井下人员定位系统读卡器存储算法分析与测试

从查找时间与存储容量2个方面分析了适用于井下人员定位系统读卡器的3种存储算法,即链式存储算法、哈希表存储算法和哈希二叉树存储算法,给出了该3种不同存储算法的使用效率与应用场合。测试结果表明,链式存储算法查找时间呈线性增长,哈希表存储算法明显优越于链式存储算法,如果测试数据增多,其优越性会更明显;当数据量较大时,哈希搜索二叉树存储算法又明显优于哈希表存储算法,当人数达到行业所要求的8 000人以上时,选择哈希搜索二叉树存储算法更为合理。     

智能集中润滑系统在平朔矿区的应用

分析了平朔矿区原有破碎机润滑系统使用中存在的问题,介绍了一种智能集中润滑系统的工作原理及其在平朔矿区应用中的系统配置、系统应用方案及系统维护。该智能集中润滑系统将PC和PLC与现场多点润滑泵、智能仪表和智能传感器相结合,采用多点润滑泵实现对破碎机多个点的集中润滑,采用电动加油泵实现自动补脂。实际应用表明,该智能集中润滑系统运行稳定、可靠,提高了设备使用效率,减少了日常维修工作量。     

基于云自适应遗传算法的NoC路径分配研究

路径分配是NoC设计流程中的两个关键步骤之一;路径分配的结果对NoC系统的性能尤其是通讯延时有着很重要的影响;多约束条件下的NoC路径分配问题是NP完全问题,要求出其最优解比较困难,目前常用的方法是利用启发式算法求得其较优解;文中提出一种基于云自适应遗传算法的NoC路径分配解决方案,该算法利用云模型对传统遗传算法加以改进,采取新的方法自动调整遗传算法过程中的交叉概率pc和变异概率pm,将适应度与云模型的3个参数Ex、En、He相互结合,从而达到优化遗传算法的目的;将此算法应用于2D-Mesh拓扑结构的NoC中,以平衡链路负载和联合优化为实验目标,以优化静态通讯分配结果;实验证明,文章所采取的算法在平衡链路负载和联合优化方面均取得了良好的效果。     

遗传算法模糊PID测控系统在环控试验台中的应用

飞机环控试验台须模拟流量0～14000kg/h、压力0～2.5MPa和常温～500℃的空气环境;项目要求测控范围广、精度±1%且不超调;空气状态具有非线性、时变等特点,且控制参数之间存在复杂耦合;针对以上难点,设计了分布式测控系统,提出了改进的智能PID控制方案;通过遗传算法分段整定PID参数,离线建立PID数据库,使系统能够根据控制目标值选择最优PID初值;在此基础上,结合模糊推理在线调整PID参数,使系统具有了自适应性,能在具体工况和干扰下达到很好的控制效果;实际应用中完全满足了指标要求,解决了传统PID的控制难点,对类似的复杂系统有一定借鉴意义。     

基于图像处理的片式元件缺陷检测算法

表面缺陷检测是电子元器件生产过程中的关键步骤,通过分析表面缺陷检测的现状问题,结合片式元件图像的自身特征,提出一种基于改进的自适应阈值canny算子的片式元件缺陷检测算法;该算法把片式元件灰度图像进行区域分割,得到不同值域范围内的灰度区域,利用带有敏感度的自适应双阈值canny算子进行边缘检测;算法实现结果表明,该算法能高效地检测并处理不同缺陷类型的片式元件,并且可以达到工业生产的检测速度。     

漫射红外技术的智能家居无线局域网设计

分析了红外技术在短距离无线通信中的优势,并将其应用在智能家居局域网中;结合安华高科技公司的HSDL-3020红外收发模块搭建了硬件电路,构成网络的主从节点;介绍了网络组建的过程,包括IrDA协议栈结构、数据编码格式、链路连接、网络管理机制等。本设计实现了对家居设备的统一管理,具有较高的应用价值。     

Direct adaptive fuzzy control with membership function tuning

In this paper, a direct self‐structured adaptive fuzzy control is introduced for the class of nonlinear systems with unknown dynamic models. Control is accomplished by an adaptive fuzzy system with a fixed number of rules and adaptive membership functions. The reference signal and state errors are used to tune the membership functions and update them instantaneously. The Lyapunov synthesis method is also used to guarantee the stability of the closed loop system. The proposed control scheme is applied to an inverted pendulum and a magnetic levitation system, and its effectiveness is shown via simulation. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Group technology based adaptive cell formation using predator-prey genetic algorithm

In recent years, there has been a considerable growth of application of group technology in cellular manufacturing. This has led to investigation of the primary cell formation problem (CFP), both in classical and soft-computing domain. Compared to more well-known and analytical techniques like mathematical programming which have been used rigorously to solve CFPs, heuristic approaches have yet gained the same level of acceptance. In the last decade we have seen some fruitful attempts to use evolutionary techniques like genetic algorithm (GA) and Ant Colony Optimization to find solutions of the CFP. The primary aim of this study is to investigate the applicability of a fine grain variant of the predator-prey GA (PPGA) in CFPs. The algorithm has been adapted to emphasize local selection strategy and to maintain a reasonable balance between prey and predator population, while avoiding premature convergence. The results show that the algorithm is competitive in identifying machine-part clusters from the initial CFP matrix with significantly less number of iterations. The algorithm scaled efficiently for large size problems with competitive performance. Optimal cluster identification is then followed by removal of the bottleneck elements to give a final solution with minimum inter-cluster transition cost. The results give considerable impetus to study similar NP-complete combinatorial problems using fine-grain GAs in future.     

Simultaneous solving of balancing and sequencing problems with station-dependent assembly times for mixed-model assembly lines

In spite of many studies, investigating balancing and sequencing problems in Mixed-Model Assembly Line (MMAL) individually, this paper solves them simultaneously aiming to minimize total utility work. A new Mixed-Integer Linear Programming (MILP) model is developed to provide the exact solution of the problem with station-dependent assembly times. Because of NP-hardness, a Simulated Annealing (SA) is applied and compared to the Co-evolutionary Genetic Algorithm (Co-GA) from the literature. To strengthen the search process, two main hypotheses, namely simultaneous search and feasible search, are developed contrasting Co-GA. Various parameters of SA are reviewed to calibrate the algorithm by means of Taguchi design of experiments. Numerical results statistically show the efficiency and effectiveness of the proposed SA in terms of both the quality of solution and the time of achieving the best solution. Finally, the contribution of each hypothesis in this superiority is analyzed.     

Soft computing approach to fault diagnosis of centrifugal pump

Fault detection and isolation in rotating machinery is very important from an industrial viewpoint as it can help in maintenance activities and significantly reduce the down-time of the machine, resulting in major cost savings. Traditional methods have been found to be not very accurate. Soft computing based methods are now being increasingly employed for the purpose. The proposed method is based on a genetic programming technique which is known as gene expression programming (GEP). GEP is somewhat a new member of the genetic programming family. The main objective of this paper is to compare the classification accuracy of the proposed evolutionary computing based method with other pattern classification approaches such as support vector machine (SVM), Wavelet-GEP, and proximal support vector machine (PSVM). For this purpose, six states viz., normal, bearing fault, impeller fault, seal fault, impeller and bearing fault together, cavitation are simulated on centrifugal pump. Decision tree algorithm is used to select the features. The results obtained using GEP is compared with the performance of Wavelet-GEP, support vector machine (SVM) and proximal support vector machine (PSVM) based classifiers. It is observed that both GEP and SVM equally outperform the other two classifiers (PSVM and Wavelet-GEP) considered in the present study.