Abstract: The consideration of reliability in controller design is able to avoid the potential actuator faults from inappropriate strategies. This work presents an optimal reliability-critical ...
Abstract: This work investigates the fault-prediction optimal output regulation problem for the structural reliability feedback (SRF) system, and it aims to design a reliability feedback controller ...
The task of designing optimized messenger RNA (mRNA) sequences has received much attention in recent years, thanks to breakthroughs in mRNA vaccines during the COVID-19 pandemic. Because most previous ...
Objectives To develop an interpretable deep learning model of lupus nephritis (LN) relapse prediction based on dynamic multivariable time-series data. Design A single-centre, retrospective cohort ...
Machine learning is the study of intelligent machines that can learn from data and anticipate future outcomes. One of the most important components of machine learning is dealing with uncertainty, ...
Dynamic programming is a powerful technique for solving optimization problems that can be divided into smaller sub-problems. The main idea of dynamic programming is to avoid recomputing the same ...
The increasing penetration of renewable energy introduces more uncertainties and creates more fluctuations in power systems than ever before, which brings great challenges for automatic generation ...
I have been in the space of artificial intelligence for a while and am aware that multiple classifications, distinctions, landscapes, and infographics exist to represent and track the different ways ...
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