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
Finding clusters based on density represents a significant class of clustering algorithms.These methods can discover clusters of various shapes and sizes.The most studied algorithm in this class is theDensity-Based Sp...Finding clusters based on density represents a significant class of clustering algorithms.These methods can discover clusters of various shapes and sizes.The most studied algorithm in this class is theDensity-Based Spatial Clustering of Applications with Noise(DBSCAN).It identifies clusters by grouping the densely connected objects into one group and discarding the noise objects.It requires two input parameters:epsilon(fixed neighborhood radius)and MinPts(the lowest number of objects in epsilon).However,it can’t handle clusters of various densities since it uses a global value for epsilon.This article proposes an adaptation of the DBSCAN method so it can discover clusters of varied densities besides reducing the required number of input parameters to only one.Only user input in the proposed method is the MinPts.Epsilon on the other hand,is computed automatically based on statistical information of the dataset.The proposed method finds the core distance for each object in the dataset,takes the average of these distances as the first value of epsilon,and finds the clusters satisfying this density level.The remaining unclustered objects will be clustered using a new value of epsilon that equals the average core distances of unclustered objects.This process continues until all objects have been clustered or the remaining unclustered objects are less than 0.006 of the dataset’s size.The proposed method requires MinPts only as an input parameter because epsilon is computed from data.Benchmark datasets were used to evaluate the effectiveness of the proposed method that produced promising results.Practical experiments demonstrate that the outstanding ability of the proposed method to detect clusters of different densities even if there is no separation between them.The accuracy of the method ranges from 92%to 100%for the experimented datasets.展开更多
针对因风电场机组异常数据而导致风电功率预测精度下降的问题,文章提出一种基于密度噪声应用空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法加上最小绝对残差(least absolute residual,LAR)法的风...针对因风电场机组异常数据而导致风电功率预测精度下降的问题,文章提出一种基于密度噪声应用空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法加上最小绝对残差(least absolute residual,LAR)法的风电场数据清洗方法。首先利用DBSCAN算法识别分散型异常数据,然后基于LAR方法构建堆积型异常数据识别模型,分别实现对风电场分散型异常数据和堆积型异常数据的清洗,最后通过Pearson相关系数和反向传播神经网络预测模型验证所提方法的效果。结果表明,基于DBSCAN+LAR的风电场数据清洗方法能有效减小风电功率预测误差。展开更多
针对水电机组状态监测数据量逐步增大,数据质量差的问题,提出了一种基于改进K维树(K-Dimensional Tree,KD-Tree)与基于密度的空间聚类算法(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)的水电机组状态监测数...针对水电机组状态监测数据量逐步增大,数据质量差的问题,提出了一种基于改进K维树(K-Dimensional Tree,KD-Tree)与基于密度的空间聚类算法(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)的水电机组状态监测数据清洗方法,首先对输入数据建立KD-Tree,再使用DBSCAN在最近邻样本上扫描完成聚类,聚类结束以后会分离出噪声点,将噪声点去除即可完成对水电机组状态监测数据清洗。选取某水电站状态监测系统上导摆度数据1 088条,再以相同时间间隔插入随机数据100条,通过算例与常规DBScan、K-means、OCSVM算法对比聚类性能与时间性能,所提出的方法识别正确率最高,为97.78%,消耗时间最少,为0.007 732 s,数据清洗效果最优,并可以大幅减少计算时间。展开更多
针对焊接之后的焊缝提取误差大、不易提取的问题,文章提出了一种DBSCAN聚类(densitybased spatial clustering of applications with noise)与改进主成分分析(principal component analysis,PCA)算法融合的焊缝提取算法。首先对焊缝图...针对焊接之后的焊缝提取误差大、不易提取的问题,文章提出了一种DBSCAN聚类(densitybased spatial clustering of applications with noise)与改进主成分分析(principal component analysis,PCA)算法融合的焊缝提取算法。首先对焊缝图像进行灰度化、自适应中值滤波等预处理;其次对图像应用Canny边缘检测算法提取焊缝边缘,并使用DBSCAN密度聚类算法聚类焊缝边缘;之后依据改进的PCA算法寻找焊缝的主成分,将焊缝向主向量映射统计,根据图像分辨率自动分配一个阈值获取焊缝的左右边界,再将焊缝的左右边界反映射到次主向量获取焊缝的上下边界;最后按照文章提出的算法完成了三组对比实验,分析了算法受分辨率、焊接方式、光照强度等因素的影响。实验证明,文章提出的算法对直缝提取效果良好,提取精度超过了95%。展开更多
为快速准确地提取地面三维激光扫描仪获取林分点云中的单株树木点云,提出一种基于密度的抗噪空间聚类(Density-Based Spatial Clustering of Application with Noise,DBSCAN)的树木分割算法。首先采用高斯滤波对林分点云去噪,在林分点...为快速准确地提取地面三维激光扫描仪获取林分点云中的单株树木点云,提出一种基于密度的抗噪空间聚类(Density-Based Spatial Clustering of Application with Noise,DBSCAN)的树木分割算法。首先采用高斯滤波对林分点云去噪,在林分点云归一化的基础上对林分点云垂直分段,然后采用DBSCAN算法垂直分段聚类,再计算每个垂直分段中每个簇的中心点,根据簇中心点间的距离判定簇间的相邻关系,并由此匹配树干段点云,最后采用RANSAC(Random Sample Consensus)算法对树干段点云拟合直线,并根据点与拟合直线间的距离判定点的归属以实现树木分割。在郁闭度分别为中与高的林分中,所提算法的调和值F范围分别为0.88~0.99与0.72~0.74,基于距离判别的树木分割算法的F范围分别为0.84~0.90与0.73~0.79。所提算法在不同郁闭度的林分点云中均能有效分割单株树木点云,特别是在郁闭度为中的林分中有较好表现,可实现对林分点云的精确树木分割。展开更多
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。
基金The author extends his appreciation to theDeputyship forResearch&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number(IFPSAU-2021/01/17758).
文摘Finding clusters based on density represents a significant class of clustering algorithms.These methods can discover clusters of various shapes and sizes.The most studied algorithm in this class is theDensity-Based Spatial Clustering of Applications with Noise(DBSCAN).It identifies clusters by grouping the densely connected objects into one group and discarding the noise objects.It requires two input parameters:epsilon(fixed neighborhood radius)and MinPts(the lowest number of objects in epsilon).However,it can’t handle clusters of various densities since it uses a global value for epsilon.This article proposes an adaptation of the DBSCAN method so it can discover clusters of varied densities besides reducing the required number of input parameters to only one.Only user input in the proposed method is the MinPts.Epsilon on the other hand,is computed automatically based on statistical information of the dataset.The proposed method finds the core distance for each object in the dataset,takes the average of these distances as the first value of epsilon,and finds the clusters satisfying this density level.The remaining unclustered objects will be clustered using a new value of epsilon that equals the average core distances of unclustered objects.This process continues until all objects have been clustered or the remaining unclustered objects are less than 0.006 of the dataset’s size.The proposed method requires MinPts only as an input parameter because epsilon is computed from data.Benchmark datasets were used to evaluate the effectiveness of the proposed method that produced promising results.Practical experiments demonstrate that the outstanding ability of the proposed method to detect clusters of different densities even if there is no separation between them.The accuracy of the method ranges from 92%to 100%for the experimented datasets.
文摘针对因风电场机组异常数据而导致风电功率预测精度下降的问题,文章提出一种基于密度噪声应用空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法加上最小绝对残差(least absolute residual,LAR)法的风电场数据清洗方法。首先利用DBSCAN算法识别分散型异常数据,然后基于LAR方法构建堆积型异常数据识别模型,分别实现对风电场分散型异常数据和堆积型异常数据的清洗,最后通过Pearson相关系数和反向传播神经网络预测模型验证所提方法的效果。结果表明,基于DBSCAN+LAR的风电场数据清洗方法能有效减小风电功率预测误差。
文摘针对焊接之后的焊缝提取误差大、不易提取的问题,文章提出了一种DBSCAN聚类(densitybased spatial clustering of applications with noise)与改进主成分分析(principal component analysis,PCA)算法融合的焊缝提取算法。首先对焊缝图像进行灰度化、自适应中值滤波等预处理;其次对图像应用Canny边缘检测算法提取焊缝边缘,并使用DBSCAN密度聚类算法聚类焊缝边缘;之后依据改进的PCA算法寻找焊缝的主成分,将焊缝向主向量映射统计,根据图像分辨率自动分配一个阈值获取焊缝的左右边界,再将焊缝的左右边界反映射到次主向量获取焊缝的上下边界;最后按照文章提出的算法完成了三组对比实验,分析了算法受分辨率、焊接方式、光照强度等因素的影响。实验证明,文章提出的算法对直缝提取效果良好,提取精度超过了95%。
文摘为快速准确地提取地面三维激光扫描仪获取林分点云中的单株树木点云,提出一种基于密度的抗噪空间聚类(Density-Based Spatial Clustering of Application with Noise,DBSCAN)的树木分割算法。首先采用高斯滤波对林分点云去噪,在林分点云归一化的基础上对林分点云垂直分段,然后采用DBSCAN算法垂直分段聚类,再计算每个垂直分段中每个簇的中心点,根据簇中心点间的距离判定簇间的相邻关系,并由此匹配树干段点云,最后采用RANSAC(Random Sample Consensus)算法对树干段点云拟合直线,并根据点与拟合直线间的距离判定点的归属以实现树木分割。在郁闭度分别为中与高的林分中,所提算法的调和值F范围分别为0.88~0.99与0.72~0.74,基于距离判别的树木分割算法的F范围分别为0.84~0.90与0.73~0.79。所提算法在不同郁闭度的林分点云中均能有效分割单株树木点云,特别是在郁闭度为中的林分中有较好表现,可实现对林分点云的精确树木分割。
