历史数据压缩算法在DSP上的实现

工业控制中需要记录大量的历史数据,因其数据量庞大,需要的存储空间也及其庞大,同时网络传输过程的实现比较困难,针对这一情况,提出了一种基于整数小波变换和多级树集合分割编码压缩新算法,并在DSP平台上实现,给出仿真结果,结果表明此算法可方便地控制数据压缩比和重构误差,并且具有压缩速度快、熏构误差小和压缩比高的特点。     

Balancing minimum spanning trees and shortest-path trees

We give a simple algorithm to find a spanning tree that simultaneously approximates a shortest-path tree and a minimum spanning tree. The algorithm provides a continuous tradeoff: given the two trees and a>0, the algorithm returns a spanning tree in which the distance between any vertex and the root of the shortest-path tree is at most 1+2 times the shortest-path distance, and yet the total weight of the tree is at most 1+2/ times the weight of a minimum spanning tree. Our algorithm runs in linear time and obtains the best-possible tradeoff. It can be implemented on a CREW PRAM to run a logarithmic time using one processor per vertex.Current research supported by NSF Research Initiation Award CCR-9307462. This work was done while this author was supported by NSF Grants CCR-8906949, CCR-9103135, and CCR-9111348.Part of this work was done while this, author was at the University of Maryland Institute for Advanced Computer Studies (UMIACS) and supported by NSF Grants CCR-8906949 and CCR-9111348.     

区间值属性决策树学习算法*

该文提出了一种区间值属性决策树的学习算法.区间值属性的值域不同于离散情况下的无序集和连续情况下的全序集,而是一种半序集.作为ID3算法在区间值意义下的推广,算法通过一种分割信息熵的极小化来选取扩展属性.通过非平稳点分析,减少了分割信息熵的计算次数,使算法的效率得到了提高.     

基于二叉树相似性检测的变形文字识别研究

文字在经过一系列艺术处理后,许多现有的识别特征会产生歧义而导致识别准确率下降。文章讨论了在文字的变形处理不会改变文字内在的框架情况下的识别特征检测。首先利用最小生成树获取文字图像所对应的树型结构,然后约简非分叉节点(即节点的邻接节点数为2的中间节点),提出一个实用的无序无标签二叉树之间的相似测度计算算法,与原文字图像所对应的约简树进行相关度分析检测,最后获得文字相似性度量结果。这种算法着重分析图像中的分枝特征,因此对于不会改变文字内在框架的艺术处理来说,文字本身的分枝特征能够很好地被检测出来,从而实现艺术文字的识别。     

Improved Boosting Algorithms Using Confidence-rated Predictions

We describe several improvements to Freund and Schapire's AdaBoost boosting algorithm, particularly in a setting in which hypotheses may assign confidences to each of their predictions. We give a simplified analysis of AdaBoost in this setting, and we show how this analysis can be used to find improved parameter settings as well as a refined criterion for training weak hypotheses. We give a specific method for assigning confidences to the predictions of decision trees, a method closely related to one used by Quinlan. This method also suggests a technique for growing decision trees which turns out to be identical to one proposed by Kearns and Mansour. We focus next on how to apply the new boosting algorithms to multiclass classification problems, particularly to the multi-label case in which each example may belong to more than one class. We give two boosting methods for this problem, plus a third method based on output coding. One of these leads to a new method for handling the single-label case which is simpler but as effective as techniques suggested by Freund and Schapire. Finally, we give some experimental results comparing a few of the algorithms discussed in this paper.     

Minimum classification error learning for sequential data in the wavelet domain

Wavelet analysis has found widespread use in signal processing and many classification tasks. Nevertheless, its use in dynamic pattern recognition have been much more restricted since most of wavelet models cannot handle variable length sequences properly. Recently, composite hidden Markov models which observe structured data in the wavelet domain were proposed to deal with this kind of sequences. In these models, hidden Markov trees account for local dynamics in a multiresolution framework, while standard hidden Markov models capture longer correlations in time. Despite these models have shown promising results in simple applications, only generative approaches have been used so far for parameter estimation. The goal of this work is to take a step forward in the development of dynamic pattern recognizers using wavelet features by introducing a new discriminative training method for this Markov models. The learning strategy relies on the minimum classification error approach and provides re-estimation formulas for fully non-tied models. Numerical experiments on phoneme recognition show important improvement over the recognition rate achieved by the same models trained using maximum likelihood estimation.     

基于Stacking元学习策略的电力系统暂态稳定评估

为提高电力系统暂态稳定评估单个模型的准确率,研究了基于元学习策略的暂态稳定评估问题,提出了支持向量机、决策树、朴素贝叶斯和K最近邻法作为基学习算法,线性回归为元学习算法的Stacking评估模型。该模型将上述基学习算法的概率输出作为新训练数据的输入特征,同时保留原始的类标识。线性回归算法在新训练集上学习得到最终暂态稳定评估结果。新英格兰39节点测试系统和IEEE50机测试系统上仿真实现了该模型,仿真结果证明所提模型比单个模型的评估性能更好,为电力系统暂态稳定评估提供了新的思路。     

基于决策树分类技术的心电自动分析软件检测系统

研究和开发了一种基于数据挖掘技术的心电自动分析软件检测系统.该系统首先通过直接从人体采集电信号和读取扫描经典心电图图片两种途径,依据M IT-B IH心律失常数据库规则和存储格式,建立国人临床心电数据库,然后采用决策树分类方法对样本进行训练和分类,最终进行心电自动分析软件的检测,并由系统输出检测结果.     

Effects of Olive Trees Age on the Minor Components of Oueslati Virgin Olive Oils Produced from Olives Harvested at Different Ripening Degrees

Phenolics, volatiles, squalene, tocopherols, and fatty acids of virgin olive oils (VOO) from adult and young olive trees of the Oueslati variety, typically cultivated in the Center of Tunisia, were analyzed at three different harvesting periods. Significant differences in contents of saturated fatty acids (p < 0.05), squalene (p < 0.05), alpha-tocopherol and total tocopherol (p < 0.02) and oxidized form of decarboxymethyl oleuropein aglycon (p < 0.05) were seen between VOO from adult and young trees during maturation. Moreover, the volatile profiles of VOO from adult and young trees showed significant differences in the amounts of hexanal, 1-penten-3-ol (p < 0.05), (Z)-3-hexenal and (Z)-2-penten-1-ol (p < 0.01). Principal component analysis showed that olives from adult trees should be harvested at the cherry stage of maturation to obtain a satisfactory level of oil quality, while olives from young trees should be harvested at the black maturation stage.     

卫星系统总体方案树合成技术研究

针对卫星数字化设计系统中分系统之间的耦合问题,分析了两种不同的分系统建模方式,研究了相应的系统总体方案合成技术,提出了针对并行和串行结构的建模方式对应的系统方案合成和校验算法,进行了合成方案的可行性分析,将总体方案树中出现的大量可能方案减少到少量的可行方案。研究表明,采用并行与串行结合的分系统建模、分系统版本验证可以降低数字化建模的难度,大量淘汰非可行总体方案,从而极大地减少了系统总体方案分析的工作量,提高卫星总体多学科设计优化能力。