Novel Minimum Passive Element Realization of All-Pass Network Using A Modified Current Conveyor

1. IntroductionA large number of networks for realizing first and second order transfer functions using a currentconveyor have been reported in the literature. Especially, the networks that can offer highinput impedance attract attention, for high input impedance has the advantage that the networksmay be used in cascade without requiring impedance matching device. In the Higashimura and

Neural network method for solving elastoplastic finite element problems

A basic optimization principle of Artificial Neural Network—the Lagrange Programming Neural Network (LPNN) model for solving elastoplastic finite element problems is presented. The nonlinear problems of mechanics are represented as a neural network based optimization problem by adopting the nonlinear function as nerve cell transfer function. Finally, two simple elastoplastic problems are numerically simulated. LPNN optimization results for elastoplastic problem are found to be comparable to traditional Hopfield neural network optimization model.

Characterizing the influence of stress-induced microcracks on the laboratory strength and fracture development in brittle rocks using a finite-discrete element method-micro discrete fracture network FDEM-μDFN approach

Heterogeneity is an inherent component of rock and may be present in different forms including mineralheterogeneity, geometrical heterogeneity, weak grain boundaries and micro-defects. Microcracks areusually observed in crystalline rocks in two forms： natural and stress-induced; the amount of stressinducedmicrocracking increases with depth and in-situ stress. Laboratory results indicate that thephysical properties of rocks such as strength, deformability, P-wave velocity and permeability areinfluenced by increase in microcrack intensity. In this study, the finite-discrete element method （FDEM）is used to model microcrack heterogeneity by introducing into a model sample sets of microcracks usingthe proposed micro discrete fracture network （mDFN） approach. The characteristics of the microcracksrequired to create mDFN models are obtained through image analyses of thin sections of Lac du Bonnetgranite adopted from published literature. A suite of two-dimensional laboratory tests including uniaxial,triaxial compression and Brazilian tests is simulated and the results are compared with laboratory data.The FDEM-mDFN models indicate that micro-heterogeneity has a profound influence on both the mechanicalbehavior and resultant fracture pattern. An increase in the microcrack intensity leads to areduction in the strength of the sample and changes the character of the rock strength envelope. Spallingand axial splitting dominate the failure mode at low confinement while shear failure is the dominantfailure mode at high confinement. Numerical results from simulated compression tests show thatmicrocracking reduces the cohesive component of strength alone, and the frictional strength componentremains unaffected. Results from simulated Brazilian tests show that the tensile strength is influenced bythe presence of microcracks, with a reduction in tensile strength as microcrack intensity increases. Theimportance of microcrack heterogeneity in reproducing a bi-linear or S-shape failure envelope and itseffects on the mechanisms leading to spalling damage near an underground opening are also discussed.

ANALYSIS OF MICROWAVE N-PORT NETWORK BY A MODIFIED BOUNDARY ELEMENT METHOD

A new approach based on resonance technique and modified boundary ele-ment method is presented to calculate the impedance parameter matrix of a microwaveN-port network of waveguide structure.A two port network is taken as a numerical ex-ample and the results show that the approach occupys the advantages of high accuracyand less computation effort.

卷积神经网络与有限元分析在棒材轧制孔型参数仿真中的应用

为了实现智能化轧制仿真过程,提出了将卷积神经网络(CNN)与有限元分析相结合的方法,利用机器代替人工选择出最佳的轧制仿真结果,并确定最优的孔型参数.首先,将CNN用于轧件道次分类,可发现“卷积神经网络+图像预处理”测试模型精度最高,为91.84%~96%;其次,使用5种评估方法对比轧制仿真结果与理想仿真结果后可得出,均方根误差和感知哈希对仿真结果更为敏感;最后,将孔型参数朝轧制仿真结果变优方向进行修正,对比仿真结果与修正前结果后发现,第2次修正更为理想.本研究中实现了CNN对棒材轧制道次分类,利用机器识别出更优的轧制仿真结果,并确定了更适合的孔型参数,在降低人力、物力消耗,以及实现仿真智能化等方面具有重要的理论意义和实际应用价值.

Determination of penetration depth at high velocity impact using finite element method and artificial neural network tools

Determination of ballistic performance of an armor solution is a complicated task and evolved significantly with the application of finite element methods(FEM) in this research field.The traditional armor design studies performed with FEM requires sophisticated procedures and intensive computational effort,therefore simpler and accurate numerical approaches are always worthwhile to decrease armor development time.This study aims to apply a hybrid method using FEM simulation and artificial neural network(ANN) analysis to approximate ballistic limit thickness for armor steels.To achieve this objective,a predictive model based on the artificial neural networks is developed to determine ballistic resistance of high hardness armor steels against 7.62 mm armor piercing ammunition.In this methodology,the FEM simulations are used to create training cases for Multilayer Perceptron(MLP) three layer networks.In order to validate FE simulation methodology,ballistic shot tests on 20 mm thickness target were performed according to standard Stanag 4569.Afterwards,the successfully trained ANN(s) is used to predict the ballistic limit thickness of 500 HB high hardness steel armor.Results show that even with limited number of data,FEM-ANN approach can be used to predict ballistic penetration depth with adequate accuracy.

基于斜坡单元灾害强度的滑坡灾害易损性评价——以湖南省湘乡市为例

以斜坡为单元,基于潜在灾害强度的区域性易损性评价是地质灾害防治亟待解决的重要问题之一。以湖南省湘乡市为研究区,在采用加权信息量方法进行易发性区划的基础上,逐个提取斜坡单元最高易发值点的高程、坡高、坡度、坡向、月平均降雨量为特征参数,分别代入BP神经网络、PSO-BP神经网络、随机森林及支持向量机模型。通过训练与精度测试对比,构建基于PSO优化BP神经网络算法的滑坡体积预测模型,建立以灾害体积为灾害强度指标,以建筑密度、人口密度、财产密度等为脆弱性指标的易损性综合评价模型。针对研究区开展基于潜在灾害强度的区域性易损性评价,完成高易损区(面积占比1.5%)、中易损区(面积占比28.5%)和低易损区(面积占比70%)的区划,实现了区域性易损性评价过程中致灾体灾害强度与承灾体脆弱性的有机结合,增强了评价的客观性和科学性。

YASA轴向磁通永磁电机定子槽漏感计算

定子槽漏感是定子无轭模块化(yokeless and segmented,YASA)轴向磁通永磁电机电感的主要分量,会降低电机的功率因数和最大输出转矩。该文提出定子槽内微小单元磁场能量法计算YASA电机的定子槽漏感,通过建立计及铁心饱和影响的二维等效磁网络模型准确计算槽内微小单元储存的磁场能量,进而计算定子槽漏感。基于所提出的方法,进一步研究定子结构参数以及极槽配合对YASA电机定子槽漏感的影响规律。有限元仿真和实验结果表明,该文所提出的YASA电机定子槽漏感计算方法的可行性和准确性。

基于有限元数据集建立髋臼周围截骨术的骨刀变形预测模型

目的建立一种用于髋臼周围截骨(periacetabular osteotomy,PAO)手术的骨刀变形预测模型,快速且精准地预测骨刀变形。方法建立包含皮质骨和松质骨的骨盆-骨刀有限元数值模型,分析节点应变与形变之间的相关性,并优选综合相关性最强的5个节点的应变作为输入,选择刀刃部分节点的位移增量作为输出。通过数据集训练模型后,采用基于有限元数据集的深度学习神经网络回归模型,建立骨刀应变-形变预测模型。在模型预测完成后,利用双目视觉定位系统确定截骨手术过程中骨刀的空间精确位置,以此对骨刀进行术中跟踪。结果预测模型的R^(2)为0.98781,骨刀离散成节点后的平均变形误差为0.07 mm。该预测模型能够快速且精确地获取骨刀变形,在降低PAO手术事故方面显示出巨大的潜力。结论建立的骨刀变形预测模型可通过骨刀的应变信息快速预测骨刀的变形,从而避免伤及组织周围神经和血管等组织,降低PAO术中难度和风险,并为临床应用提供理论支撑。

山区“铁路工程-水土流失”系统关键要素识别

建立了一种识别山区铁路工程水土流失关键要素的模型。首先确定易引发水土流失的铁路重点专业,分析“铁路工程-水土流失”的交互影响关系,识别“铁路工程-水土流失”要素,并构建其指标体系。然后基于复杂网络模型,结合熵权法、层次分析法(IGAHP)法、DWNodeRank算法构建一种多层网络关键要素识别模型。最后以山区某铁路工程为例,该山区铁路工程关键要素有弃渣处置、路基边坡防护、路基截排水沟布设等,水土保持性能关键要素有大型临时土地扰动、渣土弃置等,水土流失关键要素有水文、地表植被、流失物质。在山区铁路工程水土流失控制时需重点针对以上关键要素进行水土保持措施和工程结构的设计,以期有效地控制区域水土流失,改善和恢复破坏的生态环境。

Optimizing Data Collection Path in Sensor Networks with Mobile Elements

Exploiting mobile elements （MEs） to accomplish data collection in wireless sensor networks （WSNs） can improve the energy efficiency of sensor nodes, and prolong network lifetime. However, it will lead to large data collection latency for the network, which is unacceptable for data-critical applications. In this paper, we address this problem by minimizing the traveling length of MEs. Our methods mainly consist of two steps： we first construct a virtual grid network and select the minimal stop point set （SPS） from it; then, we make optimal scheduling for the MEs based on the SPS in order to minimize their traveling length. Different implementations of genetic algorithm （GA） are used to solve the problem. Our methods are evaluated by extensive simulations. The results show that these methods can greatly reduce the traveling length of MEs, and decrease the data collection latency.

山区铁路工程“路基-环境”绿色关键要素识别研究

为推动山区铁路路基工程与环境之间绿色优质协调发展,提出一种铁路“路基-环境”耦合关系分析及关键要素确定方法。首先,构建“路基-环境”交互影响网络,引入木桶理论构建绿色要素集;然后,运用系统耦合模型,测算系统耦合协调度并评估组合状态,分析铁路路基与环境的绿色协调状况;最后,借助复杂网络理论,以要素为节点,要素间影响强度为连边,形成协同、影响、扩散多层复杂关联网络,采取spearman和二维云模型相结合的方式确定多层网络要素耦合强度,并结合节点删除法确定铁路“路基-环境”的关键要素集。以某铁路路基为例进行研究,得出铁路路基与环境之间的耦合协调状态处于勉强协调和初级协调之间的结论,因此应对路基边坡植被防护、路堑开挖尺寸、路堤填筑尺寸、路基边坡工程防护4个关键要素加以重点管理,以实现铁路路基工程绿色化。

液压门式起重机受力及稳定性监测系统研究与应用

提出了一种液压门式起重机受力及稳定性预测监测系统。根据实际参数建立了起重机结构起升单元有限元模型,分析起升单元在不同步状态下起重机相关结构的受力情况,使用有限元分析得出的数据训练BP神经网络预测模型,最终结合测量数据来快速获取任意状态下起重机相关结构的受力情况。通过实验验证了该系统的可行性,提高了液压门式起重机的安全监测能力。

基于模糊结构元的新零售末端配送网络优化研究

新零售模式下,高时效性的物流末端配送对提高顾客满意度具有重要作用,而对末端配送中心位置和数量的确定,是优化末端配送网络的关键。首先,综合考虑影响配送中心位置与数量的模糊因素,构建以配送时间最少、配送中心覆盖范围最大以及系统成本最低为目标的模糊多目标规划模型;其次,采用结构元加权序准则法求解模型,生成三角模糊数简化求解过程;最后,以算例检验方法的有效性。结果表明,所提出的模型能够将模糊因素融入模糊决策过程中,且结构元加权序准则法有助于决策者灵活调整方案和决策,从而对新零售末端配送网络布局进行优化。

基于有限元法的油气田集输管网阴极保护布局优化

在利用有限元法建立外加电流阴极保护模型的基础上,借助Sobol灵敏度分析,确定了显著影响某油气田集输管网阴极保护效果的关键参数,并通过粒子群优化(PSO)算法对其系统布局进行优化。结果表明:由于外加电流阴极保护模型的模拟电位与测试桩的测定电位误差较小,故该模型能够较好地反映出油气田集输管网的电位分布现状;改变防护涂层电阻率等一阶敏感性系数较高的因素会对阴极保护效果产生较大影响,土壤电阻率、防护涂层电阻率以及管道埋深与其他参数之间可能存在明显交互作用;当利用PSO算法对油气田集输管网的阴极保护效果进行全局寻优时,该模型经过47700次粒子进化迭代后的最佳阴极保护系统有效覆盖率高达99.14%,优化效果显著。

红色旅游区文化元素旅游体验质量:基于网络文本数据

红色旅游区文化元素的挖掘和识别是提升游客旅游体验质量的关键。文章综合运用网络文本分析、扎根理论以及IPA分析等方法,以西柏坡红色旅游区为例,构建红色旅游区文化元素的概念模型,分析和评价大数据下红色旅游区文化元素的旅游体验质量。研究结果表明:游客对西柏坡红色旅游区的整体感知主要集中在历史回顾、红色之旅、旅游吸引物和红色精神上,且情感整体呈现积极状态;从旅游体验内容来看,风土人情、历史印记、文化体验三个层面既是游客旅游体验内容的来源,同时也构成游客对西柏坡红色旅游区文化元素的感知时间序列;就旅游体验质量而言,游客对西柏坡红色旅游区的相关评价整体呈上升趋势,自然景观和人文精神给游客带来较高质量的旅游体验,但生活氛围、建筑风貌、红色故事挖掘等方面还存在诸多问题亟待改善。

基于希尔伯特变换的电网络约束关系研究

为了研究电压波动条件下的电网络约束关系,文章基于希尔伯特变换,构建了供电电压存在波动下线性时不变负载上电压、电流的解析信号模型,推导出了以解析信号表征线性时不变电网络的拓扑约束和元件约束关系,将相量法思想拓展至对供电电压波动下线性时不变电网络响应特性的分析与求解。仿真验证结果表明,供电电压存在波动条件下,文中所建立的基于希尔伯特变换的电网络约束关系,不仅物理意义清晰、表征简明,而且正确、好用。文章所建立的电网络约束关系,能够为供电电压波动条件下电网络中线性时不变负载上电压电流关系的建模、仿真分析与计算提供指导。