scholarly journals Difusi Bebas 1D dan 2D dengan Monte Carlo: Perbandingan Distribusi Bilangan Random Normal dan Seragam dengan Box-Müller

2021 ◽  
Vol 9 (1) ◽  
pp. 55-64
Author(s):  
Fairusy Fitria Haryani ◽  
◽  
Freddy Haryanto ◽  
Sparisoma Viridi ◽  
◽  
...  
Keyword(s):  
Monte Carlo ◽  
Random Number ◽  
Particle Distribution ◽  
Random Number Generator ◽  
Particle Interaction ◽  
Number Generator ◽  
Random Number Generators ◽  
High Concentrations ◽  
The Impact ◽  
Mc Method

Many biological processes in the human body are based on the diffusion system. Diffusion is defined as a process of random movement of the particle whose the direction is from high concentrations to low concentrations. Many of various study of diffusion have been done both experimentally and computationally. Because the particle interaction is stochastic, the Monte Carlo (MC) method is used in performing particle simulations. The main of MC method is the use of random numbers. Many software have provided uniform random number generators. But based on the analytic results, the solution is normal distribution. Therefore, Box-Müller can be used as a transformation of particle distribution. The software used, MATLAB, has a normal random generator. Therefore, the aims of this study is comparing particle distribution of these two different random number generator with MATLAB and showing the impact of timestep parameter to these random number generator. This result can be used as based for the modelling of more complex biological systems.

scholarly journals ERRORS IN MONTE CARLO SIMULATIONS USING SHIFT REGISTER RANDOM NUMBER GENERATORS

1996 ◽  
Vol 06 (06) ◽  
pp. 781-787 ◽  
Author(s):  
F. SCHMID ◽  
N. B. WILDING
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Random Number ◽  
Random Number Generator ◽  
Shift Register ◽  
Magnetization Distribution ◽  
Number Generator ◽  
Random Number Generators ◽  
Single Spin ◽  
System Volume

We report large systematic errors in Monte Carlo simulations of the tricritical Blume–Capel model using single spin Metropolis updating. The error, manifest as a 20% asymmetry in the magnetization distribution, is traced to the interplay between strong triplet correlations in the shift register random number generator and the large tricritical clusters. The effect of these correlations is visible only when the system volume is a multiple of the random number generator lag parameter. No such effects are observed in related models.

Analysis of Distributed Token Circulation Algorithm with Faulty Random Number Generator

2014 ◽  
Vol 24 (01) ◽  
pp. 1450002
Author(s):  
Shinji Kawai ◽  
Fukuhito Ooshita ◽  
Hirotsugu Kakugawa ◽  
Toshimitsu Masuzawa
Keyword(s):  
Distributed Computing ◽  
Random Number ◽  
Random Number Generator ◽  
Convergence Time ◽  
Number Generator ◽  
Ring Networks ◽  
Random Number Generators ◽  
New Class ◽  
Token Circulation ◽  
The Impact

Randomization is a technique to improve efficiency and computability of distributed computing. In this paper, we investigate fault tolerance of distributed computing against faults of random number generators. We introduce an RNG (Random Number Generator)-fault as a new class of faults; a random number generator on an RNG-faulty process outputs the same number deterministically. This paper is the first work that considers faults of randomness in distributed computing. We investigate the role of randomization by observing the impact of RNG-faults on performance of a self-stabilizing token circulation algorithm on unidirectional n-node ring networks. In the analysis, we assume there exist nf (0 ≤ nf ≤ n−1) RNG-faulty nodes and each RNG-faulty node always transfers a token to the next node. Our results are threefold: (1) We derive the upper bound on the expected convergence time in the case of nf = n − 1. (2) Our simulation result shows that the expected convergence time is maximum when nf = n − 1. (3) We derive the expected token circulation time for each nf (0 ≤ nf ≤ n − 1).

scholarly journals A Gaussian-Distributed Quantum Random Number Generator Using Vacuum Shot Noise

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 618 ◽  
Author(s):  
Min Huang ◽  
Ziyang Chen ◽  
Yichen Zhang ◽  
Hong Guo
Keyword(s):  
Shot Noise ◽  
Distribution System ◽  
Random Number ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Continuous Variable ◽  
Number Generator ◽  
Sampling Device ◽  
Random Number Generators ◽  
The Impact

Among all the methods of extracting randomness, quantum random number generators are promising for their genuine randomness. However, existing quantum random number generator schemes aim at generating sequences with a uniform distribution, which may not meet the requirements of specific applications such as a continuous-variable quantum key distribution system. In this paper, we demonstrate a practical quantum random number generation scheme directly generating Gaussian distributed random sequences based on measuring vacuum shot noise. Particularly, the impact of the sampling device in the practical system is analyzed. Furthermore, a related post-processing method, which maintains the fine distribution and autocorrelation properties of raw data, is exploited to extend the precision of generated Gaussian distributed random numbers to over 20 bits, making the sequences possible to be utilized by the following system with requiring high precision numbers. Finally, the results of normality and randomness tests prove that the generated sequences satisfy Gaussian distribution and can pass the randomness testing well.

SRAM Based Random Number Generator for Non-Repeating Pattern Generation

2014 ◽  
Vol 573 ◽  
pp. 181-186 ◽  
Author(s):  
G.P. Ramesh ◽  
A. Rajan
Keyword(s):  
Random Number ◽  
Statistical Tests ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Pattern Generation ◽  
Storage Device ◽  
Number Generator ◽  
Random Number Generators ◽  
Field Programmable ◽  
The Look

—Field-programmable gate array (FPGA) optimized random number generators (RNGs) are more resource-efficient than software-optimized RNGs because they can take advantage of bitwise operations and FPGA-specific features. A random number generator (RNG) is a computational or physical device designed to generate a sequence of numbers or symbols that lack any pattern, i.e. appear random. The many applications of randomness have led to the development of several different methods for generating random data. Several computational methods for random number generation exist, but often fall short of the goal of true randomness though they may meet, with varying success, some of the statistical tests for randomness intended to measure how unpredictable their results are (that is, to what degree their patterns are discernible).LUT-SR Family of Uniform Random Number Generators are able to handle randomness only based on seeds that is loaded in the look up table. To make random generation efficient, we propose new approach based on SRAM storage device.Keywords: RNG, LFSR, SRAM

A Modification to the Monte Carlo Method—The Exodus Method

1968 ◽  
Vol 90 (3) ◽  
pp. 328-332 ◽  
Author(s):  
A. F. Emery ◽  
W. W. Carson
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Random Number ◽  
Random Number Generator ◽  
Number Generator ◽  
Calculation Time ◽  
The Monte Carlo Method

A modification to the Monte Carlo method is described which reduces calculation time and improves the accuracy. This method—termed “Exodus”—is not dependent upon a random number generator and may be applied to any problem which admits of a nodal network.

scholarly journals A new computer-controlled platform for ADC-based true random number generator and its applications

2019 ◽  
pp. 847-860 ◽  
Author(s):  
SELÇUK COŞKUN ◽  
İHSAN PEHLİVAN ◽  
AKİF AKGÜL ◽  
BİLAL GÜREVİN
Keyword(s):  
Random Number ◽  
Random Number Generator ◽  
Digital Data ◽  
Pseudorandom Number Generator ◽  
Number Generator ◽  
Random Number Generators ◽  
True Random Number Generator ◽  
Sample Application ◽  
Computer Controlled ◽  
Entropy Source

The basis of encryption techniques is random number generators (RNGs). The application areas of cryptology are increasing in number due to continuously developing technology, so the need for RNGs is increasing rapidly, too. RNGs can be divided into two categories as pseudorandom number generator (PRNGs) and true random number generator (TRNGs). TRNGs are systems that use unpredictable and uncontrollable entropy sources and generate random numbers. During the design of TRNGs, while analog signals belonging to the used entropy sources are being converted to digital data, generally comparators, flip-flops, Schmitt triggers, and ADCs are used. In this study, a computer-controlled new and flexible platform to find the most appropriate system parameters in ADC-based TRNG designs is designed and realized. As a sample application with this new platform, six different TRNGs that use three different outputs of Zhongtang, which is a continuous time chaotic system, as an entropy source are designed. Random number series generated with the six designed TRNGs are put through the NIST800–22 test, which has the internationally highest standards, and they pass all tests. With the help of the new platform designed, ADC-based high-quality TRNGs can be developed fast and also without the need for expertise. The platform has been designed to decide which entropy source and parameter are better by comparing them before complex embedded TRNG designs. In addition, this platform can be used for educational purposes to explain how to work an ADC-based TRNG. That is why it can be utilized as an experiment set in engineering education, as well.

A new random-number generator for multispin Monte Carlo algorithms

1987 ◽  
Vol 48 (1-2) ◽  
pp. 135-149 ◽  
Author(s):  
L. Pierre ◽  
T. Giamarchi ◽  
H. J. Schulz
Keyword(s):  
Monte Carlo ◽  
Random Number ◽  
Random Number Generator ◽  
Number Generator ◽  
Monte Carlo Algorithms

scholarly journals FULL QCD ON APE100 MACHINES

1995 ◽  
Vol 06 (01) ◽  
pp. 25-45
Author(s):  
STEFANO ANTONELLI ◽  
MARCO BELLACCI ◽  
ANDREA DONINI ◽  
RENATA SARNO
Keyword(s):  
Monte Carlo ◽  
Random Number ◽  
Random Number Generator ◽  
Memory Capacity ◽  
Monte Carlo Algorithm ◽  
Number Generator ◽  
Inversion Algorithm ◽  
Hybrid Monte Carlo ◽  
Pure Gauge ◽  
Wilson Fermions

We present the first tests and results from a study of QCD with two flavours of dynamical Wilson fermions using the Hybrid Monte Carlo Algorithm (HMCA) on APE100 machines. The simulations have been performed on 64 lattice for the pure gauge HMCA and on 84, 123×32 lattices for full QCD configurations. We discuss the inversion algorithm for the fermionic operator, the methods used to overcome the problems arising using a 32 bit machine and the implementation of a new random number generator for APE100 machines. We propose different scenarios for the simulation of physical observables, with respect to the memory capacity and speed of different APE100 configurations.

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