Sea state estimation based on vessel motion responses: Improved smoothness and robustness using Bézier surface and L1 optimization

Ren, Zhengru; Han, Xu; Verma, Amrit Shankar; Dirdal, Johann A.; Skjetne, Roger

doi:10.1016/j.marstruc.2020.102904

Cited by 28 publications

(5 citation statements)

References 27 publications

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“…However, their slow update rates and communication delays make them unsuitable for real-time applications. Although seabed and sea surface sensors can provide real-time measurements, the high costs of installation restrict their use [16]. Standard incoherent wave radars gather sea state data by measuring wave surface backscatter and ship motion, while advanced coherent systems estimate wave height using the Doppler effect [17].…”

Section: A Sea State Estimationmentioning

confidence: 99%

“…Modelbased methods use a transfer function to map the sea state to the ship's motion [19], dividing further into frequency-based and time-based techniques [11], [20]. For instance, Ren et al proposed a method to estimate the directional wave spectrum using a non-parametric approach, an L1 optimization, and a novel smoothness constraint [16].…”

Section: A Sea State Estimationmentioning

confidence: 99%

“…The evolution of autonomous ships confronts a multitude of challenges. A foundational hurdle involves the accurate perception of the sea state [4]. In oceanography, the sea state is characterized by the overall wind, wave, and swell conditions at a specific open-sea location [5].…”

Section: Introductionmentioning

confidence: 99%

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SAFENESS: A Semi-Supervised Transfer Learning Approach for Sea State Estimation Using Ship Motion Data

Cheng,

Li,

Skulstad

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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Autonomous vessels have been identified as a promising innovation in advancing marine transportation, providing an effective means to mitigate the risk of accidents, pollution incidents, and carbon dioxide emissions. Accurate sea state estimation (SSE) plays a critical role in facilitating onboard decision-making and optimizing operational efficiency for autonomous ships. Traditional SSE approaches relying on external sensors, such as wave buoys and wave radars, are limited by cost considerations. Model-based methods are highly relying on the understanding of human knowledge to ships. Data-driven models also provide promising solutions, but their generalization is low. To address this challenge, a semi-supervised transfer learning approach for SSE (SAFENESS) is proposed. The model is trained using sufficient data in the source ship and limited data from the target ship and finally applied to the target ship. A data alignment algorithm is utilized to use the limited data of the target ship. To enhance the learning capability of the framework, two attention mechanisms are proposed, and a multi-class adversarial discriminator is introduced that can align the distributions of different domains. The effectiveness of our approach is validated through comprehensive comparisons with eleven established transfer learning methods, demonstrating the superiority of our model. The competitiveness of the proposed attention modules is verified by comparing them with state-ofthe-art attention modules. The significance of each component and the influence of key parameters have been thoroughly explored in the ablation and sensitivity analysis. Our method has potential applications in maritime safety, navigation, and operation optimization.

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Section: A Sea State Estimationmentioning

confidence: 99%

Section: A Sea State Estimationmentioning

confidence: 99%

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SAFENESS: A Semi-Supervised Transfer Learning Approach for Sea State Estimation Using Ship Motion Data

Cheng,

Li,

Skulstad

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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“…Salah satu penyebab kecelakaan adalah tenggelam akibat buruknya stabilitas kapal. Dalam perancangan kapal stabilitas menjadi unsur yang krusial, oleh karena itu diperlukan kajian dengan analisis hidrostatis untuk meng-evaluasi properti hidrostatis seperti displacement, center of gravity, center of buoyancy dan titik metacenter [4][5] [6]. Pada umumnya analisis hidrostatis dapat dilakukan dengan pendekatan numerik dengan pemodelan komputer [7], namun untuk mengakomodasi perilaku non-linier dibutuh-kan eksperimen dengan model uji mengacu pada standar ITTC [8].…”

Section: Pendahulanunclassified

Rancang Bangun Model Uji Kapal General Cargo 8202 DWT untuk Pengujian Hidrostatis

Wibowo,

Alfaris,

Firdaus

et al. 2023

zonalaut

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Perkembangan industri maritim Indonesia tidak bisa terlepas dari pertumbuhan sarana transportasi laut, dalam hal ini yaitu pertumbuhan armada kapal. Namun dibalik pertumbuhan tersebut kecelakaan kapal masih menjadi isu yang cukup krusial. Salah satu penyebabnya adalah tenggelam yang diakibatkan buruknya stabilitas kapal. Perpindahan muatan di atas kapal dari aktivitas bongkar-muat (loading-unloading) menyebabkan berubahnya stabilitas kapal. Pada umumnya stabilitas kapal dapat dianalisis menggunakan pendekatan numerik dengan analisis hidrostatis, namun untuk mengakomodasi perilaku non-linier dibutuhkan eksperimen model uji. Penelitian ini berfokus pada rancang bangun model uji kapal General Cargo 8202 DWT. Model uji dibuat dengan skala 1:60 yang memiliki panjang model (L) 1.80m, lebar (B) 0.3m, tinggi (D) 0.23m dan sarat air (T) 0.12m. Model uji dibuat dengan memodelkan linesplane kemudian menyusunnya menjadi model 3D. Setiap station pada kapal dipolakan pada kayu, dipotong dan disusun membentuk pola kapal, kemudian dilapisi dengan multiplek dan fiber. Prosedur rancang bangun model uji yang dibuat mengacu pada standar International Towing Tank Conference (ITTC). Pengujian kolam dilakukan untuk mengidentifikasi sarat air dan kemiringan kapal pada 3 kondisi muatan: lightweight, ballasted load dan full load. Berdasarkan hasil pengujian didapatkan sarat air yang sesuai dengan desain ukuran utama serta kemiringan yang cenderung stabil.

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“…An overview of diverse sea state estimation methods proceeded is provided in [40]. However, in the absence of these motion sensors, long-term and short-term weather forecasts can supply adequate information regarding the operation region, and the engaging wave parameters can be approximated based on them [43]. After evaluating the sea state and the encountering wave attributes, if the ship faces an irregular wave, the wave collision model converts the wave into a superposition of regular waves.…”

Section: A the Wave Collision Modelmentioning

confidence: 99%

Power Management Strategies Based on Propellers Speed Control in Waves for Mitigating Power Fluctuations of Ships

Nasiri

Peyghami

Parniani

et al. 2022

IEEE Trans. Transp. Electrific.

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This paper proposes two novel Power ManagementStrategies (PMS) to enhance the electrical power quality in twinscrew All-Electric Ships (AES) during vessel-wave encountering. Soothing PMS (SPMS) focuses on mitigating power fluctuations of the propulsion system since it has significant impacts on the power system in extreme conditions. For this purpose, the PMS modifies propellers speed based on the in-and-out-of-water effect Loss Factor (LF) in wave collisions. This method does not require typical equipment, such as Energy Storage Systems (ESS), to moderate the variations. Hence, it decreases the ESS demand capacity and maintenance costs at the design and operation levels. An integrated ship model is introduced to determine this loss factor in waves thoroughly. Furthermore, due to the propeller dynamics in speed altering, an adaptive delay is embedded in SPMS to improve its efficiency, and an Advanced Soothing PMS (ASPMS) is developed. To determine this varying adaptive delay during an operation, a straightforward algorithm is proposed that does not impose the PMS to compute complex differential equations. Simulations reveal that both PMSs soothe the propulsion system power fluctuations and remarkably reduce the power system frequency and voltage variations. Meanwhile, the ASPMS surpasses the SPMS in terms of improving the power quality.

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Sea state estimation based on vessel motion responses: Improved smoothness and robustness using Bézier surface and L1 optimization

Cited by 28 publications

References 27 publications

SAFENESS: A Semi-Supervised Transfer Learning Approach for Sea State Estimation Using Ship Motion Data

SAFENESS: A Semi-Supervised Transfer Learning Approach for Sea State Estimation Using Ship Motion Data

Rancang Bangun Model Uji Kapal General Cargo 8202 DWT untuk Pengujian Hidrostatis

Power Management Strategies Based on Propellers Speed Control in Waves for Mitigating Power Fluctuations of Ships

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