An unsupervised learning method with convolutional auto-encoder for vessel trajectory similarity computation

Liang, Maohan; Liu, Ryan Wen; Li, Shichen; Xiao, Zhe; Liu, Xin; Lu, F.

doi:10.1016/j.oceaneng.2021.108803

Cited by 83 publications

(34 citation statements)

References 41 publications

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“…Similarity measures are employed for different purposes in information retrieval and data mining, such as top-K similarity queries, trajectory outlier detection and clustering techniques [48]. One of the most recent studies [49], has created a hybrid unsupervised learning method for trajectory similarity computation, by combining auto-encoders and convolutional neural networks. The original vessel trajectories are transformed into images which in turn are remapped into two-dimensional matrices.…”

Section: Trajectory Similarity Measuresmentioning

confidence: 99%

A Comparison of Trajectory Compression Algorithms Over AIS Data

et al. 2021

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Today's industry is flooded with tracking data originating from vessels across the globe that transmit their position at frequent intervals. These voluminous and high-speed streams of data has led researchers to develop novel ways to compress them in order to speed-up processing without losing valuable information. To this end, several algorithms have been developed that try to compress streams of vessel tracking data without compromising their spatio-temporal and kinematic features. In this paper, we present a wide range of several well-known trajectory compression algorithms and evaluate their performance on data originating from vessel trajectories. Trajectory compression algorithms included in this research are suitable for either historical data (offline compression) or real-time data streams (online compression). The performance evaluation is three-fold and each algorithm is evaluated in terms of compression ratio, execution speed and information loss. Experiments demonstrated that each algorithm has its own benefits and limitations and that the choice of a suitable compression algorithm is application-dependent. Finally, considering all assessed aspects, the Dead-Reckoning algorithm not only presented the best performance, but it also works over streaming data, which constitutes an important criterion in maritime surveillance.

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Section: Trajectory Similarity Measuresmentioning

confidence: 99%

A Comparison of Trajectory Compression Algorithms Over AIS Data

et al. 2021

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“…To support multiple ships anticollision decision, CAS based on multiagent was designed, in which the agents formulate collision-free strategy through information interaction [22], such as distributed algorithm [23,24] and centralized algorithm [25]. Moreover, big data processing techniques for ship AIS trajectories and video detection provided well support to identify ship encounter behaviors more accurately [26,27] or study the traffic flows in water areas [28].…”

Section: Collision Avoidance Systemmentioning

confidence: 99%

“…Rule 16 of COLREG states that "Every vessel is directed to keep out of the way of another vessel shall, so far as possible, take early and substantial action to keep well clear," the giveway vessel can take action at any time within the time interval between the time to urgent situation and the time to close-quarter situation. However, there are great differences in trajectory characteristics under the same waters based on high-quality ship trajectory data from AIS [27,48]. Further, video-based detection reflected more detailed and clear ship encounter behavior, such as moving straight, turning right, and turning left, in specific waters [49].…”

Section: Parameter Related To Ship Collision Avoidance Asmentioning

confidence: 99%

An Optimized Collision Avoidance Decision-Making System for Autonomous Ships under Human-Machine Cooperation Situations

Liu

Zhang

et al. 2021

Journal of Advanced Transportation

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Maritime Autonomous Surface Ships (MASSs) are attracting increasing attention in recent years as it brings new opportunities for water transportation. Previous studies aim to propose fully autonomous system on collision avoidance decisions and operations, either focus on supporting conflict detection or providing with collision avoidance decisions. However, the human-machine cooperation is essential in practice at the first stage of automation. An optimized collision avoidance decision-making system is proposed in this paper, which involves risk appetite (RA) as the orientation. The RA oriented collision avoidance decision-making system (RA-CADMS) is developed based on human-machine interaction during ship collision avoidance, while being consistent with the International Regulations for Preventing Collisions at Sea (COLREGS) and Ordinary Practice of Seamen (OPS). It facilitates automatic collision avoidance and safeguards the MASS remote control. Moreover, the proposed RA-CADMS are used in several encounter situations to demonstrate the preference. The results show that the RA-CADMS is capable of providing accurate collision avoidance decisions, while ensuring efficiency of MASS maneuvering under different RA.

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“…It combines the advantages and structures of auto-encoder (AE) and CNN. Th supervised AE can encode the input sample into a low-dimensional representation, w CNN is capable of quickly extracting meaningful features from the input sample [28] simple CAE network is shallow, which may make it difficult to extract more comple tures. A multi-layer CAE network is proposed to extract the features of the weight m W more comprehensively.…”

Section: Feature Extraction Of Weight Matrix Based On Caementioning

confidence: 99%

A Deep Neural Network Method for Arterial Blood Flow Profile Reconstruction

Yang

Wang

Bin

et al. 2021

Entropy

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Arterial stenosis will reduce the blood flow to various organs or tissues, causing cardiovascular diseases. Although there are mature diagnostic techniques in clinical practice, they are not suitable for early cardiovascular disease prediction and monitoring due to their high cost and complex operation. In this paper, we studied the electromagnetic effect of arterial blood flow and proposed a method based on the deep neural network for arterial blood flow profile reconstruction. The potential difference and weight matrix are used as inputs to the method, and its output is an estimate of the internal blood flow velocity distribution for arterial blood flow profile reconstruction. Firstly, the weight matrix is input into the convolutional auto-encode (CAE) network to extract its features. Then, the weight matrix features and potential difference are combined to obtain the features of the blood velocity distribution. Finally, the velocity features are reconstructed into blood flow velocity distribution by a convolution neural network (CNN). All data sets are obtained from a model of the carotid artery with different rates of stenosis in a uniform magnetic field by COMSOL. The results show that the average root mean square error of the reconstruction results obtained by the proposed method is 0.0333, and the average correlation coefficient is 0.9721, which is better than the corresponding indicators of the Tikhonov, back propagation (BP) and CNN methods. The simulation results show that the proposed method can achieve high accuracy in blood flow profile reconstruction and is of great significance for the early diagnosis of arterial stenosis and other vessel diseases.

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An unsupervised learning method with convolutional auto-encoder for vessel trajectory similarity computation

Cited by 83 publications

References 41 publications

A Comparison of Trajectory Compression Algorithms Over AIS Data

A Comparison of Trajectory Compression Algorithms Over AIS Data

An Optimized Collision Avoidance Decision-Making System for Autonomous Ships under Human-Machine Cooperation Situations

A Deep Neural Network Method for Arterial Blood Flow Profile Reconstruction

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