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Journal articles on the topic 'Trajectory anomaly detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Praczyk, Tomasz. "Ship trajectory anomaly detection." Intelligent Data Analysis 23, no. 5 (2019): 1021–40. http://dx.doi.org/10.3233/ida-184366.

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Zhang, Haiyan, Yonglong Luo, Qingying Yu, Liping Sun, Xuejing Li, and Zhenqiang Sun. "A Framework of Abnormal Behavior Detection and Classification Based on Big Trajectory Data for Mobile Networks." Security and Communication Networks 2020 (December 22, 2020): 1–15. http://dx.doi.org/10.1155/2020/8858444.

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Big trajectory data feature analysis for mobile networks is a popular big data analysis task. Due to the large coverage and complexity of the mobile networks, it is difficult to define and detect anomalies in urban motion behavior. Some existing methods are not suitable for the detection of abnormal urban vehicle trajectories because they use the limited single detection techniques, such as determining the common patterns. In this study, we propose a framework for urban trajectory modeling and anomaly detection. Our framework takes into account the fact that anomalous behavior manifests the ov

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Shuaau, Muhammed, and Ka Fei Thang. "Autonomous Anomaly Detection System for Crime Monitoring." Journal of Computational and Theoretical Nanoscience 16, no. 8 (2019): 3410–18. http://dx.doi.org/10.1166/jctn.2019.8301.

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Autonomous anomaly detection has attracted significant amount of attention in the past decade due to increased security concerns all around the world. The volume of data reported by surveillance cameras has outrun human capacity and there exists a greater need for anomaly detection systems for crime monitoring. This project proposes a solution to this problem in a reception area context by using trajectory analysis. Trajectory extraction is proposed by using Gaussian Mixture Models and Kalman Filter for data association. Then trajectory analysis is performed on extracted trajectories to detect

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Ando, Shin, Theerasak Thanomphongphan, Yoichi Seki, and Einoshin Suzuki. "Ensemble anomaly detection from multi-resolution trajectory features." Data Mining and Knowledge Discovery 29, no. 1 (2013): 39–83. http://dx.doi.org/10.1007/s10618-013-0334-x.

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Shuaau, Muhammed, Ka Fei Thang, and Nai Shyan Lai. "Autonomous Abnormal Behaviour Detection Using Trajectory Analysis." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 4 (2019): 2403. http://dx.doi.org/10.11591/ijece.v9i4.pp2403-2415.

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<span lang="EN-GB">Abnormal behaviour detection has attracted signification amount of attention in the past decade due to increased security concerns around the world. The amount of data from surveillance cameras have exceeded human capacity and there is a greater need for anomaly detection systems for crime monitoring. This paper proposes a solution to this problem in a reception area context by using trajectory extraction through Gaussian Mixture Models and Kalman Filter for data association. Here, trajectory analysis was performed on extracted trajectories to detect four different ano

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Deng, Ketao. "Anomaly Detection of Highway Vehicle Trajectory under the Internet of Things Converged with 5G Technology." Complexity 2021 (April 27, 2021): 1–12. http://dx.doi.org/10.1155/2021/9961428.

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The gradual increase in the density of highway vehicles and traffic flow makes the abnormal driving state of vehicles an indispensable tool for assisting traffic dispatch. Intelligent transportation systems can detect and track vehicles in real time, acquire characteristics such as vehicle traffic, vehicle speed, vehicle flow density, and vehicle trajectory, and further perform advanced tasks such as vehicle trajectory. The detection of abnormal vehicle trajectory is an important content of vehicle trajectory understanding. And the development of the Internet of Things (IoT) and 5G technology

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Chebiyyam, Manaswi, Rohit Desam Reddy, Debi Prosad Dogra, Harish Bhaskar, and Lyudmila Mihaylova. "Motion anomaly detection and trajectory analysis in visual surveillance." Multimedia Tools and Applications 77, no. 13 (2017): 16223–48. http://dx.doi.org/10.1007/s11042-017-5196-6.

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Lei, Po-Ruey. "A framework for anomaly detection in maritime trajectory behavior." Knowledge and Information Systems 47, no. 1 (2015): 189–214. http://dx.doi.org/10.1007/s10115-015-0845-4.

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Peltola, Pekka, Jialin Xiao, Terry Moore, Antonio Jiménez, and Fernando Seco. "GNSS Trajectory Anomaly Detection Using Similarity Comparison Methods for Pedestrian Navigation." Sensors 18, no. 9 (2018): 3165. http://dx.doi.org/10.3390/s18093165.

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The urban setting is a challenging environment for GNSS receivers. Multipath and other anomalies typically increase the positioning error of the receiver. Moreover, the error estimate of the position is often unreliable. In this study, we detect GNSS trajectory anomalies by using similarity comparison methods between a pedestrian dead reckoning trajectory, recorded using a foot-mounted inertial measurement unit, and the corresponding GNSS trajectory. During a normal walk, the foot-mounted inertial dead reckoning setup is trustworthy up to a few tens of meters. Thus, the differing GNSS trajecto

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Guo, Yuejun, and Anton Bardera. "SHNN-CAD+: An Improvement on SHNN-CAD for Adaptive Online Trajectory Anomaly Detection." Sensors 19, no. 1 (2018): 84. http://dx.doi.org/10.3390/s19010084.

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To perform anomaly detection for trajectory data, we study the Sequential Hausdorff Nearest-Neighbor Conformal Anomaly Detector (SHNN-CAD) approach, and propose an enhanced version called SHNN-CAD +. SHNN-CAD was introduced based on the theory of conformal prediction dealing with the problem of online detection. Unlike most related approaches requiring several not intuitive parameters, SHNN-CAD has the advantage of being parameter-light which enables the easy reproduction of experiments. We propose to adaptively determine the anomaly threshold during the online detection procedure instead of p

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Zhang, Zixian, Geqi Qi, Avishai (Avi) Ceder, Wei Guan, Rongge Guo, and Zhenlin Wei. "Grid-Based Anomaly Detection of Freight Vehicle Trajectory considering Local Temporal Window." Journal of Advanced Transportation 2021 (August 31, 2021): 1–18. http://dx.doi.org/10.1155/2021/8103333.

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The security travel of freight vehicles is of high societal concern and is the key issue for urban managers to effectively supervise and assess the possible social security risks. With continuous improvements in motion-based technology, the trajectories of freight vehicles are readily available, whose unusual changes may indicate hidden urban risks. Moreover, the increasing high spatial and temporal resolution of trajectories provides the opportunity for the real-time recognition of the abnormal or risky vehicle motion. However, the existing researches mainly focus on the spatial anomaly detec

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Han, X., C. Armenakis, and M. Jadidi. "DBSCAN OPTIMIZATION FOR IMPROVING MARINE TRAJECTORY CLUSTERING AND ANOMALY DETECTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020 (August 25, 2020): 455–61. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2020-455-2020.

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Abstract. Today maritime transportation represents 90% of international trade volume and there are more than 50,000 vessels sailing the ocean every day. Therefore, reducing maritime transportation security risks by systematically modelling and surveillance should be of high priority in the maritime domain. By statistics, majority of maritime accidents are caused by human error due to fatigue or misjudgment. Auto-vessels equipped with autonomous and semi-autonomous systems can reduce the reliance on human’s intervention, thus make maritime navigation safer. This paper presents a clustering meth

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Wang, Zhongqiu, Guan Yuan, Haoran Pei, Yanmei Zhang, and Xiao Liu. "Unsupervised learning trajectory anomaly detection algorithm based on deep representation." International Journal of Distributed Sensor Networks 16, no. 12 (2020): 155014772097150. http://dx.doi.org/10.1177/1550147720971504.

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Without ground-truth data, trajectory anomaly detection is a hard work and the result lacks of interpretability. Moreover, in most current methods, trajectories are represented by geometric features or their low-dimensional linear combination, and some hidden features and high-dimensional combined features cannot be found efficiently. Meanwhile, traditional methods still cannot get rid of the limitation of space attributes. Therefore, a novel trajectory anomaly detection algorithm is present in this article. Unsupervised learning mechanism is used to overcome nonground-truth problem and deep r

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Guo, Yuejun, Qing Xu, Peng Li, Mateu Sbert, and Yu Yang. "Trajectory Shape Analysis and Anomaly Detection Utilizing Information Theory Tools." Entropy 19, no. 7 (2017): 323. http://dx.doi.org/10.3390/e19070323.

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15

Wan, Yiwen, Bill Buckles, David Keathly, and Tze-I. Yang. "Dynamic scene modelling and anomaly detection based on trajectory analysis." IET Intelligent Transport Systems 8, no. 6 (2014): 526–33. http://dx.doi.org/10.1049/iet-its.2012.0119.

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Huang, Jie, Fengwei Zhu, Zejun Huang, Jian Wan, and Yongjian Ren. "Research on Real-Time Anomaly Detection of Fishing Vessels in a Marine Edge Computing Environment." Mobile Information Systems 2021 (May 4, 2021): 1–15. http://dx.doi.org/10.1155/2021/5598988.

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Fishing vessel monitoring systems (VMSs) play an important role in ensuring the safety of fishing vessel operations. Traditional VMSs use a cloud centralized computing model, and the storage, processing, and visualization of all fishing vessel data are completed in the monitoring center. Due to the limitation of maritime communications, the data generated by fishing vessels cannot be fully utilized, and communication delays lead to inadequate warnings in cases of fishing vessel abnormalities. In this paper, we present a real-time anomaly detection model (RADM) for fishing vessels based on edge

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Guo, Shaoqing, Junmin Mou, Linying Chen, and Pengfei Chen. "An Anomaly Detection Method for AIS Trajectory Based on Kinematic Interpolation." Journal of Marine Science and Engineering 9, no. 6 (2021): 609. http://dx.doi.org/10.3390/jmse9060609.

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With the enormous amount of information provided by the ship Automatic Identification System (AIS), AIS is now playing a significant role in maritime transport system-related research and development. Many kinds of research and industrial applications are based on the ship trajectory extracted from raw AIS data. However, due to the issues of equipment, the transmission environment, and human factors, the raw AIS data inevitably contain abnormal messages, which have hindered the utilization of such information in practice. Thus, in this paper, an anomaly detection method that focuses on AIS tra

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Kong, Xiangjie, Ximeng Song, Feng Xia, Haochen Guo, Jinzhong Wang, and Amr Tolba. "LoTAD: long-term traffic anomaly detection based on crowdsourced bus trajectory data." World Wide Web 21, no. 3 (2017): 825–47. http://dx.doi.org/10.1007/s11280-017-0487-4.

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Kumar, Dheeraj, James C. Bezdek, Sutharshan Rajasegarar, Christopher Leckie, and Marimuthu Palaniswami. "A visual-numeric approach to clustering and anomaly detection for trajectory data." Visual Computer 33, no. 3 (2015): 265–81. http://dx.doi.org/10.1007/s00371-015-1192-x.

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Huang, Shihong Ed, Yiheng Feng, and Henry X. Liu. "A data-driven method for falsified vehicle trajectory identification by anomaly detection." Transportation Research Part C: Emerging Technologies 128 (July 2021): 103196. http://dx.doi.org/10.1016/j.trc.2021.103196.

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Dai, Tian, Lingjuan Miao, and Yanbing Guo. "A Real-Time Mismatch Detection Method for Underwater Database-Referenced Navigation." Sensors 19, no. 2 (2019): 307. http://dx.doi.org/10.3390/s19020307.

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Database-referenced navigation (DBRN) using geophysical information is often implemented on autonomous underwater vehicles (AUVs) to correct the positional errors of the inertial navigation system (INS). The matching algorithm is a pivotal technique in DBRN. However, it is impossible to completely eliminate mismatches in practical application. Therefore, it is necessary to perform a mismatch detection method on the outputs of DBRN. In this paper, we propose a real-time triple constraint mismatch detection method. The proposed detection method is divided into three modules: the model fitting de

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22

Ai, T., and W. Yang. "THE DETECTION OF TRANSPORT LAND-USE DATA USING CROWDSOURCING TAXI TRAJECTORY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 785–88. http://dx.doi.org/10.5194/isprsarchives-xli-b8-785-2016.

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This study tries to explore the question of transport land-use change detection by large volume of vehicle trajectory data, presenting a method based on Deluanay triangulation. The whole method includes three steps. The first one is to pre-process the vehicle trajectory data including the point anomaly removing and the conversion of trajectory point to track line. Secondly, construct Deluanay triangulation within the vehicle trajectory line to detect neighborhood relation. Considering the case that some of the trajectory segments are too long, we use a interpolation measure to add more points

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Ai, T., and W. Yang. "THE DETECTION OF TRANSPORT LAND-USE DATA USING CROWDSOURCING TAXI TRAJECTORY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 785–88. http://dx.doi.org/10.5194/isprs-archives-xli-b8-785-2016.

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This study tries to explore the question of transport land-use change detection by large volume of vehicle trajectory data, presenting a method based on Deluanay triangulation. The whole method includes three steps. The first one is to pre-process the vehicle trajectory data including the point anomaly removing and the conversion of trajectory point to track line. Secondly, construct Deluanay triangulation within the vehicle trajectory line to detect neighborhood relation. Considering the case that some of the trajectory segments are too long, we use a interpolation measure to add more points

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24

Li, Zhaoxu, Qiang Ling, Jing Wu, Zhengyan Wang, and Zaiping Lin. "A Constrained Sparse-Representation-Based Spatio-Temporal Anomaly Detector for Moving Targets in Hyperspectral Imagery Sequences." Remote Sensing 12, no. 17 (2020): 2783. http://dx.doi.org/10.3390/rs12172783.

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At present, small dim moving target detection in hyperspectral imagery sequences is mainly based on anomaly detection (AD). However, most conventional detection algorithms only utilize the spatial spectral information and rarely employ the temporal spectral information. Besides, multiple targets in complex motion situations, such as multiple targets at different velocities and dense targets on the same trajectory, are still challenges for moving target detection. To address these problems, we propose a novel constrained sparse representation-based spatio-temporal anomaly detection algorithm th

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Corrado, Samantha J., Tejas G. Puranik, Oliva J. Pinon, and Dimitri N. Mavris. "Trajectory Clustering within the Terminal Airspace Utilizing a Weighted Distance Function." Proceedings 59, no. 1 (2020): 7. http://dx.doi.org/10.3390/proceedings2020059007.

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To support efforts to modernize aviation systems to be safer and more efficient, high-precision trajectory prediction and robust anomaly detection methods are required. The terminal airspace is identified as the most critical airspace for individual flight-level and system-level safety and efficiency. To support successful trajectory prediction and anomaly detection methods within the terminal airspace, accurate identification of air traffic flows is paramount. Typically, air traffic flows are identified utilizing clustering algorithms, where performance relies on the definition of an appropri

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Yang, Wanqi, Yang Gao, and Longbing Cao. "TRASMIL: A local anomaly detection framework based on trajectory segmentation and multi-instance learning." Computer Vision and Image Understanding 117, no. 10 (2013): 1273–86. http://dx.doi.org/10.1016/j.cviu.2012.08.010.

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Abreu, Fernando H. O., Amilcar Soares, Fernando V. Paulovich, and Stan Matwin. "A Trajectory Scoring Tool for Local Anomaly Detection in Maritime Traffic Using Visual Analytics." ISPRS International Journal of Geo-Information 10, no. 6 (2021): 412. http://dx.doi.org/10.3390/ijgi10060412.

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With the recent increase in the use of sea transportation, the importance of maritime surveillance for detecting unusual vessel behavior related to several illegal activities has also risen. Unfortunately, the data collected by surveillance systems are often incomplete, creating a need for the data gaps to be filled using techniques such as interpolation methods. However, such approaches do not decrease the uncertainty of ship activities. Depending on the frequency of the data generated, they may even confuse operators, inducing errors when evaluating ship activities and tagging them as unusua

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Zhao, Liangbin, and Guoyou Shi. "Maritime Anomaly Detection using Density-based Clustering and Recurrent Neural Network." Journal of Navigation 72, no. 04 (2019): 894–916. http://dx.doi.org/10.1017/s0373463319000031.

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Maritime anomaly detection can improve the situational awareness of vessel traffic supervisors and reduce maritime accidents. In order to better detect anomalous behaviour of a vessel in real time, a method that consists of a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and a recurrent neural network is presented. In the method presented, the parameters of the DBSCAN algorithm were determined through statistical analysis, and the results of clustering were taken as the traffic patterns to train a recurrent neural network composed of Long Short-Term Memory (LST

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Zhen, Rong, Yongxing Jin, Qinyou Hu, Zheping Shao, and Nikitas Nikitakos. "Maritime Anomaly Detection within Coastal Waters Based on Vessel Trajectory Clustering and Naïve Bayes Classifier." Journal of Navigation 70, no. 3 (2017): 648–70. http://dx.doi.org/10.1017/s0373463316000850.

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Maritime anomaly detection is a key technique in intelligent vessel traffic surveillance systems and implementation of maritime situational awareness. In this paper, we propose a method which combines vessel trajectory clustering and Naïve Bayes classifier to detect anomalous vessel behaviour in the maritime surveillance system. A similarity measurement between vessel trajectories is designed based on the spatial and directional characteristics of Automatic Identification System (AIS) data, then the method of hierarchical and k-medoids clustering are applied to model and learn the typical vess

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Jiang, Haixu, Ke Zhang, Jingyu Wang, Xianyu Wang, and Pengfei Huang. "Anomaly Detection and Identification in Satellite Telemetry Data Based on Pseudo-Period." Applied Sciences 10, no. 1 (2019): 103. http://dx.doi.org/10.3390/app10010103.

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To effectively detect and identify the anomaly data in massive satellite telemetry data sets, the novel detection and identification method based on the pseudo-period was proposed in this paper. First, the raw data were compressed by extracting the shape salient points. Second, the compressed data were symbolized by the tilt angle of the adjacent data points. Based on this symbolization, the pseudo-period of the data was extracted. Third, the phase-plane trajectories corresponding to the pseudo-period data were obtained by using the pseudo-period as the basic analytical unit, and then, the pha

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Huijun, Duan, Hao Shijun, and Feng Jie. "The Detection Method of Fire Abnormal Based on Directional Drilling in Complex Conditions of Mine." E3S Web of Conferences 38 (2018): 01007. http://dx.doi.org/10.1051/e3sconf/20183801007.

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In the light of more and more urgent hidden fire abnormal detection problem in complex conditions of mine, a method which is used directional drilling technology is put forward. The method can avoid the obstacles in mine, and complete the fire abnormal detection. This paper based on analyzing the trajectory control of directional drilling, measurement while drilling and the characteristic of open branch process, the project of the directional drilling is formulated combination with a complex condition mine, and the detection of fire abnormal is implemented. This method can provide technical su

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Cai, Ying Feng, Hai Wang, and Wei Gong Zhang. "Learning Patterns of Motion Trajectories Using Real-Time Tracking." Advanced Materials Research 403-408 (November 2011): 2768–71. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.2768.

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The understanding and description of behaviors for road vehicles is a hot topic of intelligent visual surveillance system. Trajectory analysis is one of the basic problems in behavior understanding, from which anomalies can be detected and also accidents can be predicted. In this paper, we proposed a hierarchical self-organizing neural network model to learn trajectory distribution pattern and a probability model for accident recognition. Sample data including motion trajectories are first get by real-time vehicle tracking. The self-organizing neural network algorithm is then applied to learn

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Cai, Lei, Peien Luo, and Guangfu Zhou. "Multistage Analysis of Abnormal Human Behavior in Complex Scenes." Journal of Sensors 2019 (November 15, 2019): 1–10. http://dx.doi.org/10.1155/2019/1276438.

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Effective abnormal human behavior analysis serves as a warning signal before emergencies. However, most abnormal human behavior detections rely on manual monitoring at present. This method is criticized for being subjective and lack of timeliness. In response to the problems above, this paper proposes a multistage analysis method of abnormal human behavior in complex scenes. This paper firstly differentiates the abnormal behavior roughly from a large monitoring area with similarity measurement applied to the social force model, and precise analysis is conducted thereafter. The multistage analy

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Lien, Yu-Hsuan, Chao-Chung Peng, and Yi-Hsuan Chen. "Adaptive Observer-Based Fault Detection and Fault-Tolerant Control of Quadrotors under Rotor Failure Conditions." Applied Sciences 10, no. 10 (2020): 3503. http://dx.doi.org/10.3390/app10103503.

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This paper aims to propose a strategy for the flight control of quad-rotors under single rotor failure conditions. The proposed control strategy consists of two stages—fault detection (FD) and fault tolerant control (FTC). A dual observer-based strategy for FD and fault estimation is developed. With the combination of the results from both observers, the decision making in whether a fault actually happened or the observed anomaly was caused by an external disturbance could be distinguished. Following the FD result, a control strategy for normal flight, as well as the abnormal one, is presented

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Page, Brian R., Reeve Lambert, Nina Mahmoudian, David H. Newby, Elizabeth L. Foley, and Thomas W. Kornack. "Compact Quantum Magnetometer System on an Agile Underwater Glider." Sensors 21, no. 4 (2021): 1092. http://dx.doi.org/10.3390/s21041092.

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This paper presents results from the integration of a compact quantum magnetometer system and an agile underwater glider for magnetic survey. A highly maneuverable underwater glider, ROUGHIE, was customized to carry an increased payload and reduce the vehicle’s magnetic signature. A sensor suite composed of a vector and scalar magnetometer was mounted in an external boom at the rear of the vehicle. The combined system was deployed in a constrained pool environment to detect seeded magnetic targets and create a magnetic map of the test area. Presented is a systematic magnetic disturbance reduct

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Ghanbari, Maryam, and Witold Kinsner. "Detecting DDoS Attacks Using Polyscale Analysis and Deep Learning." International Journal of Cognitive Informatics and Natural Intelligence 14, no. 1 (2020): 17–34. http://dx.doi.org/10.4018/ijcini.2020010102.

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Distributed denial-of-service (DDoS) attacks are serious threats to the availability of a smart grid infrastructure services because they can cause massive blackouts. This study describes an anomaly detection method for improving the detection rate of a DDoS attack in a smart grid. This improvement was achieved by increasing the classification of the training and testing phases in a convolutional neural network (CNN). A full version of the variance fractal dimension trajectory (VFDTv2) was used to extract inherent features from the stochastic fractal input data. A discrete wavelet transform (D

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Liang, Chen-Jui, and Pei-Rong Yu. "Assessment and Improvement of Two Low-Cost Particulate Matter Sensor Systems by Using Spatial Interpolation Data from Air Quality Monitoring Stations." Atmosphere 12, no. 3 (2021): 300. http://dx.doi.org/10.3390/atmos12030300.

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Two low-cost fine particulate matter (PM2.5) sensor systems have been established by the government and community in Taiwan. Each system combines hundreds of PM2.5 sensors through an Internet of Things architecture. Since these sensors have not been calibrated, their performance has been questioned. In this study, the spatial interpolation data from air quality monitoring stations (AQMSs) was used to quantify the performances of the two sensor systems. The linearity, sensitivity, offset, precision, accuracy, and bias of the two sensor systems were estimated. The results indicate that the linea

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Cho, Sungjin, Fumin Zhang, and Catherine R. Edwards. "Learning and detecting abnormal speed of marine robots." International Journal of Advanced Robotic Systems 18, no. 2 (2021): 172988142199926. http://dx.doi.org/10.1177/1729881421999268.

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This article presents anomaly detection algorithms for marine robots based on their trajectories under the influence of unknown ocean flow. A learning algorithm identifies the flow field and estimates the through-water speed of a marine robot. By comparing the through-water speed with a nominal speed range, the algorithm is able to detect anomalies causing unusual speed changes. The identified ocean flow field is used to eliminate false alarms, where an abnormal trajectory may be caused by unexpected flow. The convergence of the algorithms is justified through the theory of adaptive control. T

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Mavrakou, T., and C. Cartalis. "An assessment of the potential of earth observation data to detect and monitor storm cells associated with natural hazards – an application to an extreme weather event in southeastern Mediterranean." Natural Hazards and Earth System Sciences Discussions 3, no. 4 (2015): 2191–219. http://dx.doi.org/10.5194/nhessd-3-2191-2015.

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Abstract. Storm cells that evolve in Mesoscale Convective Systems (MCSs) can be recognised with the use of satellite images. In this study, Meteosat images are used for the early detection and monitoring of the evolution of storm cells associated with MCSs. The developed methodology is based on the estimation of the "Airmass" and "Convective storm" composites, at fifteen minutes intervals. The methodology was applied on a selected four-day case study in February 2013, when a depression was developed over Africa and moved across the Mediterranean resulting in deep convection along its trajector

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Wang, Ke, Qingwen Xue, Yingying Xing, and Chongyi Li. "Improve Aggressive Driver Recognition Using Collision Surrogate Measurement and Imbalanced Class Boosting." International Journal of Environmental Research and Public Health 17, no. 7 (2020): 2375. http://dx.doi.org/10.3390/ijerph17072375.

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Real-time recognition of risky driving behavior and aggressive drivers is a promising research domain, thanks to powerful machine learning algorithms and the big data provided by in-vehicle and roadside sensors. However, since the occurrence of aggressive drivers in real traffic is infrequent, most machine learning algorithms treat each sample equally and prone to better predict normal drivers rather than aggressive drivers, which is our real interest. This paper aims to test the advantage of imbalanced class boosting algorithms in aggressive driver recognition using vehicle trajectory data. F

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IORIO, LORENZO. "ON SOME GRAVITOMAGNETIC SPIN–SPIN EFFECTS FOR ASTRONOMICAL BODIES." International Journal of Modern Physics D 12, no. 01 (2003): 35–43. http://dx.doi.org/10.1142/s021827180300269x.

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In this paper we look at the gravitational spin–spin interaction between macroscopic astronomical bodies. In particular, we calculate their post-Newtonian orbital effects of order [Formula: see text] on the trajectory of a spinning particle with proper angular momentum s moving in the external gravitomagnetic field generated by a central spinning mass with proper angular momentum J. It turns out that, at order [Formula: see text] in the orbiter's eccentricity, the eccentricity the pericenter and the mean anomaly rates of the moving particle are affected by long-term harmonic effects. If, on on

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Ji, Ze, and Quanquan Han. "A novel image feature descriptor for SLM spattering pattern classification using a consumable camera." International Journal of Advanced Manufacturing Technology 110, no. 11-12 (2020): 2955–76. http://dx.doi.org/10.1007/s00170-020-05995-3.

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Abstract In selective laser melting (SLM), spattering is an important phenomenon that is highly related to the quality of the manufactured parts. Characterisation and monitoring of spattering behaviours are highly valuable in understanding the manufacturing process and improving the manufacturing quality of SLM. This paper introduces a method of automatic visual classification to distinguish spattering characteristics of SLM processes in different manufacturing conditions. A compact feature descriptor is proposed to represent spattering patterns and its effectiveness is evaluated using real im

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Zhuang, Naifan, The Duc Kieu, Jun Ye, and Kien A. Hua. "Convolutional Nonlinear Differential Recurrent Neural Networks for Crowd Scene Understanding." International Journal of Semantic Computing 12, no. 04 (2018): 481–500. http://dx.doi.org/10.1142/s1793351x18400196.

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With the growth of crowd phenomena in the real world, crowd scene understanding is becoming an important task in anomaly detection and public security. Visual ambiguities and occlusions, high density, low mobility, and scene semantics, however, make this problem a great challenge. In this paper, we propose an end-to-end deep architecture, convolutional nonlinear differential recurrent neural networks (CNDRNNs), for crowd scene understanding. CNDRNNs consist of GoogleNet Inception V3 convolutional neural networks (CNNs) and nonlinear differential recurrent neural networks (RNNs). Different from

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Grieman, Mackenzie M., Murat Aydin, Joseph R. McConnell, and Eric S. Saltzman. "Burning-derived vanillic acid in an Arctic ice core from Tunu, northeastern Greenland." Climate of the Past 14, no. 11 (2018): 1625–37. http://dx.doi.org/10.5194/cp-14-1625-2018.

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Abstract. In this study, vanillic acid was measured in the Tunu ice core from northeastern Greenland in samples covering the past 1700 years. Vanillic acid is an aerosol-borne aromatic methoxy acid, produced by the combustion of lignin during biomass burning. Air mass trajectory analysis indicates that North American boreal forests are likely the major source region for biomass burning aerosols deposited to the ice core site. Vanillic acid levels in the Tunu ice core range from < 0.005 to 0.08 ppb. Tunu vanillic acid exhibits centennial-scale variability in pre-industrial ice, with elevated

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Scheiben, D., C. Straub, K. Hocke, P. Forkman, and N. Kämpfer. "Middle atmospheric water vapor and ozone anomalies during the 2010 major sudden stratospheric warming." Atmospheric Chemistry and Physics Discussions 11, no. 12 (2011): 32391–422. http://dx.doi.org/10.5194/acpd-11-32391-2011.

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Abstract. A major sudden stratospheric warming (SSW) occurred in the Northern Hemisphere in January 2010. The warming started on 26 January 2010, was most pronounced by the end of January and was accompanied by a polar vortex shift towards Europe. After the warming, the polar vortex split into two weaker vortices. The zonal mean temperature in the polar upper stratosphere (35–45 km) increased by approximately 25 K in a few days, while there was a decrease in temperature in the lower stratosphere and mesosphere. Local temperature maxima were around 325 K in the upper stratosphere and minima aro

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"Wasserstein Clustering based Video Anomaly Detection for Traffic Surveillance." International Journal of Engineering and Advanced Technology 9, no. 1 (2019): 6438–43. http://dx.doi.org/10.35940/ijeat.a2222.109119.

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Anomaly Detection is very important in present scenario with huge availability of data and enormous difficulty in extraction of meaningful information out of it. In this paper we present an approach for video anomaly detection based on trajectory features and spatio – temporal features. Clustering of spatio – temporal features and trajectory features are performed in Wasserstein metric space and cluster distance and span in Wasserstein metric space is exploited to perform anomaly detection. The Performance of the Anomaly detection with Wasserstein distance based K – means and Wasserstein dista

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Zhao, Xinyu, Hao Yan, Jing Li, Yutian Pang, and Yongming Liu. "Spatio-temporal Anomaly Detection, Diagnostics, and Prediction of the Air-traffic Trajectory Deviation using the Convective Weather." Annual Conference of the PHM Society 11, no. 1 (2019). http://dx.doi.org/10.36001/phmconf.2019.v11i1.854.

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With ahead-of-time aircraft management, we are able to reduce aircraft collision and improve air traffic capacity. However, there are various impact factors which will cause a large deviation between the actual flight and the original flight plan. Such uncertainty will result in an inappropriate decision for flight management. In order to solve this problem, most of the existing research attempt to build up a stochastic trajectory prediction model to capture the influence of the weather. However, the complexity of the weather information and various human factors make it hard to build up an ac

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Karataş, Gözde Boztepe, Pinar Karagoz, and Orhan Ayran. "Trajectory pattern extraction and anomaly detection for maritime vessels." Internet of Things, August 2021, 100436. http://dx.doi.org/10.1016/j.iot.2021.100436.

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Xue, Zixuan, and Wei Wu. "Anomaly detection by exploiting the tracking trajectory in surveillance videos." Science China Information Sciences 63, no. 5 (2020). http://dx.doi.org/10.1007/s11432-018-9792-8.

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Belhadi, Asma, Youcef Djenouri, Gautam Srivastava, Alberto Cano, and Jerry Chun-Wei Lin. "Hybrid Group Anomaly Detection for Sequence Data: Application to Trajectory Data Analytics." IEEE Transactions on Intelligent Transportation Systems, 2021, 1–12. http://dx.doi.org/10.1109/tits.2021.3114064.

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