7236194
An Empirical Study on Improving the Manufacturing Informatization Index System of China
The manufacturing informatization index system (MIIS) is an indispensable tool to measure the informatization level of Chinese manufacturing industry and evaluate the implementation effect of the manufacturing informatization engineering (MIE) conducted by the government of China. Thus far, the constructs of MIIS has not been validated. This study fills this void by employing structural equation modeling (SEM) to test the MIIS model. The samples in this study come from the standard database of Chinese manufacturing informatization established by the data survey of MIE during the "Tenth Five-Year Plan" period, including 12896 enterprises samples from 11 manufacturing industries and 3472 support samples from 29 provinces of China. Based on the results of SEM analysis, some indexes of MIIS are adjusted and an improved MIIS is got at last. This empirical study proves that combining SEM technology and standard data resources would be an ideal method to improve MIIS.
5841267
The Data Mining Technology Based on CIMS and its Application on Automotive Remanufacturing
Nowadays, with the development of computer technology, data mining has been widely used in various fields. This paper describes a CIMSMINER that combines the data mining with CIMS (computer integrated manufacturing system) and instructs its objectives, model, physical architecture and methods. Considering the characteristics of remanufacturing of automotive products in China, the CIMSMINER is used to get the information concourse together and obtain the data mining results to help the improvement of products. The application in automotive remanufacturing is a reform to make the automotive product information chain be not only an information carrier, but also an information miner. Currently, the government strongly emphasizes energy saving and emission reducing, which is closely related to the sustainable development of China. Obviously, CIMSMINER is an effective tool to support the implementation of this policy.
3106142
MATLAB Simulation and Comparison of Zhang Neural Network and Gradient Neural Network for Online Solution of Linear Time-Varying Matrix Equation AXB-C=0
Different from gradient neural networks (GNN), a special kind ofrecurrent neural networks has been proposed recently by Zhanget alfor solving online linear matrix equations withtime-varying coefficients. Such recurrent neural networks, designedbased on a matrix-valued error-function, could achieve globalexponential convergence when solving online time-varying problemsin comparison with gradient neural networks. This paperinvestigates the MATLAB simulation of Zhang neural networks (ZNN)for real-time solution of linear time-varying matrix equationAXB- C= 0. Gradient neural networks aresimulated and compared as well. Simulation results substantiate thetheoretical analysis and efficacy of ZNN on linear time-varyingmatrix equation solving.
15785287
A Nonlinear Multigrid Method for Total Variation Minimization from Image Restoration
Abstract
Image restoration has been an active research topic and variational formulations are particularly effective in high quality recovery. Although there exist many modelling and theoretical results, available iterative solvers are not yet robust in solving such modeling equations. Recent attempts on developing optimisation multigrid methods have been based on first order conditions. Different from this idea, this paper proposes to use piecewise linear function spanned subspace correction to design a multilevel method for directly solving the total variation minimisation. Our method appears to be more robust than the primal-dual method (Chan et al., SIAM J. Sci. Comput. 20(6), 1964–1977, 1999) previously found reliable. Supporting numerical results are presented.

465165
Text-dependent speaker identification based on input/output HMMs: An empirical study
In this paper, we explore theInput/Output HMM (IOHMM) architecture for a substantial problem, that of text-dependent speaker identification. For subnetworks modeled with generalized linear models, we extend the IRLS algorithm to the M-step of the corresponding EM algorithm. Experimental results show that the improved EM algorithm yields significantly faster training than the original one. In comparison with the multilayer perceptron, the dynamic programming technique and hidden Markov models, we empirically demonstrate that the IOHMM architecture is a promising way to text-dependent speaker identification.
15894335
An adaptive load balancing framework for parallel database systems based on collaborative agents
Load balancing is a key technique in parallel computer supported collaborative work (CSCW) systems, parallel database system and P2P system for instance, to boost performance and improve scalability. In order to reduce total cost of ownership (TCO), adaptive/self-tuning administration techniques are gradually and extensively expected in the cyberspace. In parallel database systems, adaptive load balancing techniques are proposed to face the change in data storage patterns and access patterns in a dynamic real environment. The techniques utilized in both shared-nothing and shared-disk parallel database systems are discussed, and a general flexible framework based on collaborative agents is studied to support these techniques in both architectures. The framework supports two kinds of load balancing - one is passively executing query statements balancedly, and the other one is proactively adjusting data placement and task execution scheme, by means of data and task migration, whenever load unbalance is detected. Three categories of agents, scheduling agents, monitoring agents and task agents, are identified in the framework. The collaboration protocols and scheduling algorithms to support adaptive load balancing are described. The framework also applies to other parallel systems such as P2P systems and shared file processing systems due to their underlying commonness.
119485415
On efficient preconditioners for iterative solution of a Galerkin boundary element equation for the three-dimensional exterior Helmholtz problem
The paper presents a Galerkin numerical method for solving the hyper-singular boundary integral equations for the exterior Helmholtz problem in three dimensions with a Neumann's boundary condition. Previous work in the topic has often dealt with the collocation method with a piecewise constant approximation because high order collocation and Galerkin methods are not available due to the presence of a hypersingular integral operator. This paper proposes a high order Galerkin method by using singularity subtraction technique to reduce the hyper-singular operator to a weakly singular one. Moreover, we show here how to extend the previous work (J. Appl. Numer. Math. 36 (4) (2001) 475-489) on sparse preconditioners to the Galerkin case leading to fast convergence of two iterative solvers: the conjugate gradient normal method and the generalised minimal residual method. A comparison with the collocation method is also presented for the Helmholtz problem with several wavenumbers.