A Monte Carlo sampling method for drawing representative samples from large databases

Purpose A “spin-diode” is the spintronics equivalent of an electrical diode: applying an external magnetic field greater than the limit of spin-diode BT flips the spin-diode between an isolating state and a conducting state [1]. While conventional electrical diodes are two-terminal devices with electrical current between the two terminals modulated by an electrical field, these two-terminal magneto resistive devices can generally be referred to as “spin-diodes” in which a magnetic field modulates the electrical current between the two terminals. Design/methodology/approach Current modulation and rectification are an important subject of electronics as well as spintronics spin diode is two-terminal magnetoresistive devices in which change in resistance in response to an applied magnetic field; this magnetoresistance occurs due to a variety of phenomena and with varying magnitudes and directions. Findings In this paper, an efficient rectifying spin diode is introduced. The resulting spin diode is formed from graphene gallium and indium quantum dots and antimony-doped molybdenum disulfide. Converting an alternating bias voltage to direct current is the main achievement of this model device with an additional profit of rectified spin-current. The non-equilibrium density functional theory with a Monte Carlo sampling method is used to evaluate the flow of electrons and rectification ratio of the system. Originality/value The results indicate that spin diode displaying both spin-current and charge-current rectification should be possible and may find practical application in nanoscale devices that combine logic and memory functions.

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Capacity Expansion Problem by Monte Carlo Sampling Method

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2009.p0697 ◽

2009 ◽

Vol 13 (6) ◽

pp. 697-703

Author(s):

Takayuki Shiina ◽

Keyword(s):

Monte Carlo ◽

Variance Reduction ◽

Sampling Method ◽

Sampling Technique ◽

Capacity Expansion ◽

Monte Carlo Sampling ◽

Previous Approach ◽

Importance Sampling Technique ◽

Stochastic Vector ◽

Additive Form

We consider the stochastic programming problem with recourse in which the expectation of the recourse function requires a large number of function evaluations, and its application to the capacity expansion problem. We propose an algorithm which combines an L-shaped method and a Monte Carlo method. The importance sampling technique is applied to obtain variance reduction. In the previous approach, the recourse function is approximated as an additive form in which the function is separable in the components of the stochastic vector. In our approach, the approximate additive form of the recourse function is perturbed to define the new density function. Numerical results for the capacity expansion problem are presented.

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Accurate Loop Generation of Protein Structures using Distance-Guided Sequential Monte Carlo Sampling Method

Biophysical Journal ◽

10.1016/j.bpj.2009.12.2509 ◽

2010 ◽

Vol 98 (3) ◽

pp. 462a

Author(s):

Ke Tang ◽

Jinfeng Zhang ◽

Jian Zhang ◽

Jie Liang

Keyword(s):

Monte Carlo ◽

Sampling Method ◽

Sequential Monte Carlo ◽

Protein Structures ◽

Monte Carlo Sampling

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Study on the uncertainty of the available time under ship fire based on Monte Carlo sampling method

China Ocean Engineering ◽

10.1007/s13344-013-0012-1 ◽

2013 ◽

Vol 27 (1) ◽

pp. 131-140 ◽

Cited By ~ 8

Author(s):

Jin-hui Wang ◽

Guan-quan Chu ◽

Kai-yuan Li

Keyword(s):

Monte Carlo ◽

Sampling Method ◽

Monte Carlo Sampling

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Monte Carlo Sampling Method for a Class of Box-Constrained Stochastic Variational Inequality Problems

Mathematical Problems in Engineering ◽

10.1155/2021/6618180 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Pei-Yu Li

Keyword(s):

Monte Carlo ◽

Variational Inequality ◽

Optimization Problem ◽

Sampling Method ◽

Monte Carlo Sampling ◽

Merit Function ◽

Variational Inequality Problems ◽

Sufficient Condition ◽

Existence Of A Solution ◽

Stochastic Variational Inequality

This paper uses a merit function derived from the Fishcher–Burmeister function and formulates box-constrained stochastic variational inequality problems as an optimization problem that minimizes this merit function. A sufficient condition for the existence of a solution to the optimization problem is suggested. Finally, this paper proposes a Monte Carlo sampling method for solving the problem. Under some moderate conditions, comprehensive convergence analysis is included as well.

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