Underwater Gyros Denoising Net (UGDN): A Learning-Based Gyros Denoising Method for Underwater Navigation DOI Creative Commons
Chun Cao, Can Wang,

Shaoping Zhao

и другие.

Journal of Marine Science and Engineering, Год журнала: 2024, Номер 12(10), С. 1874 - 1874

Опубликована: Окт. 18, 2024

Autonomous Underwater Vehicles (AUVs) are widely used for hydrological monitoring, underwater exploration, and geological surveys. However, AUVs face limitations in navigation due to the high costs associated with Strapdown Inertial Navigation System (SINS) Doppler Velocity Log (DVL), hindering development of low-cost vehicles. Micro Electro Mechanical Measurement Units (MEMS IMUs) industry their low cost can output acceleration angular velocity, making them suitable as an Attitude Heading Reference (AHRS) poorly calibrated MEMS IMUs provide inaccurate leading rapid drift orientation. In environments where cannot use GPS position correction, this have severe consequences. To address issue, paper proposes Gyros Denoising Net (UGDN), a method based on dilated convolutions LSTM that learns extracts spatiotemporal features IMU sequences dynamically compensate gyroscope’s velocity measurements, reducing attitude heading errors. experimental section paper, we deployed dataset collected from field trials achieved significant results. The results show accuracy data denoised by UGDN approaches fiber-optic SINS, when integrated DVL, it serve solution.

Язык: Английский

Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions DOI Creative Commons
Nadav Cohen, Itzik Klein

Results in Engineering, Год журнала: 2024, Номер unknown, С. 103565 - 103565

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

10

A framework to assess the operational state of autonomous ships with multi-component degrading systems DOI
Xiangyu Zhou, Shiqi Jin, Xiaohang Ren

и другие.

Ocean Engineering, Год журнала: 2025, Номер 327, С. 121000 - 121000

Опубликована: Март 23, 2025

Язык: Английский

Процитировано

0

Ship Noise Characteristics in the Java Sea: A Preliminary Study on Underwater Noise Pollution in Indonesia DOI

Amron Amron,

Rizqi Rizaldi Hidayat, Iqbal Ali Husni

и другие.

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Май 16, 2025

Abstract Indonesia is the largest archipelagic nation in world, facing high environmental challenges due to underwater noise generated by activities from various types of ships. Therefore, this study aimed examine characteristics (specifically sound pressure level (SPL) and frequency) different ships operating Java Sea, categorized tonnage, namely under 30 GT, 30–100 exceeding 100 GT. Using a calibrated omnidirectional hydrophone system alongside synchronized video documentation, acoustic data were collected analyzed assess intensity, frequency, duration. The results showed that small produced higher frequency broadband noise, with SPL ranging 122 144 dB re 1 µPa based on speed. Medium-sized display dominant frequencies kHz, SPLs related engine power Large ships, such as ferries, tugboats, patrol boats, show unique spectral profiles influenced type, achieving approximately 155 µPa. This significant variability emissions type operational behavior suggesting need for mitigation strategies marine policies safeguard Indonesia's delicate ecosystems.

Язык: Английский

Процитировано

0

A novel NSSA-LSTM-based forecasting model for ship delays in the Beijing-Hangzhou Grand Canal DOI
Zhengchun Sun, Sudong Xu

Journal of Ocean Engineering and Marine Energy, Год журнала: 2024, Номер unknown

Опубликована: Дек. 21, 2024

Язык: Английский

Процитировано

0

Selective maintenance of the complex system considering maintenance time uncertainty for system components with multiple repairpersons DOI
Haipeng Wang, Kaiwen Li, Zixuan Liu

и другие.

Quality and Reliability Engineering International, Год журнала: 2024, Номер unknown

Опубликована: Окт. 2, 2024

Abstract This research presents an innovative selected maintenance model for complex systems that considers the uncertainty in time (MT) system components with multiple repairpersons. The computational of uncertain MT is established. An imperfect introduced, which has many levels not only considering do nothing, minimal repair, and replacement but also intermediate levels. Furthermore, assignment algorithm repairpersons proposed to addresses problem how assign tasks order minimize MT. And it innovatively integrated into particle swarm optimization (PSO) solve selective model, enables heuristic efficiently effectiveness advantages are verified by numerical experiments.

Язык: Английский

Процитировано

0

Underwater Gyros Denoising Net (UGDN): A Learning-Based Gyros Denoising Method for Underwater Navigation DOI Creative Commons
Chun Cao, Can Wang,

Shaoping Zhao

и другие.

Journal of Marine Science and Engineering, Год журнала: 2024, Номер 12(10), С. 1874 - 1874

Опубликована: Окт. 18, 2024

Autonomous Underwater Vehicles (AUVs) are widely used for hydrological monitoring, underwater exploration, and geological surveys. However, AUVs face limitations in navigation due to the high costs associated with Strapdown Inertial Navigation System (SINS) Doppler Velocity Log (DVL), hindering development of low-cost vehicles. Micro Electro Mechanical Measurement Units (MEMS IMUs) industry their low cost can output acceleration angular velocity, making them suitable as an Attitude Heading Reference (AHRS) poorly calibrated MEMS IMUs provide inaccurate leading rapid drift orientation. In environments where cannot use GPS position correction, this have severe consequences. To address issue, paper proposes Gyros Denoising Net (UGDN), a method based on dilated convolutions LSTM that learns extracts spatiotemporal features IMU sequences dynamically compensate gyroscope’s velocity measurements, reducing attitude heading errors. experimental section paper, we deployed dataset collected from field trials achieved significant results. The results show accuracy data denoised by UGDN approaches fiber-optic SINS, when integrated DVL, it serve solution.

Язык: Английский

Процитировано

0