Tag: Robustness

Resilience, Robustness, and Vulnerability of Transport Systems Analysis, Modelling, and Practice


Free Download Resilience, Robustness, and Vulnerability of Transport Systems: Analysis, Modelling, and Practice by Milan Janić
English | November 23, 2022 | ISBN: 3031130391 | 449 pages | MOBI | 45 Mb
This book is focused on the analysis and modelling of resilience, robustness, and vulnerability of transport systems and their complement: reliability. It provides an elaboration on their generic concepts, analysing disruptive events and nature of their impacts. It also offers an analysis and modelling of the system performances and their indicators relevant for the main actors involved. Applications of the models of performances and their resilience, robustness, and vulnerability to the selected cases of road, rail, air, and maritime transport mode affected by different internal and external disruptive events are also presented. By offering readers a systematic and comprehensive way of dealing with the topic of resilience, robustness, and vulnerability of transport systems and their complement, reliability, this book will be of interest to researchers and professionals alike within the transport industry.

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Machine Learning Algorithms Adversarial Robustness in Signal Processing (Wireless Networks)


Free Download Machine Learning Algorithms: Adversarial Robustness in Signal Processing (Wireless Networks) by Fuwei Li, Lifeng Lai, Shuguang Cui
English | November 16, 2022 | ISBN: 3031163745 | 113 pages | MOBI | 11 Mb
This book demonstrates the optimal adversarial attacks against several important signal processing algorithms. Through presenting the optimal attacks in wireless sensor networks, array signal processing, principal component analysis, etc, the authors reveal the robustness of the signal processing algorithms against adversarial attacks. Since data quality is crucial in signal processing, the adversary that can poison the data will be a significant threat to signal processing. Therefore, it is necessary and urgent to investigate the behavior of machine learning algorithms in signal processing under adversarial attacks.

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