The applications of optical coherence tomography angiography in diabetic retinopathy

Optical coherence tomography angiography(OCTA),an extent function of traditional optical coherence tomography(OCT),is a non-invasive,high-resolution imaging system designed to display vascular networks.The fundamental principle of OCTA is to achieve the signal of blood flow based on the analysis of complex OCT signal,amplitude of OCT signal or phase of OCT signal.OCTA can display and monitor the vascular abnormalities in patients with diabetic retinopathy(DR),including microaneurysms,vessel density(VD),nonperfusion,neovascularization,and other lesions.OCTA offers a new and potential horizon in the monitor of the DR progress and evaluation of DR treatment.

Mathematical Representation of WECC Composite Load Model

Composite load model of Western Electricity Coordinating Council(WECC)is a newly developed load model that has drawn great interest from the industry.To analyze its dynamic characteristics with both mathematical and engineering rigors,a detailed mathematical model is needed.Although composite load model of WECC is available in commercial software as a module and its detailed block diagrams can be found in several public reports,there is no complete mathematical representation of the full model in literature.This paper addresses a challenging problem of deriving detailed mathematical representation of composite load model of WECC from its block diagrams.In particular,we have derived the mathematical representation of the new DERA model.The developed mathematical model is verified using both MATLAB and PSS/E to show its effectiveness in representing composite load model of WECC.The derived mathematical representation serves as an important foundation for parameter identification,order reduction and other dynamic analysis.

Sizing battery storage for islanded microgrid systems to enhance robustness against attacks on energy sources

Power system security against attacks is drawing increasing attention in recent years.Battery energy storage systems(BESSs)are effective in providing emergency support.Although the benefits of BESSs have been extensively studied earlier to improve the system economics,their role in enhancing the system robustness in overcoming attacks has not been adequately investigated This paper addresses the gap by proposing a new battery storage sizing algorithm for microgrids to limit load shedding when the energy sources are attacked.Four participants are considered in a framework involving interactions between a robustness-oriented economic dispatch model and a bilevel attacker-defender model.The proposed method is tested with the data from a microgrid system in Kasabonika Lake of Canada.Comprehensive case studies are carried out to demonstrate the effectiveness and merits of the proposed approach.

Unsupervised Learning for Non-intrusive Load Monitoring in Smart Grid Based on Spiking Deep Neural Network

This paper investigates the intelligent load monitoring problem with applications to practical energy management scenarios in smart grids.As one of the critical components for paving the way to smart grids’success,an intelligent and feasible non-intrusive load monitoring(NILM)algorithm is urgently needed.However,most recent researches on NILM have not dealt with practical problems when applied to power grid,i.e.,①limited communication for slow-change systems;②requirement of low-cost hardware at the users’side;and③inconvenience to adapt to new households.Therefore,a novel NILM algorithm based on biology-inspired spiking neural network(SNN)has been developed to overcome the existing challenges.To provide intelligence in NILM,the developed SNN features an unsupervised learning rule,i.e.,spike-time dependent plasticity(STDP),which only requires the user to label one instance for each appliance while adapting to a new household.To upgrade the feasibility in NILM,the designed spiking neurons mimic the mechanism of human brain neurons that can be constructed by a resistor-capacitor(RC)circuit.In addition,a distributed computing system has been designed that divides the SNN into two parts,i.e.,smart outlets and local servers.Since the information flows as sparse binary vectors among spiking neurons in the developed SNN-based NILM,the high-frequency data can be easily compressed as the spike times,and are sent to the local server with limited communication capability,whereas it is unable to handle the traditional NILM.Finally,a series of experiments are conducted using a benchmark public dataset.Meanwhile,the effectiveness of developed SNN-based NILM can be demonstrated through comparisons with other emerging NILM algorithms such as the convolutional neural networks.