Uniform Random Number Research Articles

Regional Leader-Following Consensus of Generalized One-Sided Lipschitz Multiagents: A Monte Carlo Simulation-Based Strategy

This article addresses the leader-following exponential consensus control problem for generalized one-sided Lipschitz (OSL) multiagents (MAs) using relative-state information under a directed spanning tree. Previous works have accomplished the consensus protocol design for the globally OSL nonlinear MA systems. Unlike the existing results, the proposed strategy employs a less conservative approach in the form of matrix inequalities and convex routines by assuming that the nonlinear MAs satisfy the OSL condition in a local region by relaxing the so-called quadratic inner-boundedness constraint. A novel algorithm is provided to incorporate the value of generalized OSL constant and to consider the regional consensus protocol computation via Monte Carlo simulations, based on uniform random numbers. The proposed approach is fully automatic, easily solvable, and presented for the consensus control of generalized (locally) OSL nonlinear systems for the first time. The proposed approach also studies the criteria for the exponential consensus convergence rate for attaining a faster convergence of the consensus error. Furthermore, the developed consensus protocol for the generalized OSL systems is then extended for the case of switching topologies. The proposed consensus protocol of locally OSL nonlinear MA systems can also be employed to the global OSL nonlinear MA systems as a special case for attaining the local performance. The effectiveness of the proposed approach is verified by the leader-following consensus of eight moving agents and a practical application of F-18 aircraft.

Study of dynamic reaction of tubulins in microtubules. A qm/mm simulation

In this model one-dimensional microtubule is fixed at one of the two and simulated while the opposite end is allowed for growing in random situation. By this study at each step one tubulin has been added to the length for growing microtubule length. Computationally this can be done through generating a uniform random number between (0, 1). Microtubules are demonstrated as straight macromolecules consist of the linear chains of tubulin subunits in the length. QM/MM simulation has been applied to study dynamic instability of the microtubule length. It has been calculated a correct dimension around 10-6 meter of microtubules length consist of around 1650 tubulin dimers. Microtubule growth rate is related to the soluble tubulin dimer concentration and for all results shown here, simulation of any single condition was run 5–10 times.

Modification of Harris hawks optimization algorithm with random distribution functions for optimum power flow problem

Harris hawks optimization (HHO) algorithm, which is inspired from Harris hawks hunting strategy, uses uniform random numbers in the optimization process. This paper proposes modifying HHO with seven types of random distribution function definitions that are chi-square distribution, normal distribution, exponential distribution, Rayleigh distribution, Student’s distribution, F distribution, and lognormal distribution to show effects on stochastic search-based optimization algorithm performance. The modified HHO algorithm is tested via some benchmark test functions. Results are compared with each other and with classical HHO solutions. Then, the HHO and its modified versions are applied to optimum power flow (OPF), which is an important problem for power system engineering for decades. The algorithms are applied to IEEE 30-bus test system to minimize total fuel cost of the power system, active/reactive power losses, and emission, by comparing with recent OPF researches. Considering the applicability of the proposed approach and the results achieved, one can confirm that it might be a different alternative method for solving OPF problems. One of the important results of the paper in the IEEE 30-bus test system is that the cost of fuel is calculated as 798.9105 $/h with classical HHO, while it is calculated as 798.66 $/h with the HHO modified with SD function.

VLSI Implementation of Linear Feedback Shift Register (LFSR) based Test Pattern Generator for Pseudo Exhaustive Testing

Advanced strides of improvement in programmable logic density, enhancements in speed and hardware description language (HDL) are empowering design engineers to implement highly performing and testable digital systems. Linear feedback shift registers (LFSR) are the critical elements in the testing and self testing of contemporary complex electronic systems like processors, Built-in-self-test (BIST) controllers and integrated circuits (ICs) etc. Fundamentally BIST is a Design-for-Testability (DFT) technique meant to configure testing functions physically with the circuit under test (CUT). To enhance the percentage of fault coverage as a part of BIST operations (testing the IC), LFSRs are deployed (as test pattern generator) to generate the test vectors inside logic BIST for testing digital systems. Proposed work is focused upon designing a fast adder based variable length pseudorandom binary sequence pattern generator (PRBSPG) and experimental validations. LFSR possess characteristics of high speed, better encoding efficiency, high fault coverage, low test volume data and low power consumption specially suitable in processing environment where uniform distribution random numbers are required. Verilog HDL is employed for structuring the modular design units while Xilinx ISE tool is deployed for validating the proposed LFSR design work and associated modular units.

Clear-sky index space-time trajectories from probabilistic solar forecasts: Comparing promising copulas

Short-term probabilistic solar forecasts are an important tool in decision-making processes in which uncertainty plays a non-negligible role. Purely statistical models that produce temporal or spatiotemporal probabilistic solar forecasts are generally trained individually, and the combined forecasts therefore lack the temporal or spatiotemporal correlation present in the data. To recover the spatiotemporal dependence structure, a copula can be employed, which constructs a multivariate distribution from which spatially and temporally correlated uniform random numbers can be sampled, which in turn can be used to generate the so-called space-time trajectories via the inverse probability integral transform. In this study, we employ the recently introduced ultra-fast preselection algorithm to leverage the spatiotemporal information present in a pyranometer network and compare its accuracy to that of quantile regression forecasts that only consider temporal information. We show that the preselection algorithm improves both the calibration and sharpness of the predictive distributions. Furthermore, we employ four copulas, i.e., (1) Gaussian, (2) Student-t, (3) Clayton, and (4) empirical, to generate space-time trajectories. The results highlight the necessity to rigorously assess the calibration of the space-time trajectories and the correct modeling of the spatiotemporal dependence structure, which we show through techniques introduced in atmospheric sciences. The code used to generate the results in this study can be found at https://github.com/DWvanderMeer/SpaceTimeTrajectories.

A Hardware-efficient Weight Sampling Circuit for Bayesian Neural Networks

The main problems of deep learning are requiring a large amount of data for learning, and prediction with excessive confidence. A Bayesian neural network (BNN), in which a Bayesian approach is incorporated into a neural network (NN), has drawn attention as a method for solving these problems. In a BNN, the probability distribution is assumed for the weight, in contrast to a conventional NN, in which the weight is point estimated. This makes it possible to obtain the prediction as a distribution and to evaluate how uncertain the prediction is. However, a BNN has more computational complexity and a greater number of parameters than an NN. To obtain an inference result as a distribution, a BNN uses weight sampling to generate the respective weight values, and thus, a BNN accelerator requires weight sampling hardware based on a random number generator in addition to the standard components of a deep learning neural network accelerator. Therefore, the throughput of weight sampling must be sufficiently high at a low hardware resource cost. We propose a resource-efficient weight sampling method using inversion transform sampling and a lookup-table (LUT)-based function approximation for hardware implementation of a BNN. Inversion transform sampling simplifies the mechanism of generating a Gaussian random number from a uniform random number provided by a common random number generator, such as a linear feedback shift register. Employing an LUT-based low-bit precision function approximation enables inversion transform sampling to be implemented at a low hardware cost. The evaluation results indicate that this approach effectively reduces the occupied hardware resources while maintaining accuracy and prediction variance equivalent to that with a non-approximated sampling method.

Identification of pollution sources using artificial neural network (ANN) and multilevel breakthrough curve (BTC) characterization

A pollution source in groundwater may be active at some location for certain periods. There may be multiple potential sources responsible for observed contamination at observation wells. The contamination witnessed in observation wells at different times establishes breakthrough curves (BTCs). These BTCs are usually employed for source identification. In this work, single and multistage artificial neural network (ANN) is employed to identify the potential pollution sources. Temporally varying potential pollution sources are generated using uniform random numbers. These source fluxes are further applied to the simulation of the pollution concentration at observation wells. BTC at an observation well is characterized by statistical parameters and data mining. Characterized BTCs are inputs and source fluxes are outputs of ANN models. Initial stage ANN models are developed at the specified observation well locations, using multilevel BTC characterization. These initial meta models are utilized for the development of intermediate models. Further, the intermediate models are employed for final stage identification. These multi-stage ANN models are found to perform comparatively better than single stage ANN models.

Вихревой генератор стохастических плоскостей

Various pseudorandom number generation algorithms may be used to create a discrete stochastic plane. If a Cartesian completeness property is required of the plane, it must be uniform. The point is, employing the concept of uncontrolled random number generation may yield low-quality results, since original sequences may omit random numbers or not be sufficiently uniform. We present a novel approach for generating stochastic Cartesian planes according to the model of complete twister sequences featuring uniform random numbers without omissions or repetitions. Simulation results confirm that the random planes obtained are indeed perfectly uniform. Moreover, recombining the original complete uniform sequence parameters allows the number of planes created to be significantly increased without using any extra random access memory.

Decentralized Production-Distribution Planning in a Supply Chain: Computer Experiments

Abstract It has been realized long back by the supply chain managers that the channel partners like suppliers, manufacturers, distributors, retailers can benefit by sharing information about their domain with other channel partners. The supply chain costs such as production costs, distribution costs can be reduced by sharing the information and the service can be improved by quickly responding to the changes in demand. But a practical difficulty is that the channel partners do not like to share complete information about their domain with others. Because, they feel that by doing so, they will lose grip on their domain, and also feel that the shared information could reach their rivals. In this paper, the authors propose a production distribution planning model for a supply chain with partial information sharing. This type of supply chains are found where the third party logistics (3PL) are involved. In 3PL, the transportation facilities (vehicles) are owned by private parties (i.e. they are not owned by plant owners) in this type of distribution, the plant managers and distribution service providers share partial information. They will not share information about their capacities and costs, but share information such as the desired production quantity by the distributors and possible production quantity by the plant managers. Under such situations, the model proposed in this paper is appropriate.. The authors tested the model for problems generated with uniform random numbers between minimum and maximum values for the costs and capacities. The results indicate that the proposed model can be applied to real life problems where third party logistics are involved.

A Class of Quadratic Polynomial Chaotic Maps and its Application in Cryptography

At present, the probability density of most chaotic systems is unknown, and the statistical characteristics of chaotic sequences cannot be described by the probability density of chaotic maps. This paper constructs a class of quadratic polynomial chaotic maps with three system parameters, which are topologically conjugated with Tent maps. The probability density functions of this kind of chaotic maps are given. Then, an arcsine function is designed to transform the chaotic sequence generated by the quadratic polynomial chaotic map into a new random sequence, which obeys the uniform distribution on the interval (−0.5, 0.5). In order to show the application of the new uniform random numbers, the applications of it in generating random arrangement, Gaussian measurement matrix of compressed sensing, and pseudo random number generator are discussed.

Analytic simulator and image generator of multiple-scattering Compton camera for prompt gamma ray imaging.

For prompt gamma ray imaging for biomedical applications and environmental radiation monitoring, we propose herein a multiple-scattering Compton camera (MSCC). MSCC consists of three or more semiconductor layers with good energy resolution, and has potential for simultaneous detection and differentiation of multiple radio-isotopes based on the measured energies, as well as three-dimensional (3D) imaging of the radio-isotope distribution. In this study, we developed an analytic simulator and a 3D image generator for a MSCC, including the physical models of the radiation source emission and detection processes that can be utilized for geometry and performance prediction prior to the construction of a real system. The analytic simulator for a MSCC records coincidence detections of successive interactions in multiple detector layers. In the successive interaction processes, the emission direction of the incident gamma ray, the scattering angle, and the changed traveling path after the Compton scattering interaction in each detector, were determined by a conical surface uniform random number generator (RNG), and by a Klein-Nishina RNG. The 3D image generator has two functions: the recovery of the initial source energy spectrum and the 3D spatial distribution of the source. We evaluated the analytic simulator and image generator with two different energetic point radiation sources (Cs-137 and Co-60) and with an MSCC comprising three detector layers. The recovered initial energies of the incident radiations were well differentiated from the generated MSCC events. Correspondingly, we could obtain a multi-tracer image that combined the two differentiated images. The developed analytic simulator in this study emulated the randomness of the detection process of a multiple-scattering Compton camera, including the inherent degradation factors of the detectors, such as the limited spatial and energy resolutions. The Doppler-broadening effect owing to the momentum distribution of electrons in Compton scattering was not considered in the detection process because most interested isotopes for biomedical and environmental applications have high energies that are less sensitive to Doppler broadening. The analytic simulator and image generator for MSCC can be utilized to determine the optimal geometrical parameters, such as the distances between detectors and detector size, thus affecting the imaging performance of the Compton camera prior to the development of a real system.

Fuzzy Dynamic Parameter Adaptation in the Harmony Search Algorithm for the Optimization of the Ball and Beam Controller

This paper presents a method for dynamic parameter adaptation in the harmony search algorithm (HS) based on fuzzy logic. The adaptation is performed using Type 1 (FHS), interval Type 2 (IT2FHS), and generalized Type 2 (GT2FHS) fuzzy systems as the number of improvisations or iterations advances, achieving a better intensification and diversification. The main contribution of this work is the dynamic parameter adaptation using different types of fuzzy systems in the harmony search algorithm applied to optimization of the membership functions for a benchmark control problem; in this case it is focused on the ball and beam controller. Experiments are presented with the HS, FHS, IT2FHS, and GT2FHS with noise (uniform random number) and without noise for the controller, and the following error metrics are obtained: ITAE, ITSE, IAE, ISE, and RMSE, to validate the efficacy of the proposed methods.

The rating mode of eutrophication in Dalinuoer Lake

The Logistic Curve eutrophication level comprehensive evaluation model was used to generate model samples and verification samples based on the evaluation grade criteria and uniform random numbers and was solved using an accelerated genetic algorithm The water quality monitoring indicators in July, August, September and October of 2017 were selected to evaluate the water environment quality of Darinol Lake and determine the eutrophication level. The results showed that the degree of eutrophication in Darinol Lake is close to five extremely eutrophic state, and the eutrophication grades in July, August, September and October showed a slowly decreasing trend.

Extracting Randomness from the Trend of IPI for Cryptographic Operations in Implantable Medical Devices

Achieving secure communication between an Implantable Medical Device (IMD) and a gateway or programming device outside the body has showed its criticality in recent reports of vulnerabilities in cardiac devices, insulin pumps and neural implants, amongst others. The use of asymmetric cryptography is typically not a practical solution for IMDs due to the scarce computational and power resources. Symmetric key cryptography is preferred but its security relies on agreeing and using strong keys, which are difficult to generate. A solution to generate strong shared keys without using extensive resources, is to extract them from physiological signals already present inside the body such as the Inter-Pulse interval (IPI). The physiological signals must therefore be strong sources of randomness that meet five conditions: Universality (available on all people), Liveness (available at any-time), Robustness (strong random number), Permanence (independent from its history) and Uniqueness (independent from other sources). However, these conditions (mainly the last three) have not been systematically examined in current methods for randomness extraction from IPI. In this study, we first propose a methodology to measure the last three conditions: Information secrecy measures for Robustness, Santha-Vazirani Source delta value for Permanence and random sources dependency analysis for Uniqueness. Then, using a large dataset of IPI values (almost 900,000,000 IPIs), we show that IPI does not have Robustness and Permanence as a randomness source. Thus, extraction of a strong uniform random number from IPI values is impossible. Third, we propose to use the trend of IPI, instead of its value, as a source for a new randomness extraction method named Martingale Randomness Extraction from IPI (MRE-IPI). We evaluate MRE-IPI and show that it satisfies the Robustness condition completely and Permanence to some level. Finally, we use the NIST STS and Dieharder test suites and show that MRE-IPI is able to outperform all recent randomness extraction methods from IPIs and achieves a quality roughly half that of the AES random number generator. MRE-IPI is still not a strong random number and cannot be used as key to secure communications in general. However, it can be used as a one-time pad to securely exchange keys between the communication parties. The usage of MRE-IPI will thus be kept at a minimum and reduces the probability of breaking it. To the best of our knowledge, this is the first work in this area which uses such a comprehensive method and large dataset to examine the randomness of physiological signals.

Secure uniform random-number extraction via incoherent strategies

To guarantee the security of uniform random numbers generated by a quantum random number generator, we study secure extraction of uniform random numbers when the environment of a given quantum state is controlled by the third party, the eavesdropper. Here we restrict our operations to incoherent strategies that are composed of the measurement on the computational basis and incoherent operations (or incoherence-preserving operations). We show that the maximum secure extraction rate is equal to the relative entropy of coherence. By contrast, the coherence of formation gives the extraction rate when a certain constraint is imposed on eavesdropper's operations. The condition under which the two extraction rates coincide is then determined. Furthermore, we find that the exponential decreasing rate of the leaked information is characterized by R\'{e}nyi relative entropies of coherence. These results clarify the power of incoherent strategies in random number generation, and can be applied to guarantee the quality of random numbers generated by a quantum random number generator.

Improved Firefly Algorithm: A Novel Method for Optimal Operation of Thermal Generating Units

This paper presents a novel improved firefly algorithm (IFA) to deal the problem of the optimal operation of thermal generating units (OOTGU) with the purpose of reducing the total electricity generation fuel cost. The proposed IFA is developed based on combining three improvements. The first is to be based on the radius between two solutions, the second is updated step size for each considered solution based on different new equations, and the third is to slightly modify a formula producing new solutions by using normally distributed random numbers and canceling uniform random numbers of conventional firefly algorithm (FA). The effect of each proposed improvement on IFA is investigated by executing five benchmark functions and two different systems. The performance of IFA is investigated on six other study cases consisting of different types of objective function and complex level of constraints. The objective function considers single fuel with quadratic form and nonconvex form, and multifuels with the sum of several quadratic and nonconvex functions while a set of constraints taken into account are power loss, prohibited zone, ramp rate limit, spinning reserve, and all constraints in transmission power networks. The obtained results indicate the proposed improvements in terms of high optimal solution quality, stabilization of search ability, and fast convergence compared with FA. In addition, the comparisons with other methods also lead to a conclusion that the proposed method is a very promising optimization tool for systems with quadratic fuel cost function and with complicated constraints.

Effect of Levy Flight on the discrete optimum design of steel skeletal structures using metaheuristics

Metaheuristic algorithms in general make use of uniform random numbers in their search for optimum designs. Levy Flight (LF) is a random walk consisting of a series of consecutive random steps. The use of LF instead of uniform random numbers improves the performance of metaheuristic algorithms. In this study, three discrete optimum design algorithms are developed for steel skeletal structures each of which is based on one of the recent metaheuristic algorithms. These are biogeography-based optimization (BBO), brain storm optimization (BSO), and artificial bee colony optimization (ABC) algorithms. The optimum design problem of steel skeletal structures is formulated considering LRFD-AISC code provisions and W-sections for frames members and pipe sections for truss members are selected from available section lists. The minimum weight of steel structures is taken as the objective function. The number of steel skeletal structures is designed by using the algorithms developed and effect of LF is investigated. It is noticed that use of LF results in up to 14% lighter optimum structures.

Ternary jitter-based true random number generator

In this paper a novel family of generators producing true uniform random numbers in ternary logic is presented. The generator consists of a number of identical ternary logic combinational units connected into a ring. All the units are provided to have a random delay time, and this time is supposed to be distributed in accordance with an exponential distribution. All delays are supposed to be independent events. The theory of the generator is based on Erlang equations. The generator can be used for test production in various systems. Features of multidimensional random vectors, produced by the generator, are discussed.

Improving simulated annealing through derandomization

We propose and study a version of simulated annealing (SA) on continuous state spaces based on $$(t,s)_R$$ -sequences. The parameter $$R\in \bar{\mathbb {N}}$$ regulates the degree of randomness of the input sequence, with the case $$R=0$$ corresponding to IID uniform random numbers and the limiting case $$R=\infty $$ to (t, s)-sequences. Our main result, obtained for rectangular domains, shows that the resulting optimization method, which we refer to as QMC-SA, converges almost surely to the global optimum of the objective function $$\varphi $$ for any $$R\in \mathbb {N}$$ . When $$\varphi $$ is univariate, we are in addition able to show that the completely deterministic version of QMC-SA is convergent. A key property of these results is that they do not require objective-dependent conditions on the cooling schedule. As a corollary of our theoretical analysis, we provide a new almost sure convergence result for SA which shares this property under minimal assumptions on $$\varphi $$ . We further explain how our results in fact apply to a broader class of optimization methods including for example threshold accepting, for which to our knowledge no convergence results currently exist. We finally illustrate the superiority of QMC-SA over SA algorithms in a numerical study.

A Tutorial on Pharmacodynamic Scripting Facility inSimcyp.

The Simcyp Simulator provides a framework for mechanistic Physiologically‐Based Pharmacokinetic/Pharmacodynamic modeling of potentially interacting drugs. It also provides a scripting facility, using the Lua language, for developing customized pharmacodynamic and toxicity models driven by drug concentrations at the site of action. We present an overview of the scripting facility including the scripting language, the editor, and how scripts are embedded within the Simulator. Examples incorporating differential equations and including inter‐individual variability on parameters are presented.