In this paper, the problem of a fully actuated hexarotor performing a physical interaction with the environment through a rigidly attached tool is considered. A nonlinear model predictive impedance control (NMPIC) method is proposed to achieve the goal in which the controller is able to simultaneously handle the constraints and maintain the compliant behavior. The design of NMPIC is the combination of a nonlinear model predictive control and impedance control based on the dynamics of the system. A disturbance observer is exploited to estimate the external wrench and then provide compensation for the model which was employed in the controller. Moreover, a weight adaptive strategy is proposed to perform the online tuning of the weighting matrix of the cost function within the optimal problem of NMPIC to improve the performance and stability. The effectiveness and advantages of the proposed method are validated by several simulations in different scenarios compared with the general impedance controller. The results also indicate that the proposed method opens a novel way for interaction force regulation.
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http://dx.doi.org/10.3390/s23115231 | DOI Listing |
Front Artif Intell
January 2025
RV University, Bengaluru, India.
Introduction: Cyber situational awareness is critical for detecting and mitigating cybersecurity threats in real-time. This study introduces a comprehensive methodology that integrates the Isolation Forest and autoencoder algorithms, Structured Threat Information Expression (STIX) implementation, and ontology development to enhance cybersecurity threat detection and intelligence. The Isolation Forest algorithm excels in anomaly detection in high-dimensional datasets, while autoencoders provide nonlinear detection capabilities and adaptive feature learning.
View Article and Find Full Text PDFStat Interface
January 2024
Purdue University, West Lafayette, IN 47907, United States of America.
Graphical models have long been studied in statistics as a tool for inferring conditional independence relationships among a large set of random variables. The most existing works in graphical modeling focus on the cases that the data are Gaussian or mixed and the variables are linearly dependent. In this paper, we propose a double regression method for learning graphical models under the high-dimensional nonlinear and non-Gaussian setting, and prove that the proposed method is consistent under mild conditions.
View Article and Find Full Text PDFFront Pharmacol
January 2025
Department of Pharmacy, The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China.
Objectives: To assess the pharmacokinetics and pharmacodynamics of imipenem in a retrospective cohort of hospitalized Chinese older patients.
Methods: A population pharmacokinetic (PPK) model was constructed utilizing a nonlinear mixed-effects modeling approach. The final model underwent evaluation through bootstrap resampling and visual predictive checks.
Heliyon
January 2025
National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 100050, China.
Background: Previous studies have found an association between influenza, cardiovascular and cerebrovascular disease mortality, and all-cause mortality. And the vaccination of elderly diabetes is often recommended to reduce the risk of hospitalization and death. Nevertheless, no previous work has investigated the short-term impact of influenza on diabetes mortality in China.
View Article and Find Full Text PDFBackground: Polyfluoroalkyl chemicals (PFCs) present potential health risks due to their persistence and bioaccumulation. However, there is currently insufficient evidence regarding their impact on cardiovascular disease (CVD). Consequently, it is imperative to investigate the correlation between PFCs and CVD.
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