Название: Intelligent Automation in Renewable Energy Автор: Tetyana Baydyk, Ernst Kussul, Donald C. Wunsch II
Издательство: Springer
Серия: Computational Intelligence Methods and Applications
Год: 2019
Язык: английский
Формат: pdf (true), epub
Размер: 63.6 MB
After an introduction to renewable energy technologies, the authors present computational intelligence techniques for optimizing the manufacture of related technologies, including solar concentrators. In particular the authors present new applications for their neural classifiers for image and pattern recognition.
The book will be of interest to researchers in computational intelligence, in particular in the domain of neural networks, and engineers engaged with renewable energy technologies.
This book presents the design and development of the prototypes of solar concentrators with flat triangular mirrors. We also propose an automatic production tool based on artificial vision, to position the set of nuts that determine the parabolic surface of the solar concentrators.
At present, more sophisticated algorithms based on neural networks are being developed. Here, we describe our experiments in the development and applications of such algorithms. We consider some properties, limitations, and problems in how we can apply our methods for intelligent automation.
Some of the artificial intelligence (AI) methods, including neural networks, could be used to improve the automation system performance in manufacturing processes. However, the implementation of these AI methods in the industry is rather slow, because of the high cost of the experiments with the conventional manufacturing and AI systems. To lower the experimental cost in this field, we have developed a special micromechanical equipment, similar to conventional mechanical equipment, but of a much smaller size and therefore of lower cost. This equipment could be used for the evaluation of different AI methods in an easy and inexpensive way. The proven methods could be transferred to the industry through appropriate scaling. In this book, we describe the prototypes of low-cost micro equipment for manufacturing processes and some AI methods implementation to increase its precision, such as computer vision systems based on neural networks for micro device assembly, and genetic algorithms for micro equipment characterization and increase the micro equipment precision.
The book is intended as a professional reference and also as a textbook for graduate students in science, engineering, and micromechanics. We expect it to be particularly interesting to computer scientists and applied mathematicians engaged in neural networks, artificial intelligence, image recognition, and adaptive control.
Contents:
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