Estimation of Navigation Mark Floating Based on Fractional-Order Gradient Descent with Momentum for RBF Neural Network

Fang, Qionglin

doi:10.1155/2021/6681651

Cited by 4 publications

(2 citation statements)

References 17 publications

(17 reference statements)

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“…Xu et al constructed an improved complex-valued neural network that uses latitude and longitude as inputs to predict the drifting position of anchored buoys [27]. Fang and Jauregui-Correa designed a fractional step decline model of a radial basis function (RBF) neural network with meteorological data, hydrological data, latitude, and longitude as inputs to predict the position of navigation beacons and obtained prediction results of high accuracy [28]. Moreover, in a recent study on trajectory prediction at sea, on one hand, it is found that hybrid multi-layer ANN-based models achieved better performance than a single-layer model [29][30][31].…”

Section: Related Workmentioning

confidence: 99%

Developing an Artificial Intelligence-Based Method for Predicting the Trajectory of Surface Drifting Buoys Using a Hybrid Multi-Layer Neural Network Model

Song,

Hu,

Liu

et al. 2024

JMSE

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Accurately predicting the long-term trajectory of a surface drifting buoy (SDB) is challenging. This paper proposes a promising solution to the SDB trajectory prediction based on artificial intelligence (AI) technologies. Initially, a scalable mathematical model for trajectory prediction is developed, transforming the challenge of predicting trajectory points into predicting velocities in eastward and northward directions. Subsequently, a four-layer trajectory prediction calculation framework (FLTPCF) is established, outlining a complete workflow for the real-time online training of marine environment data and SDBs’ trajectory prediction. Thirdly, for facilitating accurate long-term trajectory prediction, a hybrid artificial neural network trajectory prediction model, named CNN–BiGRU–Attention, integrates a Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and Attention mechanism (AM), tuned for spatiotemporal feature extraction and extended time-series reasoning. Extensive experiments, including ablation studies, comparative analyses with state-of-the-art models like BiLSTM and Transformer, evaluations against numerical methods, and adaptability tests, were conducted for justifying the CNN–BiGRU–Attention model. The results highlight the CNN–BiGRU–Attention model’s excellent convergence, accuracy, and generalization capabilities in predicting 24, 48, and 72 h trajectories for SDBs with varying drogue statuses and under different sea conditions. This work has great potential to promote the intelligent degree of marine environmental monitoring.

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Section: Related Workmentioning

confidence: 99%

Developing an Artificial Intelligence-Based Method for Predicting the Trajectory of Surface Drifting Buoys Using a Hybrid Multi-Layer Neural Network Model

Song,

Hu,

Liu

et al. 2024

JMSE

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“…Li et al [ 12 ] used a BP neural network (BPNN) to predict the drift position of buoy T10 in the Tonggu waterway. Fang [ 13 ] established a fractional-order gradient descent with the momentum RBF neural network algorithm to predict the drift value of buoy #4 in Xiamen port. Xu [ 14 ] proposed an improved complex-valued neural network to implement the drift position prediction of buoys 11A and No.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Drift Prediction of Multi-Functional Buoys in Inland Rivers Based on Deep Learning

Zeng

2022

Sensors

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The multi-functional buoy is an important facility for assisting the navigation of inland waterway ships. Therefore, real-time tracking of its position is an essential process to ensure the safety of ship navigation. Aiming at the problem of the low accuracy of multi-functional buoy drift prediction, an integrated deep learning model incorporating the attention mechanism and ResNet-GRU (RGA) to predict short-term drift values of buoys is proposed. The model has the strong feature expression capability of ResNet and the temporal memory capability of GRU, and the attention mechanism can capture important information adaptively, which can solve the nonlinear time series drift prediction problem well. In this paper, the data collected from multi-functional buoy #4 at Nantong anchorage No. 2 in the Yangtze River waters in China were studied as an example, and first linear interpolation was used for filling in missing values; then, input variables were selected based on Pearson correlation analysis, and finally, the model structure was designed for training and testing. The experimental results show that the mean square error, mean absolute error, root mean square error and mean percentage error of the RGA model on the test set are 5.113036, 1.609969, 2.261202 and 15.575886, respectively, which are significantly better than other models. This study provides a new idea for predicting the short-term drift of multi-functional buoys, which is helpful for their tracking and management.

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Research on image enhancement algorithm for the monitoring system in coal mine hoist

Zhang,

Zuo,

Wang

et al. 2023

Measurement and Control

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As the mine hoist monitors video images with poor light, low brightness, heavy dust, and low contrast, the monitoring video images are not conducive to monitoring. They cannot meet the needs of applications. Based on actual video surveillance data, this paper proposes a dark channel prior (DCP) method integrated with a guided image filter video image enhancement algorithm. Firstly, we analyzed the characteristics of the mine hoist system’s video images. Then, the DCP technique was used to enhance the video images. A guided image filter algorithm was introduced to ensure that the video has more clarity and visual impact. Comparing the suggested method to the other four algorithms, it performed better both subjectively and objectively than the algorithms mentioned above. Experiments demonstrate that the proposed technique can successfully improve the entire clarity and contrast of video images while avoiding the over-enhancement of bright areas close to the light source, meeting the practical application requirements of video surveillance.

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Estimation of Navigation Mark Floating Based on Fractional-Order Gradient Descent with Momentum for RBF Neural Network

Cited by 4 publications

References 17 publications

Developing an Artificial Intelligence-Based Method for Predicting the Trajectory of Surface Drifting Buoys Using a Hybrid Multi-Layer Neural Network Model

Developing an Artificial Intelligence-Based Method for Predicting the Trajectory of Surface Drifting Buoys Using a Hybrid Multi-Layer Neural Network Model

Short-Term Drift Prediction of Multi-Functional Buoys in Inland Rivers Based on Deep Learning

Research on image enhancement algorithm for the monitoring system in coal mine hoist

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