Machine learning in sustainable ship design and operation: A review
TLDR
In this paper , the authors present an overview of applying ML techniques to enhance ships' sustainability, covering the ML fundamentals and applications in relevant areas: ship design, operational performance, and voyage planning.About:
This article is published in Ocean Engineering.The article was published on 2022-12-01 and is currently open access. It has received 7 citations till now. The article focuses on the topics: Fuel efficiency & Fidelity.read more
Citations
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A review on the progress and research directions of ocean engineering
TL;DR: In this article , the authors present a review of ocean engineering research over the last 50+ years with the aim to understand the technological challenges and evolution in the field, investigate whether ocean engineering studies meet present global demands, explore new scientific/engineering tools that may suggest pragmatic solutions to problems, and identify research and management gaps, and the way forward.
Journal ArticleDOI
Marine Accidents in the Brazilian Amazon: The Problems and Challenges in the Initiatives for Their Prevention Focused on Passenger Ships
Jassiel V. H. Fontes,Paulo R. R. de Almeida,Harlysson W. S. Maia,Irving D. Hernández,Claudio A. Rodríguez,Rodolfo Daniel Silva,Edgar Mendoza,Paulo T. T. Esperança,Ricardo Almeida Sanches,S. Mounsif +9 more
TL;DR: In this paper , the main problems that long-distance passenger ships face in the Brazilian Amazon are addressed, presenting an integrated framework towards accident prevention, and measures to help minimize passenger ship accidents are proposed, and social relevance is discussed.
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Survey on hydrodynamic analysis of ship–ship interaction during the past decade
TL;DR: In this article , the authors reviewed the hydrodynamic response of ship-ship interaction in the past decade and classified different operating conditions, including lightering operations, overtaking maneuvers, and encountering ships.
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Capturing the effect of biofouling on ships by incremental machine learning
TL;DR: In this article , a semi-empirical simulation framework was used to predict the required shaft power and to overcome the deterioration in model accuracy due to concept drift, three methods of incremental learning were applied and compared: (1) layer freezing, (2) L2 regularization, and (3) elastic weight consolidation.
References
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Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Posted Content
Deep Residual Learning for Image Recognition
TL;DR: This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Journal ArticleDOI
ImageNet classification with deep convolutional neural networks
TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.
Proceedings Article
Auto-Encoding Variational Bayes
Diederik P. Kingma,Max Welling +1 more
TL;DR: A stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case is introduced.