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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).
The Modeling Aspect of Simulation
Published in William Delaney, Erminia Vaccari, Dynamic Models and Discrete Event Simulation, 2020
William Delaney, Erminia Vaccari
As we saw in Chapter 4, real sampling is used when we want to estimate some parameter of the unknown distribution of an rv or the distribution itself. Instead, we use simulated sampling when we know the probability distribution of the rv and want to generate single observations with the aim of getting a sample that represents the population. Thus the basic problem of simulated sampling is how to select a sequence of random numbers that are distributed according to a given pdf. In the case of a uniform distribution, several methods are available to generate random numbers; the important criteria for the selection of a method are that it must be statistically reliable, reproducible, and capable of being implemented efficiently on a computer. The problem of efficiency and reproducibility led to the development of methods for the generation of pseudorandom numbers
Symmetric Algorithms I
Published in Khaleel Ahmad, M. N. Doja, Nur Izura Udzir, Manu Pratap Singh, Emerging Security Algorithms and Techniques, 2019
Faheem Syeed Masoodi, Mohammad Ubaidullah Bokhari
Random number generator is considered as one of the primary building blocks for cryptographic system essentially for the logic that they are quite unpredictable for potential attackers. Uniformly distributed random numbers are pivotal to cryptographic applications, and even though the values generated by random processes are very unpredictable, they lack uniform distribution (Random.org, 2018). Pseudorandom number generators (PRNGs) generate sequences commonly known as pseudorandom numbers by starting with a short random seed value and then recursively expand it into a large set of random-looking sequences. It is important to mention here that PRNGS are completely deterministic in nature, implying that if the generator is put back into the same state, the output sequence will be a replica of the earlier generated sequence (Van Tilborg, 2005). One of the primary requirements of these pseudorandom numbers is that they carry good statistical properties and a reasonable number of mathematical tests verify their statistical behavior. Owing to their good statistical properties, PRNGs are widely used in stream ciphers (Goldreich, 1998). A good PRNG must carry statistical properties that resemble the properties of a true random number generator, and the size of the seed value must be long enough to resist exhaustive key search attack.
A mathematically-based study of the random wheel-rail contact irregularity by wheel out-of-roundness
Published in Vehicle System Dynamics, 2022
The aim of the present work is conducting a mathematically-based prediction study to transform the geometric irregularities induced by wheel out-of-roundness (wheel flat, tread spalling and wheel polygonization) to the randomly irregular displacement excitation of the wheel-rail contact, which can be served as initial excitations for the three-dimensional wheel-rail rolling contact finite element analysis, to overcome those difficulties in the direct geometry modelling as stated above. The randomisation of the size, shape and distribution of wheel tread defects satisfied to several various random distribution functions is generated by using a computer-generated pseudo-random number approach [34]. The content of this study includes four main aspects: (i) the formula derivation of wheel-rail contact displacement irregularity caused by various tread defects based on geometrical relationships; (ii) the introduce and choice of a computer-generated pseudo-random number approach to generate random sequence; (iii) the generation of the randomly irregular wheel-rail displacement satisfied various random distribution functions; and (iv) validation of the feasibility and effectiveness of the proposed method based on a FEM-based wheel-rail simulation example.
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
When electronic digital computers became available, a different method for “looking up” random numbers could be used. Deterministic algorithms were devised that generated a sequence of pseudo random numbers. The term “pseudo” indicates that the deterministic algorithm can repeat the sequence, and therefore, the random numbers are not truly random: they imitate randomness. In this paper, the term “random number” means pseudo random number. Tests for randomness—no observed patterns or regularities—were developed to assess how random these deterministic sequences appeared to be.
Assessment of rock mass erosion in unlined spillways using developed vulnerability and fragility functions
Published in Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 2020
Ali Saeidi, Esmaeil Eslami, Marco Quirion, Mahdiyeh Seifaddini
Within this methodology, regardless of the chosen rock mass erodibility method, the process of computing fragility and vulnerability curves always begins by generating populations of several series of random numbers (example n = 1000) through Monte Carlo simulation. We developed a MATLAB-based programme to apply the adapted Monte Carlo method. We used the Mersenne Twister algorithm (Matsumoto and Nishimura 1998) to generate pseudorandom numbers that have passed numerous randomness tests, among them includes the strict Diehard test (Marsaglia 1995). The statistical properties of the random numbers are also important; as such, we tested them by verifying that the integral deviation calculated using the Monte Carlo algorithm drops by 1/N from its true value. All computations of random numbers rely on either a uniform or normal distribution algorithm within a distinct interval according to the rock mass erodibility class. Two important methods in the domain, i.e. the Van Schalkwyk, Jordaan, and Dooge (1994) and Pells et al. (2016, 2017) approaches, are used to present the methodology and to perform a sensitivity analysis. For the remainder of this paper, mention of the Van Schalkwyk et al. and Pells et al. (2016) methods refer respectively to these aforementioned papers. A database of n = 1000 random cases can then be applied to one of the existing rock mass classes-based on the chosen method-to evaluate the damage for one value of the intensity criterion-stream power dissipation. In regard to erosion level, Van Schalkwyk, Jordaan, and Dooge (1994) stated that categorising the erosion condition into multiple classes, rather than two classes-scour and no-scour-provided more accurate predictions of erosion risk. Consequently, we propose multiple scour thresholds (erosion levels) as a function of erosion depth (Table 1). Four damage grades are identified: negligible erosion, minor erosion, moderate erosion, and large erosion. The erosion condition in Pells’s approach is based on the maximum depth and extension of the eroded gully based on the five classes presented in Table 1. The class of the Van Schalkwyk’s (1994) method discretizes to the two classes to , the large and extensive damage classes of Pells et al. (2016). For developing the vulnerability functions, the same damage categories are used for each method.