钢拱桥的线形监测是桥梁健康监测系统的重要组成部分。运用三维激光扫描技术,融合随机抽样一致(random sample consensus,RANSAC)算法对传统的具有噪声的基于密度的聚类方法(density-based spatial clustering of applications with noi...钢拱桥的线形监测是桥梁健康监测系统的重要组成部分。运用三维激光扫描技术,融合随机抽样一致(random sample consensus,RANSAC)算法对传统的具有噪声的基于密度的聚类方法(density-based spatial clustering of applications with noise,DBSCAN)算法进行改进,对钢拱桥拱肋线形进行提取。三维激光点云数据具有全面性和细节体现的优势,能够完整地呈现桥梁结构的形状和变形信息,融合RANSAC的改进DBSCAN算法根据钢拱桥结构特征对聚类结果进行约束,能够很好地实现删除离散点及桥面、横撑、横联和腹杆部分的点云这一目的。根据融合RANSAC的改进DBSCAN算法提取出的点云进行关键点拟合,与人工提取结果进行对比,拱肋关键点提取误差均在毫米级,最大误差为9.2 mm,最小误差为0.1 mm,此提取方法能够更加准确有效地完成钢拱桥线形提取,使线形提取精度达到毫米级,大大降低了人力成本和时间成本,对钢拱桥的复杂结构有更好的鲁棒性,能很好地适应实际生产需求。展开更多
新一代智能变电站使用采集执行单元统一采样,后续设备都将依赖此装置数据,因此该装置采样环节产生的异常数据会影响变电站保护、测控等多个系统的正常运行。如何高效地识别这些异常数据是电力系统采样中至关重要的部分,也是智能变电站...新一代智能变电站使用采集执行单元统一采样,后续设备都将依赖此装置数据,因此该装置采样环节产生的异常数据会影响变电站保护、测控等多个系统的正常运行。如何高效地识别这些异常数据是电力系统采样中至关重要的部分,也是智能变电站安全性和稳定性的基础。传统的异常数据检测方法主要设计用于处理低采样率场景下偶尔出现的异常采样点。随着智能变电站采样率的提高和电磁干扰问题的加剧,采样时连续多个采样点同时异常成为普遍现象。这使得原有识别算法准确率降低,而未被识别的异常数据可能会造成后续测控装置精度降低甚至保护装置误动。针对传统检测方法的不足,提出基于密度的噪声空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法的交流采样异常数据识别实时算法。该算法利用异常数据与正常数据的空间密度差异,有效区分出密度较低的异常采样点,从而实现智能变电站异常采样数据的识别。相比于传统方法,所提方法在采样异常数据识别上具有更准确的结果。展开更多
内河水上交通事故时有发生,对水路运输安全、高效发展带来威胁。研究提出一种基于自适应参数的DBSCAN(Density-Based Spatial Clustering of Applications with Noise)方法,用于识别内河事故黑点水域。该方法支持对邻域半径ε和邻域中...内河水上交通事故时有发生,对水路运输安全、高效发展带来威胁。研究提出一种基于自适应参数的DBSCAN(Density-Based Spatial Clustering of Applications with Noise)方法,用于识别内河事故黑点水域。该方法支持对邻域半径ε和邻域中数据对象数目阈值P_(min)参数的自动选取,可提高聚类分析的精度和效率。基于2010—2019年长江干线下游散货船舶事故数据开展案例研究,对各典型事故黑点段的事故特征和事故原因进行分析,得到8个事故黑点。此外,采用Getis-Ord General G聚类识别事故黑点中的高等级事故区域,得到事故黑点及高等级事故主要分布于江心洲、桥区、港口码头区域。研究结果与实际情况基本吻合,一定程度上表明了该方法在内河水上交通事故分布特征分析上的科学性和实用性。展开更多
为解决大数据下船舶会遇识别算法效率不高且存在误判等问题,提出一种融合国际海上避碰规则(International Regulations for Preventing Collisions at Sea,COLREGs)的带噪声的基于密度的空间聚类(density-based spatial clustering of a...为解决大数据下船舶会遇识别算法效率不高且存在误判等问题,提出一种融合国际海上避碰规则(International Regulations for Preventing Collisions at Sea,COLREGs)的带噪声的基于密度的空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法,建立船舶会遇识别模型。在DBSCAN算法对邻域内的船舶数量进行统计时,计算船舶间的最近会遇距离(distance to closest point of approach,DCPA)和最近会遇时间(time to closest point of approach,TCPA),初步筛选邻域内的噪声点;基于模糊综合评价模型计算船舶会遇风险,对邻域内的船舶进行二次筛选,实现船舶会遇态势的提取。结果表明:改进后的DBSCAN算法过滤掉传统DBSCAN算法识别到的非会遇局面,并且在同一会遇局面下的船舶数量均保持在4艘以内;输出的会遇船舶风险演变趋势对实际水域内高风险船舶的监控适用性较好,能有效辅助船舶避碰。所提识别模型对保障航行安全和提高海事监管效率具有重要意义。展开更多
针对点云数据中噪声点的剔除问题,提出了一种基于改进DBSCAN(density-based spatial clustering of applications with noise)算法的多尺度点云去噪方法。应用统计滤波对孤立离群点进行预筛选,去除点云中的大尺度噪声;对DBSCAN算法进行...针对点云数据中噪声点的剔除问题,提出了一种基于改进DBSCAN(density-based spatial clustering of applications with noise)算法的多尺度点云去噪方法。应用统计滤波对孤立离群点进行预筛选,去除点云中的大尺度噪声;对DBSCAN算法进行优化,减少算法时间复杂度和实现参数的自适应调整,以此将点云分为正常簇、疑似簇及异常簇,并立即去除异常簇;利用距离共识评估法对疑似簇进行精细判定,通过计算疑似点与其最近的正常点拟合表面之间的距离,判定其是否为异常,有效保持了数据的关键特征和模型敏感度。利用该方法对两个船体分段点云进行去噪,并与其他去噪算法进行对比,结果表明,该方法在去噪效率和特征保持方面具有优势,精确地保留了点云数据的几何特性。展开更多
Clustering is one of the unsupervised learning problems.It is a procedure which partitions data objects into groups.Many algorithms could not overcome the problems of morphology,overlapping and the large number of clu...Clustering is one of the unsupervised learning problems.It is a procedure which partitions data objects into groups.Many algorithms could not overcome the problems of morphology,overlapping and the large number of clusters at the same time.Many scientific communities have used the clustering algorithm from the perspective of density,which is one of the best methods in clustering.This study proposes a density-based spatial clustering of applications with noise(DBSCAN)algorithm based on the selected high-density areas by automatic fuzzy-DBSCAN(AFD)which works with the initialization of two parameters.AFD,by using fuzzy and DBSCAN features,is modeled by the selection of high-density areas and generates two parameters for merging and separating automatically.The two generated parameters provide a state of sub-cluster rules in the Cartesian coordinate system for the dataset.The model overcomes the problems of clustering such as morphology,overlapping,and the number of clusters in a dataset simultaneously.In the experiments,all algorithms are performed on eight data sets with 30 times of running.Three of them are related to overlapping real datasets and the rest are morphologic and synthetic datasets.It is demonstrated that the AFD algorithm outperforms other recently developed clustering algorithms.展开更多
In order to improve image quality, a novel Retinex algorithm for image enhancement was presented. Different from conventional algorithms, it was based on certain defined points containing the illumination information ...In order to improve image quality, a novel Retinex algorithm for image enhancement was presented. Different from conventional algorithms, it was based on certain defined points containing the illumination information in the intensity image to estimate the illumination. After locating the points, the whole illumination image was computed by an interpolation technique. When attempting to recover the reflectance image, an adaptive method which can be considered as an optimization problem was employed to suppress noise in dark environments and keep details in other areas. For color images, it was taken in the band of each channel separately. Experimental results demonstrate that the proposed algorithm is superior to the traditional Retinex algorithms in image entropy.展开更多
A novel method under the interactive multiple model (IMM) filtering framework is presented in this paper, in which the expectation-maximization (EM) algorithm is used to identify the process noise covariance Q online....A novel method under the interactive multiple model (IMM) filtering framework is presented in this paper, in which the expectation-maximization (EM) algorithm is used to identify the process noise covariance Q online. For the existing IMM filtering theory, the matrix Q is determined by means of design experience, but Q is actually changed with the state of the maneuvering target. Meanwhile it is severely influenced by the environment around the target, i.e., it is a variable of time. Therefore, the experiential covariance Q can not represent the influence of state noise in the maneuvering process exactly. Firstly, it is assumed that the evolved state and the initial conditions of the system can be modeled by using Gaussian distribution, although the dynamic system is of a nonlinear measurement equation, and furthermore the EM algorithm based on IMM filtering with the Q identification online is proposed. Secondly, the truncated error analysis is performed. Finally, the Monte Carlo simulation results are given to show that the proposed algorithm outperforms the existing algorithms and the tracking precision for the maneuvering targets is improved efficiently.展开更多
文摘新一代智能变电站使用采集执行单元统一采样,后续设备都将依赖此装置数据,因此该装置采样环节产生的异常数据会影响变电站保护、测控等多个系统的正常运行。如何高效地识别这些异常数据是电力系统采样中至关重要的部分,也是智能变电站安全性和稳定性的基础。传统的异常数据检测方法主要设计用于处理低采样率场景下偶尔出现的异常采样点。随着智能变电站采样率的提高和电磁干扰问题的加剧,采样时连续多个采样点同时异常成为普遍现象。这使得原有识别算法准确率降低,而未被识别的异常数据可能会造成后续测控装置精度降低甚至保护装置误动。针对传统检测方法的不足,提出基于密度的噪声空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法的交流采样异常数据识别实时算法。该算法利用异常数据与正常数据的空间密度差异,有效区分出密度较低的异常采样点,从而实现智能变电站异常采样数据的识别。相比于传统方法,所提方法在采样异常数据识别上具有更准确的结果。
文摘内河水上交通事故时有发生,对水路运输安全、高效发展带来威胁。研究提出一种基于自适应参数的DBSCAN(Density-Based Spatial Clustering of Applications with Noise)方法,用于识别内河事故黑点水域。该方法支持对邻域半径ε和邻域中数据对象数目阈值P_(min)参数的自动选取,可提高聚类分析的精度和效率。基于2010—2019年长江干线下游散货船舶事故数据开展案例研究,对各典型事故黑点段的事故特征和事故原因进行分析,得到8个事故黑点。此外,采用Getis-Ord General G聚类识别事故黑点中的高等级事故区域,得到事故黑点及高等级事故主要分布于江心洲、桥区、港口码头区域。研究结果与实际情况基本吻合,一定程度上表明了该方法在内河水上交通事故分布特征分析上的科学性和实用性。
文摘为解决大数据下船舶会遇识别算法效率不高且存在误判等问题,提出一种融合国际海上避碰规则(International Regulations for Preventing Collisions at Sea,COLREGs)的带噪声的基于密度的空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法,建立船舶会遇识别模型。在DBSCAN算法对邻域内的船舶数量进行统计时,计算船舶间的最近会遇距离(distance to closest point of approach,DCPA)和最近会遇时间(time to closest point of approach,TCPA),初步筛选邻域内的噪声点;基于模糊综合评价模型计算船舶会遇风险,对邻域内的船舶进行二次筛选,实现船舶会遇态势的提取。结果表明:改进后的DBSCAN算法过滤掉传统DBSCAN算法识别到的非会遇局面,并且在同一会遇局面下的船舶数量均保持在4艘以内;输出的会遇船舶风险演变趋势对实际水域内高风险船舶的监控适用性较好,能有效辅助船舶避碰。所提识别模型对保障航行安全和提高海事监管效率具有重要意义。
文摘针对点云数据中噪声点的剔除问题,提出了一种基于改进DBSCAN(density-based spatial clustering of applications with noise)算法的多尺度点云去噪方法。应用统计滤波对孤立离群点进行预筛选,去除点云中的大尺度噪声;对DBSCAN算法进行优化,减少算法时间复杂度和实现参数的自适应调整,以此将点云分为正常簇、疑似簇及异常簇,并立即去除异常簇;利用距离共识评估法对疑似簇进行精细判定,通过计算疑似点与其最近的正常点拟合表面之间的距离,判定其是否为异常,有效保持了数据的关键特征和模型敏感度。利用该方法对两个船体分段点云进行去噪,并与其他去噪算法进行对比,结果表明,该方法在去噪效率和特征保持方面具有优势,精确地保留了点云数据的几何特性。
文摘Clustering is one of the unsupervised learning problems.It is a procedure which partitions data objects into groups.Many algorithms could not overcome the problems of morphology,overlapping and the large number of clusters at the same time.Many scientific communities have used the clustering algorithm from the perspective of density,which is one of the best methods in clustering.This study proposes a density-based spatial clustering of applications with noise(DBSCAN)algorithm based on the selected high-density areas by automatic fuzzy-DBSCAN(AFD)which works with the initialization of two parameters.AFD,by using fuzzy and DBSCAN features,is modeled by the selection of high-density areas and generates two parameters for merging and separating automatically.The two generated parameters provide a state of sub-cluster rules in the Cartesian coordinate system for the dataset.The model overcomes the problems of clustering such as morphology,overlapping,and the number of clusters in a dataset simultaneously.In the experiments,all algorithms are performed on eight data sets with 30 times of running.Three of them are related to overlapping real datasets and the rest are morphologic and synthetic datasets.It is demonstrated that the AFD algorithm outperforms other recently developed clustering algorithms.
基金Project(61071162) supported by the National Natural Science Foundation of China
文摘In order to improve image quality, a novel Retinex algorithm for image enhancement was presented. Different from conventional algorithms, it was based on certain defined points containing the illumination information in the intensity image to estimate the illumination. After locating the points, the whole illumination image was computed by an interpolation technique. When attempting to recover the reflectance image, an adaptive method which can be considered as an optimization problem was employed to suppress noise in dark environments and keep details in other areas. For color images, it was taken in the band of each channel separately. Experimental results demonstrate that the proposed algorithm is superior to the traditional Retinex algorithms in image entropy.
基金Supported by the National Key Fundamental Research & Development Programs of P. R. China (2001CB309403)
文摘A novel method under the interactive multiple model (IMM) filtering framework is presented in this paper, in which the expectation-maximization (EM) algorithm is used to identify the process noise covariance Q online. For the existing IMM filtering theory, the matrix Q is determined by means of design experience, but Q is actually changed with the state of the maneuvering target. Meanwhile it is severely influenced by the environment around the target, i.e., it is a variable of time. Therefore, the experiential covariance Q can not represent the influence of state noise in the maneuvering process exactly. Firstly, it is assumed that the evolved state and the initial conditions of the system can be modeled by using Gaussian distribution, although the dynamic system is of a nonlinear measurement equation, and furthermore the EM algorithm based on IMM filtering with the Q identification online is proposed. Secondly, the truncated error analysis is performed. Finally, the Monte Carlo simulation results are given to show that the proposed algorithm outperforms the existing algorithms and the tracking precision for the maneuvering targets is improved efficiently.
