星载单光子激光雷达在浅水域测深方面展现出巨大的潜力,但其接收到的点云数据中存在大量噪声,给高程信息的提取带来了困难。目前,基于OPTICS(Ordering Points to Identify the Clustering Structure)的去噪算法因其精度高,在强背景噪声...星载单光子激光雷达在浅水域测深方面展现出巨大的潜力,但其接收到的点云数据中存在大量噪声,给高程信息的提取带来了困难。目前,基于OPTICS(Ordering Points to Identify the Clustering Structure)的去噪算法因其精度高,在强背景噪声下表现良好,得到了广泛的应用,但此算法在水底附近区域存在去噪效果不佳的现象。为了解决这个问题,文章提出了一种参量自适应的OPTICS单光子点云去噪算法,该算法与现有OPTICS算法固定输入参数的方式不同,是通过场景自适应获取更合适的输入参数来保证水底附近区域光子的分布参数的测量准确性,从而提升水底附近区域的去噪效果。凝聚度结果显示,新算法在水底附近区域的去噪效果较现有OPTICS算法提升了约13.67%,可为之后的水深测量等工作提供更高精度的单光子点云图像。展开更多
Bug isolation is a popular approach for multi-fault localization(MFL),where all failed test cases are clustered into several groups,and then the failed test cases in each group combined with all passed test cases are ...Bug isolation is a popular approach for multi-fault localization(MFL),where all failed test cases are clustered into several groups,and then the failed test cases in each group combined with all passed test cases are used to localize only a single fault.However,existing clustering algorithms cannot always obtain completely correct clustering results,which is a potential threat for bug isolation based MFL approaches.To address this issue,we first analyze the influence of the accuracy of the clustering on the performance of MFL,and the results of a controlled study indicate that using the clustering algorithm with the highest accuracy can achieve the best performance of MFL.Moreover,previous studies on clustering algorithms also show that the elements in a higher density cluster have a higher similarity.Based on the above motivation,we propose a novel approach FATOC(One-Fault-at-a-Time via OPTICS Clustering).In particular,FATOC first leverages the OPTICS(Ordering Points to Identify the Clustering Structure)clustering algorithm to group failed test cases,and then identifies a cluster with the highest density.OPTICS clustering is a density-based clustering algorithm,which can reduce the misgrouping and calculate a density value for each cluster.Such a density value of each cluster is helpful for finding a cluster with the highest clustering effectiveness.FATOC then combines the failed test cases in this cluster with all passed test cases to localize a single-fault through the traditional spectrum-based fault localization(SBFL)formula.After this fault is localized and fixed,FATOC will use the same method to localize the next single-fault,until all the test cases are passed.Our evaluation results show that FATOC can significantly outperform the traditional SBFL technique and a state-of-the-art MFL approach MSeer on 804 multi-faulty versions from nine real-world programs.Specifically,FATOC’s performance is 10.32%higher than that of traditional SBFL when using Ochiai formula in terms of metric A-EXAM.Besides,the results also indicate that,when checking 1%,3%and 5%statements of all subject programs,FATOC can locate 36.91%,48.50%and 66.93%of all faults respectively,which is also better than the traditional SBFL and the MFL approach MSeer.展开更多
随着移动定位技术的发展和移动定位设备普及,移动对象轨迹数据分析逐渐成为空间数据挖掘领域的研究热点。基于出租车GPS轨迹数据进行空间聚类研究可以发现出租车移动的热点路径以及运动趋势。在传统OPTICS(Ordering Points To Identify ...随着移动定位技术的发展和移动定位设备普及,移动对象轨迹数据分析逐渐成为空间数据挖掘领域的研究热点。基于出租车GPS轨迹数据进行空间聚类研究可以发现出租车移动的热点路径以及运动趋势。在传统OPTICS(Ordering Points To Identify the Clustering Structure)算法的基础上根据轨迹数据的特征提出了适合海量轨迹空间聚类的TR-OPTICS(Trajectory OPTICS)算法。该方法选取出租车轨迹中的载客轨迹为研究对象,经过轨迹特征点选取后采用MDL(Minimum Description Length)方式进行轨迹的二次划分,通过计算子轨迹间的水平距离、垂直距离、角度距离来度量轨迹的相似性。在聚类算法上采用外包矩形作为核心轨迹的搜索邻域,同时重新定义轨迹核心距离与轨迹可达距离,用邻接表代替空间索引来降低算法的复杂度。通过对南京市出租车载客轨迹的聚类分析,得到了出租车载客热点轨迹簇,并且经过多次实验与传统OPTICS算法、DBSCAN(Density-Based Spatial Clustering of Applications with Noise)算法对比,提出的TR-OPTICS算法在算法执行效率上均优于其他两种算法,在聚类结果上该算法可以发现载客子轨迹簇主要集中在市中心的中央路、大桥南路、北京东路、中山东路、中山北路、建宁路、瑞金路、板仓街、迈皋桥等道路,并且聚类效果优于其他两种算法。结果表明,提出的TR-OPTICS算法提高了算法执行效率,同时也提高了聚类结果的准确性。展开更多
针对现有数据库入侵检测系统高误报率的问题,提出了一种基于密度聚类数据库入侵检测系统,其检测系统过程分为2个部分,①数据训练阶段:执行事务属性的数据预处理,然后将数据集划分为训练集和测试集,使用点排序识别聚类结构(Ordering of P...针对现有数据库入侵检测系统高误报率的问题,提出了一种基于密度聚类数据库入侵检测系统,其检测系统过程分为2个部分,①数据训练阶段:执行事务属性的数据预处理,然后将数据集划分为训练集和测试集,使用点排序识别聚类结构(Ordering of Points To Identify Clustering Structure,OPTICS)来构建用户的正常配置文件;②入侵检测阶段:每个传入行为有2种状态,位于群集内或是集群外,根据其局部异常因子(Local Outlier Factor,LOF)值来确定事务的异常程度,对于LOF<1的行为允许访问数据库,其他行为通过采用不同的监督机器学习技术进一步验证是正常值或异常值,实现入侵检测.实验结果表明,与其他现有数据库入侵检测系统相比,本文系统性能优于其他2种系统.展开更多
文摘星载单光子激光雷达在浅水域测深方面展现出巨大的潜力,但其接收到的点云数据中存在大量噪声,给高程信息的提取带来了困难。目前,基于OPTICS(Ordering Points to Identify the Clustering Structure)的去噪算法因其精度高,在强背景噪声下表现良好,得到了广泛的应用,但此算法在水底附近区域存在去噪效果不佳的现象。为了解决这个问题,文章提出了一种参量自适应的OPTICS单光子点云去噪算法,该算法与现有OPTICS算法固定输入参数的方式不同,是通过场景自适应获取更合适的输入参数来保证水底附近区域光子的分布参数的测量准确性,从而提升水底附近区域的去噪效果。凝聚度结果显示,新算法在水底附近区域的去噪效果较现有OPTICS算法提升了约13.67%,可为之后的水深测量等工作提供更高精度的单光子点云图像。
基金supported in part by the National Natural Science Foundation of China under Grant Nos.61902015,61872026,and 61672085the Nantong Application Research Plan under Grant No:JC2019106the Open Project of State Key Laboratory of Information Security(Institute of Information Engineering,Chinese Academy of Sciences)under Grant No.2020-MS-07.
文摘Bug isolation is a popular approach for multi-fault localization(MFL),where all failed test cases are clustered into several groups,and then the failed test cases in each group combined with all passed test cases are used to localize only a single fault.However,existing clustering algorithms cannot always obtain completely correct clustering results,which is a potential threat for bug isolation based MFL approaches.To address this issue,we first analyze the influence of the accuracy of the clustering on the performance of MFL,and the results of a controlled study indicate that using the clustering algorithm with the highest accuracy can achieve the best performance of MFL.Moreover,previous studies on clustering algorithms also show that the elements in a higher density cluster have a higher similarity.Based on the above motivation,we propose a novel approach FATOC(One-Fault-at-a-Time via OPTICS Clustering).In particular,FATOC first leverages the OPTICS(Ordering Points to Identify the Clustering Structure)clustering algorithm to group failed test cases,and then identifies a cluster with the highest density.OPTICS clustering is a density-based clustering algorithm,which can reduce the misgrouping and calculate a density value for each cluster.Such a density value of each cluster is helpful for finding a cluster with the highest clustering effectiveness.FATOC then combines the failed test cases in this cluster with all passed test cases to localize a single-fault through the traditional spectrum-based fault localization(SBFL)formula.After this fault is localized and fixed,FATOC will use the same method to localize the next single-fault,until all the test cases are passed.Our evaluation results show that FATOC can significantly outperform the traditional SBFL technique and a state-of-the-art MFL approach MSeer on 804 multi-faulty versions from nine real-world programs.Specifically,FATOC’s performance is 10.32%higher than that of traditional SBFL when using Ochiai formula in terms of metric A-EXAM.Besides,the results also indicate that,when checking 1%,3%and 5%statements of all subject programs,FATOC can locate 36.91%,48.50%and 66.93%of all faults respectively,which is also better than the traditional SBFL and the MFL approach MSeer.
文摘随着移动定位技术的发展和移动定位设备普及,移动对象轨迹数据分析逐渐成为空间数据挖掘领域的研究热点。基于出租车GPS轨迹数据进行空间聚类研究可以发现出租车移动的热点路径以及运动趋势。在传统OPTICS(Ordering Points To Identify the Clustering Structure)算法的基础上根据轨迹数据的特征提出了适合海量轨迹空间聚类的TR-OPTICS(Trajectory OPTICS)算法。该方法选取出租车轨迹中的载客轨迹为研究对象,经过轨迹特征点选取后采用MDL(Minimum Description Length)方式进行轨迹的二次划分,通过计算子轨迹间的水平距离、垂直距离、角度距离来度量轨迹的相似性。在聚类算法上采用外包矩形作为核心轨迹的搜索邻域,同时重新定义轨迹核心距离与轨迹可达距离,用邻接表代替空间索引来降低算法的复杂度。通过对南京市出租车载客轨迹的聚类分析,得到了出租车载客热点轨迹簇,并且经过多次实验与传统OPTICS算法、DBSCAN(Density-Based Spatial Clustering of Applications with Noise)算法对比,提出的TR-OPTICS算法在算法执行效率上均优于其他两种算法,在聚类结果上该算法可以发现载客子轨迹簇主要集中在市中心的中央路、大桥南路、北京东路、中山东路、中山北路、建宁路、瑞金路、板仓街、迈皋桥等道路,并且聚类效果优于其他两种算法。结果表明,提出的TR-OPTICS算法提高了算法执行效率,同时也提高了聚类结果的准确性。
文摘针对现有数据库入侵检测系统高误报率的问题,提出了一种基于密度聚类数据库入侵检测系统,其检测系统过程分为2个部分,①数据训练阶段:执行事务属性的数据预处理,然后将数据集划分为训练集和测试集,使用点排序识别聚类结构(Ordering of Points To Identify Clustering Structure,OPTICS)来构建用户的正常配置文件;②入侵检测阶段:每个传入行为有2种状态,位于群集内或是集群外,根据其局部异常因子(Local Outlier Factor,LOF)值来确定事务的异常程度,对于LOF<1的行为允许访问数据库,其他行为通过采用不同的监督机器学习技术进一步验证是正常值或异常值,实现入侵检测.实验结果表明,与其他现有数据库入侵检测系统相比,本文系统性能优于其他2种系统.
