A Nonlinear Double Model for Multisensor-Integrated Navigation Using the Federated EKF Algorithm for Small UAVs

Aimed at improving upon the disadvantages of the single centralized Kalman filter for integrated navigation, including its fragile robustness and low solution accuracy, a nonlinear double model based on the improved decentralized federated extended Kalman filter (EKF) for integrated navigation is proposed. The multisensor error model is established and simplified in this paper according to the near-ground short distance navigation applications of small unmanned aerial vehicles (UAVs). In order to overcome the centralized Kalman filter that is used in the linear Gaussian system, the improved federated EKF is designed for multisensor-integrated navigation. Subsequently, because of the navigation requirements of UAVs, especially for the attitude solution accuracy, this paper presents a nonlinear double model that consists of the nonlinear attitude heading reference system (AHRS) model and nonlinear strapdown inertial navigation system (SINS)/GPS-integrated navigation model. Moreover, the common state parameters of the nonlinear double model are optimized by the federated filter to obtain a better attitude. The proposed algorithm is compared with multisensor complementary filtering (MSCF) and multisensor EKF (MSEKF) using collected flight sensors data. The simulation and experimental tests demonstrate that the proposed algorithm has a good robustness and state estimation solution accuracy

FC-RRT*: An Improved Path Planning Algorithm for UAV in 3D Complex Environment

In complex environments, path planning is the key for unmanned aerial vehicles (UAVs) to perform military missions autonomously. This paper proposes a novel algorithm called flight cost-based Rapidly-exploring Random Tree star (FC-RRT*) extending the standard Rapidly-exploring Random Tree star (RRT*) to deal with the safety requirements and flight constraints of UAVs in a complex 3D environment. First, a flight cost function that includes threat strength and path length was designed to comprehensively evaluate the connection between two path nodes. Second, in order to solve the UAV path planning problem from the front-end, the flight cost function and flight constraints were used to inspire the expansion of new nodes. Third, the designed cost function was used to guide the update of the parent node to allow the algorithm to consider both the threat and the length of the path when generating the path. The simulation and comparison results show that FC-RRT* effectively overcomes the shortcomings of standard RRT*. FC-RRT* is able to plan an optimal path that significantly improves path safety as well as maintains has the shortest distance while satisfying flight constraints in the complex environment. This paper has application value in UAV 3D global path planning

A Fast Weakly-Coupled Double-Layer ESKF Attitude Estimation Algorithm and Application

Aimed at the problem of small unmanned aerial vehicle (UAV) attitude solution accuracy and real-time performance in short-range navigation flight, in this paper, we propose a fast weakly-coupled double-layer error-state Kalman filter (DL-ESKF) attitude estimation algorithm. Considering the application of short-range navigation, we designed an improved attitude error model for low-cost gyroscope/accelerometer/magnetometer devices. In addition, we reasonably simplified certain factors that affect the attitude solution to reduce the filtering calculation burden. For the data coupling phenomenon caused by the different sampling frequencies of the attitude sensor data in the filtering process, we designed a new attitude algorithm combined with the ESKF and hierarchical filter. The first layer of filters used an accelerometer and the second layer used a magnetometer to correct the attitude error. We also built an offline and real-time test platform to verify the performance of the proposed algorithm in a simulation and flight test environment compared with the classic attitude algorithms. The experimental results demonstrated that the proposed algorithm not only improved the attitude solution accuracy and stability but also reduced the filter running time

A deep learning‐based interpretable decision tool for predicting high risk of chemotherapy‐induced nausea and vomiting in cancer patients prescribed highly emetogenic chemotherapy

Abstract Objective This study aims to develop a risk prediction model for chemotherapy‐induced nausea and vomiting (CINV) in cancer patients receiving highly emetogenic chemotherapy (HEC) and identify the variables that have the most significant impact on prediction. Methods Data from Tianjin Medical University General Hospital were collected and subjected to stepwise data preprocessing. Deep learning algorithms, including deep forest, and typical machine learning algorithms such as support vector machine (SVM), categorical boosting (CatBoost), random forest, decision tree, and neural network were used to develop the prediction model. After training the model and conducting hyperparameter optimization (HPO) through cross‐validation in the training set, the performance was evaluated using the test set. Shapley additive explanations (SHAP), partial dependence plot (PDP), and Local Interpretable Model‐Agnostic Explanations (LIME) techniques were employed to explain the optimal model. Model performance was assessed using AUC, F1 score, accuracy, specificity, sensitivity, and Brier score. Results The deep forest model exhibited good discrimination, outperforming typical machine learning models, with an AUC of 0.850 (95%CI, 0.780–0.919), an F1 score of 0.757, an accuracy of 0.852, a specificity of 0.863, a sensitivity of 0.784, and a Brier score of 0.082. The top five important features in the model were creatinine clearance (Ccr), age, gender, anticipatory nausea and vomiting, and antiemetic regimen. Among these, Ccr had the most significant predictive value. The risk of CINV decreased with increased Ccr and age, while it was higher in the presence of anticipatory nausea and vomiting, female gender, and non‐standard antiemetic regimen. Conclusion The deep forest model demonstrated good discrimination in predicting the risk of CINV in cancer patients prescribed HEC. Kidney function, as represented by Ccr, played a crucial role in the model's prediction. The clinical application of this predictive tool can help assess individual risks and improve patient care by proactively optimizing the use of antiemetics in cancer patients receiving HEC

Unzipping MWCNTs for controlled edge- and heteroatom-defects in revealing their roles in gas-phase oxidative dehydrogenation of ethanol to acetaldehyde

Bioethanol is a promising candidate for acetaldehyde production. In this study, we controllably unzipped multi-walled carbon nanotubes into open-edged nanotube/nanoribbon hybrids via a nano-cutting strategy for metal-free oxidative dehydrogenation of ethanol to acetaldehyde and unravelled the catalytic role of edge defects in the reaction. The edge-rich structure of the 1D-nanotube/2D-nanoribbon hybrid can accelerate the catalytic reaction more efficiently than pristine carbon sample. Moreover, edges can further accommodate nitrogen defects to preferentially form edge-doped nitrogen. Through engineering the concentration and speciation of defects, the structure-performance relationship between the defective structure and ethanol conversion rate is intensively investigated. Theoretical calculations unveil that the nitrogen doped at edge sites other than in basal planes can effectively facilitate O2 dissociation and formation of oxygen-containing active centers. Temperature-programmed ethanol desorption and kinetic measurements further supplement the catalytic interplay of edge and nitrogen defects on ethanol adsorption and reaction kinetics. The synergistic edge and nitrogen defects of the engineered hybrid produced a steady ethanol conversion of 47.9% and acetaldehyde selectivity of 90.2% at the gas hourly space velocity of 48,000 mL gcat-1h−1 on stream of 48 h. This work offers more insights to intrinsic properties and mechanism of enriched defective structures for development of effective carbocatalysts in catalytic applications