支持产品工程分析的集成产品模型

本文分析了基于产品几何模型、装配信息，支持产品工程分析的集成产品模型描述。该模型在产品几何模型和装配模型的基础上集成了零部件设计参数、参数之间的约束关系、受力状况、力学性能。阐述了Pro/Engineer平台应用系统原理。     

周期式冷轧管机的发展

叙述了周期式冷轧管机国内外开发、研制的历史和现状。介绍了典型型号的周期式冷轧管机的技术参数,结构特点及目前应用情况。     

导流洞封堵体温控有限元仿真分析

该文通过对有限元分析软件ANSYS二次开发从而进行封堵体三维温度场及温度应力场仿真分析,根据实际工程模拟了混凝土浇筑的温升温降过程并计算得出混凝土的应力变化,计算结果为封堵体的施工提供参考。     

Mooring system diagnosis and structural reliability control for position moored vessels

Early diagnosis and fault-tolerant control are essential for safe operation of floating platforms where mooring systems maintain vessel position and must withstand environmental loads. This paper considers two critical faults, line breakage and loss of a buoyancy element and employs vector statistical change detection for timely diagnosis of faults. Diagnosis design is scrutinised and a procedure is proposed based on specified false alarm probability and estimation of the distribution of the test statistics on which change detection is based. A structural reliability index is applied for monitoring the safety level of each mooring line and a set-point chasing algorithm accommodates the effects of line failure, as an integral part of the reliability-based set-point chasing control algorithm. The feasibility of the diagnosis and of the fault-tolerant control strategy is verified in model basin tests.     

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.     

引入迁移学习的癫痫EEG信号自适应识别

在脑电图( EEG)信号识别中,EEG信号的采样环境、病人状态的多样性导致分类器训练所用的源域与分类器测试所用的目标域不匹配,分类器在目标域上表现不佳。为此,引入邻域适应策略,提出一种基于子空间相似度的改进主成分分析特征提取方法( SSM-PCA),在选择主成分时,考虑源域和目标域数据的几何和统计特性,并结合迁移学习分类器大间隔投射迁移支持向量机( LMPROJ),给出以SSM-PCA为基础的LMPROJ分类识别方法。实验结果表明,与结合PCA特征抽取技术和K近邻分类器实现的识别方法相比,该方法在识别正确率方面得到较大提升。     

二次回归学习及其在软件开发工作量预测上的应用

回归学习是用于对具有实值标记样本进行学习建模的监督学习技术。为获得良好的预测性能,通常需要大量的训练样本,然而,在实际应用中可收集到的训练样本数量极少。针对该问题,提出一种基于二次学习框架的新型二次回归学习方法———基于神经网络集成的回归树算法( NERT)。该方法借助虚拟样本生成技术,通过串行执行的两个学习阶段对其进行有效利用,有效缓解训练样本不足的困难,从而提升学习性能。同时,通过为两个阶段分别选择泛化能力强和理解性好的学习方法,可得到预测性能好且可理解性高的模型。实验结果表明在训练样本极少的软件开发工作量预测问题上,NERT方法能够从小样本数据得到比现有方法更好的预测性能,同时其模型内在可理解性能够揭示工作量预测的关键因素。     

正则化半监督判别分析方法

为了克服加权线性判别分析(WLDA)只利用有标签的训练样本而不能反映样本数据流形结构的缺点,提出一种正则化的半监督判别分析方法。首先构建所有样本的近邻图来估计数据的局部流形结构,然后将此作为正则项引入WLDA的准则函数中。该方法避免了类内散度矩阵奇异,同时保持了样本数据的判别结构和几何结构。在ORL和YALE人脸数据库上的实验结果证明了该算法的有效性。     

基于奇异谱分析的DGPS浮标海面高测量误差研究

在高度计绝对定标实验中,利用差分GPS(Differential GPS,DGPS)浮标进行现场海面高测量得到的海面高(Sea Surface Height,SSH)序列之中,除了包含有由风浪引起的高频噪声外,还包括了仪器误差造成的影响。前者可通过低通滤波方式消除,而后者由于是全频段的白噪声,不能通过低通滤波的方式消除。通过研究奇异谱分析(Singular Spectrum Analysis,SSA)的方法,确定了嵌入维数和截断长度的选取准则,并对其进行仿真,验证了该方法在消除海面高序列的仪器误差噪声中的有效性,同时也验证了嵌入维数和截断长度选取准则的有效性。最后,利用该方法处理了某次DGPS浮标实验获得的海面高序列,处理结果与原始序列相比较为平滑,且在精度上有一定的改善,验证了奇异谱分析方法的有效性。     

Comparing interpolation techniques for monthly rainfall mapping using multiple evaluation criteria and auxiliary data sources: A case study of Sri Lanka

Interpolating climatic variables such as rainfall is challenging due to the highly variable nature of meteorological processes, the effects of terrain and geography, and the difficulty in establishing a representative network of stations. While interpolation models are being adapted to include these effects, often the rainfall data contain significant gaps in coverage. In this paper, we evaluated rainfall data from an agro-ecological monitoring network for producing maps of total monthly rainfall in Sri Lanka. We compared four spatial interpolation techniques: inverse distance weighting, thin-plate splines, ordinary kriging, and Bayesian kriging. Error metrics were used to validate interpolations against independent data. Satellite data were used to assess the spatial pattern of rainfall. Results indicated that Bayesian kriging and splines performed best in low and high rainfall, respectively. Rainfall maps generated from the agro-ecological network were found to have accuracies consistent with previous studies in Sri Lanka.