A genetic algorithm for the optimisation of assembly sequences

This paper describes a Genetic Algorithm (GA) designed to optimise the Assembly Sequence Planning Problem (ASPP), an extremely diverse, large scale and highly constrained combinatorial problem. The modelling of the ASPP problem, which has to be able to encode any industrial-size product with realistic constraints, and the GA have been designed to accommodate any type of assembly plan and component. A number of specific modelling issues necessary for understanding the manner in which the algorithm works and how it relates to real-life problems, are succinctly presented, as they have to be taken into account/adapted/solved prior to Solving and Optimising (S/O) the problem. The GA has a classical structure but modified genetic operators, to avoid the combinatorial explosion. It works only with feasible assembly sequences and has the ability to search the entire solution space of full-scale, unabridged problems of industrial size. A case study illustrates the application of the proposed GA for a 25-components product.     

The IS Organization of the Future: Four Scenarios for 2020

Four scenarios for the IS organization of 2020 are described, based on differing assumptions about two drivers: the advances in the reliability of international telecommunications and the value placed on computerization in businesses and society.     

Nonlinear suboptimal filtration of pseudorandom sequences

The critical importance of studies of alternative methods of representation and processing of signals constructed from pseudorandom sequences of maximal period is demonstrated. For the first time, it is proposed that a multidimensional nonlinear finite-difference representation of an arbitrary pseudorandom sequence be employed. A suboptimal algorithm for nonlinear discrete Kalman filtration of a pseudorandom sequence is obtained. A numerical example of filtration of a sequence formed on the basis of a given recurrence rule is considered and its efficiency is demonstrated.     

前向人工神经网络模型用于时间序列分析

介绍前向人工神经网络模型，并与传统的统计学线性模型进行比较。为了适应时间序列变量的特点，对逆传播模型进行了改进，并对太阳黑子活动的观察值这一典型的时间序列进行了分析，并检验了模型的预测能力，取得了较好的结果     

PTA装置自动控制系统实现安稳运行的对策

针对PTA装置的工艺生产特点，对自动控制系统当中出现的不同种类的问题，进行了多方面的简要探讨与分析，并提出实现安稳运行相应的对策。     

CS3000顺控表的组态和应用

介绍CS3000系统顺控表的结构、参数定义和功能块组态,以聚丙烯加氢控制为例,描述顺控表的组态方法、顺控动作和操作步骤等。     

改进的蠕虫特征自动提取模型及算法设计

文章提出了一种基于序列比对的蠕虫特征自动提取模型,该模型针对现有蠕虫特征自动提取系统的可疑蠕虫样本流量单来源和粗预处理等问题,提出了对网络边界可疑流量和蜜罐捕获网络流量统一的聚类预处理,并使用改进的T-Coffee多序列比对算法进行蠕虫特征提取。实验分别对Apache-Knacker和TSIG这两种蠕虫病毒进行特征提取,从实验结果可以看出文章提出的模型产生的特征质量优于比较流行的Polygraph、Hamsa两种技术。     

动态规划法求解加工顺序问题的研究

提出动态规划法求解加工顺序问题的方法,分析最优子结构性质,建立最优值的递归关系式,分析Johnson-Bell-man’s Rule并设计算法流程,最后通过构造实例表明了算法的有效性。     

三相电压不平衡情况下电机车整流技术的研究

针对煤矿井下直流电机车变流系统中存在三相电压不平衡而使传统的PWM整流器的交流侧出现负序电流分量的问题,提出了一种在三相电压不平衡情况下抑制PWM整流器交流侧负序电流分量的控制策略;详细介绍了该控制策略的具体实现。仿真及现场实验结果表明,采用该控制策略后,PWM整流器的交流侧电流波形基本接近正弦波形,实现了单位功率因数运行。