Search Results (949)

Model-based sensorless control of interior permanent-magnet synchronous motors (IPMSMs) typically employs an estimation observer with embedded position information, followed by a position extraction process. Although a type-2 phase-locked loop (PLL) is widely adopted for position and speed extraction, it suffers from steady-state tracking errors under variable-speed operation, leading to torque bias in IPMSM torque control. To mitigate this issue, this paper first proposes an adaptive bandpass full-order observer in the stationary reference frame. Subsequently, a Kalman filter (KF)-based compensation strategy is introduced for the PLL to eliminate tracking errors while maintaining system stability. Experimental validation on a 300 kW platform confirms the effectiveness of the proposed sensorless torque control algorithm, demonstrating significant reductions in position error and torque fluctuations during acceleration and deceleration. Full article

The low-cost Strapdown Inertial Navigation System (SINS)/Global Navigation Satellite System (GNSS) is widely used in autonomous vehicles for positioning and navigation. Initial alignment is a critical stage for SINS operations, and the alignment time and accuracy directly affect the SINS navigation performance. To address the issue that low-cost SINS/GNSS cannot effectively achieve rapid and high-accuracy alignment in complex environments that contain noise and external interference, an adaptive multiple backtracking robust alignment method is proposed. The sliding window that constructs observation and reference vectors is established, which effectively avoids the accumulation of sensor errors during the full integration process. A new observation vector based on the magnitude matching is then constructed to effectively reduce the effect of outliers on the alignment process. An adaptive multiple backtracking method is designed in which the window size can be dynamically adjusted based on the innovation gradient; thus, the alignment time can be significantly shortened. Furthermore, the modified variational Bayesian Kalman filter (VBKF) that accurately adjusts the measurement noise covariance matrix is proposed, and the Expectation–Maximization (EM) algorithm is employed to refine the prior parameter of the predicted error covariance matrix. Simulation and experimental results demonstrate that the proposed method significantly reduces alignment time and improves alignment accuracy. Taking heading error as the critical evaluation indicator, the proposed method achieves rapid alignment within 120 s and maintains a stable error below 1.2° after 80 s, yielding an improvement of over 63% compared to the backtracking-based Kalman filter (BKF) method and over 57% compared to the fuzzy adaptive KF (FAKF) method. Full article

The Varnous Mt. area in the northern Pelagonian Nappe is characterized by the intrusion of an Early Permian pluton, with its tectonic setting and igneous petrology well constrained in earlier studies. The metamorphic basement rocks warrant further detailed investigation due to their complex history. These rocks are polymetamorphosed, preserving a sequence of overprinting metamorphic and deformational events. The metapelitic rocks have undergone an initial, pre-Carboniferous regional metamorphism of unknown grade before or during Hercynian Orogeny, followed by a thermal metamorphic event associated with the intrusion of the Varnous pluton at 297 Ma. The assemblage attributed to this event is And + Crd + Bt + Ms (west), while the first assemblage identified at the eastern part is Sil + Bt + Gt. Additionally, three regional tectonometamorphic events occurred during the Alpine Orogeny. For the Alpine events, the assemblages are as follows: first, the development of St + Gt + Chl + Kfs + Pl + Qtz at 150–130 Ma; second, retrograde metamorphism of these assemblages with Cld + Gt + Ser + Mrg + Chl ± Sil (Fi) at 110–90 Ma; and finally, mylonitization of all previous assemblages at 90–70 Ma with simultaneous annealing and formation of Cld + Chl + Ms. Full article

In industrial wireless networks beyond 5G and toward 6G, combining uplink non-orthogonal multiple access (NOMA) with the Kalman filter (KF) effectively reduces interruption risks and transmission delays in remote state estimation. However, the complexity of wireless environments and concurrent multi-sensor transmissions introduce significant interference and latency, impairing the KF’s ability to continuously obtain reliable observations. Meanwhile, existing remote state estimation systems typically rely on oversimplified wireless communication models, unable to adequately handle the dynamics and interference in realistic network scenarios. To address these limitations, this paper formulates a novel dynamic wireless resource allocation problem as a mixed-integer nonlinear programming (MINLP) model. By jointly optimizing sensor grouping and power allocation—considering sensor available power and outage probability constraints—the proposed scheme minimizes both estimation outage and transmission delay. Simulation results demonstrate that, compared to conventional approaches, our method significantly improves transmission reliability and KF estimation performance, thus providing robust technical support for remote state estimation in next-generation industrial wireless networks. Full article

Since its publication in the 1960s, the Kalman Filter (KF) has been a powerful tool in optimal state estimation. However, the KF and most of its variants have mainly focused on the state estimation of smooth systems. In this work, we propose a new algorithm called the Discrete Unilateral Constrained Extended Kalman Filter (DUCEKF) that expands the capabilities of the Extended Kalman Filter (EKF) to a class of hybrid mechanical systems known as systems with unilateral constraints. Such systems are non-smooth in position and discontinuous in velocity. Lyapunov stability theory is invoked to establish sufficient conditions for the estimation error stability of the proposed algorithm. A comparison of the proposed algorithm with the EKF is conducted in simulation through a case study to demonstrate the superiority of the DUCEKF for the state estimation tasks in this class of systems. Simulations and an experiment were developed in this case study to validate the performance of the proposed algorithm. The experiment was conducted using electronic hardware that consists of an Embedded System (ES) called “Mikromedia for dsPIC33EP” and an external DAC-12 Click board, which includes a Digital-to-Analog Converter (DAC) from Texas Instruments. Full article

It is fundamental, yet challenging, to accurately predict water levels at hydrological stations located along the banks of an open channel river due to the complex interactions between different hydraulic structures. This paper presents a novel application for short-term multivariate prediction applied to hydrological variables based on a multivariate NARX model coupled to a nonlinear recursive Ensemble Kalman Filter (EnKF). The proposed approach is designed for two hydrological stations of the Atrato river in Colombia, where the variables, water level, water flow, and water precipitation, are correlated using a NARX model based on neural networks. The NARX model is designed to consider the complex dynamics of the hydrological variables and their corresponding cross-correlations. The short-term two-day water-level forecast is designed with a fourth-order NARX model. It is observed that the NARX model coupled with EnKF improves the robustness of the proposed approach in terms of external disturbances. Furthermore, the proposed approach is validated by subjecting the NARX–EnKF coupled model to five levels of additive white noise. The proposed approach employs metric regressions to evaluate the proposed model by means of the Root Mean Squared Error (RMSE) and the Nash–Sutcliffe model efficiency (NSE) coefficient. Full article

Natural-fiber-reinforced polypropylene (PP) composites are gaining increasing interest as lightweight, sustainable alternatives for various packaging and applications. This study investigates the effect of filler addition sequence on the mechanical, morphological, thermal, and dynamic mechanical properties of PP-based composites reinforced with graphite nanoplatelets (GnP) and kenaf fiber (KF). Two filler incorporation sequences were evaluated: GnP/KF/PP (GnP initially mixed with KF before PP addition) and GnP/PP/KF (KF added after mixing GnP with PP). The GnP/KF/PP composite exhibited superior mechanical properties, with tensile strength and flexural strength increasing by up to 25% compared to the control, while GnP/PP/KF showed a 13% improvement. SEM analyses revealed that initial mixing of GnP with KF significantly improved filler dispersion and interfacial bonding, enhancing stress transfer within the composite. XRD and DSC analyses showed reduced crystallinity and lower crystallization temperatures in the addition of KF due to restricted polymer chain mobility. Thermal stability assessed by TGA indicated minimal differences between the composites regardless of filler sequence. DMA results demonstrated a significantly higher storage modulus and enhanced elastic response in the addition of KF, alongside a slight decrease in glass transition temperature (T_g). The results emphasize the importance of optimizing filler addition sequences to enhance mechanical performance, confirming the potential of these composites in sustainable packaging and structural automotive applications. Full article

Stylolites are a common mineral dissolution feature in rocks that develop during compression and form distinct tooth structures. On a tectonic plate scale, mid-ocean ridges (MORs) and transform faults are a significant feature of the Earth’s surface that develop due to accretion of new material in an extensional regime. We present a comparison between the two features and argue that transform faults in MOR are similar to the sides of stylolite teeth, with both features representing kinematic faults (KFs). First, we present a numerical model of both stylolite and MOR growth and show that in both cases, KFs nucleate and grow spontaneously. In addition, we use a well-established technique (Family–Vicsek scaling) of describing fractal self-affine interfaces, which has been used for stylolites, to characterize the pattern of MOR systems in both simulations and natural examples. Our results show that stylolites and MOR have self-affine scaling characteristics with similar scaling regimes. They both show a larger roughness exponent at the small scale, a smaller exponent at the intermediate scale, followed by a flattening of the system at the largest scale. For stylolites, the physical forces behind the scaling are the surface energy at the small mineral scale, the elastic energy at the intermediate scale, followed by the system reaching the correlation length where growth stops. For MORs, the physical forces behind the scaling are not yet clear; however, the self-affine scaling shows that transform faults at MORs do not have a preferred spacing, but that the spacing is fractal. Our study offers a new perspective on the study of natural roughening phenomena on various scales, from minerals to tectonic plates, and a new view on the development of MORs. Full article

Circulating current control is a critical part of the Modular Multilevel Converter (MMC) control system. Existing control methods rely on arm current information obtained from complex current measurement devices. However, these devices are susceptible to failures, which can lead to distorted arm currents, increased peak arm current values, and higher losses. In extreme cases, this can result in system instability. This paper first analyzes four typical arm current measurement faults, i.e., constant gain faults, amplitude deviation faults, phase shift faults, and stuck faults. Then, a Kalman Filter (KF)-based fault detection method is proposed, which allows for the simultaneous monitoring status of all six arm current measurements. Moreover, to facilitate fault detection, the Moving Root Mean Square (MRMS) value of the observation residual is defined, which effectively detects faults while suppressing noise. The entire fault detection process takes less than 20 ms. Finally, the feasibility and effectiveness of the proposed method are validated through MATLAB/Simulink simulations and experimental results. Full article

Coronavirus non-structural protein 1 (nsp1) is a pathogenic determinant of Betacoronaviruses. Previous studies demonstrated that the nsp1 of various coronaviruses induces host shutoff through a variety of mechanisms; however, there is little information on the function of bovine coronavirus (BCoV) nsp1. We aimed to characterize the host gene expression suppression function of BCoV nsp1. We first confirmed that the expression of BCoV nsp1 in MAC-T cells, a bovine mammary epithelial cell line, suppressed host and reporter gene expression. Subsequently, lysine and phenylalanine at amino acid positions 232 and 233, respectively, were identified as key residues required for this suppressive effect. Expression levels of housekeeping genes are comparable in cells expressing wild-type BCoV nsp1 and a mutant with alanine substitutions at positions 232 and 233 (BCoV nsp1-KF). Wild-type BCoV nsp1 localized to both the cytoplasm and nucleus; however, BCoV nsp1-KF exhibited prominent nuclear accumulation with dot-like structures. Using confocal microscopy and co-sedimentation analysis, we identified an association between wild-type BCoV nsp1, but not BCoV nsp1-KF, and ribosomes, suggesting that ribosome binding is required for BCoV nsp1-mediated suppression of host gene expression. This is the first study of the characterization of host gene expression suppression by BCoV nsp1. Full article

Satellite-based monitoring of atmospheric column-averaged dry-air mole fraction (XCH₄) is essential for quantifying methane (CH₄) emissions, yet uncharacterized spatially varying biases in XCH₄ observations can cause misattribution in flux estimates. This study assesses the potential of the upcoming TanSat-2 satellite mission to estimate China’s CH₄ emission using a series of Observing System Simulation Experiments (OSSEs) based on an Ensemble Kalman Filter (EnKF) inversion framework coupled with GEOS-Chem on a 0.5° × 0.625° grid, alongside an evaluation of current TROPOMI-based products against Total Carbon Column Observing Network (TCCON) observations. Assuming a target precision of 8 ppb, TanSat-2 could achieve an annual national emission estimate accuracy of 2.9% ± 4.2%, reducing prior uncertainty by 84%, with regional deviations below 5.0% across Northeast, Central, East, and Southwest China. In contrast, limited coverage in South China due to persistent cloud cover leads to a 26.1% discrepancy—also evident in pseudo TROPOMI OSSEs—highlighting the need for complementary ground-based monitoring strategies. Sensitivity analyses show that satellite retrieval biases strongly affect inversion robustness, reducing the accuracy in China’s total emission estimates by 5.8% for every 1 ppb increase in bias level across scenarios, particularly in Northeast, Central and East China. We recommend expanding ground-based XCH₄ observations in these regions to support the correction of satellite-derived biases and improve the reliability of satellite-constrained inversion results. Full article

Recent advances in high-fidelity modeling, numerical computing, and data science have spurred interest in model-data integration for nuclear reactor applications. While machine learning often prioritizes data-driven predictions, this study focuses on data assimilation (DA) to synergize physical models with measured data, aiming to enhance predictive accuracy and reduce uncertainties. We implemented deterministic DA methods—Kalman filter (KF) and three-dimensional variational (3D-VAR)—in a one-dimensional single-phase natural circulation loop and extended 3D-VAR to RELAP5, a system code for two-phase loop analysis. Unlike surrogate-based or model-reduction strategies, our approach leverages full-model propagation without relying on computationally intensive sampling. The results demonstrate that KF and 3D-VAR exhibit robustness against varied noise types, intensities, and distributions, achieving significant uncertainty reduction in state variables and parameter estimation. The framework’s adaptability is further validated under oceanic conditions, suggesting its potential to augment baseline models beyond conventional extrapolation boundaries. These findings highlight DA’s capacity to improve model calibration, safety margin quantification, and reactor field reconstruction. By integrating high-fidelity simulations with real-world data corrections, the study establishes a scalable pathway to enhance the reliability of nuclear system predictions, emphasizing DA’s role in bridging theoretical models and operational demands without compromising computational efficiency. Full article

High-resolution radar observations are essential to improving the numerical predictions of high-impact weather systems with data assimilation techniques. The numerical simulations of the landfall of Typhoon Lekima (2019) are conducted in the framework of the WRF model, investigating the impact of assimilating radar radial velocity observations via the Ensemble Kalman Filter (EnKF) on the typhoon’s analysis and forecast performance. The results demonstrate that the EnKF method significantly improves forecast accuracy for Typhoon Lekima, including track, intensity and the 24 h cumulative precipitation. To be specific, the control experiment significantly underestimated typhoon intensity, while EnKF-based radar radial velocity assimilation markedly improved near-surface winds (>48 m/s) in the typhoon core, refined vortex structure and reduced track forecast errors by 50–60%. Compared with the control and 3DVAR experiments, EnKF assimilation better captured typhoon precipitation patterns, with the highest ETS scores, especially for moderate-to-high precipitation intensities. Moreover, the detailed analysis and diagnostics of Lekima show that the warm core structure is better captured in the assimilation experiment. The typhoon system is also improved, as reflected by enhanced potential temperature and a more robust wind field analysis. Full article

Mineralogical variability exerts a profound influence on the flotation performance of Platinum Group Metal (PGM) ores, particularly those from the Platreef deposit, where complex associations and textures influence recovery, grade, and kinetics. This study integrates the Mode of Occurrence (MOC) and mineral associations into a modified Kelsall flotation kinetics model, optimized using a Particle Swarm Optimization (PSO) algorithm, to improve prediction accuracy. Batch flotation tests were conducted on eight samples from two lithologies—Pegmatoidal Feldspathic Pyroxenite (P-FPX) and Feldspathic Pyroxenite (FPX)—with mineralogical characterization performed using MLA, QEMSCAN, and XRD. PGMs in liberated (L) and sulfide-associated (SL) forms accounted for up to 90.6% (FPX1), exhibiting high fast-floating fractions (θ_f = 0.77–0.84) and fast flotation rate constants (K_f = 1.45–1.78 min⁻¹). In contrast, PGMs locked in silicates (G class) showed suppressed kinetics (K_f < 0.09 min⁻¹, K_s anomalies up to 8.67 min⁻¹) and were associated with lower recovery (P-FPX3 = 83.25%) and increased model error (P-FPX4 = 57.3). FPX lithologies achieved the highest cumulative recovery (FPX4 = 90.35%) and the best concentrate grades (FPX3 = 116.5 g/t at 1 min), while P-FPX1 had the highest gold content (10.45%) and peak recovery (94.37%). Grade-recovery profiles showed steep declines after 7 min, particularly in slow-floating types (e.g., P-FPX2, FPX2), with fast-floating lithologies stabilizing above 85% recovery at 20 min. The model yielded R² values above 0.97 across all samples. This validates the predictive power of MOC-integrated flotation kinetics for complex PGM ores and supports its application in geometallurgical plant design. Model limitations in capturing complex locked ore textures (SAG, G classes) highlight the need for reclassification based on floatability indices and further integration of machine learning methods. Full article

This study evaluated the adsorption and desorption of imidacloprid (IMI), thiamethoxam (THM) and clothianidin (CLO) in an andisol (Freire soil) and an inceptisol (Chufquén soil) from southern Chile with different organic matter and clay contents. The soils had a slightly acidic pH and clay and clay-loam textures. The tests were carried out at 20 °C with CaCl₂ 0.01 M as the electrolyte. Kinetic experiments were performed and isotherms were fitted to the pseudo-second-order, Elovich, Weber–Morris, Freundlich and Langmuir models. The kinetics were best described by the pseudo-second-order model (R² > 0.99), indicating chemisorption; the rate was the highest for THM, although IMI and CLO achieved the highest retention capacities. The Chufquén samples, with lower organic matter but 52% clay, exhibited the highest K_f and q_m of up to 12.4 and 270 mg kg⁻¹, respectively, while the K_d (2.3–6.9 L kg⁻¹) and K_oc (24–167 L kg⁻¹) coefficients revealed a moderate leaching risk. THM was the most mobile compound due to its high solubility. Desorption was partially irreversible (H = 0.48–1.48), indicating persistence in soil. FTIR analysis confirmed the interaction with O-Al-O/O-O-Si-O groups without alterations in the mineral structure. In the soils examined in this study, the clay fraction and variable-charge minerals, rather than organic matter, were more closely associated with the adsorption behaviour of these NNIs. Full article