无约束最优化问题的BFGS并行算法与实现

介绍无约束最优化问题的BFGS算法及其收敛性,提出利用行卷帘格式并行Cholesky分解法、同步并行Wolfe-Powell非线性搜索和并行处理BFGS修正公式来构建BFGS的并行算法,并对该算法的时间复杂性、加速比进行分析。在PC机群数值实验的结果表明,BFGS并行算法提高了无约束最优化问题的求解速度,理论分析与实验结果相一致,并行算法具有线性加速比。     

基于网格的散乱点曲面重构技术

提出一种利用网格技术实现散乱点曲面重构的方法,设计适合网格并行的松耦合分层重构算法,在此基础上研究基于Agent技术的智能任务分解机制和基于Condor-G技术的任务分配与调度策略,并搭建网格实验平台进行测试。对重构结果的分析表明,该技术可提高基于散乱点的曲面重构速度,降低应用成本。     

基于多代理的CSCL系统成员感知实现

针对一般基于多代理技术的CSCL系统中没有有效感知机制的问题,提出将具有感知功能的学习者Agent应用于协作学习系统中,描述学习者Agent的内部结构,给出感知功能模块中小组任务的分解和原子任务图的构造算法,定义感知强度的计算函数,对感知信息的呈现过程进行描述,有效解决现有协作学习系统中群体互动效果差、感知信息混乱的问题。     

基于小波分解和鉴别共同矢量的人耳识别

针对高维、小样本的情况下使用Fisher线形鉴别分析进行特征提取存在的病态奇异问题,提出一种新的特征提取方法,即先对人耳样本图像进行二维离散小波分解,再利用DCV算法对小波分解后的低频信息分量作进一步的降维处理。不仅克服了小样本问题,也解决了直接使用DCV算法对人耳图像降维所引起的计算量大和计算速度过慢的问题。实验证明,该方法具有较好的识别率,是一种有效的特征提取算法。     

Prediction of landslide displacement with controlling factors using extreme learning adaptive neuro-fuzzy inference system (ELANFIS)

Landslide is a major geo-environmental hazard which imparts serious threat to lives and properties. The slope failures are due to adverse inherent geological conditions triggered by an external factor. This paper proposes a new method for the prediction of displacement of step-like landslides, by accounting the controlling factors, using recently proposed extreme learning adaptive neuro-fuzzy inference system (ELANFIS) with empirical mode decomposition (EMD) technique. ELANFIS reduces the computational complexity of conventional ANFIS by incorporating the theoretical idea of extreme learning machines (ELM). The rainfall data and reservoir level elevation data are also integrated into the study. The nonlinear original landslide displacement series, rainfall data, and reservoir level elevation data are first converted into a limited number of intrinsic mode functions (IMF) and one residue. Then decomposed displacement data are predicted by using appropriate ELANFIS model. Final prediction is obtained by the summation of outputs of all ELANFIS sub models. The performance of proposed the technique is tested for the prediction Baishuihe and Shiliushubao landslides. The results show that ELANFIS with EMD model outperforms other methods in terms of generalization performance.     

Large-scale linear system solver using secondary storage: Self-energy in hybrid nanostructures

We present a Fortran library which can be used to solve large-scale dense linear systems, Ax=b. The library is based on the LU decomposition included in the parallel linear algebra library PLAPACK and on its out-of-core extension POOCLAPACK. The library is complemented with a code which calculates the self-polarization charges and self-energy potential of axially symmetric nanostructures, following an induced charge computation method. Illustrative calculations are provided for hybrid semiconductor–quasi-metal zero-dimensional nanostructures. In these systems, the numerical integration of the self-polarization equations requires using a very fine mesh. This translates into very large and dense linear systems, which we solve for ranks up to 3×¹⁰⁵. It is shown that the self-energy potential on the semiconductor–metal interface has important effects on the electronic wavefunction.

Program summary

Program title: HDSS (Huge Dense System Solver)Catalogue identifier: AEHU_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEHU_v1_0.htmlProgram obtainable from: CPC Program Library, Queen's University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 98 889No. of bytes in distributed program, including test data, etc.: 1 009 622Distribution format: tar.gzProgramming language: Fortran 90, CComputer: Parallel architectures: multiprocessors, computer clustersOperating system: Linux/UnixHas the code been vectorized or parallelized?: Yes. 4 processors used in the sample tests; tested from 1 to 288 processorsRAM: 2 GB for the sample tests; tested for up to 80 GBClassification: 7.3External routines: MPI, BLAS, PLAPACK, POOCLAPACK. PLAPACK and POOCLAPACK are included in the distribution file.Nature of problem: Huge scale dense systems of linear equations, Ax=B, beyond standard LAPACK capabilities. Application to calculations of self-energy potential in dielectrically mismatched semiconductor quantum dots.Solution method: The linear systems are solved by means of parallelized routines based on the LU factorization, using efficient secondary storage algorithms when the available main memory is insufficient. The self-energy solver relies on an induced charge computation method. The differential equation is discretized to yield linear systems of equations, which we then solve by calling the HDSS library.Restrictions: Simple precision. For the self-energy solver, axially symmetric systems must be considered.Running time: About 32 minutes to solve a system with approximately 100 000 equations and more than 6000 right-hand side vectors using a four-node commodity cluster with a total of 32 Intel cores.     

Research and Implementation of Task Management Model in Product Development Process Management

The implementation of product development process management (PDPM) is an effective means of developing products with higher quality in shorter lead time. It is argued in this paper that product, data, person and activity are basic factors in PDPM With detailed analysis of these basic factors and their relations in product developmed process, all product development activities are considered as tasks and the management of product development process is regarded as the management of task execution A task decomposition based product development model is proposed with methods of constructing task relation matrix from layer model and constraint model resulted from task decomposition. An algorithm for constructing directed task graph is given and is used in the management of tasks. Finally, the usage and limitation of the proposed PDPM model is given with further work proposed.     

Influence-based model decomposition for reasoning about spatially distributed physical systems

Many important science and engineering applications, such as regulating the temperature distribution over a semiconductor wafer and controlling the noise from a photocopy machine, require interpreting distributed data and designing decentralized controllers for spatially distributed systems. Developing effective computational techniques for representing and reasoning about these systems, which are usually modeled with partial differential equations (PDEs), is one of the major challenge problems for qualitative and spatial reasoning research.

This paper introduces a novel approach to decentralized control design, influence-based model decomposition, and applies it in the context of thermal regulation. Influence-based model decomposition uses a decentralized model, called an influence graph, as a key data abstraction representing influences of controls on distributed physical fields. It serves as the basis for novel algorithms for control placement and parameter design for distributed systems with large numbers of coupled variables. These algorithms exploit physical knowledge of locality, linear superposability, and continuity, encapsulated in influence graphs representing dependencies of field nodes on control nodes. The control placement design algorithms utilize influence graphs to decompose a problem domain so as to decouple the resulting regions. The decentralized control parameter optimization algorithms utilize influence graphs to efficiently evaluate thermal fields and to explicitly trade off computation, communication, and control quality. By leveraging the physical knowledge encapsulated in influence graphs, these control design algorithms are more efficient than standard techniques, and produce designs explainable in terms of problem structures.     

基于增广矩阵束方法的平面天线阵列综合

针对平面阵列的稀布优化问题,提出了一种基于增广矩阵束方法的减少阵元数目、求解阵元位置和设计幅度激励的优化方法。首先对期望平面阵的方向图进行采样并由采样点数据构造增广矩阵,对此矩阵进行奇异值(SVD)分解,确定在误差允许范围内所需的最小阵元数目;然后基于广义特征值分解分别计算两组特征值,并根据类ESPRIT算法对特征值进行配对;最后在最小二乘准则条件下根据正确的特征值对求解平面阵列的阵元位置和激励。仿真结果表明该算法具有较高的计算效率和数值精度。