Results 21 to 30 of about 116,653,606 (265)
Stochastic variational variable selection for high-dimensional microbiome data
Microbiome, 2022 Background The rapid and accurate identification of a minimal-size core set of representative microbial species plays an important role in the clustering of microbial community data and interpretation of clustering results.
Tung Dang +9 more
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Frontiers in Energy Research, 2022 Increasing penetration of wind power with intermittency and variability threatens the stability of the power system frequency. The fast response capability of the energy storage system (ESS) makes it an effective measure to improve frequency regulation ...
Zizhao Wang +4 more
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Application of Improved Q-Learning Algorithm in Dynamic Path Planning for Aircraft at Airports
IEEE Access, 2023 Guiding and controlling aircraft within an airport is a decision-making process based on safety and efficiency in a highly dynamic and stochastic environment.
Zheng Xiang, Heyang Sun, Jiahao Zhang
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A posteriori error estimation for stochastic static problems [PDF]
, 2014 To solve stochastic static field problems, a discretization by the Finite Element Method can be used. A system of equations is obtained with the unknowns (scalar potential at nodes for example) being random variables. To solve this stochastic system, the
MAC, Hung, CLENET, Stephane
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Virtual Sensoring of Motion Using Pontryagin’s Treatment of Hamiltonian Systems
Sensors, 2021 To aid the development of future unmanned naval vessels, this manuscript investigates algorithm options for combining physical (noisy) sensors and computational models to provide additional information about system states, inputs, and parameters ...
Timothy Sands
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Mathematics, 2022 Nowadays, much of the world has a regional air pollution strategy to limit and decrease the pollution levels across governmental borders and control their impact on human health and ecological systems.
Venelin Todorov, Ivan Dimov
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ActuatorsThis study explores a stochastic guarantee cost control (GCC) for time-varying systems with random parameters and asymmetric saturation actuators by employing the integral reinforcement learning (IRL) method in the dynamic event-triggered (DET) mode ...
Yuling Liang +4 more
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π-Economy, 2023 Reinforcement learning is a class of machine learning and artificial intelligence methods, a field for the applied problem studied, as well as methods for solving it.
Orlova Ekaterina
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Parallel implementation of stochastic simulation for large-scale cellular processes [PDF]
, 2005 Experimental and theoretical studies have shown the importance of stochastic processes in genetic regulatory networks and cellular processes. Cellular networks and genetic circuits often involve small numbers of key proteins such as transcriptional ...
Kevin Burrage +6 more
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Neural Network-Based Imitation Learning for Approximating Stochastic Battery Management Systems
IEEE AccessLithium-ion batteries play a pivotal role in enabling eco-friendly mobility, particularly in electric vehicles, but optimizing their charging process to improve battery lifespan, safety, and overall efficiency remains a significant challenge. Traditional
Andrea Pozzi +2 more
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