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Scope and Application of VANET
Published in Sonali P. Botkar, Sachin P. Godse, Parikshit N. Mahalle, Gitanjali R. Shinde, VANET, 2021
Sonali P. Botkar, Sachin P. Godse, Parikshit N. Mahalle, Gitanjali R. Shinde
Random number generator: Random number generator (RNG) is used to generate randomness in a simulation model. It is generated by sequentially taking numbers from a deterministic sequence of pseudo-random number. Number selected from the sequence is appeared to be random. In some cases, pseudo-random sequence is predefined and used by every RNG. In some cases, RNG takes number from different locations of pseudo-random sequence. Location is called as seed. The actual implementation of RNG is initialized with seed. A seed identifies the starting location in a pseudo-random sequence from which RNG starts to pick numbers. In different simulations, seeds are different and thus generate different results. Consider an ideal example as computer network simulation, where packet arrival process, waiting process, and service process are usually modeled as random processes. A random process is expressed by sequences of random variables. These random processes are usually implemented with the aid of an RNG. for a comprehensive treatment on random process implementation (e.g., those having the uniform, exponential, Gaussian, Poisson, binomial distribution functions).
Semi-Formal Design Verification
Published in Chris Hobbs, Embedded Software Development for Safety-Critical Systems, 2019
Rather than genuinely random numbers, pseudo-random numbers are therefore generally used. A well-known quotation from reference [3] by John von Neumannvon Neumann, John is that “Anyone who considers arithmetical methods of producing random digits is, of course, in a state of sin. For, as has been pointed out several times, there is no such thing as a random number — there are only methods to produce random numbers, and a strict arithmetic procedure of course is not such a method.”†Pseudo-random numbers are generated deterministically from an original “seed.” Given the same initial seed, the same sequence of pseudo-random numbers will be generated.
Semi-Formal Design Verification
Published in Chris Hobbs, Embedded Software Development for Safety-Critical Systems, 2017
Rather than genuinely random numbers, pseudo-random numbers are therefore generally used. A well-known quotation from reference [3] by John von Neumann is that “Anyone who considers arithmetical methods of producing random digits is, of course, in a state of sin. For, as has been pointed out several times, there is no such thing as a random number — there are only methods to produce random numbers, and a strict arithmetic procedure of course is not such a method.”* Pseudo-random numbers are generated deterministically from an original “seed.” Given the same seed twice, the same sequence of pseudo-random numbers will be generated.
A two-level strategy for aeroelastic optimization of a 3D wing with constant and variable stiffness skins
Published in Engineering Optimization, 2023
The particle in PSO is a set of design variables defined as an N-dimensional vector, where N is the number of design variables. The particle updates are given based on two equations, written here in a format defined by Bratton and Kennedy (2007): where is the position of particle j, is its velocity, all given in steps i and i+1. Two random numbers and are considered along with an inertia term λ and two trust parameters and . The velocity depends on the best value found for a certain particle () and the overall best (). The search is grouped into a swarm of an arbitrary number of particles, . The process was implemented in C++, and used a pseudo-random number generator to chose the initial PSO points through the function rand, given by the cstdlib library. The random number generation seed is calculated based on the time difference between the program's execution and the clock's epoch, ensuring that it changes with each run.
On the use of common random numbers in activity-based travel demand modeling for scenario comparison
Published in Transportation Planning and Technology, 2023
H. Zhou, J. L. Dorsman, M. Mandjes, M. Snelder
To apply CRN in this additional ABM component, one needs to make sure that across scenarios the same error samples are used for every traveler/mode combination and every traveler/mode/trip combination respectively. To make this happen, we use the notion of initial seeds. Every time the same initial seed is set in a random number generator (RNG), it will generate the same sequence of random numbers. Therefore, incorporating CRN for traveler/mode combinations errors can be done by associating with each traveler a seed. Then, every time a different scenario is considered, the RNG will still generate the same traveler/mode errors, independent of the actual scenario. For the traveler/mode/trip errors, this can be done at a trip level: we associate with each trip a seed, so that each time the trip is considered, the same traveler/model/trip errors are computed. This way, the errors between scenarios are maximally synchronized. Furthermore, this strategy has the additional computational advantage that, when a trip is not undertaken in a certain scenario, the required traveler/mode/trip errors will not be generated either, saving computation time.
Neutronics Calculation Advances at Los Alamos: Manhattan Project to Monte Carlo
Published in Nuclear Technology, 2021
Avneet Sood, R. Arthur Forster, B. J. Archer, R. C. Little
The big improvement in RNGs was the Linear Congruential Generator (LCG) that first appeared in 1951 (Ref. 69) and was later improved in 1958 (Ref. 70). LCGs are the best known and most studied RNGs. Each random number is calculated based on a recurrence relation involving the previous random number. The initial random number is called the seed. LCGs have many desirable characteristics for Monte Carlo calculations.60 They have been extensively inspected and tested. They are repeatable, have long and known periods, are robust, require little computer memory, and are computationally very efficient. It is easy to skip ahead to any location in the sequence so that the i’th source particle always starts with the same sequence for the specified LCG seed. This feature enables correlated sampling comparisons between similar calculations and is crucial for performing parallel Monte Carlo calculations. For these reasons, the LCG is used in many Monte Carlo radiation transport codes, including the MCNP code.