Simulating Nonlinear Circuits With Python Power Electronics
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Simulating Nonlinear Circuits with Python Power Electronics
This book provides readers with an in-depth discussion of circuit simulation, combining basic electrical engineering circuit theory with Python programming. It fills an information gap by describing the development of Python Power Electronics, an open-source software for simulating circuits, and demonstrating its use in a sample circuit. Unlike typical books on circuit theory that describe how circuits can be solved mathematically, followed by examples of simulating circuits using specific, commercial software, this book has a different approach and focus. The author begins by describing every aspect of the open-source software, in the context of non-linear power electronic circuits, as a foundation for aspiring or practicing engineers to embark on further development of open source software for different purposes. By demonstrating explicitly the operation of the software through algorithms, this book brings together the fields of electrical engineering and software technology.
Modeling and Python Simulation of Magnetics for Power Electronics Applications
This book describes the role of magnetism in electrical engineering, starting from the most basic laws of physics, converted into simulation models such that electrical engineering students can learn by example and practice. The author demystifies a topic that many electrical engineers take for granted, providing readers the tools to be able to understand how any magnetic component works. He describes magnetic components like inductors and transformers in simple understandable language. Mathematical equations related to the basic laws of physics are described in detail along with the physical significance of the equations. Every application is supported by a simulation. All simulations are performed using free and open source software based on Python making the material in this book universally accessible.
Deep Learning Models explored with help of Python Programming
Author: Editor IJSMI
language: en
Publisher: International Journal of Statistics and Medical Informatics
Release Date: 2020-11-04
This is the second book in the Deep Learning models series by the author. Deep learning models are widely used in different fields due to its capability to handle large and complex datasets and produce the desired results with more accuracy at a greater speed. In Deep learning models, features are selected automatically through the iterative process wherein the model learns the features by going deep into the dataset and selects the features to be modeled. In the traditional models the features of the dataset needs to be specified in advance. The Deep Learning algorithms are derived from Artificial Neural Network concepts and it is a part of broader Machine Learning Models. The book starts with the Introduction part which is adopted from Author’s Deep Learning Models and its application: An overview with the help of R software book and move on to the Python’s important data processing packages such Numpy, and Pandas. Book then explores the Deep Learning models with the help of packages such as Pytorch, Tensor Flow and Keras and their applications in image processing, stock market prediction, recommender systems and natural language processing. Editor International Journal of Statistics and Medical Informatics www.ijsmi.com/book.php ISBN: 9798558877953 E-Books: https://www.amazon.com/dp/B08MQTM1ZP Paperbacks: https://www.amazon.com/dp/B08MSQ3R8R