Deep Learning For Marine Science
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Deep Learning For Marine Science
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Author : Haiyong Zheng
language : en
Publisher: Frontiers Media SA
Release Date : 2024-05-15
Deep Learning For Marine Science written by Haiyong Zheng and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-15 with Science categories.
Deep learning (DL), mainly composed of deep and complex neural networks such as recurrent network and convolutional network, is an emerging research branch in the field of artificial intelligence and machine learning. DL revolution has a far-reaching impact on all scientific disciplines and every corner of our lives. With continuing technological advances, marine science is entering into the big data era with the exponential growth of information. DL is an effective means of harnessing the power of big data. Combined with unprecedented data from cameras, acoustic recorders, satellite remote sensing, and large model outputs, DL enables scientists to solve complex problems in biology, ecosystems, climate, energy, as well as physical and chemical interactions. Although DL has made great strides, it is still only beginning to emerge in many fields of marine science, especially towards representative applications and best practices for the automatic analysis of marine organisms and marine environments. DL in nowadays' marine science mainly leverages cutting-edge techniques of deep neural networks and massive data which collected by in-situ optical or acoustic imaging sensors for underwater applications, such as plankton classification and coral reef detection. This research topic aims to expand the applications of marine science to cover all aspects of detection, classification, segmentation, localization, and density estimation of marine objects, organisms, and phenomena.
Artificial Intelligence Oceanography
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Author : Xiaofeng Li
language : en
Publisher: Springer Nature
Release Date : 2023-02-03
Artificial Intelligence Oceanography written by Xiaofeng Li and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-03 with Science categories.
This open access book invites readers to learn how to develop artificial intelligence (AI)-based algorithms to perform their research in oceanography. Various examples are exhibited to guide details of how to feed the big ocean data into the AI models to analyze and achieve optimized results. The number of scholars engaged in AI oceanography research will increase exponentially in the next decade. Therefore, this book will serve as a benchmark providing insights for scholars and graduate students interested in oceanography, computer science, and remote sensing.
Spatiotemporal Modeling And Analysis In Marine Science
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Author : Junyu He
language : en
Publisher: Frontiers Media SA
Release Date : 2023-11-29
Spatiotemporal Modeling And Analysis In Marine Science written by Junyu He and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-29 with Science categories.
With the development of earth observation technologies (such as satellite remote sensing, unmanned aerial vehicle, autonomous underwater vehicle, etc.), an era of big data with important and non-negligible spatial/temporal attributes comes. Novel and rigorous spatiotemporal methodologies and models are needed to process and analyze marine big data. Since many marine environmental processes, such as pollutants diffusion, algae distributions etc., vary or evolve across spatiotemporal domains, detecting the distributions and patterns of marine fauna and, particularly in the coastal regions, will improve our understanding of marine systems and can be beneficial in marine environmental management. The goals of this Research Topic, therefore, are two-fold: (a) to develop methodologies and models in theory and applications, including spatiotemporal geostatistics, geographic information system, deep learning, etc.; (b) to quantitatively gain the knowledge of the marine environment. This Research Topic will provide a platform for researchers to share and exchange their new knowledge gained in a spatiotemporal domain of marine or coastal regions. This Research Topic will cover, but is not limited to, the following areas: • Spatiotemporal variations of physical/chemical/biological indicators (such as chlorophyll, temperature, salinity, colorful dissolved organic matter, suspended solids, nutrients, microplastic, etc.) in marine. • Spatiotemporal variations of potential fishing grounds in marine. • Spatiotemporal variations of the ecosystems in coastal regions, such as salt marshes, mangroves, seagrass, macroalgae, etc. • Spatiotemporal distributions of the pollutants (such as heavy metals, polycyclic aromatic hydrocarbon, etc.) in marine and sediments. • Spatiotemporal evolution pattern modeling and prediction of the marine disasters and abnormal phenomena (such as algal bloom, typhoons, SST anomalies, etc).
Marine Big Data
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Author : Dongmei Huang
language : en
Publisher: World Scientific Publishing Company
Release Date : 2019
Marine Big Data written by Dongmei Huang and has been published by World Scientific Publishing Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Computers categories.
As the volume of marine big data has increased dramatically, one of the main concerns is how to fully exploit the value of such data in the development of marine economy and marine science and technology. The book covers data acquisition, feature classification, processing and applications of marine big data in evaluation and decision-making, using case studies such as storm surge and marine oil spill disaster.
Machine Learning To Improve Marine Science For The Sustainability Of Living Ocean Resources
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Author : William L. Michaels
language : en
Publisher:
Release Date : 2019
Machine Learning To Improve Marine Science For The Sustainability Of Living Ocean Resources written by William L. Michaels and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Artificial intelligence categories.
Optics And Machine Vision For Marine Observation
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Author : Hong Song
language : en
Publisher: Frontiers Media SA
Release Date : 2023-10-13
Optics And Machine Vision For Marine Observation written by Hong Song and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-13 with Science categories.
Advanced Concepts For Intelligent Vision Systems
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Author : Jacques Blanc-Talon
language : en
Publisher: Springer
Release Date : 2017-11-22
Advanced Concepts For Intelligent Vision Systems written by Jacques Blanc-Talon and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-22 with Computers categories.
This book constitutes the refereed proceedings of the 18th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2017, held in Antwerp, Belgium, in September 2017. The 63 full papers presented in this volume were carefully selected from 134 submissions. They deal with human-computer interaction; classification and recognition; navigation, mapping, robotics, and transports; video processing and retrieval; security, forensics, surveillance; and image processing.
Artificial Intelligence And Edge Computing For Sustainable Ocean Health
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Author : Debashis De
language : en
Publisher: Springer
Release Date : 2024-10-02
Artificial Intelligence And Edge Computing For Sustainable Ocean Health written by Debashis De and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-02 with Mathematics categories.
Artificial Intelligence and Edge Computing for Sustainable Ocean Health explores the transformative role of AI and edge computing in preserving and enhancing ocean health. The growing influence of Artificial Intelligence (AI), along with the Internet of Things (IoT) in generating wide coverage of sensor networks, and Edge Computing (EC) has paved the way for investigation of underwater as well as massive marine data, thereby generating huge potential for credible research opportunities for these domains. This book’s journey begins with a broad overview of Artificial Intelligence for Sustainable Ocean Health, setting the foundation for understanding AI's potential in marine conservation. The subsequent chapter, Role of Artificial Intelligence and Technologies in Improving Ocean Health in Promoting Tourism, illustrates the synergy between technological advancements and sustainable tourism practices, demonstrating how AI can enhance the attractiveness and preservation of marine destinations. The identification, restoration, and monitoring of marine resources along with the utilization of technology continues in Utilization of Underwater Wireless Sensor Network through Supervising a Random Network Environment in the Ocean Environment has been extensively dealt with. The technical challenges of underwater imaging, essential for accurate data collection and analysis has been discussed. The importance of Explainable AI is discussed in chapters like Sustainable Development Goal 14: Explainable AI (XAI) for Ocean Health, Explainable AI (XAI) for Ocean Health: Exploring the Role of Explainable AI in Enhancing Ocean Health, and A Comprehensive Study of AI (XAI) for Ocean Health Monitoring, which emphasize transparency and trust in AI systems. Further, Revolutionizing Internet of Underwater Things with Federated Learning, Underwater Drone, Underwater Imagery with AI/ML and IoT in ROV Technology and Ocean Cleanup has been demonstrated using innovative approaches to addressing underwater challenges. The book also includes a Review on the Optics and Photonics in Environmental Sustainability, focusing on the role of optics in marine conservation. Security issues are tackled in Intelligent Hash Function Based Key-Exchange Scheme for Ocean Underwater Data Transmission, and the overarching potential of AI in marine resource management is discussed in Artificial Intelligence as Key-enabler for Safeguarding the Marine Resources.
Quantitative Monitoring Of The Underwater Environment
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Author : Benoît Zerr
language : en
Publisher: Springer
Release Date : 2016-06-03
Quantitative Monitoring Of The Underwater Environment written by Benoît Zerr and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-03 with Technology & Engineering categories.
This volume constitutes the results of the International Conference on Underwater Environment, MOQESM’14, held at “Le Quartz” Conference Center in Brest, France, on October 14-15, 2014, within the framework of the 9th Sea Tech Week, International Marine Science and Technology Event. The objective of MOQESM'14 was to bring together researchers from both academia and industry, interested in marine robotics and hydrography with application to the coastal environment mapping and underwater infrastructures surveys. The common thread of the conference is the combination of technical control, perception, and localization, typically used in robotics, with the methods of mapping and bathymetry. The papers presented in this book focus on two main topics. Firstly, coastal and infrastructure mapping is addressed, focusing not only on hydrographic systems, but also on positioning systems, bathymetry, and remote sensing. The proposed methods rely on acoustic sensors such as side scan sonars, multibeam echo sounders, phase-measuring bathymetric sonars, as well as optical systems such as underwater laser scanners. Accurate underwater positioning is also addressed in the case of the use of a single acoustic beacon, and the latest advances in increasing the vertical precision of Global Navigation Satellite System (GNSS) are also presented. Most of the above mentioned works are closely related to autonomous marine vehicles. Consequently, the second part of the book describes some works concerning the methods associated with such type of vehicles. The selected papers focus on autonomous surface or underwater vehicles, detailing new approaches for localization, modeling, control, mapping, obstacle detection and avoidance, surfacing, and software development. Some of these works imply acoustics sensing as well as image processing. Set membership methods are also used in some papers. The applications of the work presented in this book concern in particular oceanography, monitoring of oil and gas infrastructures, and military field.
Deep Learning For Unmanned Systems
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Author : Anis Koubaa
language : en
Publisher: Springer Nature
Release Date : 2021-10-01
Deep Learning For Unmanned Systems written by Anis Koubaa and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-01 with Technology & Engineering categories.
This book is used at the graduate or advanced undergraduate level and many others. Manned and unmanned ground, aerial and marine vehicles enable many promising and revolutionary civilian and military applications that will change our life in the near future. These applications include, but are not limited to, surveillance, search and rescue, environment monitoring, infrastructure monitoring, self-driving cars, contactless last-mile delivery vehicles, autonomous ships, precision agriculture and transmission line inspection to name just a few. These vehicles will benefit from advances of deep learning as a subfield of machine learning able to endow these vehicles with different capability such as perception, situation awareness, planning and intelligent control. Deep learning models also have the ability to generate actionable insights into the complex structures of large data sets. In recent years, deep learning research has received an increasing amount of attention from researchers in academia, government laboratories and industry. These research activities have borne some fruit in tackling some of the challenging problems of manned and unmanned ground, aerial and marine vehicles that are still open. Moreover, deep learning methods have been recently actively developed in other areas of machine learning, including reinforcement training and transfer/meta-learning, whereas standard, deep learning methods such as recent neural network (RNN) and coevolutionary neural networks (CNN). The book is primarily meant for researchers from academia and industry, who are working on in the research areas such as engineering, control engineering, robotics, mechatronics, biomedical engineering, mechanical engineering and computer science. The book chapters deal with the recent research problems in the areas of reinforcement learning-based control of UAVs and deep learning for unmanned aerial systems (UAS) The book chapters present various techniques of deep learning for robotic applications. The book chapters contain a good literature survey with a long list of references. The book chapters are well written with a good exposition of the research problem, methodology, block diagrams and mathematical techniques. The book chapters are lucidly illustrated with numerical examples and simulations. The book chapters discuss details of applications and future research areas.