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

Author: Grafiati

Published: 10 March 2023

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1

Miller, Paul H. "Fatigue Prediction Verification of Fiberglass Hulls." Marine Technology and SNAME News 38, no. 04 (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 havi

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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 (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(conges

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Konon, N. M. "ANALYTICAL MODELLING OF SEAKEEPING QUALITIES OF CONTAINER VESSEL." Shipping & Navigation 30, no. 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 wav

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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 (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%

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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 (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 (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 (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 th

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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 (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

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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 (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 (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 (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 (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 (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 (2019): 6865–76. http://dx.doi.org/10.1109/tgrs.2019.2909057.

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18

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 (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 co

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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 (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

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20

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 (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

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21

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 (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, transfo

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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 (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

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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 (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 di

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24

Palmieri, Luigi, Rudenko Andrey, Jim Mainprice, et al. "Guest Editorial: Introduction to the Special Issue on Long-Term Human Motion Prediction." IEEE Robotics and Automation Letters 6, no. 3 (2021): 5613–17. http://dx.doi.org/10.1109/lra.2021.3077964.

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25

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 (2017): 333–43. http://dx.doi.org/10.1007/s00190-017-1065-3.

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26

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 (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 mode

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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 (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 fact

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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 traject

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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 le

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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 (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

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Li, Chong-hui, Zhang-lei Chen, Xin-jiang Liu, et al. "Adaptively robust filtering algorithm for maritime celestial navigation." Journal of Navigation 75, no. 1 (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

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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 (2019): 085010. http://dx.doi.org/10.1088/1361-6560/ab13fa.

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33

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 (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 0.18

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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 (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 networ

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35

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 (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 numbe

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36

Koketsu, Kazuki, and Hiroe Miyake. "Earthquake Observation and Strong Motion Seismology in Japan from 1975 to 2005." Journal of Disaster Research 1, no. 3 (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 ne

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37

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 (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 p

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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 (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

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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 structur

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Ma, Wenda, and Zhihong Wu. "Vehicle Motion Prediction Algorithm with Driving Intention Classification." Applied Sciences 12, no. 15 (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 bas

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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 bra

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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 inte

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Lim, Gilbert, Zhan Wei Lim, Dejiang Xu, et al. "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 estab

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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 (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 tempor

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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, 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 (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 (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

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