... neural network marine transportation artificial intelligence neural network model bpnn sustainable development resistance resistance coefficient prediction model social responsibility sustainability machine learning freight & logistics services oil tanker model oil tanker...

... machine learning reduction drag reduction technology mesh reduction effect drag reduction effect resistance reduction technology united states government freight & logistics services calculation ship model simulation accuracy resistance calculation result equation reduction technology...

... responsibility machine learning marine transportation freight & logistics services resistance trim angle artificial intelligence ship level trim trim optimization degree trim calculation consumption optimization knot dynamic pressure distribution sustainability sustainable development...

... intelligence machine learning control point deformation total resistance ABSTRACT A ship hull form optimization is conducted for KCS focusing on the shape of bow and stern. Shape of the hull form is expressed in a radial basis function to facilitate its deformation during the course of optimization...

... of Tahara (Campana et al., 2009 and Tahara et al., 2011). hull form deep learning marine transportation optimization problem bayesian inference neural network machine learning geometry requirement design region reduction tank test objective engineering practice artificial intelligence...

...). The difference is whether the linear conversion is used in the dimensionality reduction process. hull form data mining optimization neural network consumer health reduction health & medicine machine learning dimensionality design variable dimensionality reduction method hull form...

... intelligence, machine learning, and deep learning to ship design. The computer vision (CV) technology could import the recognition functionally of geometry to programs, and there are many successful engineering applications already. As the pictures and voices, the ship offset table is a matrix carrying...

... model on the CFD calculation results (Guo at all., 2013). This paper uses the CFD method to demonstrate the effect of scale on ship resistance and investigate the uncertainty of full-scale resistance simulations. (Terziev at all., 2019). In recent years, artificial intelligence and machine learning have...

... celeris physical result manning coefficient dissipation crest freeboard artificial intelligence seawall calibration vertical seawall simulation machine learning equation shallow foreshore eng additional dissipative term coefficient numerical model discharge university modeling...

... an important issue. In this study, we investigate the methodology of using machine learning to extract data that requires detailed confirmation from the inspection and diagnosis results in the maintenance of port infrastructure. Our findings confirmed that abnormal data can be extracted from the field data...

... are often installed at fatigue critical locations to collect structural response data that can be effectively utilized to reduce uncertainties associated with fatigue deterioration. machine learning wind energy artificial intelligence probability distribution dataset monitoring neural network...

...-resolved prediction, the linear wave theory (LWT) based on potential flow was proposed in early 1990, it is simple but only works for small steepness and short-term prediction (Morris et al., 1998; Ruban, 2016). Thanks to advances in the machine learning (ML) in computer science, the non-linear problem...

... that a single neural network only capture limited features, Hybrid model and multi-scale model were proposed. artificial intelligence flow field high frequency data prediction method prediction low frequency data submarine time step deep learning machine learning prediction result neural...

... by capitalizing on the multi-source data obtained from aids telemetry and remote sensing. neural network machine learning bp neural network ground transportation aids formula artificial intelligence ds evidence theory evidence theory recognition result state recognition sub neural network...

... ABSTRACT Nowadays, the problem of short- and long-term forecasts in the Russian Arctic becomes important for global logistics and engineering. Here, we present our first findings on the application of machine learning (ML) methods to oceanographic data. The Barents Sea is the region of our...

.... This algorithm was developed based on non-holonomic constraints and steady-state component constraints which were integrated with navigation algorithm by indirect Kalman filter based on state error propagation model. robot machine learning upstream oil & gas reservoir characterization artificial...

... ABSTRACT The main objective is to investigate the possibility that machine learning can be used in the real-time simulation of ship motion. A short-term prediction of seakeeping and maneuvering is strongly required for the navigation process. However, accurate and instant prediction remains...

.... Subsequent experiments will use observers of real-time real-world waves as target waves for further learning. machine learning simulation algorithm reinforcement learning observation point target wave artificial intelligence irregular wave openfoam solitary wave superposition wave...

... model of AUV, PD controller and three fuzzy controllers are designed: Mamdani PD controller, T-S PD controller and adaptive fuzzy controller. Based on SIMLINK platform simulation, the heading angle control curve is obtained. fuzzy logic controller vehicle characteristic machine learning...

... artificial intelligence expert system machine learning node algorithm auv underwater high-speed unmanned craft subsystem node function self-propelled model underwater vehicle actual output fault diagnosis emergency system operation mechanism high-speed unmanned vehicle emergency system...