Intent prediction of vessels in intersection waterway based on learning vessel motion patterns with early observations

2021 ◽  
Vol 232 ◽  
pp. 109154
Author(s):  
Jie Ma ◽  
Chengfeng Jia ◽  
Yaqing Shu ◽  
Kezhong Liu ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Motion Patterns ◽  
Intent Prediction ◽  
Vessel Motion

Vessel Track Recovery With Incomplete AIS Data Using Tensor CANDECOM/PARAFAC Decomposition

2013 ◽  
Vol 67 (1) ◽  
pp. 83-99 ◽  
Author(s):  
Changqing Liu ◽  
Xiaoqian Chen
Keyword(s):  
Link Prediction ◽  
Global Analysis ◽  
Automatic Identification ◽  
Identification System ◽  
Motion Patterns ◽  
Maritime Surveillance ◽  
Cp Decomposition ◽  
Mode Characteristics ◽  
Vessel Motion ◽  
Parafac Decomposition

Global analysis of vessel motion patterns has become possible using satellite-based Automatic Identification System (AIS). The concept of space-based AIS needs several satellites to provide complete coverage and high detection probability. However, in early development stages, often only one satellite is launched and due to its limitation of orbit and footprint, received AIS messages are discontinuous. In this paper, we have analysed real AIS data obtained by satellite to form a global maritime surveillance picture. Furthermore, we propose to take advantage of the tensor CANDECOMP/PARAFAC (CP) decomposition to analyse three mode characteristics of the data, which are location, vessel and time. For incomplete data, we exploit the link prediction technique based on tensor factorisation to recover vessel tracks in a specified area. A variant of temporal link prediction based on CP is presented. We illustrate the usefulness of exploiting the three-mode structure of AIS data by simulation, and demonstrate that the track recovery result has acceptable precision.

User modelling for intent prediction in information analysis

2003 ◽  
Author(s):  
Eugene Santos ◽  
Hien Nguyen ◽  
Qunhua Zhao ◽  
Hua Wang
Keyword(s):  
User Modelling ◽  
Information Analysis ◽  
Intent Prediction

Tongue and Lip Motion Patterns in Alaryngeal Speech

2020 ◽  
Author(s):  
Kristin J. Teplansky ◽  
Alan Wisler ◽  
Beiming Cao ◽  
Wendy Liang ◽  
Chad W. Whited ◽  
...  
Keyword(s):  
Motion Patterns ◽  
Alaryngeal Speech

scholarly journals Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 798
Author(s):  
Hamed Darbandi ◽  
Filipe Serra Bragança ◽  
Berend Jan van der Zwaag ◽  
John Voskamp ◽  
Annik Imogen Gmel ◽  
...  
Keyword(s):  
Machine Learning ◽  
The Body ◽  
Biomechanical Analysis ◽  
Estimation Accuracy ◽  
Speed Estimation ◽  
Motion Patterns ◽  
Positioning Systems ◽  
Machine Learning Approach ◽  
Global Positioning ◽  
Measurement Units

Speed is an essential parameter in biomechanical analysis and general locomotion research. It is possible to estimate the speed using global positioning systems (GPS) or inertial measurement units (IMUs). However, GPS requires a consistent signal connection to satellites, and errors accumulate during IMU signals integration. In an attempt to overcome these issues, we have investigated the possibility of estimating the horse speed by developing machine learning (ML) models using the signals from seven body-mounted IMUs. Since motion patterns extracted from IMU signals are different between breeds and gaits, we trained the models based on data from 40 Icelandic and Franches-Montagnes horses during walk, trot, tölt, pace, and canter. In addition, we studied the estimation accuracy between IMU locations on the body (sacrum, withers, head, and limbs). The models were evaluated per gait and were compared between ML algorithms and IMU location. The model yielded the highest estimation accuracy of speed (RMSE = 0.25 m/s) within equine and most of human speed estimation literature. In conclusion, highly accurate horse speed estimation models, independent of IMU(s) location on-body and gait, were developed using ML.

A prediction model of vessel trajectory based on generative adversarial network

2021 ◽  
pp. 1-11
Author(s):  
Senjie Wang ◽  
Zhengwei He
Keyword(s):  
Safety Management ◽  
Route Planning ◽  
Local Area ◽  
Trajectory Data ◽  
Trajectory Prediction ◽  
Generative Adversarial Network ◽  
Ship Traffic ◽  
Adversarial Network ◽  
Historical Trajectory ◽  
Vessel Motion

Abstract Trajectory prediction is an important support for analysing the vessel motion behaviour, judging the vessel traffic risk and collision avoidance route planning of intelligent ships. To improve the accuracy of trajectory prediction in complex situations, a Generative Adversarial Network with Attention Module and Interaction Module (GAN-AI) is proposed to predict the trajectories of multiple vessels. Firstly, GAN-AI can infer all vessels’ future trajectories simultaneously when in the same local area. Secondly, GAN-AI is based on adversarial architecture and trained by competition for better convergence. Thirdly, an interactive module is designed to extract the group motion features of the multiple vessels, to achieve better performance at the ship encounter situations. GAN-AI has been tested on the historical trajectory data of Zhoushan port in China; the experimental results show that the GAN-AI model improves the prediction accuracy by 20%, 24% and 72% compared with sequence to sequence (seq2seq), plain GAN, and the Kalman model. It is of great significance to improve the safety management level of the vessel traffic service system and judge the degree of ship traffic risk.

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