Search Results (3,493)

This paper addresses the voltage regulation problem for DC microgrids modeled as nonlinear dynamical systems subject to parametric uncertainties and external disturbances. A data-driven predictive hybrid control scheme is developed, combining a nonlinear sliding mode law that guarantees finite-time current convergence, an adaptive fuzzy universal approximator that compensates for unknown residual dynamics and mitigates chattering, and a recursive predictor built online via forgetting-factor recursive least squares. Real-time gain optimization is achieved through the minimization of a quadratic predictive performance index. A composite Lyapunov analysis rigorously establishes uniform ultimate boundedness of the low level Adaptive Fuzzy Sliding Mode Control (AFSMC) inner loop, assuming bounded reference currents provided by the DQL agent and characterizes the convergence residual set of the tracking error. Comparative simulations against conventional fuzzy logic and a standard (non augmented) Deep Q-Learning baseline with fixed gain SMC corroborate the theoretical guarantees, demonstrating superior voltage regulation, reduced battery deep discharges, and improved load management. Full article

Aiming at frequency fluctuations caused by high-penetration renewable energy, a coordinated secondary frequency regulation strategy is proposed for hybrid energy storage stations in regional power grid. Hybrid energy storage stations contain electrochemical energy storage stations (EESSs) and compressed air energy storage stations (CAESSs). Complete mathematical models of concerned energy storage stations are constructed, and a multi-objective exponential distribution optimization (MOEDO) algorithm is proposed to optimize regulation cost, automatic generating control (AGC) command tracking and constrain the state of charge (SoC) of energy storage equipment. The proposed strategy rationally distributes secondary regulation instructions for hybrid energy storage stations and thermal power plants. In the end, simulation results on a two-area interconnected power grid are given to verify the effectiveness of proposed strategy. Results reveal that the presented method can effectively suppress frequency deviation and tie-line power oscillation and maintain SoC within a safe operating range. Full article

Rapid and accurate detection of pathogenic bacteria is of critical concern in the food and pharmaceutical sectors. In this study, a gene membrane chip detection method combining multiplex PCR with reverse dot blot hybridization was developed to enable visual, high-throughput simultaneous identification of 12 common pathogenic bacteria. Specific primers and probes were designed targeting Klebsiella pneumoniae, Proteus mirabilis, Vibrio parahaemolyticus, Enterobacter cloacae, Pseudomonas putida, Listeria monocytogenes, Salmonella spp., Pseudomonas aeruginosa, Escherichia coli, Acinetobacter baumannii, Clostridium perfringens, and Staphylococcus aureus. The multiplex PCR reaction system, hybridization temperature, and color development conditions were systematically optimized to construct the gene membrane chip detection platform. The specificity, limit of detection, and stability of the method were rigorously evaluated. Results demonstrated that the 12 selected specific primer pairs effectively amplified the corresponding targets, yielding amplicon lengths ranging from 81 to 298 bp, with no nonspecific amplification observed. Under the optimized detection system, each probe produced clear, visually distinct color spots exclusively for its target, with no cross-reactivity. The method achieved a limit of detection as low as 0.01 ng/μL for mixed templates, and exhibited excellent intra- and inter-assay reproducibility. The established visual gene membrane chip assay successfully enables simultaneous differentiation of 12 control bacteria in a single reaction, providing a robust technical foundation and promising potential for rapid screening in food and drug safety, but its practical application requires further validation through authentic or spiked food samples. Full article

Vertical farming (VF) is a critical solution for sustainable urban agriculture; however, its economic viability remains constrained by high labour and energy costs. The integration of Artificial Intelligence (AI) and Computer Vision (CV) offers opportunities to automate monitoring and optimize environmental control. This systematic review synthesizes peer-reviewed research published between 2015 and 2026 on Deep Learning (DL) applications for lettuce (Lactuca sativa) cultivated in Controlled Environment Agriculture (CEA). Literature was retrieved from Google Scholar, Scopus, PubMed, Semantic Scholar, OpenAlex, and Web of Science, resulting in 34 eligible studies selected from an initial pool of 893 records. The analysis indicates that Convolutional Neural Networks (CNNs) and You Only Look Once (YOLO)-based object detection models are the most widely adopted architectures for non-invasive growth monitoring and disease detection, frequently reporting accuracy metrics exceeding 90%. In parallel, hybrid approaches that integrate AI with biophysical constraints are gaining attention for yield estimation and nutrient prediction tasks. Nevertheless, significant challenges remain, particularly concerning data availability and reproducibility, as 87% of the reviewed studies rely on private datasets. Overall, the findings underscore the need for standardized benchmarking datasets and computationally efficient, edge-deployable architectures to facilitate the transition from experimental prototypes to scalable commercial applications. Full article

In this paper, we propose a correntropy weighted extended Kalman filter (CWEKF) method to address the challenges of low estimation accuracy and poor robustness in sensorless rotor speed estimation for doubly-fed induction generators (DFIGs). Firstly, based on Faraday’s law of electromagnetic induction and the mechanical motion equation, we derive a DFIG nonlinear state-space model. This model quantifies the sources of nonlinearity arising from cross-coupling terms and product terms, providing a precise model foundation for rotor speed estimation. Secondly, we introduce correntropy theory to design a residual dynamic weighting scheme. By quantifying the local similarity between current and historical residuals, the scheme adaptively adjusts the noise covariance estimation weights, suppressing the interference of outdated data. Combined with the Chi-squared test, we derive an adaptive kernel bandwidth mechanism, balancing the response speed to noise variations and the estimation accuracy in steady-state. Additionally, we further integrate Huber robust weighting and regularization techniques for constructing a hybrid weighting mechanism and optimizing the covariance positive-definiteness correction to address the numerical stability deficiencies of the original algorithm. Using the Lipschitz condition and Lyapunov theory, we prove the mean-square exponential boundedness of the CWEKF estimation error. Finally, we build a DFIG vector control model using MATLAB R2021a and conduct comprehensive experiments, including simulation comparative experiments, open-loop speed identification experiments, and closed-loop sensorless control experiments. Comparative simulation experiments are conducted with EKF, AEKF, and RWEKF under three operating conditions; open-loop experiments verify that the constructed platform meets variable-speed constant-frequency (VSCF) power generation requirements, and closed-loop experiments compare CWEKF with MRAS under different speeds and parameter variations. The results show that the CWEKF has a maximum rotor speed estimation error <5 r/min, the response time has been reduced by over 65% compared to the traditional EKF, and it outperforms EKF, AEKF, RWEKF, and MRAS in estimation accuracy and stability, exhibiting significantly improved robustness under parameter variations and strong noise conditions. Full article

Edge detection is an essential problem in computer vision and is used in applications such as object recognition, scene analysis, and medical imaging. Conventional edge detectors based on integer-order derivatives are computationally efficient but sensitive to noise, whereas modern deep learning approaches generally achieve higher accuracy at the cost of increased model complexity. This paper presents GLF-ResFormer a lightweight hybrid CNN–Transformer architecture incorporating Grünwald–Letnikov (GL) fractional preprocessing. The discrete GL operator is approximated using a finite-difference convolution with a truncation level of $N=15$, where the fractional order $\alpha \in (0,1]$ controls the spatial memory of the operator. We establish an upper bound for the truncation error of the discrete GL approximation, $\mathcal{O}\left(h^{N-\alpha }\right)$ (Theorem 1), and present a gradient-sensitivity analysis (Lemma 1) that provides theoretical support for the proposed preprocessing strategy. Extensive experiments using 10 independent random seeds on the MNIST dataset show that, at the optimal fractional order of $\alpha =0.01$, GLF-ResFormer achieves a pixel-wise F1 score of $0.9967\pm 0.0002$ compared with $0.9891\pm 0.0033$ for a CNN baseline, while reducing the validation loss to $0.0043\pm 0.0003$. Additional experiments on the CIFAR-10 dataset and comparisons with the BSDS500 benchmark further demonstrate the effectiveness of the proposed framework across multiple edge detection evaluation settings while maintaining a lightweight architecture. Full article

Precision agriculture and sustainable farming practices require autonomous environmental monitoring systems capable of operating in remote areas with limited energy and connectivity. However, the high cost of existing professional technology remains a significant barrier to widespread adoption. This study presents the development of a solar-powered smart sensing node designed for autonomous operation that integrates TinyML and dual-mode wireless connectivity via LoRaWAN and Wi-Fi for intelligent monitoring. The system features a custom-designed cup anemometer and multispectral sensing capabilities integrated into a compact single-tower architecture. All structural components, including radiation shields and a modular PVC frame, were designed for low-cost manufacturing and mass production. A single hermetic housing protects the core control electronics and is designed to improve durability in harsh outdoor environments. A Multi-Layer Perceptron model was implemented on the edge to enable intelligent data fusion and compensation, while a dynamic sampling strategy optimized power consumption. Experimental results demonstrate the feasibility of the proposed architecture through adaptive spectral acquisition over a daily illumination cycle, embedded MLP-based sensor fusion, and telemetry-oriented data compression that substantially reduces the number of transmitted samples. The main contribution of this work is a system-level architecture that integrates sensing, embedded intelligence, solar-energy harvesting, hybrid wireless communication, and telemetry optimization into a compact, low-cost, and field-deployable prototype IoT platform for sustainable precision agriculture. Full article

The convergence of optical burst switching (OBS) and elastic optical networks (EON) offers a promising pathway for 6G IoT backhaul. However, existing solutions treat OBS and EON separately and rely on heuristic resource allocation that fails to meet stringent QoS demands. This paper proposes a fully AI-driven converged OBS/EON architecture integrating a hybrid switching fabric, a multi-agent deep reinforcement learning (DRL) orchestrator, and a federated learning (FL) plane for autonomous, QoS-aware resource provisioning. The control plane implements multi-agent Proximal Policy Optimization (PPO) for joint burst scheduling, routing, modulation selection, and spectrum allocation. The orchestration plane employs q-fair FL for privacy-preserving cross-domain traffic prediction. Mathematical formulations of the optimization problem with spectrum, GSNR, and delay constraints are provided, along with pseudo-algorithms. Simulations over a 14-node NSFNET topology demonstrate a 78% reduction in blocking probability, a 42% improvement in spectral efficiency, sub-millisecond URLLC delays, and a Jain’s fairness index of 0.92, while preserving data privacy. The framework builds upon SDN principles for seamless integration with optical transport infrastructures. Full article

Urban traffic congestion remains a major challenge for modern cities, requiring intelligent traffic signal control (TSC) strategies capable of adapting to dynamic traffic conditions. This paper proposes a hybrid reinforcement learning approach for traffic signal control that combines the complementary learning mechanisms of Q-learning, SARSA, and Monte Carlo algorithms to improve both learning efficiency and control performance. The proposed approach is implemented and evaluated using the Simulation of Urban MObility (SUMO) simulator on a realistic road network corresponding to the “Route de Sefrou” in Fez, Morocco. The traffic signal controller is trained through continuous interaction with the simulated environment and compared with the three individual reinforcement learning algorithms under identical experimental conditions. The experimental results demonstrate that the proposed hybrid approach provides more efficient traffic management, faster convergence, and greater learning stability than the individual algorithms. These findings demonstrate the potential of hybrid reinforcement learning as an effective solution for adaptive traffic signal control in realistic urban environments. Full article

Hybrid quantum–classical machine learning offers a promising direction for advancing automated quality control in industrial settings. In this study, we investigate two hybrid quantum–classical approaches for classifying defects in aluminum TIG welding images and benchmarking their performance against a conventional deep learning model. A convolutional neural network is used to extract compact and informative feature vectors from weld images, effectively reducing the higher-dimensional pixel space to a lower-dimensional feature space. Our first quantum approach encodes these features into quantum states using a parameterized quantum feature map composed of rotation and entangling gates. We compute a quantum kernel matrix from the inner products of these states, defining a linear system in a higher-dimensional Hilbert space corresponding to the support vector machine (SVM) optimization problem and solving it using a Variational Quantum Linear Solver (VQLS). We also examine the effect of the quantum kernel condition number on classification performance. In our second method, we apply angle encoding to the extracted features in a variational quantum circuit and use a classical optimizer for model training. Both quantum models are tested on binary and multiclass classification tasks, and the performance is compared with the classical CNN model. Our results show that while the CNN model demonstrates robust performance, hybrid quantum–classical models perform competitively. This highlights the potential of hybrid quantum–classical approaches for near-term real-world applications in industrial defect detection and quality assurance. Full article

This study presents an experimental framework for hybrid grey-box–ARX identification and constrained model predictive control (MPC) in a 250 L jacketed batch milk pasteurizer. The plant was represented as a closed, stirred, energy-accumulating system rather than as a continuous high-temperature short-time process. The workflow combined manufacturer specifications, field measurements, a batch–jacket grey-box model, local autoregressive with exogenous input (ARX) identification in the approach and holding region, and controller evaluation under common sampling, reference, actuator, and exclusion conditions. In five paired experimental blocks, the nominal MPC–ARX reduced RMSE by 1.959 °C, IAE by 7801 °C s, specific energy consumption by 0.0081 kWh/kg, overshoot by 3.573 °C, actuator saturation by 44.46 percentage points, and thermal overexposure by 4717 °C s relative to a fixed-parameter PI controller with anti-windup. The optimization remained feasible at all evaluated instants, with a mean computation time of 6.4 ms for a 5 s control period. Supervised adaptive strategies provided diagnostic value but did not outperform the nominal MPC. The conclusions are restricted to batches near the nominal fill condition and the 65 °C/30 min milk recipe. Full article

This paper presents an advanced load frequency regulation strategy for interconnected thermal power systems integrated with renewable energy sources and energy storage systems. A two-area non-reheat thermal power system is investigated, where photovoltaic generation is incorporated in Area 1 and wind turbine generation in Area 2 to assess the impact of renewable penetration on system dynamics and frequency stability. To improve the dynamic response under varying operating conditions, a novel multi-stage TDn(1+PIDn) controller is proposed. The TDn stage enhances transient shaping, while the PIDn stage provides superior damping and steady-state accuracy. The controller parameters are optimally tuned using the pied kingfisher optimizer (PKO) and compared with particle swarm and grey wolf-based optimizers. Furthermore, vanadium redox flow batteries, superconducting magnetic energy storage, and hydrogen–air fuel cells are integrated into the hybrid system to mitigate frequency oscillations caused by renewable intermittency. Offline simulations and real-time validation using the OPAL-RT OP4512 simulator are conducted under different dynamic scenarios. The obtained results demonstrate that the proposed PKO-TDn(1+PIDn) method achieves the best transient performance, for example, reducing the $∆F_1$ overshoot, undershoot and settling time by 28%, 15% and 7.5%, respectively, relative to its closest-performing counterpart while attaining the minimum ITAE value of 0.037309. Consistent improvements are observed across the key performance metrics, confirming the robustness and effectiveness of the scheme for modern hybrid power systems. Full article

Rotary tillage–seeding combined operations require stable power take-off (PTO) speed during rotary tillage and accurate tracking of the prescribed travel speed for seeding. Existing energy management strategies for hybrid electric tractors mainly focus on fuel economy and commonly use fixed objective weights, limiting their ability to adjust control priorities under changing operating conditions. To address this issue, an operation-quality-oriented energy management strategy based on model predictive control, termed OQ-EMS/MPC, is proposed. An equivalent combined-operation condition was constructed using the PTO-side rotary-tillage load, drive-side equivalent traction load, segmented travel-speed reference, and equivalent seeding-quality risk. A condition-severity index integrating the PTO-load coefficient of variation, PTO-load impact intensity, and equivalent seeding-quality risk was developed to distinguish steady, fluctuating, and impact-dominated conditions. Based on the identified condition, the weights assigned to PTO-speed regulation, equivalent seed synchronization, and energy economy were adjusted online. These weights were used in the MPC to optimize torque allocation among the engine, motor-generator 1 (MG1), and motor-generator 2 (MG2). The proposed strategy was validated on a dual-side loading bench and compared with a rule-based energy management strategy and a fixed-weight MPC strategy. The overall PTO-speed root-mean-square error (RMSE) was reduced to 1.76 r/min, representing reductions of 58.40% and 45.66% relative to the two comparative strategies, respectively. The equivalent seed-synchronization RMSE was reduced by 69.15% and 52.66%, respectively. Under the impact-dominated condition, the PTO-speed RMSE decreased to 1.65 r/min. The normalized composite cost decreased by 13.53% and 6.26%, while the equivalent fuel consumption increased by 3.40% and 3.76%, respectively. The results demonstrate that the proposed strategy improves PTO-speed stability and equivalent seed-synchronization performance as operating severity increases while accounting for energy economy. Full article

The integration of artificial intelligence (AI) with coagulation–flocculation (C-F) processes represents a significant advancement for optimizing wastewater treatment. However, a comprehensive analysis of the research landscape, trends and collaborative networks in this interdisciplinary field remains lacking. This study addresses this gap by conducting a bibliometric analysis of 251 Scopus-indexed publications (2000–2025) using VOSviewer and Bibliometrix. The objective was to map the intellectual structure, quantify growth trends and identify key research themes and contributors. Results indicate a surge in publications post-2015, with environmental science and engineering as dominant subject areas. China, Iran and Nigeria are leading contributors though geographical concentration suggests a need for broader collaboration. Keyword analysis reveals a thematic evolution from basic artificial neural networks towards advanced machine learning and deep learning, primarily focused on optimizing coagulant dosage and predictive control. Despite promising advancements, challenges related to data dependency, model interpretability and infrastructure integration persist. Future prospects hinge on developing explainable and hybrid AI models, leveraging the Internet of Things for real-time adaptation and fostering interdisciplinary research to bridge the gap between data science and process engineering. This analysis provides a foundational overview to guide future research towards more robust, transparent and widely applicable AI-driven C-F systems in sustainable wastewater management. Full article

Polybenzoxazines (PBz) represent a versatile class of high-performance thermosetting polymers that have attracted significant attention for advanced composite applications due to their unique combination of properties including high glass transition temperatures, low polymerization shrinkage, excellent thermal stability, and molecular design flexibility. This comprehensive review examines the structure–property relationships governing PBz composite performance, from molecular design principles through network formation, composite reinforcement strategies, and ultimate application performance. The review systematically addresses benzoxazine monomer structure and its influence on polymer network architecture, explores the polymerization mechanism, and critically evaluates composite design strategies incorporating carbon-based nanofillers, fiber reinforcements, and hybrid filler systems. Detailed analysis of structure–property relationships reveals how molecular and composite architecture control thermal stability (glass transition temperatures exceeding 350 °C and char yields up to 92%), mechanical performance, electrical properties (dielectric constants as low as 2.67), and chemical durability. Processing techniques ranging from conventional compression molding to emerging additive manufacturing approaches are discussed in the context of morphological control and property optimization. Applications spanning aerospace structures, high-frequency electronics and protective coatings demonstrate the technological relevance of PBz composites. Critical challenges including network brittleness, high cure temperatures, and recyclability limitations are addressed alongside recent advances in dynamic covalent networks, vitrimer chemistry, and self-healing systems that promise to overcome these barriers. This review provides a comprehensive framework for understanding and engineering polybenzoxazine composites for next-generation advanced applications. Full article