Data Augmentation for Chemical Gas Sensing
Neural Networks, Data Analysis, Machine Learning
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Beschreibung
By providing a comprehensive yet accessible guide to both the theory and practical implementation of data augmentation, this book will equip researchers, engineers, and practitioners to enhance their sensor systems, ensuring more accurate, robust, and scalable predictive models that can handle complex real-world scenarios.
In recent years, chemical gas sensors have become crucial to diverse real-world applications, from monitoring air quality in urban areas to ensuring safety in industrial environments. However, one of the most critical limitations of these sensors is the challenge of obtaining sufficient, high-quality data to train predictive models. Sensor data collection is often labor intensive, costly, and limited in scope, particularly when attempting to capture the dynamic behavior of multiple gases in varying environmental conditions. These challenges make it difficult to build predictive models that are widely effective in real-world scenarios, where gas concentrations fluctuate unpredictably.
This book aims to tackle this issue by providing a comprehensive introduction to data augmentation techniques in the context of chemical gas sensing. The objective is to show how artificial data generation can overcome the limitations of real-world sensor data, thus enhancing the accuracy and generalization of machine learning models. The book will cover traditional data augmentation methods, such as geometric transformations as well as more advanced techniques like recurrent neural networks (RNNs), Long Short-Term Memory (LSTM) networks, Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs).
What makes this book unique is its blend of theory and hands-on application. It will provide a solid foundation for readers who may not have advanced knowledge in machine learning, making it accessible to those with basic programming skills. Each chapter will not only explain the theoretical concepts behind various data augmentation methods but also provide practical and ready-to-use code snippets in Python. These code examples will allow readers to quickly implement and test the techniques described, enabling them to apply the methods directly to real-world sensor data.
Autor*in
Ajit Khosla
Themen in »Data Augmentation for Chemical Gas Sensing«
Gas-Sensoren Data Augmentation Maschinelles Lernen Datenwissenschaft Gas Sensors Machine Learning Data Science