scholarly journals Algebraic Analysis of Variants of Multi-State k-out-of-n Systems

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2042
Author(s):  
Patricia Pascual-Ortigosa ◽  
Eduardo Sáenz-de-Cabezón
Keyword(s):  
Computer Experiments ◽  
Efficient Algorithms ◽  
Algebraic Analysis ◽  
Reliability Method

We apply the algebraic reliability method to the analysis of several variants of multi-state k-out-of-n systems. We describe and use the reliability ideals of multi-state consecutive k-out-of-n systems with and without sparse, and show the results of computer experiments on these kinds of systems. We also give an algebraic analysis of weighted multi-state k-out-of-n systems and show that this provides an efficient algorithms for the computation of their reliability.

Efficient algorithms for the algebraic analysis of system reliability

2010 ◽  
Vol 43 (3/4) ◽  
pp. 98-99
Author(s):  
Eduardo Sáenz de Cabezón ◽  
Henry P. Wynn
Keyword(s):  
System Reliability ◽  
Efficient Algorithms ◽  
Algebraic Analysis

PROBABILITY-BASED DESIGN OPTIMIZATION OF DYNAMIC SYSTEMS

2012 ◽  
Vol 19 (01) ◽  
pp. 1250001
Author(s):  
TURUNA S. SEECHARAN ◽  
GORDON J. SAVAGE
Keyword(s):  
Monte Carlo ◽  
Design Optimization ◽  
Performance Measures ◽  
Mechanistic Model ◽  
Limit State ◽  
Computer Experiments ◽  
Specific Information ◽  
Design Variables ◽  
Reliability Method ◽  
Time Invariant

The mechanistic model of a dynamic system is often so complex that it is not conducive to probability-based design optimization. This is so because the common method to evaluate probability is the Monte Carlo method which requires thousands of lifetime simulations to provide probability distributions. This paper presents a methodology that (1) replaces the implicit mechanistic model with a simple explicit model, and (2), transforms the dynamic, probabilistic, problem into a time invariant probability problem. Probabilities may be evaluated by any convenient method, although the first-order reliability method is particularly attractive because of its speed and accuracy. A part of the methodology invokes design of computer experiments and approximating functions. Training sets of the design variables are selected, a few computer experiments are run to produce a matrix of corresponding responses at discrete times, and then the matrix is replaced with a vector of so-called metamodels. Responses at an arbitrary design set and at any time are easily calculated and then used to formulate common, time-invariant, performance measures. Design variables are treated as random variables and limit-state functions are formed in standard normal probability space. Probability-based design is now straightforward and optimization determines the best set of distribution parameters. Systems reliability methods may be invoked for multiple competing performance measures. Further, singular value decomposition may be used to reduce greatly the number of metamodels needed by transforming the response matrix into two smaller matrices: One containing the design variable-specific information and the other the time-specific information. An error analysis is presented. A case study of a servo-control mechanism shows the new methodology provides controllable accuracy and a substantial time reduction when compared to the traditional mechanistic model with Monte Carlo sampling.

Tidal Flow: A Fast and Teachable Maximum Flow Algorithm

2018 ◽  
Vol 12 ◽  
pp. 25-41
Author(s):  
Matthew C. FONTAINE
Keyword(s):  
Computer Science ◽  
Tidal Flow ◽  
Maximum Flow ◽  
Efficient Algorithms ◽  
Science Students ◽  
Flow Algorithm ◽  
Maximum Flows

Among the most interesting problems in competitive programming involve maximum flows. However, efficient algorithms for solving these problems are often difficult for students to understand at an intuitive level. One reason for this difficulty may be a lack of suitable metaphors relating these algorithms to concepts that the students already understand. This paper introduces a novel maximum flow algorithm, Tidal Flow, that is designed to be intuitive to undergraduate andpre-university computer science students.

Efficient Algorithms for the Partial Sum Dispersion Problem

2020 ◽  
Vol E103.A (10) ◽  
pp. 1206-1210
Author(s):  
Toshihiro AKAGI ◽  
Tetsuya ARAKI ◽  
Shin-ichi NAKANO
Keyword(s):  
Efficient Algorithms ◽  
Partial Sum ◽  
Dispersion Problem

Efficient Algorithms for Mining Top-K High Utility Itemsets

2018 ◽  
Vol 6 (7) ◽  
pp. 1274-1280 ◽  
Author(s):  
Ameena Aiman ◽  
Raafiya Gulmeher
Keyword(s):  
Efficient Algorithms ◽  
High Utility ◽  
High Utility Itemsets

Energy-Efficient Algorithms for Distributed File System HDFS

2014 ◽  
Vol 36 (5) ◽  
pp. 1047-1064 ◽  
Author(s):  
Bin LIAO ◽  
Jiong YU ◽  
Tao ZHANG ◽  
Xing-Yao YANG
Keyword(s):  
Energy Efficient ◽  
File System ◽  
Efficient Algorithms ◽  
Distributed File System

Network Energy Consumption Models and Energy Efficient Algorithms

2012 ◽  
Vol 35 (3) ◽  
pp. 603-615 ◽  
Author(s):  
Fa ZHANG ◽  
Antonio Fernandez Anta ◽  
Lin WANG ◽  
Chen-Ying HOU ◽  
Zhi-Yong LIU
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Efficient Algorithms

Waves in Plasma Sheaths and at Boundaries: Theory and Computer Experiments.

1995 ◽  
Author(s):  
Charles K. Birdsall
Keyword(s):  
Computer Experiments ◽  
Plasma Sheaths
Sign in / Sign up

Export Citation Format

Share Document