A Review of Current Research Trends in Power-Electronic Innovations in Cyber-Physical Systems.

In this paper, a broad overview of the current research trends in power-electronic innovations in cyber-physical systems (CPSs) is presented. The recent advances in semiconductor device technologies, control architectures, and communication methodologies have enabled researchers to develop integrated smart CPSs that can cater to the emerging requirements of smart grids, renewable energy, electric vehicles, trains, ships, internet of things (IoTs), etc. The topics presented in this paper include novel power-distribution architectures, protection techniques considering large renewable integration in smart grids, wireless charging in electric vehicles, simultaneous power and information transmission, multi-hop network-based coordination, power technologies for renewable energy and smart transformer, CPS reliability, transactive smart railway grid, and real-time simulation of shipboard power systems. It is anticipated that the research trends presented in this paper will provide a timely and useful overview to the power-electronics researchers with broad applications in CPSs.post-print2.019 K

Modeling structure property relationships with Kernel recursive least squares

Motivation: Modeling structure property relationships accurately is a challenging task and newly developed kernel based methods may provide the accuracy for building these relationships. Method: Kernelized variant of traditional recursive least squares algorithm is used to model two QSPR datasets. Results: All the datasets showed a good correlation between actual and predicted values of boiling points with root mean squared errors (RMSEs) comparable to other conventional methods. For the datasets from Espinosa et al., KRLS showed good prediction statistics with R value in the range of 0.97-0.99 and S value in the range 5.5- 8 as compared to multiple linear regression (MLR) with R value in the range 0.85-0.88 and S value in the range 22-26. For the dataset from Trinajstiu et al., KRLS performed consistently well with R values lying in the range of 0.95-0.99 and S in the range of 5-10 as compared to MLR with R values in the range of 0.7-0.85 and S in the range of 25-30. Conclusions: The KRLS method works better when more number of variables from the dataset are included as against other methods such as support vector learning or lazy learning technique which works better for smaller number of reduced relevant variables from the dataset