一种包含异常传播的类间数据依赖分析方法

类间数据依赖分析是类间数据流测试的基础。本文通过分析类簇级测试中的异常传播对程序数据依赖的影响,提出一种包括异常结构在内的类间C++程序数据依赖分析方法,根据类间关系增量式地构造类间数据依赖图,并给出类间数据依赖图的构造算法。最后,在程序切片中应用了该数据依赖分析方法。结果证明,该方法通过分析异常传播对数据依赖的影响能够带来切片精度的提高。

非平衡光纤传感数据集类间数据重合的识别与分离算法

传统基于LDA的聚类算法,进行非平衡光纤传感数据集类间数据重合的识别以及分类时,结果存在严重的类倾斜问题,聚类效果不理想。因此,提出新非平衡光纤传感数据集类间数据重合的识别与分离算法,采用K最邻近算法实现非平衡光纤传感数据集类间数据重合的识别,基于识别结果通过GAK-KNN算法实现数据集类间数据重合的分离,对非平衡光纤传感数据集类间数据样本分配合理的权重,通过K-means算法操作非均衡数据集,采用SMOTE算法实现初步聚类,通过三角质心TSMOTE算法解决非平衡光纤传感数据集分类结果倾向性问题,获取更为精确的非平衡光纤传感数据集类间数据聚类结果。实验结果说明,所提算法取得了较好的非平衡光纤传感数据聚类效果,聚类效率高。

基于决策树算法的客服终端冗余数据迭代消除方法

为了提高客服终端数据可利用性,降低冗余数据干扰程度,挖掘潜在客户,制定销售策略,研究一种基于决策树算法的客服终端冗余数据迭代消除方法。采用数据仓库法抽取并集成客服终端数据,对字符类数据进行去停用词和中文分词预处理,对数值类数据进行缺失值填补和离散值删除预处理。构建ID3决策树,分类客服终端数据,计算同一类数据的类间相似度,构建冗余数据判断规则,检测客服终端冗余数据,联合消除器消除冗余数据。实验结果表明:所研究方法应用后,可以消除客服终端冗余数据,空间缩减比更接近冗余率。

高度不平衡肠道图像数据集均衡策略

医学图像分类天然受到数据集高度不平衡问题影响。文章首先阐述医学图像的数据不平衡问题及其对人工智能技术分类性能产生的影响。随后研究现有对数据不平衡问题提供类间数据平衡分布的方法,讨论肠道图像中遇到的暗区以及病灶区域旋转等特殊挑战,根据肠道图像的特点,设计一种适用于肠道图像数据集的类间数据平衡方法。核心思路包括利用数量巨大的健康图像为基底,线性组合病灶图像,在选取图像时去除暗区部分较大的健康图像以降低暗区对分类性能的影响,同时通过病灶图像的旋转和平移模拟临床中肠道病灶的旋转特性和位置不确定的特点。通过这种简洁的方法,能够快速地进行高度不平衡的肠道图像数据集平衡。基于肠道图像疾病数据集进行实验,通过肠道溃疡和健康二分类仿真验证文章方法的有效性。

Fuzzy Clustering Validity for Spatia Data

The validity measurement of fuzzy clustering is a key problem. If clustering is formed, it needs a kind of machine to verify its validity. To make mining more accountable, comprehensible and with a usable spatial pattern, it is necessary to first detect whether the data set has a clustered structure or not before clustering. This paper discusses a detection method for clustered patterns and a fuzzy clustering algorithm, and studies the validity function of the result produced by fuzzy clustering based on two aspects, which reflect the un-certainty of classification during fuzzy partition and spatial location features of spatial data, and proposes a new validity function of fuzzy clustering for spatial data. The experimental result indicates that the new validity function can accurately measure the validity of the results of fuzzy clustering. Especially, for the result of fuzzy clustering of spatial data, it is robust and its classification result is better when compared to other indices.

Realization of R-tree for GIS on hybrid clustering algorithm

The characteristic of geographic information system（GfS） spatial data operation is that query is much more frequent than insertion and deletion, and a new hybrid spatial clustering method used to build R-tree for GIS spatial data was proposed in this paper. According to the aggregation of clustering method, R-tree was used to construct rules and specialty of spatial data. HCR-tree was the R-tree built with HCR algorithm. To test the efficiency of HCR algorithm, it was applied not only to the data organization of static R-tree but also to the nodes splitting of dynamic R-tree. The results show that R-tree with HCR has some advantages such as higher searching efficiency, less disk accesses and so on.

Phylogenetic analyses of four species of Ulva and Monostroma grevillei using ITS, rbcL and 18S rDNA sequence data

Chlorophyta species are common in the southern and northern coastal areas of China. In recent years, frequent green tide incidents in Chinese coastal waters have raised concerns and attracted the attention of scientists. In this paper, we sequenced the 18S rDNA genes, the internal transcribed spacer （ITS） regions and the rbcL genes in seven organisms and obtained 536-566 bp long ITS sequences, 1 377-I 407 bp long rbcL sequences and 1 718-1 761 bp long partial 18S rDNA sequences. The GC base pair content was highest in the ITS regions and lowest in the rbcL genes. The sequencing results showed that the three Ulvaprolifera （or U. pertusa） gene sequences from Qingdao and Nan＇ao Island were identical. The ITS, 18S rDNA and rbcL genes in U. prolifera and U. pertusa from different sea areas in China were unchanged by geographic distance. U.flexuosa had the least evolutionary distance from U. californica in both the ITS regions （0.009） and the 18S rDNA （0.002）. These data verified that Ulva and Enteromorpha are not separate genera.

DCAD:a Dual Clustering Algorithm for Distributed Spatial Databases

Spatial objects have two types of attributes： geometrical attributes and non-geometrical attributes, which belong to two different attribute domains （geometrical and non-geometrical domains）. Although geometrically scattered in a geometrical domain, spatial objects may be similar to each other in a non-geometrical domain. Most existing clustering algorithms group spatial datasets into different compact regions in a geometrical domain without considering the aspect of a non-geometrical domain. However, many application scenarios require clustering results in which a cluster has not only high proximity in a geometrical domain, but also high similarity in a non-geometrical domain. This means constraints are imposed on the clustering goal from both geometrical and non-geometrical domains simultaneously. Such a clustering problem is called dual clustering. As distributed clustering applications become more and more popular, it is necessary to tackle the dual clustering problem in distributed databases. The DCAD algorithm is proposed to solve this problem. DCAD consists of two levels of clustering： local clustering and global clustering. First, clustering is conducted at each local site with a local clustering algorithm, and the features of local clusters are extracted clustering is obtained based on those features fective and efficient. Second, local features from each site are sent to a central site where global Experiments on both artificial and real spatial datasets show that DCAD is effective and efficient.

Dynamic Change of Landscape Pattern at Jilin Province from 1980 to 2000

By using remote sensing images from three periods (1980, 1995, 2000) and with the support of GIS and RS, the spatial information of landscape elements of Jilin Province from 1980 to 2000 was interpreted and extracted. Using models of landscape indices such as diversity, fragmentation, and mean patch fractal dimension, dynamic spatio-temporal changes of landscape patterns of the province were analyzed. The results: ① cropland and forestland were the main landscape types, and forestland became a landscape matrix; ② in the study area, landscapes were distributed unevenly, and there was low heterogeneity, a simple ecosystem structure and a tendency of irrational landscape patterns. There were also simple spatial shapes of patches and strong self-similarities, and in terms of dynamic change analysis, patch shapes tended to be more simple; ③ from 1980 to 2000, holistic landscape fragmentation was low and changed slightly. As far as landscape elements were concerned, the fragmentation of grassland, water area, land for residential area and factory facilities was relatively low; land distribution for residential areas and factory facilities was dispersed; and cropland and forestland were most concentrated-an indication that the trend will continue. Comprehensive effects among human activity, local policy, regional climate and environmental change led to the results.

Seasonal variation of the Taiwan Warm Current Water and its underlying mechanism

Based on the historical observed data and the modeling results,this paper investigated the seasonal variations in the Taiwan Warm Current Water(TWCW)using a cluster analysis method and examined the contributions of the Kuroshio onshore intrusion and the Taiwan Strait Warm Current(TSWC)to the TWCW on seasonal time scales.The TWCW has obviously seasonal variation in its horizontal distribution,T-S characteristics and volume.The volume of TWCW is maximum(13746 km^3)in winter and minimum(11397 km^3)in autumn.As to the contributions to the TWCW,the TSWC is greatest in summer and smallest in winter,while the Kuroshio onshore intrusion northeast of Taiwan Island is strongest in winter and weakest in summer.By comparison,the Kuroshio onshore intrusion make greater contributions to the Taiwan Warm Current Surface Water(TWCSW)than the TSWC for most of the year,except for in the summertime(from June to August),while the Kuroshio Subsurface Water(KSSW)dominate the Taiwan Warm Current Deep Water(TWCDW).The analysis results demonstrate that the local monsoon winds is the dominant factor controlling the seasonal variation in the TWCW volume via Ekman dynamics,while the surface heat fl ux can play a secondary role via the joint ef fect of baroclinicity and relief.

Application of Two-Order Difference to Gap Statistic

Gap statistic is a well-known index of clustering validity, but its realization is difficult to be comprehended and accurately determined. A direct method is presented to improve the performance of the Gap statistic, which applies the two-order difference of within-cluster dispersion to replace the constructed null reference distribution in the Gap statistic. Hence, the realization of the Gap statistic becomes easy and is reformulated, and its uncertainty in applications is reduced. Also, the limitation of the Gap statistic is analyzed by two typical examples, that is, the Gap statistic is difficult to be applied to the dataset that contains strong-overlap or uneven-density clusters. Experiments verify the usefulness of the proposed method.

HPLC-DAD data coupled with second-order calibration method applied to food analysis: Simultaneous determination of six benzoylurea insecticides in various fruit samples by selecting time region of chromatogram

This work presents a novel application of second-order calibration based on self-weighted alternating trilinear decomposition(SWATLD)algorithm for analyzing the HPLC-DAD data.The proposed method makes it possible to simultaneously determine teflubenzuron,hexaflumuron,flufenoxuron,chlorfluazuron,diflubenzuron and benzoylurea in different fruit samples,i.e.pear,apple and banana,in the selected time region of chromatogram.The concentration,elution time and spectral information of these benzoylurea insecticides are selectively extracted from complex matrices even in the presence of unknown interferences.The root-mean-square error of prediction(RMSEP)and figures of merit,including sensitivity(SEN),selectivity(SEL)and limit of detection(LOD)are employed to access the performance of the method.The LODs obtained for these insecticides are within the range 0.017–0.26 ppm in pears,0.039–0.33 ppm in apples,0.041–0.44 ppm in bananas,respectively.Such a chemometrics-based protocol holds great potential to be extended as a promising alternative for more practical applications in food safety and quality monitoring.