Digital Filter Design Using Python For Power Engineering Applications
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Digital Filter Design Using Python For Power Engineering Applications
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Author : Shivkumar Venkatraman Iyer
language : en
Publisher:
Release Date : 2020
Digital Filter Design Using Python For Power Engineering Applications written by Shivkumar Venkatraman Iyer and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Electric filters, Digital categories.
This book is an in-depth description on how to design digital filters. The presentation is geared for practicing engineers, using open source computational tools, while incorporating fundamental signal processing theory. The author includes theory as-needed, with an emphasis on translating to practical application. The book describes tools in detail that can be used for filter design, along with the steps needed to automate the entire process. Breaks down signal processing theory into simple, understandable language for practicing engineers; Provides readers with a highly-practical introduction to digital filter design; Uses open source computational tools, while incorporating fundamental signal processing theory; Describes examples of digital systems in engineering and a description of how they are implemented in practice; Includes case studies where filter design is described in depth from inception to final implementation.
Digital Filter Design Using Python For Power Engineering Applications
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Author : Shivkumar Venkatraman Iyer
language : en
Publisher: Springer Nature
Release Date : 2020-11-30
Digital Filter Design Using Python For Power Engineering Applications written by Shivkumar Venkatraman Iyer and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-30 with Technology & Engineering categories.
This book is an in-depth description on how to design digital filters. The presentation is geared for practicing engineers, using open source computational tools, while incorporating fundamental signal processing theory. The author includes theory as-needed, with an emphasis on translating to practical application. The book describes tools in detail that can be used for filter design, along with the steps needed to automate the entire process. Breaks down signal processing theory into simple, understandable language for practicing engineers; Provides readers with a highly-practical introduction to digital filter design; Uses open source computational tools, while incorporating fundamental signal processing theory; Describes examples of digital systems in engineering and a description of how they are implemented in practice; Includes case studies where filter design is described in depth from inception to final implementation.
Electrical Engineering
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Author : IBRAHIM MURITALA
language : en
Publisher: Ibrahim Muritala
Release Date :
Electrical Engineering written by IBRAHIM MURITALA and has been published by Ibrahim Muritala this book supported file pdf, txt, epub, kindle and other format this book has been release on with Technology & Engineering categories.
Electrical Engineering: Harnessing Python Programming for Circuit Design, Simulation, and Data Analysis in Electrical Systems Are you an electrical engineering student, hobbyist, or professional looking to level up your skills using modern tools? Discover how Python programming is transforming the world of circuit design, electrical simulation, and data analysis. This hands-on guide bridges the gap between classical electrical engineering principles and modern computational methods. Learn how to apply Python for electrical engineering tasks, from creating and simulating circuits to analysing system data and automating workflows. Inside, you'll master: Python libraries like NumPy, SciPy, Matplotlib, and PySpice Circuit modelling and real-time simulations Signal processing and waveform analysis Data acquisition and visualisation of electrical systems Practical, project-based applications and code samples Whether you're building power systems, working on embedded devices, or analysing circuit behaviour, this book gives you the coding tools and engineering insights to become more efficient, innovative, and future-ready. No more juggling outdated tools, Python is your all-in-one powerhouse for modern electrical design. Perfect for those searching "Python for electrical engineering", "circuit simulation with Python", or "engineering data analysis with Python", this book is your step-by-step blueprint to mastering both programming and engineering fundamentals. 👉 Start building smarter circuits and analysing systems like a pro , grab your copy today!
Real World Applications And Implementations Of Iot
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Author : Aritra Acharyya
language : en
Publisher: Springer Nature
Release Date : 2025-02-11
Real World Applications And Implementations Of Iot written by Aritra Acharyya and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-11 with Computers categories.
This book explores state-of-the-art internet of things (IoT) solutions for energy conservation, security, agricultural advancements, mining security, healthcare, and environmental protection. This book delves deep into the technology, offering a comprehensive analysis, detailed descriptions, and in-depth discussions of recently developed IoT applications. With a strong focus on the cutting-edge research at a global scale, the book combines IoT with artificial intelligence (AI), shedding light on emerging possibilities and advancements. Designed to cater to a broad audience, from those with a foundational understanding of science to seasoned engineering and technology experts, this book can serve as an essential resource for engineering students and science master's programs. Researchers seeking to stay at the forefront of IoT and AI will also find it invaluable.
Applications Of Artificial Intelligence In Electrical Engineering
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Author : Khalid, Saifullah
language : en
Publisher: IGI Global
Release Date : 2020-03-27
Applications Of Artificial Intelligence In Electrical Engineering written by Khalid, Saifullah and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-27 with Technology & Engineering categories.
Artificial intelligence is increasingly finding its way into industrial and manufacturing contexts. The prevalence of AI in industry from stock market trading to manufacturing makes it easy to forget how complex artificial intelligence has become. Engineering provides various current and prospective applications of these new and complex artificial intelligence technologies. Applications of Artificial Intelligence in Electrical Engineering is a critical research book that examines the advancing developments in artificial intelligence with a focus on theory and research and their implications. Highlighting a wide range of topics such as evolutionary computing, image processing, and swarm intelligence, this book is essential for engineers, manufacturers, technology developers, IT specialists, managers, academicians, researchers, computer scientists, and students.
Digital Filters
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Author : Fred Taylor
language : en
Publisher: John Wiley & Sons
Release Date : 2011-09-20
Digital Filters written by Fred Taylor and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-20 with Technology & Engineering categories.
The book is not an exposition on digital signal processing (DSP) but rather a treatise on digital filters. The material and coverage is comprehensive, presented in a consistent that first develops topics and subtopics in terms it their purpose, relationship to other core ideas, theoretical and conceptual framework, and finally instruction in the implementation of digital filter devices. Each major study is supported by Matlab-enabled activities and examples, with each Chapter culminating in a comprehensive design case study.
Digitales Filterdesign Mit Python F R Anwendungen In Der Energietechnik
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Author : Shivkumar Venkatraman Iyer
language : de
Publisher: Springer-Verlag
Release Date : 2024-08-13
Digitales Filterdesign Mit Python F R Anwendungen In Der Energietechnik written by Shivkumar Venkatraman Iyer and has been published by Springer-Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-13 with Technology & Engineering categories.
Diese Buch bietet eine ausführliche Beschreibung darüber, wie digitale Filter entworfen werden. Die Darstellung richtet sich an praktizierende Ingenieure und verwendet Open-Source-Computertools, wobei gleichzeitig grundlegende Signalverarbeitungstheorie einfließt. Der Autor integriert Theorie nach Bedarf und legt dabei einen Schwerpunkt auf die Umsetzung in die Praxis. Das Buch beschreibt Werkzeuge im Detail, die für das Filterdesign verwendet werden können, sowie die Schritte, um den gesamten Prozess zu automatisieren. Zerlegt die Theorie der Signalverarbeitung in einfache, verständliche Sprache für praktizierende Ingenieure. Bietet den Lesern eine äußerst praktische Einführung in das Design digitaler Filter. Verwendet Open-Source-Computertools und integriert dabei grundlegende Signalverarbeitungstheorie. Beschreibt Beispiele digitaler Systeme in der Ingenieurwissenschaft und wie sie in der Praxis umgesetzt werden. Enthält Fallstudien, in denen das Filterdesign von der Konzeption bis zur endgültigen Implementierung ausführlich beschrieben wird.
Wind Power Analysis And Forecasting Using Machine Learning With Python
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-07-09
Wind Power Analysis And Forecasting Using Machine Learning With Python written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-09 with Computers categories.
In this project on wind power analysis and forecasting using machine learning with Python, we started by exploring the dataset. We examined the available features and the target variable, which is the active power generated by wind turbines. The dataset likely contained information about various meteorological parameters and the corresponding active power measurements. To begin our analysis, we focused on the regression task of predicting the active power using regression algorithms. We split the dataset into training and testing sets and preprocessed the data by handling missing values and performing feature scaling. The preprocessing step ensured that the data was suitable for training machine learning models. Next, we trained several regression models on the preprocessed data. We utilized algorithms such as Linear Regression, Decision Tree Regression, Random Forest Regression, and Gradient Boosting Regression. Each model was trained on the training set and evaluated on the testing set using performance metrics like mean squared error (MSE) and R-squared score. After obtaining regression models for active power prediction, we shifted our focus to predicting categorized active power using machine learning models. This involved converting the continuous active power values into discrete categories or classes. We defined categories based on certain thresholds or ranges of active power values. For the categorized active power prediction task, we employed classification algorithms. Similar to the regression task, we split the dataset, preprocessed the data, and trained various classification models. Common classification algorithms used were Logistic Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Decision Trees, Random Forests, Gradient Boosting, Extreme Gradient Boosting, Multi-Layer Perceptron, and Light Gradient Boosting models. During the training and evaluation of classification models, we used performance metrics like accuracy, precision, recall, and F1-score to assess the models' predictive capabilities. Additionally, we analyzed the classification reports to gain insights into the models' performance for each category. Throughout the process, we paid attention to feature scaling techniques such as normalization and standardization. These techniques were applied to ensure that the features were on a similar scale and to prevent any bias or dominance of certain features during model training. The results of predicting categorized active power using machine learning models were highly encouraging. The models demonstrated exceptional accuracy and exhibited strong classification performance across all categories. The findings from this analysis have significant implications for wind power forecasting and monitoring systems, allowing for more effective categorization and management of wind power generation based on predicted active power levels. To summarize, the wind power analysis and forecasting session involved dataset exploration, active power regression using regression algorithms, and predicting categorized active power using various machine learning models. The regression task aimed to predict continuous active power values, while the classification task aimed to predict discrete categories of active power. Preprocessing, training, evaluation, and performance analysis were key steps throughout the session. The selected models, algorithms, and performance metrics varied depending on the specific task at hand. Overall, the project provided a comprehensive overview of applying machine learning techniques to analyze and forecast wind power generation.
Data Analytics In System Engineering
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Author : Radek Silhavy
language : en
Publisher: Springer Nature
Release Date : 2024-02-23
Data Analytics In System Engineering written by Radek Silhavy and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-23 with Computers categories.
These proceedings offer an insightful exploration of integrating data analytics in system engineering. This book highlights the essential role of data in driving innovation, optimizing processes, and solving complex challenges in the field. Targeted at industry professionals, researchers, and enthusiasts, this book serves as a comprehensive resource, providing actionable insights and showcasing transformative applications of data in engineering. It is a must-read for anyone keen on understanding and participating in the ongoing evolution of system engineering in our data-centric world.
Household Electric Power Consumption Analysis Clustering And Prediction With Python
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2022-03-03
Household Electric Power Consumption Analysis Clustering And Prediction With Python written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-03 with Technology & Engineering categories.
In this project, you will perform analysis, clustering, and prediction on household electric power consumption with python. The dataset used in this project contains 2075259 measurements gathered between December 2006 and November 2010 (47 months). Following are the attributes in the dataset: date: Date in format dd/mm/yyyy; time: time in format hh:mm:ss; globalactivepower: household global minute-averaged active power (in kilowatt); globalreactivepower: household global minute-averaged reactive power (in kilowatt); voltage: minute-averaged voltage (in volt); global_intensity: household global minute-averaged current intensity (in ampere); submetering1: energy sub-metering No. 1 (in watt-hour of active energy). It corresponds to the kitchen, containing mainly a dishwasher, an oven and a microwave (hot plates are not electric but gas powered); submetering2: energy sub-metering No. 2 (in watt-hour of active energy). It corresponds to the laundry room, containing a washing-machine, a tumble-drier, a refrigerator and a light; and submetering3: energy sub-metering No. 3 (in watt-hour of active energy). It corresponds to an electric water-heater and an air-conditioner. In this project, you will perform clustering using KMeans to get 5 clusters. The machine learning models used in this project to perform regression on total number of purchase and to predict clusters as target variable are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM, Gradient Boosting, XGB, and MLP. Finally, you will plot boundary decision, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy.