增力式夹具的设计与应用

增力式夹具结构紧凑，夹紧迅速可靠。它可以用较少的原动力获得较大的夹紧力，其增力倍数据实际须要同设计者调整。而且，若按机构自锁条件设计，该夹具夹紧之后，实现自锁。本文对该夹具结构做了介绍，并通过理论分析，指出增力系数和夹具自锁的设计条件。     

UniMAT PLC在塑料挤出机控制系统中的应用

针对塑料挤出机控制系统成本高、速度低、精度低的问题,提出采用UniMATPLC控制方式,实践证明效果良好。     

S7-200在KYS-1500/50型打捆机中的应用

采用西门子S7-200系列PLC设计电气控制系统,成功实现打捆机系统的自动控制,满足了生产的工艺要求。     

西门子PLC在合金钢棒材连轧机上的应用

介绍基于西门子S7-400PLC的控制系统,通过Ethernet、Profibus-DP、MPI网完成系统数据的采集、交换与监控,满足合金钢棒材轧制线高动态品质、高调速精度的要求。     

基于数字PID算法的台达运动控制器在音叉多线切割机上的应用

在连续一时间控制系统中,PID控制器在各类自动化系统中应用得非常广泛。其设计技术成熟,长期以来形成了典型的结构,参数整定方便,结构变化灵活,能满足一般的控制要求。数字PID控制比连续PID控制更为优越,因为计算机程序的灵活性,很容易克服连续PID控制中存在的问题,经修正可得到更为完善的数字PID算法。     

基于Profibus-DP的西门子S7—300 PLC与ABB 800XA数据通信的实现

本文介绍基于Profibus—DP通信协议的西门子S7—300PLC与ABB 800XA系统之间的数据通信。详细描述了分别通过S7—300 PLC CPU的DP接口和CP342—5通信模块与ABB 800XA系统通信两种不同条件下的有关通信配置及实现数据通信的过程。     

智能交通信号控制系统初探

城市交通拥挤是当今世界普遍关注的问题,它所带来的严重危害日益影响到人们的日常生活和社会经济的发展,而城市交通信号控制系统对解决这一问题发挥着不可替代的作用。从高性能智能交通信号控制系统的要求出发,针对目前我国城市交通问题的现状．对智能交通信号协调控制系统进行初步的研究和探讨。     

基于SOM的模糊聚类在Web日志挖掘中的应用

将自组织映射神经网络（SOM）与FCM结合,利用SOM的并行计算能够减少模糊C均值算法在处理海量数据时的聚类时间,可以提高聚类算法的速度和效果,同时使用该算法对校园网Web日志进行数据挖掘,能够对用户行为进行分析,从而提出相应的方法,更好地提高服务效率和管理质量。     

Analysis of oceanaut operating performance using an integrated Bayesian network aided by the fuzzy logic theory

Because the oceanaut plays a significant role in safety and capability during manned deep-diving scientific tasks, preventing oceanaut performance decline is of paramount importance. However, the factors responsible for oceanaut performance are almost entirely unexplored. To address the preceding issues, a quantitative method of fuzzy integrated Bayesian network (FIBN) was modeled within the limits of oceanaut operating procedures. To quantify the probabilities of the influencing factors, the probability of each node in the FIBN was calculated using integrated expert judgement, fuzzy logic theory, and Bayesian network. By considering a total of 28 factors related to oceanaut performance in the “Jiaolong” manned submersible, this study found that difficult sampling, long sampling times, cabin equipment failure, oceanaut physical decline, and declining decision-making ability are important factors that affect oceanaut performance. The FIBN proposed in our study fused the qualitative and quantitative methods and can be developed into a versatile tool for analysis of comprehensive systems that contain both static and dynamic factors.Relevance to industryThe results provide a powerful basis for the design of manned submersible and assignment of tasks to oceanauts, while the fuzzy integrated Bayesian network (FIBN) method proposed can be effectively applied to various quantitative assessment fields which direct researchers to deal with analysis problems of complex systems.     

A fuzzy hierarchical reinforcement learning based scheduling method for semiconductor wafer manufacturing systems

Scheduling semiconductor wafer manufacturing systems has been viewed as one of the most challenging optimization problems owing to the complicated constraints, and dynamic system environment. This paper proposes a fuzzy hierarchical reinforcement learning (FHRL) approach to schedule a SWFS, which controls the cycle time (CT) of each wafer lot to improve on-time delivery by adjusting the priority of each wafer lot. To cope with the layer correlation and wafer correlation of CT due to the re-entrant process constraint, a hierarchical model is presented with a recurrent reinforcement learning (RL) unit in each layer to control the corresponding sub-CT of each integrated circuit layer. In each RL unit, a fuzzy reward calculator is designed to reduce the impact of uncertainty of expected finishing time caused by the rematching of a lot to a delivery batch. The results demonstrate that the mean deviation (MD) between the actual and expected completion time of wafer lots under the scheduling of the FHRL approach is only about 30 % of the compared methods in the whole SWFS.