Ship motion attitude prediction model based on IWOA-TCN-Attention

Wang, Sheng; Deng, Liwei; Jia, Mingqi; Xu, Jiazhong

doi:10.1016/j.oceaneng.2023.113911

Cited by 33 publications

(6 citation statements)

References 19 publications

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“…Paper [35] introduces the improved whale optimization algorithm (IWOA), which continuously optimizes the nonlinear weights of the Elman neural network during the iterative process. Paper [36,37] uses an improved whale algorithm to optimize the hidden layer of the LSTM network, the number of neurons in the first LSTM layer, etc. Paper [38] obtained a proposed IWOA optimization algorithm by improving the whale optimization algorithm (WOA).…”

Section: Source Vantage Drawbackmentioning

confidence: 99%

Load Day-Ahead Automatic Generation Control Reserve Capacity Demand Prediction Based on the Attention-BiLSTM Network Model Optimized by Improved Whale Algorithm

Li,

Liang

et al. 2024

Energies

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Load forecasting is a research hotspot in academia; in the context of new power systems, the prediction and determination of load reserve capacity is also important. In order to adapt to new forms of power systems, a day-ahead automatic generation control (AGC) reserve capacity demand prediction method based on the Fourier transform and the attention mechanism combined with a bidirectional long and short-term memory neural network model (Attention-BiLSTM) optimized by an improved whale optimization algorithm (IWOA) is proposed. Firstly, based on the response time, Fourier transform is used to refine the distinction between various types of load reserve demand, and the power of the AGC reserve band is calculated using Parseval’s theorem to obtain the reserve capacity demand sequence. The maximum mutual information coefficient method is used to explore the relevant influencing factors of the AGC reserve sequence concerning the data characteristics of the AGC reserve sequence. Then, the historical daily AGC reserve demand sequences with relevant features are input into the Attention-BiLSTM prediction model, and the improved whale algorithm is used to automatically find the optimal hyperparameters to obtain better prediction results. Finally, the arithmetic simulation results show that the model proposed in this paper has the best prediction performance with the upper (0.8810) and lower (0.6651) bounds of the coefficient of determination (R2) higher than the other models, and it has the smallest mean absolute percentage error (MAPE) and root mean square error (RMSE).

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Section: Source Vantage Drawbackmentioning

confidence: 99%

Load Day-Ahead Automatic Generation Control Reserve Capacity Demand Prediction Based on the Attention-BiLSTM Network Model Optimized by Improved Whale Algorithm

Li,

Liang

et al. 2024

Energies

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“…These models are based on the interrelationships of data at different times and aim to explore their patterns of change. Utilizing historical observational data, these models can effectively make predictions and inferences [14]. Therefore, knowing only the historical data of network security posture allows us to build a time-series model to estimate future network security conditions.…”

Section: B Related Workmentioning

confidence: 99%

Network Security Situation Prediction Model Based on VMD Decomposition and DWOA Optimized BiGRU-ATTN Neural Network

Zhang,

Fu,

2023

IEEE Access

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The widespread adoption of Internet-of-Things (IoT) devices has resulted in a comprehensive transformation of human life. However, the network security challenges posed by the IoT devices have become increasingly severe, necessitating the implementation of effective security mechanisms. Network security situational awareness enables an effective network state prediction for better formulation of network security defense strategies. Existing network security situational prediction methods are typically constrained by situational sequence data, especially those sequences with a high degree of non-stationarity, leading to unstable predictions and low performance. Moreover, in real-world application scenarios, the network security situational sequences are often highly non-stationary. To address these challenges, we introduce a novel hybrid prediction model named Variational Mode Decomposition (VMD) -Dynamic Whale Optimization Algorithm (DWOA) -Bidirectional Gated Recurrent Unit (BiGRU) -Attention Mechanism (ATTN). The proposed model integrates VMD, BiGRU, ATTN, and DWOA. Initially, network security situational awareness sequences are processed using VMD to decompose them into a series of subsequences, thus reducing the non-stationarity of the original sequences. Subsequently, an enhanced DWOA optimization algorithm is introduced for tuning the hyperparameters of the BiGRU-ATTN network. Ultimately, BiGRU-ATTN is employed to predict each of these subsequences, which are then aggregated to yield the final network security situational prediction value. When compared with several existing methods on public network security datasets, the proposed VMD-DWOA-BiGRU-ATTN method demonstrated an improvement in the R^2 values ranging from 6.34% to 52.61%. These results substantiate that the model significantly enhances predictive performance.

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“…Moving on to ship motion attitude prediction, Zhang et al Zhang et al (2023a) present a model called IWOA-TCN-Attention. This model utilizes an improved whale optimization algorithm (IWOA), temporal convolutional networks, and an attention mechanism to address the non-stationary, nonlinear, and stochastic characteristics of ship motion.…”

Section: Ta B L E 1 3 Imu Datasetsmentioning

confidence: 99%

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

Asgharpoor

Sabour

2023

Preprint

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This review article presents recent advancements in deep learning methodologies and applications for autonomous nav- igation. It analyzes state-of-the-art deep learning frameworks used in tasks like signal processing, attitude estimation, ob- stacle detection, scene perception, and path planning. The implementation and testing methodologies of these approaches are critically evaluated, highlighting their strengths, limita- tions, and areas for further development. The review em- phasizes the interdisciplinary nature of autonomous naviga- tion and addresses challenges posed by dynamic and com- plex environments, uncertainty, and obstacles. With a par- ticular focus on mobile robots, self-driving cars, unmanned aerial vehicles, and space vehicles to underscore the impor- tance of navigation in these domains. By synthesizing find- ings from multiple studies, the review aims to be a valuable resource for researchers and practitioners, contributing to the advancement of novel approaches. Key aspects covered include the classification of deep learning applications, re- cent advancements in methods, general applications in the field, innovations, challenges, and limitations associated with learning-based navigation systems. This review also explores current research trends and future directions in the field. This extensive overview, initiated in 2020, provides a valuable re- source for researchers of all levels, from seasoned experts to newcomers. Its main purpose is to streamline the process of identifying, evaluating, and interpreting relevant research, ultimately contributing to the progress and development of autonomous navigation technologies.

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Ship motion attitude prediction model based on IWOA-TCN-Attention

Cited by 33 publications

References 19 publications

Load Day-Ahead Automatic Generation Control Reserve Capacity Demand Prediction Based on the Attention-BiLSTM Network Model Optimized by Improved Whale Algorithm

Load Day-Ahead Automatic Generation Control Reserve Capacity Demand Prediction Based on the Attention-BiLSTM Network Model Optimized by Improved Whale Algorithm

Network Security Situation Prediction Model Based on VMD Decomposition and DWOA Optimized BiGRU-ATTN Neural Network

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

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