Active redundancy allocation in systems

An effective way of improving the reliability of a system is the allocation of active redundancy. Let $X_{1}$, $X_{2}$ be independent lifetimes of the components $C_{1}$ and $C_{2}$, respectively, which form a series system. Let denote $U_{1} = \min ( \max (X_{1},X),X_{2})$ and $U_{2} = \min (X_{1},\max (X_{2},X))$, where X is the lifetime of a redundancy (say S) independent of $X_{1}$ and $X_{2}$. That is $U_{1}(U_{2})$ denote the lifetime of a system obtained by allocating S to $C_{1}(C_{2})$ as an active redundancy. Singh and Misra (1994) considered the criterion where $C_{1}$ is preferred to $C_{2}$ for redundancy allocation if $P(U_{1} > U_{2})\geq P(U_{2} > U_{1})$. In this paper we use the same criterion of Singh and Misra (1994) and we investigate the allocation of one active redundancy when it differs depending on the component with which it is to be allocated. We find sufficient conditions for the optimization which depend on the components and redundancies probability distributions. We also compare the allocation of two active redundancies (say $S_{1}$ and $S_{2}$) in two different ways, that is $S_{1}$ with $C_{1}$ and $S_{2}$ with $C_{2}$ and viceversa. For this case the hazard rate order plays an important role. We obtain results for the allocation of more than two active redundancies to a k-out-of-n systems

Active redundancy allocation in systems

An effective way of improving the reliability of a system is the allocation of active redundancy. Let 1 X , 2 X be independent lifetimes of the components 1 C and 2 C , respectively, which form a series system. Let denote ( ) ( ) 2 1 1 , , max min X X X U = and ( ) ( ) X X X U , max , min 2 1 2 = , where X is the lifetime of a redundancy (say S ) independent of 1 X and 2 X . That is ( ) 2 1 U U denote the lifetime of a system obtained by allocating S to ( ) 2 1 C C as an active redundancy. Singh and Misra (1994) considered the criterion where 1 C is preferred to 2 C for redundancy allocation if ( ) ( ) 1 2 2 1 U U P U U P > ³ > . In this paper we use the same criterion of Singh and Misra (1994) and we investigate the allocation of one active redundancy when it differs depending on the component with which it is to be allocated. We find sufficient conditions for the optimization which depend on the components and redundancies probability distributions. We also compare the allocation of two active redundancies (say 1 S and 2 S ) in two different ways, that is, 1 S with 1 C and 2 S with 2 C and viceversa. For this case the hazard rate order plays an important role. We obtain results for the allocation of more than two active redundancies to a k-out- of-n systems.

Predicting Future Instance Segmentation by Forecasting Convolutional Features

Anticipating future events is an important prerequisite towards intelligent behavior. Video forecasting has been studied as a proxy task towards this goal. Recent work has shown that to predict semantic segmentation of future frames, forecasting at the semantic level is more effective than forecasting RGB frames and then segmenting these. In this paper we consider the more challenging problem of future instance segmentation, which additionally segments out individual objects. To deal with a varying number of output labels per image, we develop a predictive model in the space of fixed-sized convolutional features of the Mask R-CNN instance segmentation model. We apply the "detection head'" of Mask R-CNN on the predicted features to produce the instance segmentation of future frames. Experiments show that this approach significantly improves over strong baselines based on optical flow and repurposed instance segmentation architectures

A Generalized Statistical Complexity Measure: Applications to Quantum Systems

A two-parameter family of complexity measures $\tilde{C}^{(\alpha,\beta)}$ based on the R\'enyi entropies is introduced and characterized by a detailed study of its mathematical properties. This family is the generalization of a continuous version of the LMC complexity, which is recovered for $\alpha=1$ and $\beta=2$. These complexity measures are obtained by multiplying two quantities bringing global information on the probability distribution defining the system. When one of the parameters, $\alpha$ or $\beta$, goes to infinity, one of the global factors becomes a local factor. For this special case, the complexity is calculated on different quantum systems: H-atom, harmonic oscillator and square well.Comment: 15 pages, 3 figure