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