Results 41 to 50 of about 2,291,111 (185)
Data-driven Economic NMPC using Reinforcement Learning [PDF]
, 2019 Reinforcement Learning (RL) is a powerful tool to perform data-driven optimal control without relying on a model of the system. However, RL struggles to provide hard guarantees on the behavior of the resulting control scheme. In contrast, Nonlinear Model
Gros, Sébastien, Zanon, Mario
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Energy InformaticsElectricity markets increasingly rely on residential demand-side flexibility to integrate renewables and stabilize the grid. While dynamic tariffs can unlock short-term flexibility, they expose households to a risk–reward trade-off. This paper quantifies
Justus Ameling +3 more
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Data-driven structured realization [PDF]
Linear Algebra and its Applications, 2018 We present a framework for constructing structured realizations of linear dynamical systems having transfer functions of the form $\widetilde{C}(\sum_{k=1}^K h_k(s)\widetilde{A}_k)^{-1}\widetilde{B}$ where $h_1, h_2, \ldots, h_K$ are prescribed functions that specify the surmised structure of the model. Our construction is data-driven in the sense that
Schulze, Philipp +3 more
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Few-shot prediction of amyloid β accumulation from mainly unpaired data on biomarker candidates
npj Systems Biology and Applications, 2023 The pair-wise observation of the input and target values obtained from the same sample is mandatory in any prediction problem. In the biomarker discovery of Alzheimer’s disease (AD), however, obtaining such paired data is laborious and often avoided ...
Yuichiro Yada, Honda Naoki
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Workout Detection by Wearable Device Data Using Machine Learning
Applied Sciences, 2023 There are many reports that workouts relieve daily stress and are effective in improving mental and physical health. In recent years, there has been a demand for quick and easy methods to analyze and evaluate living organisms using biological information
Yutaka Yoshida, Emi Yuda
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Data-Driven Algorithm Design [PDF]
, 2020 Chapter 29 of the book Beyond the Worst-Case Analysis of Algorithms, edited by Tim Roughgarden and published by Cambridge University Press (2020)
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ETNA - Electronic Transactions on Numerical Analysis, 2022 We present a framework enabling variational data assimilation for gradient flows in general metric spaces, based on the minimizing movement (or Jordan-Kinderlehrer-Otto) approximation scheme. After discussing stability properties in the most general case, we specialise to the space of probability measures endowed with the Wasserstein distance.
Pietschmann, Jan-Frederik +1 more
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The penalty in data driven Neyman's tests [PDF]
, 2002 Data driven Neyman's tests are based on two elements: Neyman's smooth tests in finite dimensional submodels and a selection rule to choose the "right'' submodel. As selection rule usually (a modification of) Schwarz's rule is applied.
Kallenberg, W.C.M.
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Nature CommunicationsPediatric low-grade gliomas (pLGGs) exhibit heterogeneous prognoses and variable responses to treatment, leading to tumor progression and adverse outcomes in cases where complete resection is unachievable. Early prediction of treatment responsiveness and
Anahita Fathi Kazerooni +31 more
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Contamination Detection From Highly Cluttered Waste Scenes Using Computer Vision
IEEE AccessAs the global production of waste continues to rise, there is a growing demand for more effective waste management strategies to handle this expanding problem.
Dishant Mewada +8 more
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