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Yahui Hu, Jiaqi Yan, Ertai Cao, Yimeng Yu, Haiming Tian and Heyuan Huang
The statistical analysis of civil aircraft accidents reveals that the highest incidence of mishaps occurs during the approach and landing stages. Predominantly, these accidents are marked by abnormal energy states, leading to critical situations like sta...
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Rui Zhou, Jiyin Cao, Gang Zhang, Xia Yang and Xinyu Wang
High heat load on diesel engines is a main cause of ship failure, which can lead to ship downtime and pose a risk to personal safety and the environment. As such, predictive detection and maintenance measures are highly important. During the operation of...
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Weiqing Zhuang and Yongbo Cao
In the previous research on traffic flow prediction models, most of the models mainly studied the time series of traffic flow, and the spatial correlation of traffic flow was not fully considered. To solve this problem, this paper proposes a method to pr...
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Siyao Yan, Jing Zhang, Mosharaf Md Parvej and Tianchi Zhang
This paper proposes a novel Sea Drift Trajectory Prediction method based on the Quantum Convolutional Long Short-Term Memory (QCNN-LSTM) model. Accurately predicting sea drift trajectories is a challenging task, as they are influenced by various complex ...
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Wenqi Cheng and Baigang Mi
A new high-efficiency method based on a particle swarm optimization and long short-term memory network is proposed in this study to predict the aerodynamic forces in an unsteady state. Based on the predicted aerodynamic forces, the dynamic derivative is ...
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Muhammad Rifqi Maarif, Arif Rahman Saleh, Muhammad Habibi, Norma Latif Fitriyani and Muhammad Syafrudin
The accurate forecasting of energy consumption is essential for companies, primarily for planning energy procurement. An overestimated or underestimated forecasting value may lead to inefficient energy usage. Inefficient energy usage could also lead to f...
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Shuo Zhang, Emma Robinson and Malabika Basu
The operation and maintenance (O&M) issues of offshore wind turbines (WTs) are more challenging because of the harsh operational environment and hard accessibility. As sudden component failures within WTs bring about durable downtimes and significant...
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Yuan Chen and Abdul Q. M. Khaliq
The Lee?Carter model could be considered as one of the most important mortality prediction models among stochastic models in the field of mortality. With the recent developments of machine learning and deep learning, many studies have applied deep learni...
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Dianrui Wang, Junhe Wan, Yue Shen, Ping Qin and Bo He
An accurate mathematical model is a basis for controlling and estimating the state of an Autonomous underwater vehicle (AUV) system, so how to improve its accuracy is a fundamental problem in the field of automatic control. However, AUV systems are compl...
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Ching-Hsue Cheng, Ming-Chi Tsai and Yi-Chen Cheng
Public transportation systems are an effective way to reduce traffic congestion, air pollution, and energy consumption. Today, smartcard technology is used to shorten the time spent boarding/exiting buses and other types of public transportation; however...
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Tae Ho Kwon, Jaehwan Kim, Ki-Tae Park and Kyu-San Jung
The proposed methodology creates LSTM-based carbonation models using the data from existing bridges. The proposed methodology and results can help bridge managers to conduct preventive maintenance.
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Pengyuan Wang, Xiao Huang, Joseph Mango, Di Zhang, Dong Xu and Xiang Li
Studying population prediction under micro-spatiotemporal granularity is of great significance for modern and refined urban traffic management and emergency response to disasters. Existing population studies are mostly based on census and statistical yea...
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Xiaoyu Zhang, Yongqing Li, Song Gao and Peng Ren
This paper investigates the possibility of using machine learning technology to correct wave height series numerical predictions. This is done by incorporating numerical predictions into long short-term memory (LSTM). Specifically, a novel ocean wave hei...
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Shurui Fan, Dongxia Hao, Yu Feng, Kewen Xia and Wenbiao Yang
Accurate and reliable air quality predictions are critical to the ecological environment and public health. For the traditional model fails to make full use of the high and low frequency information obtained after wavelet decomposition, which easily lead...
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Zhen Li, Tao Tang and Chunhai Gao
The automatic train operation system is a significant component of the intelligent railway transportation. As a fundamental problem, the construction of the train dynamic model has been extensively researched using parametric approaches. The parametric b...
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Liwen Xu, Chengdong Li, Xiuying Xie and Guiqing Zhang
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Zhangping Wei and Hai Cong Nguyen
This study presents an encoder?decoder neural network model to forecast storm surges on the US North Atlantic Coast. The proposed multivariate time-series forecast model consists of two long short-term memory (LSTM) models. The first LSTM model encodes t...
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Zhi Yung Tay, Januwar Hadi, Favian Chow, De Jin Loh and Dimitrios Konovessis
The global greenhouse gas emitted from shipping activities is one of the factors contributing to global warming; thus, there is an urgent need to mitigate the adverse effect of climate change. One of the key strategies is to build a vibrant maritime indu...
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Yuchao Wang, Hui Wang, Dexin Zou and Huixuan Fu
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 n...
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Tianao Qin, Ruixin Chen, Rufu Qin and Yang Yu
Time series prediction is an effective tool for marine scientific research. The Hierarchical Temporal Memory (HTM) model has advantages over traditional recurrent neural network (RNN)-based models due to its online learning and prediction capabilities. G...
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