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Deep Learning in Medical Image Analysis: Challenges and Applications (Advances in Experimental Medicine and Biology #1213)
by Gobert Lee Hiroshi FujitaThis book presents cutting-edge research and applications of deep learning in a broad range of medical imaging scenarios, such as computer-aided diagnosis, image segmentation, tissue recognition and classification, and other areas of medical and healthcare problems. Each of its chapters covers a topic in depth, ranging from medical image synthesis and techniques for muskuloskeletal analysis to diagnostic tools for breast lesions on digital mammograms and glaucoma on retinal fundus images. It also provides an overview of deep learning in medical image analysis and highlights issues and challenges encountered by researchers and clinicians, surveying and discussing practical approaches in general and in the context of specific problems. Academics, clinical and industry researchers, as well as young researchers and graduate students in medical imaging, computer-aided-diagnosis, biomedical engineering and computer vision will find this book a great reference and very useful learning resource.
Deep Learning in Multi-step Prediction of Chaotic Dynamics: From Deterministic Models to Real-World Systems (SpringerBriefs in Applied Sciences and Technology)
by Giorgio Guariso Fabio Dercole Matteo SangiorgioThe book represents the first attempt to systematically deal with the use of deep neural networks to forecast chaotic time series. Differently from most of the current literature, it implements a multi-step approach, i.e., the forecast of an entire interval of future values. This is relevant for many applications, such as model predictive control, that requires predicting the values for the whole receding horizon. Going progressively from deterministic models with different degrees of complexity and chaoticity to noisy systems and then to real-world cases, the book compares the performances of various neural network architectures (feed-forward and recurrent). It also introduces an innovative and powerful approach for training recurrent structures specific for sequence-to-sequence tasks. The book also presents one of the first attempts in the context of environmental time series forecasting of applying transfer-learning techniques such as domain adaptation.
Deep Learning in Solar Astronomy (SpringerBriefs in Computer Science)
by Long Xu Yihua Yan Xin HuangThe volume of data being collected in solar astronomy has exponentially increased over the past decade and we will be entering the age of petabyte solar data. Deep learning has been an invaluable tool exploited to efficiently extract key information from the massive solar observation data, to solve the tasks of data archiving/classification, object detection and recognition. Astronomical study starts with imaging from recorded raw data, followed by image processing, such as image reconstruction, inpainting and generation, to enhance imaging quality. We study deep learning for solar image processing. First, image deconvolution is investigated for synthesis aperture imaging. Second, image inpainting is explored to repair over-saturated solar image due to light intensity beyond threshold of optical lens. Third, image translation among UV/EUV observation of the chromosphere/corona, Ha observation of the chromosphere and magnetogram of the photosphere is realized by using GAN, exhibiting powerful image domain transfer ability among multiple wavebands and different observation devices. It can compensate the lack of observation time or waveband. In addition, time series model, e.g., LSTM, is exploited to forecast solar burst and solar activity indices. This book presents a comprehensive overview of the deep learning applications in solar astronomy. It is suitable for the students and young researchers who are major in astronomy and computer science, especially interdisciplinary research of them.
Deep Learning in Visual Computing: Explanations and Examples
by Hassan UgailDeep learning is an artificially intelligent entity that teaches itself and can be utilized to make predictions. Deep learning mimics the human brain and provides learned solutions addressing many challenging problems in the area of visual computing. From object recognition to image classification for diagnostics, deep learning has shown the power of artificial deep neural networks in solving real world visual computing problems with super-human accuracy. The introduction of deep learning into the field of visual computing has meant to be the death of most of the traditional image processing and computer vision techniques. Today, deep learning is considered to be the most powerful, accurate, efficient and effective method with the potential to solve many of the most challenging problems in visual computing. This book provides an insight into deep machine learning and the challenges in visual computing to tackle the novel method of machine learning. It introduces readers to the world of deep neural network architectures with easy-to-understand explanations. From face recognition to image classification for diagnosis of cancer, the book provides unique examples of solved problems in applied visual computing using deep learning. Interested and enthusiastic readers of modern machine learning methods will find this book easy to follow. They will find it a handy guide for designing and implementing their own projects in the field of visual computing.
Deep Learning in Visual Computing: Explanations and Examples
by Hassan UgailDeep learning is an artificially intelligent entity that teaches itself and can be utilized to make predictions. Deep learning mimics the human brain and provides learned solutions addressing many challenging problems in the area of visual computing. From object recognition to image classification for diagnostics, deep learning has shown the power of artificial deep neural networks in solving real world visual computing problems with super-human accuracy. The introduction of deep learning into the field of visual computing has meant to be the death of most of the traditional image processing and computer vision techniques. Today, deep learning is considered to be the most powerful, accurate, efficient and effective method with the potential to solve many of the most challenging problems in visual computing. This book provides an insight into deep machine learning and the challenges in visual computing to tackle the novel method of machine learning. It introduces readers to the world of deep neural network architectures with easy-to-understand explanations. From face recognition to image classification for diagnosis of cancer, the book provides unique examples of solved problems in applied visual computing using deep learning. Interested and enthusiastic readers of modern machine learning methods will find this book easy to follow. They will find it a handy guide for designing and implementing their own projects in the field of visual computing.
Deep Learning in Visual Computing and Signal Processing
by Krishna Kant Singh Vibhav Kumar Sachan Akansha Singh Sanjeevikumar PadmanabanAn enlightening amalgamation of deep learning concepts with visual computing and signal processing applications, this new volume covers the fundamentals and advanced topics in designing and deploying techniques using deep architectures and their application in visual computing and signal processing. The volume first lays out the fundamentals of deep learning as well as deep learning architectures and frameworks. It goes on to discuss deep learning in neural networks and deep learning for object recognition and detection models. It looks at the various specific applications of deep learning in visual and signal processing, such as in biorobotics, for automated brain tumor segmentation in MRI images, in neural networks for use in seizure classification, for digital forensic investigation based on deep learning, and more.
Deep Learning in Visual Computing and Signal Processing
by Krishna Kant Singh Vibhav Kumar Sachan Akansha Singh Sanjeevikumar PadmanabanAn enlightening amalgamation of deep learning concepts with visual computing and signal processing applications, this new volume covers the fundamentals and advanced topics in designing and deploying techniques using deep architectures and their application in visual computing and signal processing. The volume first lays out the fundamentals of deep learning as well as deep learning architectures and frameworks. It goes on to discuss deep learning in neural networks and deep learning for object recognition and detection models. It looks at the various specific applications of deep learning in visual and signal processing, such as in biorobotics, for automated brain tumor segmentation in MRI images, in neural networks for use in seizure classification, for digital forensic investigation based on deep learning, and more.
Deep Learning, Machine Learning and IoT in Biomedical and Health Informatics: Techniques and Applications (Biomedical Engineering)
by Sujata Dash Subhendu Kumar Pani Joel J. P. C. Rodrigues Babita MajhiBiomedical and Health Informatics is an important field that brings tremendous opportunities and helps address challenges due to an abundance of available biomedical data. This book examines and demonstrates state-of-the-art approaches for IoT and Machine Learning based biomedical and health related applications. This book aims to provide computational methods for accumulating, updating and changing knowledge in intelligent systems and particularly learning mechanisms that help us to induce knowledge from the data. It is helpful in cases where direct algorithmic solutions are unavailable, there is lack of formal models, or the knowledge about the application domain is inadequately defined. In the future IoT has the impending capability to change the way we work and live. These computing methods also play a significant role in design and optimization in diverse engineering disciplines. With the influence and the development of the IoT concept, the need for AI (artificial intelligence) techniques has become more significant than ever. The aim of these techniques is to accept imprecision, uncertainties and approximations to get a rapid solution. However, recent advancements in representation of intelligent IoTsystems generate a more intelligent and robust system providing a human interpretable, low-cost, and approximate solution. Intelligent IoT systems have demonstrated great performance to a variety of areas including big data analytics, time series, biomedical and health informatics. This book will be very beneficial for the new researchers and practitioners working in the biomedical and healthcare fields to quickly know the best performing methods. It will also be suitable for a wide range of readers who may not be scientists but who are also interested in the practice of such areas as medical image retrieval, brain image segmentation, among others. • Discusses deep learning, IoT, machine learning, and biomedical data analysis with broad coverage of basic scientific applications • Presents deep learning and the tremendous improvement in accuracy, robustness, and cross- language generalizability it has over conventional approaches • Discusses various techniques of IoT systems for healthcare data analytics • Provides state-of-the-art methods of deep learning, machine learning and IoT in biomedical and health informatics • Focuses more on the application of algorithms in various real life biomedical and engineering problems
Deep Learning, Machine Learning and IoT in Biomedical and Health Informatics: Techniques and Applications (Biomedical Engineering)
by Subhendu Kumar Pani Joel J. P. C. Rodrigues Babita Majhi Sujata DashBiomedical and Health Informatics is an important field that brings tremendous opportunities and helps address challenges due to an abundance of available biomedical data. This book examines and demonstrates state-of-the-art approaches for IoT and Machine Learning based biomedical and health related applications. This book aims to provide computational methods for accumulating, updating and changing knowledge in intelligent systems and particularly learning mechanisms that help us to induce knowledge from the data. It is helpful in cases where direct algorithmic solutions are unavailable, there is lack of formal models, or the knowledge about the application domain is inadequately defined. In the future IoT has the impending capability to change the way we work and live. These computing methods also play a significant role in design and optimization in diverse engineering disciplines. With the influence and the development of the IoT concept, the need for AI (artificial intelligence) techniques has become more significant than ever. The aim of these techniques is to accept imprecision, uncertainties and approximations to get a rapid solution. However, recent advancements in representation of intelligent IoTsystems generate a more intelligent and robust system providing a human interpretable, low-cost, and approximate solution. Intelligent IoT systems have demonstrated great performance to a variety of areas including big data analytics, time series, biomedical and health informatics. This book will be very beneficial for the new researchers and practitioners working in the biomedical and healthcare fields to quickly know the best performing methods. It will also be suitable for a wide range of readers who may not be scientists but who are also interested in the practice of such areas as medical image retrieval, brain image segmentation, among others. • Discusses deep learning, IoT, machine learning, and biomedical data analysis with broad coverage of basic scientific applications • Presents deep learning and the tremendous improvement in accuracy, robustness, and cross- language generalizability it has over conventional approaches • Discusses various techniques of IoT systems for healthcare data analytics • Provides state-of-the-art methods of deep learning, machine learning and IoT in biomedical and health informatics • Focuses more on the application of algorithms in various real life biomedical and engineering problems
Deep Learning Techniques for Biomedical and Health Informatics (Studies in Big Data #68)
by Sujata Dash Biswa Ranjan Acharya Mamta Mittal Ajith Abraham Arpad KelemenThis book presents a collection of state-of-the-art approaches for deep-learning-based biomedical and health-related applications. The aim of healthcare informatics is to ensure high-quality, efficient health care, and better treatment and quality of life by efficiently analyzing abundant biomedical and healthcare data, including patient data and electronic health records (EHRs), as well as lifestyle problems. In the past, it was common to have a domain expert to develop a model for biomedical or health care applications; however, recent advances in the representation of learning algorithms (deep learning techniques) make it possible to automatically recognize the patterns and represent the given data for the development of such model. This book allows new researchers and practitioners working in the field to quickly understand the best-performing methods. It also enables them to compare different approaches and carry forward their research in an important area that has a direct impact on improving the human life and health. It is intended for researchers, academics, industry professionals, and those at technical institutes and R&D organizations, as well as students working in the fields of machine learning, deep learning, biomedical engineering, health informatics, and related fields.
Deep Learning Techniques for Biomedical and Health Informatics
by Lakhmi C. Jain Basant Agarwal Valentina Emilia Balas Ramesh Chandra Poonia ManishaDeep Learning Techniques for Biomedical and Health Informatics provides readers with the state-of-the-art in deep learning-based methods for biomedical and health informatics. The book covers not only the best-performing methods, it also presents implementation methods. The book includes all the prerequisite methodologies in each chapter so that new researchers and practitioners will find it very useful. Chapters go from basic methodology to advanced methods, including detailed descriptions of proposed approaches and comprehensive critical discussions on experimental results and how they are applied to Biomedical Engineering, Electronic Health Records, and medical image processing. Examines a wide range of Deep Learning applications for Biomedical Engineering and Health Informatics, including Deep Learning for drug discovery, clinical decision support systems, disease diagnosis, prediction and monitoringDiscusses Deep Learning applied to Electronic Health Records (EHR), including health data structures and management, deep patient similarity learning, natural language processing, and how to improve clinical decision-makingProvides detailed coverage of Deep Learning for medical image processing, including optimizing medical big data, brain image analysis, brain tumor segmentation in MRI imaging, and the future of biomedical image analysis
Deep Learning Technologies for the Sustainable Development Goals: Issues and Solutions in the Post-COVID Era (Advanced Technologies and Societal Change)
by Virender Kadyan T. P. Singh Chidiebere UgwuThis book provides insights into deep learning techniques that impact the implementation strategies toward achieving the Sustainable Development Goals (SDGs) laid down by the United Nations for its 2030 agenda, elaborating on the promises, limits, and the new challenges. It also covers the challenges, hurdles, and opportunities in various applications of deep learning for the SDGs. A comprehensive survey on the major applications and research, based on deep learning techniques focused on SDGs through speech and image processing, IoT, security, AR-VR, formal methods, and blockchain, is a feature of this book. In particular, there is a need to extend research into deep learning and its broader application to many sectors and to assess its impact on achieving the SDGs. The chapters in this book help in finding the use of deep learning across all sections of SDGs. The rapid development of deep learning needs to be supported by the organizational insight and oversight necessary for AI-based technologies in general; hence, this book presents and discusses the implications of how deep learning enables the delivery agenda for sustainable development.
Deep Learning to See: Towards New Foundations of Computer Vision (SpringerBriefs in Computer Science)
by Alessandro Betti Marco Gori Stefano MelacciThe remarkable progress in computer vision over the last few years is, by and large, attributed to deep learning, fueled by the availability of huge sets of labeled data, and paired with the explosive growth of the GPU paradigm. While subscribing to this view, this work criticizes the supposed scientific progress in the field, and proposes the investigation of vision within the framework of information-based laws of nature. This work poses fundamental questions about vision that remain far from understood, leading the reader on a journey populated by novel challenges resonating with the foundations of machine learning. The central thesis proposed is that for a deeper understanding of visual computational processes, it is necessary to look beyond the applications of general purpose machine learning algorithms, and focus instead on appropriate learning theories that take into account the spatiotemporal nature of the visual signal.Serving to inspire and stimulate critical reflection and discussion, yet requiring no prior advanced technical knowledge, the text can naturally be paired with classic textbooks on computer vision to better frame the current state of the art, open problems, and novel potential solutions. As such, it will be of great benefit to graduate and advanced undergraduate students in computer science, computational neuroscience, physics, and other related disciplines.
Deep Learning With Fastai Cookbook (PDF): Leverage The Easy-to-use Fastai Framework To Unlock The Power Of Deep Learning
by Mark RyanHarness the power of the easy-to-use, high-performance fastai framework to rapidly create complete deep learning solutions with few lines of code Key Features Discover how to apply state-of-the-art deep learning techniques to real-world problems Build and train neural networks using the power and flexibility of the fastai framework Use deep learning to tackle problems such as image classification and text classification Book Description fastai is an easy-to-use deep learning framework built on top of PyTorch that lets you rapidly create complete deep learning solutions with as few as 10 lines of code. Both predominant low-level deep learning frameworks, TensorFlow and PyTorch, require a lot of code, even for straightforward applications. In contrast, fastai handles the messy details for you and lets you focus on applying deep learning to actually solve problems. The book begins by summarizing the value of fastai and showing you how to create a simple 'hello world' deep learning application with fastai. You'll then learn how to use fastai for all four application areas that the framework explicitly supports: tabular data, text data (NLP), recommender systems, and vision data. As you advance, you'll work through a series of practical examples that illustrate how to create real-world applications of each type. Next, you'll learn how to deploy fastai models, including creating a simple web application that predicts what object is depicted in an image. The book wraps up with an overview of the advanced features of fastai. By the end of this fastai book, you'll be able to create your own deep learning applications using fastai. You'll also have learned how to use fastai to prepare raw datasets, explore datasets, train deep learning models, and deploy trained models. What you will learn Prepare real-world raw datasets to train fastai deep learning models Train fastai deep learning models using text and tabular data Create recommender systems with fastai Find out how to assess whether fastai is a good fit for a given problem Deploy fastai deep learning models in web applications Train fastai deep learning models for image classification Who this book is for This book is for data scientists, machine learning developers, and deep learning enthusiasts looking to explore the fastai framework using a recipe-based approach. Working knowledge of the Python programming language and machine learning basics is strongly recommended to get the most out of this deep learning book.
The Deep Learning With Keras Workshop: Learn How To Define And Train Neural Network Models With Just A Few Lines Of Code
by Matthew Moocarme Mahla Abdolahnejad Ritesh BhagwatNew experiences can be intimidating, but not this one! This beginner's guide to deep learning is here to help you explore deep learning from scratch with Keras, and be on your way to training your first ever neural networks. What sets Keras apart from other deep learning frameworks is its simplicity. With over two hundred thousand users, Keras has a stronger adoption in industry and the research community than any other deep learning framework. The Deep Learning with Keras Workshop starts by introducing you to the fundamental concepts of machine learning using the scikit-learn package. After learning how to perform the linear transformations that are necessary for building neural networks, you'll build your first neural network with the Keras library. As you advance, you'll learn how to build multi-layer neural networks and recognize when your model is underfitting or overfitting to the training data. With the help of practical exercises, you'll learn to use cross-validation techniques to evaluate your models and then choose the optimal hyperparameters to fine-tune their performance. Finally, you'll explore recurrent neural networks and learn how to train them to predict values in sequential data. By the end of this book, you'll have developed the skills you need to confidently train your own neural network models.
Deep Learning With Tensorflow And Keras: Build And Deploy Supervised, Unsupervised, Deep, And Reinforcement Learning Models
by Antonio Gulli Sujit Pal Amita Kapoor François CholletDeep Learning with TensorFlow and Keras, Third Edition teaches deep learning techniques alongside TensorFlow (TF) and Keras. The book introduces neural networks with TensorFlow, runs through the main applications, covers two working example apps, and then dives into TF and cloud in production, TF mobile, and using TensorFlow with AutoML.
The Deep Learning Workshop: Learn The Skills You Need To Develop Your Own Next-generation Deep Learning Models With Tensorflow And Keras
by Mirza Rahim Baig Thomas V. Joseph Nipun Sadvilkar Mohan Kumar Silaparasetty Anthony SoAre you fascinated by how deep learning powers intelligent applications such as self-driving cars, virtual assistants, facial recognition devices, and chatbots to process data and solve complex problems? Whether you are familiar with machine learning or are new to this domain, The Deep Learning Workshop will make it easy for you to understand deep learning with the help of interesting examples and exercises throughout. The book starts by highlighting the relationship between deep learning, machine learning, and artificial intelligence and helps you get comfortable with the TensorFlow 2.0 programming structure using hands-on exercises. You'll understand neural networks, the structure of a perceptron, and how to use TensorFlow to create and train models. The book will then let you explore the fundamentals of computer vision by performing image recognition exercises with convolutional neural networks (CNNs) using Keras. As you advance, you'll be able to make your model more powerful by implementing text embedding and sequencing the data using popular deep learning solutions. Finally, you'll get to grips with bidirectional recurrent neural networks (RNNs) and build generative adversarial networks (GANs) for image synthesis. By the end of this deep learning book, you'll have learned the skills essential for building deep learning models with TensorFlow and Keras.
Deep Life: The Hunt for the Hidden Biology of Earth, Mars, and Beyond
by Tullis C. OnstottDeep Life takes readers to uncharted regions deep beneath Earth's crust in search of life in extreme environments and reveals how astonishing new discoveries by geomicrobiologists are helping the quest to find life in the solar system.Tullis Onstott, named one of the 100 most influential people in America by Time magazine, provides an insider’s look at the pioneering fieldwork that is shining vital new light on Earth’s hidden biology—a thriving subterranean biosphere that scientists once thought to be impossible. Come along on epic descents two miles underground into South African gold mines to experience the challenges that Onstott and his team had to overcome. Join them in their search for microbes in the ancient seabed below the desert floor in the American Southwest, and travel deep beneath the frozen wastelands of the Arctic tundra to discover life as it could exist on Mars.Blending cutting-edge science with thrilling scientific adventure, Deep Life features rare and unusual encounters with exotic life forms, including a bacterium living off radiation and a hermaphroditic troglodytic worm that has changed our understanding of how complex subsurface life can really be. This unforgettable book takes you to the absolute limits of life—the biotic fringe—where today’s scientists hope to discover the very origins of life itself.
Deep Life: The Hunt for the Hidden Biology of Earth, Mars, and Beyond (PDF)
by Tullis C. OnstottDeep Life takes readers to uncharted regions deep beneath Earth's crust in search of life in extreme environments and reveals how astonishing new discoveries by geomicrobiologists are helping the quest to find life in the solar system.Tullis Onstott, named one of the 100 most influential people in America by Time magazine, provides an insider’s look at the pioneering fieldwork that is shining vital new light on Earth’s hidden biology—a thriving subterranean biosphere that scientists once thought to be impossible. Come along on epic descents two miles underground into South African gold mines to experience the challenges that Onstott and his team had to overcome. Join them in their search for microbes in the ancient seabed below the desert floor in the American Southwest, and travel deep beneath the frozen wastelands of the Arctic tundra to discover life as it could exist on Mars.Blending cutting-edge science with thrilling scientific adventure, Deep Life features rare and unusual encounters with exotic life forms, including a bacterium living off radiation and a hermaphroditic troglodytic worm that has changed our understanding of how complex subsurface life can really be. This unforgettable book takes you to the absolute limits of life—the biotic fringe—where today’s scientists hope to discover the very origins of life itself.
Deep Marine Mineral Resources
by Yves Fouquet Denis LacroixThe risks of shortages for some crucial metals and uncertainty about the land-based reserves of several others justify the search to diversify our sources of supply and investigate their potential. Mineral resources in the deep sea are attracting increasing interest with the progressive discovery of various forms of ores. France possesses large areas of deep seafloor in the three oceans as well as world-class human and technological resources and know-how, resulting from over 40 years of experience. This study takes stock of knowledge about mineralisations and associated metals, technologies for exploring and exploiting them, biodiversity and the potential impact of exploitation on the deep environment and the partnerships which are vital for France and Europe. This information will be useful for decision-makers in drawing up strategies, defining research and development programmes and in enhancing and developing commercial utilizations for these high-potential resources.
Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again
by Eric TopolA Science Friday pick for book of the year, 2019One of America's top doctors reveals how AI will empower physicians and revolutionize patient care Medicine has become inhuman, to disastrous effect. The doctor-patient relationship--the heart of medicine--is broken: doctors are too distracted and overwhelmed to truly connect with their patients, and medical errors and misdiagnoses abound. In Deep Medicine, leading physician Eric Topol reveals how artificial intelligence can help. AI has the potential to transform everything doctors do, from notetaking and medical scans to diagnosis and treatment, greatly cutting down the cost of medicine and reducing human mortality. By freeing physicians from the tasks that interfere with human connection, AI will create space for the real healing that takes place between a doctor who can listen and a patient who needs to be heard. Innovative, provocative, and hopeful, Deep Medicine shows us how the awesome power of AI can make medicine better, for all the humans involved.
The Deep Metaphysics of Space: An Alternative History and Ontology Beyond Substantivalism and Relationism (European Studies in Philosophy of Science #3)
by Edward SlowikThis volume explores the inadequacies of the two standard conceptions of space or spacetime, substantivalism and relationism, and in the process, proposes a new historical interpretation of these physical theories. This book also examines and develops alternative ontological conceptions of space, and explores additional historical elements of seventeenth century theories and other metaphysical themes.The author first discusses the two main opposing theories of the ontology of space. One, known as substantivalism, proposes space to be an entity that can exist independently of material things. The other, relationism, contends that space is a relation among material things. Readers will learn about specific problems with this dichotomy. First, Newton and Leibniz are often upheld as the retrospective forerunners of substantivalism and relationism. But, their work often contradicts the central tenets of these views. Second, these theories have proven problematic when transferred to a modern setting, especially with regards to general relativity and the recent quantum gravity hypotheses.The author details an alternative set of concepts that address these problems. The author also develops a new classificational system that provides a more accurate taxonomy for the elements of all spatial ontologies. This classification obtains successful analogies between Newton, Leibniz, and other natural philosophers with contemporary physical theories.
Deep Oil Spills: Facts, Fate, and Effects
by Steven A. Murawski Cameron H. Ainsworth Sherryl Gilbert David J. Hollander Claire B. Paris Michael Schlüter Dana L. WetzelThe demand for oil and gas has brought exploration and production to unprecedented depths of the world’s oceans. Currently, over 50% of the oil from the Gulf of Mexico now comes from waters in excess of 1,500 meters (one mile) deep, where no oil was produced just 20 years ago. The Deepwater Horizon oil spill blowout did much to change the perception of oil spills as coming just from tanker accidents, train derailments, and pipeline ruptures. In fact, beginning with the Ixtoc 1 spill off Campeche, Mexico in 1979-1980, there have been a series of large spill events originating at the sea bottom and creating a myriad of new environmental and well control challenges. This volume explores the physics, chemistry, sub-surface oil deposition and environmental impacts of deep oil spills. Key lessons learned from the responses to previous deep spills, as well as unresolved scientific questions for additional research are highlighted, all of which are appropriate for governmental regulators, politicians, industry decision-makers, first responders, researchers and students wanting an incisive overview of issues surrounding deep-water oil and gas production.
Deep Reinforcement Learning with Guaranteed Performance: A Lyapunov-Based Approach (Studies in Systems, Decision and Control #265)
by Yinyan Zhang Shuai Li Xuefeng ZhouThis book discusses methods and algorithms for the near-optimal adaptive control of nonlinear systems, including the corresponding theoretical analysis and simulative examples, and presents two innovative methods for the redundancy resolution of redundant manipulators with consideration of parameter uncertainty and periodic disturbances.It also reports on a series of systematic investigations on a near-optimal adaptive control method based on the Taylor expansion, neural networks, estimator design approaches, and the idea of sliding mode control, focusing on the tracking control problem of nonlinear systems under different scenarios. The book culminates with a presentation of two new redundancy resolution methods; one addresses adaptive kinematic control of redundant manipulators, and the other centers on the effect of periodic input disturbance on redundancy resolution.Each self-contained chapter is clearly written, making the book accessible to graduate students as well as academic and industrial researchers in the fields of adaptive and optimal control, robotics, and dynamic neural networks.
Deep-Sea Ecosystems Off Mauritania: Research of Marine Biodiversity and Habitats in the Northwest African Margin
by Ana Ramos Fran Ramil José Luis SanzThis book compiles the main findings of the multidisciplinary long-term research program developed in the continental margin of one of the more productive and unknown areas of the world oceans, Northwest Africa. The more than 25,000 preserved fishes and benthic invertebrates and quantitative data collected in 342 trawling stations, the 267 oceanographic profiles, the 211 sediment samples and the 28,122 km2 prospected by multi˗beam echo sounding allowed to obtain an overview of the amazing biodiversity of the demersal and benthic fauna inhabiting soft- and hard-bottom habitats, as well as the fascinating geomorphology and oceanography, hidden in the Mauritanian slope.