The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of ...The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of the number of clusters to be identified. Density-based spatial clustering of applications with noise （DBSCAN） is the first algorithm proposed in the literature that uses density based notion for cluster detection. Since most of the real data set, today contains feature space of adjacent nested clusters, clearly DBSCAN is not suitable to detect variable adjacent density clusters due to the use of global density parameter neighborhood radius Y,.ad and minimum number of points in neighborhood Np~,. So the efficiency of DBSCAN depends on these initial parameter settings, for DBSCAN to work properly, the neighborhood radius must be less than the distance between two clusters otherwise algorithm merges two clusters and detects them as a single cluster. Through this paper： 1） We have proposed improved version of DBSCAN algorithm to detect clusters of varying density adjacent clusters by using the concept of neighborhood difference and using the notion of density based approach without introducing much additional computational complexity to original DBSCAN algorithm. 2） We validated our experimental results using one of our authors recently proposed space density indexing （SDI） internal cluster measure to demonstrate the quality of proposed clustering method. Also our experimental results suggested that proposed method is effective in detecting variable density adjacent nested clusters.展开更多
点排序识别聚类结构(Ordering Points to Identify the Clustering Structure,OPTICS)的密度聚类算法能以可视化的方式导出数据集的内在聚类结构,并且可以通过簇排序提取基本的聚类信息。但是该算法由于时空复杂度较高,不能很好地适应...点排序识别聚类结构(Ordering Points to Identify the Clustering Structure,OPTICS)的密度聚类算法能以可视化的方式导出数据集的内在聚类结构,并且可以通过簇排序提取基本的聚类信息。但是该算法由于时空复杂度较高,不能很好地适应当今社会出现的大型数据集。随着云计算和并行计算的发展,提供了一种解决OPTICS算法复杂度缺陷的方法和一种建立在基于Spark内存计算平台的点排序识别聚类结构并行算法。测试的实验结果表明,它能极大地降低OPTICS算法对时间和空间的需要。展开更多
Process safety in chemical industries is considered to be one of the important goals towards sustainable development. This is due to the fact that, major accidents still occur and continue to exert significant reputat...Process safety in chemical industries is considered to be one of the important goals towards sustainable development. This is due to the fact that, major accidents still occur and continue to exert significant reputational and financial impacts on process industries. Alarm systems constitute an indispensable component of automation as they draw the attention of process operators to any abnormal conditi on in the plant. Therefore, if deployed properly, alarm systems can play a critical role in helping plant operators ensure process safety and profitability. How-ever, in practice, many process plants suffer from poor alarm system configuration which leads to nuisance alarms and alarm floods that compromise safety. A vast amount of research has primarily focused on developing sophisticated alarm management algorithms to address specific issues. In this article, we provide a simple, practical, systematic approach that can be applied by plant engineers (i.e., non-experts) to improve industrial alarm system performance. The proposed approach is demonstrated using an industrial power plant case study.展开更多
文摘The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of the number of clusters to be identified. Density-based spatial clustering of applications with noise （DBSCAN） is the first algorithm proposed in the literature that uses density based notion for cluster detection. Since most of the real data set, today contains feature space of adjacent nested clusters, clearly DBSCAN is not suitable to detect variable adjacent density clusters due to the use of global density parameter neighborhood radius Y,.ad and minimum number of points in neighborhood Np~,. So the efficiency of DBSCAN depends on these initial parameter settings, for DBSCAN to work properly, the neighborhood radius must be less than the distance between two clusters otherwise algorithm merges two clusters and detects them as a single cluster. Through this paper： 1） We have proposed improved version of DBSCAN algorithm to detect clusters of varying density adjacent clusters by using the concept of neighborhood difference and using the notion of density based approach without introducing much additional computational complexity to original DBSCAN algorithm. 2） We validated our experimental results using one of our authors recently proposed space density indexing （SDI） internal cluster measure to demonstrate the quality of proposed clustering method. Also our experimental results suggested that proposed method is effective in detecting variable density adjacent nested clusters.
文摘点排序识别聚类结构(Ordering Points to Identify the Clustering Structure,OPTICS)的密度聚类算法能以可视化的方式导出数据集的内在聚类结构,并且可以通过簇排序提取基本的聚类信息。但是该算法由于时空复杂度较高,不能很好地适应当今社会出现的大型数据集。随着云计算和并行计算的发展,提供了一种解决OPTICS算法复杂度缺陷的方法和一种建立在基于Spark内存计算平台的点排序识别聚类结构并行算法。测试的实验结果表明,它能极大地降低OPTICS算法对时间和空间的需要。
文摘Process safety in chemical industries is considered to be one of the important goals towards sustainable development. This is due to the fact that, major accidents still occur and continue to exert significant reputational and financial impacts on process industries. Alarm systems constitute an indispensable component of automation as they draw the attention of process operators to any abnormal conditi on in the plant. Therefore, if deployed properly, alarm systems can play a critical role in helping plant operators ensure process safety and profitability. How-ever, in practice, many process plants suffer from poor alarm system configuration which leads to nuisance alarms and alarm floods that compromise safety. A vast amount of research has primarily focused on developing sophisticated alarm management algorithms to address specific issues. In this article, we provide a simple, practical, systematic approach that can be applied by plant engineers (i.e., non-experts) to improve industrial alarm system performance. The proposed approach is demonstrated using an industrial power plant case study.
基金国家自然科学基金( the National Natural Science Foundation of China under Grant No6037405) 山东省自然科学基金( the NaturalScience Foundation of Shandong Province of China under Grant NoZ2004G02) +1 种基金 山东省中青年科学家奖励基金资助项目( No03BS003) "泰山学者"建设工程专项经费资助
