用混合模型反演法计算水电站坝基弹性模量

给出了混合模型反演坝基弹模的基本公式和基本步骤,给出了实测位移为相对位移条件下的计算公式并对白水峪水电站大坝进行了具体计算,对坝基进行了安全评价.     

修正剑桥渗流耦合模型参数的估计

修正剑桥模型在国内外应用相当广泛 ,其与Biot固结理论耦合构成渗流耦合模型 .该模型含有 5个参数 ,若直接对其进行反分析结果不甚理想 .为此以上海地区软土为研究对象 ,回归得到了修正剑桥模型的压缩指数λ、回弹指数κ和应力比M与塑性指数Ip 之间的相关关系 ,相关系数分别为 0 .96 ,0 .98和 0 .92 .这样使修正剑桥渗流耦合模型参数由原来的 5个减少到 3个 .编制相应的基于遗传算法的反分析程序 ,对一高速公路路堤工程进行参数的反分析及位移预测 ,显示结果与实测位移值比较吻合 ,反分析效率较高 .同时又提出了根据土性和软硬程度在反分析之前给出泊松比的设想 ,以进一步提高反分析效率     

Optimization Method of the Side Member of the Automobile Seat

Buckling is the primary cause of failure of a sidemember in condition of overloading,so buckling stability should be taken into consideration in the design optimization of side member. On the other hand,lightweight is always a pursuing objective of manufacturer of automobile seat. Thus both the buckling stability and lightweight are considered in the suggested dcsign optimization mcthed of the side member of automobile seats. The method has two design phases. Firstly,the optimal shape of beck curve by using shape optimization was obtained in which the lightweight of the side member was set to be the design objective.Secondly,optimal size and distribution of grooves,on side side member was obtained by topography optimization in which the buckling critical load was set to be the design objective. A typical design example shows that the buckling critical load of the optirnal side member is increased by 25. 98%,and the weight is decreased by 3.7% simultaneously.     

自适应变异粒子群优化BP的短期风电功率预测模型

针对反向传播(BP)神经网络预测模型在风电预测中预测精度低、输入变量多的问题,提出一种基于互信息的自适应变异粒子群优化BP的短期风电功率预测模型.首先,采用互信息筛选出原始数据中与输出功率相关度较大的影响因素,减少冗余信息;然后,引入具有自适应惯性权重系数和变异因子思想的粒子群算法对预测模型进行优化.结果表明:与传统预测模型相比,该预测模型具有收敛速度快、预测精度高等特点.     

Soft Fault Diagnosis for Analog Circuits Based on Slope Fault Feature and BP Neural Networks

Fault diagnosis is very important for development and maintenance of safe and reliable electronic circuits and systems. This paper describes an approach of soft fault diagnosis for analog circuits based on slope fault feature and back propagation neural networks (BPNN). The reported approach uses the voltage relation function between two nodes as fault features; and for linear analog circuits, the voltage relation function is a linear function, thus the slope is invariant as fault feature. Therefore, a unified fault feature for both hard fault (open or short fault) and soft fault (parametric fault) is extracted. Unlike other NN-based diagnosis methods which utilize node voltages or frequency response as fault features, the reported BPNN is trained by the extracted feature vectors, the slope features are calculated by just simulating once for each component, and the trained BPNN can achieve all the soft faults diagnosis of the component. Experiments show that our approach is promising.     

弱电源侧稳态电压对称分量选相元件

针对输电线路弱馈侧母线稳态电压向量和故障点电压向量之间基本同向的特点，通过对输电线路各种短路故障情况下电压序分量关系的分析，提出了基于稳态电压序分量间相位关系和幅值关系的选相新原理．该原理所用的是电压稳态值，避免使用突变量，所以该选相元件可以在故障启动后整个过程都适用．该选相元件在选相过程中裕度角较大，有效地解决了弱电源侧的选相问题．EMTP仿真结果和动模试验表明，该选相元件能准确地选出故障相，完全能满足现场弱馈选相的要求．     

位移反分析的求解方法

对于位移反分析对应的线性代数方程组，本文说明了其解可能出现不稳定；根据线性方程组系数矩阵的奇异程度，介绍了合适的求解方法，最后，提出了一种有效的确定任意矩阵奇异值的迭代方法。     

Combinatorial Optimization Based Analog Circuit Fault Diagnosis with Back Propagation Neural Network

Electronic components＇ reliability has become the key of the complex system mission execution. Analog circuit is an important part of electronic components. Its fault diagnosis is far more challenging than that of digital circuit. Simulations and applications have shown that the methods based on BP neural network are effective in analog circuit fault diagnosis. Aiming at the tolerance of analog circuit,a combinatorial optimization diagnosis scheme was proposed with back propagation（ BP） neural network（ BPNN）.The main contributions of this scheme included two parts：（ 1） the random tolerance samples were added into the nominal training samples to establish new training samples,which were used to train the BP neural network based diagnosis model;（ 2） the initial weights of the BP neural network were optimized by genetic algorithm（ GA） to avoid local minima,and the BP neural network was tuned with Levenberg-Marquardt algorithm（ LMA） in the local solution space to look for the optimum solution or approximate optimal solutions. The experimental results show preliminarily that the scheme substantially improves the whole learning process approximation and generalization ability,and effectively promotes analog circuit fault diagnosis performance based on BPNN.     

一种基于灰度映射与融合技术的微光图像增强模型

针对低照度图像对比度差、暗区视觉质量差的问题,本文提出了一种全新的低照度图像增强算法.该方法能够有效地避免传统增强方法容易导致图像色彩失真以及亮区过度增强的问题.首先,设计了一种针对像素含量极少的灰度区域进行压缩的线性映射函数,该函数能够在保持图像灰度分布特征基本不变的前提下提升图像的整体亮度.然后,利用图像的灰度分布特征来生成相应的增强映射函数.最后,通过图像融合技术来进一步提升图像的整体色彩表现.实验结果表明,本算法在色彩表现方面以及亮度提升方面均有较好的性能表现,并在视觉质量和定量测量方面优于目前最先进的方法.     

BP小波神经网络在大断面隧道变形预测中的应用

将BP算法引入小波神经网络,自适应地调整小波系数和网络权重,同时利用自适应算法调节BP算法的学习率,提高收敛效率.以4车道隧道——前欧隧道的监测数据为基础,建立BP小波神经网络变形预测模型.预测结果表明:BP小波神经网络对地质条件相似,施工及初期支护方法相同的隧道断面变形进行预测,其预测结果满足工程精度要求,能较准确地...