This paper compares a conventional interacting multiple model Kalman filter (IMM-KF) filter and an interacting multiple models with maximum correntropy Kalman filter (IMM-MCKF). A nonlinear UAV dynamics model was used...
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An accurate and straightforward symplectic method is presented for the fracture analysis of fractional two-dimensional(2D)viscoelastic *** fractional Kelvin-Zener constitutive model is used to describe the time-depend...
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An accurate and straightforward symplectic method is presented for the fracture analysis of fractional two-dimensional(2D)viscoelastic *** fractional Kelvin-Zener constitutive model is used to describe the time-dependent behavior of viscoelastic *** the framework of symplectic elasticity,the governing equations in the Hamiltonian form for the frequency domain(s-domain)can be directly and rigorously *** the s-domain,the analytical solutions of the displacement and stress fields are constructed by superposing the symplectic eigensolutions without any trial function,and the explicit expressions of the intensity factors and J-integral are derived *** studies are provided to validate the accuracy and effectiveness of the present solutions.A detailed analysis is made to reveal the effects of viscoelastic parameters and applied loads on the intensity factors and J-integral.
In this study, a robust controller design using an Integral Sliding Mode control (ISMC) approach for controlling the roll motion during the Vertical Take-off and Landing of Unmanned Air Vehicles (VTOL-UAV) is introduc...
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Wepresent a comparative study of traditional "norm-based" and recently developed "norm free" event-triggered control architectures. For this purpose, the benchmark problem of scheduling control dat...
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An advantage of bio-inspired robots is the versatility of their locomotion on a wide range of terrains that conventional robots are not able to traverse. The snake-like robot, which is a mechanism designed to move in ...
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Wind energy has good potential as a renewable energy source. Savonius-type wind turbines can provide power generation at a low cost with minimal environmental impact. Due to its low efficiency, researchers were focuse...
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This article presents a comparative study of two topologies of three-phase photovoltaic inverters connected to the grid, between the usual two-level inverter and three-level NPC (Neutral Point Clamped) inverter. Inver...
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Accelerated MRI involves a trade-off between sampling sufficiency and acquisition time. Supervised deep learning methods have shown great success in MRI reconstruction from under-sampled measurements, but they typical...
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ISBN:
(数字)9798350371499
ISBN:
(纸本)9798350371505
Accelerated MRI involves a trade-off between sampling sufficiency and acquisition time. Supervised deep learning methods have shown great success in MRI reconstruction from under-sampled measurements, but they typically require a large set of fully-sampled MR images for training, which can be difficult to obtain. In this paper, we present a novel fully self-supervised method based on implicit neural representation, which requires only a single under-sampled MRI instance for training. To effectively guide the self-supervised learning process, we introduced multiple novel supervisory signals in both the image and frequency domains. Experimental results indicate that the proposed method outperforms existing self-supervised methods and even a supervised method, demonstrating its strong reliability and flexibility. Our code is publicly available at https://***/YSongxiao/*** relevance— The proposed method can significantly enhance the image quality of under-sampled MR images without the need of ground-truth fully-sampled MR images for supervision and additional prior images for guidance.
Stochastic differential equation (SDE)-based random process models of renewable energy sources (RESs) jointly capture evolving probability distribution and temporal correlation in continuous time. It enabled recent st...
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Stochastic differential equation (SDE)-based random process models of renewable energy sources (RESs) jointly capture evolving probability distribution and temporal correlation in continuous time. It enabled recent studies to remarkably improve performance of power system dynamic uncertainty quantification and optimization. However, considering the non-homogeneous random process nature of PV, there still remains a challenging question: how can a realistic and accurate daily SDE model for PV power be obtained that reflects its weather-dependent and non-Gaussian uncertainty in operation, especially when high-resolution numerical weather prediction (NWP) or sky imager is unavailable for many distributed plants? To fill this gap, this article finds that an accurate SDE model for PV power can be constructed only using the data from low-resolution public weather reports. Specifically, for each day, an hourly parameterized Jacobi diffusion process recreates temporal patterns of PV volatility. Its parameters are mapped from the day's public weather reports to reflect varying weather conditions using a simple learning model. The SDE model jointly captures intraday and intrahour volatility. Statistical examination shows that the proposed approach outperforms a selection of the latest deep learning-based time series models on real-world data collected in Macao.
This paper proposes an online controller tuning method using fictitious reference iterative tuning (FRIT) design method based on recursive least-squares (RLS) method for quadrotor flight control. FRIT is a method of d...
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