双机械臂协调：以运动学为基础的模型分析与动态补偿控制方法

本文建立了双机械臂协调系统的主从式控制的非结构模型，并针对两臂具有相同特性和不同特性这两种情形作了稳定性分析，提出特性校正的方法，本文还利用内模原理设计出动态补偿器来克服标定约束的不确定性对系统控制品质的影响，最后给出了实验结果。     

韶钢热轧宽板厂工艺设备参数的优化

采用卷轧与中板轧制相结合的轧制工艺具有投资省、见效快等优点,对于资金不是很充裕需要采取滚动发展的中小型企业是一种可取之路.韶钢通过与国内外专家的技术交流、技术谈判及优化,在有限的场地上(长620m、宽120 m)布置一套先进、实用的3 450现代宽中厚板(卷)生产线,取得了较好的投资效益.     

提高E^2PROM中NMOS管源漏穿通电压的实验研究

为了提高Ｅ＾２ＰＲＯＭ中Ｎ管源漏穿通电压，用实验的方法对制造工艺进行了研究。结果表明，高能量注入是提高ＶＰＴ的有效手段，但受到ｐｎ结击穿的限制，只适用于低区短沟Ｎ管；ＤＤＤ工艺大幅度高ＶＰＴ，但ｐｎ结击穿电压低于２０Ｖ，不能应用于高压ＭＯＳ管；采用适量的防穿通注入和适当增大沟道长度为最理想的工艺途径。     

SVPWM三相电压型逆变器的仿真研究

以三相电压型逆变器为基础,根据空间矢量脉宽调制（SVPWM）基本原理详细分析研究了电压矢量作用时间和扇区分配问题,以α-β坐标为基础,建立了互差π/3的x-y-z时间坐标系,提出了不同扇区矢量作用时间长短根据期望极值判断的原则。介绍了MATLAB中M文件编程的仿真方法,建立了MATLAB程序嵌入式主要功能模块,逆变器采用数字PID控制,利用Simulink对系统进行了综合仿真分析及试验,给出了仿真及实验波形,为实现SVPWM逆变器的数字控制提供参考。     

A new algorithm for fast mining frequent itemsets using N-lists

Mining frequent itemsets has emerged as a fundamental problem in data mining and plays an essential role in many important data mining tasks.In this paper,we propose a novel vertical data representation called N-list,which originates from an FP-tree-like coding prefix tree called PPC-tree that stores crucial information about frequent itemsets.Based on the N-list data structure,we develop an efficient mining algorithm,PrePost,for mining all frequent itemsets.Efficiency of PrePost is achieved by the following three reasons.First,N-list is compact since transactions with common prefixes share the same nodes of the PPC-tree.Second,the counting of itemsets’ supports is transformed into the intersection of N-lists and the complexity of intersecting two N-lists can be reduced to O(m + n) by an efficient strategy,where m and n are the cardinalities of the two N-lists respectively.Third,PrePost can directly find frequent itemsets without generating candidate itemsets in some cases by making use of the single path property of N-list.We have experimentally evaluated PrePost against four state-of-the-art algorithms for mining frequent itemsets on a variety of real and synthetic datasets.The experimental results show that the PrePost algorithm is the fastest in most cases.Even though the algorithm consumes more memory when the datasets are sparse,it is still the fastest one.