花键轴增量式滚轧成形工艺的分流方式及温度效应研究

为了降低花键轴增量式滚轧成形工艺中工件初始直径对滚轧载荷和齿形填充质量的负面影响,根据分流锻造思想提出了一种齿顶分流方式,分析了温度效应对滚轧载荷和齿形填充质量的影响,并通过DEFORM-3D有限元软件对该分流方式进行了数值分析验证.研究结果表明:在校正滚轧阶段,齿顶分流方式的应用使得花键轴增量式滚轧成形工艺中滚轧载荷最大值平均下降了约60％,并降低了工件初始直径对花键轴齿形填充质量和齿距累计误差的影响.工件材料在400、800、1 000℃(相对室温20℃)时,温度效应使得最大滚轧载荷分别平均下降了约20％、67％、80％,并且明显提高了齿形处工件材料的填充质量.     

基于增量学习的关节式目标跟踪算法

为实现对关节式目标的稳定跟踪,提出了基于增量学习的关节式目标跟踪算法.该算法应用图割法对目标矩形窗进行前景与背景分割,得到前景图像;然后对前景图像进行快速傅里叶变换,得到傅里叶系数矩阵,进而得到振幅图像,并将振幅图像作为跟踪目标的描述;最后将多个目标描述进行奇异值分解和主元分析,实现对跟踪目标的低维子空间描述.文中在粒子滤波框架下实现了整个跟踪算法.实验结果表明,该算法具有较稳定的关节式目标跟踪效果.     

基于视觉技术智能车系统的设计

介绍了一种以OV6620数字摄像头为视觉传感器的自循迹智能车系统,详细阐述了视觉传感器有效信号的小范围黑线提取法,采用增量式数字PID控制算法,实现智能车调速。实验测试表明,本智能车系统具有良好的稳定性、快速性和前瞻性。．     

基于增量动力分析的钢框架结构抗震性能评估

为了预测结构的抗震能力,以分别代表低层、中层及高层结构的3层、9层、20层钢框架为例,采用增量动力分析方法对3个结构模型进行了近断层地震作用下不同强度指标的地震性能评估．基于增量动力分析,从概率的角度得到了结构在不同强度指标下的地震危险性曲线、年平均超越概率及重现周期．通过对比分析发现不同层数的结构模型选取不同的强度指标进行性能评估的有效性不同：3个结构模型均适合采用Sa作为地震动强度指标;3层钢框架不适合采用aPGA作为强度指标,9层钢框架较适合采用aPGA作为强度指标,而20层钢框架选取aPGA作为强度指标的有效性尚不确定．     

关于扩散方程漂移系数估计方法的改进

本文提出了一种新的基于扩散过程轨道构造漂移系数样本的方法—对数增量法,通过理论及模拟分析说明了在适当条件下,特别是对于大多数金融数据,基于对数增量法获得的漂移系数估计量的收敛速度及有限样本性质均比基于传统的"直接增量法"所得到的结果要好。     

Bond graph model-based system mode identification and mode-dependent fault thresholds for hybrid systems

Hybrid system models exploit the modelling abstraction that fast state transitions take place instantaneously so that they encompass discrete events and the continuous time behaviour for the while of a system mode. If a system is in a certain mode, e.g. two rigid bodies stick together, then residuals of analytical redundancy relations (ARRs) within certain small bounds indicate that the system is healthy. An unobserved mode change, however, invalidates the current model for the dynamic behaviour. As a result, ARR residuals may exceed current thresholds indicating faults in system components that have not happened. The paper shows that ARR residuals derived from a bond graph cannot only serve as fault indicators but may also be used for bond graph model-based system mode identification. ARR residuals are numerically computed in an off-line simulation by coupling a bond graph of the faulty system to a non-faulty system bond graph through residual sinks. In real-time simulation, the faulty system model is to be replaced by measurements from the real system. As parameter values are uncertain, it is important to determine adaptive ARR thresholds that, given uncertain parameters, allow to decide whether the dynamic behaviour in a current system mode is the one of the healthy system so that false alarms or overlooking of true faults can be avoided. The paper shows how incremental bond graphs can be used to determine adaptive mode-dependent ARR thresholds for switched linear time-invariant systems with uncertain parameters in order to support robust fault detection. Bond graph-based hybrid system mode identification as well as the determination of adaptive fault thresholds is illustrated by application to a power electronic system easy to survey. Some simulation results have been analytically validated.     

A comparison of the von Mises and Hencky equivalent strains for use in simple shear experiments

The von Mises equivalent strain increment is derived for the case of large strain simple shear (torsion testing). This is used, in conjunction with the von Mises yield surface, to define the von Mises equivalent stress as well as the incremental work per unit volume. Integration of the equivalent strain increment leads to the definition of the von Mises equivalent strain for torsion. The Hencky equivalent strain increment is derived from the Hencky strain defined as the logarithm of the semi major and minor axes of the strain ellipse. This is then used, via the incremental work, to derive the ‘Hencky equivalent stress’. In the Onaka approach, the numerical values of the principal strain increments were integrated without taking into account the continuous rotation of the strain ellipse. This invalid operation leads to an expression for the equivalent strain increment that cannot be applied to the large strains considered by Onaka. Using the correct increments of the Hencky strain, it is shown that the shear strain increments turn negative and consequently, the incremental work becomes negative when the shear is large.     

基于三支决策的二阶段分类模型研究

当对三支决策边界域进一步划分时,边界域知识存在划分信息不足,从而导致分类精度不高,针对上述问题提出一种新的基于三支决策的二阶段分类模型(TWD-TP).第一阶段根据贝叶斯规则构建三支决策中样本的条件概率,通过求解最优化损失函数得到所需阈值,然后按照三支决策规则对数据集进行划分.三支决策是基于最小风险贝叶斯决策理论的划分,在其正域、负域中包含一定的误分类样本;在第二阶段通过类标签索引分别将正域、负域中误分样本作为增量信息引入延迟决策域,形成重构边界域,最后对重构边界域进行划分.实验结果表明:所提出的TWD-TP模型不仅能在三支决策划分中筛选出高误分类特征的样本,同时其重构边界域中不能被划分的样本得到正确划分,分类精度进一步提高.     

基于频繁项集分类统计的增量式关联规则应用

针对商业交易数据构成项目繁多、动态数据增加量大、历史数据量更大的特点,根据频繁项集的商业特征,分为新生、成熟、老化、过期4种类型并分类统计;提出了基于分类统计增量地挖掘新增业务数据中关联规则的算法,算法只需两次扫描新增数据库,无需扫描历史数据库,算法将发现的规则按照其反应的商业特征分为4种类型:新生规则、成熟规则、老化规则、过期规则,在提升规则内容识别效率的同时,强化规则特点的识别能力。