bluuebunny/crossref_metadata_2025_split huggingface dataset:
Spatiotemporal Characteristics of the Evaporation Duct in the Kuroshio Extension
Authors: Gao Yuan , Zhang Yongchui , Hong Mei , Wang Ning , Hu Wangjiang , Yan Hengqian , Chen Zhentao 
Published: March 2024

Abstract
The evaporation duct over the sea affects the propagation of electromagnetic waves, impacts the performance of electromagnetic systems accordingly, such as the shipborne radars. In order to reveal the spatiotemporal variations of the evaporation duct in the Kuroshio Extension (KE) region, where is one of the most intense air-sea interaction regions, the study utilized a 30-year ERA5 reanalysis dataset (from 1993 to 2022) and conducted statistical analysis on the monthly, seasonal variations and spatial distribution characteristics of the evaporation duct based on the NPS evaporation duct diagnostic model. The results enrich the database of maritime evaporation duct characteristics and improve the resolution and accuracy compared to previous studies.
Experimental Investigation on a convex Mode-2 internal solitary wave and its induced flow field
Authors: Wang Shaodong , Du Hui , Wei Gang , Xu Junnan , Peng Pai , Xuan Pu , Wang Guanjing 
Published: March 2024

Abstract
The second mode internal solitary wave (Mode-2 ISW) and its wave-induced flow field structure are investigated by conducted physical experiments in a large stratified fluid flume. The Mode-2 ISW is generated by the method of the gravity collapse of fluid, and the measurements of its wave-flow structure are obtained by the combined method of conductivity probe array and particle image velocimetry (PIV). The flow field structure and its influencing factors were analysed quantitatively. The results reveal the characteristic of the Mode-2 ISW waveform and density field and the effect of the wave amplitude on the flow and shear characteristics of induced flow field.
Risk assessment of wave hazard in Guangdong Province
Authors: Jiang Lifang , Xu Weiming , Luo Jun , Hu Jinlei , Yin Yi , Xu Boliang 
Published: March 2024

Abstract
In order to improve the prediction ability of wave hazard levels in the ocean under the jurisdiction of Guangdong Province, this paper uses the wave simulation data to evaluate the wave hazard index of the ocean under the jurisdiction of Guangdong Province, and analyses the risk assessment analysis of wave hazards in the Guangdong Province, which provides data support for future wave disaster prevention and mitigation, coastal development, and spatial planning in Guangdong Province. The significant wave height of the 50-year return period in the coastal areas of Guangdong Province is generally distributed within 9 meters of the territorial sea baseline. The intensity of the return period gradually increases with the distance from the mainland and decreases with the decrease of water depth. In the past 30 years, the sea areas under the jurisdiction of the Guangdong Province are mainly dominated by class III and IV wave heights, with class IV being higher than class III. The frequency of Class IV wave heights in the mainly marine area is more than 90%. The risk level of wave disasters in the coastal waters of Guangdong Province is mainly level IV. However, the risk level of wave disasters in the waters under the jurisdiction of Guangdong Province is mainly Level I.
Response of tidal current to the construction of Yangshan Deepwater Harbor, Shanghai, China
Authors: Mao Wei , Ge Jianzhong , Guo Wenyun 
Published: March 2024

Abstract
A high-resolution ocean model for the Yangshan Deepwater Harbor (YDH), Shanghai, China was established based on FVCOM. Changes of tidal current and water fluxes due the construction of YDH was then investigated thoroughly. Results show that the construction of YDH reduced current velocities in the harbor basin, but led to significant increase of tidal current in the channels.
The Effect of Vehicles Attitude Angle Error on Magnetic Compass Heading Estimation
Authors: Li Ziyaun , Zhang Yanmin , Yang Wentie 
Published: March 2024

Abstract
The three-component magnetic sensor is used to measure the heading of the vehicle. Under working, the magnetic sensor be rotated to the horizontal plane according to the attitude angle of the vehicle, and then the heading estimation is performed. However, the vehicle’s attitude angle measurement errors affect the accuracy of the magnetic sensor measurement’s east and north components and then lead to a false heading result. Based on the relation of magnetic field measurement error and magnetic heading error, the effect model of geomagnetic measurement error on magnetic heading error is derived in this paper. Finally, the characteristics of attitude angle errors on magnetic heading are verified by experiments, and the results show that particular angles can avoid the magnetic compass’s effect by the vehicle’s attitude angle error.
Seawater Dual Parametric Fiber Optic Temperature and Pressure Sensors Demodulated by Machine Learning Method
Authors: Chen Yuren , Yu Yang , Xu Yan , Wang Zhencheng , Li Ning , Xu Wenjie , Zhang Zhenrong 
Published: March 2024

Abstract
By employing polydimethylsiloxane (PDMS) characterized by elevated thermo-optical and elastic-optical coefficients for encapsulating an optical microfiber coupler integrated with a sagnac loop (OMCSL) structure, which exhibits large abrupt field characteristics, a fiber optic sensor capable of simultaneously measuring seawater temperature and pressure can be created. Nevertheless, the utilization of the traditional sensitivity matrix method (SMM) for demodulating the sensor led to unstable and considerably erroneous demodulation results. To enhance the accuracy of the demodulation process, this paper investigates and employs a machine learning method (MLM) for sensor demodulation. This paper centers on the investigation and application of MLM for sensor demodulation. The experimental results exhibit a significant decrease in demodulation error attained via MLM when contrasted with the traditional SMM.
Marine Compass Data Quality Control Method Based on Robust Surface Fitting
Authors: Sang Kaiwei , Kuang Cuilin , Duan Chufeng , Zhang Haonan , Yang Baocai 
Published: March 2024

Abstract
Towed streamer positioning is a key step in offshore seismic exploration, and its positioning accuracy directly affects the imaging quality of subsequent seismic data. Accurate and clean magnetic compass observations are an important guarantee for high-precision towed streamer positioning. However, the existing compass data quality control methods have the problem of insufficient utilization of spatial distribution prior information, which heavily relies on mathematical statistical characteristics of the data while neglecting the crucial factors of streamer shape, bringing about an inadequate elimination of gross errors. In this work, a compass data quality control algorithm based on robust surface fitting is proposed to improve the accuracy of positioning. Firstly, the towed streamer positioning algorithm and the prior information of the spatial structure distribution of the compass are introduced. Secondly, the robust surface fitting to control the quality of compass data is proposed, and the specific process of the algorithm is given. Lastly, the effectiveness of the algorithm is verified by simulation and measured data. The results of simulation experiments indicate that compared with the traditional single-streamer threshold robust algorithm, the new algorithm has a significant effect on the quality control of compass data, and the positioning accuracy of the towed streamer is significantly improved. The positioning precision of along-line direction is improved by 52.1%, and that of the across-line direction by 52.4%. These demonstrate that the proposed new algorithm is effective for detecting and eliminating gross errors in compass observations in real-time operation scenarios, improving the stability of towed streamer shape calculation, which can better ensure the smooth progress of offshore towed streamer seismic exploration operations.
Storm surge forecasting based on physics-informed neural networks in the Bohai Sea
Authors: Fu Cifu , Xiong Jie , Yu Fujiang 
Published: March 2024

Abstract
Physics-informed neural networks (PINN), as a new method of integrating artificial neural networks (ANN) and physical laws, have been considered and applied in the fields of ocean forecasting and ocean research. In this paper, the simplified two-dimensional (2D) storm surge governing equation is introduced into an ANN to establish a PINN-based storm surge forecast model. The numerical simulation results of 14 storm surge events in the Bohai Sea are selected as the PINN training set, and 6.3% of the training set data are randomly selected to reconstruct the storm surge field information. The storm surge reconstructed at each tide station is nearly identical to the storm surge curve simulated by the numerical model, with the root mean square error (RMSE) less than 0.12 m and absolute error of maximum storm surge less than 0.2 m. The analysis of the storm surge field at key moments (storm surge height lager than 1 m) shows that the difference in storm surge field between the PINN reconstruction and the numerical model is generally less than 0.4 m. Two storm surge events in the Bohai Sea are selected as forecast cases, and the same network structure, parameters, and storm surge data assimilation scheme are used for predictions by the ANN, PINN, and numerical model. The results show that compared to the ANN and numerical models, the average relative error of the maximum storm surge predicted by the PINN is reduced by approximately 25%, which significantly improves the forecast accuracy, therefore, the PINN is suitable for storm surge forecasting and research due to its advantages in small sample data training and strong physical meaning.
Research on Underdriven HOV Fault Diagnosis based on Physical Attention-Multidimensional Recurrent Neural Networks
Authors: Zhao Yuchen , Hu Haolong , Jiao Huifeng , Chu Zhenzhong 
Published: March 2024

Abstract
In recent years, underdriven human occupied vehicles (HOVs) have gained immense popularity due to their exceptional maneuverability and efficiency. However, the intricate nature of their propulsion systems, coupled with the harsh underwater environment, presents substantial challenges in terms of fault diagnosis. To address this issue, this study proposes a fault diagnosis approach that leverages the Physical Attention-Multidimensional Recurrent Neural Networks (PA-MDRNN) for underdriven HOV propulsion systems. The PA-MDRNN model is trained using a dataset comprising both normal and faulty HOV propulsion systems. Subsequently, the trained model is utilized for real-time identification and diagnosis of faults. Distinguishing itself from conventional data-driven models, our approach introduces a novel non-consistent attention mechanism based on fundamental computational physical properties. This mechanism operates within the framework of hybrid networks, where attention is computed on the multidimensional mapping results. This computation enhances the focus on desired features that adhere to traditional physical properties, thereby eliminating any training results that contradict established physical laws. Consequently, this approach not only excludes incongruous training outcomes but also reinforces the adherence to physical properties in the finalized training results.
Gaussian Process-Based Model Predictive Control for Autonomous Underwater Vehicles
Authors: Xuyu Shen , Gongwu Sun , Ying Mao , Xuanyu Hu , Zhenzhong Chu 
Published: March 2024

Abstract
Traditional MPC algorithms, assuming constant values, suffer from performance degradation caused by model mismatch. This paper addresses the enhancement of predictability in Autonomous Underwater Vehicles (AUVs) under uncertain disturbances and unknown system dynamics through the design of Model Predictive Controllers (MPCs). We propose a hybrid model that integrates a first-principles nominal model with a learning-based model utilizing Gaussian Processes (GPs). The algorithm addresses the problem of model mismatch in AUV motion control by constructing a precise GP model, which captures the dynamic characteristics of the process through the collection and learning of deviations between the reference model and the controlled system. Additionally, the GP model transforms stochastic constraints into deterministic convex constraints, enhancing safety guarantees in complex and challenging environments. The effectiveness of the proposed algorithm is demonstrated through two simulation examples.
Underwater target recognition algorithm based on improved convolutional neural network: YOLOV5-Improved
Authors: Wu Sheng , Zhang Chaolan , Liu Chao , Jin Yuqiang 
Published: March 2024

Abstract
This paper proposes an underwater identification method based on the YOLOV5-Improved model. The MobileOne module is added to the Backbone network of the previous model to improve the lightweight model and reduce the network complexity. The amount of parameters, the amount of calculation, and the inference time; the CA full-dimensional dynamic convolution module is embedded in the MP module of the previous model to focus on the main features, and better focus on the discriminative features of fishing nets, thereby improving the accuracy of fishing net recognition; in The adaptive spatial feature fusion module ASFF is introduced into the head network of the previous model to perform multi-scale feature fusion and remove useless background information to improve the detection and mining capabilities of smaller targets in complex backgrounds under natural conditions. The final experiment proved that the YOLOV5-Improved model has a good learning effect and faster convergence speed during the training process, and the experimental accuracy can reach 99.6%, which can better detect underwater fishing nets and provide assistance for safe navigation at sea.
Prediction of the sea surface temperature in Perhentian Islands by EMD-LSTM model
Authors: Jiang Lifang , Xu Weiming , Zeng Lihong , Xing Huibin , Yin Yi 
Published: March 2024

Abstract
The increase in the sea surface temperature (SST) is currently an important factor in the decline of coral reef ecosystems worldwide, and SST prediction has always been an important research direction in operational oceanography. This paper collects and analyses the buoy data deployed at Malaysian Perhentian islands and combines CRW data to develop coral bleaching warning products for Malaysian Perhentian islands. The Long Short-Term Memory Network(LSTM) and Empirical Mode Decomposition(EMD)-LSTM methods are used to research the SST prediction, and the differences between the two prediction methods were compared. The research results show that both the LSTM prediction model and the EMD-LSTM prediction model can accurately predict SST, with almost all prediction errors at 0.11°C and 0.01°C, respectively.
A scaled tank experiment for layered stochastic rough interface scattering reflection coefficient
Authors: Wang Mingkai , Gao Bo , Zhou Zhen , Pang Jie 
Published: March 2024

Abstract
The stochastic reflection coefficient of the seabed holds valuable acoustic information about the seabed, making it vital for seabed parameter detection. Therefore, it is crucial to achieve some improved results. In this study, we utilized the small-slope approximation method to obtain the fluctuation characteristics of the reflection coefficient for a layered medium with rough interface. Moreover, we addressed the changes in grazing angle by combining the stochastic rough interface scattering with the layered medium reflection. Initially, we present a simulation result, and subsequently, we conducted a scaled tank experiment to verify the variation characteristics of the reflection coefficient. These findings can serve as a theoretical reference for future seabed exploration.
Discrete Distribution Model of Ship Noise Source in Shipping Channel
Authors: Shan Yuanchun , Jiang Pengfei , Sun Junping , Yi Xuejuan , Lin Jianheng 
Published: March 2024

Abstract
Ship noise plays an important role in low-frequency ocean ambient noise. With the increasingly busy sea traffic, the randomness of the spatiotemporal distribution characteristics of ship noise is also increasing. However, most ships sail in a relatively fixed range in different sea areas. Most of the previous studies assumed that the ship noise sources in shipping channel satisfy a uniformly distribution, actually all kinds of large ships are not uniformly distributed in the shipping channel, and there are also many small boats distributed on the channel that contribute significantly to low-frequency ocean ambient noise. The channel within a certain sea area will be divided into different regions, and ship information (AIS) in different regions will be analyzed statistically in this paper. It is assumed that the ship noise sources in different channel regions are discrete point noise sources that obey a certain spatiotemporal statistical distribution, combined with the noise source level models of different types of ships to form a discrete source model of ship noise in shipping channel. Using the ray propagation theory considering the horizontal refraction of sound energy, the vertical distribution of the ship noise level in this sea area is calculated, and compared with the vertical noise level distribution of ship noise source uniform distribution model, the variation range of noise level vertical distribution of two models is close calculated noise level (such as maximum and minimum values) is consistent with the measured data. The discrete source model of ship noise established in this paper may provide a technical support for predicting low-frequency ambient noise characteristics near the channel.
Research on Noise Reduction Method of Underwater Acoustic Signal Based on CEEMDAN Decomposition-Improved Wavelet Threshold
Authors: Jiang Si Yuan , Zhang Xin Xin , Mo Yi , Huang Yan Jie 
Published: March 2024

Abstract
Due to the complex noise in the ocean environment, the signal-to-noise ratio of the hydrophone receiving signal is often low, making subsequent signal processing difficult. To solve this problem, this paper proposes using CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) decomposition algorithm combined with an improved wavelet threshold algorithm to process the signal, and obtain the reconstructed signal after denoising. In this method, the noise-containing signal is transformed by the function and decomposed into multiple natural mode components with frequencies ranging from high to low using the CEEMDAN algorithm. The correlation component and the non-correlation component are then determined using the cross-correlation function. The non-correlated compinents are denoised using the improved wavelet threshold method and the denoised signal is obtained by reconstructing the signal. Experimental results show that this method can improve the performance of underwater acoustic signal denoising.
Research on Volume Array Based on Curved Disk Transducers
Authors: Cheng Qihang , Yu Siyuan , Wu Hengfei 
Published: March 2024

Abstract
A curved disk transducer with a dual stack structure with a resonant frequency of 800Hz is used as the array element, and the mutual radiation effect between the transducer elements is utilized to form a densely arranged volume array. Based on the formation structure, establish corresponding finite element models in COMSOL software to simulate and analyse the acoustic performance of different volume arrays. Finally, prototype production and array acoustic performance testing were conducted, and the measured data basically matched the simulation results. The results show that compared to a single transducer, utilizing the mutual radiation effect between the transducer elements can effectively reduce the resonant frequency of the volumetric array and expand its operating bandwidth, while also improving the maximum sound source level after formation. In addition, with a reasonable number of array elements, keeping the spacing between the elements constant, the more elements there are, the lower the resonant frequency of the volumetric array, the smaller the quality factor, and the higher the emission voltage response.
A hybrid algorithm based on TDOA and DOA for underwater target localization
Authors: Xing Chuanxi , Cui Jing , Jiang Jiayuan , Wei Guangchun , Dong Saimeng 
Published: March 2024

Abstract
To address the problems of complex scenarios, low accuracy of a single localization algorithm, and high requirements for localization system equipment, this paper proposes a Taylor-weighted least squares algorithm based on Time Differences of Arrival (TDOA) and Direction of Arrival (DOA) joint localization method. The method first performs DOA estimation of the target by the Bayesian algorithm with off-grid sparse reconstruction and converts the obtained orientation information into position coordinates, and finds the final position of the target through iterative operations. The proposed algorithm is compared with the existing TDOA method based on the Chan algorithm and the Taylor algorithm based on the TDOA with the Chan algorithm as the initial value and the Two-Step Weighted Least Squares (TSWLS) algorithm in a simulation environment. The results show that the proposed algorithm has better performance in terms of localization accuracy, noise immunity, stability and is suitable for target localization in underwater.
Weak fluctuating spectral line reconstruction using deep learning
Authors: Li Zhen , Guo Junyuan , Wang Xiaohan 
Published: March 2024

Abstract
The detection of weak fluctuating spectral lines emitted by underwater and surface vehicles poses a challenging problem for passive sonar system. Therefore, a spectral line reconstruction algorithm based on deep learning called the DEDAN, is proposed. The DEDAN learns the time-frequency correlation of spectral lines through end-to-end training and then reconstructs the spatial location of spectral lines. Simulation results show that the DEDAN is robust to ambient noise, and outperforms other reconstruction algorithms at a mixed signal-to-noise ratio as low as -22 dB to -26 dB. Its reconstruction performance is also verified by the measured South China Sea data.
Design of an acoustic direction finding system based on split array frequency domain beamforming algorithm
Authors: Gao Qingze , Xia Shixuan , Yu Fan 
Published: March 2024

Abstract
According to the technical requirement, the principle of the acoustic direction finding and positioning algorithm is introduced, and based on the engineering implementation requirements of split array frequency domain beam forming algorithm, a hardware implementation method based on FPGA +DSP is introduced, which use FPGA to realize the logic control and two pieces of MS320C6678 multi-core DSP to realize the digital beam forming. The hardware system design scheme can realize the program-controlled amplification, filtering, collection, real-time data storage and beam forming processing of the multi-channel acoustic analog signals for acoustic receiving array. At the same time, it can also communicate with the upper computer to achieve upper computer instructions, data transmission, and parameter settings. It has been verified through engineering practice that this scheme has high data processing efficiency and reliable direction finding calculation effect.
Line spectrum detection and motion parameters estimation for underwater moving target
Authors: Shi Yunjia , Piao Shengchun , Guo Junyuan 
Published: March 2024

Abstract
For the detection of underwater moving target with constant speed at low signal-to-noise ratio (SNR), a track-before-detect method is proposed based on motion parameters estimation and hidden Markov model (HMM). Optimization algorithm is used for the moving target motion parameter estimation with cost function obtained by energy accumulation along line spectrum tracked with 1-D Hidden Markov model in spatially filtered LOFAR spectrum. This method is suitable for detecting weak targets with fast changes in azimuth.