基于神经网络与光纤传感阵列的结构状态监测方法

以探测机敏复合材料与结构内应变、应力及损伤等物理状态的位置信息为目的．提出了一种新颖的可内埋于材料与结构内作为结构状态监测用的强度调制型光纤传感阵列网络，并采用人工神经网络来处理传感阵列输出的并行分布式传感信号，阐述了适用的Ｋｏｈｏｎｅｎ网模型及其变化形式，给出了仿真实验结果     

Necessary conditions for shear thickening in associating polymer networks

We study shear-thickening phenomena observed in thermoreversible gels of telechelic associating polymers on the basis of the transient network theory. We introduce nonlinear models for the elongational properties of the middle chain; the tension along middle chains contains a nonlinear term with an amplitude A that diverges if the chain is fully stretched. Models for the dissociation rate of end chains from network junctions are also introduced; the dissociation rate is coupled to the middle-chain tension by a coupling constant g. The balance between the nonlinearity strength and coupling intensity determines the necessary condition for thickening. A strong nonlinearity in the tension caused by a large A leads to thickening if the dissociation rate of the end chains is weakly coupled to the middle-chain tension. In contrast, if it is strongly coupled, the end chains easily dissociate upon a small nonlinear elongation of the middle chains, thus leading to thinning. We present theoretical phase diagrams showing the boundary of the thickening/thinning transition in the A–g plane, and we show the existence of a critical point for thickening in these diagrams.     

Neural network optimization of material composition of a functionally graded material plate at arbitrary temperature range and temperature rise

Summary A neural network model is applied to optimization problems of material compositions for a functionally graded material plate with arbitrarily distributed and continuously varied material properties in the thickness direction. Unsteady temperature distribution is evaluated by taking into account the bounds of the number of the layers. Thermal stress components for an infinite functionally graded material plate are formulated under traction-free mechanical conditions. As a numerical example, a plate composed of zirconium oxide and titanium alloy is considered. In the optimization problem of minimizing the thermal stress distribution, the numerical calculations are carried out making use of the neural network. The optimum material composition is determined by taking into account the effect of temperature-dependence of material properties. The results obtained by neural network and ordinary nonlinear programming method are compared. Received 3 March 1998; accepted for publication 22 May 1998     

Pore-scale network model for drainage-dominated three-phase flow in porous media

Drainage displacements in three-phase flow under strongly wetting conditions are completely described by a simple generalisation of well understood two-phase drainage mechanisms. As in two-phase flow, the sequence of throat invasions in three-phase flow is determined by fluid connectivity and threshold capillary pressure for the invading interface. Flow through wetting and intermediate spreading films is important in determining fluid recoveries and the progress of the displacement in three-phase flow. Viscous pressure drops associated with flow through films give rise to multiple filling and emptying of pores. A three-phase, two-dimensional network model based on the pore-scale fluid distributions and displacement mechanisms reported by Øren et al. and which accounts for flow through both wetting and intermediate fluid films is shown to correctly predict all the important characteristics of three-phase flow observed in glass micromodel experiments.     

Filtration in a Porous Granular Medium: 2. Application of Bubble Model to 1-D Column Experiments

This paper describes the formulation of a quasi-1-D network model, referred to as the ‘bubble model’, and its application for simulating particle transport and filtration through a granular filter bed. The model comprises a series of homogeneous sites linked through bundles of cylindrical bonds that represent flow pathways through distributions of pores and pore throats. This model incorporates pore scale processes of particle sieving and infiltration are based on numerical simulations described in a companion paper. The modeling of infiltration is further refined based on detailed experimental observations and measurements of the filtration of a dilute suspension of acrylic particles through a column of glass beads reported by Yoon et al. (2005 Water Resour. Res., to appear). Their data distinguish (a) between the collection of particles on grain surfaces and at grain-to-grain contact points, and (b) between particles that are fully entrapped and those that are hindered (temporarily collected) and can later become detached. These effects are represented by two parameters that characterize the probability of attachment and are linked to the surface roughness of the grains; one that describes the minimum particle size that can be fully entrapped, and one that describes the detachment rate. These parameters can be readily calibrated from conventional measurements of effluent concentration and effluent particle size distribution. Detailed comparisons with the data reported by Yoon et al. show that the proposed bubble model is able to achieve reliable predictions of the spatial distribution of particles within the filter bed following phases of particle injection and washing.     

基于多级BP神经网络的无线电罗盘多故障诊断研究

【】本文针对过拟合现象提出了基于提前停止法的学习率可变BP算法,运用多级BP神经网络诊断思想,提出了基于多级BP神经网络的多故障诊断方法。文中根据BP神经网络改进算法和网络撕裂法,对具体的机载无线电罗盘测向电路建模仿真,将复杂的无线电罗盘电路分解为3个子网络,并对每个子网络建立合适的故障集,快速准确诊断得出电路中的故障模块。     

基于跳频通信的移动机械遥控系统

为解决在同一个工作区域多个无线通信网络容易彼此干扰无法协同工作的问题,以移动机械工程车辆为控制对象,设计JRCJ500无线遥控系统,由用户手持终端接收用户操作指令,现场控制器驱动远程设备动作,实现对车辆的远程操作控制与状态信息反馈;采用STM32L低功耗处理器设计无线通信模块,选择公用通信频段,基于跳频扩频通信技术,约定无线通信数据格式与跳频频点,通过简单高效的跳频图案生成算法,在每一帧数据中添加同步字头,实现了无线通信网络的快速同步组网,保证了通信的稳定性,实现了在限定工作区域多台无线通信设备同时工作的应用需求。由于采用模块化设计,系统具有良好的通用性和可移植性,能够在多种复杂恶劣工作场合实现远程控制功能。     

Efficient quantification of the importance of contacts for the dynamical stability of proteins

Understanding the stability of the native state and the dynamics of a protein is of great importance for all areas of biomolecular design. The efficient estimation of the influence of individual contacts between amino acids in a protein structure is a first step in the reengineering of a particular protein for technological or pharmacological purposes. At the same time, the functional annotation of molecular evolution can be facilitated by such insight. Here, we use a recently suggested, information theoretical measure in biomolecular design - the Kullback-Leibler-divergence - to quantify and therefore rank residue-residue contacts within proteins according to their overall contribution to the molecular mechanics. We implement this protocol on the basis of a reduced molecular model, which allows us to use a well-known lemma of linear algebra to speed up the computation. The increase in computational performance is around 10(1)- to 10(4)-fold. We applied the method to two proteins to illustrate the protocol and its results. We found that our method can reliably identify key residues in the molecular mechanics and the protein fold in comparison to well-known properties in the serine protease inhibitor. We found significant correlations to experimental results, e.g., dissociation constants and Φ values.     

基于改进BP神经网络的煤催化气化预测模型研究

采用改进的三层BP神经网络建立了煤催化气化反应失重率、气化初始温度和最大气化速率所对应温度的预测模型。结果表明,采用改进BP神经网络模型在此研究中可达到较高的精度,其最大预测误差分别为5.18% 、5.65% 、2.33%,明显小于归回公式的预测误差。
     

Study on tribological behaviors of short glass fiber and polytetrafluoroethylene reinforced polycarbonate composites via a d‐optimal mixture design

This study analyzed variations of tribological behaviors that depend on the injection molding techniques during the blending of short glass fiber (SGF) and polytetrafluoroethylene (PTFE) reinforced polycarbonate (PC) composites. The proposed planning of blending experiments is to use a D‐optimal mixture design (DMD). The tribological behaviors of friction coefficient and wear mass loss were selected for discussion. Nine experimental runs, based on a DMD method, utilized to train the back‐propagation neural network (BPNN) and then the simulated annealing algorithm (SAA) approach is applied to search for an optimal mixture ratio setting. In addition, the result of BPNN integrating SAA was also compared with response surface methodology (RSM) approach. The results of confirmation experiment show that DMD, RSM, and BPNN integrating SAA method are effective tools for the optimization of reinforced process. Furthermore, the scanning electron microscope (SEM) images show that the abundant debris are peeled off from the matrix materials and predominant delamination mechanisms and plastic deformation are shown on the worn surface after tribological behavior tests. Copyright © 2010 John Wiley & Sons, Ltd.