Kalman filtering was applied to the current vs. time data obtained at the growing mercury drop of a DME under d.c. polarographic conditions, to separate the faradaic and capacitive components of the electrode current. Polarograms consisting of the pure faradaic current vs. applied d.c. potential were subjected to a four-parameter curve-fitting procedure to obtain the polarographic characteristics, viz. half-wave potential, limiting current and slope of the log plot together with the baseline current. The method was tested with cadmium and zinc in the 10(-6)-10(-5)M range. The standard deviations of the half-wave potentials and the limiting current/concentration ratios were found to be 1.0 mV and 0.04 respectively.
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http://dx.doi.org/10.1016/0039-9140(86)80134-5 | DOI Listing |
Front Neurorobot
January 2025
Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou, China.
Introduction: Tracking the hidden states of dynamic systems is a fundamental task in signal processing. Recursive Kalman Filters (KF) are widely regarded as an efficient solution for linear and Gaussian systems, offering low computational complexity. However, real-world applications often involve non-linear dynamics, making it challenging for traditional Kalman Filters to achieve accurate state estimation.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, China.
Aerial manipulators can manipulate objects while flying, allowing them to perform tasks in dangerous or inaccessible areas. Advanced aerial manipulation systems are often based on rigid-link mechanisms, but the balance between dexterity and payload capacity limits their broader application. Combining unmanned aerial vehicles with continuum manipulators emerges as a solution to this trade-off, but these systems face challenges with large actuation systems and unstable control.
View Article and Find Full Text PDFISA Trans
December 2024
State Key Laboratory of Intelligent Control and Decision of Complex System, Beijing Institute of Technology, School of Automation, Beijing, China.
This paper investigates the initial dynamic docking problem to mobile and trajectory-disturbed targets for tracking and recovering drones by Unmanned Ground Vehicles (UGVs). First, the target status is estimated by employing the Extended Kalman Filter (EKF). Then, the drone's perturbation is mapped to a dynamic docking point, quantifying the target motion deviation.
View Article and Find Full Text PDFSci Rep
January 2025
College of Mathematical Sciences, Harbin Engineering University, Nangang District, Heilongjiang, Harbin, 150001, China.
This study introduces a hybrid data assimilation method that significantly improves the predictive accuracy of the time-dependent Susceptible-Exposed-Asymptomatic-Infected-Quarantined-Removed (SEAIQR) model for epidemic forecasting. The approach integrates real-time Ensemble Kalman Filtering (EnKF) with the K-Nearest Neighbors (KNN) algorithm, combining dynamic real-time adjustments with pattern recognition techniques tailored to the specific dynamics of epidemics. This hybrid methodology overcomes the limitations of single-model predictions in the face of increasingly complex transmission pathways in modern society.
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
Civil Engineering, SRM Institute of Science and Technology, Kattankulathur, 603203, India.
Papermaking wastewater consists of a sizable amount of industrial wastewater; hence, real-time access to precise and trustworthy effluent indices is crucial. Because wastewater treatment processes are complicated, nonlinear, and time-varying, it is essential to adequately monitor critical quality indices, especially chemical oxygen demand (COD). Traditional models for predicting COD often struggle with sensitivity to parameter tuning and lack interpretability, underscoring the need for improvement in industrial wastewater treatment.
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