PLC顺控程序的计算机辅助设计

本文用C／C 语言研制了一个计算机辅助设计软件，可以自动设计PLC的SFC应用程序，并针对具体机型S7—200，利用其编程软件，将程序快速下载到PLC，极大地提高了效率。     

"Recovery of spatial alternation deficits following selective hippocampal destruction with kainic acid": Correction to Kesslak and Gage (1986).

Reports an error in "Recovery of spatial alternation deficits following selective hippocampal destruction with kainic acid" by J. Patrick Kesslak and Fred H. Gage (Behavioral Neuroscience, 1986[Apr], Vol 100[2], 280-283). In the aforementioned article, the degrees of freedom reported in the Results section are incorrect. In the sixth paragraph on page 281, the second sentence should read as follows: Results of the ANOVA indicated a significant effect for surgical treatments. F(2, 25)=25.44, p1986-21445-001.) Examined whether the sympathetic ingrowth of superior cervical ganglion (SCG) fibers sprouting into the hippocampus following kainic acid (KA) lesion of CA3 and CA4 pyramidal cells in male Sprague-Dawley rats would contribute to behavioral recovery. 31 Ss were trained on a forced-choice task. After reaching criterion performance levels, Ss received either KA (8 nM/0.4 μl) or saline injections into the hippocampus and were again tested on the forced-choice task. Half of the Ss had their SCG removed 35 days after injections, and all were again tested on the forced-choice task. Analysis of variance (ANOVA) showed Ss receiving KA took significantly longer to reach criterion following injections. Removal of the SCG after recovery reintroduced the performance deficit of KA-treated Ss on the alternation task; no other group showed any effect for SCG removal. Results indicate that the SCG may have a modulatory effect in behavioral recovery, although other mechanisms may also be operating. (22 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Tree-based partitioning of date for association rule mining

The most computationally demanding aspect of Association Rule Mining is the identification and counting of support of the frequent sets of items that occur together sufficiently often to be the basis of potentially interesting rules. The task increases in difficulty with the scale of the data and also with its density. The greatest challenge is posed by data that is too large to be contained in primary memory, especially when high data density and/or low support thresholds give rise to very large numbers of candidates that must be counted. In this paper, we consider strategies for partitioning the data to deal effectively with such cases. We describe a partitioning approach which organises the data into tree structures that can be processed independently. We present experimental results that show the method scales well for increasing dimensions of data and performs significantly better than alternatives, especially when dealing with dense data and low support thresholds. Shakil Ahmed received a first class BSc (Hons) degree from Dhaka University, Bangladesh, in 1990; and an MSc (first class), also Dhaka University, in 1992. He received his PhD from The University of Liverpool, UK, in 2005. From 2000 onwards he is a member of the Data Mining Group at the Department of Computer Science of the University of Liverpool, UK. His research interests include data mining, Association Rule Mining and pattern recognition. Frans Coenen has been working in the field of Data Mining for many years and has written widely on the subject. He received his PhD from Liverpool Polytechnic in 1989, after which he took up a post as a RA within the Department of Computer Science at the University of Liverpool. In 1997, he took up a lecturing post within the same department. His current Data Mining research interests include Association rule Mining, Classification algorithms and text mining. He is on the programme committee for ICDM'05 and was the chair for the UK KDD symposium (UKKDD'05). Paul Leng is professor of e-Learning at the University of Liverpool and director of the e-Learning Unit, which is responsible for overseeing the University's online degree programmes, leading to degrees of MSc in IT and MBA. Along with e-Learning, his main research interests are in Data Mining, especially in methods of discovering Association Rules. In collaboration with Frans Coenen, he has developed efficient new algorithms for finding frequent sets and is exploring applications in text mining and classification.     

Analysis of polarization plane rotation characteristic in 2D photonic crystal waveguide with chiral medium by condensed node spatial network

Using the photonic band gap in photonic crystals, the fundamental waveguide structures for the light wavelength range have been developed. Based on the fine structure of these many functional devices have been proposed by analytical or numerical simulation methods and the experiments of trial manufacture. In this paper, the treatment of chiral dielectric in the Condensed Node Spatial Network for the vector potential is explained, and we show the polarization plane rotation property in air‐hole and pillar type photonic crystal waveguide structures with the chiral medium substrate. Then, we show the fundamental advantage of the air‐hole type photonic crystal waveguide structure in application to a mode converter. © 2005 Wiley Periodicals, Inc. Electr Eng Jpn, 152(1): 7–14, 2005; Published online in Wiley InterScience ( www.interscience. wiley.com ). DOI 10.1002/eej.20098     

浅论建设项目投资控制的重点

建设项目控制的重点应放在投资决策和设计阶段中,否则会造成巨大的损失。这不仅需要人们改革组织结构和管理体制,更需要从根本上转变思想观念。     

环形孔径对部分相干光光谱的影响

本文推导了部分相干光在经过环形孔径之后,远场轴上点的光谱S(ω)与环形孔径处光的光谱S~(0)(ω)之间的关系。数值计算结果表明,S(ω)不仅依赖于S~(0)(ω),而且还与环形孔径处光的相干性以及环形孔径的参数有关。     

一种降低电离层梯度影响的Hatch滤波算法

目前,地基增强系统（Ground-Based Augment System,GBAS）中的主要问题是：尽管使用载波平滑降低了伪距噪,利用差分技术消除了用户和基准站间的大部分共同误差,从而减少了差分改正值中的误差。但是电离层时间梯度和空间梯度于这两个过程中产生了额外的误差,阻碍了差分改正值质量的进一步提高。基于对电离层时间和空间梯度引起的额外误差的分析,并考虑平滑伪距噪声,文章提出了一种新的Hatch滤波方法,该滤波器的时间常数是随卫星仰角、电离层时间和空间梯度以及用户与参考站之间的距离而实时变化的。该方法降低了差分改正值误差,提高了定位精度。并且通过差分定位仿真并对比传统的单频Hatch滤波方法,发现使用该方法后,减小了差分校正值的误差。     

一种基于遗传算法的模糊关联规则挖掘方法

文中提出了一种基于遗传算法的生成隶属度函数的方法,该方法通过遗传算法对初始种群进行优化,获得一个适应度较高的隶属度函数编码,然后再根据机场噪声数据的实际标准对优化后得到的隶属度函数进行修正,进而得到梯形分布的隶属度函数编码.最后通过得到的隶属度函数对数据进行模糊化,并采用FP-trees算法生成模糊关联规则.该文针对数量型属性提出了这种方法,它的优点是能够使通过遗传算法得到的较优的隶属度函数更加适用于实际的数据集.     

基于间接关联规则的数据挖掘算法研究

简单数据集可以通过关联规则得到在数据间的相互关系;相当多的情况下,由于不能从关联规则得到隐藏在数据间的相互关系,需要按间接关联规则分析出数据项集在交易集合中出现的频度,挖掘隐藏在数据间的相互关系。文中通过使用概念分层和基于近邻的方法,探讨利用FP树产生的频繁项集,对候选关联检验其是否满足项对支持度条件,并利用这个频繁项集挖掘事务的间接关联,找到挖掘事务的间接关联的内在规律,构造出不依赖中介条件的间接关联挖掘算法。