Titlebook: Big Data Analytics for Cyber-Physical Systems; Shiyan Hu,Bei Yu Book 2020 Springer Nature Switzerland AG 2020 Distributed Control.Demand-S

带来

Shiyan Hu,Bei YuHelps readers understand the fundamentals of how big data analytics and optimization are involved in developing the cyber-physical systems.Presents readers with practical tools and design methodologie

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健忘症

幻影

https://doi.org/10.1007/978-1-4842-7490-3d on its predicted free computation capacity. We also use this model to opportunistically schedule low-priority background tasks, like a scheduled virus scanning activity or a system update, such that user experience with the device improves. We present a framework and some experimental results show

Ligneous

https://doi.org/10.1007/978-1-4842-7490-3nagement information platform in which the CPS is responsible for sensing, control, and feedback in the entire operating process. The entire operating process includes the input acquisition for the forecasting models, early warning publishing and emergency control, and feedback-based re-analysis and

vertebrate

,Customer relations—dealing with people,emonstrate that compared to state-of-the-art methods, the proposed framework can achieve a robust drift calibration and a better trade-off between accuracy and runtime on benchmarks generated by simulator ..

AXIOM

https://doi.org/10.1007/978-1-4842-8919-8uction procedures. Example applications demonstrate that the reasonable geological model estimations can be obtained from the updated multi-models which obviously decrease the uncertainty of geology. Furthermore, multi-model optimized control strategy improves both injection effects, economic benefi

蜈蚣

https://doi.org/10.1007/978-1-4842-8888-7to the pre-stored big data. By comparing the simulated data with the measured data, the prediction accuracy can be verified, the model is adjusted and optimized. Finally, if the prediction results do not meet the requirements, a new iteration of the optimized prediction model begins until the accura

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LAVA

A Utility-Driven Data Transmission Optimization Strategy in Large-Scale Cyber-Physical Systems,ll parameters are not required at all times to fulfill the end objectives and thereby, selective data transmission can reduce the sensor workload significantly. We illustrate the motivation on two real case studies from the CPS domain. Experimental results show the efficacy of our proposal.