Experimental verification of an out-of-plane repulsive-force electrostatic actuator using a macroscopic mechanism

A macroscopic mechanism is developed to verify a repulsive-force electrostatic actuator, which consists of an array of fixed finger electrodes and an array of moving finger electrodes. The actuator is able to generate an asymmetric electric field surrounding the top and bottom surfaces of each moving finger electrode to push the moving finger up and away from the fixed fingers. The macroscopic mechanism consists of a macro repulsive force actuator, a high voltage power supply, a z-stage, a high precision balance and a LCR meter. The force and capacitance characteristic curves of the actuator are obtained using the macro mechanism. The 3-stage force (repulsive, zero and attractive forces) of the actuator is verified, as well as the effects of the moving finger width on the actuator’s performance. Experimental tests show that the macro repulsive-force actuator can generate a repulsive force of 3,000 μN with a maximum gap of 9.5 mm for generating a repulsive force.     

State estimation in high-mix semiconductor manufacturing

The traditional way of state estimation in semiconductor manufacturing, known as “threaded” state estimation, segregates the process data into different bins and uses the ones that match the current event of the specific context information (such as tools, layers, products) to update the process state. The limitation of threaded state estimation is that a narrowly defined process stream can result in too many different threads and insufficient data for each thread. This limitation becomes more severe in high-mix manufacturing, where there can be many products and many tools. Hence there is great interest in estimation methods that utilize all available data in the analysis. In this work, the characteristics inherent in state estimation of high-mix semiconductor manufacturing processes are analyzed, and a general framework is introduced for the non-threaded state estimation methods, i.e., state estimation without segregating the process data into different bins. The framework is based on the best linear unbiased estimate (BLUE) of a simplified stationary singular Gauss–Markov process, and non-threaded state estimation methods based on the Kalman filter, least squares and recursive least squares (RLS) are analyzed using the general framework. Simulation examples are presented to illustrate the equivalence between different algorithms. As real processes are rarely stationary, modifications to the Kalman filter and RLS are discussed. We show that in non-threaded state estimation, how to regulate the estimate covariance plays a significant role in estimation performance. To handle nonstationary disturbances that often occur in semiconductor processes, Bayesian-enhanced adaptive versions of the Kalman filter and RLS are proposed. Both simulated and industrial nonstationary processes are used to demonstrate the effectiveness of the proposed adaptive methods.     

基于权重信息挖掘社会网络中的隐含社团

社团结构是一种普遍存在于各类真实网络中的结构特性.挖掘网络的社团结构对于理解网络的功能与行为有着重要作用.然而,现有的各种社团挖掘算法仅仅基于网络拓扑结构信息,而忽视了蕴涵于真实社会网络边权信息中丰富的语义信息.目前普遍使用的基于模块性最大化的社团挖掘算法倾向于将小社团合并,这使得语义上丰富的小社团容易湮灭于基于拓扑结构信息所挖掘出的大社团中.而挖掘出这些隐含于大社团中的有着丰富语义内涵的小社团对于加深社会网络语义层面的理解有着重要作用.为此,提出一个接近线性复杂度的有权网络社团挖掘算法.通过充分利用权重信息,算法可以将社会网络划分为富含语义信息的粒度较细且相对较小的隐含社团.通过对基于DBLP作者合作网络的实证分析,证实了新算法的有效性和高效性.     

基于OSP的端元个数估计方法

针对在缺乏先验知识的情况下难以确定高光谱影像端元个数的问题,提出一种新的虚拟维数估计方法,其结果可作为端元个数的估计。该方法采用正交子空间投影(OSP)原理,逐个提取并剥离端元信号,通过比较残余值与阈值,实现虚拟维数的估计。对模拟高光谱数据和PHI高光谱影像数据的实验结果验证方法的可行性,与Nerman-Pearson法相比其具有更高的灵活性和准确性。     

多跳无线 Ad hoc网络的分布式公平调度算法

为兼顾无线Ad hoc网络信道资源的公平利用及网络吞吐量,提出针对多跳无线Ad hoc网络的分布式公平调度算法。该算法采用链路竞争图说明网络中各子流的竞争关系,通过着色算法得到最大无竞争流的集合,通过邻居节点之间的信息交换得到局部(本地)流的信息,而无需全局流的信息交换。实验模拟结果表明,该算法可以在保证Ad hoc网络中各流公平性的同时,有效提高网络资源的利用率。     

负关联规则增量更新算法

讨论负关联规则的更新问题。与正关联规则增量更新不同,负关联规则不仅存在于频繁项集中,更多存在于非频繁项集中。针对该问题提出一种负关联规则增量更新算法NIUA,利用改进的Apriori算法以及集合的性质挖掘出频繁、非频繁项集和负关联规则。实验结果表明,该算法是可取的。     

基于张量的XML相似度计算方法

扩展标记语言(XML) 带有一定的结构和语义信息, 与普通文本相比, XML具有描述精确、表现形式丰富等特点, 但同时也使得传统的自然语言处理和数据挖掘等技术不能直接应用. 根据XML内容和结构并非独立, 内容影响结构, 结构作用于内容, 提出一种基于张量的XML特征降维及综合相似度计算方法. 针对XML文档, 使用张量表示并采用基于最大互信息的方法对其进行降维, 采用将XML结构和内容相融合的综合相似度度量方法确定结构和内容的内在联系及共同作用方式, 提高XML综合相似度计算性能. 实验及结果分析验证了所提出方法的有效性.

     

基于PCNN改进算法的手写体识别研究

脉冲神经网络(PCNN)被广泛应用于图像处理、模式识别等领域。提出了一种基于PCNN的凹点检测改进算法。首先改进神经元激励函数,并利用小波收缩法去噪,保持图像的层次性,然后通过凹点检测识别手写体。实验结果表明,提出的方法能有效提高手写字母的识别率,尤其是在噪声环境下,识别率得到大幅提升。     

Adaptive neural control for an uncertain robotic manipulator with joint space constraints

In this paper, adaptive neural tracking control is proposed for a robotic manipulator with uncertainties in both manipulator dynamics and joint actuator dynamics. The manipulator joints are subject to inequality constraints, i.e., the joint angles are required to remain in some compact sets. Integral barrier Lyapunov functionals (iBLFs) are employed to address the joint space constraints directly without performing an additional mapping to the error space. Neural networks (NNs) are utilised to compensate for the unknown robot dynamics and external force. Adapting parameters are developed to estimate the unknown bounds on NN approximations. By the Lyapunov synthesis, the proposed control can guarantee the semi-global uniform ultimate boundedness of the closed-loop system, and the practical tracking of joint reference trajectory is achieved without the violation of predefined joint space constraints. Simulation results are given to validate the effectiveness of the proposed control scheme.