Stochastic Random Walk Algorithm for Predicting Fatal Events in Multi-component Systems

Filipov, Stefan M.; Tomova, Fani N.; Atanassov, Atanas; Panov, Stefan

doi:10.1109/icai50593.2020.9311329

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…One such method for finding absolute target meeting tests, called simulated annealing, uses importance Monte Carlo sampling [14]. This is a stochastic method [15] that uses parameters T and E akin to temperature and energy in the Monte Carlo molecular simulations [16], [17], [18]. We propose to use this method for finding target exceeding tests.…”

Section:  mentioning

confidence: 99%

Exceeding Information Targets in Fixed-Form Test Assembly

Gospodinov

Emira

Filipov

2021

2021 International Conference Automatics and Informatics (ICAI)

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This work studies the assembly of ability estimation test forms (called tests, for short) drawn from an item bank. The goal of fixed-from test assembly is to generate a large number of different tests with information functions that meet a target information function. Thus, every test has the same ability estimation error. This work proposes a new way of test assembly, namely, drawing tests with information functions that exceed the target. This guarantees that every test has an ability estimation error that is less than the error set by the target. The work estimates the number of target exceeding tests as a function of the number of items in the test. It demonstrates that the number of target exceeding tests is far greater than the number of target meeting tests. A Monte Carlo importance sampling algorithm is proposed for target exceeding test assembly.

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Section:  mentioning

confidence: 99%

Exceeding Information Targets in Fixed-Form Test Assembly

Gospodinov

Emira

Filipov

2021

2021 International Conference Automatics and Informatics (ICAI)

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“…To solve the optimization problem, a stochastic algorithm [13] based on the simulated annealing technique is proposed [14 -20]. The simulated annealing is an importance sampling Monte Carlo method [21 -23].…”

Section: Introductionmentioning

confidence: 99%

Covering a Maximum Number of Points by a Fixed Number of Equal Disks via Simulated Annealing

Tomova,

Filipov,

Avdzhieva

2024

JCTM

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The presented paper considers the problem of covering a maximum number of n given points in the plane by m equal disks of radius r. A point is covered if it is inside one or more than one disk. The disks need to be placed in the plane in such a way that a maximum number of points are covered. To solve the problem, an objective function, called energy, is introduced in such a way that the greater the covering is, the lower the energy is. Thus, a configuration of disks with minimum energy is a configuration with maximum covering. To find a configuration of disks that minimizes the energy, a stochastic algorithm based on the Monte Carlo simulated annealing technique is proposed. The algorithm overcomes potential local minima, which, as shown in the paper, are quite likely to occur. The computational complexity of the algorithm is O(mn). The algorithm is tested on several cases demonstrating its efficiency in finding global minima of the energy, i.e. configurations with maximum covering.

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