Sanjeet Kumar Dwivedi – författare
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4 produkter
4 produkter
Häftad, Engelska, 2018
1 426 kr
Skickas inom 10-15 vardagar
Modeling and Control of Power Electronics Converter Systems for Power Quality Improvements provides grounded theory for the modeling, analysis and control of different converter topologies that improve the power quality of mains. Intended for researchers and practitioners working in the field, topics include modeling equations and the state of research to improve power quality converters. By presenting control methods for different converter topologies and aspects related to multi-level inverters and specific analysis related to the AC interface of drives, the book helps users by putting a particular emphasis on different control algorithms that enhance knowledge and research work.
Present In-depth coverage of modeling and control methods for different converter topology Includes a particular emphasis on different control algorithms to give readers an easier understanding Provides a results and discussion chapter and MATLAB simulation to support worked examples and real-life application scenarios
E-bok
Engelska, 20181 975 kr
Läs direkt efter köp
Modeling and Control of Power Electronics Converter Systems for Power Quality Improvements provides grounded theory for the modeling, analysis and control of different converter topologies that improve the power quality of mains. Intended for researchers and practitioners working in the field, topics include modeling equations and the state of research to improve power quality converters. By presenting control methods for different converter topologies and aspects related to multi-level inverters and specific analysis related to the AC interface of drives, the book helps users by putting a particular emphasis on different control algorithms that enhance knowledge and research work.- Present In-depth coverage of modeling and control methods for different converter topology- Includes a particular emphasis on different control algorithms to give readers an easier understanding- Provides a results and discussion chapter and MATLAB simulation to support worked examples and real-life application scenarios
Häftad, Engelska, 2024
1 146 kr
Skickas inom 10-15 vardagar
Cognitive Science, Computational Intelligence, and Data Analytics: Methods and Applications with Python introduces readers to the foundational concepts of data analysis, cognitive science, and computational intelligence, including AI and Machine Learning. The book's focus is on fundamental ideas, procedures, and computational intelligence tools that can be applied to a wide range of data analysis approaches, with applications that include mathematical programming, evolutionary simulation, machine learning, and logic-based models. It offers readers the fundamental and practical aspects of cognitive science and data analysis, exploring data analytics in terms of description, evolution, and applicability in real-life problems.The authors cover the history and evolution of cognitive analytics, methodological concerns in philosophy, syntax and semantics, understanding of generative linguistics, theory of memory and processing theory, structured and unstructured data, qualitative and quantitative data, measurement of variables, nominal, ordinals, intervals, and ratio scale data. The content in this book is tailored to the reader's needs in terms of both type and fundamentals, including coverage of multivariate analysis, CRISP methodology and SEMMA methodology. Each chapter provides practical, hands-on learning with real-world applications, including case studies and Python programs related to the key concepts being presented.Demystifies the theory of data analytics using a step-by-step approachCovers the intersection of cognitive science, computational intelligence, and data analytics by providing examples and case studies with applied algorithms, mathematics, and Python programming codeIntroduces foundational data analytics techniques such as CRISP-DM, SEMMA, and Object Detection Models in the context of computational intelligence methods and toolsCovers key concepts of multivariate and cognitive data analytics such as factor analytics, principal component analytics, linear regression analysis, logistic regression analysis, and value chain applications
E-bok
Engelska, 20241 627 kr
Läs direkt efter köp
Cognitive Science, Computational Intelligence, and Data Analytics: Methods and Applications with Python introduces readers to the foundational concepts of data analysis, cognitive science, and computational intelligence, including AI and Machine Learning. The book''s focus is on fundamental ideas, procedures, and computational intelligence tools that can be applied to a wide range of data analysis approaches, with applications that include mathematical programming, evolutionary simulation, machine learning, and logic-based models. It offers readers the fundamental and practical aspects of cognitive science and data analysis, exploring data analytics in terms of description, evolution, and applicability in real-life problems.The authors cover the history and evolution of cognitive analytics, methodological concerns in philosophy, syntax and semantics, understanding of generative linguistics, theory of memory and processing theory, structured and unstructured data, qualitative and quantitative data, measurement of variables, nominal, ordinals, intervals, and ratio scale data. The content in this book is tailored to the reader''s needs in terms of both type and fundamentals, including coverage of multivariate analysis, CRISP methodology and SEMMA methodology. Each chapter provides practical, hands-on learning with real-world applications, including case studies and Python programs related to the key concepts being presented.- Demystifies the theory of data analytics using a step-by-step approach- Covers the intersection of cognitive science, computational intelligence, and data analytics by providing examples and case studies with applied algorithms, mathematics, and Python programming code- Introduces foundational data analytics techniques such as CRISP-DM, SEMMA, and Object Detection Models in the context of computational intelligence methods and tools- Covers key concepts of multivariate and cognitive data analytics such as factor analytics, principal component analytics, linear regression analysis, logistic regression analysis, and value chain applications