Detection of delamination in laminated composites with limited measurements combining PCA and dynamic QPSO

This paper presents an output only damage diagnostic algorithm based on frequency response functions and the principal components for health monitoring of laminated composite structures. The principal components evaluated from frequency response data, are employed as dynamical invariants to handle the effects of operational/environmental variability on the dynamic response of the structure. Finite element models of a laminated composite beam and plate are used to generate vibration data for healthy and damaged structures. Three numerical examples include a laminated composite beam, cantilever plate made of carbon–epoxy and a laminated composite simply supported plate. Varied levels of delamination of laminated composite plies and matrix cracking at varied locations in the plies are simulated at different spatial locations of the structure. Numerical investigations have been carried out to identify the spatial location of damage using the proposed principal component analysis (PCA) based algorithm. In order to limit the number of sensors on the structure, an optimal sensor placement algorithm based on PCA is employed in the present work and the effectiveness of the proposed algorithm with a limited number of sensors is also investigated. Finally, the inverse problem associated with the detection of delamination and matrix cracking is formulated as an optimization problem and is solved using the newly developed dynamic quantum particle swarm optimization (DQPSO) algorithm. Studies carried out and presented in this paper clearly indicate that the proposed SHM scheme can robustly identify the instant of damage, spatial location, the extent of delamination and matrix cracking even with limited sensor measurements and also with noisy data.     

A novel control algorithm for dephosphorization in an LD converter

This paper proposes a new control algorithm for dephosphorization in the steelmaking process using a LD converter. The control algorithm is composed of two steps, which achieves a low phosphorous content in the hot metal after treatment. The first step creates target concentration for the iron oxide in the slag for the next heat, which is optimal to achieve effective dephosphorization. The target concentration is obtained after applying independent component analysis (ICA) to the concentration data of past heats. The second step decides the control inputs for the next heat, namely the oxygen blowing rate and the lance height by calculating a weighted summation of past control inputs. The weight is based on the difference between the target concentration and concentration of each past heat. The proposed algorithm was applied to an actual process, and the results showed high control performance at the end of this paper.     

Automatic generation algorithm of expected results for testing of component-based software system

ContextA component-based software (CBS) system is a typical example of a composite component, which is composed of two or more atomic components. In the test of a CBS system, it is necessary to automatically generate expected results because they are compared with the actual results.ObjectiveThis paper proposes an automatic generation algorithm of expected results for the testing of a CBS system.MethodThe algorithm analyzes Input/Output (I/O) relationships of a CBS system to identify inputs that influence its outputs. Then, the algorithm uses test cases of atomic components for each input and automatically generates expected results. To evaluate the proposed approach, we compare the proposed approach with the other I/O relationship based approach via two case studies which are the CBS systems for guide robot. The comparison shows the effectiveness of our approach.ResultsTo verify effectiveness of the proposed approach, we measure the cost of manual generation of expected results for each case study. The costs are from 0.001 to 0.015 in the first case study, and are from 0.590 to 0.998 in the second case study.ConclusionThis proposed approach reduces the time-consuming and error-prone task which manually generates expected results for the testing of a CBS system.     

离散Walsh变换在超声治疗无损测温中的应用

利用高强度聚焦超声对新鲜的离体猪肉进行辐射,并通过B超获取辐射前后的超声图像。采用二维离散Walsh变换法,从辐射前后的超声图像的差图像中分离出低频分量,作为反映温度信息的参数。选取确定系数作为衡量误差的参数,通过曲线拟合,发现二维离散Walsh变换直流分量与温度呈近似线性关系,且多组数据的斜率非常接近,与传统的灰度均值测温法相比,实验结果表明,该方法误差更小,温度分辨率更高,使用正交矩阵变换,符合计算机语言的逻辑,仿真检测时间较短,在实际应用中能为超声无损测温提供准确可靠的依据。     

地下结构震害风险信息融合方法研究

为提高地下结构震害风险判决的科学性和智能化水平,提出一种基于证据理论的地下结构震害风险信息融合方法。在地下结构震害风险智能监测的基础上,根据震害风险融合原理,构建基于智能监测和专家判决的地下结构震害风险融合模型。遵循证据理论的原则和模型规则,构造统一的地下结构震害状态空间和风险概率表达方式,建立风险融合证据框架,确定判决规则,设计风险融合证据模型组件。计算结果表明,该模型能有效提高地下结构震害信息诊断的可信度,充分显示了该融合模型组件的有效性。     

基于ARM Cortex-M0+KL系列MCU的构件化汇编框架研究

ARM Cortex-M0+是2012年新发布的一款将逐步8位/16位微处理器市场的32位低功耗、高性能处理器,规范的汇编框架与样例是一个新微处理器深层次应用开发的基础。鉴于目前ARM Cortex-M0+的汇编程序资料与样例程序十分匮乏,加之汇编设计的复杂性,在充分分析ARM Cortex-M0+汇编寻址方式与指令系统的基础上,以Freescale半导体公司于2013年正式发布的ARM Cortex-M0+KL系列MCU为蓝本,基于CW10.3开发环境,结合软件构件设计思想,提出构件化ARM Cortex-M0+汇编框架,并编制GPIO汇编构件,给出应用实例。旨在降低读者学习ARM Cortex-M0+汇编程序的难度,完成第一个ARM Cortex-M0+规范化汇编程序。     

基于PCA与主动学习AdaBoost的频谱感知算法

针对认知网络中各低信噪比环境下主用户信号检测率偏低的问题,提出一种基于主成分分析和主动学习AdaBoost的主用户信号频谱感知算法.该算法首先采用主成分分析算法对信号特征参数进行提取,获得信号的主成分,之后利用主动学习算法通过多次迭代抽样,获取有利于提高分类性能的样本,并对AdaBoost分类器进行训练,最后利用训练完成的AdaBoost分类器对待测信号进行分类检测.仿真实验表明,在各低信噪比情况下与ANN和MME算法相比较,所提算法具有较高的分类感知性能,有效地实现了对主用户信号的频谱感知.     

基于SKPCA的卫星整流罩空调系统传感器故障检测研究

针对卫星整流罩空调系统对传感器故障检测的高可靠性的要求,在分析常见故障模式的基础上,采用了基于统计量核主元分析（ SKPCA）的故障检测方法,建立整流罩空调系统传感器故障检测模型,对整流罩空调系统传感器容易出现的偏置故障进行了验证实验。实验结果验证了SKPCA方法在整流罩空调系统传感器故障检测中的正确性和有效性。     

基于特征保持的三维模型四面体化及优化

三维模型四面体化是一种重要的有限元网格生成技术.介绍了一种特征保持的四面体网格生成及优化算法.首先使用三维模型主成分分析进行预处理,然后用体心立方构建初始四面体,接着通过拉普拉斯坐标改变模型边界切点的移动方式保持模型的局部特征,最后构造改进的密度能量误差函数优化四面体网格质量.实验结果表明,该方法可行、有效,且能很好地保持模型特征.     

基于主元分析的径向基神经网络预测模型研究

结合主元分析(PCA)和径向基函数(RBF)神经网络,建立了地下水动态模拟与软测量预测模型。通过主元分析法提取主要成分,实现数据预处理;将选取的主要成分作为RBF神经网络的输入;采用k均值聚类算法确定RBF网络隐含层参数,并用递进最小二乘法确定输出层权值。仿真结果表明,该模型优化了网络结构,提高了预测精度。