The compleat modellist [[electronic resource] ] : shewing the true and exact way of raising the model of any ship or vessel, small or great, either in proportion, or out of proportion. Also the manner how to find the length of every rope exactly. And tables which give the true bigness of every rope in each vessel. Together with the weights of their cables and anchors. / / Performed by Thomas Miller of Great Yarmouth, seaman and master in the art of raising the model

• Record Nr.: UNISA996395898003316
• Autore: Miller Thomas, seaman
• Pubbl/distr/stampa: London, : Printed for William Fisher at the Postern Gate near Tower Hill. Tho Passenger at the Three Bibles on London Bridg. and Ralph Smith under the Royal Exchange, 1684
• Descrizione fisica: [4], 8, [12] p., [2] folded leaves of plates : ill
• Soggetti: Ship models - England; Masts and rigging - England; Shipbuilding - England
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Reproduction of original in: British Library.
• Sommario/riassunto: eebo-0018

Artificial Intelligence Oceanography / / edited by Xiaofeng Li, Fan Wang

• Record Nr.: UNINA9910647486903321
• Autore: Li Xiaofeng
• Pubbl/distr/stampa: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023
• ISBN: 981-19-6375-4
• Edizione: [1st ed. 2023.]
• Descrizione fisica: 1 online resource (xii, 346 pages) : illustrations (chiefly color)
• Classificazione: COM004000COM018000SCI026000SCI030000SCI042000SCI052000
• Altri autori (Persone): LiXiaofeng; WangFan
• Disciplina: 551.46
• Soggetti: Oceanography; Atmospheric science; Geographic information systems; Artificial intelligence; Sustainability; Ocean Sciences; Atmospheric Science; Geographical Information System; Artificial Intelligence
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Theory and technology of artificial intelligence for oceanography -- Satellite data-driven internal wave forecast model based on machine learning techniques -- Detection and analysis of marine macroalgae based on artificial intelligence -- Tropical cyclone intensity estimation from geostationary satellite imagery -- Reconstructing marine environmental data based on deep learning -- Detecting oceanic processes from space-borne sar imagery using machine learning -- Deep convolutional neural networks-based coastal inundation mapping for un-defined least developed countries: taking madagascar and mozambique as examples -- Ai- based mesoscale eddy study -- Classifying sea ice types from sar images based on deep fully convolutional networks -- Detecting ships and extracting ship's size from SAR images based on deep learning -- Quality control of ocean temperature and salinity data based on machine learning technology -- ; automatic extraction of internal wave signature from multiple satellite sensors based on deep convolutional neural networks -- Automatic extraction of waterlines from large-scale tidal flats on SAR images and applications based on deep convolutional neural networks -- Forecast of tropical instability waves using deep learning -- Sea surface height prediction based on artificial intelligence.
• Sommario/riassunto: 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. .