Random Generation Algorithm Research Articles

Effect of aggregate gradation and mortar rheology on static segregation of self-compacting concrete

Due to the high flowability of self-compacting concrete (SCC), compared with ordinary concrete, it is more prone to have stability problems and segregation. In order to study the segregation of SCC, a random irregular aggregate generation algorithm is developed to simulate the actual aggregate gradation, and a fluid-particle coupling method based on immersed boundary method (IBM) is proposed to simulate the rheological behavior of SCC. To verify the accuracy of the simulation method, a validation experiment is carried out, by comparing the results of the experiment and the simulation, it can be found that the numerical simulation method is reasonable and feasible. The influences of aggregates and mortar on the SCC static segregation are systematically studied by comparing the simulation results of different aggregate gradation and mortar rheological properties. The stress generated by gravity and buoyancy of large aggregates can easily overcome the mortar yield stress and settle in the mortar when the large aggregate content is higher and the mortar yield stress is lower. Then SCC is more prone to segregation while mortar viscosity only affects the time required to achieve stability and does not affect the static segregation results. Therefore, by adjusting aggregate gradation and mortar rheology, SCC can reach a relatively stable state, which has a guiding significance for the practical preparation of SCC in preventing its segregation.

A two-dimensional random aggregate structure generation method: Determining effective thermo-mechanical properties of asphalt concrete

This study presents a 2D random aggregate structure generation algorithm for asphalt concrete. It involves two steps: first, the “convex-concave surface method” is proposed to generate random aggregate particles; second, the “double background grid method” is developed to place the generated aggregates and form the asphalt concrete mesostructure. The convex-concave surface method can produce aggregate particles randomly with both irregular convex and concave surfaces, enabling the generated aggregates to look realistic. Very few parameters are required during the particle generation, significantly simplifying the operation of input parameters. The double background grid method employs two types of background grids: the coarse grids are used to determine the placing position of the particle, whereas the fine grids are used to inspect if the particle overlaps with the placed ones; in this way, it effectively improves the placing efficiency. Besides, it allows for a predefined minimum gap between the adjacent particles, successfully guaranteeing the quality of finite element meshing. Based on the developed 2D random aggregate structure of asphalt concrete, a unified computational methodology for determining the effective thermo-mechanical properties is presented. The effectiveness of these methods is demonstrated using experimental data and typical values reported in the literature.

Automatic “Ground Truth” Annotation and Industrial Workpiece Dataset Generation for Deep Learning

In industry, it is becoming common to detect and recognize industrial workpieces using deep learning methods. In this field, the lack of datasets is a big problem, and collecting and annotating datasets in this field is very labor intensive. The researchers need to perform dataset annotation if a dataset is generated by themselves. It is also one of the restrictive factors that the current method based on deep learning cannot expand well. At present, there are very few workpiece datasets for industrial fields, and the existing datasets are generated from ideal workpiece computer aided design (CAD) models, for which few actual workpiece images were collected and utilized. We propose an automatic industrial workpiece dataset generation method and an automatic ground truth annotation method. Included in our methods are three algorithms that we proposed: a point cloud based spatial plane segmentation algorithm to segment the workpieces in the real scene and to obtain the annotation information of the workpieces in the images captured in the real scene; a random multiple workpiece generation algorithm to generate abundant composition datasets with random rotation workpiece angles and positions; and a tangent vector based contour tracking and completion algorithm to get improved contour images. With our procedures, annotation information can be obtained using the algorithms proposed in this paper. Upon completion of the annotation process, a json format file is generated. Faster R-CNN (Faster R-convolutional neural network), SSD (single shot multibox detector) and YOLO (you only look once: unified, real-time object detection) are trained using the datasets proposed in this paper. The experimental results show the effectiveness and integrity of this dataset generation and annotation method.

Nonadditivity Index Based Quasi-Random Generation of Capacities and Its Application in Comprehensive Decision Aiding

The capacity is a powerful tool with exponential coefficients to represent the interaction phenomenon among decision criteria, but its random generation becomes a tough issue for dealing with the monotonicity with all inclusion subsets as well as the complex constraints of decision preference. In this paper, we adopt a kind of explicit interaction index, the nonadditivity index, to construct two types of quasi-random generation methods of capacity under a given decision interaction preference. Compared to the existing random generation algorithms, the methods have relatively satisfactory performance on the statistics characteristic of generated capacities but need rather less calculation effort on the generation process. We also show the effectiveness of proposed quasi-random generation methods by an illustrative decision example.

A Practical Approach to Testing Random Number Generators in Computer Algebra Systems

This paper has a practical aim. For a long time, implementations of pseudorandom number generators in standard libraries of programming languages had poor quality. The situation started to improve only recently. Up to now, a large number of libraries and weakly supported mathematical packages use outdated algorithms for random number generation. Four modern sets of statistical tests that can be used for verifying random number generators are described. It is proposed to use command line utilities, which makes it possible to avoid low-level programming in such languages as C or C++. Only free open source systems are considered.

A Dial-a-Ride Problem Applied to Saharan Countries: The Case of Taxi Woro-Woro

In a context of increasing competition and sustainable development, service prices and unused capacity enhancement play a crucial role in freight or people transportation management. The focus of the paper is on a Dial-a-ride Problem in the Saharan country context, for the particular of Taxi “woro-woro”1. More precisely, these taxis help to transport groups of people without any affinity, from point A to point B without stopping. Also, we propose for this problem an exact solution based on a mix integer program (MIP). A secondary study on a random instance generation algorithm is presented, which allows us to have a diversified and varied benchmark on which to apply our MIP program.

SPAD-Based Quantum Random Number Generator With an $N^{\rm{th}}$ -Order Rank Algorithm on FPGA

We present a compact, all-solid-state, low-cost quantum random number generator (QRNG) based on a single-photon avalanche diode (SPAD) and a field programmable gate array (FPGA). A new algorithm for random bit generation is described, ranking the inter-arrival times of a group of $M$ photons detected by the SPAD device, and processed directly on the FPGA. The proposed approach improves the efficiency of generated random bits per detected photon, spanning from 0.5 bits/photon in case of 0 order rank, up to 0.875 bits/photon for second order rank. By extending the algorithm to higher orders, the proposed system approaches the maximum theoretical value of 1.0 bit/photon. The rate of generation of random numbers is limited by the SPAD minimum deadtime, achieving an experimentally proven bit rate of 7.3 Mbps. The standard randomness statistical tests are passed for a wide range of photon fluxes and for all the implemented rank orders with no additional post-processing on the generated sequence.

SO–CNN based urban functional zone fine division with VHR remote sensing image

Functional zone reflects city's spatial structures, and as a carrier of social and economic activities, it is of critical significance to urban management, resource allocation and planning. However, most researches on functional zone division are based on a large spatial scale such as blocks or other scales larger than it. Aiming at a subtle fine functional result, the concept of Super Object (SO) was especially explained, also a Super Object - Convolutional Neural Network (SO–CNN) based urban functional zone fine division method with very high resolution (VHR) remote sensing image was proposed. The original image was firstly segmented into different SOs which correspond to the basic functional zone units in geography. A random point generation algorithm was used to generate the voting points for functional zone category identification, and then a trained CNN model was employed to assign functional attributes to those voting points. Then a statistical method was involved to count the frequency of the classified voting points of different functional attributes in each basic functional zone units. By voting process, the functional attribute with the highest frequency was assigned to the basic functional zone unit, which corrected the misclassification results of CNN to some extent. This paper also explored the scale effect of the SO on the final functional zone classification result from two aspects, spatial scale of SO and the sampling window size of CNN model. Because of the natural differences between functional zone division and land cover classification, region based overall accuracy assessment method was used to evaluate functional zone division result. Compared with other methods, SO–CNN method can generate higher accuracy and subtle result, based on which larger spatial scale results can be available by scaling-up, so SO–CNN method plays a great significant role on small scale functional space structure research.

Faster FPTASes for counting and random generation of Knapsack solutions

In the #P-complete problem of counting 0/1 Knapsack solutions, the input consists of a sequence of n nonnegative integer weights w1,…,wn and an integer C, and we have to find the number of subsequences (subsets of indices) with total weight at most C. We give faster and simpler fully polynomial-time approximation schemes (FPTASes) for this problem, and for its random generation counterpart. Our method is based on dynamic programming and discretization of large numbers through floating-point arithmetic. We improve both deterministic counting FPTASes from Gopalan et al. (2011) [9], Štefankovič et al. (2012) [6] and the randomized counting and random generation algorithms in Dyer (2003) [5].Our method is general, and it can be directly applied on top of combinatorial decompositions (such as dynamic programming solutions) of various problems. For example, we also improve the complexity of the problem of counting 0/1 Knapsack solutions in an arc-weighted DAG.

Fast Algorithm for Generating Random Bit Strings and Multispin Coding for Directed Percolation

We present efficient algorithms to generate a bit string in which each bit is set with arbitrary probability. By adopting a hybrid algorithm, i.e., a finite-bit density approximation with correction techniques, we achieve 3.8 times faster random bit generation than the simple algorithm for the 32-bit case and 6.8 times faster for the 64-bit case. Employing the developed algorithm, we apply the multispin coding technique to one-dimensional bond-directed percolation. The simulations are accelerated by up to a factor of 14 compared with an optimized scalar implementation. The random bit string generation algorithm proposed here is applicable to general Monte Carlo methods.

Efficient generator of random fiber distribution with diverse volume fractions by random fiber removal

In this paper, we propose a simple and efficient generator of random fiber distribution with diverse fiber volume fractions (Vf) for unidirectional composites by a random fiber removal technique. From the representative volume element (RVE) consisting of 100 fibers that have a maximum Vf of about 65% in this work, also termed the master model, we randomly eliminate fibers to match the predefined Vf ranging from 60%, 55%, 45%, 35%, 25%, 15%, and 5%, which are lower than that of the master model. Accordingly, 100 RVE samples for each Vf can be constructed in a straightforward manner.To demonstrate the performance of the proposed algorithm, its fiber locations are verified in terms of statistical spatial metrics, such as the nearest neighbor orientation, Ripley's K function, and pair distribution function. Furthermore, the elastic properties and the anisotropic ratios of the generated RVEs are investigated and compared to those of other random fiber generation algorithms.

Randomize IPv6 Stateless Address Autoconfiguration in None-stable Storage Arduino Devices

<p>The pervasiveness of IoT device requires each device to have a unique address number in order to communicate. Internet Standard specified in RFC4941-Privacy Extension for SLAAC had question raised on the randomness of the IPv6 address generated due to the shortcoming of device random generator algorithm. Improvements to the RFC’s have been proposed and Arduino Uno had been selected as IoT platform since it currently supports IPv6 implementation. An enhancement algorithm was developed. The generated IPv6 address is then tested against ENT Random Test tool for observation and conclusion.</p>

Settling the mystery of Zr = r in RC4

In this paper, using a matrix, at first we revisit the work of Mantin on finding the probability distribution of the RC4 permutation after the completion of the KSA. After that, we extend the same idea to analyse the probabilities during any iteration of the Pseudo Random Generation Algorithm. Next, we study the bias of Zr = r (where Zr is the r-th output keystream byte), which is one of the significant biases observed in the RC4 output keystream. This bias has played an important role in the plaintext recovery attack proposed by Isobe et al. in FSE 2013. However, the accurate theoretical explanation of the bias of Zr = r is still a mystery. Though several attempts have been made to prove this bias, none of those provides an accurate justification. Here, using the results found with the help of the probability transition matrix we justify this bias of Zr = r accurately and settle this issue. The bias obtained from our proof matches the experimental observations perfectly.

Effect of Randomness of Interfacial Properties on Fracture Behavior of Concrete Under Uniaxial Tension

Interfacial transition zones (ITZs) between aggregates and mortar are the weakest parts in concrete. The random aggregate generation and packing algorithm was utilized to create a two-phase concrete model, and the zero-thickness cohesive elements with different normal distribution parameters were used to model the ITZs with random mechanical properties. A number of uniaxial tension-induced fracture simulations were carried out, and the effects of the random parameters on the fracture behavior of concrete were statistically analyzed. The results show that, different from the dissipated fracture energy, the peak load of concrete does not always obey a normal distribution, when the elastic stiffness, tensile strength, or fracture energy of ITZs is normally distributed. The tensile strength of the ITZs has a significant effect on the fracture behavior of concrete, and its large standard deviation leads to obvious diversity of the fracture path in both location and shape.

Efficient secure system of data in cloud using steganography based cryptosystem with FSN

Data sharing is one of the most important functionality in cloud and one of the challenging problem is sharing data securely with others. Sharing the data with security and confidentiality on cloud with data integrity is discussed in this paper. We describe random key generation algorithm using that random sized public/master-secret key pair is created by data owner. The novelty is that data owner encrypts the data using feistal structure network along with the public key and the data index into an image and this is done by using steganography & then the data are uploaded in the Cloud Server. Aggregate Decryption Key (ADK) is mainly used to generate the Master- secret key where it is used for sharing the data to other users by transferring its ADK to those who are interested in accessing the contents through E-mail by the data owner. Original Data is downloaded only after Verification of ADK and the authentication that are done by 3D scan of finger print authentication.

Cryptography using Random Rc4 Stream Cipher on SMS for Android-Based Smartphones

Messages sent using the default Short Message Service (SMS) application have to pass the SMS Center (SMSC) to record the communication between the sender and recipient. Therefore, the message security is not guaranteed because it may read by irresponsible people. This research proposes the RC4 stream cipher method for security in sending SMS. However, RC4 has any limitation in the Key Scheduling Algorithm (KSA) and Pseudo Random Generation Algorithm (PRGA) phases. Therefore, this research developed RC4 with a random initial state to increase the randomness level of the keystream. This SMS cryptography method applied the processes of encryption against the sent SMS followed by decryption against the received SMS. The performance of the proposed method is evaluated based on the time of encryption and decryption as well as the average correlation value. Based on the time, it shows that the length of the SMS characters sent affects the time of encryption and decryption. Meanwhile, the best correlation value achieved 0.00482.

Assorted carrier‐variable frequency‐random PWM scheme for voltage source inverter

This study presents an assorted carrier-variable frequency random pulse width modulation (AC-VF-RPWM) method in which a new type of mixed carrier wave is proposed for a three-phase voltage source inverter-fed induction motor drive. This study has focused on the reduction of acoustic noise where a deterministic PWM strategy is replaced with a proposed RPWM. Inverted sine carrier, sine carrier and triangle carrier are used to compare with the reference sinusoidal wave to generate the pulse (lead and lag) positions. The two groups (lead and lag) each with three pulse positions are randomly selected and it aims at dispersing the harmonic voltage over a wide band area. The linear feedback shift register-based random bit generation algorithms are used to select the three pulse positions from the lead and lag groups. In addition, a frequency modulator converts the carriers into a randomised frequency, which will give more potency to the randomisation process. The results of co-simulation and experimental evaluation that the proposed method could provide harmonic power spectra of the voltage and current spreading over a much broader band area, as compared with the conventional schemes. In addition, Xilinx XC3S500E FPGA device synthesis results are presented.

Dual-stage constructed random graph algorithm to generate random graphs featuring the same topological characteristics with power grids

It is a common practice to simulate some historical or test systems to validate the efficiency of new methods or concepts. However, there are only a small number of existing power system test cases, and validation and evaluation results, obtained using such a limited number of test cases, may not be deemed sufficient or convincing. In order to provide more available test cases, a new random graph generation algorithm, named “dual-stage constructed random graph” algorithm, is proposed to effectively model the power grid topology. The algorithm generates a spanning tree to guarantee the connectivity of random graphs and is capable of controlling the number of lines precisely. No matter how much the average degree is, whether sparse or not, random graphs can be quickly formed to satisfy the requirements. An approach is developed to generate random graphs with prescribed numbers of connected components, in order to simulate the power grid topology under fault conditions. Our experimental study on several realistic power grid topologies proves that the proposed algorithm can quickly generate a large number of random graphs with the topology characteristics of real-world power grid.

A Sustainable City Planning Algorithm Based on TLBO and Local Search

Nowadays, how to design a city with more sustainable features has become a center problem in the field of social development, meanwhile it has provided a broad stage for the application of artificial intelligence theories and methods. Because the design of sustainable city is essentially a constraint optimization problem, the swarm intelligence algorithm of extensive research has become a natural candidate for solving the problem. TLBO (Teaching-Learning-Based Optimization) algorithm is a new swarm intelligence algorithm. Its inspiration comes from the “teaching” and “learning” behavior of teaching class in the life. The evolution of the population is realized by simulating the “teaching” of the teacher and the student “learning” from each other, with features of less parameters, efficient, simple thinking, easy to achieve and so on. It has been successfully applied to scheduling, planning, configuration and other fields, which achieved a good effect and has been paid more and more attention by artificial intelligence researchers. Based on the classical TLBO algorithm, we propose a TLBO_LS algorithm combined with local search. We design and implement the random generation algorithm and evaluation model of urban planning problem. The experiments on the small and medium-sized random generation problem showed that our proposed algorithm has obvious advantages over DE algorithm and classical TLBO algorithm in terms of convergence speed and solution quality.

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 $1/k$.