MAGA-WNN模型在明胶浓度软测量中的应用

胶液浓度的测量在明胶的生产过程中是一项很重要的任务,为解决明胶浓度胶液手工检测中存在的滞后时间长、测量误差大等实际问题,将软测量技术应用到明胶的浓度测量中。通过对明胶生产工艺的分析,采用小波神经网络建立软测量模型,利用混合变异自适应遗传算法对模型参数进行优化。用现场采集并归一化后的120组数据做仿真实验,其中80组作为训练,40组作为预测。仿真结果表明,该算法迭代67次后误差平方和低于0.01并趋于稳定。证明混合变异自适应遗传算法可增强小波神经网络模型的鲁棒性和提高收敛速度。所以该算法用于明胶浓度的软测量中是可行的。     

基于虚拟时序机的隐式软故障精确定位

虚拟时序机中的不完全封闭顺序时序机的工作模式识别,特别地隐式软故障精确定位是重大难题.在虚拟状态表、虚拟状态差表、虚拟自状态差表的基础上,提出了一系列定义,导出了一系列重要定理、性质及规律,建立了状态差理论及自状态差法.可简单、直观地识别虚拟时序机的全部工作模式,精确定位隐式软故障.     

多台高压同步电动机液态软起动控制方案

利用液态软起动的原理起动多台高压同步电动机,可以节省投资.介绍了使用Quantum系列PLC实现对多台高压大功率同步电动机起动过程的控制方案.     

电沉积软磁铁箔连续化生产的关键技术研究

就电沉积方法制备软磁铁箔时要实现连续化生产的一些关键技术进行讨论。结果表明,经高精度抛光及钝化处理后的纯钛是理想的阴极材料;随电解液温度和阴极电流密度的提高,电成型铁箔的均匀程度均提高;电解液温度大于60℃时,添加10g·L-1的铁粉对溶液中的Fe2+氧化抑制程度在80%以上。     

新型钴基非晶态合金屏蔽材料的磁性研究

研究了不同退火条件下，新型屏蔽材料钴基非晶态合金Co66Fe4Ni1Si15B14的磁阻抗比（MIR）和磁导率比（PR）。采用了两步冷却法（F＆W），通过协同降低内热应力和磁有序，提高了样品的初始磁导率。研究结果表明，F＆W法试样的初始磁导率（μi）比单独用熔炉冷却法（FC）和水淬火法（WQ）分别提高了50％和100％。通过测量试样的MIR和PR，研究了试样软磁性的退火效应，3．1MHz下FC法试样和WQ法试样在420℃退火温度下具有比F＆W法更优越的软磁性。     

用 BP神经网络在线检测松节油含量

在用蒸汽蒸馏连续法生产松香和松节油的过程中，在线检测松节油的含油量是一难点。由于目前尚无此类仪表，因此提出用BP神经网络搭建软仪表模型在线检测松节油含量，以生产过程中较易检测到的两个量（温度和黏度）作为软仪器的输入量，间接检测出松节油的含油量。用MATLAB软件对模型进行了仿真，检验了方法的可行性，说明神经网络在辨识非线性系统模型中具有很好的应用前景。     

Linux系统实时性能增强方法的研究

标准Linux系统采用非抢占式内核,中断服务例程在执行过程中处于屏蔽状态,实时任务响应因为中断屏蔽可能被延时,实时任务响应具有不确定性,因此标准Linux系统不具有硬实时特性,其实时性能是软实时特性。增强标准Linux系统的实时性能,使其具有硬实时特性,是当前实时操作系统研究领域的一个热点。论文研究了影响Linux实时性能的因素,分析目前流行的增强标准Linux实时性能的方法,比较了相应实时Linux产品的优缺点,引出了基于资源的新型实时操作系统的模型。     

商品软件试用版的发布一法

试用版的使用可通过设置有效日期或使用次数来控制，前者较简单，文中以Visual Basic6.0为例主要说明了后者的实现：建立包含检测使用次数代码的工程，编译工程并打包发布。     

Boosting Search with Variable Elimination in Constraint Optimization and Constraint Satisfaction Problems

There are two main solving schemas for constraint satisfaction and optimization problems: i) search, whose basic step is branching over the values of a variables, and ii) dynamic programming, whose basic step is variable elimination. Variable elimination is time and space exponential in a graph parameter called induced width, which renders the approach infeasible for many problem classes. However, by restricting variable elimination so that only low arity constraints are processed and recorded, it can be effectively combined with search, because the elimination of variables may reduce drastically the search tree size.In this paper we introduce BE-BB(k), a hybrid general algorithm that combines search and variable elimination. The parameter k controls the tradeoff between the two strategies. The algorithm is space exponential in k. Regarding time, we show that its complexity is bounded by k and a structural parameter from the constraint graph. We provide experimental evidence that the hybrid algorithm can outperform state-of-the-art algorithms in constraint satisfaction, Max-CSP and Weighted CSP. Especially in optimization tasks, the advantage of our approach over plain search can be overwhelming.     

Dual-Priority versus Background Scheduling: A Path-Wise Comparison

In this paper, two well-known scheduling policies for real-time systems, namely background scheduling (BS) and dual-priority (DP) are compared in terms of response times for soft real-time traffic (SRT). It is proved in the preemptive as well as in the non-preemptive case that, when the SRT traffic is FIFO, the DP policy always outperforms BS for all instances of SRT tasks. When the SRT traffic is not FIFO but if all tasks are of equal size then, in the non-preemptive case, the average response times is shown to be always better under DP than under BS. As a complementary result, some non-FIFO examples where BS behaves better than DP for some SRT tasks but also on the average of the SRT response times, are given. The proofs are based on a trajectorial method that may be used for comparing other scheduling policies.