面向工程设计的数据管理模式的实现

通过介绍压力容器CAD系统(PVCAD)的数据管理机制,旨在给出一种面向工程设计的产品数据管理模式.这种模式能够使系统实现对通常工程设计所需的产品数据进行有效地管理和自动地访问.为此,较详细地说明了系统中产品数据模型的数据分类、表达方式、数据结构及其实现方法.     

基于自校正模糊神经控制的无刷直流传动系统

提出了一种自校正模糊神经网络控制器(SCFNNC)来实现无刷直流电动机起动、调速、制动等各运行阶段的性能指标.该SCFNNC是采用调整系统增益参数的方法完成较完善的控制规则的.重点研究了系统自校正增益参数的确定方法,模糊控制器的设计,人工神经网络实现模糊控制规则的方法等.自校正增益参数是根据系统对超调量、转速稳态误差、动态速降的期望值来确定的.设计模糊控制器时是根据系统的性能指标,确定出合适的模糊控制规则表,用于训练神经网络.为使系统的性能达到最佳,采用了自校正模糊神经控制、开关控制和比例控制相结合的复合控制方法,通过数学仿真证实配备SCFNNC的系统具有优良的动、静态特性,及较强的鲁棒性.     

矢量喷管数学模型研究

采用计算流体力学的有限体积法和神经网络方法,对矢量喷管的统一数学模型进行了研究,并得到了一种以神经网络描述形式的数学模型。该模型为矢量喷管控制规律和带有推力矢量的飞行／推进系统综合控制的研究提供了一定基础。     

俄罗斯电弧离子镀技术的特点及其在刀具涂层上的应用

俄罗斯电弧离子镀技术中先进行强化层处理,再进行涂层处理的复合强化技术对提高涂层与基体的结合强度,解决涂层剥落问题十分有效,特别是对高速钢刀具,效果更好.这是俄罗斯涂层技术与欧美涂层技术相比最大的特点.     

从量传溯源过程探求扭矩工具不确定度控制方法

通过对量值溯源传递系统传递检索,结合实际操作各环节控制要点,从影响量值传递准确性、稳定性、不确定度各分量进行归纳分析和验证,探讨扭矩工具量值不确定度控制措施方法.     

“隐喻自洽”理论在民航术语翻译中的应用研究

基于认知语言学的隐喻构建理论，本文分析了翻译者的受喻者和施喻者双重身份，并且在专业经验和感知选择两个维度上应用自洽工具来翻译隐喻性的民航专业术语，以改进民航专业术语的翻译和教学工作。     

遥测校准数据特性方程的构建与求解方法

紧密结合靶场数据处理的实际,基于传感器的动态变化特性,建立遥测校准数据的特性方程,提供相应的求解方法,对其理论特性精度进行评估,并给出具体的应用实例。     

Bernoulli particle flter with observer altitude for maritime radiation source tracking in the presence of measurement uncertainty

For maritime radiation source target tracking in particular electronic counter measures（ECM）environment,there exists two main problems which can deteriorate the tracking performance of traditional approaches.The frst problem is the poor observability of the radiation source.The second one is the measurement uncertainty which includes the uncertainty of the target appearing/disappearing and the detection uncertainty（false and missed detections）.A novel approach is proposed in this paper for tracking maritime radiation source in the presence of measurement uncertainty.To solve the poor observability of maritime radiation source target,using the radiation source motion restriction,the observer altitude information is incorporated into the bearings-only tracking（BOT）method to obtain the unique target localization.Then the two uncertainties in the ECM environment are modeled by the random fnite set（RFS）theory and the Bernoulli fltering method with the observer altitude is adopted to solve the tracking problem of maritime radiation source in such context.Simulation experiments verify the validity of the proposed approach for tracking maritime radiation source,and also demonstrate the superiority of the method compared with the traditional integrated probabilistic data association（IPDA）method.The tracking performance under different conditions,particularly those involving different duration of radiation source opening and switching-off,indicates that the method to solve our problem is robust and effective.     

Estimation of Transverse Thermal Conductivity of Doubly-periodic Fiber Reinforced Composites

For steady-state heat conduction, a new variational functional for a unit cell of composites with periodic microstructures is constructed by considering the quasi-periodicity of the temperature field and in the periodicity of the heat flux fields. Then by combining with the eigenfunction expansion of complex potential which satisfies the fiber-matrix interface conditions, an eigenfunction expansion-variational method(EEVM) based on a unit cell is developed. The effective transverse thermal conductivities of doubly-periodic fiber reinforced composites are calculated, and the first-order approximation formula for the square and hexagonal arrays is presented, which is convenient for engineering application. The numerical results show a good convergency of the presented method, even though the fiber volume fraction is relatively high. Comparisons with the existing analytical and experimental results are made to demonstrate the accuracy and validity of the first-order approximation formula for the hexagonal array.     

SPATIAL REGULARIZATION OF CANONICAL CORRELATION ANALYSIS FOR LOW-RESOLUTION FACE RECOGNITION

Canonical correlation analysis ( CCA ) based methods for low-resolution ( LR ) face recognition involve face images with different resolutions ( or multi-resolutions ), i.e.LR and high-resolution ( HR ) .For single-resolution face recognition , researchers have shown that utilizing spatial information is beneficial to improving the recognition accuracy , mainly because the pixels of each face are not independent but spatially correlated.However , for a multi-resolution scenario , there are no related works.Therefore , a method named spatial regularization of canonical correlation analysis ( SRCCA ) is developed for LR face recognition to improve the performance of CCA by the regularization utilizing spatial information of different resolution faces.Furthermore , the impact of LR and HR spatial regularization terms on LR face recognition is analyzed through experiments.