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Contingency Detection in Modern Power Systems: A Stochastic Hybrid System Method
The framework uses stochastic hybrid system representations in state space models to expand and facilitate capability of contingency detection.
Stochastic Hybrid System Modeling and State Estimation of Modern Power Systems under Contingency
This paper formulates stochastic hybrid system models for MPSs, introduces coordinated observer design algorithms for state estimation, and establishes their convergence and reliability properties.
Kitchen Food Waste Image Segmentation and Classification for Compost Nutrients Estimation
The escalating global concern over extensive food wastage necessitates innovative solutions to foster a net-zero lifestyle and reduce emissions.
Large Deviations Principles for Langevin Equations in Random Environment and Applications
This paper aims to consider large deviations principles (LDPs) of Langevin equations involving a random environment that is a process taking value in a measurable space and that is allowed to interact with the systems, without specified formulation on the random environment.
Probability Mathematical Physics Mathematical Physics
Adaptive Non-reversible Stochastic Gradient Langevin Dynamics
It is well known that adding any skew symmetric matrix to the gradient of Langevin dynamics algorithm results in a non-reversible diffusion with improved convergence rate.
Multi-kernel Passive Stochastic Gradient Algorithms and Transfer Learning
This paper develops a novel passive stochastic gradient algorithm.
Langevin Dynamics for Adaptive Inverse Reinforcement Learning of Stochastic Gradient Algorithms
Inverse reinforcement learning (IRL) aims to estimate the reward function of optimizing agents by observing their response (estimates or actions).
