):  
Anthony G. Pakes
Keyword(s):  
Markov Chain ◽  
Random Walk ◽  
Limit Theorems ◽  
Age Distribution ◽  
Weak Limit ◽  
Absorbing State ◽  
Absorbing Markov Chain ◽  
Continuous Random Walk ◽  
A Chain ◽  
Watson Process

This paper develops the notion of the limiting age of an absorbing Markov chain, conditional on the present state. Chains with a single absorbing state {0} are considered and with such a chain can be associated a return chain,obtained by restarting the original chain at a fixed state after each absorption. The limiting age,A(j), is the weak limit of the timegivenXn=j(n → ∞).A criterion for the existence of this limit is given and this is shown to be fulfilled in the case of the return chains constructed from the Galton–Watson process and the left-continuous random walk. Limit theorems forA(J) (J →∞) are given for these examples.

MARKOV CHAIN METHOD FOR COMPUTING THE RELIABILITY OF HAMMOCK NETWORKS

2020 ◽  
pp. 1-18
Author(s):  
Marilena Jianu ◽  
Daniel Ciuiu ◽  
Leonard Dăuş ◽  
Mihail Jianu
Keyword(s):  
Markov Chain ◽  
Lower Bound ◽  
Relative Error ◽  
Fundamental Matrix ◽  
Approximation Methods ◽  
Arbitrary Length ◽  
Reliability Polynomial ◽  
Absorbing Markov Chain ◽  
Markov Chain Method ◽  
The Mean

In this paper, we develop a new method for evaluating the reliability polynomial of a hammock network. The method is based on a homogeneous absorbing Markov chain and provides the exact reliability for networks of width less than 5 and arbitrary length. Moreover, it produces a lower bound for the reliability polynomial for networks of width greater than or equal to 5. To investigate how sharp this lower bound is, we compare our method with other approximation methods and it proves to be the most accurate in terms of absolute as well as relative error. Using the fundamental matrix, we also calculate the average time to absorption, which provides the mean length of a network that is expected to work.

scholarly journals A Counterexample to Temporal Differences Learning

1995 ◽  
Vol 7 (2) ◽  
pp. 270-279 ◽  
Author(s):  
Dimitri P. Bertsekas
Keyword(s):  
Neural Network ◽  
Markov Chain ◽  
Cost Function ◽  
Least Squares ◽  
Temporal Differences ◽  
Q Learning ◽  
Absorbing Markov Chain ◽  
Transition Costs ◽  
Compact Representations ◽  
The Cost

Sutton's TD(λ) method aims to provide a representation of the cost function in an absorbing Markov chain with transition costs. A simple example is given where the representation obtained depends on λ. For λ = 1 the representation is optimal with respect to a least-squares error criterion, but as λ decreases toward 0 the representation becomes progressively worse and, in some cases, very poor. The example suggests a need to understand better the circumstances under which TD(0) and Q-learning obtain satisfactory neural network-based compact representations of the cost function. A variation of TD(0) is also given, which performs better on the example.

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