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Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Herein, a smooth preprocessing method of robotic end-effector trajectory, based on Nurbs curve is proposed for obtaining the complete and stable envelope trajectory of the robot. Initially, the winding pattern of pressure vessel based on non-geodesic is deduced, followed by positioning of yarn point based on doffing point distribution and preprocessing of the trajectory through Nurbs curve. Variation trend and stationarity of velocity and acceleration in carriage and cross-carriage directions in Cartesian coordinate system are analyzed. Finally, the winding experiment of type IV pressure vessel is wound with the robotic winding workstation. Results show that the fiber winding can be carried out stably by the end of the robot. The realtime movement curve of the robot is consistent with the simulation curve, and the pressure liner is completely covered without yarn sliding and overhead problems, verifying the feasibility of the as-reported preprocessing method. [ABSTRACT FROM AUTHOR]

Trajectory data represents a trace of an object that changes its position in space over time. This kind of data is complex to handle and analyze, since it is generally produced in huge quantities, often prone to errors generated by the geolocation device, human mishandling, or area coverage limitations. Therefore, there is a need for software specifically tailored to preprocess trajectory data. In this work we propose PTRAIL, a python package offering several trajectory preprocessing steps, including filtering, feature extraction, and interpolation. PTRAIL uses parallel computation and vectorization, being suitable for large datasets and fast compared to other python libraries.

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Tangles are a common anomaly in Automatic Identification System (AIS) data, resulting from disorder in time sequences. They increase the spatial complexity of trajectories, making existing methods unable to fully recognize and process them. This will seriously affect the analysis of ship behavior. Furthermore, existing methods often directly remove after recognition, resulting in a significant loss of local information. To address these challenges, this paper proposes an algorithm called Graph Signal Variation Detection (GSVD) to recognize and reconstruct tangles. Initially, data preprocessing is conducted to remove redundant and stationary points. Then, construct the vertices and edges of the graph using indicators integrating AIS data features and ship trajectories, respectively. The tangled region is identified by calculating the total variation theory of the graph. The tangled region is reconstructed using the greedy heuristic, and the optimal reconstruction path is determined by combining total variation quantization. Finally, the GSVD is compared with four prevailing methods, through qualitative and quantitative analyses. The results demonstrate that the GSVD surpasses the other methods in terms of recognition accuracy, recall rates, and false alarm rates. Additionally, the GSVD can effectively reconstruct the tangle. • Fusion trajectory data features to quantify tangle degree. • A tangled trajectories recognition method based on graph is proposed. • Realizing the reconstruction of tangled trajectories. [ABSTRACT FROM AUTHOR]

Analysis of trajectory such as detection of an outlying trajectory can produce inaccurate results due to the existence of noise, an outlying point-locations that can change statistical properties of the trajectory. Some trajectories with noise are repairable by noise filtering or by trajectory-simplification. We herein propose the application of a trajectory-simplification approach in both batch and streaming environments, followed by benchmarking of various outlier-detection algorithms for detection of outlying trajectories from among simplified trajectories. Experimental evaluation in a case study using real-world trajectories from a shipyard in South Korea shows the benefit of the new approach.

Analysis of trajectory such as detection of an outlying trajectory can produce inaccurate results due to the existence of noise, an outlying point-locations that can change statistical properties of the trajectory. Some trajectories with noise are repairable by noise filtering or by trajectory-simplification. We herein propose the application of a trajectory-simplification approach in both batch and streaming environments, followed by benchmarking of various outlier-detection algorithms for detection of outlying trajectories from among simplified trajectories. Experimental evaluation in a case study using real-world trajectories from a shipyard in South Korea shows the benefit of the new approach. [ABSTRACT FROM AUTHOR]