Random Generation Algorithm Research Articles

Preserving the Number of Cycles of Length $k$ in a Growing Uniform Permutation

The goal of this work is to describe a uniform generation tree for permutations which preserves the number of $k$-cycles between any permutation (except for a small unavoidable subset of optimal size) of the tree and its direct children. Moreover, the tree we describe has the property that if the number of $k$-cycles does not change during any $k$ consecutive levels, then any further random descent will always yield permutations with that same number of $k$-cycles. This specific additional property yields interesting applications for exact sampling. We describe a new random generation algorithm for permutations with a fixed number of $k$-cycles in $n+\mathcal{O}(1)$ expected calls to a random integer sampler. Another application is a combinatorial algorithm for exact sampling from the Poisson distribution with parameter $1/k$.

A coupled Cellular Automaton–Lattice Boltzmann model for grain structure simulation during additive manufacturing

A 2D coupled Cellular Automaton (CA)–Lattice Boltzmann (LB) model has been developed to simulate grain structure evolution during powder–bed–based, layer by layer, additive manufacturing (AM). The presented model includes algorithms for random powder layer generation, electron beam energy absorption, evaporation, capillarity and wetting, meltpool dynamics, its temperature evolution and face centered cubic grain solidification. The model is first validated against the experimental findings of single track electron beam melting and solidification of a baseplate and then applied to simulate the grain structure produced during AM. Influence of the hatching strategy on grain structure is presented as well as stray grain formation resulting from partially molten powder particles.

Heterogeneous Fracture Simulation of Three-point Bending Plain-concrete Beam with Double Notches

Plain concrete is regarded as a two-phase material comprising randomly distributed aggregates and mortar matrix. A series of three-point bending concrete beams with symmetric or asymmetric double notches are modeled using the modified random aggregate generation and packing algorithm. The cohesive zone model is used as the fracture criterion and the cohesive elements are inserted into both the mortar matrix and the aggregate-mortar interfaces as potential micro-cracking zones. The dead and alive crack phenomena are studied experimentally and numerically; and the influences of notch location, aggregate distribution and gradation on fracture are numerically evaluated. Some important conclusions are given.

Automated Transient Input Stimuli Generation for Analog Circuits

We present an automated directed random input stimulus generation algorithm with high coverage for nonlinear analog circuits. Our methodology is able to generate input stimuli to meet two kinds of objectives: 1) to reach user-defined goal regions and 2) increased coverage of state space. The principal benefit of our approach is that it can provide directed input stimulus generation, as opposed to the randomly generated input stimulus by Monte Carlo-based methods. The methodology introduces multiobjective rapidly-exploring random trees (MORRTs), which add a bias and a feedback loop to the standard rapidly-exploring random trees algorithm. The biasing is provided by a statistical inference algorithm. Simultaneous biasing toward goal regions and coverage is possible in MORRT to a user-defined extent. Our methodology generates several input stimuli that are concentrated in the goals or relevant operating regions, while providing high coverage of the state space. We demonstrate the efficiency and scalability of our approach on high-dimensional analog case studies.

An MDE performance testing framework based on random model generation

The scalability of model-related operations (e.g., model transformations), when they are to be applied in industrial model-driven engineering, becomes an important issue. However, there is a lack of an automated performance testing framework for those operations, since the existing ones for ordinary programs are ill-suited. Such a framework is required to provide the function of creating and organizing test cases, and the ability of generating test input of large size automatically, because large scale models are not widely available, making it hard to test the performance and coverage of those operations without any bias. This paper proposes a performance testing framework, integrated with a random model generation algorithm, for model-related operations. The framework, based on a test model, can be used to specify and arrange test cases into test suites. And the model generation algorithm can generate a random model correctly and efficiently, according to the metamodel and user-defined constraints. Finally, we present two case studies, one experiment in randomness, and two experiments in generation efficiency to evaluate the framework and algorithm. Results show that the framework is competent to support performance testing of model-related operations, and the algorithm is random and efficient enough to generate test data for performance testing.

Modeling the thermal radiation properties of thermal barrier coatings based on a random generation algorithm

Thermal barrier coatings (TBCs) are porous media in which many different pores and cracks are induced by different manufacturing procedures. Many studies have been conducted to investigate the impact of microstructures of TBCs on thermal conductivity; nevertheless, the influence of microstructures on the radiative properties of TBCs has not drawn significant attention. In addition, the working condition of thermal barrier coatings is at high temperatures at which the contribution of radiative heat transfer plays a very crucial role. Therefore, it is necessary to study the radiative properties of TBCs to characterize their insulation performance. In this work, the microstructures of air-plasma- sprayed (APS) 8wt% yttria stabilized zirconia (8YSZ) thermal barrier coatings (TBCs) are constructed by the quartet structure generation set (QSGS) algorithm. A finite-difference-time-domain (FDTD) method is carried out to simulate radiative heat transfer through TBCs. Three parameters—average pore size, directional growth probability Di (especially horizontal growth probability D13) and porosity—have been investigated to study the microstructural effect on the radiative properties of TBCs. The reflectance of freestanding 50-μm-thick thermal barrier coatings is studied using Lumerical FDTD Solutions in the wavelength range from 1 to 6μm at normal incidence. The absorption and scattering coefficient as a function of wavelength are extracted using the four-flux model. The results will help us to characterize the radiative heat transfer process across the TBCs and provide us with a theoretical guide to design TBCs with a high thermal insulation property.

Rapid Generation of Particle Packs at High Packing Ratios for DEM Simulations of Granular Compacts

In this work we present a simple, fast technique for generating particle packs at high packing ratios aiming at the simulation of granular compacts via the discrete element method (DEM). We start from a random sequence addition particle generation algorithm to generate a layer of non-overlapping spherical particles that are let to evolve dynamically in time under the action of compacting or jamming pseudo forces. A layer-by-layer approach is then followed to generate multiple layers on top of each other. In the end, very dense packs with pre-defined bulk shapes and sizes (e.g. rectangles in two dimensions and prisms in three dimensions) are achieved. The influence of rolling motion (with particle rotation and spin) along with inter-particle friction on the density and ordering of the generated packs is assessed. Both congruent and inhomogeneous packs (with respect to particle sizes) are created and their packing properties evaluated. We believe that simple techniques for fast generation of particle packs at high packing ratios are essential tools for the DEM simulation of granular compacts.

Investigation of permeability, formation factor, and porosity relationships for Mesaverde tight gas sandstones using random network models

Replicating the pore topology/structure of tight gas reservoirs is essential to model fluid flow through such porous media. Constitutive relationships between the macroscopic properties of the medium can often help with such modeling efforts. Permeability and formation factor are rock properties providing useful information for assessing the potential of hydrocarbon recovery. Pore topology/structure and pore–throat radius distributions are the major factors having influence on permeability and formation factor estimation. A stochastic random generation algorithm is employed to study the effect of pore structure and geometries on the relationships of formation factor–permeability and permeability–porosity on physically realistic 3D random networks. These relationships are derived by constructing two sets of physically equivalent pore networks of tight porous media and are validated using experimental measurements of Mesaverde tight gas sandstones. The first set of networks were based on Berea sand network properties, which are then reduced and derived using a Weibull truncated equation to produce physically sound tight porous media. The second equivalent networks are constructed according to experimentally derived throat size distributions obtained from ambient mercury injection capillary pressure for 17 selected core samples from three Mesaverde tight gas sandstones basins in the U.S. Imperial college Pore-Scale Modeling software is used to model the single liquid flow properties through constructed equivalent networks. The estimated porosity, absolute liquid permeability and formation factor of the constructed physically equivalent networks are in good agreement with measured data obtained by Byrnes et al. (Analysis of critical permeability, capillary and electrical properties for Mesaverde tight gas sandstones from Western US basins: final scientific. Technical report submitted to DOE and NETL 355, 2009). However, a variation between estimated absolute permeability to liquid and measured routine gas permeability is accounted in core samples that have measured permeability smaller than 0.1 mD. Networks based on Berea sand properties show qualitative agreement between modeled data points and experiment data. However, modeled data are off by two orders of magnitude and all fall more or less on the same line.

IMPROVE THE PERFORMANCE OF MPEG VIDEO ENCRYPTION ALGORITHM USING MODIFIED RC4 ALGORITHM BASEDON CHAOTIC MAP

With rapid growth in communication techniques and multimedia application, security is becoming more important for multimedia data storage or transmission. Traditional encryption algorithm such as Advance Encryption Standard (AES) and Data Encryption Standard (DES) are not suitable for video data because it caused large computational overhead and reduce the MPEG compression efficiency. The encryption algorithm that has less computation overhead is needed. The proposed algorithm combines encryption process with MPEG video compression. Some modifications of the RC4 algorithm were proposed in this paper to enhance the performance of video encryption algorithm in terms of encryption time, compression ratio and to provide accepted level of the security. The modification of the RC4 algorithm depends on the maximum value of the plain text, chaotic map and circular shift operation. This modification will reduce the number of iteration in the Key scheduling Algorithm (KSA) and make the Pseudo Random Generator Algorithm (PRGA) depend on initial condition of chaotic maps to provide the strong shuffling inside the PRGA. The random rotation technique based on chaotic map is proposed to increase security of MPEG video. This technique depends on the value generated from chaotic map to rotate the Y blocks in I frame. The experimental results indicate that the proposed method provided better encryption times and provides sufficient level of the security. Thus the proposed method is suitable to protect the MPEG video with minimizing the overhead.

A predictive machine learning approach for microstructure optimization and materials design.

This paper addresses an important materials engineering question: How can one identify the complete space (or as much of it as possible) of microstructures that are theoretically predicted to yield the desired combination of properties demanded by a selected application? We present a problem involving design of magnetoelastic Fe-Ga alloy microstructure for enhanced elastic, plastic and magnetostrictive properties. While theoretical models for computing properties given the microstructure are known for this alloy, inversion of these relationships to obtain microstructures that lead to desired properties is challenging, primarily due to the high dimensionality of microstructure space, multi-objective design requirement and non-uniqueness of solutions. These challenges render traditional search-based optimization methods incompetent in terms of both searching efficiency and result optimality. In this paper, a route to address these challenges using a machine learning methodology is proposed. A systematic framework consisting of random data generation, feature selection and classification algorithms is developed. Experiments with five design problems that involve identification of microstructures that satisfy both linear and nonlinear property constraints show that our framework outperforms traditional optimization methods with the average running time reduced by as much as 80% and with optimality that would not be achieved otherwise.

An economical acceptance–rejection algorithm for uniform random variate generation over constrained simplexes

This paper develops an algorithm for uniform random generation over a constrained simplex, which is the intersection of a standard simplex and a given set. Uniform sampling from constrained simplexes has numerous applications in different fields, such as portfolio optimization, stochastic multi-criteria decision analysis, experimental design with mixtures and decision problems involving discrete joint distributions with imprecise probabilities. The proposed algorithm is developed by combining the acceptance---rejection and conditional methods along with the use of optimization tools. The acceptance rate of the algorithm is analytically compared to that of a crude acceptance---rejection algorithm, which generates points over the simplex and then rejects any points falling outside the intersecting set. Finally, using convex optimization, the setup phase of the algorithm is detailed for the special cases where the intersecting set is a general convex set, a convex set defined by a finite number of convex constraints or a polyhedron.

Experimental and numerical investigation of fracture behavior of asphalt mixture under direct shear loading

Experiments and heterogeneous simulations are combined to investigate cracking behavior of sheared asphalt mixture. A series of direct shear tests are performed at different temperatures with a self-manufactured direct shear test setup, and then a heterogeneous fracture modeling approach based on the random aggregate generation and packing algorithm and the bilinear cohesive crack model is utilized to simulate shear fracture processes. Two kinds of fracture, the single crack type for −20 and −10°C, and the double crack type for 0 and 5°C, are observed. Some internal mechanisms are analyzed based on numerical simulations.

From A to B, randomly: a point-to-point random trajectory generator for animal movement

For applications in animal movement, we propose a random trajectory generator (RTG) algorithm that combines the concepts of random walks, space-time prisms, and the Brownian bridge movement model and is capable of efficiently generating random trajectories between a given origin and a destination point, with the least directional bias possible. Since we provide both a planar and a spherical version of the algorithm, it is suitable for simulating trajectories ranging from the local scale up to the (inter-)continental scale, as exemplified by the movement of migrating birds. The algorithm accounts for physical limitations, including maximum speed and maximum movement time, and provides the user with either single or multiple trajectories as a result. Single trajectories generated by the RTG algorithm can be used as a null model to test hypotheses about movement stimuli, while the multiple trajectories can be used to create a probability density surface akin to Brownian bridges.

Grid Method 기법을 이용한 베이지안 비정상성 확률강수량 산정

A Bayesian nonstationary probability rainfall estimation model using the Grid method is developed. A hierarchical Bayesian framework is consisted with prior and hyper-prior distributions associated with parameters of the Gumbel distribution which is selected for rainfall extreme data. In this study, the Grid method is adopted instead of the Matropolis Hastings algorithm for random number generation since it has advantage that it can provide a thorough sampling of parameter space. This method is good for situations where the best-fit parameter values are not easily inferred a priori, and where there is a high probability of false minima. The developed model was applied to estimated target year probability rainfall using hourly rainfall data of Seoul station from 1973 to 2012. Results demonstrated that the target year estimate using nonstationary assumption is about 5~8% larger than the estimate using stationary assumption.

Efficient and Secure Data Sharing in Cloud Storage by Using Encryption Techniques

Using cloud storage, users can remotely store their data and enjoy the on-demand high-quality applications and services from a shared pool of configurable computing resources, without the burden of local data storage and maintenance. However, the fact that users no longer have physical possession of the outsourced data makes the data integrity protection in cloud computing formidable task, especially for users with constrained computing resources. Moreover, users should be able to just use the cloud storage as if it is local, without worrying about the need to verify its integrity. This paper enables security in cloud using encryption key that is generated while uploaded files by the user. The random key is generated using random key generation algorithm. The user can transfer their files with the permission of the owner. Multi-keyword ranked search over encrypted cloud data establish a set of strict privacy requirements for such a secure cloud data utilization system to become a reality. This provides high security while sharing files in cloud. The user can audit their uploaded files and verify the contents of the uploaded file in the cloud.

Three-dimensional heterogeneous fracture simulation of asphalt mixture under uniaxial tension with cohesive crack model

A three-dimensional (3D) heterogeneous fracture modeling technology is presented to simulate complex crack evolution in quasi-brittle asphalt mixture. In this technology, the random aggregate generation and packing algorithm is employed to create 3D heterogeneous numerical model of asphalt mixture, and the cohesive elements with the tension/shear softening laws are inserted into both the mastic matrix and the aggregate–mastic interfaces as potential cracks. The nucleation and coalescence of microcracks, and inception and propagation of main macrocracks are carefully studied under uniaxial tension and temperature of −10°C. The effects of the averaged coarse aggregate size and the cohesive fracture parameters on performance of asphalt mixture are also evaluated.

Test Pattern Generation with Low Power for Delay Faults in Digital Circuits by Evolution Method with Hybrid Strategies

The high power consumption during circuit test process can produce unwanted failures or take effects on circuit reliability, therefore the reduction of both peak power and average power of circuit test is necessary. A test pattern generation approach is presented in this paper for the delay faults in digital circuits, the approach makes use of the evolution method with the hybrid strategies to produce the test vectors with low power consumption. First of all, a pair of vectors that may detect a delay fault is coded as an individual. A lot of individuals constitute the populations. Secondly, the test vectors with low power are produced by the evolution of these populations. Many new individuals are randomly produced and are added into every evolution step, and the mutation mode of individuals is related to other individuals in the current population. A lot of experimental results show that the test vectors with low power for the delay faults in digital circuits can be produced by the approach proposed in this paper, and the approach can get the large reduction of power consumption when compared with random test generation algorithm.

Geometrical characterization and micro-structural modeling of short steel fiber composites

Short steel fiber reinforced polymer composites are a newly introduced class of materials which combines outstanding properties of stiffness and ductility. The first step in modeling composite mechanical properties is the generation of a representative volume element (RVE) which accurately describes the distinctive microstructure of the material. Injection molded short steel fiber composites exhibit a complex microstructure due to the three-dimensional orientation, waviness and entanglements of the fibers. The present work proposes a data structure for description of the RVE geometry and algorithms for RVE random generation. The micro-structure of short steel fiber reinforced polycarbonate samples was determined using X-ray micro-computed tomography (micro-CT) and the model input parameters were identified using a specialized image processing methodology, based on the Mimics software package. Model generated RVEs were satisfactory compared qualitatively as well as quantitatively against the real micro-CT reconstructed volumes.

The Design of Software Protection Mechanism Based on Virtual Machine

Software security is facing enormous challenges with the rapid development of attacking technology.Obsolete software protection methods can no longer meet the needs of modern software security. In this paper,we proposed a software protection mechanism based on virtual machine which can compile the x86 assembly instructions into virtual instructions.The execution of instructions will be completed in the virtue machine so this mechanism can resist the attack of reverse analysis,meanwhile, the rights of software developers and intellectual property will be protected. furthermore, we design a random instruction generation algorithm to make the code of instructions haphazard, so that the protective ability of VM can be enhanced. We use some experiment to illustrate its superiority and the experimental results indicated that the protective effect of the proposed mechanism is excellent.

Fracture simulation of pre-cracked heterogeneous asphalt mixture beam with movable three-point bending load

The random aggregate generation and packing algorithm is employed to model pre-cracked three-point bending beams of asphalt mixture which is treated as a two-phase material comprising randomly distributed coarse aggregates and asphalt mastic, and the cohesive elements are inserted inside mastic and into aggregate-mastic interfaces to simulate crack initiation and propagation. The mode I and mixed mode cracking behaviors are simulated through changing the beam location in the loading setup. The predicted results compare very well with the experimental ones, and then the influences of crack location and coarse aggregate distribution on cracking behavior are evaluated.