Processor Time Research Articles

On the Optimal Strategy for Circuit Design

The circuit optimization process is defined as a controlled dynamic system with a special control vector. This vector serves as the main tool for generalizing the problem of circuit optimization and produces a huge number of different optimization strategies. In this case, we can formulate the task of finding the best optimization strategy that minimizes processor time. We need to find the optimal structure of the control vector that minimizes processor time. A special function, which is a combination of the Lyapunov function of the optimization process and its time derivative, was proposed to predict the optimal structure of the control vector. The found optimal positions of the switching points of the control vector give a large gain in CPU time in comparison with the traditional approach.

Rapid species-level metagenome profiling and containment estimation with sylph.

Profiling metagenomes against databases allows for the detection and quantification of microorganisms, even at low abundances where assembly is not possible. We introduce sylph, a species-level metagenome profiler that estimates genome-to-metagenome containment average nucleotide identity (ANI) through zero-inflated Poisson k-mer statistics, enabling ANI-based taxa detection. On the Critical Assessment of Metagenome Interpretation II (CAMI2) Marine dataset, sylph was the most accurate profiling method of seven tested. For multisample profiling, sylph took >10-fold less central processing unit time compared to Kraken2 and used 30-fold less memory. Sylph's ANI estimates provided an orthogonal signal to abundance, allowing for an ANI-based metagenome-wide association study for Parkinson disease (PD) against 289,232 genomes while confirming known butyrate-PD associations at the strain level. Sylph took <1 min and 16 GB of random-access memory to profile metagenomes against 85,205 prokaryotic and 2,917,516 viral genomes, detecting 30-fold more viral sequences in the human gut compared to RefSeq. Sylph offers precise, efficient profiling with accurate containment ANI estimation even for low-coverage genomes.

Method of determining timing parameters of real time systems

Real-time systems are widely used and actively implemented in advanced developments across many industries, from medicine to aerospace. Research and modelling of real-time systems are crucial tasks at the design stage, as they help in the determination of whether the system being modelled meets the specified timing characteristics and, accordingly, assess the system's ability to satisfy timing requirements. After all, the success of a real-time system depends not only on their logical correctness but also on the time it takes for the system to generate a result. Considering the various types of tasks, such as synchronous and asynchronous, parallel and sequential, determining their timing characteristics at the design stage is quite a complex problem. It is also essential to consider the sequence of tasks and their arrival times since there are cases where one task depends on the result of another task, and thus, the arrival time of a new task to the scheduler's queue depends on the completion time of the previous task. Previous studies have investigated methods for evaluating the timing characteristics of tasks in real-time systems by analyzing data obtained from modeling the distribution of processor time among tasks according to selected scheduler algorithms using Petri net models in both single-processor and multi-processor systems. These methods ensured the acquisition of task timing characteristics when choosing a specific type of processor and scheduler, which is necessary for the initial technical design of a real-time system. However, the methods have not considered the dynamic nature of task yielding, which is an essential component of modern real-time systems. This paper proposes a method for determining the timing characteristics of real-time systems at the design stage. The proposed method helps to identify the arrival time of tasks, taking into account various types of tasks and their dependencies. The identified timing characteristics can subsequently be used for modelling the system's operation and determining the optimal task scheduling algorithm.

Cubic q-Bézier Triangular Patch for Scattered Data Interpolation and Its Algorithm

This paper presents an approach to scattered data interpolation using q-Bézier triangular patches via an efficient algorithm. While existing studies have formed q-Bézier triangular patches through convex combination, their application to scattered data interpolation has not been previously explored. Therefore, this study aims to extend the use of q-Bézier triangular patches to scattered data interpolation by achieving C1 continuity throughout the data points. We test the proposed scheme using both established data points and real-life engineering problems. We compared the performance of the proposed interpolation scheme with a well-known existing scheme by varying the q parameter. The comparison was based on visualization and error analysis. Numerical and graphical results were generated using MATLAB. The findings indicate that the proposed scheme outperforms the existing scheme, demonstrating a higher coefficient of determination (R2), smaller root mean square error (RMSE), and faster central processing unit (CPU) time. These results highlight the potential of the proposed q-Bézier triangular patches scheme for more accurate and reliable scattered data interpolation via the proposed algorithm.

Stochastic Modeling of Two-Phase Transport in Fractured Porous Media Under Geological Uncertainty Using an Improved Probabilistic Collocation Method

Summary Simulation of multiphase transport through fractured porous media is highly affected by the uncertainty in fracture distribution and matrix block size that arises from inherent heterogeneity. To quantify the effect of such uncertainties on displacement performance in porous media, the probabilistic collocation method (PCM) has been applied as a feasible and accurate approach. However, propagation of uncertainty during the simulation of unsteady-state transport through porous media could not be computed by this method or even by the direct-sampling Monte Carlo (MC) approach. Therefore, with this research, we implement a novel numerical modeling workflow that improves PCM on sparse grids and combines it with the Smolyak algorithm for selection of collocation points sets, Karhunen-Loeve (KL) decomposition, and polynomial chaos expansion (PCE) to compute the uncertainty propagation in oil-gas flow through fractured porous media in which gravity drainage force is enabled. The effect of uncertainty in the vertical dimension of matrix blocks, which are frequently an uncertain and history-matching parameter, on simulation results of randomly synthetic 3D fractured media is explored. The developed numerical model is innovatively coupled with solving governing deterministic partial differential equations (PDEs) to compute uncertainty propagation from the first timestep to the last timestep of the simulation. The uncertainty interval and aggregation of uncertainty in ultimate recovery are quantified, and statistical moments for simulation outputs are presented at each timestep. The results reveal that the model properly quantifies uncertainty and extremely reduces central processing unit (or CPU) time in comparison with MC simulation.

Comparative analysis of the implementation performance using selected scripting languages in the Godot game engine

This article describes a comparative analysis of the implementation performance of selected scripting languages on the Godot game engine. In order to analyze the implementation of scripting languages, research scenarios were designed in which the scripts were written in a similar way to facilitate the analysis of the performance of their implementation. The study took into account parameters such as the execution time of a given script, processor time and the amount of RAM used. Based on the results obtained, averages were determined and presented in charts to facilitate their interpretation. The conducted research allowed for a comparative analysis between scripting languages. The analysis showed that each language is better suited for different types of projects, with GDScript being better for smaller projects and C# for more complex projects.

Magnetohydrodynamics flow and heat transfer of novel generalized Kelvin–Voigt viscoelastic nanofluids over a moving plate

In this work, the unsteady magnetohydrodynamics boundary layer flow and heat transfer of novel generalized Kelvin–Voigt viscoelastic nanofluids over a moving plate are investigated. The classical Kelvin–Voigt constitutive relation is generalized to incorporate a time-fractional derivative to characterize the fluid behavior, which is proved to be of significance and physically justified. The newly developed fractional Kelvin–Voigt constitutive correlation and a dual-phase-lagging constitutive equation are applied to the momentum and energy equations, respectively, for a nanofluid model over a moving plate. The formulated integrodifferential velocity and thermal boundary layer equations are solved using the finite difference method together with a fast algorithm, which reduces the consumed central processing unit time significantly. Several numerical examples are presented to illustrate the influence of the critical parameters on the nanofluid motion and thermal characteristics. Compared to the fractional Maxwell nanofluid model, the velocity boundary layer for the fractional Kelvin–Voigt nanofluid model is thinner. Although the fractional indexes show similar effects on the velocity boundary layer, the impacts of the relaxation parameters are in contrast. This work provides valuable insights into the feasibility of using the fractional Kelvin–Voigt viscoelastic model to depict the fluid flow and heat transfer characteristics of nanofluids.

A three-term conjugate gradient descent method with some applications

The stationary point of optimization problems can be obtained via conjugate gradient (CG) methods without the second derivative. Many researchers have used this method to solve applications in various fields, such as neural networks and image restoration. In this study, we construct a three-term CG method that fulfills convergence analysis and a descent property. Next, in the second term, we employ a Hestenses-Stiefel CG formula with some restrictions to be positive. The third term includes a negative gradient used as a search direction multiplied by an accelerating expression. We also provide some numerical results collected using a strong Wolfe line search with different sigma values over 166 optimization functions from the CUTEr library. The result shows the proposed approach is far more efficient than alternative prevalent CG methods regarding central processing unit (CPU) time, number of iterations, number of function evaluations, and gradient evaluations. Moreover, we present some applications for the proposed three-term search direction in image restoration, and we compare the results with well-known CG methods with respect to the number of iterations, CPU time, as well as root-mean-square error (RMSE). Finally, we present three applications in regression analysis, image restoration, and electrical engineering.

A Comprehensive Analysis of Parallel Genetic Algorithms

The paper introduces an optimal approach for optimizing the idle time of processors through the utilization of parallel genetic algorithms. By harnessing the power of parallelism, we propose a method that effectively minimizes idle periods in processor operations. Our approach, outlined in detail within this paper, demonstrates significant improvements in resource utilization and efficiency. Through rigorous experimentation and analysis, we illustrate the efficacy of our parallel genetic algorithm in optimizing processor idle time. The findings presented herein offer promising insights into enhancing computational resource utilization, particularly in multi-processor systems. This research contributes to the ongoing efforts in maximizing computational efficiency and lays a foundation for further advancements in parallel optimization techniques. Keywords: Optimization; Metaheuristic; Parallel Genetic algorithm; Crossover; Mutation; Selection; Evolution;

The shortest-path and bee colony optimization algorithms for traffic control at single intersection with NetworkX application

AbstractIn this article, we study the application of NetworkX, a Python library for dealing with traffic networks, to the problem of signal optimization at a single intersection. We use the shortest-path algorithms such as Bellman-Ford (Dynamic Programming), A star (A*), and Dijkstra’s algorithm to compute an optimal solution to the problem. We consider both undersaturated and oversaturated traffic conditions. The results show that we find optimal results with short Central Processor Unit (CPU) time using all the applied algorithms, where Dijkstra’s algorithm slightly outperformed others. Moreover, we show that bee colony optimization can find the optimal solution for all tested problems with different degrees of computational complexity for less CPU time, which is a new contribution to knowledge in this field.

Predictive controllers for synchronous reluctance motor drive systems

This paper proposes the design and implementation of predictive controllers for synchronous reluctance motor drive systems to enhance their dynamic responses. The predictive speed and current controllers in this paper are designed in systematic procedures. The predictive speed controller is implemented by using Laguerre function procedure. The Laguerre function is used to simplify the algorithm and to minimize the execution time of the digital signal processor. For predictive current controller, a finite control set method improves the current tracking ability. The measured currents are used to predict the future phase-current based on the motor model. The optimal control inputs of both predictive controllers are determined by using a cost function minimization method. Experimental results show the proposed drive system provides a wide adjustable speed range, from 2 r/min to 1800 r/min. It has better performance than a proportional-integral (PI) controller including fast rise time, which is 0.9 second, small steady state error, which is 0.32 r/min, and small current ripples. A 32-bit floating-point digital signal processor, TMS-320-F-28335 DSP, is employed to implement the control algorithms.

A MapReduce-Based Approach for Fast Connected Components Detection from Large-Scale Networks.

Owing to increasing size of the real-world networks, their processing using classical techniques has become infeasible. The amount of storage and central processing unit time required for processing large networks is far beyond the capabilities of a high-end computing machine. Moreover, real-world network data are generally distributed in nature because they are collected and stored on distributed platforms. This has popularized the use of the MapReduce, a distributed data processing framework, for analyzing real-world network data. Existing MapReduce-based methods for connected components detection mainly struggle to minimize the number of MapReduce rounds and the amount of data generated and forwarded to the subsequent rounds. This article presents an efficient MapReduce-based approach for finding connected components, which does not forward the complete set of connected components to the subsequent rounds; instead, it writes them to the Hadoop Distributed File System as soon as they are found to reduce the amount of data forwarded to the subsequent rounds. It also presents an application of the proposed method in contact tracing. The proposed method is evaluated on several network data sets and compared with two state-of-the-art methods. The empirical results reveal that the proposed method performs significantly better and is scalable to find connected components in large-scale networks.

Analysis of the Circuit Optimization Process Based on a Generalized Approach and a Genetic Algorithm

Recently - based on generalized optimization - we developed an approach and successfully applied it to the problem of designing electronic circuits using deterministic optimization methods. In this paper, a similar approach is extended to the problem of optimizing electronic circuits using a genetic algorithm (GA) as the main optimization method. The fundamental element of the generalized optimization is an artificially introduced control vector that generates different strategies within the optimization process and determines the number of independent variables of the optimization problem, as well as the length and structure of chromosomes in the GA. In this case, the GA forms a set of populations defined by a fitness function specified in different ways depending on the strategy chosen within the framework of the idea of generalized optimization. The control vector allows generating different strategies, as well as building composite strategies that significantly increase the accuracy of the resulting solution. This, in turn, makes it possible to reduce both the number of generations - required during the operation of the GA - and the processor time by 3–5 orders of magnitude when solving the circuit optimization problem compared to the traditional GA. The performed analysis of the optimization procedure for some electronic circuits shows the effectiveness of this approach. The obtained results prove that the applied modification of the GA makes it possible to overcome premature convergence and increase the minimization accuracy by 3-4 orders of magnitude.

Investigation on the Application of Computer System Reliability and Data Security in GPN Networks

With the progress of science and the development of human society, the demand for complex software and hardware systems has sharply increased. Its development speed far exceeds the ability to design, implement, test, and maintain software and hardware. This article mainly studied the computer system reliability and data security of GPN networks. The automatic testing module of the program called the program to be tested into the simulator, started these test cases, and automatically simulated the situations in the test cases for testing. The test results were automatically saved to the reliability test case result document, and the database was encrypted using a GPN network. In password processing, a random key was generated and encrypted with a public key associated with a specific user identification code. Finally, the key was attached to the specified database. During the testing process, the average clock period of multiple experiments was used. The CPU (Central Processing Unit) time estimated the number of clock cycles required by the processor to execute tasks without considering factors such as pipeline. After introducing the GPN network in this article, the system throughput was 175 mpbs. Therefore, GPN networks had a certain positive impact on the reliability of computer systems and data security.

Nonlinear diffusion‐limited two‐dimensional colliding‐plume simulations with very high‐order numerical approximations

AbstractAtmospheric numerical models play a crucial role in operational weather forecasting, as well as in improving our understanding of atmospheric dynamics via research studies. Maximising their accuracy is of paramount importance. Use of advective flux schemes greater than O7 in atmospheric models is largely undocumented, with no studies considering O3–O17 fluxes with formal accuracy‐preserving high‐order interpolation, pressure gradient/divergence, and subgrid‐scale (SGS) turbulent fluxes. Higher‐order numerical approximations can reduce truncation, amplitude and phase errors, and potentially improve model accuracy and effective resolution. Here, simulations are presented using very‐high‐order O3–O17 fluxes, with/without high‐order O2–O18 Lagrangian interpolations, pressure gradient/divergence approximations, and SGS turbulent fluxes for a two‐dimensional, highly‐viscous (Re ∼ 100) diffusion‐limited, nonlinear colliding‐plumes problem using 25–200 m spatial resolutions. The highest‐order flux schemes coupled with higher‐order interpolations, pressure gradient/divergence and SGS flux approximations produced the best solutions, with higher‐order fluxes and interpolations being most important. Overall solution convergence of order about O1–O2 with mode‐split (fast sound/slow advective waves) O3 Runge–Kutta temporal schemes was negatively impacted by order at most O1 temporal convergence with SGS fluxes, divergence damping, and especially spatial filters, compared to about order O3 convergence with these inactivated. While very‐high‐order schemes were shown to improve solution accuracy, few cost‐effective higher‐order highly‐viscous test problem solutions (higher order vs finer resolution) were found using theoretical floating‐point operations (FPO) with Courant‐Friedrichs‐Lewy (CFL)‐limited or constant stable Courant number‐based time steps. However, employing central processing unit (CPU) time, rather than FPOs, demonstrated there was reduced computational burden using higher‐order approximations. We conclude that O9–O17 flux schemes with or without high‐order (≥O4) interpolations, pressure gradient/divergence approximations, and SGS fluxes can improve atmospheric model solution accuracy, without prohibitive computation costs, compared to O3–O7 flux with O2 interpolations, pressure gradient/divergence approximations, and SGS fluxes.

Sparse-view X-ray CT based on a box-constrained nonlinear weighted anisotropic TV regularization.

Sparse-view computed tomography (CT) is an important way to reduce the negative effect of radiation exposure in medical imaging by skipping some X-ray projections. However, due to violating the Nyquist/Shannon sampling criterion, there are severe streaking artifacts in the reconstructed CT images that could mislead diagnosis. Noting the ill-posedness nature of the corresponding inverse problem in a sparse-view CT, minimizing an energy functional composed by an image fidelity term together with properly chosen regularization terms is widely used to reconstruct a medical meaningful attenuation image. In this paper, we propose a regularization, called the box-constrained nonlinear weighted anisotropic total variation (box-constrained NWATV), and minimize the regularization term accompanying the least square fitting using an alternative direction method of multipliers (ADMM) type method. The proposed method is validated through the Shepp-Logan phantom model, alongisde the actual walnut X-ray projections provided by Finnish Inverse Problems Society and the human lung images. The experimental results show that the reconstruction speed of the proposed method is significantly accelerated compared to the existing $ L_1/L_2 $ regularization method. Precisely, the central processing unit (CPU) time is reduced more than 8 times.

Upsampling Monte Carlo reactor simulation tallies in depleted LWR assemblies fueled with LEU and HALEU using a convolutional neural network

Simulating nuclear reactor cores at the highest achievable spatial and energy resolution is critical in modeling these systems accurately. Increasing the resolution, however, can dramatically increase the memory and central processing unit time required to run simulations. A convolutional neural network was shown previously to accurately upsample tally results of simulated light water reactor assemblies fueled with fresh, low enriched uranium. Here, we show that a convolutional neural network can be used to upsample tally results in assemblies containing fresh and depleted fuel enriched from 1.6 to 19.9 atom percent. The network was trained using neutron flux tallies from simulations of light water reactor assemblies with a range of fuel and coolant temperatures and a diverse selection of geometries. Accurate predictions of flux tallies are possible even on test assemblies with geometries and burnup levels well outside the range of those present in the training and validation data. The network improves the data density by a factor of 8 over a broad range of light water reactor assemblies while incurring insignificant additional computational cost to a Monte Carlo simulation.

Application of machine learning methods for process safety assessments

AbstractThe accurate prediction of gas dispersion and the potential consequences of gas explosions hold a pivotal role in the assessment of explosion design loads for oil and gas processing facilities. This often involves the use of computational fluid dynamics (CFD) simulations, a widely adopted practice in the field. The extent of CFD simulations required depends on the specific characteristics and size of the facility. In many cases, a substantial number of simulations, often in the thousands, are needed to comprehensively assess the potential outcomes in the event of a hydrocarbon loss of containment. These simulations account for the complex three‐dimensional nature of the facility, the surrounding environmental conditions, and the properties of the leaking hydrocarbon fluids. Although unquestionably invaluable, CFD simulations impose significant temporal constraints upon their execution and necessitate the allocation of substantial efforts and Central Processing Unit (CPU) time. In this paper we develop a neural model tailored specifically for the analysis of CFD gas dispersion and gas explosion scenarios. This model leverages the capabilities of machine learning algorithms to expedite the execution of these complex studies. The proposed neural network model has the advantage of being able to handle a wide range of scenarios in a fraction of time it takes to perform the CFD simulations, making it particularly useful for large‐scale processes facilities. The accuracy of the predictions is remarkably high, providing a high level of confidence in the predictions of the flammable gas clouds sizes across various scenarios, as well as the resulting explosion overpressures.

Tetra geoemtric mean Runge-Kutta analysis of bioeconomic fisheries model

The optimal management and utilization of natural resources is eventually important, due to increase in the consumption of natural resources and in view of this, the main aim of fisheries is to maintain the marine organisms at levels that produces maximum sustainable yield (MSY). In this study, the one step numerical approximations schemes are developed to find the solution of such bioeconomic fisheries models whose analytical solution is not possible. In the proposed work, the explicit Runge-Kutta scheme based on fourth order tetra geometric mean is established and this method solve fishery management models of Schaefer and Gordon-Schaefer. The models analyze bioeconomic growth rate, carrying capacity, total and marginal costs and revenues. In solving, the models through proposed tetra geometric mean Runge-Kutta method, it is obtained that the method is easy to implement. The results obtained are much better as compared to other methods when compared in terms of errors and central processing unit (CPU) time.

A fast third order algorithm for two dimensional inhomogeneous fractional parabolic partial differential equations

A computationally fast third order numerical algorithm is developed for inhomogeneous parabolic partial differential equations. The algorithm is based on a third order method developed by using a rational approximation with single Gaussian quadrature pole to avoid complex arithmetic and to achieve high efficiency and accuracy. Difficulties with computational efficiency and accuracy are addressed using partial fraction decomposition technique. Third order accuracy and convergence of the method is proved analytically and verified numerically. Several classical as well as more challenging fractional and distributed order inhomogeneous problems are considered to perform numerical experiments. Computational efficiency of the method is demonstrated through central processing unit (CPU) time and is given in the convergence tables.