Journal articles: 'Long term prediction of vessel motion'

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Miller, Paul H. "Fatigue Prediction Verification of Fiberglass Hulls." Marine Technology and SNAME News 38, no. 04 (October 1, 2001): 278–92. http://dx.doi.org/10.5957/mt1.2001.38.4.278.

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The growing use of marine composite materials has led to many technical challenges and one is predicting lifetime durability. This analysis step has a large uncertainty due to the lack of data from in-service composite vessels. Analytical models based on classical lamination theory, finite-element analysis, ship motions, probability and wind and wave mechanicswere used in this project to predict hull laminate strains, and fatigue tests were used to determine S-N residual stiffness properties of coupons. These predictions and test data were compared against two cored fiberglass sisterships having significantly different fatigue histories and undamaged laminates representing a new vessel. Strains were measured while underway and good correlation was achieved between predictions and measurements. Fatigue damage indicators were identified which could be used in vessel inspection procedures. Endurance limits were found to be near 25% of static failure load, indicating that a fatigue design factor of four is required for infinite service with this material. Standard moisture experiments using boiling water were compared with long-term exposure. Results indicated the boiling water test yielded significantly conservative values and was not a reliable means of predicting long-term effects. Panel tests were compared with a combined coupon and finite-element procedure. Results indicated the proposed procedure was a viable substitute, at least for the materials studied. A rational explanation for using thicker outer skin laminates in marine composites was identified through single-sided moisture flex tests. These showed that the reduced strength and stiffness due to moisture of the outer hull skin laminate could be compensated by increased thickness. Although the resulting unbalanced laminate is not ideal from a warping standpoint, the approach leads to consistent tensile failure of the inner skin when subjected to normal loads. Permeability considerations make this desirable for hull laminates.

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Siniuta, K. O. "SHIP HANDLING IN CASE OF DISTURBANCE DURING SEQUENTIAL CALCULATION AND OBSERVATION OF SHIP MOTION." Shipping & Navigation 32, no. 2 (December 12, 2021): 88–94. http://dx.doi.org/10.31653/2306-5761.32.2021.88-94.

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Controllability is an important maritime quality that determines the efficiency of ship handling. When developing course control systems, it is necessary to take into account the operational characteristics of the vessel, as well as external factors affecting it. The complexity of ship handling, as an object of handling, arises due to the continuous influence of various factors that affect the controllability of the ship. The environmental conditions in which the course management task has to be solved are diverse - stormy weather, ice conditions, shallow water, tides, restricted waters(congested areas), proximity of other vessels, etc. All these factors cannot be comprehensively taken into account by traditional mathematical methods, while ensuring the necessary adequacy of real processes. This paper considers existing approaches to controlling the movement of a ship on a course, such as course control, disturbance control, ship movement control on a course based on the principle of long-term prediction, lateral deviation, intellectual approaches to ship control. The most necessary way to improve the quality of the vessel's course is to control the disturbance by consistently calculating and observing the vessel's movement. The main disturbing effect in stabilizing the course is caused by sea waves. In stormy weather, forced oscillations are imposed on the ship's own motions on the course. The amplitude and period of yawing depend on the level of sea state, the direction and strength of the wind, the tonnage of the vessel, its loading condition, speed, effectiveness of the rudder and the law of control. There is a need to increase the accuracy of determining the direct relationship between the measured value of the external perturbation and the magnitude of the yaw angle. The article provides an algorithm for calculating the return of the vessel to the path line, taking into account the modulus and direction of natural disturbance obtained as a result of observation of the ship's position.

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Konon, N. M. "ANALYTICAL MODELLING OF SEAKEEPING QUALITIES OF CONTAINER VESSEL." Shipping & Navigation 30, no. 1 (December 1, 2020): 78–87. http://dx.doi.org/10.31653/2306-5761.30.2020.78-87.

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The design of ships or any other floating systems intended to operate on or close to the surface of the sea is controlled to a large extent by what is usually referred to as seakeeping, or, in more common terminology, safety at sea. This is a primary consideration and criteria, which has to be fully met. Safety of a ship naturally includes the crew, cargo and the hull itself. Seakeeping is, indeed, a generalized term and reflects the ship's capability to survive all hazards at sea such as collision, grounding, fire, as well as heavy-weather effects related to the environment in general and waves in particular. The two most likely types of failure under these conditions are due to structural causes and capsizing resulting from insufficient stability under severe weather conditions. Such criteria as economical navigation of the ship as related to speed-keeping abilities, fuel consumption, avoidance of damage to ship components and cargo, and comfort to crew or passengers, or both, are key items. The operational limits of electronic equipment, mechanical components and weapon systems on board warships are other aspects of sea keeping. In this work it is highlighted that seakeeping is a generalized term that includes a wide variety of subjects such as ship motions (amplitudes, accelerations, phases), deck wetness, slamming, steering in waves, added resistance, hydrodynamic loadings (pressures, forces, moments) and transient loads. Since the ship environmental operability or its sea keeping characteristics are closely linked to the severity of the sea, the description of the seaway is usually considered as an integral part of sea keeping. It is taken into consideration that the severity of the sea cannot be considered in absolute terms, since for each floating system, be it a ship, a platform or a buoy, the intensity of the sea state can only be determined in terms of the system's responses. Hence, different thresholds apply to different problems, and sea state 4 may be just as severe for a small patrol craft as sea state 8 may be for a larger containership. Hence, the characteristics and frequency of occurrence of waves in specific sea zones are required if a possible reduction in the system environmental operability is expected. It is demonstrated that most texts or papers, which deal with the overall question of sea keeping, devote some attention to the basic phenomena, that is, the seaway and the motions of the ship or other floating platforms as a result of the excitation imposed by the seaway. Ship motions, as such, do not always constitute the criteria for sea keeping, and much more often other responses directly related to the magnitude and phasing of the motions or the resulting velocities and accelerations constitute the prime cause for exhibiting good or bad sea keeping qualities. Such responses could be a function of the motion only, as in the case of added resistance or hydrodynamic pressures, or they could be a function of motion and other design parameters, such as freeboard in the case of deck wetness or the longitudinal weight distribution in the case of vertical bending moments. In this work, latest methods of modeling and computation for body-wave interactions described and compared with data observed for container carrier. The foregoing calculation routine Судноводіння | Shipping & Navigation ISSN 2306-5761 | 2618-0073 30-2020 Національний університет «Одеська морська академія» 79 is fairly well accepted today among naval architects specializing in the sea keeping aspects of the ship design process. Differences between the results obtained by various techniques as presented by the available computer programs are insignificant. However, since the regular-wave results are of little or no value except as input for the more realistic long- and short-term response predictions in a real seaway environment, it is important to determine which wave data information and what statistical extrapolation techniques are used to obtain the latter. The format used to describe the seaway in most ship response calculations is the wave spectrum. However, since measured spectrum for a specific sea zone or route are very rarely available, it is often necessary to use spectrum measured in one location for predictions in another location. In such a case, while the basic spectruml shape and scatter remain unchanged, the percentage of wave height distribution would vary to represent realistic conditions for the sea area in question. Such data usually are based on observations, and assuming the sample is large enough the distribution of expected wave heights should be quite reliable. An alternative approach often used in ship design is to utilize one of several theoretical spectruml formulations [2, 3, 4] such as the Pierson-Moskowitz one-parameter spectrum, the ISSC spectrum, the JONSWAP spectrum, and other. In each of these cases, some input parameters are required usually in the form of wave height, period, peak frequency, fetch, etc. The reliability of the wave data depends in these cases both on the quality of the input parameter and the adequacy of the theoretical formulation.

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Li, Xingyang, Kaiqiang Wu, and Haijing Deng. "Blood Pressure Monitoring Based on Carbonized Lens Cleaning Paper-Based Flexible Strain Sensor." Science of Advanced Materials 13, no. 9 (September 1, 2021): 1789–96. http://dx.doi.org/10.1166/sam.2021.4070.

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Blood pressure (BP) is an important indicator for measuring human health, especially continuous BP, which can indirectly reflect the operating conditions of the heart and blood vessels. The increase in wearable devices has promoted the development of high-performance flexible strain sensors that can monitor various physiological signals and human motion signals. In this work, we used carbonized non-woven lens cleaning paper as the sensitive element to prepare a wide working range (0–100% strain) and high sensitivity (sensitivity in the range of 0–60% is 32, sensitivity in the range of 60–100% strain is as high as 162), fast response capability (response time less than 20 ms), with long-term cycle stability (greater than 10,000 cycles). Based on this sensor, we collected the user’s radial artery pressure pulse wave signal, and proposed a wearable continuous BP monitoring method based on the pressure pulse wave signal. This method includes pulse wave signal monitoring, pulse wave feature extraction, and establishment of a BP prediction model. The results show that there is a strong correlation and satisfactory accuracy between the predicted BP value and the reference BP value, thus demonstrating its application potential in personal BP monitoring.

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Wiegand, T., Xiaozheng Zhang, and B. Girod. "Long-term memory motion-compensated prediction." IEEE Transactions on Circuits and Systems for Video Technology 9, no. 1 (1999): 70–84. http://dx.doi.org/10.1109/76.744276.

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Tang, Gang, Jinman Lei, Chentong Shao, Xiong Hu, Weidong Cao, and Shaoyang Men. "Short-Term Prediction in Vessel Heave Motion Based on Improved LSTM Model." IEEE Access 9 (2021): 58067–78. http://dx.doi.org/10.1109/access.2021.3072420.

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Kim, Jonghee, Chanho Jung, Dokeun Kang, and Chang Jin Lee. "A New Vessel Path Prediction Method using Long Short-term Memory." Transactions of The Korean Institute of Electrical Engineers 69, no. 7 (July 31, 2020): 1131–34. http://dx.doi.org/10.5370/kiee.2020.69.7.1131.

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Su, Xiaoqing, Lintao Liu, Hsu Houtse, and Guocheng Wang. "Long-term polar motion prediction using normal time–frequency transform." Journal of Geodesy 88, no. 2 (November 30, 2013): 145–55. http://dx.doi.org/10.1007/s00190-013-0675-7.

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Liu, Chao, Shuai Guo, Yuan Feng, Feng Hong, Haiguang Huang, and Zhongwen Guo. "L-VTP: Long-Term Vessel Trajectory Prediction Based on Multi-Source Data Analysis." Sensors 19, no. 20 (October 9, 2019): 4365. http://dx.doi.org/10.3390/s19204365.

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With the rapid development of marine IoT (Internet of Things), ocean MDTN (Mobile Delay Tolerant Network) has become a research hot spot. Long-term trajectory prediction is a key issue in MDTN. There are no long-term fine-grained trajectory prediction methods proposed for ocean vessels because a vessel’s mobility pattern lacks map topology support and can be easily influenced by the fish moratorium, sunshine duration, etc. A traditional on-land trajectory prediction algorithm cannot be directly utilized in this field because trajectory characteristics of ocean vessels are far different from that on land. To address the problem above, we propose a novel long-term trajectory prediction algorithm for ocean vessels, called L-VTP, by utilizing multiple sailing related parameters and K-order multivariate Markov Chain. L-VTP utilizes multiple sailing related parameters to build multiple state-transition matrices for trajectory prediction based on quantitative uncertainty analysis of trajectories. Trajectories’ sparsity of ocean vessels results in a critical state missing problem of a high-order state-transition matrix. L-VTP automatically traverses other matrices in a specific sequence in terms of quantitative uncertainty results to overcome this problem. Furthermore, the different mobility models of the same vessel during the day and the night are also exploited to improve the prediction accuracy. Privacy issues have been taken into consideration in this paper. A quantitative model considering Markov order, training metadata and privacy leak degree is proposed to help the participant make the trade-off based on their customized requirements. We have performed extensive experiments on two years of real-world trajectory data that include more than two thousand vessels. The experiment results demonstrate that L-VTP can realize fine-grained long-term trajectory prediction with the consideration of privacy issues. The average error of 4.5-hour fine-grained prediction is less than 500 m. In addition, the proposed method can be extended to 10-hour prediction with an average error of 2.16 km, which is also far less than the communication range of ocean vessel communication devices.

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Zhang, Minglong, Liang Huang, Yuanqiao Wen, Jinfen Zhang, Yamin Huang, and Man Zhu. "Short-Term Trajectory Prediction of Maritime Vessel Using k-Nearest Neighbor Points." Journal of Marine Science and Engineering 10, no. 12 (December 7, 2022): 1939. http://dx.doi.org/10.3390/jmse10121939.

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The prediction of ship location has become an increasingly popular research hotspot in the field of maritime transportation engineering, which benefits maritime safety supervision and security. Existing methods of ship location prediction based on motion characteristics have a large uncertainty and cannot guarantee trajectory prediction accuracy of the target ship. An improved method of location prediction using k-nearest neighbor (KNN) is proposed in this paper. An expanded circle area of the latest point of the target ship is first generated to find the reference points with similar movement characteristics in the constraints of distance and time intervals. Then, the top k-nearest neighbors are determined based on the degree of similarity. Relationships between the reference point of each neighbor and the latest points of the target ship are calculated. The predicted location of the target ship can then be determined by a weighted calculation of the locations of all neighbors at the predicted time and their relationships with the target ship. Experiments of ship location prediction in 10 min, 20 min, and 30 min were conducted. The correlation coefficient of the location prediction error for the three experiments was 0.992, 0.99, and 0.9875, respectively. The results show that ship location prediction with reference to multiple nearest neighbors with similar movements can provide better accuracy.

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Shan, Mao, Stewart Worrall, and Eduardo Nebot. "Probabilistic Long-Term Vehicle Motion Prediction and Tracking in Large Environments." IEEE Transactions on Intelligent Transportation Systems 14, no. 2 (June 2013): 539–52. http://dx.doi.org/10.1109/tits.2012.2224657.

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Wiegand, T., N. Farber, K. Stuhlmuller, and B. Girod. "Error-resilient video transmission using long-term memory motion-compensated prediction." IEEE Journal on Selected Areas in Communications 18, no. 6 (June 2000): 1050–62. http://dx.doi.org/10.1109/49.848255.

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Bennett, Simon J., Jean-Jacques Orban de Xivry, Philippe Lefèvre, and Graham R. Barnes. "Oculomotor prediction of accelerative target motion during occlusion: long-term and short-term effects." Experimental Brain Research 204, no. 4 (June 17, 2010): 493–504. http://dx.doi.org/10.1007/s00221-010-2313-4.

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Millefiori, Leonardo M., Paolo Braca, Karna Bryan, and Peter Willett. "Modeling vessel kinematics using a stochastic mean-reverting process for long-term prediction." IEEE Transactions on Aerospace and Electronic Systems 52, no. 5 (October 2016): 2313–30. http://dx.doi.org/10.1109/taes.2016.150596.

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Vivone, Gemine, Leonardo M. Millefiori, Paolo Braca, and Peter Willett. "Performance Assessment of Vessel Dynamic Models for Long-Term Prediction Using Heterogeneous Data." IEEE Transactions on Geoscience and Remote Sensing 55, no. 11 (November 2017): 6533–46. http://dx.doi.org/10.1109/tgrs.2017.2729622.

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Ra, W. S., and I. H. Whang. "Real-time long-term prediction of ship motion for fire control applications." Electronics Letters 42, no. 18 (2006): 1020. http://dx.doi.org/10.1049/el:20061053.

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Petrou, Zisis I., and Yingli Tian. "Prediction of Sea Ice Motion With Convolutional Long Short-Term Memory Networks." IEEE Transactions on Geoscience and Remote Sensing 57, no. 9 (September 2019): 6865–76. http://dx.doi.org/10.1109/tgrs.2019.2909057.

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Wang, Dangli, Yangran Meng, Shuzhe Chen, Cheng Xie, and Zhao Liu. "A Hybrid Model for Vessel Traffic Flow Prediction Based on Wavelet and Prophet." Journal of Marine Science and Engineering 9, no. 11 (November 7, 2021): 1231. http://dx.doi.org/10.3390/jmse9111231.

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Accurate vessel traffic flow prediction is significant for maritime traffic guidance and control. According to the characteristics of vessel traffic flow data, a new hybrid model, named DWT–Prophet, is proposed based on the discrete wavelet decomposition and Prophet framework for the prediction of vessel traffic flow. First, vessel traffic flow was decomposed into a low-frequency component and several high-frequency components by wavelet decomposition. Second, Prophet was trained to predict the components, respectively. Finally, the prediction results of the components were reconstructed to complete the prediction. The experimental results demonstrate that the hybrid DWT–Prophet outperformed the single Prophet, long short-term memory, random forest, and support vector regression (SVR). Moreover, the practicability of the new forecasting method was improved effectively.

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Mounasri, Mrs, V. Ujwala, and R. Gowthami. "Motion Pattern Classification on Online/Active Data-Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 8 (August 31, 2022): 1013–16. http://dx.doi.org/10.22214/ijraset.2022.45338.

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Abstract: Ship behaviour recognition and prediction is very important for the early warning of risky behaviour, identifying potential ship collision, improving maritime traffic efficiency etc., and thus is a very active topic in the intelligent maritime navigation community. The high flow of vessel traffic affects the difficulty of monitoring vessel in the middle of the sea because of limited human visibility, occurrence of vessel accidents at the sea and other illegal activities that illustrate abnormal vessel behaviour such as oil bunkering, piracy, illegal fishing and other crimes that will continue and will certainly have an impact on losses in several aspects. An existing system involves, Automatic Identification System (AIS) for short-range operation, Long-Range Identification and Tracking (LRIT), Vessel Monitoring System (VMS) are widely used automatic reporting systems for the ship/vessels. Further few classification algorithms like Bayesian, CNN and many other methods which does not permit to draw definite conclusions about the overall effectiveness of the identification procedure because of noise level. Automatic identification system (AIS) trajectory will collect data from multiple sensors that record dynamic and static ship information. AIS sequences (and records) are affected by subjective ship-officer behavior such as collision-avoidance decisionmaking and good seamanship

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Yang, Cheng-Hong, Guan-Cheng Lin, Chih-Hsien Wu, Yen-Hsien Liu, Yi-Chuan Wang, and Kuo-Chang Chen. "Deep Learning for Vessel Trajectory Prediction Using Clustered AIS Data." Mathematics 10, no. 16 (August 15, 2022): 2936. http://dx.doi.org/10.3390/math10162936.

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Accurate vessel track prediction is key for maritime traffic control and management. Accurate prediction results can enable collision avoidance, in addition to being suitable for planning routes in advance, shortening the sailing distance, and improving navigation efficiency. Vessel track prediction using automatic identification system (AIS) data has attracted extensive attention in the maritime traffic community. In this study, a combining density-based spatial clustering of applications with noise (DBSCAN)-based long short-term memory (LSTM) model (denoted as DLSTM) was developed for vessel prediction. DBSCAN was used to cluster vessel tracks, and LSTM was then used for training and prediction. The performance of the DLSTM model was compared with that of support vector regression, recurrent neural network, and conventional LSTM models. The results revealed that the proposed DLSTM model outperformed these models by approximately 2–8%. The proposed model is able to provide a better prediction performance of vessel tracks, which can subsequently improve the efficiency and safety of maritime traffic control.

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Peng, Xiuyan, Biao Zhang, and Haiguang Zhou. "An improved particle swarm optimization algorithm applied to long short-term memory neural network for ship motion attitude prediction." Transactions of the Institute of Measurement and Control 41, no. 15 (July 15, 2019): 4462–71. http://dx.doi.org/10.1177/0142331219860731.

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This paper proposes a prediction method of ship motion attitude with high accuracy based on the long short-term memory neural network. The model parameters should be initialized randomly, resulting in critical decreases of the nonlinear learning ability of current parameter optimization methods. Therefore, a multilayer heterogeneous particle swarm optimization is proposed to optimize the parameters of long short-term memory neural network and applied to the prediction of ship motion. In multilayer heterogeneous particle swarm optimization, this paper proposes the concept of attractors, transforms the speed update equation, enhances the information interaction ability between particles, improves the optimization performance of the particle swarm optimization algorithm, and improves its optimization effect on the parameters of the long short-term memory networks. In the simulations, the measured data were used as input to predict the results of the ship motion. The results showed that the proposed method offers higher learning accuracy, faster convergence speed, and better prediction performance for accurate estimation of ship motion attitude than existing methods.

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Tao, Lu, Yousuke Watanabe, and Hiroaki Takada. "A Lightweight Long-Term Vehicular Motion Prediction Method Leveraging Spatial Database and Kinematic Trajectory Data." ISPRS International Journal of Geo-Information 11, no. 9 (August 29, 2022): 463. http://dx.doi.org/10.3390/ijgi11090463.

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Long-term vehicular motion prediction is a crucial function for both autonomous driving and advanced driver-assistant systems. However, due to the uncertainties of vehicle dynamics and complexities of surroundings, long-term motion prediction is never trivial work. As they combine effects of humans, vehicles and environments, kinematic trajectory data reflect several aspects of vehicles’ spatial behaviors. In this paper, we propose a novel method that leverages spatial database and kinematic trajectory data to achieve long-term vehicular motion prediction in a lightweight way. In our system, a spatial database system is initially embedded in an extended Kalman filter (EKF) framework. The spatial kinematic trajectory data are managed through the database and directly used in motion prediction; namely, weighted means are derived from the spatially retrieved kinematic data and used to update EKF predictions. The proposed method is validated in the real world. The experiments indicate that different weighting methods make a slight accuracy difference. Our method is not data-and-computation-consumed; its performance is acceptable in the limited data conditions and its prediction accuracy is improved as the size of used data sets increases; our method can predict in real time. The efficiency of an unscented Kalman filter (UKF) is compared with that of the EKF. The results show that the UKF can hardly meet real-time requirements.

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Zhang, Lixiang, Yian Zhu, Jiang Su, Wei Lu, Jiayu Li, and Ye Yao. "A Hybrid Prediction Model Based on KNN-LSTM for Vessel Trajectory." Mathematics 10, no. 23 (November 28, 2022): 4493. http://dx.doi.org/10.3390/math10234493.

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Trajectory prediction technology uses the trajectory data of historical ships to predict future ship trajectory, which has significant application value in the field of ship driving and ship management. With the popularization of Automatic Identification System (AIS) equipment in-stalled on ships, many ship trajectory data are collected and stored, providing a data basis for ship trajectory prediction. Currently, most of the ship trajectory prediction methods do not fully consider the influence of ship density in different sea areas, leading to a large difference in the prediction effect in different sea areas. This paper proposes a hybrid trajectory prediction model based on K-Nearest Neighbor (KNN) and Long Short-Term Memory (LSTM) methods. In this model, different methods are used to predict trajectory based on trajectory density. For offshore waters with a high density of trajectory, an optimized K-Nearest Neighbor algorithm is used for prediction. For open sea waters with low density of trajectory, the Long Short-Term Memory model is used for prediction. To further improve the prediction effect, the spatio-temporal characteristics of the trajectory are fully considered in the prediction process of the model. The experimental results for the dataset of historical data show that the mean square error of the proposed method is less than 2.92 × 10−9. Compared to the prediction methods based on the Kalman filter, the mean square error decreases by two orders of magnitude. Compared to the prediction methods based on recurrent neural network, the mean square error decreases by 82%. The advantage of the proposed model is that it can always obtain a better prediction result under different conditions of trajectory density available for different sea areas.

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Palmieri, Luigi, Rudenko Andrey, Jim Mainprice, Marc Hanheide, Alexandre Alahi, Achim Lilienthal, and Kai O. Arras. "Guest Editorial: Introduction to the Special Issue on Long-Term Human Motion Prediction." IEEE Robotics and Automation Letters 6, no. 3 (July 2021): 5613–17. http://dx.doi.org/10.1109/lra.2021.3077964.

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Shen, Yi, Jinyun Guo, Xin Liu, Qiaoli Kong, Linxi Guo, and Wang Li. "Long-term prediction of polar motion using a combined SSA and ARMA model." Journal of Geodesy 92, no. 3 (September 12, 2017): 333–43. http://dx.doi.org/10.1007/s00190-017-1065-3.

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Son, Hye-young, Gi-yong Kim, Hee-jin Kang, Jin Choi, Dong-kon Lee, and Sung-chul Shin. "Ship Motion-Based Prediction of Damage Locations Using Bidirectional Long Short-Term Memory." Journal of Ocean Engineering and Technology 36, no. 5 (October 31, 2022): 295–302. http://dx.doi.org/10.26748/ksoe.2022.026.

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<i>The initial response to a marine accident can play a key role to minimize the accident. Therefore, various decision support systems have been developed using sensors, simulations, and active response equipment. In this study, we developed an algorithm to predict damage locations using ship motion data with bidirectional long short-term memory (BiLSTM), a type of recurrent neural network. To reflect the low frequency ship motion characteristics, 200 time-series data collected for 100 s were considered as input values. Heave, roll, and pitch were used as features for the prediction model. The F1-score of the BiLSTM model was 0.92; this was an improvement over the F1-score of 0.90 of a prior model. Furthermore, 53 of 75 locations of damage had an F1-score above 0.90. The model predicted the damage location with high accuracy, allowing for a quick initial response even if the ship did not have flood sensors. The model can be used as input data with high accuracy for a real-time progressive flooding simulator on board.</i>

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Huang, Yen-Chun, Kuan-Yu Chen, Shao-Jung Li, Chih-Kuang Liu, Yang-Chao Lin, and Mingchih Chen. "Implementing an Ensemble Learning Model with Feature Selection to Predict Mortality among Patients Who Underwent Three-Vessel Percutaneous Coronary Intervention." Applied Sciences 12, no. 16 (August 14, 2022): 8135. http://dx.doi.org/10.3390/app12168135.

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Coronary artery disease (CAD) is a common major disease. Revascularization with percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG) could relieve symptoms and myocardial ischemia. As the treatment improves and evolves, the number of aged patients with complex diseases and multiple comorbidities gradually increases. Furthermore, in patients with multivessel disease, 3-vessel PCI may lead to a higher risk of complications during the procedure, leading to further ischemia and higher long-term mortality than PCI for one vessel or two vessels. Nevertheless, the risk factors for accurately predicting patient mortality after 3-vessel PCI are unclear. Thus, a new risk prediction model for primary PCI (PPCI) patients’ needs to be established to help physicians and patients make decisions more quickly and accurately. This research aimed to construct a prediction model and find which risk factors will affect mortality in 3-vessel PPCI patients. This nationwide population-based cohort study crossed multiple hospitals and selected 3-vessel PPCI patients from January 2007 to December 2009. Then five different single machine learning methods were applied to select significant predictors and implement ensemble models to predict the mortality rate. Of the 2337 patients who underwent 3-vessel PPCI, a total of 1188 (50.83%) survived and 1149 (49.17%) died. Age, congestive heart failure (CHF), and chronic renal failure (CRF) are mortality’s most important variables. When CRF patients accept 3-vessel PPCI at ages between 68–75, they will possibly have a 94% death rate; Furthermore, this study used the top 15 variables averaged by each machine learning method to make a prediction model, and the ensemble learning model can accurately predict the long-term survival of 3-vessel PPCI patients, the accurate predictions rate achieved in 88.7%. Prediction models can provide helpful information for the clinical physician and enhance clinical decision-making. Furthermore, it can help physicians quickly identify the risk features, design clinical trials, and allocate hospital resources effectively.

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Song, Fei, Yong Li, Wei Cheng, and Limeng Dong. "Learning to Track Multiple Radar Targets with Long Short-Term Memory Networks." Wireless Communications and Mobile Computing 2023 (February 15, 2023): 1–9. http://dx.doi.org/10.1155/2023/1033371.

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Radar multitarget tracking in a dense clutter environment remains a complex problem to be solved. Most existing solutions still rely on complex motion models and prior distribution knowledge. In this paper, a new online tracking method based on a long short-term memory (LSTM) network is proposed. It combines state prediction, measurement association, and trajectory management functions in an end-to-end manner. We employ LSTM networks to model target motion and trajectory associations, relying on their strong learning ability to learn target motion properties and long-term dependence of trajectory associations from noisy data. Moreover, to address the problem of missing appearance information of radar targets, we propose an architecture based on the LSTM network to calculate similarity function by extracting long-term motion features. And the similarity is applied to trajectory associations to improve their robustness. Our proposed method is validated in simulation scenarios and achieves good results.

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Liu, Chao, Yingbin Li, Ruobing Jiang, Yong Du, Qian Lu, and Zhongwen Guo. "TPR-DTVN: A Routing Algorithm in Delay Tolerant Vessel Network Based on Long-Term Trajectory Prediction." Wireless Communications and Mobile Computing 2021 (January 29, 2021): 1–15. http://dx.doi.org/10.1155/2021/6630265.

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An efficient and low-cost communication system has great significance in maritime communication, but it faces enormous challenges because of high communication costs, incomplete communication infrastructure, and inefficient routing algorithms. Delay Tolerant Vessel Networks (DTVNs), which can create low-cost communication opportunities among vessels, have recently attracted considerable attention in the academic community. Most existing maritime ad hoc routing algorithms focus on predicting vessels’ future contacts by mining coarse-grained social relations or spatial distribution, which has led to poor performance. In this paper, we analyze 3-year trajectory data of 5123 fishery vessels in the China East Sea. Using entropy theory, we observe that the trajectory of the vessel has strongly spatial-temporal distribution regularity, especially when previous states were given. To predict accurate future trajectories, we develop a long-term accurate trajectory prediction model by improving the Bidirectional Long-Short Term Memory (Bi-LSTM) model. Based on predicted trajectories and the confident degree of each prediction step, we propose a series of routing algorithms called TPR-DTVN to achieve efficient communication performance. Finally, we carry out simulation experiments with extensive real data. Compared with existing algorithms, the simulation results show that TPR-DTVN can achieve a higher delivery ratio with lower cost and transmission delay.

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Cui, Jianwei, and Zhigang Li. "Prediction of Upper Limb Action Intention Based on Long Short-Term Memory Neural Network." Electronics 11, no. 9 (April 21, 2022): 1320. http://dx.doi.org/10.3390/electronics11091320.

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The use of an inertial measurement unit (IMU) to measure the motion data of the upper limb is a mature method, and the IMU has gradually become an important device for obtaining information sources to control assistive prosthetic hands. However, the control method of the assistive prosthetic hand based on the IMU often has problems with high delay. Therefore, this paper proposes a method for predicting the action intentions of upper limbs based on a long short-term memory (LSTM) neural network. First, the degree of correlation between palm movement and arm movement is compared, and the Pearson correlation coefficient is calculated. The correlation coefficients are all greater than 0.6, indicating that there is a strong correlation between palm movement and arm movement. Then, the motion state of the upper limb is divided into the acceleration state, deceleration state and rest state. The rest state of the upper limb is used as a sign to control the assistive prosthetic hand. Using the LSTM to identify the motion state of the upper limb, the accuracy rate is 99%. When predicting the action intention of the upper limb based on the angular velocity of the shoulder and forearm, the LSTM is used to predict the angular velocity of the palm, and the average prediction error of palm motion is 1.5 rad/s. Finally, the feasibility of the method is verified through experiments, in the form of holding an assistive prosthetic hand to imitate a disabled person wearing a prosthesis. The assistive prosthetic hand is used to reproduce foot actions, and the average delay time of foot action was 0.65 s, which was measured by using the method based on the LSTM neural network. However, the average delay time of the manipulator control method based on threshold analysis is 1.35 s. Our experiments show that the prediction method based on the LSTM can achieve low prediction error and delay.

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Li, Chong-hui, Zhang-lei Chen, Xin-jiang Liu, Bin Chen, Yong Zheng, Shuai Tong, and Ruo-pu Wang. "Adaptively robust filtering algorithm for maritime celestial navigation." Journal of Navigation 75, no. 1 (October 29, 2021): 200–212. http://dx.doi.org/10.1017/s0373463321000758.

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AbstractCelestial navigation is an important means of maritime navigation; it can automatically achieve inertially referenced positioning and orientation after a long period of development. However, the impact of different accuracy of observations and the influence of nonstationary states, such as ship speed change and steering, are not taken into account in existing algorithms. To solve this problem, this paper proposes an adaptively robust maritime celestial navigation algorithm, in which each observation value is given an equivalent weight according to the robust estimation theory, and the dynamic balance between astronomical observation and prediction values of vessel motion is adjusted by applying the adaptive factor. With this system, compared with the frequently used least square method and extended Kalman filter algorithm, not only are the real-time and high-precision navigation parameters, such as position, course, and speed for the vessel, calculated simultaneously, but also the influence of abnormal observation and vessel motion status change could be well suppressed.

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Lin, Hui, Chengyu Shi, Brian Wang, Maria F. Chan, Xiaoli Tang, and Wei Ji. "Towards real-time respiratory motion prediction based on long short-term memory neural networks." Physics in Medicine & Biology 64, no. 8 (April 10, 2019): 085010. http://dx.doi.org/10.1088/1361-6560/ab13fa.

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Yu, Kehao, Kai Yang, Tonghui Shen, Lihua Li, Haowei Shi, and Xu Song. "Estimation of Earth Rotation Parameters and Prediction of Polar Motion Using Hybrid CNN–LSTM Model." Remote Sensing 15, no. 2 (January 10, 2023): 427. http://dx.doi.org/10.3390/rs15020427.

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The Earth rotation parameters (ERPs), including polar motion (PMX and PMY) and universal time (UT1-UTC), play a central role in functions such as monitoring the Earth’s rotation and high-precision navigation and positioning. Variations in ERPs reflect not only the overall state of movement of the Earth, but also the interactions among the atmosphere, ocean, and land on the spatial and temporal scales. In this paper, we estimated ERP series based on very long baseline interferometry (VLBI) observations between 2011–2020. The results show that the average root mean square errors (RMSEs) are 0.187 mas for PMX, 0.205 mas for PMY, and 0.022 ms for UT1-UTC. Furthermore, to explore the high-frequency variations in more detail, we analyzed the polar motion time series spectrum based on fast Fourier transform (FFT), and our findings show that the Chandler motion was approximately 426 days and that the annual motion was about 360 days. In addition, the results also validate the presence of a weaker retrograde oscillation with an amplitude of about 3.5 mas. This paper proposes a hybrid prediction model that combines convolutional neural network (CNN) and long short-term memory (LSTM) neural network: the CNN–LSTM model. The advantages can be attributed to the CNN’s ability to extract and optimize features related to polar motion series, and the LSTM’s ability to make medium- to long-term predictions based on historical time series. Compared with Bulletin A, the prediction accuracies of PMX and PMY are improved by 42% and 13%, respectively. Notably, the hybrid CNN–LSTM model can effectively improve the accuracy of medium- and long-term polar motion prediction.

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Zhang, Wenjie, Pin Wu, Yan Peng, and Dongke Liu. "Roll Motion Prediction of Unmanned Surface Vehicle Based on Coupled CNN and LSTM." Future Internet 11, no. 11 (November 18, 2019): 243. http://dx.doi.org/10.3390/fi11110243.

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The prediction of roll motion in unmanned surface vehicles (USVs) is vital for marine safety and the efficiency of USV operations. However, the USV roll motion at sea is a complex time-varying nonlinear and non-stationary dynamic system, which varies with time-varying environmental disturbances as well as various sailing conditions. The conventional methods have the disadvantages of low accuracy, poor robustness, and insufficient practical application ability. The rise of deep learning provides new opportunities for USV motion modeling and prediction. In this paper, a data-driven neural network model is constructed by combining a convolution neural network (CNN) with long short-term memory (LSTM) for USV roll motion prediction. The CNN is used to extract spatially relevant and local time series features of the USV sensor data. The LSTM layer is exploited to reflect the long-term movement process of the USV and predict roll motion for the next moment. The fully connected layer is utilized to decode the LSTM output and calculate the final prediction results. The effectiveness of the proposed model was proved using USV roll motion prediction experiments based on two case studies from “JingHai-VI” and “JingHai-III” USVS of Shanghai University. Experimental results on a real data set indicated that our proposed model obviously outperformed the state-of-the-art methods.

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Kumar, Y. V. Satish, Madhujit Mukhopadhyay, and Tanmay Sarkar. "Long-Term Structural Analysis of 3-D Ship Structures Using a New Stiffened Plate Element." Journal of Offshore Mechanics and Arctic Engineering 123, no. 1 (October 27, 2000): 29–37. http://dx.doi.org/10.1115/1.1336800.

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The paper presents the development of a technique for long-term 3-D structural analysis of the complete ship using a new stiffened plate element. The 3-D analysis involves the 3-D finite element modeling of the vessel and evaluation of hydrodynamic pressures using the 3-D linear diffraction theory. The elegance of the present stiffened plate element is that it can accommodate any number of arbitrarily oriented stiffeners within the plate element. Thus, the formulation obviates the use of mesh lines strictly along the longitudinals and transverses of the ship, which minimizes the required number of degrees of freedom of the finite element model of the complete vessel and reduces the computational effort considerably. The long-term prediction for the worst hydrodynamic pressures in the lifetime of the ship is carried out using the ISSC spectrum and scatter tables of Indian coastal waters. As an example problem, long-term structural analysis of a mini-bulk carrier in Indian coastal waters is presented in the paper. The long-term pressures are estimated with a probability of exceedence of 10−4 and principal stresses are calculated. It is shown that the present method provides a new, elegant, and economic technique for long-term 3-D structural analysis of the complete ship.

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Koketsu, Kazuki, and Hiroe Miyake. "Earthquake Observation and Strong Motion Seismology in Japan from 1975 to 2005." Journal of Disaster Research 1, no. 3 (December 1, 2006): 407–14. http://dx.doi.org/10.20965/jdr.2006.p0407.

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We review earthquake observation and strong motion seismology in Japan over the three decades starting in 1975. Preceding the 1995 Kobe earthquake, earthquake prediction research programs played an important role in earthquake observation research. The devastating damage from this earthquake, however, forced a change in emphasis from empirical shortterm prediction to long-term forecasting of earthquakes and the prediction of strong ground motion. Nationwide observation networks were set up, and progress in strong motion seismology was applied to projects of national seismic hazard maps. The next disastrous earthquake may even force their reexamination in the near future.

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Ren, Bin, Zhiqiang Zhang, Chi Zhang, and Silu Chen. "Motion Trajectories Prediction of Lower Limb Exoskeleton Based on Long Short-Term Memory (LSTM) Networks." Actuators 11, no. 3 (February 26, 2022): 73. http://dx.doi.org/10.3390/act11030073.

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A typical man–machine coupling system could provide the wearer a coordinated and assisted movement by the lower limb exoskeleton. The process of cooperative movement relies on the accurate perception of the wearer’s human movement information and the accurate planning and control of the joint movement of the lower limb exoskeleton. In this paper, a neural network and a Long-Short Term Memory (LSTM) machine learning model method is proposed to predict the actual movement trajectory of the human body’s lower limbs. Then a wearable joint angle measurement device was designed for gait trajectory prediction, which can be used for predictive control through machine learning methods. The experimental results show that the LSTM model can accurately predict the gait trajectory with an average mean square error. This method has practical significance for prediction the trajectory of the lower limb exoskeleton.

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Wang, Liang, Baicun Wang, Shuang Wei, Yifeng Hong, and Chuanxiang Zheng. "Prediction of long-term fatigue life of CFRP composite hydrogen storage vessel based on micromechanics of failure." Composites Part B: Engineering 97 (July 2016): 274–81. http://dx.doi.org/10.1016/j.compositesb.2016.05.012.

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Iwadate, T., J. Watanabe, and Y. Tanaka. "Prediction of the Remaining Life of High-Temperature/Pressure Reactors Made of Cr-Mo Steels." Journal of Pressure Vessel Technology 107, no. 3 (August 1, 1985): 230–38. http://dx.doi.org/10.1115/1.3264441.

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The Cr-Mo steels widely used for pressure vessels have a potential for temper embrittlement. Therefore, embrittlement during long-term service is expected, and it leads to the decrease of the critical flaw size of brittle fracture and/or to the reduction of the remaining life of a pressure vessel. In this paper, the concept of a remaining life prediction model is presented. And also, experimental data on the temper embrittlement and fracture toughness after long-term exposure and sub-critical crack growth rate, such as creep crack growth rate, were collected, and the data were analyzed for use in the remaining life prediction model. Examples of the remaining life prediction of a 2 1/4 Cr-1Mo steel hydrogenation reactor and a 1 1/4Cr-1/2Mo steel catalytic reforming reactor were calculated from the statistical data base.

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Guo, Xiao, and Jongmoo Choi. "Human Motion Prediction via Learning Local Structure Representations and Temporal Dependencies." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2580–87. http://dx.doi.org/10.1609/aaai.v33i01.33012580.

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Human motion prediction from motion capture data is a classical problem in the computer vision, and conventional methods take the holistic human body as input. These methods ignore the fact that, in various human activities, different body components (limbs and the torso) have distinctive characteristics in terms of the moving pattern. In this paper, we argue local representations on different body components should be learned separately and, based on such idea, propose a network, Skeleton Network (SkelNet), for long-term human motion prediction. Specifically, at each time-step, local structure representations of input (human body) are obtained via SkelNet’s branches of component-specific layers, then the shared layer uses local spatial representations to predict the future human pose. Our SkelNet is the first to use local structure representations for predicting the human motion. Then, for short-term human motion prediction, we propose the second network, named as Skeleton Temporal Network (Skel-TNet). Skel-TNet consists of three components: SkelNet and a Recurrent Neural Network, they have advantages in learning spatial and temporal dependencies for predicting human motion, respectively; a feed-forward network that outputs the final estimation. Our methods achieve promising results on the Human3.6M dataset and the CMU motion capture dataset, and the code is publicly available 1.

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Ma, Wenda, and Zhihong Wu. "Vehicle Motion Prediction Algorithm with Driving Intention Classification." Applied Sciences 12, no. 15 (July 25, 2022): 7443. http://dx.doi.org/10.3390/app12157443.

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The future motion prediction of vehicles in the front is widely valued for its great potential to improve a vehicle’s safety, fuel consumption, and efficiency. However, due to the uncertainty of a driver’s driving intentions and vehicle dynamics, future motion prediction faces great challenges. In order to break the bottleneck in the prediction of leading vehicle motion, this paper proposes a prediction idea of decoupling the prediction of leading vehicle motion into vertical vehicle speed prediction based on the Gaussian process regression algorithm and horizontal heading angle prediction based on the long short-term memory method, which combines the predicted vehicle speed and heading angle to derive the future trajectory of the leading vehicle. Moreover, we propose a prediction algorithm of the leading vehicle motion based on the combination of driving intention recognition and multimodel prediction results by the Fuzzy C-means algorithm, which tries to solve the problem of the unclear driving intention of the predicted object and the nonlinearity between the future motion of the vehicle and the environment. Finally, the algorithm is validated using real vehicle data, proving that it has high prediction accuracy.

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Fan, Hehe, Linchao Zhu, and Yi Yang. "Cubic LSTMs for Video Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8263–70. http://dx.doi.org/10.1609/aaai.v33i01.33018263.

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Predicting future frames in videos has become a promising direction of research for both computer vision and robot learning communities. The core of this problem involves moving object capture and future motion prediction. While object capture specifies which objects are moving in videos, motion prediction describes their future dynamics. Motivated by this analysis, we propose a Cubic Long Short-Term Memory (CubicLSTM) unit for video prediction. CubicLSTM consists of three branches, i.e., a spatial branch for capturing moving objects, a temporal branch for processing motions, and an output branch for combining the first two branches to generate predicted frames. Stacking multiple CubicLSTM units along the spatial branch and output branch, and then evolving along the temporal branch can form a cubic recurrent neural network (CubicRNN). Experiment shows that CubicRNN produces more accurate video predictions than prior methods on both synthetic and real-world datasets.

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Liu, Shaohua, Haibo Liu, Yisu Wang, Jingkai Sun, and Tianlu Mao. "MDST-DGCN: A Multilevel Dynamic Spatiotemporal Directed Graph Convolutional Network for Pedestrian Trajectory Prediction." Computational Intelligence and Neuroscience 2022 (April 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/4192367.

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Pedestrian trajectory prediction is an essential but challenging task. Social interactions between pedestrians have an immense impact on trajectories. A better way to model social interactions generally achieves a more accurate trajectory prediction. To comprehensively model the interactions between pedestrians, we propose a multilevel dynamic spatiotemporal digraph convolutional network (MDST-DGCN). It consists of three parts: a motion encoder to capture the pedestrians’ specific motion features, a multilevel dynamic spatiotemporal directed graph encoder (MDST-DGEN) to capture the social interaction features of multiple levels and adaptively fuse them, and a motion decoder to produce the future trajectories. Experimental results on public datasets demonstrate that our model achieves state-of-the-art results in both long-term and short-term predictions for both high-density and low-density crowds.

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Lim, Gilbert, Zhan Wei Lim, Dejiang Xu, Daniel S. W. Ting, Tien Yin Wong, Mong Li Lee, and Wynne Hsu. "Feature Isolation for Hypothesis Testing in Retinal Imaging: An Ischemic Stroke Prediction Case Study." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9510–15. http://dx.doi.org/10.1609/aaai.v33i01.33019510.

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Ischemic stroke is a leading cause of death and long-term disability that is difficult to predict reliably. Retinal fundus photography has been proposed for stroke risk assessment, due to its non-invasiveness and the similarity between retinal and cerebral microcirculations, with past studies claiming a correlation between venular caliber and stroke risk. However, it may be that other retinal features are more appropriate. In this paper, extensive experiments with deep learning on six retinal datasets are described. Feature isolation involving segmented vascular tree images is applied to establish the effectiveness of vessel caliber and shape alone for stroke classification, and dataset ablation is applied to investigate model generalizability on unseen sources. The results suggest that vessel caliber and shape could be indicative of ischemic stroke, and sourcespecific features could influence model performance.

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Wang, Yuchao, Hui Wang, Dexin Zou, and Huixuan Fu. "Ship Roll Prediction Algorithm Based on Bi-LSTM-TPA Combined Model." Journal of Marine Science and Engineering 9, no. 4 (April 6, 2021): 387. http://dx.doi.org/10.3390/jmse9040387.

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When ships sail on the sea, the changes of ship motion attitude presents the characteristics of nonlinearity and high randomness. Aiming at the problem of low accuracy of ship roll angle prediction by traditional prediction algorithms and single neural network model, a ship roll angle prediction method based on bidirectional long short-term memory network (Bi-LSTM) and temporal pattern attention mechanism (TPA) combined deep learning model is proposed. Bidirectional long short-term memory network extracts time features from the forward and reverse of the ship roll angle time series, and temporal pattern attention mechanism extracts the time patterns from the deep features of a bidirectional long short-term memory network output state that are beneficial to ship roll angle prediction, ignore other features that contribute less to the prediction. The experimental results of real ship data show that the proposed Bi-LSTM-TPA combined model has a significant reduction in MAPE, MAE, and MSE compared with the LSTM model and the SVM model, which verifies the effectiveness of the proposed algorithm.

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Hu, Xiong, Boyi Zhang, and Gang Tang. "Research on Ship Motion Prediction Algorithm Based on Dual-Pass Long Short-Term Memory Neural Network." IEEE Access 9 (2021): 28429–38. http://dx.doi.org/10.1109/access.2021.3055253.

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Jeong, Yonghwan, and Kyongsu Yi. "Bidirectional Long Shot-Term Memory-Based Interactive Motion Prediction of Cut-In Vehicles in Urban Environments." IEEE Access 8 (2020): 106183–97. http://dx.doi.org/10.1109/access.2020.2994929.

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Tamaru, Hiroto, Kenichi Fujii, Masashi Fukunaga, Takahiro Imanaka, Kojiro Miki, Tetsuo Horimatsu, Machiko Nishimura, et al. "Impact of spotty calcification on long-term prediction of future revascularization: a prospective three-vessel intravascular ultrasound study." Heart and Vessels 31, no. 6 (May 12, 2015): 881–89. http://dx.doi.org/10.1007/s00380-015-0687-8.

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Jang, Da-Un, and Joo-Sung Kim. "Analysis and Prediction Methods of Marine Accident Patterns related to Vessel Traffic using Long Short-Term Memory Networks." Journal of the Korean Society of Marine Environment and Safety 28, no. 5 (August 30, 2022): 780–90. http://dx.doi.org/10.7837/kosomes.2022.28.5.780.

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Li, Xiao Min, Hai Yan Guo, and Peng Li. "Combination of Random Waves and Vessel Motions Effects on the Fatigue Damage of Top Tensioned Riser." Applied Mechanics and Materials 90-93 (September 2011): 2659–64. http://dx.doi.org/10.4028/www.scientific.net/amm.90-93.2659.

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As one of the main configurations of the riser, top tensioned riser(TTR) encounters harsh environment in its whole service life. In order to ensure that the riser will fulfil its intended functions, a fatigue assessment should be carried out for each representative riser, which is subjected to dynamic fatigue loading. The fatigue life of TTR under the combination excitation of random waves, current and vessel motion is analyzed in this paper. The long-term stress histories of the riser are calculated and the mean stresses, the number of stress cycles and amplitudes are determined by rain flow counting method. The Palmgren-Miner rule for cumulative damage theory with a specified S–N curve is used to estimate the fatigue life of the riser. The corresponding numerical programs which can be used to calculate the response and fatigue life of the riser are compiled. The significant influences of internal flow velocities and low frequency motion of the vessel are analyzed in detail.

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Journal articles on the topic 'Long term prediction of vessel motion'

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