Search Results (4,858)

The integration of distributed renewable energy sources into distribution networks is a key approach to achieving sustainable and low-carbon power systems. However, high renewable penetration significantly increases the volatility and uncertainty of distribution systems, posing challenges to renewable energy accommodation and reliable operation. To address these challenges, active control of distribution networks is required, which in turn relies on accurate system states. In practice, the limited number and accuracy of measurement devices in distribution networks make dynamic state estimation a critical technology for sustainable distribution systems. In this paper, a novel dynamic state estimation method for sustainable distribution systems is proposed, incorporating spatiotemporal data correlation and adaptiveness to process and measurement noise. A CNN-BiGRU-Attention model is developed to reconstruct high-accuracy real-time pseudo-measurements, compensating for insufficient sensing infrastructure. Furthermore, a noise adaptive dynamic state estimation method is proposed based on an improved unscented Kalman filter. An amplitude modulation factor (AMF) is applied to track time-varying process noise, while an evaluation method based on robust Mahalanobis distance (RMD) is embedded to deal with non-Gaussian measurement noise. Finally, simulation studies on the IEEE 33-bus three-phase unbalanced distribution network demonstrate the effectiveness and robustness of the proposed method. Full article

The Global Navigation Satellite System (GNSS) is commonly used for outdoor positioning. However, its effectiveness diminishes in urban canyons and indoor environments attributed to signal blockage. This study aims to explore the potential of GNSS signals penetrating indoor spaces through windows and to enhance indoor positioning with 3D Mapping-Aided (3DMA) GNSS, a concept generally applied outdoors. The research employs a 3D model of a corridor with manually labeled window locations to predict satellite visibility within indoor areas. The study integrates Pedestrian Dead Reckoning (PDR) with an indoor Shadow-matching (I-SM) technique, utilizing an Extended Kalman Filter (EKF) to improve positioning accuracy. One of the findings indicates that the proposed method significantly enhances positioning performance and its availability, achieving a root mean square error (RMSE) that is 2 m better than using PDR alone or single epoch I-SM. The study concludes that integrating GNSS with I-SM technique and PDR can optimize an indoor positioning solution and highlights the potential for improved navigation solutions in complex urban environments. Full article

This study introduces the Deep Learning Assisted Composite Clock (DLACC), aiming to improve the robustness of the GNSS timescale. If traditional Kalman filter-based composite clocks are today used in systems like GPS and EGNOS, the non-linear, non-Gaussian, and non-stationary behavior of atomic clocks can impact the performance of such model-based filtering. DLACC, built from the KalmanNet approach, proposes to enhance Kalman filters by computing its gain through a neural network to better model clock dynamics and manage ensemble clock reconfigurations. In particular, this study evaluates this method’s performance against conventional filters, demonstrating its potential for more resilient and adaptive GNSS timescales. Full article

To address the performance degradation of the traditional Extended Kalman Filter (EKF) in state estimation for sensorless brushless DC motor (BLDC) control under dynamic operating conditions, such as sudden speed and load changes—a degradation caused primarily by model mismatches—this paper proposes an Adaptive Extended Kalman Filter (AEKF) algorithm. The proposed algorithm incorporates a robust weighting strategy based on the Mahalanobis distance and a dynamically adjusted adaptive forgetting factor. This integration establishes an estimation mechanism capable of online updating of the innovation covariance, thereby enhancing the state observer’s adaptability to system uncertainties and external disturbances. Simulation results demonstrate that, compared to the traditional EKF, the designed AEKF algorithm significantly improves the estimation accuracy of rotor position and speed under various operating conditions, including low-speed start-up, speed step changes, and sudden load applications. Furthermore, it accelerates dynamic response, suppresses overshoot, and enhances the system’s disturbance rejection robustness. This work provides an effective state estimation solution for high-dynamic performance sensorless control of BLDC. Full article

Image-Based Visual Servoing (IBVS) systems often suffer from instability due to measurement noise, modeling errors, and external disturbances. To address these issues, this study proposes a Visual Predictive Control framework integrating Radial Basis Function (RBF) and Extended Kalman Filter (EKF) coupled state-disturbance estimation and task-oriented K-means clustering. First, a feedback linearization Model Predictive Control (MPC) law is designed to handle system nonlinearities and physical constraints. Second, a coupled estimation mechanism is established where the EKF suppresses noise while the RBF network learns lumped disturbances. Crucially, to optimize network efficiency, a task-oriented K-means clustering method is introduced to select RBF centers based on the nominal IBVS path. Lyapunov analysis confirms the Uniformly Ultimately Bounded (UUB) stability. Simulation results demonstrate that the proposed method significantly reduces estimation errors and improves tracking accuracy compared to traditional schemes. Ultimately, this approach enhances the robustness and engineering practicality of robotic visual servoing through the deep coordination of control and estimation. Full article

Non-line-of-sight (NLOS) propagation remains a major obstacle to high-accuracy ultra-wideband (UWB) indoor positioning. To address this issue, this study investigates solutions from two complementary perspectives: NLOS identification and error mitigation. First, an NLOS signal classification model is proposed based on multidimensional statistics of the channel impulse response (CIR). The model incorporates an attention mechanism and an improved snake optimization (ISO) algorithm, achieving significantly enhanced classification accuracy and robustness. For error mitigation, a UKF–BiLSTM dual-directional mutual calibration framework is proposed to dynamically compensate for NLOS errors. The framework embeds the constant turn rate and velocity (CTRV) motion model within an unscented Kalman filter (UKF) to enhance trajectory modeling. It establishes a bidirectional correction loop with a bidirectional long short-term memory (BiLSTM) network. Through the synergy of physical constraints and data-driven learning, the framework adaptively suppresses NLOS errors. Experimental results show that the proposed framework achieves state-of-the-art–comparable performance with improved model efficiency in complex indoor UWB positioning scenarios. Full article

Shoreline variations in closed-basin lakes are closely linked to hydrological fluctuations and long-term changes in water balance, making them important indicators of environmental change. This study analyzes historical shoreline dynamics in Lake Van (Türkiye), the world’s largest soda lake, and provides scenario-based shoreline projections for 2032 and 2042 to support hydrological assessment and water-related management. Multi-temporal Landsat satellite images from 1982, 1992, 2002, 2012, and 2022 were processed using the Digital Shoreline Analysis System (DSAS 5.0) to quantify shoreline retreat and accretion, while future shoreline positions were estimated using the Kalman filter model. The results show pronounced spatial variability, with the most significant shoreline retreat observed in the Çelebibağ and Karahan regions, where sediment supplied by major inflowing streams contributes to shoreline instability through reworking and redistribution rather than stable accretion. Net shoreline movement values reached −2580.1 m for erosion and up to 1700 m for accretion. Model projections indicate an increasing trend of shoreline retreat by 2032 and 2042, accompanied by localized accretion zones. These hydrological-driven shoreline changes have potential implications for littoral habitats, water–land interactions, and human use of the shoreline, including fisheries infrastructure. The study demonstrates the value of integrating remote sensing and statistical forecasting for monitoring shoreline dynamics in closed-basin lake systems. Full article

Background/Objectives: Recent investigations point to red foxes (Vulpes vulpes) as a very potent sentinel species for monitoring the dissemination of antimicrobial bacteria in wildlife habitats. Methods: This study investigated antimicrobial resistance in red foxes from 16 hunting grounds (peri-urban and peri-rural) in western Romania, between 2022 and 2024, in order to evaluate the species as “One Health” sentinels at the wildlife–human–animal interface. During this period, 137 bacterial strains previously identified from 216 samples were phenotypically tested using both the Kirby–Bauer disk diffusion method and the Vitek 2 Compact system. Results: Among the Gram-negative isolates, particularly Escherichia coli and Salmonella enterica, notable antimicrobial resistance and multidrug-resistant (MDR) phenotypes were observed, including resistance to third-generation cephalosporins (ceftazidime) and reduced susceptibility to carbapenems. Resistance patterns observed in Proteus spp. largely reflected intrinsic resistance traits. Methicillin-resistant and MDR staphylococci (Staphylococcus aureus, S. pseudintermedius and S. sciuri) were detected in both peri-urban and peri-rural hunting grounds, with higher frequencies observed in peri-rural areas. Although MDR prevalence was slightly higher in peri-urban compared to peri-rural sites, no statistically significant association was identified between area of isolation and antimicrobial resistance or MDR status. Antimicrobial susceptibility results obtained by disk diffusion and the Vitek 2 Compact system showed a high level of concordance for antibiotics tested in common. Conclusions: Overall, these findings support the use of red foxes as effective One Health sentinels for monitoring environmental antimicrobial resistance occurrence across wildlife, domestic animals, and human-impacted habitats. Full article

Background/Objective: Women after cesarean delivery (CD) may feel discomfort and pain until the gastrointestinal (GI) activation. Standard care approaches following an elective cesarean delivery often fail to address the needs of patients. Nurses care for women after CD, managing their symptoms and promoting GI activity to prevent ileus. Randomized controlled trials (RCTs) have shown that gum chewing is an effective method compared to standard care. Additionally, pilot RCTs have found Paula method exercises to be beneficial in comparison to standard care. This study aims to compare the time of first flatus and first defecation between the Paula method group and the gum-chewing group in women after an elective CD. Methods: A randomized controlled trial was conducted with 90 women; forty-four women were randomized to the Paula method exercises, and forty-six to gum chewing. Both groups received standard care. The primary outcomes were the time to the passage of the first flatus and the time to the first defecation from the delivery. Results: There was no significant difference between groups in time to flatus or time to defecation, yet there was a median 8.2 h shortening of time to flatus in the Paula group (19.7 h [IQR 15.7–28.3] in the Paula group versus 27.9 h [IQR 17.6–38.2] in the gum-chewing group). In an exploratory analysis of the first 16 h post-cesarean delivery, the gum-chewing group showed a shorter time to passage of the first flatus compared to the Paula group. Conclusions: Gum chewing is recommended as part of the current guidelines to enhance recovery after surgery, yet it is not suitable for all. While the Paula method appears safe and demonstrates promise, definitive conclusions require validation from larger, adequately powered trials. Full article

Managing unknown, time-varying noise and outliers presents a critical challenge in GPS applications. Variational Bayesian (VB) inference effectively estimates unknown noise statistics but lacks robustness to outliers, while robust filters such as the centered error entropy (CEE) suppress outliers but rely on fixed noise assumptions. To address both limitations, we propose the centered error entropy-based variational Bayesian extended Kalman filter (CEEVB-EKF), which integrates VB inference with the CEE criterion in a unified framework. The method estimates time-varying noise covariance via recursive VB updates and applies the CEE cost function for robustness to heavy-tailed disturbances and outliers. This dual-stage design improves adaptability and reliability, with simulations showing superior, stable state estimation, making CEEVB-EKF suitable for positioning, tracking, and autonomous navigation. Full article

The escalating development of Maritime Autonomous Surface Ships (MASS) has imposed rigorous demands on the precision, continuity, and resilience of onboard integrated navigation systems. However, in complicated marine settings, data from the Global Navigation Satellite System (GNSS) and Doppler Velocity Log (DVL) are frequently corrupted by multipath effects and non-line-of-sight (NLOS) interference. These disturbances introduce anomalous observations that violate Gaussian noise assumptions, thereby severely deteriorating the robustness and estimation quality of traditional sliding-window factor graph optimization (SW-FGO). To mitigate this problem, this study introduces a novel integrated navigation strategy termed gradient-adaptive factor graph optimization (GA-FGO). By designing a gradient-adaptive robust objective function within the factor graph structure, the proposed method dynamically re-weights constraints from the inertial navigation system (INS), GNSS, and DVL. This mechanism adequately suppresses the influence of measurement outliers at the optimization level. Furthermore, a unified solution framework utilizing iterative reweighted least squares (IRLS) and the Gauss–Newton method is established to simultaneously perform adaptive weight updates and state estimation. Validation was based on offline field data benchmarked against the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and standard SW-FGO. The simulation results demonstrated that the GA-FGO algorithm achieves superior positioning accuracy and estimation stability under realistic measurement conditions. Full article

Viticulture is facing growing economic and environmental pressures that demand a transition toward intelligent and autonomous crop management systems. Phytopathologies remain one of the most critical threats, causing substantial yield losses and reducing grape quality, while regulatory restrictions on agrochemicals and sustainability goals are driving the development of precision agriculture solutions. In this context, early disease detection is crucial; however, current visual inspection methods are hindered by subjectivity, cost, and delayed symptom recognition. This study presents a fully autonomous robotic platform developed within the Agrimet project, enabling continuous, high-frequency monitoring in vineyard environments. The system integrates a tracked mobility base, multimodal sensing using RGB-D and thermal cameras, an AI-based perception framework for leaf localisation, and a compliant six-axis manipulator for biological sampling. A custom control architecture bridges standard autopilot PWM signals with industrial CANopen motor drivers, achieving seamless coordination among all subsystems. Field validation in a Sicilian vineyard demonstrated the platform’s capability to navigate autonomously, acquire multimodal data, and perform precise georeferenced sampling under unstructured conditions. The results confirm the feasibility of holistic robotic systems as a key enabler for sustainable, data-driven viticulture and early disease management. The YOLOv10s detection model achieved good precision and F1-score for leaf detection, while the integrated Kalman filtering visual servoing system demonstrated low spatial tolerance under field conditions despite foliage sway and vibrations. Full article

Background/Objectives: Canine otitis externa (OE) is frequently complicated by Pseudomonas aeruginosa (P. aeruginosa) infections, which are often associated with treatment failure due to intrinsic and acquired antimicrobial resistance. This study aimed to assess the prevalence and antimicrobial susceptibility of P. aeruginosa isolates from dogs with OE in Timiș County, Romania, with a focus on aminoglycosides and fluoroquinolones, to provide region-specific, clinically relevant data and address potential public health implications. Methods: Exudate samples were collected from 435 dogs diagnosed with OE across multiple veterinary clinics between 2022 and 2025. P. aeruginosa isolates were identified using standard culture methods, and antimicrobial susceptibility was determined using the VITEK^® 2 Compact system according to CLSI VET01, Fifth Edition (2018) guidelines. Tested antibiotics included amikacin, gentamicin, enrofloxacin, marbofloxacin, and pradofloxacin. Resistance profiles were analyzed at both the individual antibiotic and class levels. Results:P. aeruginosa was isolated in 14.0% (61/435) of dogs. All isolates were susceptible to amikacin and gentamicin, whereas resistance to enrofloxacin and marbofloxacin was 27.9%, and pradofloxacin resistance reached 63.9%. A total of 24.6% of isolates were susceptible to all tested antibiotics. The most frequent multidrug-resistant combination among fluoroquinolones was ENR (R) + MAR (R) + PRA (R), observed in 23.0% of isolates. Conclusions: This study provides recent, region-specific data on P. aeruginosa prevalence and antimicrobial susceptibility in canine OE, offering clinically relevant insights into aminoglycoside and fluoroquinolone resistance. The findings highlight the potential public health significance of resistant P. aeruginosa strains at the human–animal interface and underscore the importance of antimicrobial stewardship in veterinary practice. Full article

PAX6 haploinsufficiency-related congenital aniridia is frequently associated with ocular surface disease, including meibomian gland dysfunction (MGD), dry eye, limbal stem cell deficiency (LSCD), aniridia-associated keratopathy (AAK), and inflammation. This study measured ocular surface temperature (OST) at the corneal center and four paracentral points (2 mm from center) in patients with congenital aniridia and examined factors influencing OST. Forty-five eyes from 26 aniridia patients (55.6% female; 26.29 ± 17.78 years) with PAX6 pathogenic variants and 47 eyes from 25 controls (68.1% female; 24.81 ± 4.73 years; p = 0.1639) were included. Body temperature, OSDI, and OST (TG-1000) were recorded; clinical assessment evaluated MGD, LSCD, AAK, iris malformation, epithelial defects, Salzmann nodules, glaucoma and previous ocular surgery. Body temperature and OSDI did not differ in aniridia and controls (p ≥ 0.606). LSCD was mainly Grade 2 (46.7%) or Grade 4 (40.0%), and AAK Grade 1 (33.3%) or Grade 2 (31.1%). MGD affected 51.1%, Salzmann nodules 22.2%, epithelial defects 2.2%, glaucoma 60.0%, and previous ocular surgery 35.5%. Superior OST was higher in aniridia (34.98 ± 0.55 °C vs. 34.75 ± 0.47 °C; p = 0.012). Exploratory univariate analyses identified that higher AAK grade correlated with lower inferior OST (p = 0.030), iris malformation with reduced central/paracentral OST (p ≤ 0.029), and Salzmann nodules with lower OST overall (p ≤ 0.011). However, in a multivariate model, age, Salzmann nodular degeneration, and prior ocular surgery emerged as key determinants of OST. OST may serve as a noninvasive biomarker in congenital aniridia. Full article

Reliable state estimation is critical for the safe operation of second-life battery systems but is severely hindered by significant parameter heterogeneity arising from diverse historical aging conditions. Traditional static models struggle to adapt to such variability, while online identification methods are prone to divergence under dynamic loads. To overcome these challenges, this paper proposes a Dual-Anchor Adaptive Fusion Framework for robust State of Charge (SOC) estimation and State of Health (SOH) soft-sensing. Specifically, to establish a reliable physical baseline, an automated Dynamic Relaxation Interval Selection (DRIS) strategy is introduced. By minimizing the fitting Root Mean Square Error (RMSE), DRIS systematically extracts high-fidelity parameters to construct two “anchor models” that rigorously define the boundaries of the aging space. Subsequently, a residual-driven Bayesian fusion mechanism is developed to seamlessly interpolate between these anchors based on real-time voltage feedback, enabling the model to adapt to uncalibrated target batteries. Concurrently, a novel “SOH Soft-Sensing” capability is unlocked by interpreting the adaptive fusion weights as real-time health indicators. Experimental results demonstrate that the proposed framework achieves robust SOC estimation with an RMSE of 0.42%, significantly outperforming the standard Adaptive Extended Kalman Filter (A-EKF, RMSE 1.53%), which exhibits parameter drift under dynamic loading. Moreover, the a posteriori voltage tracking residual is compressed to ~0.085 mV, effectively approaching the hardware’s ADC quantization limit. Furthermore, SOH is inferred with a relative error of 0.84% without additional capacity tests. This work establishes a robust methodological foundation for calibration-free state estimation in heterogeneous retired battery packs. Full article