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Advanced Sensing Technologies for Marine Intelligent Systems

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Electronic Sensors".

Deadline for manuscript submissions: 30 August 2024 | Viewed by 11208

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Special Issue Editor

Prof. Dr. Minyi Xu

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Dalian Key Lab of Marine Micro/Nano Energy and Self-Powered System, Marine Engineering College, Dalian Maritime University, Dalian 116026, China
Interests: self-powered sensor; underwater sensor; energy harvesting technology; triboelectric nanogenerator
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Special Issue Information

Dear Colleagues,

Advanced sensing technologies are greatly demanded for developing marine intelligent systems, such as underwater robots, unmanned vessels, marine Internet of Things, floating/submerged buoys, etc. With the development of material science, microelectronics and intelligence, there is more and more research on advanced marine sensors that can still work in harsh marine environments with high resolution, high sensitivity and long service times. This Special Issue aims to highlight the advanced technologies in marine information sensing, distributed marine sensor power supply, sensing signals intelligent analysis, marine sensor wireless communication, etc. The key topics of interest include (but are not limited to):

Marine intelligent unmanned systems;
Marine electrochemical sensing technology;
Marine photoelectric sensing technology;
Marine acoustic sensing technology;
Marine micro/nano sensing technology;
Marine self-powered sensing technology;
Marine Energy harvesting for distributed sensors;
Marine internet of things;
Marine sensor wireless communication;
Marine sensing signals intelligent analysis.

Prof. Dr. Minyi Xu
Guest Editors

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Keywords

marine sensing technology
marine intelligent system
marine self-powered sensor
marine advanced sensor
marine distributed sensor
marine energy harvesting
wireless communication
signals intelligent analysis

Published Papers (6 papers)

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Research

21 pages, 10212 KiB

Open AccessArticle

Robust Learning with Noisy Ship Trajectories by Adaptive Noise Rate Estimation

by Haoyu Yang, Mao Wang, Zhihao Chen, Kaiming Xiao, Xuan Li and Hongbin Huang

Sensors 2023, 23(15), 6723; https://doi.org/10.3390/s23156723 - 27 Jul 2023

Viewed by 709

Abstract

Ship trajectory classification is of great significance for shipping analysis and marine security governance. However, in order to cover up their illegal fishing or espionage activities, some illicit ships will forge the ship type information in the Automatic Identification System (AIS), and this label noise will significantly impact the algorithm’s classification accuracy. Sample selection is a common and effective approach in the field of learning from noisy labels. However, most of the existing methods based on sample selection need to determine the noise rate of the data through prior means. To address these issues, we propose a noise rate adaptive learning mechanism that operates without prior conditions. This mechanism is integrated with the robust training paradigm JoCoR (joint training with co-regularization), giving rise to a noise rate adaptive learning robust training paradigm called A-JoCoR. Experimental results on real-world trajectories provided by the Danish Maritime Authority verified the effectiveness of A-JoCoR. It not only realizes the adaptive learning of the data noise rate during the training process, but also significantly improves the classification performance compared with the original method. Full article

(This article belongs to the Special Issue Advanced Sensing Technologies for Marine Intelligent Systems)

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14 pages, 6283 KiB

Open AccessArticle

A Novel Thermal Tactile Sensor Based on Micro Thermoelectric Generator for Underwater Flow Direction Perception

by Changxin Liu, Nanxi Chen, Guangyi Xing, Runhe Chen, Tong Shao, Baichuan Shan, Yilin Pan and Minyi Xu

Sensors 2023, 23(12), 5375; https://doi.org/10.3390/s23125375 - 6 Jun 2023

Cited by 3 | Viewed by 1577

Abstract

Underwater vehicles can operate independently in the exploitation of marine resources. However, water flow disturbance is one of the challenges underwater vehicles must face. The underwater flow direction sensing method is a feasible way to overcome the challenges but faces difficulties such as integrating the existing sensors with underwater vehicles and high-cost maintenance fees. In this research, an underwater flow direction sensing method based on the thermal tactility of the micro thermoelectric generator (MTEG) is proposed, with the theoretical model established. To verify the model, a flow direction sensing prototype is fabricated to carry out experiments under three typical working conditions. The three typical flow direction conditions are: condition No. 1, in which the flow direction is parallel to the x-axis; condition No. 2, in which the flow direction is at an angle of 45° to the x-axis; and condition No. 3, which is a variable flow direction condition based on condition No. 1 and condition No. 2. According to the experimental data, the variations and orders of the prototype output voltages under three conditions fit the theoretical model, which means the prototype can identify the flow direction of three conditions. Besides, experimental data show that in the flow velocity range of 0~5 m/s and the flow direction variation range of 0~90°, the prototype can accurately identify the flow direction in 0~2 s. The first time utilizing MTEG on underwater flow direction perception, the underwater flow direction sensing method proposed in this research is cheaper and easier to be applied on the underwater vehicles than traditional underwater flow direction sensing methods, which means it has great application prospects in underwater vehicles. Besides, the MTEG can utilize the waste heat of the underwater vehicle battery as the energy source to achieve self-powered work, which greatly enhances its practical value. Full article

(This article belongs to the Special Issue Advanced Sensing Technologies for Marine Intelligent Systems)

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Graphical abstract

9 pages, 3929 KiB

Open AccessArticle

Experimental Study of Deep Submersible Structure Defect Monitoring Based on Flexible Interdigital Transducer Surface Acoustic Wave Technology

by Zhongjun Ding, Zhiliang Feng, Hongyu Li, Dejian Meng, Yi Zhang and Dewei Li

Sensors 2023, 23(3), 1184; https://doi.org/10.3390/s23031184 - 20 Jan 2023

Cited by 2 | Viewed by 1344

Abstract

In view of the shortage of structural defect monitoring methods for deep submersibles, numerical simulation and experimental research on underwater SAW propagation based on interdigital transducers are carried out in this paper. PVDF interdigital transducer (PVDF-IDT) has shown considerable potential in the application of structural health monitoring because of its micro size, soft material characteristics, and the characteristics of long-term bonding on the surface of the tested structure. In order to realize the application of IDT on submersible or underwater structures, it is necessary to understand the influence of underwater environment on IDTs with different structures. The underwater attenuation of IDT with 2–5 mm wavelength and the underwater attenuation of Lamb (A0 mode) wave on a 4 mm thick titanium alloy plate is obtained through COMSOL software simulation. The experimental verification shows that the simulation results match with the actual situation, which proves that COMSOL software can accurately calculate the acoustic attenuation of surface waves at the solid–liquid interface. At the same time, the underwater attenuation of IDT with different structures is very different, providing important design parameters for the underwater interdigital transducer. In this paper, it is found that the Lamb wave has significant advantages over the Rayleigh wave in the health monitoring of underwater thin plate structures. Full article

(This article belongs to the Special Issue Advanced Sensing Technologies for Marine Intelligent Systems)

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21 pages, 7245 KiB

Open AccessArticle

Feature Attribution Analysis to Quantify the Impact of Oceanographic and Maneuverability Factors on Vessel Shaft Power Using Explainable Tree-Based Model

by Donghyun Kim, Melia Putri Handayani, Sangbong Lee and Jihwan Lee

Sensors 2023, 23(3), 1072; https://doi.org/10.3390/s23031072 - 17 Jan 2023

Cited by 2 | Viewed by 1754

Abstract

A vessel sails above the ocean against sea resistance, such as waves, wind, and currents on the ocean surface. Concerning the energy efficiency issue in the marine ecosystem, assigning the right magnitude of shaft power to the propeller system that is needed to move the ship during its operations can be a contributive study. To provide both desired maneuverability and economic factors related to the vessel’s functionality, this research studied the shaft power utilization of a factual vessel operational data of a general cargo ship recorded during 16 months of voyage. A machine learning-based prediction model that is developed using Random Forest Regressor achieved a 0.95 coefficient of determination considering the oceanographic factors and additional maneuver settings from the noon report data as the model’s predictors. To better understand the learning process of the prediction model, this study specifically implemented the SHapley Additive exPlanations (SHAP) method to disclose the contribution of each predictor to the prediction results. The individualized attributions of each important feature affecting the prediction results are presented. Full article

(This article belongs to the Special Issue Advanced Sensing Technologies for Marine Intelligent Systems)

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18 pages, 15510 KiB

Open AccessArticle

Dynamic Maritime Traffic Pattern Recognition with Online Cleaning, Compression, Partition, and Clustering of AIS Data

by Yuanqiang Zhang and Weifeng Li

Sensors 2022, 22(16), 6307; https://doi.org/10.3390/s22166307 - 22 Aug 2022

Cited by 13 | Viewed by 2871

Abstract

Maritime traffic pattern recognition plays a major role in intelligent transportation services, ship monitoring, route planning, and other fields. Facilitated by the establishment of terrestrial networks and satellite constellations of the automatic identification system (AIS), large quantities of spatial and temporal information make ships’ paths trackable and are useful in maritime traffic pattern research. The maritime traffic pattern may vary with changes in the traffic environment, so the recognition method of the maritime traffic pattern should be adaptable to changes in the traffic environment. To achieve this goal, a dynamic maritime traffic pattern recognition method is presented using AIS data, which are cleaned, compressed, partitioned, and clustered online. Old patterns are removed as expired trajectories are deleted, and new patterns are created as new trajectories are added. This method is suitable for processing massive stream data. Experiments show that when the marine traffic route changes due to the navigation environment, the maritime traffic pattern adjusts automatically. Full article

(This article belongs to the Special Issue Advanced Sensing Technologies for Marine Intelligent Systems)

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13 pages, 4226 KiB

Open AccessArticle

A Cantilever Beam-Based Triboelectric Nanogenerator as a Drill Pipe Transverse Vibration Energy Harvester Powering Intelligent Exploitation System

by Zhenhui Lian, Qunyi Wang, Chuanqing Zhu, Cong Zhao, Qiang Zhao, Yan Wang, Zhiyuan Hu, Ruijiang Xu, Yukai Lin, Tianyu Chen, Xiangyu Liu, Xiaoyan Xu, Ling Liu, Xiu Xiao and Minyi Xu

Sensors 2022, 22(11), 4287; https://doi.org/10.3390/s22114287 - 4 Jun 2022

Cited by 4 | Viewed by 2023

Abstract

Measurement While Drilling (MWD) is the most commonly used real-time information acquisition technique in offshore intelligent drilling, its power supply has always been a concern. Triboelectric nanogenerators have been shown to harvest low-frequency vibrational energy in the environment and convert it into electricity to power small sensors and electrical devices. This work proposed a cantilever-beam-based triboelectric nanogenerator (CB-TENG) for transverse vibration energy harvesting of a drill pipe. The CB-TENG consists of two vibrators composed of spring steel with PTFE attached and Al electrodes. The structurally optimized CB-TENG can output a peak power of 2.56 mW under the vibration condition of f = 3.0 Hz and A = 50 mm, and the electrical output can be further enhanced with the increased vibration parameters. An array-type vibration energy harvester integrated with eight CB-TENGs is designed to fully adapt to the interior of the drill pipe and improve output performance. The device can realize omnidirectional vibration energy harvesting in the two-dimensional plane with good robustness. Under the typical vibration condition, the short-circuit current and the peak power can reach 49.85 μA and 30.95 mW, respectively. Finally, a series of demonstration experiments have been carried out, indicating the application prospects of the device. Full article

(This article belongs to the Special Issue Advanced Sensing Technologies for Marine Intelligent Systems)

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