[1] 张树波,唐强荣.基于AIS数据的船舶异常行为检测方法[J].人工智能与机器人研究,2015,4(4):23-31.
ZHANG Shubo, TANG Qiangrong. Abnormal vessel behavior detection based on AIS data[J]. Artificial Intelligence and Robotics Research, 2015, 4(4): 23-31.
[2] LEI P R. A framework for anomaly detection in maritime trajectory behavior[J]. Knowledge and Information Systems, 2016, 47(1):189-214.
[3] MARTINEAU E, ROY J. Maritime Anomaly Detection: Domain Intro-duction and Review of Selected Literature[R]. Canada: Defense Research and Development Canada, 2011.
[4] 曲琳,周凡,陈耀武.基于Hausdorff距离的视觉监控轨迹分类算法[J].吉林大学学报(工学版),2009,39(6):1618-1624.
QU Lin, ZHOU Fan, CHEN Yaowu. Trajectory classification based on Hausdorff distance for visual surveillance system[J]. Journal of Jilin University(Engineering and Technology Edition),2009,39(6):1618-1624.
[5] 胡宏宇,王庆年,曲昭伟,等.运动目标空间模式辨识与异常交通行为检测[J].吉林大学学报(工学版),2011,41(6):1598-1602.
HU Hongyu, WANG Qingnian, QU Zhaowei, et al. Spatial pattern recognition and abnormal traffic behavior detection of moving object[J]. Journal of Jilin University(Engineering and Technology Edition), 2011,41(6):1598-1602.
[6] 曹妍妍,崔志明,吴健,等.一种改进Hausdorff距离和谱聚类的车辆轨迹模式学习方法[J].计算机应用与软件,2012,29(5):38-40.
CAO Yanyan, CUI Zhiming, WU Jian, et al. An improved Hausdorff distance and spectral clustering vehicle trajectory pattern learning approach[J]. Computer Applications and Software,2012,29(5):38-40.
[7] 魏龙翔,何小海,滕奇志,等.结合Hausdorff距离和最长公共子序列的轨迹分类[J].电子与信息学报,2013,35(4):784-790.
WEI Longxiang, HE Xiaohai, TENG Qizhi, et al. Trajectory classification based on Hausdorff distance and longest common subsequence[J]. Journal of Electronics & Information Technology, 2013,35(4):784-790.
[8] 胡晶.基于AIS的船舶轨迹分析与应用系统的设计与实现[D].武汉:华中师范大学,2015.
HU Jin.Design and Implementation of Vessel Trajectory Analysis and Application System Based on AIS[D]. Wuhan: Central China Normal University,2015.
[9] AGRAWAL R, FALOUTSOS C, SWAMI A N. Efficient similarity search in sequence databases[C]//International Conference on Foundations of Data Organization and Algorithms. Berlin: Springer-Verlag, 1993:69-84.
[10] CHEN Lei, ZSU M T, ORIA V. Robust and fast similarity search for moving object trajectories[C]// Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data. New York: ACM, 2005:491-502.
[11] LEE Seok-Lyong, CHUN Seok-Ju, KIM D H, et al. Similarity search for multidimensional data sequences[C]// Proceedings of IEEE 16th International Conference on Data Engineering. San Diego: IEEE, 2000:599-608.
[12] ELNEKAVE S, LAST M, MAIMON O. Incremental clustering of mobile objects[C]// IEEE International Conference on Data Engineering Workshop. Washington, D. C.,: IEEE, 2007:585-592.
[13] BASHIR F, KHOKHAR A, SCHONFELD D. Segmented trajectory based indexing and retrieval of video data[C]// International Conference on Image Processing 2003. Barcelona, Spain: IEEE, 2003:623-626.
[14] PORIKLI F, HAGA T. Event detection by eigenvector decomposition using object and frame features[C]// 2004 Conference on Computer Vision and Pattern Recognition Workshop. Washington, D. C.,: IEEE, 2004.
[15] 袁和金,张艳宁,周涛,等.基于归一化编辑距离和谱聚类的轨迹模式学习方法[J].计算机辅助设计与图形学学报,2008,20(6):753-758.
YUAN Hejin, ZHANG Yanning, ZHOU Tao, et al. A trajectory pattern learning approach based on the normalized edit distance and spectral clustering algorithm[J]. Journal of Computer-Aided Design and Computer Graphics, 2008,20(6):753-758.
[16] 毛嘉莉,金澈清,章志刚,等.轨迹大数据异常检测:研究进展及系统框架[J].软件学报,2017,28(1):17-34.
MAO Jiali, JIN Ceqing, ZHANG Zhigang, et al. Anomaly detection for trajectory big data: advancements and framework[J]. Journal of Software, 2017,28(1):17-34.
[17] ZHANG Zhang, HUANG Kaiqi, TAN Tan. Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes[C]// International Conference on Pattern Recognition 2006. Washington, D. C.,: IEEE, 2006:1135-1138.
[18] LEE Jae-Gil, HAN Jiawei, WHANG Kyu-Young. Trajectory clustering:a partition-and-group framework[C]// Proceedingsof the 2007 ACM SIGMOD International Conference on Management of Data. New York: ACM, 2007:593-604.
[19] 袁冠,夏士雄,张磊,等.基于结构相似度的轨迹聚类算法[J].通信学报,2011,32(9):103-110.
YUAN Guan, XIA Shixiong, ZHANG Lei, et al. Trajectory clustering algorithm based on structural similarity[J]. Journal of Communica-tions, 2011,32(9):103-110.
[20] 王天真,郝瑞吉,汤天浩,等.一种基于数据挖掘的GIS及在航海中的应用[J].中国航海,2003(3):1-4.
WANG Tianzhen, HAO Ruijie, TANG Tianhao, et al. A data mining method for GIS in marine engineering[J]. Navigation of China, 2003(3):1-4.
[21] 邱洪生.基于卡尔曼滤波的船舶航行轨迹异常行为预测算法研究[D].天津:河北工业大学,2012.
HONG Qiusheng. Research on Forecasting Ship Sailed Track Behavioral Abnormalities Algorithm Based on Kalman Filter[D]. Tianjin: Hebei University of Technology, 2012.
[22] 胡晶.基于AIS的船舶轨迹分析与应用系统的设计与实现[D].武汉:华中师范大学,2015.
HU Jing. Design and Implementation of Vessel Trajectory Analysis and Application System Based on AIS[D]. Wuhan: Central China Normal University, 2015.
[23] 肖潇.基于AIS信息的船舶轨迹聚类模型研究[D].厦门:集美大学,2015.
XIAO Xiao. Study on Ships Trajectory Clustering Model Based on AIS Data[D]. Xiamen: Jimei University, 2015.
[24] 魏照坤.基于AIS的船舶轨迹聚类与应用[D].大连:大连海事大学,2015.
WEI Zhaokun. The Vessels Trajectory Clustering and Its Application Based on AIS[D]. Dalian: Dalian Maritime University, 2015.
[25] 谢斌,陈莉明.密度聚类方法在船舶航迹图谱挖掘中的应用研究[J].舰船科学技术,2016,38(7A):100-102.
XIE Bin, CHEN Liming. Research on the density clustering method in ship track patternmining[J]. Ship Science and Technology, 2016, 38(7A):100-102.
[26] 张洛蒙.出租车异常轨迹挖掘系统设计与实现[J].福建电脑,2018(1):134-135.
ZHANG Luomeng. Design and implementation of taxi abnormal trajectory mining system[J]. Fujian Computer, 2018(1): 134-135.
[27] 赵梁滨.船舶轨迹的数据挖掘框架及应用[D].大连:大连海事大学,2016.
ZHAO Liangbin. Research and Application of the Mining Framework of Trajectory Data for Vessels[D]. Dalian: Dalian Maritime University, 2016.
[28] 王建军.基于出租车轨迹挖掘的寻客路线推荐[D].湘潭:湖南科技大学,2016.
WANG Jianjun. Mining Taxis Trajectories for Cruising Routes Recommendation[D]. Xiangtan: Hunan University of Science and Technology, 2016.
[29] 周宇鹏,牛保宁.密度聚类划分时间段的动态热度路网构建[J].计算机工程与设计,2017,38(11):3023-3028.
ZHOU Yupeng, NIU Baoning. Constructing dynamic hot road networks using time points density clustering[J]. Computer Engineering and Design, 2017, 38(11):3023- 3028.
[30] DAHLBOM A, NIKLASSON L. Trajectory clustering for coastal surveillance[C]// 2017 10th International Conference on Information Fusion. Washington, D. C.,: IEEE, 2007:1-8.
[31] PALLOTTA G, VESPE M, BRYAN K. Vessel pattern knowledge discovery from AIS data: aframework for anomaly detection and route prediction[J]. Entropy, 2013, 15(6):2218-2245.
[32] NG A Y, JORDAN M I, WEISS Y. On spectral clustering: analysis and an algorithm[C]// Proceedings of 14th International Conference on Neural Information Processing Systems: Natural and Synthetic. MA, U.S.A.: MIT Press, 2002:849-856.
[33] RIVEIRO M, JOHANSSON F, ZIEMKE T, et al. Supporting maritime situation awareness using self organizing maps and gaussian mixture models[C]// Proceedings of the 2008 conference on Tenth Scandinavian Conference on Artificial Intelligence: SCAI 2008. Amsterdam, the Netherlands: IOS Press, 2008:84-91.
[34] LAXHAMMAR R. Anomaly detection for sea surveillance[C]// 2008 11th International Conference on Information Fusion. Washington, D. C.,: IEEE, 2008:1-8.
[35] RISTIC B, SCALA B L, MORELANDE M, et al. Statistical analysis of motion patterns in AIS data: anomaly detection and motion prediction[J].International Conference on Information Fusion, 2008, 29(1):1-7.
[36] 熊勇,瞿小豪,郭俊维,等.基于AIS数据的渡轮异常运动模式监测[J].中国安全科学学报,2016,26(1):100-103.
XIONG Yong, QU Xiaohao, GUO Junwei, et al. Monitoring of abnormal movement patterns of ferry based on AIS data[J]. China Safety Science Journal, 2016, 26(1):100 -103.
[37] BOMBERGER N A, RHODES B J, SEIBERT M, et al. Associative learning of vessel motion patterns for maritime situation awareness[C]// 2006 9th International Conference on Information Fusion. Washington, D. C.,: IEEE, 2007:1-8.
[38] RHODES B J, BOMBERGER N A, SEIBERT M, et al. Maritime situation monitoring and awareness using learning mechanisms[C]// 2005 IEEE Military Communications Conference. Washington, D. C.,: IEEE, 2005:646-652.
[39] RHODES B J, BOMBERGER N A, ZANDIPOUR M. Probabilistic associative learning of vessel motion patterns at multiple spatial scales for maritime situation awareness[C]// International Conference on Information Fusion. Washington, D. C.,: IEEE, 2007:1-8.
[40] LAXHAMMAR R, FALKMAN G. Sequential conformal anomaly detection in trajectories based on Hausdorff distance[C]// Proceedings of the International Conference on Information Fusion. Washington, D. C.: IEEE, 2011:1-8.
[41] LAXHAMMAR R, FALKMAN G. Conformal prediction for distribution-independent anomaly detection in streaming vessel data[C]// Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques. New York: ACM, 2011:47-55.
[42] LAXHAMMAR R, FALKMAN G. Online detection of anomalous sub-trajectories: asliding window approach based on conformal anomaly detection and local outlier factor[M]// Artificial Intelligence Applications and Innovations. Berlin: Springer-Verlag, 2012:192-202.
[43] LAXHAMMAR R. Anomaly Detection in Trajectory Data for Surveillance Applications[D]. Orebro, Sweden: Orebro University, 2011.
[44] 甄荣,金永兴,胡勤友,等.基于AIS信息和BP神经网络的船舶航行行为预测[J].中国航海,2017,40(2):6-10.
ZHEN Rong, JIN Yongxing, HU Qingyou, et al. Vessel behavior predection based on AIS data and BP neural network[J]. Navigation of China, 2017, 40(2):6-10.
[45] 杨龙海,徐洪,张春.基于GPS数据的高速公路车辆异常行为检测[J].重庆交通大学学报(自然科学版),2018,37(5):97-103.
YANG Longhai, XU Hong, ZHANG Chun. Vehicle abnormal behavior detection on freeway based on global positioning system data[J]. Journal of Chongqing Jiaotong University(Natural Science), 2018,37(5):97-103.
[46] 马文耀,吴兆麟,杨家轩,等.基于单向距离的谱聚类船舶运动模式识别[J].重庆交通大学学报(自然科学版),2015,34(5):130-134.
MA Wenyao,WU Zhaolin, YANG Jiaxuan, et al. Vessel motion pattern recognition based on one-way distance spectral clustering algorithm[J]. Journal of Chongqing Jiaotong University(Natural Science), 2015,34(5):130-134.
[47] 李永攀,刘正江,郑中义.基于时空密度的船载AIS数据聚类分析方法研究[J].重庆交通大学学报(自然科学版),2018,37(10):117-122.
LI Yongpan, LIU Zhengjiang, ZHENG Zhongyi. Clustering analysis method of shipborne AIS data based on spatio-temporal density[J]. Journal of Chongqing Jiaotong University(Natural Science), 2018,37(10):117-122. |