parallel

摘要： In low signal-to-noise ratio(SNR)environments,the traditional radar emitter recognition(RER)method struggles to recognize multiple radar emitter signals in parallel.This paper proposes a multi-label classification and recognition method for multiple radar-emitter modulation types based on a residual network.This method can quickly perform parallel classification and recognition of multi-modulation radar time-domain aliasing signals under low SNRs.First,we perform time-frequency analysis on the received signal to extract the normalized time-frequency image through the short-time Fourier transform(STFT).The time-frequency distribution image is then denoised using a deep normalized convolutional neural network(DNCNN).Secondly,the multi-label classification and recognition model for multi-modulation radar emitter time-domain aliasing signals is established,and learning the characteristics of radar signal time-frequency distribution image dataset to achieve the purpose of training model.Finally,time-frequency image is recognized and classified through the model,thus completing the automatic classification and recognition of the time-domain aliasing signal.Simulation results show that the proposed method can classify and recognize radar emitter signals of different modulation types in parallel under low SNRs.

摘要： In this paper,a new computation scheme based on parallelization is proposed for Isogeometric analysis.The parallel computing is introduced to the whole progress of Isogeometric analysis.Firstly,with the help of the“tensorproduct”and“iso-parametric”feature,all the Gaussian integral points in particular element can be mapped to a global matrix using a transformation matrix that varies from element.Then the derivatives of Gauss integral points are computed in parallel,the results of which can be stored in a global matrix.And a middle layer is constructed to assemble the final stiffness matrices in parallel.The numerical example results show that:the method presented in the paper can reduce calculation time and improve the use rate of computing resources.

摘要： Higher-order displacement-based finite element methods are useful for simulating bending problems and potentially addressing mesh-locking associated with nearly-incompressible elasticity,yet are computationally expensive.To address the computational expense,the paper presents a matrix-free,displacement-based,higher-order,hexahedral finite element implementation of compressible and nearly-compressible(ν→0.5)linear isotropic elasticity at small strain with p-multigrid preconditioning.The cost,solve time,and scalability of the implementation with respect to strain energy error are investigated for polynomial order p=1,2,3,4 for compressible elasticity,and p=2,3,4 for nearly-incompressible elasticity,on different number of CPU cores for a tube bending problem.In the context of this matrix-free implementation,higher-order polynomials(p=3,4)generally are faster in achieving better accuracy in the solution than lower-order polynomials(p=1,2).However,for a beam bending simulation with stress concentration(singularity),it is demonstrated that higher-order finite elements do not improve the spatial order of convergence,even though accuracy is improved.

摘要： In this paper, we construct a class of semi-implicit difference method for time fractional diffusion equations—the group explicit (GE) difference scheme, which is a difference scheme with good parallelism constructed using Saul’yev asymmetric scheme. The stability and convergence of the GE scheme of time fractional diffusion equation are analyzed by mathematical induction. Then, the theoretical analysis is verified by numerical experiments, which shows that the GE scheme is effective for solving the time fractional diffusion equation.

摘要： Parallel Kinematic Machines(PKMs)are being widely used for precise applications to achieve complex motions and variable poses for the end effector tool.PKMs are found in medical,assembly and manufacturing industries where accuracy is necessary.It is often desired to have a compact and simple architecture for the robotic mechanism.In this paper,the kinematic and dynamic analysis of a novel 3-PRUS(P:prismatic joint,R:revolute joint,U:universal joint,S:spherical joint)parallel manipulator with a mobile platform having 6 Degree of Freedom(Do F)is explained.The kinematic equations for the proposed spatial parallel mechanism are formulated using the Modified Denavit-Hartenberg(DH)technique considering both active and passive joints.The kinematic equations are used to derive the Jacobian matrix of the mechanism to identify the singular points within the workspace.A Jacobian based sti ness analysis is done to understand the variations in sti ness for different poses of the mobile platform and further,it is used to decide trajectories for the end effector within the singularity free region.The analytical model of the robot dynamics is presented using the Euler-Lagrangian approach with Lagrangian multipliers to include the system constraints.The gravity and inertial forces of all links are considered in the mathematical model.The analytical results of the dynamic model are compared with ADAMS simulation results for a pre-defined trajectory of the end effector.

摘要： If Goldbach’s conjecture is true, then for each prime number p there is at least one pair of primes symmetric with respect to p and whose sum is 2p. In the multiplicative number theory, covering the positive integers with primes, during the prime factorization, may be viewed as being the outcome of a parallel system which functions properly if and only if Euler’s formula of the product of the reciprocals of the primes is true. An exact formula for the number of primes less than or equal to an arbitrary bound is given. This formula may be implemented using Wolfram’s computer package Mathematica.

摘要： Accurate and timely monthly rainfall forecasting is a major challenge for the scientific community in hydrological research such as river management project and design of flood warning systems. Support Vector Regression (SVR) is a very useful precipitation prediction model. In this paper, a novel parallel co-evolution algorithm is presented to determine the appropriate parameters of the SVR in rainfall prediction based on parallel co-evolution by hybrid Genetic Algorithm and Particle Swarm Optimization algorithm, namely SVRGAPSO, for monthly rainfall prediction. The framework of the parallel co-evolutionary algorithm is to iterate two GA and PSO populations simultaneously, which is a mechanism for information exchange between GA and PSO populations to overcome premature local optimum. Our methodology adopts a hybrid PSO and GA for the optimal parameters of SVR by parallel co-evolving. The proposed technique is applied over rainfall forecasting to test its generalization capability as well as to make comparative evaluations with the several competing techniques, such as the other alternative methods, namely SVRPSO (SVR with PSO), SVRGA (SVR with GA), and SVR model. The empirical results indicate that the SVRGAPSO results have a superior generalization capability with the lowest prediction error values in rainfall forecasting. The SVRGAPSO can significantly improve the rainfall forecasting accuracy. Therefore, the SVRGAPSO model is a promising alternative for rainfall forecasting.

摘要： In this paper, LPT (largest processing time) algorithm is considered for scheduling jobs with similar sizes on three machines. The objective function is to minimize the maximum completion time of all machines. The worst case performance ratio of the LPT algorithm is given as a piecewise linear function of r if job sizes fall in [1, r]. Our result is better than the existing result. Furthermore, the ratio given here is the best. That means our result cannot be improved any more.

摘要： We apply the recent important result of serial sorting of n real numbers in time to the design of a parallel algorithm for sorting real numbers in time and operations. This is the first NC algorithm known to take operations for sorting real numbers.

摘要： Particle accelerators play an important role in a wide range of scientific discoveries and industrial applications. The self-consistent multi-particle simulation based on the particle-in-cell (PIC) method has been used to study charged particle beam dynamics inside those accelerators. However, the PIC simulation is time-consuming and needs to use modern parallel computers for high-resolution applications. In this paper, we implemented a parallel beam dynamics PIC code on multi-node hybrid architecture computers with multiple Graphics Processing Units (GPUs). We used two methods to parallelize the PIC code on multiple GPUs and observed that the replication method is a better choice for moderate problem size and current computer hardware while the domain decomposition method might be a better choice for large problem size and more advanced computer hardware that allows direct communications among multiple GPUs. Using the multi-node hybrid architectures at Oak Ridge Leadership Computing Facility (OLCF), the optimized GPU PIC code achieves a reasonable parallel performance and scales up to 64 GPUs with 16 million particles.

摘要： 本发明公开一种基于Parallel_ModCTANE的一致性检测模型构建方法，包括对CTANE算法进行改进；对水文数据进行分布式并行条件函数依赖挖掘；对条件函数依赖集进行过滤；对水文数据进行基于主数据的联表不一致性检测，得到水文数据的不一致性检测结果。本发明对数据进行初步清洗后，结合Parallel_ModCTANE方法进行分布式并行条件函数依赖挖掘，使得条件函数依赖挖掘的效率更高，使用基于主数据的联表不一致性检测算法进行一致性检测，使得检测的效率更高。

摘要： 本发明公开了一种提取Parallels Desktop虚拟机的hds镜像数据的方法，其特征在于包括以下步骤：S100：获取并解析磁盘中所述hds镜像数据的头文件，获取头文件信息，并计算表项大小＝所述块管理表大小/所述块管理表的表项数；S200：以所述头文件的结束地址的下一字节地址为起始地址，根据所述块管理表的表项数及所述表项大小，读取块管理表，所述块管理表以X字节为一组，其中，X等于所述表项大小；S300：遍历块管理表：根据步骤S200所获取的各个表项，获取所述hds镜像数据的文件系统；S400：根据所述hds镜像数据的文件系统与目标文件系统的对应关系，在所述hds镜像数据的文件系统中提取目标文件。

摘要： 本发明涉及建筑结构风压预测技术领域，公开了一种基于parallel CNN‑GRU的风压短期预测方法、装置和计算机设备，其中，方法包括：获取建筑结构的原始风压数据；构建parallelCNN‑GRU神经网络模型；利用所述原始风压数据对所述parallel CNN‑GRU神经网络模型进行训练，得到训练完成的parallel CNN‑GRU神经网络模型；采集建筑结构的实时风压数据，并将所述实时风压数据输入所述训练完成的parallel CNN‑GRU神经网络模型，得到所述建筑结构的完整风压数据。本发明提供了的基于parallel CNN‑GRU的风压短期预测方法、装置和计算机设备，解决了现有风压数据采集中异常、失真、损失数据频繁出现的问题。