Optimal Estimation Research Articles

Paliperidone-loaded nose to brain targeted NLCS: optimisation, evaluation, histopathology and pharmacokinetic estimation for schizophernia.

Study was to develop a nanostructured-lipid-careers (NLCs) of paliperidone (PLP) for nose-to-brain targeting. NLCs was prepared by sonication, high-shear homogenisation method, and characterised their mean diameter, PDI, zeta-potential, morphology (by SEM, TEM and AFM), entrapment efficiency, drug loading, in vitro release, interaction study (by FTIR), and stability. Further, ex vivo permeation and ciliotoxicity performed in sheep nasal mucosa, and in vivo biodistribution/pharmacokinetic was performed in rats for schizophernia. Developed NLCs showed spherical and clearly 3-dimentinal structure with 129 ± 2.7 nm mean diameter, 0.304 ± 0.003 PDI, -7.61 ± 0.56 mV zeta-potential, 58.16 ± 0.17% entrapment efficiency, 65.8 ± 2% drug loading and 74.32 ± 0.003% release in 12 h, followed by Higuchi model. Ex vivo study showed NLCs have three times higher permeation, compare to pure drug (around 71.50.32% in 6 h) and 3.98 g/cm2/h steady sate flux. The blood/brain ratio given by intranasally have higher compare to IV route, and 94.53 ± 21.45% drug targeting efficiency in brain. In conclusion, NLCs have easily crossed BBB, higher drug delivery and effective for schizophrenia in given by intranasal.

Error governor for active fault tolerance in PID control of MIMO systems

Input saturation and actuator faults are common issues in control system engineering. This paper proposes an Error Governor (EG) policy that dynamically manages the feedback error to enhance the tracking error in Multiple Input-Multiple Output (MIMO) closed-loop system controlled by Proportional-Integral-Derivative (PID) regulators. By integrating an Adaptive Kalman Filter (AKF) for optimal fault estimation with the EG scheme, the solution enables fault-tolerant control without requiring modifications to the baseline controller, which can be impractical or unsuitable in certain applications. Furthermore, the proposed control scheme completely avoids windup, thereby replacing conventional Anti-Windup (AW) schemes for PIDs, and is computationally cheap and easy to implement, needing only the same inputs as conventional AW algorithms and the actuator fault estimation. Simulation results on the MIMO model of the Zagi flying-wing aerial vehicle show that the EG reduces the tracking error in presence of actuator faults by − 49.92 % in terms of Integral Absolute Error (IAE), outperforming conventional AW methods.

RBCKF-Based Vehicle State Estimation by Adaptive Weighted Fusion Strategy Considering Composite-State Tire Model

The acquisition of vehicle driving status information is a key function of vehicle dynamics systems, and research on high-precision and high-reliability estimation of key vehicle states has significant value. To improve the state observation effect, a vehicle sideslip angle estimation method adopting a robust bias compensation Kalman filter and adaptive weight fusion strategy is proposed. On the basis of the extended Kalman filter algorithm, and with the goals of estimation exactitude and robustness, considering the potential signal deviation, a vehicle state robust deviation compensation Kalman filter estimation algorithm considering bias compensation and residual covariance matrix weighting is proposed. Meanwhile, considering the adaptive and dynamic adjustment capabilities of the observation system in complex state-change scenarios, an estimation strategy based on adaptive weight fusion and a model-based estimator is proposed. The results confirm that the robust bias compensation Kalman filter can ensure estimation exactitude and robustness when the vehicle state fluctuates greatly, and the proposed fusion strategy can ensure that the vehicle maintains optimal estimation performance during operating condition switching.

Optimal Estimation of Reliability Parameters for Modified Frechet-Exponential Distribution Using Progressive Type-II Censored Samples with Mechanical and Medical Data

The aim of this research is to estimate the parameters of the modified Frechet-exponential (MFE) distribution using different methods when applied to progressive type-II censored samples. These methods include using the maximum likelihood technique and the Bayesian approach, which were used to determine the values of parameters in addition to calculating the reliability and failure functions at time t. The approximate confidence intervals (ACIs) and credible intervals (CRIs) are derived for these parameters. Two bootstrap techniques of parametric type are provided to compute the bootstrap confidence intervals. Both symmetric loss functions such as the squared error loss (SEL) and asymmetric loss functions such as the linear-exponential (LINEX) loss are used in the Bayesian method to obtain the estimates. The Markov Chain Monte Carlo (MCMC) technique is utilized in the Metropolis–Hasting sampler approach to obtain the unknown parameters using the Bayes approach. Two actual datasets are utilized to examine the various progressive schemes and different estimation methods considered in this paper. Additionally, a simulation study is performed to compare the schemes and estimation techniques.

Effectiveness of Musa Balbisiana bract toward chromium removal from industrial tannery effluent: optimization, kinetics, isotherms, regeneration, and cost estimation

Eradicating chromium from industrial effluent is essential for environmental security and economic reasons. This study investigates the potency of activated Musa balbisiana bract biomass as a biosorbent to remove hexavalent chromium (Cr6+) from real industrial tannery effluent (ITE). The characterization study exemplifies the existence of irregular structures and excessive cavities, and the occurrence of functional groups (hydroxyl, carboxyl, esters, and alkynes) are benefitting the deposition of Cr6+ on the biosorbent. A maximum biosorption capacity of 42.75 ± 0.21mg/g was observed at an optimum pH of 6.5, biosorbent dosage of 0.3g, initial Cr6+ concentration of 50mg/L, and contact time of 120min. The validation experiment confirmed the removal efficiency of total chromium, trivalent chromium, and Cr6+ 92.56 ± 0.80%, 98.63 ± 0.20%, and 96.21 ± 0.50%, respectively. Among the models, Langmuir isotherm (R2: 0.9992) and pseudo-second order (R2: 0.9999) kinetic models greatly correlate with the equilibrium data. A 2-tier membrane module was examined for continuous study and reached 88.23 ± 0.60% Cr6+ removal. Statistical analysis was performed to confirm the significance of adsorption results. The likelihood of the desorption and regeneration of the treated biosorbent was investigated. The estimated cost per volume of ITE treated and unit of pollutant removal from ITE employing Musa balbisiana bract biosorbent is around $3.08/m3 and $3.75/kg.

Embedded State Estimation for Optimization of Cislunar Space Domain Awareness Constellation Design

The traffic in cislunar space is expected to increase over the coming years, leading to a higher likelihood of conjunction events among active satellites, orbital debris, and noncooperative satellites. This increase necessitates enhanced space domain awareness (SDA) capabilities that include state estimation for targets of interest. Both Earth surface-based and space-based observation platforms in geosynchronous orbit or below face challenges such as range, exclusion, and occlusion that hinder observation. Motivated by the need to place space-based observers in the cislunar space regime to overcome these challenges, this paper proposes a cislunar SDA constellation design and analysis framework that integrates state estimation into an optimization problem for determining the placement of observers for optimal state estimation performance on a set of targets. The proposed multiobserver placement optimization problem samples from a range of possible target orbits. Upon convergence, the optimized constellation is validated against a broader set of targets to assess its effectiveness. Two comparative analyses are presented to evaluate the effects of changes in the sensor tasking procedure and sensor fidelity on the optimized constellation, comparing these to a single observer baseline case. The results demonstrate that the optimized constellations can provide accurate state estimation for various orbit families.

ODEFTC: Optimal distributed estimation based on fixed-time consensus

ODEFTC: Optimal distributed estimation based on fixed-time consensus

Detection of Earth’s free oscillation and analysis of the non-synchronous oscillation phenomenon of normal modes

Earth’s free oscillation can provide essential constraints for refining Earth models, inverting seismic source mechanisms, and studying the deep internal structure of the Earth. Large earthquakes can simultaneously excite numerous normal modes. Due to the Earth’s ellipticity, rotation, and internal heterogeneities, these normal modes undergo splitting, with the frequencies of singlets of normal modes becoming very close (only a few µHz apart). This imposes greater demands on the detection of normal modes. This paper introduces a novel method for normal mode detection based on the normal time–frequency transform (NTFT). Compared to classical FT spectrum methods and recent optimal sequence estimation (OSE), the proposed method not only detects more weak normal modes but also reveals the spatial distribution of the phase of each normal mode. Taking the detection of 0S2 as an example, the phase measurements of each singlet are spatially inconsistent. This phenomenon can provide prior information for other methods, such as product spectrum analysis (PSA), spherical harmonic stacking (SHS), multistation experiments (MSE), and OSE. Additionally, understanding the phase distribution patterns contributes to further study of geological structures, offering crucial foundational data and observational support.

Optimal Estimation of Frequency-Selective Channels in OFDM-Based Superimposed Training Schemes

Optimal Estimation of Frequency-Selective Channels in OFDM-Based Superimposed Training Schemes

Artificial Neural Network-Based Data-Driven Parameter Estimation Approach: Applications in PMDC Motors

The optimal performance of direct current (DC) motors is intrinsically linked to their mathematical models’ precision and their controllers’ effectiveness. However, the limited availability of motor characteristic information poses significant challenges to achieving accurate modeling and robust control. This study introduces an approach employing artificial neural networks (ANNs) to estimate critical DC motor parameters by defining practical constraints that simplify the estimation process. A mathematical model was introduced for optimal parameter estimation, and two advanced learning algorithms were proposed to efficiently train the ANN. The performance of the algorithms was thoroughly analyzed using metrics such as the mean squared error, epoch count, and execution time to ensure the reliability of dynamic priority arbitration and data integrity. Dynamic priority arbitration involves automatically assigning tasks in real-time depending on their relevance for smooth operations, whereas data integrity ensures that information remains accurate, consistent, and reliable throughout the entire process. The ANN-based estimator successfully predicts electromechanical and electrical characteristics, such as back-EMF, moment of inertia, viscous friction coefficient, armature inductance, and armature resistance. Compared to conventional methods, which are often resource-intensive and time-consuming, the proposed solution offers superior accuracy, significantly reduced estimation time, and lower computational costs. The simulation results validated the effectiveness of the proposed ANN under diverse real-world operating conditions, making it a powerful tool for enhancing DC motor performance with practical applications in industrial automation and control systems.

An improved Kepler optimization algorithm for module parameter identification supporting PV power estimation

Identification of photovoltaic (PV) module characteristics in solar systems is a vital task, nowadays, for optimal PV power estimation. In this paper, this challenge task has been studied using a novel advanced Kepler optimization algorithm (KOA). The standard version of KOA is adopted and assessed for getting the nine parameters of the PV triple diode model (3DM) considering three different practical PV modules. Kepler's principles of planetary motion are used by KOA to forecast the location and velocity of planets at any particular moment. However, the success rate of the KOA is not compatible, and its efficiency needs to be enhanced. As a result, an Improved KOA (IKOA) is created by incorporating an advanced mechanism of Local Escaping Operator (LEO), resulting in improved process of searching with evading local optima. This mechanism means that the exploitation approach will activate with around half of the solutions for every iteration starting at the initial phase of the iteration journey. The suggested IKOA besides the standard KOA are developed for predicting PV parameters for three distinct PV modules which are Photowatt PWP201, R.T.C France and STM6-40/36. The results corresponding to the latest algorithms are also compared with the proposed IKOA about different published works. The simulation findings reveal that the suggested IKOA exhibits notable average improvement rates for the three modules of 62.27 %, 55.1 %, and 32.12 %, respectively. Furthermore, the suggested IKOA asserts significant superiority and robustness over previously reported results.

Real-Time LiDAR–Inertial Simultaneous Localization and Mesh Reconstruction

In this paper, a novel LiDAR–inertial-based Simultaneous Localization and Mesh Reconstruction (LI-SLAMesh) system is proposed, which can achieve fast and robust pose tracking and online mesh reconstruction in an outdoor environment. The LI-SLAMesh system consists of two components, including LiDAR–inertial odometry and a Truncated Signed Distance Field (TSDF) free online reconstruction module. Firstly, to reduce the odometry drift errors we use scan-to-map matching, and inter-frame inertial information is used to generate prior relative pose estimation for later LiDAR-dominated optimization. Then, based on the motivation that the unevenly distributed residual terms tend to degrade the nonlinear optimizer, a novel residual density-driven Gauss–Newton method is proposed to obtain the optimal pose estimation. Secondly, to achieve fast and accurate 3D reconstruction, compared with TSDF-based mapping mechanism, a more compact map representation is proposed, which only maintains the occupied voxels and computes the vertices’ SDF values of each occupied voxels using an iterative Implicit Moving Least Squares (IMLS) algorithm. Then, marching cube is performed on the voxels and a dense mesh map is generated online. Extensive experiments are conducted on public datasets. The experimental results demonstrate that our method can achieve significant localization and online reconstruction performance improvements. The source code will be made public for the benefit of the robotic community.

Motion State Estimation with Bandwidth Constraints and Mixed Cyber-Attacks for Unmanned Surface Vehicles: A Resilient Set-Membership Filtering Framework.

This paper investigates the motion state estimation problem of the unmanned surface vehicle (USV) steering system in wireless sensor networks based on the binary coding scheme (BCS). In response to the presence of bandwidth constraints and mixed cyber-attacks in USV communication networks, this paper proposes an improved set-membership state estimation algorithm based on BCS. This algorithm partially addresses the problem of degraded performance in USV steering motion state estimation caused by mixed cyber-attacks and bandwidth constraints. Furthermore, this paper proposes a robust resilient filtering framework considering the possible occurrence of unknown but bounded (UBB) noises, model parameter uncertainties, and estimator gain perturbations in practical scenarios. The proposed framework can accurately estimate the sway velocity, yaw velocity, and roll velocity of the USV under the concurrent presence situation of mixed cyber-attacks, communication capacity constraints, UBB noises, model parameter uncertainties, and estimator gain perturbations. This paper first utilizes mathematical induction to provide the sufficient conditions for the existence of the desired estimator, and obtains the estimator gain by solving a set of linear matrix inequalities. Then, a recursive optimization algorithm is utilized to achieve optimal estimation performance. Finally, the effectiveness of the proposed estimation algorithm is verified through a simulation experiment.

Robust Maximum Correntropy Kalman Filter

ABSTRACTThe Kalman filter provides an optimal estimation for a linear system with Gaussian noise. However, when the noises are non‐Gaussian in nature, its performance deteriorates rapidly. For non‐Gaussian noises, maximum correntropy Kalman filter (MCKF) is developed which provides a more accurate result. In a scenario, where the actual system model differs from nominal consideration, the performance of the MCKF degrades. For such cases, in this article, we have proposed a new robust filtering technique for a linear system which maximizes a cost function defined by exponential of weighted past and present errors weighted with the kernel bandwidth. During filtering, at each time step, the kernel bandwidth is selected by maximizing the correntropy function of error. Further, a convergence condition of the proposed algorithm is derived. Numerical examples are presented to show the usefulness of the proposed filtering technique.

Predicting refractive index of inorganic compounds using machine learning

Refractive index (RI) is one of the most important optical properties of materials. Due to the high importance of this physical parameter, there has always been a demand to find a method that provides the most optimal estimation. In this research, we utilize experimentally measured RI values of 272 inorganic compounds to build a machine learning model capable of predicting the RI of materials with low computational cost. Considering the significant relationship between the band gap and RI, we select this parameter as a predictor. In addition to the band gap, the atomic properties related to the building elements of the compounds form our data set in this work. To find the most optimal model and set of suitable predictors, we examine our data in four categories with 1, 5, 10, and 21 features. In addition, we compare the predicted RIs of 6 different independent regression methods, namely, ordinary least squares (OLSR), Gaussian process (GPR), support vector (SVR), random forest (RFR), gradient boosted trees (GBTR), and extremely randomized trees regression(ERTR). We notice that ERTR predicts RI with the highest accuracy compared to other regression methods. The prediction strength of our model excels in empirical relations and provides accurate results for a wide range of RIs. Thus, we demonstrate the high potential of machine learning methods for evaluating the RI, especially when it comes to providing an estimation of a desired physical quantity.

Adaptive sensor attack detection and defense framework for autonomous vehicles based on density

The security of autonomous vehicles heavily depends on localization systems that integrate multiple sensors, which are vulnerable to sensor attacks and increase the risk of accidents. Given the diversity of sensor attacks and the dynamic changing of driving scenarios of autonomous vehicles, an adaptive and effective attack detection and defense framework faces a considerable challenge. This paper proposes a novel real-time adaptive attack detection and defense framework based on density, which can detect and identify attacked sensors and effectively recover data. We first develop a reinforcement learning multi-armed Bandit-based Density-Based Spatial Clustering of Applications with Noise (BDBSCAN) algorithm that selects hyperparameters adaptively. The Adaptive Extended Kalman Filter (AEKF) combines with the vehicle dynamic model on the localization system and extracts data features used for the BDBSCAN algorithm to monitor potential sensor attacks. If attack detection indicates possible system compromise, AEKF is further employed on localization sensors with anomalies identified through the BDBSCAN algorithm of the attacked sensors. To ensure precision and reliability, the data recovery incorporates a redundancy mechanism to apply a decision tree to select the optimal state estimation between AEKF and Extended Kalman Filter (EKF) to replace corrupted sensor data. To evaluate the effectiveness and adaptability of the proposed framework, we conducted 15,000 experiments using the real-world KITTI and V2V4Real datasets across various driving and sensor attack scenarios. The results demonstrate that our proposed framework achieves 100% accuracy and 0% false alarm rate in various driving scenarios for attack detection within 0.15 s, with a recovery time of 0.08 s.

High-precision geometric positioning for optical remote sensing satellite in dynamic imaging

ABSTRACT Dynamic imaging of optical remote sensing satellites refers to the active acquisition of images while the satellite is maneuvering at a high angular velocity, which significantly enhances the efficiency and application value of remote sensing imagery. However, due to the influence of rapid maneuvering, the random error of star sensors significantly increases, resulting in a decrease in the geometric positioning accuracy of dynamic imaging remote sensing images. This paper proposes a dynamic fusion method for multisource attitude measurement data based on the noise adaptive estimation and bidirectional filter to achieve high-precision attitude determination and geometric positioning in dynamic imaging. Based on the measurement error model of star sensors, the weights of the star sensor and gyroscope are adaptively adjusted in multisource data fusion to reduce the impact of star sensor measurement errors on the gyroscope. Moreover, a bidirectional fusion filter that includes a low-velocity maneuvering stage is proposed to realize the optimal estimation of the satellite attitude parameter. The simulation data and onboard data of the Luojia3-01 (LJ3–01) satellite were tested to verify the effectiveness of the proposed method. The geometric positioning accuracy of the staring images of LJ3–01 improved from 10.048 m to 7.538 m. The registration accuracy of the sequential images improved from 3.568 pixels to 1.179 pixels. The proposed method can significantly improve the attitude determination accuracy and the geometric positioning accuracy of LJ3–01 satellite staring images. Moreover, for simulation data with various angular velocities, the attitude determination accuracies of the proposed method are better than 0.93”. The experimental results show that the proposed method can achieve high-precision attitude determination in dynamic imaging, reaching the accuracy in the traditional passive imaging.

Catalytic performance of FeCo2O4 spinel cobaltite for degradation of ethylparaben in a peroxymonosulfate activation process: Response surface optimization, reaction kinetics and cost estimation

The presence of cosmetics and personal care products in water resources has become an increasing concern for environment due to their toxicity and persistence in aquatic medium. Peroxymonosulfate mediated advanced oxidation methods have a great potential for the efficient removal of complex organic compounds due to the generation of highly reactive radicals with high oxidation ability. Iron cobaltite, FeCo2O4 catalyst was used as a peroxymonosulfate (PMS) activator for the degradation of ethylparaben which was selected as a model personal care product ingredient. Paraben degradation was modelled and the optimum operating parameters were determined by using Box-Behnken design. The ethylparaben degradation efficiency was calculated as 94.6 % under the optimized conditions which were determined as 0.9 g/L FeCo2O4 loading, pH 6.15, and 0.26 g/L PMS dosage. The ethylparaben degradation reaction followed second-order reaction kinetics. The activation energy was evaluated as 40.1 kJ/mol. Quenching radical tests showed that the dominant reactive species were hydroxyl radicals. Toxicity of the treated samples in terms of L. sativum growth inhibition was evaluated as 1.5 %. The total cost of the treatment including the operating and the amortization costs was calculated as 0.91 €/L.

Troposphere–stratosphere-integrated bromine monoxide (BrO) profile retrieval over the central Pacific Ocean

Abstract. Bromine monoxide (BrO) is relevant to atmospheric oxidative capacity, affecting the lifetime of greenhouse gases (i.e., methane, dimethylsulfide) and mercury oxidation. However, measurements of BrO radical vertical profiles are rare, and BrO is highly variable. As a result, the few available aircraft observations in different regions of the atmosphere are not easily reconciled. Autonomous multi-axis differential optical absorption spectroscopy (MAX-DOAS) instruments placed at remote mountaintop observatories (MT-DOAS) present a cost-effective alternative to aircraft, with the potential to probe the climate-relevant yet understudied free troposphere more routinely. Here, we describe an innovative full-atmosphere BrO and formaldehyde (HCHO) profile retrieval algorithm using MT-DOAS measurements at Mauna Loa Observatory (MLO – 19.536° N, 155.577° W; 3401 m a.s.l.). The retrieval is based on time-dependent optimal estimation and simultaneously inverts 190+ individual BrO (and formaldehyde, HCHO) SCDs (slant column densities; SCD = dSCD + SCDRef) from solar stray light spectra measured in the zenith and off-axis geometries at high and low solar zenith angles (92° > SZA > 30°) to derive BrO concentration profiles from 1.9 to 35 km with 7.5 degrees of freedom (DoFs). Two case study days are characterized by the absence (26 April 2017, base case) and presence of a Rossby-wave-breaking double tropopause (29 April 2017, RW-DT case). Stratospheric-BrO vertical columns are nearly identical on both days (VCD = (1.5 ± 0.2) × 1013 molec. cm−2), and the stratospheric-BrO profile peaks at a lower altitude during the RW-DT (1.6–2.0 DoFs). Tropospheric-BrO VCDs increase from (0.70 ± 0.14) × 1013 molec. cm−2 (base case) to (1.00 ± 0.14) × 1013 molec. cm−2 (RW-DT) owing to a 3-fold increase in BrO in the upper troposphere (1.7–1.9 DoFs). BrO at MLO increases from (0.23 ± 0.03) pptv (base case) to (0.46 ± 0.03) pptv (RW-DT) and is characterized by an added time resolution (∼ 3.8 DoFs). Up to (0.9 ± 0.1) pptv BrO is observed above MLO in the lower free troposphere in the absence of the double tropopause. We validate the retrieval using aircraft BrO profiles and in situ HCHO measurements aboard the NSF/NCAR GV aircraft above MLO (11 January 2014) that establish BrO peaks around 2.4 pptv above 13 km in the upper troposphere–lower stratosphere (UTLS) during a similar RW-DT event (0.83 × 1013 molec. cm2 tropospheric-BrO VCD above 2 km). The tropospheric-BrO profile measured using MT-DOAS (RW-DT case) and using the aircraft agree well (after averaging-kernel smoothing). Furthermore, these tropospheric-BrO profiles over the central Pacific Ocean are found to closely resemble those over the eastern Pacific Ocean (2–14 km) and are in contrast to those over the western Pacific Ocean, where a C-shaped tropospheric-BrO profile shape has been observed.

Grain Crop Yield Prediction Using Machine Learning Based on UAV Remote Sensing: A Systematic Literature Review

Preharvest crop yield estimation is crucial for achieving food security and managing crop growth. Unmanned aerial vehicles (UAVs) can quickly and accurately acquire field crop growth data and are important mediums for collecting agricultural remote sensing data. With the rapid development of machine learning, especially deep learning, research on yield estimation based on UAV remote sensing data and machine learning has achieved excellent results. This paper systematically reviews the current research of yield estimation research based on UAV remote sensing and machine learning through a search of 76 articles, covering aspects such as the grain crops studied, research questions, data collection, feature selection, optimal yield estimation models, and optimal growth periods for yield estimation. Through visual and narrative analysis, the conclusion covers all the proposed research questions. Wheat, corn, rice, and soybeans are the main research objects, and the mechanisms of nitrogen fertilizer application, irrigation, crop variety diversity, and gene diversity have received widespread attention. In the modeling process, feature selection is the key to improving the robustness and accuracy of the model. Whether based on single modal features or multimodal features for yield estimation research, multispectral images are the main source of feature information. The optimal yield estimation model may vary depending on the selected features and the period of data collection, but random forest and convolutional neural networks still perform the best in most cases. Finally, this study delves into the challenges currently faced in terms of data volume, feature selection and optimization, determining the optimal growth period, algorithm selection and application, and the limitations of UAVs. Further research is needed in areas such as data augmentation, feature engineering, algorithm improvement, and real-time yield estimation in the future.