Название: Advanced Optimization Applications in EngineeringАвтор: Afaq Ahmad, Charles V. Camp
Издательство: IGI Global
Год: 2024
Страниц: 308
Язык: английский
Формат: pdf (true), epub
Размер: 45.3 MB
In the ever-evolving landscape of engineering, a pressing challenge looms large—the need to navigate the complexities of modern problems with precision and efficiency. As industries grapple with an array of intricate issues, from sustainable materials to resilient infrastructure, the demand for optimal solutions has never been more pronounced. Traditional approaches are often inadequate, prompting the search for advanced optimization techniques capable of unraveling the intricacies inherent in engineering systems. The problem at hand is clear: how can engineers, researchers, and practitioners harness cutting-edge methodologies to address the multifaceted challenges shaping our technological future?
Advanced Optimization Applications in Engineering, is a definitive guide poised to revolutionize problem-solving in civil engineering. This book offers a comprehensive exploration of state-of-the-art optimization algorithms and their transformative applications. By delving into genetic algorithms, particle swarm optimization, neural networks, and other metaheuristic strategies, this collection provides a roadmap for automating design processes, reducing costs, and unlocking innovative solutions. The chapters not only introduce these advanced techniques but also showcase their practical implementation across diverse engineering domains, making this book an indispensable resource for those seeking to stay at the forefront of technological advancements.
This book is tailored for a discerning audience comprising engineers, academicians, researchers, practitioners, and students eager to leverage advanced optimization, Artificial Intelligence, and Machine Learning in civil engineering. While not a conventional textbook, it offers a rich tapestry of insights suitable for postgraduate courses focused on contemporary methods in civil engineering. Beyond academia, professionals in insurance, government, civil protection, and emergency management will find invaluable guidance for assessing and planning community resilience. As the global skills gap widens, this book is a solution-oriented guide for individuals and organizations to move toward a future where technology and engineering seamlessly converge for unparalleled problem-solving.
CNN is a type of image processing tool that uses Deep Learning (DL) algorithms to achieve mainly three types of tasks, firstly classification of the object or image, secondly the detection of the object in such a manner that the boundary box, and thirdly the segmentation of the image in which groups of pixels are separated of the image. To extract the object from the image, different techniques are widely adopted. One of them is using morphology. The term “morphology” refers to various image-processing techniques that analyze and manipulate pictures based on their geometrical forms. Morphological operations use an input picture template to generate an identically sized new image. Each pixel in the final picture is assigned a value determined by how the input image’s relevant pixel is compared to its neighbours.
Coverage:
The many academic areas covered in this publication include, but are not limited to:
Introduction to Optimization in Engineering
Machine Learning in Engineering
Mathematical Programming in Engineering
Optimization Concepts and Algorithms
Optimization in Advance Materials
Optimization in Energy Systems
Optimization in Hydraulics Engineering
Optimization in Manufacturing Engineering
Optimization in Structural Engineering
Optimization in Sustainability
Optimization in Transportation Engineering
Real-world Applications of Engineering Optimization
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