From artificial intelligence to deep learning in bio-medical applications

Since their introduction in late 80s, convolutional neural networks and auto-encoder architectures have shown to be powerful for automatic feature extraction and information simplification. Using convolution kernels from image processing in 2D and 3D spaces for the stage by stage features retrieval processes, allows the architecture to be as flexible as the designer wants, considering that this is not a lucky fact. With the recent ten years of technological progress now we can compute and train those architectures and they have faced so many challenges for applications originating the most famous CNN architectures. This chapter presents an author position related to the artificial intelligence field and machine learning/deep learning appearance in the scientific world scene describing hastily the basis for each one and later, focusing on medical applications most of the socialized on the Annual IEEE Engineering in Medicine and Biology Society conference held in Hawaii in July 2018. While addressing the medical applications from cardiovascular to cancer diagnosis, we will briefly describe the architectures and discuss some features. Finally, we will present a contribution to the deep learning by introducing a new architecture called Convolutional Laguerre-Gauss Network with a kernel based on a spiral phase function ranging from 0 to 2p and a toroidal amplitude band-pass filter, known as the Laguerre-Gauss transform. © Springer Nature Switzerland AG 2020

An optimal approach for load-frequency control of islanded microgrids based on nonlinear model

Due to the increased environmental and economic challenges, in recent years, renewable based distribution generation has been developed. More penetrations from the side of consumers caused a new concept called microgrids which are able to stand with or without connection to the bulk power system. Control of microgrids in islanded mode is very crucial for decreasing the amplitude of frequency deviations as well as damping speed. This chapter aims to propose an optimal combination of FOPD and fuzzy pre-compensated FOPI approach for load-frequency control of microgrids in islanded mode. The optimization parameter of the control scheme is designed by the differential evolution (DE) algorithm which has been improved by a fuzzy approach. In the optimization, control effort is considered as a constraint. Due to the robustness and flexibility of the proposed method, the simulation results have been improved substantially. Robust performance of the proposed control method is examined through sensitivity analysis.fi=vertaisarvioitu|en=peerReviewed