... the system’s effectiveness. To investigate a possible improvement to modern systems, an artificial neural network-based controller, trained by potential flow simulations, was used to prescribe the optimum future actuation of a ride control system. By prescribing the optimum future actuation in anticipation...

... The main purpose of this study is to apply a Deep Neural Network (DNN) method to linear systems and to predict in a relatively short time span the ultimate vertical bending moment (VBM) for damaged ships. A Deep Neural Network approach, which is composed of multiple fully connected layers...

... is on building and testing machine learning models that can accurately predict the shaft power of a vessel under different conditions. The models examined include pure empirical models, pure neural network models, and combinations of the two. Using data on two car carrying vessels for 8 years it was found...

.... However, current work observation requires both time and labor, and in some cases, shipyards hesitate to implement work observation. The aim of this study is to develop a methodology that uses deep neural networks to reduce the disadvantages of current work observation approaches while identifying work...

... Emerging heterogeneous computing, computing at the edge, machine learning and AI at the edge technology drives approaches and techniques for processing and analysing onboard instrument data in near real-time. The author has used edge computing and neural networks combined with high performance...

..., this article reveals the potentials of this promising technology and future challenges. machine learning freight & logistics services upstream oil & gas energy conservation renewable energy climate change artificial intelligence marine transportation reinforcement learning neural network...

...-of-the-art in machine learning techniques with the increasingly available ship design data in order to improve the hull form design process. artificial intelligence design space optimal hull form neural network training data iteration machine learning node design variable geometry ship design...

... work observation. The aim of this study is to develop a new work observation method by use of deep neural networks to reduce the disadvantages of current work observation approaches while improving the accuracy of work identification. dnn work observation air chipper identification ability...

... with little, if any, human involvement. In the DARPA Flexible Automation program, a group of Ph.D. Scientists and Engineers, began applying a new Artificial Neural Network technology, called P/NA3. Today, Neural Networks produced using the P/NA3 technology are accurate and fast so that they can be run in real...

..., which can be adapted for shipbuilding. This technology is the key to realizing the cost reduction and productivity found in industries, such as automobile manufacturing, for shipbuilding. neural network artificial intelligence marine transportation criteria us government renewable energy...

... sustainable development neural network metals & mining ground transportation artificial intelligence shipbuilding turner renewable energy machine learning social responsibility sustainability scale metal forming forming metal part forming thickness direction automation lits...

... machine learning neural network modeling us government experimentation information distortion application artificial intelligence marine transportation neural network model experiment the shipyard metals & mining shipbuilding criterion portable automated plate straightener...

... Based on the orientation and travel speed of a welding torch, virtual reality technology simulates gas metal arc welding in near-real time using a neural network. upstream oil & gas characteristic application neural network simulation module virtual reality welder training author...