4,739 research outputs found
An Improved Unscented Kalman Filter for Discrete Nonlinear Systems with Random Parameters
This paper investigates the nonlinear unscented Kalman filtering (UKF) problem for discrete nonlinear dynamic systems with random parameters. We develop an improved unscented transformation by incorporating the random parameters into the state vector to enlarge the number of sigma points. The theoretical analysis reveals that the approximated mean and covariance via the improved unscented transformation match the true values correctly up to the third order of Taylor series expansion. Based on the improved unscented transformation, an improved UKF method is proposed to expand the application of the UKF for nonlinear systems with random parameters. An application to the mobile source localization with time difference of arrival (TDOA) measurements and sensor position uncertainties is provided where the simulation results illustrate that the improved UKF method leads to a superior performance in comparison with the normal UKF method
rac-7-Oxabicyclo[2.2.1]heptane-2,3-dicarboxylic acid–2-amino-1,3,4-thiadiazole–water (1/1/1)
The title compound, C8H10O5·C2H3N3S·H2O, was synthesized by the reaction of 2-amino-1,3,4-thiadiazole with norcantharidin. The crystal structure is stabilized by N—H⋯O, N—H⋯N, O—H⋯O and O—H⋯N hydrogen bonds. In addition, weak π–π interactions are observed between symmetry-related thiadiazole ring systems [centroid–centroid distance = 3.9110 (3) Å, interplanar spacing = 3.4845 Å]
Alpha Lipoic Acid Modulated High Glucose-Induced Rat Mesangial Cell Dysfunction via mTOR/p70S6K/4E-BP1 Pathway
The aim of this study was to investigate whether alpha lipoic acid (LA) regulates high glucose-induced mesangial cell proliferation and extracellular matrix production via mTOR/p70S6K/4E-BP1 signaling. The effect of LA on high glucose-induced cell proliferation, fibronectin (FN), and collagen type I (collagen-I) expression and its mechanisms were examined in cultured rat mesangial cells by methylthiazol tetrazolium (MTT) assay, flow cytometry, ELISA assay, and western blot, respectively. LA at a relatively low concentration (0.25 mmol/L) acted as a growth factor in rat mesangial cells, promoted entry of cell cycle into S phase, extracellular matrix formation, and phosphorylated AKT, mTOR, p70S6K, and 4E-BP1. These effects disappeared when AKT expression was downregulated with PI3K/AKT inhibitor LY294002. Conversely, LA at a higher concentration (1.0 mmol/L) inhibited high glucose-induced rat mesangial cell proliferation, entry of cell cycle into S phase, and extracellular matrix exertion, as well as phosphorylation of mTOR, p70S6K, and 4E-BP1 but enhanced the activity of AMPK. However, these effects disappeared when AMPK activity was inhibited with CaMKK inhibitor STO-609. These results suggest that LA dose-dependently regulates mesangial cell proliferation and matrix protein secretion by mTOR/p70S6K/4E-BP1 signaling pathway under high glucose conditions
Tris(2-amino-1,3-thiazole-κN 3)(7-oxabicyclo[2.2.1]heptane-2,3-dicarboxylato-κ3 O 2,O 3,O 7)cadmium(II) dihydrate
In the crystal structure of the title complex, [Cd(C8H8O5)(C3H4N2S)3]·2H2O, the CdII atom exhibits a slightly distorted octahedral CdO3N3 coordination, defined by the bridging O atom of the bicycloheptane unit, two O atoms from the carboxylate groups and by three N atoms from three 2-aminothiazole ligands. Uncoordinated lattice water molecules are also present in the crystal structure. N—H⋯O and O—H⋯O hydrogen-bonding interactions link the components into a three-dimensional structure
ATASI-Net: An Efficient Sparse Reconstruction Network for Tomographic SAR Imaging with Adaptive Threshold
Tomographic SAR technique has attracted remarkable interest for its ability
of three-dimensional resolving along the elevation direction via a stack of SAR
images collected from different cross-track angles. The emerged compressed
sensing (CS)-based algorithms have been introduced into TomoSAR considering its
super-resolution ability with limited samples. However, the conventional
CS-based methods suffer from several drawbacks, including weak noise
resistance, high computational complexity, and complex parameter fine-tuning.
Aiming at efficient TomoSAR imaging, this paper proposes a novel efficient
sparse unfolding network based on the analytic learned iterative shrinkage
thresholding algorithm (ALISTA) architecture with adaptive threshold, named
Adaptive Threshold ALISTA-based Sparse Imaging Network (ATASI-Net). The weight
matrix in each layer of ATASI-Net is pre-computed as the solution of an
off-line optimization problem, leaving only two scalar parameters to be learned
from data, which significantly simplifies the training stage. In addition,
adaptive threshold is introduced for each azimuth-range pixel, enabling the
threshold shrinkage to be not only layer-varied but also element-wise.
Moreover, the final learned thresholds can be visualized and combined with the
SAR image semantics for mutual feedback. Finally, extensive experiments on
simulated and real data are carried out to demonstrate the effectiveness and
efficiency of the proposed method
Sub-optimal Policy Aided Multi-Agent Reinforcement Learning for Flocking Control
Flocking control is a challenging problem, where multiple agents, such as
drones or vehicles, need to reach a target position while maintaining the flock
and avoiding collisions with obstacles and collisions among agents in the
environment. Multi-agent reinforcement learning has achieved promising
performance in flocking control. However, methods based on traditional
reinforcement learning require a considerable number of interactions between
agents and the environment. This paper proposes a sub-optimal policy aided
multi-agent reinforcement learning algorithm (SPA-MARL) to boost sample
efficiency. SPA-MARL directly leverages a prior policy that can be manually
designed or solved with a non-learning method to aid agents in learning, where
the performance of the policy can be sub-optimal. SPA-MARL recognizes the
difference in performance between the sub-optimal policy and itself, and then
imitates the sub-optimal policy if the sub-optimal policy is better. We
leverage SPA-MARL to solve the flocking control problem. A traditional control
method based on artificial potential fields is used to generate a sub-optimal
policy. Experiments demonstrate that SPA-MARL can speed up the training process
and outperform both the MARL baseline and the used sub-optimal policy.Comment: Accepted by IEEE International Conference on Systems, Man, and
Cybernetics (SMC) 202
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