Effects of modeling means on properties of monitoring models of spot welding quality

Analyzing and modeling the relation between monitoring information during welding and quality information of the joints is the foundation of monitoring resistance spot welding quality. According to the means of modeling, the known models can be divided into three large categories: single linear regression models, multiple linear regression models and multiple non-linear models. By modeling the relations between dynamic resistance information and welding quality parameters with different means, this paper analyzes effects of modeling means on performances of monitoring models of resistance spot welding quality. From the test results, the following conclusions can be drawn: By comparison with two other kinds of models, artificial neural network (ANN) model can describe non-linear and high coupling relationship between monitoring information and quality information more reasonably, improve performance of monitoring model remarkably, and make the estimated values of welding quality parameters more accurate and reliable.     

基于PⅢ工控机VI测控系统远程控制研究

虚拟仪器（VI）将计算机采集测试分析引入到电子测量领域，用数字化和软件技术极大地提高了测量的灵活性。而VI远程测控系统是网络技术、通信技术一计算机虚拟技术结合的产物。远程控制技术能够使操作突破地域的限制，已在网络环境下PⅢ工控机上实现。     

基于一类T-模的模糊Hopfield网络的稳定性分析

本文建立了基于一类T-模的一种动态模糊神经网络-(∨,T)Hopfield网.文章首先证明了系统本身的稳定性及系统平衡态的Lyapunov稳定性,然后建立了吸引子的一个非平凡吸引域,使系统具有良好的容错性.最后,通过例子验证了得到的结论.     

Specification methods for material-handling control algorithms in flexible manufacturing systems

Good methods are needed to specify, test, and debug material-handling control logic. This article surveys a number of representative methods for defining and describing control algorithms for programmable material-handling equipment used in flexible manufacturing systems. The methods are evaluated with regard to their suitability for communication between people and as bases for interfaces to automatic program generators. It is concluded that no single method is entirely satisfactory. Three methods (position diagrams, function block diagrams, and operation networks) have potential to be combined into an effective hybrid approach that minimizes the need for the user to switch between various conceptual models.     

Synthesis and characterization of an interpenetrating polymer network composed of poly(methacrylic acid) and poly(vinyl alcohol)

Temperature and pH‐responsive interpenetrating polymer network (IPN) hydrogels, constructed with poly(methacrylic acid) (PMAA) and poly(vinyl alcohol) (PVA), by a sequential IPN method, were studied. The characterization of IPN hydrogels was investigated by Fourier‐transform infrared spectroscopy, differential scanning calorimetry (DSC) and swelling under various conditions. The IPN hydrogels exhibited relatively high swelling ratios, in the range 230–380 %, at 25 °C. The swelling ratios of the PMAA/PVA IPN hydrogels were pH and temperature dependent. DSC was used for the quantitative determination of the amounts of freezing and non‐freezing water. The amount of free water increased with increasing PMAA content in the IPN hydrogels. Copyright © 2004 Society of Chemical Industry     

Optimal ensemble construction via meta-evolutionary ensembles

In this paper, we propose a meta-evolutionary approach to improve on the performance of individual classifiers. In the proposed system, individual classifiers evolve, competing to correctly classify test points, and are given extra rewards for getting difficult points right. Ensembles consisting of multiple classifiers also compete for member classifiers, and are rewarded based on their predictive performance. In this way we aim to build small-sized optimal ensembles rather than form large-sized ensembles of individually-optimized classifiers. Experimental results on 15 data sets suggest that our algorithms can generate ensembles that are more effective than single classifiers and traditional ensemble methods.     

Simulations of the mobile phase of polyethylene

Results are presented from atomistic molecular dynamics simulations of the mobile pseudo-hexagonal phase of polyethylene, which occurs under conditions of elevated pressure and temperature. Three different types of model are considered, all of which employ periodic boundary conditions. The first model consists of n-alkane sequences (48×-C₂₄H₄₈-) that are bonded across the simulation box boundaries to produce chains that are effectively infinite in extent. On heating, at high pressure, this system displays a rotator phase, in which the chains retain an all-trans conformation, and rotate as semi-rigid units. A second model, consisting of finite n-alkanes (48×C₂₄H₅₀) displays the same behaviour at low temperatures, but at high temperature and pressure forms a conformationally disordered rotator phase, characterised by a large proportion of gauche defects and a significant lattice expansion. The final model considered contains long n-alkanes (24×C₁₀₂H₂₀₆) which contain jog defects and each pass twice through the simulation box. This model forms a conformationally disordered rotator phase at high temperature and ambient pressure. The behaviour of the three models, in terms of the variations in chain conformation and rotational and translational dynamics, are compared. The conformationally disordered phases provide useful representations of the experimentally observed mobile phase.     

Wavefront reduction using graphs,neural networks and genetic algorithm

This paper presents an algorithm for nodal numbering in order to obtain a small wavefront. Element clique graphs are employed as the mathematical models of finite element meshes. A priority function containing five vectors is used, which can be viewed as a generalization of Sloan's function. These vectors represent different connectivity properties of the graph models. Unlike Sloan's algorithm, which uses two fixed coefficients, here, five coefficients are employed, based on an evaluation by artificial neural networks. The networks weights are obtained using a simple genetic algorithm. Examples are included to illustrate the performance of the present hybrid method. Copyright © 2004 John Wiley & Sons, Ltd.     

神经网络在缺陷识别中的应用

为了准确、快速地进行缺陷识别，介绍了一种新型的前馈神经网络模型，即径向基概率神经网络。与以往的算法相比，该方法具有分类识别精度高且速度快的优点。仿真获得了很好的结果。     

Disorder effects on the strain response of model polymer networks

Molecular dynamics simulations are used to investigate polymer networks made by either end-linking or randomly crosslinking a melt of linear precursor chains. The resulting network structures are very different, since end-linking leads to nearly ideal monodisperse networks, while random crosslinking leads to polydisperse networks, characterized by an exponential strand length distribution. Networks with average strand length 20 and 100 were generated. These networks were used to study the effects of disorder in the network connectivity on observables averaged either over the entire network or selected sub-structures. Heterogeneities in the randomly crosslinked networks cause significant differences in the localization of monomers, however, neither the localization of crosslinks nor the microscopic strain response are significantly affected. Compared to end-linked networks, randomly crosslinked networks have a slightly increased tube diameter, and as a result a slightly decreased shear modulus, but otherwise identical stress-strain behavior. For the investigated systems, we conclude that the microscopic strain response, tube diameter, and stress-strain relation are all insensitive to the heterogeneities due to the linking process by which the network were made.