Joint State and Input Estimation of Agent Based on Recursive Kalman Filter Given Prior Knowledge

Modern autonomous systems are purposed for many challenging scenarios, where agents will face unexpected events and complicated tasks. The presence of disturbance noise with control command and unknown inputs can negatively impact robot performance. Previous research of joint input and state estimation separately studied the continuous and discrete cases without any prior information. This paper combines the continuous and discrete input cases into a unified theory based on the Expectation-Maximum (EM) algorithm. By introducing prior knowledge of events as the constraint, inequality optimization problems are formulated to determine a gain matrix or dynamic weights to realize an optimal input estimation with lower variance and more accurate decision-making. Finally, statistical results from experiments show that our algorithm owns 81\% improvement of the variance than KF and 47\% improvement than RKF in continuous space; a remarkable improvement of right decision-making probability of our input estimator in discrete space, identification ability is also analyzed by experiments

A cost-effective pH-sensitive release system for water source pH detection

A facile and cost-effective strategy has been developed to form basic cobalt carbonate nanovalves at the orifice of mesoporous nanocontainers, which facilitate the pH sensitive release of functional cargo for up-scaling towards applications in water source pH detection

Point Cloud-Based Target-Oriented 3D Path Planning for UAVs

This paper explores 3D path planning for unmanned aerial vehicles (UAVs) in 3D point cloud environments. Derivative maps such as dense point clouds, mesh maps, octomaps, etc. are frequently used for path planning purposes. A target-oriented 3D path planning algorithm, directly using point clouds to compute optimized trajectories for an UAV, is presented in this article. This approach searches for obstacle-free, low computational cost, smooth, and dynamically feasible paths by analyzing a point cloud of the target environment, using a modified connect RRT-based path planning algorithm, with a k-d tree based obstacle avoidance strategy and three-step optimization. This presented approach bypasses the common 3D map discretization, directly leveraging point cloud data. Following trajectory generation, the algorithm creates way-point based, closed loop quadrotor controls for pitch, roll, and yaw attitude angle as well as dynamics commands for the UAV. Simulations of UAV 3D path planning based on different target points in the point cloud map are presented, showing the effectiveness and feasibility of this approach

V2XP-ASG: Generating Adversarial Scenes for Vehicle-to-Everything Perception

Recent advancements in Vehicle-to-Everything communication technology have enabled autonomous vehicles to share sensory information to obtain better perception performance. With the rapid growth of autonomous vehicles and intelligent infrastructure, the V2X perception systems will soon be deployed at scale, which raises a safety-critical question: \textit{how can we evaluate and improve its performance under challenging traffic scenarios before the real-world deployment?} Collecting diverse large-scale real-world test scenes seems to be the most straightforward solution, but it is expensive and time-consuming, and the collections can only cover limited scenarios. To this end, we propose the first open adversarial scene generator V2XP-ASG that can produce realistic, challenging scenes for modern LiDAR-based multi-agent perception systems. V2XP-ASG learns to construct an adversarial collaboration graph and simultaneously perturb multiple agents' poses in an adversarial and plausible manner. The experiments demonstrate that V2XP-ASG can effectively identify challenging scenes for a large range of V2X perception systems. Meanwhile, by training on the limited number of generated challenging scenes, the accuracy of V2X perception systems can be further improved by 12.3\% on challenging and 4\% on normal scenes. Our code will be released at https://github.com/XHwind/V2XP-ASG.Comment: ICRA 2023, see https://github.com/XHwind/V2XP-AS

Influence of Functionalization of Nanocontainers on Self-Healing Anticorrosive Coatings

Feedback coating based on pH-induced release of inhibitor from organosilyl-functionalized containers is considered as a compelling candidate to achieve smart self-healing corrosion protection. Four key factors that determine the overall coating performance include 1) the uptake and release capacity of containers, 2) prevention of the premature leakage, 3) compatibility of containers in coating matrix and 4) cost and procedure simplicity consideration. The critical influence introduced by organosilyl-functionalization of containers is systematically demonstrated by investigating MCM-41 silica nanoparticles modified with ethylenediamine (en), en-4-oxobutanoic acid salt (en-COO-) and en-triacetate (en-(COO-)3) with higher and lower organic contents. The properties of the modified silica nanoparticles as containers were mainly characterized by solid-state 13C nuclear magnetic resonance, scanning and transmission electron microscopy, N2 sorption, thermogravimetric analysis, small-angle X-ray scattering, dynamic light scattering and UV-vis spectroscopy. Finally, the self-healing ability and anticorrosive performances of hybrid coatings were examined through scanning vibrating electrode technique (SVET) and electrochemical impedance spectroscopy (EIS). en-(COO-)3- type functionalization with content of only 0.23 mmol/g was found to perform the best as a candidate for establishing pH-induced release system. It is because the resulting capped and loaded (C-L) functionalized silica nanocontainers (FSNs) exhibit a high loading (26 wt%) and release capacity (80%) for inhibitor, prevention of premature leakage (less than 2%), good dispersibility in coating matrix and cost effectiveness

Mussel-Inspired Self-Healing Coatings Based on Polydopamine-Coated Nanocontainers for Corrosion Protection

The mussel-inspired properties of dopamine have attracted immense scientific interest for surface modification of nanoparticles due to the high potential of dopamine functional groups to increase the adhesion of nanoparticles to flat surfaces. Here, we report for the first time a novel type of inhibitor-loaded nanocontainer using polydopamine (PDA) as a pH-sensitive gatekeeper for mesoporous silica nanoparticles (MSNs). The encapsulated inhibitor (benzotriazole) was loaded into MSNs at neutral pH, demonstrating fast release in an acidic environment. The self-healing effect of water-borne alkyd coatings doped with nanocontainers was achieved by both on-demand release of benzotriazole during the corrosion process and formation of the complexes between the dopamine functional groups and iron oxides, thus providing dual self-healing protection for the mild steel substrate. The coatings were characterized by electrochemical impedance spectroscopy, visual observations, and confocal Raman microscopy. In all cases, the coatings with embedded benzotriazole-loaded MSNs with PDA-decorated outer surfaces demonstrated superior self-healing effects on the damaged areas. We anticipate that dopamine-based multifunctional gatekeepers can find application potential not only in intelligent self-healing anticorrosive coatings but also in drug delivery, antimicrobial protection, and other fields

Selective catalytic reduction of NOx with NH3 over short-range ordered W-O-Fe structures with high thermal stability

This work was supported by National Natural Science Foundation of China (Nos. 21477046, 21333003, and 21673072) and Key Technology R&D Program of Shandong Province (No. 2016ZDJS11A03).Peer reviewedPostprintPostprin

Active Site Identification and Modification of Electronic States by Atomic-Scale Doping To Enhance Oxide Catalyst Innovation

This work was supported by National Natural Science Foundation of China (No. 21477046) and Key Technology R&D Program of Shandong Province (No. 2016ZDJS11A03).Peer reviewedPostprin

KD_ConvNeXt: knowledge distillation-based image classification of lung tumor surgical specimen sections

Introduction: Lung cancer is currently among the most prevalent and lethal cancers in the world in terms of incidence and fatality rates. In clinical practice, identifying the specific subtypes of lung cancer is essential in diagnosing and treating lung lesions.Methods: This paper aims to collect histopathological section images of lung tumor surgical specimens to construct a clinical dataset for researching and addressing the classification problem of specific subtypes of lung tumors. Our method proposes a teacher-student network architecture based on a knowledge distillation mechanism for the specific subtype classification of lung tumor histopathological section images to assist clinical applications, namely KD_ConvNeXt. The proposed approach enables the student network (ConvNeXt) to extract knowledge from the intermediate feature layers of the teacher network (Swin Transformer), improving the feature extraction and fitting capabilities of ConvNeXt. Meanwhile, Swin Transformer provides soft labels containing information about the distribution of images in various categories, making the model focused more on the information carried by types with smaller sample sizes while training.Results: This work has designed many experiments on a clinical lung tumor image dataset, and the KD_ConvNeXt achieved a superior classification accuracy of 85.64% and an F1-score of 0.7717 compared with other advanced image classification method

Molecular-Level Insight into Selective Catalytic Reduction of NOx with NH3 to N-2 over a Highly Efficient Bifunctional V-alpha-MnOx Catalyst at Low Temperature

This work was supported by the National Natural Science Foundation of China (Nos. 21477046, 21333003, and 21673072) and Key Technology R&D Program of Shandong Province (No. 2016ZDJS11A03). The authors also acknowledge computing time support from the Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase) under Grant No. U1501501.Peer reviewedPostprin