Results 21 to 30 of about 380,569 (245)
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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Tree-Sparse Modeling and Solution of Multistage Stochastic Programs [PDF]
, 2005 Multistage stochastic programs are prototypical for nonlinear programs with an inherent tree structure inducing characteristic sparsity patterns in the KKT systems of interior methods. We present an integrated modeling and solution approach for such tree-
Steinbach, Marc
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Seismic Fragility Analysis of Structures Based on Adaptive Gaussian Process Regression Metamodel
Shock and Vibration, 2021 Metamodel-based seismic fragility analysis methods can overcome the challenge of high computational costs of problems considering the uncertainties of earthquakes and structural parameters; however, the accuracy of metamodels is difficult to control.
Yanjie Xiao, Feng Yue, Xun'an Zhang
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Time-delayed models of gene regulatory networks [PDF]
, 2015 We discuss different mathematical models of gene regulatory networks as relevant to the onset and development of cancer. After discussion of alternativemodelling approaches, we use a paradigmatic two-gene network to focus on the role played by time ...
Blyuss, K B +3 more
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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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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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Artificial Intelligence for sEMG-Based Muscular Movement Recognition for Hand Prosthesis
IEEE Access, 2023 The muscular activities gathered by real-time myoelectric interfaces of surface electromyography (sEMG) can be used to develop myoelectric prosthetic hands for physically disabled people.
Nafe Muhtasim Hye +4 more
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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 ...
Burrage, K., Tian, T.
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Aggressive prostate cancer is associated with pericyte dysfunction
Molecular Oncology, EarlyView.Tumor‐produced TGF‐β drives pericyte dysfunction in prostate cancer. This dysfunction is characterized by downregulation of some canonical pericyte markers (i.e., DES, CSPG4, and ACTA2) while maintaining the expression of others (i.e., PDGFRB, NOTCH3, and RGS5).
Anabel Martinez‐Romero +11 more
wiley +1 more source
Comparisons of Robustness and Sensitivity between Cancer and Normal Cells by Microarray Data
Cancer Informatics, 2008 Robustness is defined as the ability to uphold performance in face of perturbations and uncertainties, and sensitivity is a measure of the system deviations generated by perturbations to the system.
Liang-Hui Chu, Bor-Sen Chen
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