A new reverse reduce-error ensemble pruning algorithm

Although greedy algorithms possess high efficiency, they often receive suboptimal solutions of the ensemble pruning problem, since their exploration areas are limited in large extent. And another marked defect of almost all the currently existing ensemble pruning algorithms, including greedy ones, consists in: they simply abandon all of the classifiers which fail in the competition of ensemble selection, causing a considerable waste of useful resources and information. Inspired by these observations, an interesting greedy Reverse Reduce-Error (RRE) pruning algorithm incorporated with the operation of subtraction is proposed in this work. The RRE algorithm makes the best of the defeated candidate networks in a way that, the Worst Single Model (WSM) is chosen, and then, its votes are subtracted from the votes made by those selected components within the pruned ensemble. The reason is because, for most cases, the WSM might make mistakes in its estimation for the test samples. And, different from the classical RE, the near-optimal solution is produced based on the pruned error of all the available sequential subensembles. Besides, the backfitting step of RE algorithm is replaced with the selection step of a WSM in RRE. Moreover, the problem of ties might be solved more naturally with RRE. Finally, soft voting approach is employed in the testing to RRE algorithm. The performances of RE and RRE algorithms, and two baseline methods, i.e., the method which selects the Best Single Model (BSM) in the initial ensemble, and the method which retains all member networks of the initial ensemble (ALL), are evaluated on seven benchmark classification tasks under different initial ensemble setups. The results of the empirical investigation show the superiority of RRE over the other three ensemble pruning algorithms.     

Acquisition and pattern recognition of spectrum information of welding metal transfer

With the creation of a pattern recognition system for metal transfer mode, this article has collected five kinds of spectrum signals in gas metal arc welding (MIG, MAG and CO₂) and take them as training samples. These samples have been pretreated by computer, several key characteristic parameters of the spectrum signal have been creatively extracted, and a corresponding recognition function and a minimum-distance-classifier have been constructed. The results show that the pattern recognition of several kinds of metal transfer modes for the metal gas arc welding can be done successfully, and relative important parameters in welding process, such as the frequency of droplet transfer and the approximate diameter of each droplet, can also be obtained.     

基站智能天线多波束合成技术

采用数字波束形成的技术，给出了基站智能天线多波束合成的有效算法。对圆环阵分布的基站天线多波束方向图进行了综合，得到了空间隔离度很好的多个波束，并将副瓣控制在30dB以下，结果令人满意。该方法可用于任意单元构成的不均匀阵列天线，还可用于波束展宽及扇形波束的合成。     

基于Powell图的模式识别法建立化学反应速度方程

本文基于Ｐｏｗｅｌｌ图，提出了用模式识别技术中的相似系数法来确定化学反应级数的方法，并给出了计算实例。     

LMG200型可调高滚筒式露天采煤机

介绍了可调高滚筒式露天采煤机的结构特点及在露天煤矿应用所产生的效益和其广 阔的发展前景。     

薄板坯连铸工艺液芯压下的有限元分析

简要介绍世界几种主要薄板坯连铸工艺。着重叙述应用大型通用商业软件MSC.MARC。模拟液芯压下时铸坯的变形，确定最佳压下方案的有限元分析，液芯压下应该在结晶器出口处及二冷区扇形段开始部分进行，这样能减小铸坯的应变。     

离心萃取装置中硝酸羟胺还原反萃钚的研究

用十六级分离式混合—离心澄清萃取装置,在20-24℃,30-34℃,45-50℃等不同温度下进行了3B槽硝酸羟胺还原反萃钚的实验。结果表明,钚的收率均可达99.9%以上。在20-24℃时,有机相出口级附近几级的钚浓度较高,实验条件一旦波动,有可能造成钚的流失,且钚在萃取设备中有明显的积累。温度升高,14级反萃(停留时间约18min)时,钚的收率基本得到保证。     

Pattern expressions and pattern automata

We define the pattern expressions as an extension of both regular expressions and patterns. We prove several properties of the new family of languages, similar to those of extended regex languages [Câmpeanu et al., Int. J. Found. Comput. Sci. 14 (6) (2003) 1007-1018]. We also define an automata system that recognizes these languages. Differences between regex and pattern expressions are also discussed.     

基于奇异值分解和神经网络的个性化信息模式识别

随着信息资源的持续增长，信息获取系统为特定用户提供特定信息服务的需求越来越重要。该文提出了基于奇异值分解和神经网络的个性化信息模式识别的方法，可以方便地获取用户信息需求的特征，并且利用这些特征信息完成个性化的信息识别和匹配，即实现个性化信息的模式识别，进而实现信息的个性化获取和管理。该文中的算法实现简单，可以方便地应用到目前的一些信息管理、服务系统中。