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Author: Joshua Ighalo
Publisher:
ISBN:
Category :
Languages : en
Pages : 55
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Book Description
This thesis proposes a new machine learning model for predicting the corrosion rate of 3C steel in seawater. The corrosion rate of a material depends not just on the nature of the material but also on the material's environmental conditions. The proposed machine learning model comes with a selection framework based on the hyperparameter optimization method and a performance evaluation metric to determine the models that qualify for further implementation in the proposed models' ensembles architecture. The major aim of the selection framework is to select the least number of models that will fit efficiently (while already hyperparameter-optimized) into the architecture of the proposed model. Subsequently, the proposed predictive model is fitted on some portion of a dataset generated from an experiment on corrosion rate in five different seawater conditions. The remaining portion of this dataset is implemented in estimating the corrosion rate. Furthermore, the performance of the proposed models' predictions was evaluated using three major performance evaluation metrics. These metrics were also used to evaluate the performance of two hyperparameter-optimized models (Smart Firefly Algorithm and Least Squares Support Vector Regression (SFA-LSSVR) and Support Vector Regression integrating Leave Out One Cross-Validation (SVR-LOOCV)) to facilitate their comparison with the proposed predictive model and its constituent models. The test results show that the proposed model performs slightly below the SFA-LSSVR model and above the SVR-LOOCV model by an RMSE score difference of 0.305 and RMSE score of 0.792. Despite its poor performance against the SFA-LSSVR model, the super learner model outperforms both hyperparameter-optimized models in the utilization of memory and computation time (graphically presented in this thesis).
Author: Joshua Ighalo
Publisher:
ISBN:
Category :
Languages : en
Pages : 55
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Book Description
This thesis proposes a new machine learning model for predicting the corrosion rate of 3C steel in seawater. The corrosion rate of a material depends not just on the nature of the material but also on the material's environmental conditions. The proposed machine learning model comes with a selection framework based on the hyperparameter optimization method and a performance evaluation metric to determine the models that qualify for further implementation in the proposed models' ensembles architecture. The major aim of the selection framework is to select the least number of models that will fit efficiently (while already hyperparameter-optimized) into the architecture of the proposed model. Subsequently, the proposed predictive model is fitted on some portion of a dataset generated from an experiment on corrosion rate in five different seawater conditions. The remaining portion of this dataset is implemented in estimating the corrosion rate. Furthermore, the performance of the proposed models' predictions was evaluated using three major performance evaluation metrics. These metrics were also used to evaluate the performance of two hyperparameter-optimized models (Smart Firefly Algorithm and Least Squares Support Vector Regression (SFA-LSSVR) and Support Vector Regression integrating Leave Out One Cross-Validation (SVR-LOOCV)) to facilitate their comparison with the proposed predictive model and its constituent models. The test results show that the proposed model performs slightly below the SFA-LSSVR model and above the SVR-LOOCV model by an RMSE score difference of 0.305 and RMSE score of 0.792. Despite its poor performance against the SFA-LSSVR model, the super learner model outperforms both hyperparameter-optimized models in the utilization of memory and computation time (graphically presented in this thesis).
Author: Fi-John Chang
Publisher: MDPI
ISBN: 3038975486
Category : Technology & Engineering
Languages : en
Pages : 376
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Book Description
Nowadays, the degree and scale of flood hazards has been massively increasing as a result of the changing climate, and large-scale floods jeopardize lives and properties, causing great economic losses, in the inundation-prone areas of the world. Early flood warning systems are promising countermeasures against flood hazards and losses. A collaborative assessment according to multiple disciplines, comprising hydrology, remote sensing, and meteorology, of the magnitude and impacts of flood hazards on inundation areas significantly contributes to model the integrity and precision of flood forecasting. Methodologically oriented countermeasures against flood hazards may involve the forecasting of reservoir inflows, river flows, tropical cyclone tracks, and flooding at different lead times and/or scales. Analyses of impacts, risks, uncertainty, resilience, and scenarios coupled with policy-oriented suggestions will give information for flood hazard mitigation. Emerging advances in computing technologies coupled with big-data mining have boosted data-driven applications, among which Machine Learning technology, with its flexibility and scalability in pattern extraction, has modernized not only scientific thinking but also predictive applications. This book explores recent Machine Learning advances on flood forecast and management in a timely manner and presents interdisciplinary approaches to modelling the complexity of flood hazards-related issues, with contributions to integrative solutions from a local, regional or global perspective.
Author: Pierre Roberge
Publisher: McGraw-Hill Prof Med/Tech
ISBN: 0070765162
Category : Technology & Engineering
Languages : en
Pages : 1154
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Book Description
Reduce the enormous economic and environmental impact of corrosion Emphasizing quantitative techniques, this guide provides you with: *Theory essential for understanding aqueous, atmospheric, and high temperature corrosion processes Corrosion resistance data for various materials Management techniques for dealing with corrosion control, including life prediction and cost analysis, information systems, and knowledge re-use Techniques for the detection, analysis, and prevention of corrosion damage, including protective coatings and cathodic protection More
Author: Thomas K Abraham
Publisher: Packt Publishing Ltd
ISBN: 1789130271
Category : Computers
Languages : en
Pages : 331
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Book Description
Implement machine learning, cognitive services, and artificial intelligence solutions by leveraging Azure cloud technologies Key FeaturesLearn advanced concepts in Azure ML and the Cortana Intelligence Suite architectureExplore ML Server using SQL Server and HDInsight capabilitiesImplement various tools in Azure to build and deploy machine learning modelsBook Description Implementing Machine learning (ML) and Artificial Intelligence (AI) in the cloud had not been possible earlier due to the lack of processing power and storage. However, Azure has created ML and AI services that are easy to implement in the cloud. Hands-On Machine Learning with Azure teaches you how to perform advanced ML projects in the cloud in a cost-effective way. The book begins by covering the benefits of ML and AI in the cloud. You will then explore Microsoft’s Team Data Science Process to establish a repeatable process for successful AI development and implementation. You will also gain an understanding of AI technologies available in Azure and the Cognitive Services APIs to integrate them into bot applications. This book lets you explore prebuilt templates with Azure Machine Learning Studio and build a model using canned algorithms that can be deployed as web services. The book then takes you through a preconfigured series of virtual machines in Azure targeted at AI development scenarios. You will get to grips with the ML Server and its capabilities in SQL and HDInsight. In the concluding chapters, you’ll integrate patterns with other non-AI services in Azure. By the end of this book, you will be fully equipped to implement smart cognitive actions in your models. What you will learnDiscover the benefits of leveraging the cloud for ML and AIUse Cognitive Services APIs to build intelligent botsBuild a model using canned algorithms from Microsoft and deploy it as a web serviceDeploy virtual machines in AI development scenariosApply R, Python, SQL Server, and Spark in AzureBuild and deploy deep learning solutions with CNTK, MMLSpark, and TensorFlowImplement model retraining in IoT, Streaming, and Blockchain solutionsExplore best practices for integrating ML and AI functions with ADLA and logic appsWho this book is for If you are a data scientist or developer familiar with Azure ML and cognitive services and want to create smart models and make sense of data in the cloud, this book is for you. You’ll also find this book useful if you want to bring powerful machine learning services into your cloud applications. Some experience with data manipulation and processing, using languages like SQL, Python, and R, will aid in understanding the concepts covered in this book
Author: Christofer Leygraf
Publisher: John Wiley & Sons
ISBN: 1118762185
Category : Science
Languages : en
Pages : 509
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Book Description
ATMOSPHERIC CORROSION Presents a comprehensive look at atmospheric corrosion, combining expertise in corrosion science and atmospheric chemistry Atmospheric corrosion has been a subject of engineering study, largely empirical, for nearly a century. Scientists came to the field rather later on and had considerable difficulty bringing their arsenal of tools to bear on the problem. Atmospheric corrosion was traditionally studied by specialists in corrosion having little knowledge of atmospheric chemistry, history, or prospects. Atmospheric Corrosion provides a combined approach bringing together experimental corrosion and atmospheric chemistry. The second edition expands on this approach by including environmental aspects of corrosion, atmospheric corrosion modeling, and international corrosion exposure programs. The combination of specialties provides a more comprehensive coverage of the topic. These scientific insights into the corrosion process and its amelioration are the focus of this book. Key topics include the following: Basic principles of atmospheric corrosion chemistry Corrosion mechanisms in controlled and uncontrolled environments Degradation of materials in architectural, transport, and structural applications; electronic devices; and cultural artifacts Protection of existing materials and choosing new ones that resist corrosion Prediction of how and where atmospheric corrosion may evolve in the future Complete with appendices discussing experimental techniques, computer models, and the degradation of specific metals, Atmospheric Corrosion, Second Edition continues to be an invaluable resource for corrosion scientists, corrosion engineers, conservators, environmental scientists, and anyone interested in the theory and application of this evolving field. The book concerns primarily the atmospheric corrosion of metals and is written at a level suitable for advanced undergraduates or beginning graduate students in any of the physical or engineering sciences.
Author: Raul Villamarin Rodriguez
Publisher: CRC Press
ISBN: 1000629074
Category : Computers
Languages : en
Pages : 133
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Book Description
Coded Leadership: Developing Scalable Management in an AI-induced Quantum World will assist researchers and industry experts working towards improvising their processes and developing and deploying strategies in an AI-induced world of quantum computing. The book introduces the necessary background to understand the challenges in today’s organizational leadership and how artificial intelligence enables learning to be viewed from a probabilistic framework. Key Features Introduction to Quantum Natural Language Processing. Overview of Leadership and AI. The Age of Quantum Superiority. Challenges to Today’s Leadership. AI-induced Strategic Implementation and Organizational Performance. This book serves as a reference for researchers that need to know how AI and quantum can assist in leadership and organizational performance. The book will also be helpful for students that want to learn more about AI and quantum computing in various business applications.
Author:
Publisher:
ISBN:
Category : Aeronautics
Languages : en
Pages : 384
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Book Description
Author: Gurminder Singh
Publisher: Elsevier
ISBN: 0443221464
Category : Technology & Engineering
Languages : en
Pages : 291
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Book Description
Machine Learning for Powder-based Metal Additive Manufacturing outlines machine learning (ML) methods for additive manufacturing (AM) of metals that will improve product quality, optimize manufacturing processes, and reduce costs. The book combines ML and AM methods to develop intelligent models that train AM techniques in pre-processing, process optimization, and post-processing for optimized microstructure, tensile and fatigue properties, and biocompatibility for various applications. The book covers ML for design in AM, ML for materials development and intelligent monitoring in metal AM, both geometrical deviation and physics informed machine learning modeling, as well as data-driven cost estimation by ML. In addition, optimization for slicing and orientation, ML to create models of materials for AM processes, ML prediction for better mechanical and microstructure prediction, and feature extraction by sensing data are all covered, and each chapter includes a case study. - Covers machine learning (ML) methods for additive manufacturing (AM) of metals that will improve product quality, optimize manufacturing processes, and reduce costs - Combines ML and AM methods to develop intelligent models that train AM techniques in pre-processing, process optimization, and post-processing for optimized microstructure, tensile and fatigue properties, and biocompatibility for various applications - Discusses algorithm development of ML for metal AM, metal AM process modeling and optimization, mathematical and simulation studies of metal AM, and pre- and post-processing smart methods for metal AM
Author: Jens K. Nørskov
Publisher: John Wiley & Sons
ISBN: 1118888952
Category : Technology & Engineering
Languages : en
Pages : 228
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Book Description
This book is based on a graduate course and suitable as a primer for any newcomer to the field, this book is a detailed introduction to the experimental and computational methods that are used to study how solid surfaces act as catalysts. Features include: First comprehensive description of modern theory of heterogeneous catalysis Basis for understanding and designing experiments in the field Allows reader to understand catalyst design principles Introduction to important elements of energy transformation technology Test driven at Stanford University over several semesters
Author: Radek Silhavy
Publisher: Springer
ISBN: 303000211X
Category : Technology & Engineering
Languages : en
Pages : 399
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Book Description
This book presents real-world problems and pioneering research in computational statistics, mathematical modeling, artificial intelligence and software engineering in the context of intelligent systems. It gathers the peer-reviewed proceedings of the 2nd Computational Methods in Systems and Software 2018 (CoMeSySo 2018), a conference that broke down traditional barriers by being held online. The goal of the event was to provide an international forum for discussing the latest high-quality research results.
