Dirac-Based Reduction Techniques for Quantitative Analysis of Discrete-Time Markov Models

2020 ◽  
pp. 1-16
Author(s):  
Mohammadsadegh Mohagheghi ◽  
Behrang Chaboki
Keyword(s):  
Quantitative Analysis ◽  
Discrete Time ◽  
Markov Models ◽  
Reduction Techniques

scholarly journals Alternating Current Loss of Superconductors Applied to Superconducting Electrical Machines

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2234
Author(s):  
Hongye Zhang ◽  
Zezhao Wen ◽  
Francesco Grilli ◽  
Konstantinos Gyftakis ◽  
Markus Mueller
Keyword(s):  
Quantitative Analysis ◽  
Power Density ◽  
High Efficiency ◽  
Alternating Current ◽  
Coated Conductors ◽  
Ac Loss ◽  
Electrical Machines ◽  
Qualitative And Quantitative Analysis ◽  
Qualitative And Quantitative ◽  
Reduction Techniques

Superconductor technology has recently attracted increasing attention in power-generation- and electrical-propulsion-related domains, as it provides a solution to the limited power density seen by the core component, electrical machines. Superconducting machines, characterized by both high power density and high efficiency, can effectively reduce the size and mass compared to conventional machine designs. This opens the way to large-scale purely electrical applications, e.g., all-electrical aircrafts. The alternating current (AC) loss of superconductors caused by time-varying transport currents or magnetic fields (or both) has impaired the efficiency and reliability of superconducting machines, bringing severe challenges to the cryogenic systems, too. Although much research has been conducted in terms of the qualitative and quantitative analysis of AC loss and its reduction methods, AC loss remains a crucial problem for the design of highly efficient superconducting machines, especially for those operating at high speeds for future aviation. Given that a critical review on the research advancement regarding the AC loss of superconductors has not been reported during the last dozen years, especially combined with electrical machines, this paper aims to clarify its research status and provide a useful reference for researchers working on superconducting machines. The adopted superconducting materials, analytical formulae, modelling methods, measurement approaches, as well as reduction techniques for AC loss of low-temperature superconductors (LTSs) and high-temperature superconductors (HTSs) in both low- and high-frequency fields have been systematically analyzed and summarized. Based on the authors’ previous research on the AC loss characteristics of HTS coated conductors (CCs), stacks, and coils at high frequencies, the challenges for the existing AC loss quantification methods have been elucidated, and multiple suggestions with respect to the AC loss reduction in superconducting machines have been put forward. This article systematically reviews the qualitative and quantitative analysis methods of AC loss as well as its reduction techniques in superconductors applied to electrical machines for the first time. It is believed to help deepen the understanding of AC loss and deliver a helpful guideline for the future development of superconducting machines and applied superconductivity.

scholarly journals Nested particle filters for online parameter estimation in discrete-time state-space Markov models

Bernoulli ◽  
2018 ◽  
Vol 24 (4A) ◽  
pp. 3039-3086 ◽  
Author(s):  
Dan Crisan ◽  
Joaquín Míguez
Keyword(s):  
Parameter Estimation ◽  
State Space ◽  
Discrete Time ◽  
Particle Filters ◽  
Markov Models

Reduction techniques for discrete-time Markov chains on totally ordered state space using stochastic comparisons

2000 ◽  
Vol 37 (03) ◽  
pp. 795-806 ◽  
Author(s):  
Laurent Truffet
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
State Space ◽  
Discrete Time ◽  
Stochastic Comparison ◽  
Homogeneous Markov Chain ◽  
Monotone Functions ◽  
Stochastic Comparisons ◽  
Reduction Techniques ◽  
Ordered State

We propose in this paper two methods to compute Markovian bounds for monotone functions of a discrete time homogeneous Markov chain evolving in a totally ordered state space. The main interest of such methods is to propose algorithms to simplify analysis of transient characteristics such as the output process of a queue, or sojourn time in a subset of states. Construction of bounds are based on two kinds of results: well-known results on stochastic comparison between Markov chains with the same state space; and the fact that in some cases a function of Markov chain is again a homogeneous Markov chain but with smaller state space. Indeed, computation of bounds uses knowledge on the whole initial model. However, only part of this data is necessary at each step of the algorithms.

scholarly journals A discrete time event-history approach to informative drop-out in mixed latent Markov models with covariates

Biometrics ◽  
2014 ◽  
Vol 71 (1) ◽  
pp. 80-89 ◽  
Author(s):  
Francesco Bartolucci ◽  
Alessio Farcomeni
Keyword(s):  
Discrete Time ◽  
Markov Models ◽  
Drop Out ◽  
Event History ◽  
History Approach

scholarly journals Application of Discrete-Time Markov Models

2013 ◽  
Vol 2 (1) ◽  
Author(s):  
Narela Bajram ◽  
Daria Ler
Keyword(s):  
Discrete Time ◽  
Markov Models
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