面向产品创新的计算机辅助概念设计系统的研究

将面向产品外形、布局、人机工程学的产品概念设计的研究提到一个新的高度.本文将产品几何特征拓广为涵盖几何特征、工程特征、语义特征等三大内容的产品组件特征,重组为总体特征、布局特征、形状特征和人机特征,以三元组、条件约束集、多值依赖图建立组件特征模型,进而,建立了基于组件特征模型的产品信息模型,并研究了基于组件特征模型的产品布局设计技术、面向概念设计的人机工程设计技术,开发了一套计算机辅助概念设计系统.最后,以吸尘器概念设计为例,对本文提出的理论、方法、技术和系统加以验证,完成对机电信息一体化产品概念设计的支持.     

一种面向风格创新设计的汽车形态特征识别方法

以汽车为例,研究风格计算自动化的关键基础技术--产品形态特征识别,提出一套框架、模型和实现方法,建立一个比较完善的形态特征识别框架.从风格创新角度出发,结合汽车造型特征,定义3个与汽车特征识别密切相关的关键类集合.基于关键类集合,定义汽车关键特征点集合与局部特征组件集合之间的映射,以及局部特征组件集合与汽车车型集合之间的映射,在此基础上提出汽车形态特征识别模型,并给出关键特征点提取算法、局部特征分割算法以及车型识别算法的示例.最后通过一个实例系统对识别模型和方法做了介绍.     

积灰对光伏组件输出特性影响建模与分析

灰尘沉积在光伏组件上严重影响光伏系统输出的稳定性,导致发电量降低的同时缩短了组件的使用寿命。准确地评估光伏现场积灰浓度,将有助提升光伏发电功率预测模型的精度。本文以光伏电站现场采集的灰尘颗粒为研究对象,首先分析了灰尘颗粒的元素组成、含量、形貌特征和粒径分布,根据光伏组件实际的发电效率和环境参数,建立了积灰浓度软测量模型,用于快速定量评估光伏电站积灰程度;其次,为了准确地获取模型的相关参数,开展了多组积灰浓度影响发电性能实验,得到了组件输出功率和辐照度、积灰浓度、组件温度的关系;最后,在自然条件下,验证了模型的准确性和可靠性。对比其他传统方法,结果表明:本文提出的模型具有更好的预测性能,准确率可达89.6%。     

基于特定领域建模的仿真组件建模方法

针对当前仿真组件模型(SCM,Simulation Component Model)开发存在的问题,首先采用特定领域建模(DSM,Domain-Specific Modeling)理论,通过分析仿真组件的基本结构和特征,确定仿真组件元模型整体的各部分模块及其相互关系,然后提出一种基于特定领域建模的仿真组件的建模方法和仿真组件模型开发流程。利用VMSDK(Visual Studio Visualization and Modeling SDK)工具,构建了描述仿真组件领域建模语言(SCMML,Simulation Component Model Modeling language)的元模型。最后,通过建模实例,表明该方法可显著提高仿真组件的开发效率。     

基于组件的分布式CAPP系统的体系结构研究

对利用组件技术构造分布式CAPP系统进行了研究,将CAPP系统建立在一种基于组件的对象模型上,通过组件对象模型对系统功能的抽象和划分,将CAPP系统的各个功能模块分离成一些组件,这些组件在实现上可独立进行调用,组件之间也可互操作,使用时按照组件标准进行无缝集成。     

FMS中AGV的实时控制系统设计与实现

将AGV控制系统作为FMS控制系统中的一个通用组件来研究其建模与设计方法.首先,采用面向对象Petri网建模技术建立了AGV控制组件的动态模型.其次,进行了AGV控制组件与FMS中其它组件的基于CORBA的通信接口的定义.在此基础上,为了使AGV控制组件具有良好的维护性、重用性和柔性,建立了AGV控制组件的面向对象类的统一建模语言(UML)模型.最后进行了AGV控制组件设计和开发.     

面向复杂产品配置设计的组件模型构建

对预定义的产品组件进行配置设计、实现产品快速定制,是解决用户多样化需求与产品开发时间成本之间矛盾的有效方法。其中,构建产品组件模型是实现配置设计的前提。引入软件工程中面向对象的思想,对复杂产品的组件模型构建方法进行研究。首先对目前复杂产品的组件构建方法进行了文献分析,然后对基于面向对象思想的组件构建过程进行了研究,包括组件封装、组件实例化及扩展机制等,最后以工业机器人中的零部件为例,对所提的组件构建方法进行了验证。论文研究对复杂产品重用以往知识,实现快速配置设计,具有重要意义。     

基于COM组件的专家系统开发研究

介绍了COM组件和专家系统等相关技术,并给出了专家系统结构模型,研究用COM技术来实现专家系统的组件,并阐述了关键组件的实现方法。     

基于特征分解的逆向建模技术研究

将组件分析的思想融入已有的建模方法,提出一种基于特征分解的逆向建模方法。将模型分解为规则特征和不规则特征,建立特征的组件关系,并以Geomagic Design Direct软件为平台,结合其特征提取功能拟合特征的实体,通过布尔运算建立完整的实体模型。运用特征分解的思路可建立包含原始设计意图的参数化计算机辅助设计(Computer Aided Design,CAD)模型,且建模过程清晰、高效。基于特征分解的逆向建模技术为产品的快速建模和创新设计提供了一种系统的方法。     

企业信息系统集成中业务组件复用的研究与设计

本文研究了在快速可重构企业信息系统集成中业务组件的复用问题。通过对业务组件域中不同组件功能的相同点进行分析和提取,提出了功能组件模型,功能组件的编译参数代表了业务组件类型,其运行参数代表了业务组件的不同。同时,提出了功能组件的语义描述,通过设计功能组件进行业务组件的复用,提升了企业信息系统的集成。     

A flexible process information reuse method for similar machining feature

Dynamically changing machining conditions and uncertain manufacturing resource availability are forcing manufacturing enterprises to search advanced process planning in order to increase productivity and ensure product quality. As growing quantities of the three-dimensional process models are gradually applied, reusing the embedded manufacturing information in process models with less time and lower cost attracts a lot of attention. In this paper, a new flexible method is presented to reuse the existing process information based on retrieval of the similar machining feature. First, the three-level organization model is introduced to represent the process information; the machining feature which is seen as the parent layer carries the corresponding manufacturing information. To ensure accurately that the process information are obtained, the associated mechanism between the machining feature and process information is created. Second, an eight-node representation scheme is designed to represent the similar machining feature having same variations in topology and geometry. For accelerating similar feature retrieval, the extension-attributed adjacency graph and the topological relationship of the machining feature faces are built. Finally, some aircraft structural parts are utilized in the developed prototype module to verify the effectiveness of the proposed method. This method can be used as the basis for accumulation of the process information; it can promote the development and application of the intelligent process planning.     

基于对比学习辅助训练的超短期风功率预测方法

利用深度学习方法提高风功率超短期预测精度能够给电力系统日内机组组合、超短期经济调度、和电力备用安排提供更精确的风功率预测结果,对进一步提高电力系统运行的安全性和经济性具有重要意义。本文针对当前深度学习特征提取模块对时序曲线中的隐式特征和趋势变化的相似性提取不充分的问题,提出一种基于对比学习辅助训练的超短期风功率预测模型,主要包括输入模块、特征提取模块、对比学习辅助模块和回归模块。该模型通过自监督的对比学习算法自主生成正负样本、并以拉开正负样本的映射空间距离为目标来辅助训练特征提取模块的网络参数,使得特征提取模块的映射结果中包含了输入信息相似性的隐式特征,进而减少数据冗余信息、增强样本关联性,最终提高风功率预测精度。实验结果表明,对比学习方法的平均绝对误差比长短期记忆网络和轻量梯度提升机方法分别下降了19.9%和6.5%,有效提高了风功率预测精度。     

结合特征复用注意力与精细化分层残差的 细微裂纹密集连续检测

细微裂纹的高效识别对结构体早期故障诊断具有重要意义。图像分割等方法在处理复杂且带有断裂的细微裂纹时难以达到满意效果。因此,将细微裂纹的识别问题转变为密集连续的中心点预测问题,利用精细化分层残差模块构造特征提取器并结合具有特征复用的注意力模块提出一种细微裂纹检测方法。首先使用相同的矩形框沿裂纹轨迹密集连续地标注;其次对不同的精细化分层残差模块进行消融实验,得到有利于细微裂纹特征提取的骨干网络;最后结合具有特征复用的注意力模块与骨干网络对比了六种不同的特征复用方式。实验结果表明,本文方法的最高和平均精度分别为61.0%和54.7%,与原模型相比分别提升4.9%和6.3%,成功识别细微裂纹及其局部断裂区域并抑制背景干扰。     

Detection of premature ventricular contractions using MLP neural networks: A comparative study

This paper proposes a three stage technique for detection of premature ventricular contraction (PVC) from normal beats and other heart diseases. This method includes a denoising module, a feature extraction module and a classification module. In the first module we investigate the application of stationary wavelet transform (SWT) for noise reduction of the electrocardiogram (ECG) signals. The feature extraction module extracts 10 ECG morphological features and one timing interval feature. Then a number of multilayer perceptron (MLP) neural networks with different number of layers and nine training algorithms are designed. The performances of the networks for speed of convergence and accuracy classifications are evaluated for seven files from the MIT–BIH arrhythmia database. Among the different training algorithms, the resilient back-propagation (RP) algorithm illustrated the best convergence rate and the Levenberg–Marquardt (LM) algorithm achieved the best overall detection accuracy.     

基于特征造型的轴类零件设计系统

以特征造型思想为基础,用VB 6.0和Access 2003为开发工具对Autodesk Inventor进行二次开发,开发出轴类零件设计系统。系统采用模块化结构,主要阐述了信息输入及处理模块、特征造型模块、工程图绘制模块等主要模块及其实现方法。系统能够对不同形状的轴类零件进行计算机辅助设计并生成工程图,具有记录建模过程并实现人机交互功能的特点,显著的提高了设计效率。     

Statistical process control using optimized neural networks: A case study

The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy.     

Digital communication signals identification using an efficient recognizer

Automatic recognition of the communication signals plays an important role for various applications. Most of the existing techniques require high levels of signal to noise ratio (SNR). In this paper, we propose a high efficient technique for classification of the digital modulations that requires a low level of SNRs. This technique includes two main modules: feature extraction module and the classifier module. In the feature extraction module we use the auto-regressive modeling together other useful features. These features are a combination set of the entropy and energy of the signal, variance of the coefficients wavelet packet transform, fourth order of moment and zero-crossing rate. In the classifier module we have used the two structures of the neural networks: multi-layer perceptron (MLP) neural network and radial basis neural networks. Simulation results show the proposed technique has very high recognition accuracy for identification of the considered digital modulations even at very low SNRs.     

Control chart pattern recognition using an optimized neural network and efficient features

Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. This study investigates the design of an accurate system for control chart pattern (CCP) recognition from two aspects. First, an efficient system is introduced that includes two main modules: the feature extraction module and the classifier module. The feature extraction module uses the entropies of the wavelet packets. These are applied for the first time in this area. In the classifier module several neural networks, such as the multilayer perceptron and radial basis function, are investigated. Using an experimental study, we choose the best classifier in order to recognize the CCPs. Second, we propose a hybrid heuristic recognition system based on particle swarm optimization to improve the generalization performance of the classifier. The results obtained clearly confirm that further improvements in terms of recognition accuracy can be achieved by the proposed recognition system.     

基于PXI平台的传感器自动测试系统

文章以压力传感器为例,描述了基于PXI平台的传感器自动测试系统,利用PXI高速多路可复用开关模块、高精度万用表模块以及数据采集模块,结合自行设计的程控电压源及电流源,实现了对大批量压力传感器、变送器的合格性测试及特性分析。     

基于UG的装配特征自动识别及其逆向参数化方法研究

在三维CAD系统中,不论是自下而上还是自上而下的设计模式,对于组成装配体的不同零件之间仅能实现一对一的尺寸参数关联.提出了一种基于UG装配体装配特征自动识别的方法,分析了装配特征的各种定义和表达方法,建立了基于配合尺寸链的相关零件的尺寸关联,实现了逆向参数化设计,并在UG NX3.0环境开发了一个实现该功能的独立模块.