bluuebunny/crossref_metadata_2025_split huggingface dataset:
Error Analysis on Ultra-Short Baseline Positioning System
Authors: Yu Yang , He Chuanlin , Sun Shouyang , Liu Zeqi 
Published: June 2023

Abstract
Aiming at the problem of low accuracy of ultrashort baseline positioning system in practical applications, this paper introduces hydroacoustic channel response error and transducer distortion error, and analyses the influence of each factor on positioning accuracy in the positioning process according to the time delay estimation method. Finally, the major and minor factors are distinguished according to the influence mechanism and the degree of influence of the error factors. The results show that the ship attitude error and hydroacoustic channel error are the main factors affecting the positioning accuracy.
Influence and remedial measures of missing test data on magnetic field source location of underwater vehicle
Authors: Hu Ping , Jiao Dawen , Qi Jiahui , Chen Sidi 
Published: June 2023

Abstract
Due to the incomplete data collected, it will increase the difficulty of inversion location in the field source inversion of the static magnetic field data of underwater vehicles. Therefore, it is very necessary to study the influence of data loss on the static magnetic source location of underwater vehicles and the field source location for timely remedy of missing data. In this paper, the static magnetic equivalent source model of underwater vehicle was established. Then the inversion positioning algorithm was used to locate the magnetic field source of the underwater vehicle obtained from the test, and the influence of the location result under the incomplete data information was analyzed. We used deleting method, linear interpolation method and sliding filling method to populate the data to fill the remedy of missing data. Using the localization algorithm to carried out the inversion location of source, then we took the location of source inversion and the size of the magnetic dipole moment to the ellipsoid model to get fitting curve of magnetic field. The simulation curve of contrast verification test data with experimental data and the lack of remedial method was feasible. The results show that the missing data can not be ignored for the location of the underwater vehicle’s magnetic field source, and the data pairs filled with missing data can be effectively located for the equivalent static magnetic field source of the underwater vehicle through using deletion method, linear interpolation method and sliding filling method.
On the Reproducibility and Directionality of Underwater Laser-induced Acoustic Signal
Authors: Liu Qingxi , Song Wenhua , Tian Ye , Zhang Zeying , Yuan Guang 
Published: June 2023

Abstract
An intense laser pulse focused in water could induce plasma expansion, which can be used as an impulsive sound source. The optoacoustic underwater source is very useful in marine applications; however, the poor reproductivity and the sharp directivity of the emitted signals is a big problem. The reproductivity and directivity of the acoustic emission by laser breakdown are measured in the laboratory experiment in this paper, and the results show that the reproductivity and directivity are both direction-dependent. The measurement also shows the feasibility of improving the reproductivity and directivity by adjusting the focusing system of the laser beam.
SDPNet: A Novel DeepLearning Method for Ocean Surface Current Prediction
Authors: Zhang Zhiyuan , Yan Yan , Wang Zihe , Song Tao , Yin Jianping , He Xiyu , Guo Peiming , Wang Shaoke 
Published: June 2023

Abstract
Ocean surface currents (OSC), abiotic features of the environment, are continuous and directed movements of ocean water. Prediction to OSC is of significant interests in physical oceanography. Recently, deep learning technology has shown feasibility in mining the intrinsic change pattern of marine numerical values, such as SSS, SST and SSHA, but not been applied to OSC prediction yet. In this work, a deep learning method, named skipped dual path network (SDPNet), is proposed for OSC prediction. Specifically, SDPNet has a convolutional neural network (CNN) module with a one-dimensional convolution layer, and a recurrent neural network (RNN) module with a dual-path structure. Each path consists of a stack of three layers of long short-term memory (LSTM) and gated recurrent unit (GRU), respectively. As well, a skipped-connection structure is added in the both paths. It aims to gradually mine the intrinsic change pattern contained in OSC time series data itself. Experiments are conducted on the South China Sea OSC data set in REDOS. SDPNet achieves accuracy 80.83%, 75.9%, 74.9%, 73.9%, 72.58%, 70.35%, 69.93% in predicting the coming 7 days OSC values and directions. It performs better than state-of-the-art machine learning methods, include Artificial Neural Network, Simple RNN, LSTM and GRU.
A Convolutional Neural Network Combining Classification and Regression for Source Localization in Shallow Water
Authors: Liu Mingda , Niu Haiqiang , Li Zhenglin , Guo Yonggang , Liu Yining , Liu Jingben , Wu Shuanglin , Nie Leixin 
Published: June 2023

Abstract
Machine learning (ML) has been widely applied to ocean acoustic source localization. The localization problem is often regarded as a classification problem or a regression problem in the previous works. This paper proposes a convolutional neural network (CNN) combining classification and regression (CR-CNN) for source localization in shallow water with vertical array data. The normalized sample covariance matrices (SCMs) of the broadband data received by a vertical line array calculated by an acoustic propagation model are used as the input features of the network in the training process. The proposed method is verified by the observation data in the shallow water area of the East China Sea. The results of simulation and real data show that the performance of proposed method is better than that of the separate classification CNN (C-CNN) and MFP. And the localization results of the experimental data are basically consistent with the geoacoustic parameters sensitivity analysis. The depth estimation of the CR-CNN is much better than MFP, while the range estimation performance of CR-CNN, C-CNN and MFP are equivalent.
A Seabed Sediment Semantic Segmentation Method Based on Deep Transfer Learning with Self-Attention Mechanism
Authors: Zhang Y. S. , Li Y. Z. , Zuo L. J. , Deng K. L. 
Published: June 2023

Abstract
Semantic seabed sediment segmentation is one of the key research contents in the fields of marine resources development and marine engineering. In today’s intelligent and digitalized world, more refined semantic segmentation of seabed sediment can provide the basis for downstream tasks as building digital seabed models. In this paper, a seabed sediment semantic segmentation method based on deep transfer learning with self-attention mechanism is proposed. The overall framework is based on the UNet model. The transfer learning method of parameter sharing is used, while the parameters of backbone pretrained from ImageNet datasets are used as the initial weights of model. According to limited acoustic data of seabed sediment, this paper uses original side-scan sonar strip images to produce dataset. The proposed method is also compared with traditional machine learning methods as SVM (Support Vector Machine) and RF (Random Forest). The self-attention UNet (backbone: VGG16) model performed the best, with mIOU of 92.28%, mPrecision of 94.78%, and mRecall of 96.84% on the test set. The self-attention mechanism improved mIOU by 2.5%. The mIOU of deep learning methods is on average over 20% higher than that of traditional machine learning methods.
A Spatial Detection Method of Underwater Acoustic Target Based on Radiated Noise Features
Authors: Chen L. , Zhu C. Q. , An L. 
Published: June 2023

Abstract
In order to solve the problem that the traditional sonar detection method based on beam energy is not able to detect weak targets, a feature-based spatial beam target detection method is proposed in this paper. Firstly, the feature is extracted from the pre-formed beams in the whole spatial domain, and then using the relevance vector machine (RVM) for training, the probability output of target detection is obtained. Compared with traditional methods, this method can obtain excellent detection performance under the condition of low signal-to-noise ratio or signal-to-interference ratio. The probability output of this method can better represent the uncertainty of target detection, and it is also convenient for information fusion and other processing, which is different from the output of traditional binary detection. At the end of the paper, simulated data and experimental data both proved that this method is effective.
Adaptive Underwater Acoustic OFDM Modulation Method Based on Improved Deep Reinforcement Learning
Authors: Cui Xuerong , Yan Peihao , Li Juan , Li Shibao , Liu Jianhang 
Published: June 2023

Abstract
High environmental noise, narrow bandwidth, low carrier frequency, large transmission delay and other factors restrict the data transmission rate in underwater acoustic communication. To this end, improving communication quality and efficiency through adaptive adjustment is necessary for underwater acoustic communication system because of the restrict factor. The proposed method uses Deep Reinforcement Learning (DRL) to select the optimal transportation parameters and maximize the transmission efficiency with. The adaptive communication parameter setting is mapped into a Markov decision process with an unknown state transition probability. The system returns channel information including Channel State Information (CSI) and Signal Noise Ratio (SNR). We validate our estimation and adaptation techniques using the well-known simulator SWellEx_96 in varying underwater environments. Our results show that, for many practical cases, a dynamic adjustment of the prefix length, number of sub-carriers, filter length, and over-sampling factor may enable the utilization of OFDM modulations in underwater communications.
Study on effects of complex bathymetry on the deep acoustic field in Western Pacific Ocean
Authors: Zhou Xiaoyi , Duan Rui , Hao Wang , Gong Guangyu 
Published: June 2023

Abstract
Seafloor bathymetry has an important effect on acoustic propagation. In a deep water experiment in the Western Pacific in 2020, acoustic field in the reliable acoustic path (RAP) zone, the shadow zone, and the refracted zones were measured with shallow sources and deep receivers. Two important phenomena were observed. First, in the first shadow zone, the signal level (S) changing with source range presents a “W” shape: it sharply decreases by 19 dB after the source just pass the RAP boundary and then increases by 11 dB immediately, which produces the first S trough; after remaining stable for a range interval of 29 km, it drops again by 12 dB and then soars by 16 dB, which produces the second S trough. Secondly, the range spans of refracted zones that expected to be 29 km narrows down to only 13 km. Based on the precisely measured bathymetry and the range-dependent acoustic model-parabolic equation (RAM-PE), the S distribution were reproduced and its characteristics were explained. It was found that rather than the large-scale bathymetry change (e.g, large seamount or break shelf), it is the local bathymetry change that have significant effects on the S distribution.
Recognition of Whistle-like Signals Based on FM Harmonic Characteristics
Authors: Gui Y. , Cao H. L. , Wang X. Y. , An L. 
Published: June 2023

AbstractWhile human hearing can easily identify whistle-like sounds made by dolphins and whales, manually identifying them from the increasingly variety and quantity of underwater acoustic signals (such as other marine life sounds and underwater acoustic communication signals) is undoubtedly laborious. To solve this question, a recognition method based on the frequency modulated feature and harmonic characteristic is analyzed in this paper. A harmonic energy analysis algorithm is proposed and is compared to a harmonic detection method. The experimental results prove that the proposed method could identify the whistle-like sounds with an accuracy over 90% and the traditional one could only reach 50%. The recognition method may be applied to the investigation of marine biological habits and the monitoring of marine ecological environment, and can also potentially be further applied to the detection of bionic communication signal.
The analysis of the depth dependence of ambient noise in the South China Sea
Authors: Ren C , Huang Y W 
Published: June 2023

Abstract
A 64-element vertical array was deployed in the South China Sea for ocean ambient noise observation. A rainstorm passed over the area during the experiment, lasting a total of 35 minutes. The differences between the depth distribution and vertical coherence of wind-driven noise and rain noise are compared, and the influence of rainfall rates on them is analyzed. The wind-driven noise intensity decreases with increasing depth, and it is basically consistent with the theoretical results. The depth distribution of rain noise is different from that of wind-driven noise. When the rainfall rate exceeds 48mm/h, the rain noise intensity in the frequency band above 3200Hz increases with increasing depth. Compared with wind-driven noise, the vertical coherence function depth distribution of rain noise is more uniform. The vertical coherence function of rain noise is in good agreement with the simulation results when the noise source directivity index take a larger value.
Acoustic radiation force on two-layered elastic sphere in standing wave field
Authors: Zhu J L , Wu Y S , Wu H Q , Zheng C H 
Published: June 2023

Abstract
A mathematical relationship between acoustic radiation force (ARF) on two-layered stratified elastic spheres immersed in water in acoustic standing wave field and dimensionless size parameter kr is established based on acoustic scattering theory. Calculating by using the relationship, we discussed the variation of ARF with kr under different conditions including changing the materials for shells or cores and changing the ratio of outer radium and inner radium. We find that the peaks of ARF have some linkage with different impedances of shells and different ratios of outer radium and inner radium.
Recursive Decoding Based Multi-channel M-ary Spread Spectrum Underwater Acoustic Communication
Authors: Wang Liang , Gao Mingyu 
Published: June 2023

Abstract
The spread spectrum communication method has strong anti-noise and anti-fading capability, but it also brings the problem of low information transmission efficiency. The paper proposes a multi-channel M-ary spread spectrum communication method, using a recursive decoding algorithm to reduce the communication bit error rate. In combination with the channel equalization technique based on the virtual time reversal mirror technique, the scheme improves the communication rate of spread spectrum communication. It is verified through computer simulation and sea trial data that the communication method can effectively reduce the mutual interference between different channel signals, which provides a new idea for the design of hydroacoustic spread spectrum communication scheme.
The polarization filtering line spectrum extraction algorithm of underwater target signal
Authors: Han Xue , Wang Yang 
Published: June 2023

Abstract
In order to increase the performance of underwater target line spectrum extraction, an adaptive polarization filtering line spectrum extraction method is researched in the paper. By using of the polarization characteristics of underwater target signals, the polarization characteristics of underwater target line spectrum signal is analyzed via singular value decomposition (SVD) method in frequency domain. Firstly, the line spectrum signal is presented by the eigenimage corresponding to the maximum singular value to increase the signal-to-noise ratio (SNR) of the signal. Then the polarization parameters are extracted according to the polarization characteristics of the line spectrum signals. And the line spectrum signal is filtered by constructing the polarization filtering function. The simulation results show that the output SNR of the proposed method are larger than that of conventional FFT analysis method when the SNR of the signal is larger than -9dB, and the detection probability of the proposed method is 100% when the SNR of the signal is -1 dB, while the detection probability of the conventional FFT analysis method is 27.73%. It shows that the proposed method provides a new approach for underwater line spectrum signal extraction techniques.
Pressure Sensitivity Kernel and Travel-Time Gradient for Geoacoustic Parameters Perturbations of Sediment in Shallow Water
Authors: Zheng J. , Zhang X. , Ma Z. 
Published: June 2023

Abstract
In shallow sea, the effect of geoacoustic characteristics of seabed sediments on acoustic propagation cannot be neglected, which is closely related to acoustic detection and seabed inversion. Aiming at the problem of low-frequency acoustic propagation, the sound pressure sensitivity kernel (PSK) and travel-time gradient (TTG) of the shallow low-frequency acoustic field for the geoacoustic parameters of sediment were presented by using normal mode model and perturbation approximation. Combined with the benchmark shallow sea model, the numerical simulation was carried out. The simulation results show that the PSK for the sound velocity of sediment are showing some interference pattern in velocity-range plane and velocity-frequency plane, and its strength is attenuated with the increase of range. TTGs have different sensitivity to the sound velocity of sediment at different range, which is more sensitive at mid-range. The PSK and TTG show some frequency selectivity.
Study on Catecholamine Sensors Based on Noble Metal Nanoparticles Modified Ti3C2 Mxene
Authors: Shou Jialong , Zhan Shuwang , Zhang Yi , Liang Bo , Fang Lu 
Published: June 2023

Abstract
Catecholamines are neurotransmitters in the peripheral and central nervous systems, which maintain the normal physiological state of the human body. Studies have shown that the occurrence of many diseases are often accompanied by changes in catecholamine levels. Clinically, the detection of catecholamine levels in human plasma or urine can assist in the diagnosis of endocrine-related diseases and neurological diseases. However, catecholamines are easily oxidized, and the content in human body is very low. So the rapid detection of catecholamines is very important for the prevention and diagnosis of related diseases. In this paper, the electrochemical response characteristics of Ti3C2Tx MXene and noble metal nanoparticles modified Ti3C2Tx to dopamine (one type of catecholamine) were explored. The results shown that Ti3C2Tx would undergo irreversible oxidation at the anode potential and the single Ti3C2Tx modified electrode was not suitable for the detection of dopamine. So we further explored the modification of Pt and Au nanoparticles on the Ti3C2Tx/GCE electrode, and the experimental results shown that Au/Ti3C2Tx/GCE had higher sensitivity for measuring dopamine, while Pt/Ti3C2Tx/GCE had a larger linear range. Both electrodes had good stability and repeatability and could be used for the detection of catecholamines.
Multi-Modal and Self-Reconstruction Conditioning Circuit of Semiconductor Gas Sensors
Authors: Wang Tao , Wu Yu , Ni Wangze , Zhu Jianbo , Zheng Mingxu , Zeng Min , Yang Jianhua , Yang Zhi 
Published: June 2023

Abstract
Semiconductor gas sensors (chemiresistors) have been widely used in many fields. The emerging novel gas sensors usually have large initial resistance and ultra-high sensitivity, and their resistance value may change significantly during the response process. It is difficult for conventional front-end conditioning circuits to meet the requirements of large measurement range and high accuracy. Meanwhile, the circuit needs to be compatible with several forms of signals generated from different types of sensors, such as voltage, current, and resistance. This study designs a front-end conditioning circuit that is compatible with various types of gas sensors and can adjust the measurement range in real time. The circuit has different functional states, which can realize four functions: constant voltage measurement, constant current measurement, electrochemical measurement, and parameter calibration. The modal transformation of the circuit is controlled by a multiplexer, which can perform automatic transformation according to software instructions. Our work provides a flexible circuit framework composed of different functional units, with the characteristics of multi-modal and self-reconstruction. It is suitable for designing integrated or fixed-function circuits and is convenient for miniaturization and cost reduction.
Influence Factors and Uncertainty Analysis of Relative Humidity Measured by Psychrometer
Authors: Liu Ying , Mo Hongbo , Wang Xiaolin 
Published: June 2023

Abstract
Based on the measurement principle of psychrometer and experimental data, the influence of wind speed on humidity measurement is analyzed, and the uncertainty of humidity measured by psychrometer is evaluated by GUM method. The research results show that under the condition of high temperature and high humidity, the relative humidity measured by psychrometer is not sensitive to wind speed. When the relative humidity is 90%RH and air temperature is 85°C, the difference of relative humidity calculated by using different psychrometer coefficients is 0.03% RH. With the decrease of dry-bulb temperature and relative humidity, the influence of wind speed on relative humidity measurement gradually increases. When the relative humidity is 20%RH and air temperature is 20°C, the difference of relative humidity calculated by using different psychrometer coefficients is 2.64% RH. The main uncertainty of relative humidity measured by psychrometer is introduced by psychrometer coefficient which is a function of wind speed. Measurement uncertainty decreases with the increase of relative humidity while at the same dry-bulb temperature.
Smart Insoles for Gait Analysis Based on Meshless Conductive Rubber Sensors and Neural Networks
Authors: Dai Yijie , Gao Jiale , Zhang Weidong , Wu Xingyi , Zhu Xiaobo , Gu Wenhua 
Published: June 2023

Abstract
In recent years, advances in wearable medical devices have been driven by the development of flexible sensors and wireless IoT technologies, with research and applications in this field rapidly increasing. Plantar pressure analysis is a gait method that can be used to analyze foot pressure during human movement to identify potential health problems, but most of the devices based on this method are currently single-functional, poorly portable, and expensive. This work proposes a smart insole using meshless conductive rubber sensors, as well as a multi-channel data acquisition and transmission system based on these sensors, deep learning networks, and Bluetooth technology. The smart insole sensing unit and circuit board were made of flexible materials and powered by rechargeable lithium batteries. The system can be used to collect plantar pressure data from the sensors in real time, analyze the pressure in different regions of the foot through a neural network, and infer the gait cycle and abnormal gait of the user. The effective communication range can be up to 70 meters under barrier-free conditions. This smart insole has a bright future thanks to the pressure detection range, fast response time, and good durability.