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Materials Discovery and Design
This book addresses the current status, challenges and future directions of data-driven materials discovery and design. It presents the analysis and learning from data as a key theme in many science and cyber related applications. The challenging open questions as well as future directions in the application of data science to materials problems are sketched. Computational and experimental facilities today generate vast amounts of data at an unprecedented rate. The book gives guidance to discover new knowledge that enables materials innovation to address grand challenges in energy, environment and security, the clearer link needed between the data from these facilities and the theory and und...
Experimental Techniques, Rotating Machinery, and Acoustics, Volume 8
Experimental Techniques, Rotating Machinery & Acoustics, Volume 8: Proceedings of the 33rd IMAC, A Conference and Exposition on Structural Dynamics, 2015, the eighth volume of ten from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Structural Dynamics, including papers on: Experimental Techniques Processing Modal Data Rotating Machinery Acoustics Adaptive Structures Biodynamics Damping
Sensors and Instrumentation, Volume 5
Sensors and Instrumentation, Volume 5. Proceedings of the 34th IMAC, A Conference and Exposition on Dynamics of Multiphysical Systems: From Active Materials to Vibroacoustics, 2016, the fift h volume of ten from the Conference brings together contributions to this important area of research and engineering. Th e collection presents early findings and case studies on fundamental and applied aspects of Structural Dynamics, including papers on: • Experimental Techniques“/p> • Smart Sensing • Rotational Eff ects • Dynamic Calibration • Systems & Sensing Technologies • Modal Transducers • Novel Excitation Methods
Data Science in Engineering, Volume 9
Data Science in Engineering, Volume 9: Proceedings of the 40th IMAC, A Conference and Exposition on Structural Dynamics, 2022, the nineth volume of nine from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Data Science in Engineering, including papers on: Novel Data-driven Analysis Methods Deep Learning Gaussian Process Analysis Real-time Video-based Analysis Applications to Nonlinear Dynamics and Damage Detection High-rate Structural Monitoring and Prognostics
Advances in Cancer Research
Advances in Cancer Research provides invaluable information on the exciting and fast-moving field of cancer research. Here, once again, outstanding and original reviews are presented on a variety of topics. - 113th volume of Advances in Cancer Research - Expert authors - Chapters on topics including microRNA regulatory network, multifaceted oncoprotein tax, and underlying chromosomal translocations
Structural Health Monitoring, Photogrammetry & DIC, Volume 6
Structural Health Monitoring Photogrammetry & DIC, Volume 6: Proceedings of the 36th IMAC, A Conference and Exposition on Structural Dynamics, 2018, the sixth volume of nine from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Structural Health Monitoring & Damage Detection, including papers on: Structural Health Monitoring Damage Detection System Identification Active Controls
Application of Artificial Intelligence and Machine Learning to Accelerators
Artificial Intelligence (AI) and Machine learning (ML) promise significant enhancements for particle accelerator operations, including applications in diagnostics, controls, and modeling. Challenges still exist in experimentally verifying AI/ML methods before deployment at user facilities. The ability to quickly generalize and adapt these methods to new operating configurations at the same facility or between facilities also remains a challenge and requires combining model-independent adaptive feedback with traditional ML tools. These methods also apply to the detection, classification, and prevention of operational anomalies that can cause accelerator damage or excessive beam loss in the case of abnormal operations. Opportunity exists in broadening AI/ML methods for early detection of a broad range of accelerator component or subsystem failures.
Model-Free Stabilization by Extremum Seeking
With this brief, the authors present algorithms for model-free stabilization of unstable dynamic systems. An extremum-seeking algorithm assigns the role of a cost function to the dynamic system’s control Lyapunov function (clf) aiming at its minimization. The minimization of the clf drives the clf to zero and achieves asymptotic stabilization. This approach does not rely on, or require knowledge of, the system model. Instead, it employs periodic perturbation signals, along with the clf. The same effect is achieved as by using clf-based feedback laws that profit from modeling knowledge, but in a time-average sense. Rather than use integrals of the systems vector field, we employ Lie-bracket-based (i.e., derivative-based) averaging. The brief contains numerous examples and applications, including examples with unknown control directions and experiments with charged particle accelerators. It is intended for theoretical control engineers and mathematicians, and practitioners working in various industrial areas and in robotics.
