Search Results (3,111)

To address the impact of distributed energy resource volatility on distribution network fault restoration, this paper proposes a strategy that incorporates net load forecasting. A Bayesian-optimized long short-term memory neural network is used to accurately predict the net load within fault-affected areas, achieving an R² of 0.9569 and an RMSE of 12.15 kW. Based on the forecasting results, a fast restoration optimization model is established, with objectives to maximize critical load recovery, minimize switching operations, and reduce network losses. The model is solved using a genetic algorithm enhanced with quantum particle swarm optimization (GA-QPSO), a hybrid metaheuristic known for its superior global exploration and local refinement capabilities. GA-QPSO has been successfully applied in various power system optimization problems, including service restoration, network reconfiguration, and distributed generation planning, owing to its effectiveness in navigating large, complex solution spaces. Simulation results on the IEEE 33-bus system show that the proposed method reduces network losses by 33.2%, extends the power supply duration from 60 to 120 min, and improves load recovery from 72.7% to 75.8%, demonstrating enhanced accuracy and efficiency of the restoration process. Full article

In high-power insulated gate bipolar transistor (IGBT) module thermal management, the structural design of microchannel cooling plates plays a crucial role in determining heat dissipation efficiency and temperature uniformity. This study focuses on the effects of honeycomb-structured unit dimensions and arrangements, as well as inlet/outlet configurations of the cooling plate on its thermal and flow performance. Additionally, the influence of different coolant inlet velocities and temperatures is investigated. Under constant coolant flow rate and boundary conditions, four design configurations with varying pore widths and channel spacings were evaluated numerically. The results indicate that the optimized honeycomb structure can reduce the module’s peak temperature by approximately 8.7 K while significantly improving temperature uniformity and maintaining a moderate pressure drop. Moreover, increasing the number of inlets and outlets effectively lowers the pressure drop and enhances thermal uniformity. Although increasing the coolant flow rate and reducing the inlet temperature can further improve cooling performance, these measures also lead to notable increases in energy consumption and pressure loss. Therefore, a trade-off between thermal enhancement and system energy efficiency must be considered in practical applications. The findings of this study provide practical guidance for the design optimization of high-efficiency microchannel liquid cooling systems in power electronic applications. Full article

In response to the instability fluctuations and erosion characteristic changes in pantograph-catenary system (PCS) arcs induced by humidity variations in an open environment, a single-variable controlled experimental approach based on multi-source data fusion is proposed. This study innovatively establishes a humidity-controlled reciprocating current-carrying arc initiation test platform, integrating digital image processing with the dynamic analysis of multi-physics sensor signals (current, voltage, temperature). The study quantitatively evaluates the arc motion characteristics and the erosion effects on the frictional contact pair under different relative humidity levels (30%, 50%, 70%, and 90%) with a DC power supply (120 V/25 A). The experimental data and analysis reveal that increasing humidity results in higher contact resistance and accumulated arc energy, with arc stability first improving and then decreasing. At low humidity, arc behavior is more intense, and the erosion rate is faster. As humidity increases, the electrode wear transitions from adhesive wear to electrochemical wear, accompanied by copper transfer. The results suggest that the arc stability is optimal at moderate humidity (50% RH), with a peak current-carrying efficiency of 66% and a minimum loss rate of 14.5%. This threshold offers a vital theoretical framework for the optimization and risk assessments of PCS design. Full article

Integrating renewable energy, particularly wind power, into modern power systems introduces challenges concerning stability and reliability. These issues require enhanced regulation to balance power supply with load demand. Flexible loads and energy storage provide viable solutions to stabilize the grid without relying on new resources. This paper proposes building thermostatically controlled loads (BTLs), such as heating, ventilation, and air conditioning (HVAC) systems, as flexible demand-side management tools to address the challenges of intermittent energy sources. A new concept is introduced, portraying BTLs as a stochastic battery with losses, offering a compact representation of their dynamics. BTLs’ thermal characteristics, user-defined set points, and ambient temperature changes determine the power limits and energy capacity of this stochastic battery. The model is simulated using DIgSILENT Power Factory, which includes thermal power plants, gas turbines, wind power plants, and BTLs. A dynamic dispatch strategy optimizes power generation while utilizing BTLs to balance grid fluctuations caused by variable wind energy. Performance analysis shows that integrating BTLs with conventional thermal plants can reduce variability and improve grid stability. The study highlights the dual role of simulating overall flexibility and applying dynamic dispatch strategies to enhance power systems with high renewable energy integration. Full article

Received Signal Strength Indicator (RSSI) prediction is valuable for network planning and optimization as it helps determine the optimal placements of wireless access points and enables better coverage planning. It is also crucial for efficient handover management between cells or access points, reducing dropped connections and improving service quality. Additionally, RSSI prediction supports indoor positioning systems, power management optimization, and cost-efficient network deployment. Path loss models have historically served as the foundation for RSSI prediction, providing a theoretical framework for estimating signal strength degradation. However, modern machine learning approaches have emerged as a revolutionary solution for network optimization, providing more versatile and data-driven methods to enhance wireless network performance. In this paper, an adaptive machine learning framework integrating environmental sensing parameters such as temperature, relative humidity, barometric pressure, and particulate matter for RSSI prediction is proposed. Performance analysis reveals that RSSI values are influenced by environmental factors through complex, non-linear interactions, thereby challenging the conventional linear assumptions of traditional path loss models. The proposed model demonstrates improved predictive accuracy over the baseline, with relative increases in variance explained of 6.02% and 2.04% compared to the baseline model excluding and including environmental parameters, respectively. Additionally, the root mean squared error is reduced to 1.40 dB. These results demonstrate that cognitive methods incorporating environmental data can substantially enhance RSSI prediction accuracy in wireless communications. Full article

Myocardial infarction (MI) remains a leading cause of in-hospital mortality. Early identification of high-risk patients is essential for improving clinical outcomes and optimizing hospital resource allocation. This study presents a machine learning framework for predicting mortality following MI using a publicly available dataset of 1700 patient records, and after excluding records with over 20 missing values and features with more than 300 missing entries, the final dataset included 1547 patients and 113 variables, categorized as binary, categorical, integer, or continuous. Missing values were addressed using denoising autoencoders for continuous features and variational autoencoders for the remaining data. In contrast, feature selection was performed using Random Forest, and PowerTransformer scaling was applied, addressing class imbalance by using SMOTE. Twelve models were evaluated, including Focal-Loss Neural Networks, TabNet, XGBoost, LightGBM, CatBoost, Random Forest, SVM, Logistic Regression, and a voting ensemble. Performance was assessed using multiple metrics, with SVM achieving the highest F1 score (0.6905), ROC-AUC (0.8970), and MCC (0.6464), while Random Forest yielded perfect precision and specificity. To assess generalizability, a subpopulation external validation was conducted by training on male patients and testing on female patients. XGBoost and CatBoost reached the highest ROC-AUC (0.90), while Focal-Loss Neural Network achieved the best MCC (0.53). Overall, the proposed framework outperformed previous studies in key metrics and maintained better performance under demographic shift, supporting its potential for clinical decision-making in post-MI care. Full article

Litz wires are extensively employed in contemporary high-frequency switching power electronics to mitigate conductor losses. Minimizing additional winding losses caused by high-frequency phenomena, such as skin and proximity effects, is a critical design consideration for achieving high power density in modern power electronics. However, accurately predicting losses in structures composed of numerous twisted and insulated strands remains a challenge. With the increasing accessibility of commercial numerical tools, such as finite element method (FEM) solvers, simulation-based approaches have become indispensable tools for analyzing electromagnetic phenomena in complex magnetic device structures under high-frequency conditions. In parallel, data-driven modeling has emerged as a powerful method, enabling pattern identification based on datasets; however, such approaches rely on the availability of large amounts of reliable high-quality data. Generating such large-scale FEM datasets, however, is often constrained by long computation times and high memory consumption. Despite the remarkable advancements in computing power, full three-dimensional (3D) FEM analysis at the strand level for Litz wire windings often remains infeasible within personal computing environments. To address these challenges, this study presents a computationally efficient two-dimensional FEM-based framework that integrates a data-driven fitting model with optimized geometric discretization and meshing strategies, enabling accurate analysis with reduced computational load. The proposed approach, which incorporates optimal meshing conditions into commercially available 2D FEM tools and a simple data-driven fitting model, enables accurate prediction of the frequency-dependent AC resistance of multi-turn Litz windings using a typical personal computer. Its feasibility is further demonstrated through experimental frequency response measurements on both 12-turn and 21-turn windings fabricated with 150-strand Litz wire, which show strong agreement with the corrected simulation results, confirming the model’s accuracy and practical applicability. Full article

Over the last several years, a significant advancement in high-voltage electronic packaging techniques has paved the way for next-generation power electronics. However, controlling the thermal properties of these new packaging solutions is still a major challenge. The utilization of wide bandgap semiconductors such as SiC and GaN offers effective methods to minimize thermal inefficiencies caused by conduction losses through high-frequency switching topologies. Nevertheless, the need for high voltage in electrical systems continues to pose significant barriers, as heat generation remains one of the most significant obstacles to widespread implementation. The trend of electronics design miniaturization has driven the development of high-performance cooling concepts to address the needs of high-power-density systems. As a result, the design of effective cooling systems has emerged as a crucial aspect for successful implementation, requiring seamless integration with electronic packaging to achieve optimal performance. This review article explores various thermal management approaches demonstrated in electronic systems. This paper aims to provide a comprehensive overview of heat transfer enhancement techniques employed in electronics thermal management, focusing on core concepts. The review categorizes these techniques into concepts based on fin design, microchannel cooling, jet impingement, phase change materials, nanofluids, and hybrid designs. Recent advancements in high-power density devices, alongside innovative cooling systems such as phase change materials and nanofluids, demonstrate potential for enhanced heat dissipation in power electronics. Improved designs in finned heat sinks, microchannel cooling, and jet impingement techniques have enabled more efficient thermal management in high-density power electronics. By fixing key insights into one reference, this review serves as a valuable resource for researchers and engineers navigating the complex landscape of high-performance cooling for modern electronic systems. Full article

We propose a novel systematic approach for the deployment optimization of unmanned aerial vehicles (UAVs). In this context, this study focuses on enhancing the coverage of UAV-mounted 6G mobile base stations. The number and placement optimization of UAV-mounted 6G mobile base stations, deployed to support terrestrial base stations during periods of increased population density in a given area, are addressed using a symbolic limited optimal discrete controller synthesis technique. Within the scope of this study, the UAVs’ altitude and attitude behaviors are optimized to ensure the most efficient trajectory toward the designated base station coordinates. Additionally, at their new locations, these behaviors are adjusted to facilitate accurate coverage estimation from the base stations they serve. In the deployment optimization of UAVs, the placement of base stations is determined using received signal strength data obtained through the ray-tracing-based channel modeling technique. The channel model considered critical parameters such as path loss, received power, weather loss, and foliage loss. Final average path loss values of 102.3 dB, 111.7 dB, and 127.4 dB were obtained at the carrier frequencies of 7 GHz, 26 GHz, and 140 GHz, respectively. These findings were confirmed with MATLAB-based ray tracing simulations. Our proposed approach is validated through experimental evaluations, demonstrating superior performance compared to existing methods reported in the literature. Full article

Detection of defects on steel surface is crucial for industrial quality control. To address the issues of structural complexity, high parameter volume, and poor real-time performance in current detection models, this study proposes a lightweight model based on an improved YOLOv11. The model first reconstructs the backbone network by introducing a Reversible Connected Multi-Column Network (RevCol) to effectively preserve multi-level feature information. Second, the lightweight FasterNet is embedded into the C3k2 module, utilizing Partial Convolution (PConv) to reduce computational overhead. Additionally, a Group Convolution-driven EfficientDetect head is designed to maintain high-performance feature extraction while minimizing consumption of computational resources. Finally, a novel WISEPIoU loss function is developed by integrating WISE-IoU and POWERFUL-IoU to accelerate the model convergence and optimize the accuracy of bounding box regression. The experiments on the NEU-DET dataset demonstrate that the improved model achieves a parameter reduction of 39.1% from the baseline and computational complexity of 49.2% reduction in comparison with the baseline, with an mAP@0.5 of 0.758 and real-time performance of 91 FPS. On the DeepPCB dataset, the model exhibits reduction of parameters and computations by 39.1% and 49.2%, respectively, with mAP@0.5 = 0.985 and real-time performance of 64 FPS. The study validates that the proposed lightweight framework effectively balances accuracy and efficiency, and proves to be a practical solution for real-time defect detection in resource-constrained environments. Full article

In recent years, graph neural networks (GNNs) have shown powerful performance in processing non-Euclidean data. However, similar to other machine-learning algorithms, GNNs can amplify data bias in high-risk decision-making systems, which can easily lead to unfairness in the final decision-making results. At present, a large number of studies focus on solving the fairness problem of GNNs, but the existing methods mostly rely on building complex model architectures or rely on technical means in the field of non-GNNs. To this end, this paper proposes FairCNCB (Fair Graph Neural Network based on Counterfactual and Category Balance) to address the problem of class imbalancing in minority sensitive attribute groups. First, we conduct a causal analysis of fair representation and employ the adversarial network to generate counterfactual node samples, effectively mitigating bias induced by sensitive attributes. Secondly, we calculate the weights for minority sensitive attribute groups, and reconstruct the loss function to achieve the fairness of sensitive attribute classes among different groups. The synergy between the two modules optimizes GNNs from multiple dimensions and significantly improves the performance of GNNs in terms of fairness. The experimental results on the three datasets show the effectiveness and fairness of FairCNCB. The performance metrics (such as AUC, F1, and ACC) have been improved by approximately 2%, and the fairness metrics (△sp, △eo) have been enhanced by approximately 5%. Full article

The increasing demand for high-performance power converters for electric vehicle (EV) applications places a significant emphasis on developing effective and robust control strategies for DC-DC converter operation. This paper deals with the development, simulation, and experimental validation of an adaptive Lyapunov-type Nonlinear Sliding Mode Control (L-SMC) strategy for a DC–DC boost converter, addressing significant uncertainties caused by large variations in system parameters (R and L) and ensuring the tracking of a voltage reference. The proposed control strategy employs the Lyapunov stability theory to build an adaptive law to update the parameters of the sliding surface so the system can achieve global asymptotic stability in the presence of uncertainty in inductance, capacitance, load resistance, and input voltage. The nonlinear sliding manifold is also considered, which contributes to a more robust and faster convergence in the controller. In addition, a logic optimization technique was implemented that minimizes switching (chattering) operations significantly, and as a result of this, increases ease of implementation. The proposed L-SMC is validated through both simulation and experimental tests under various conditions, including abrupt increases in input voltage and load disturbances. Simulation results demonstrate that, whether under nominal parameters (R = 320 Ω, L = 2.7 mH) or with parameter variations, the voltage overshoot in all cases remains below 0.5%, while the steady-state error stays under 0.4 V except during the startup, which is a transitional phase lasting a very short time. The current responds smoothly to voltage reference and parameter variations, with very insignificant chattering and overshoot. The current remains stable and constant, with a noticeable presence of a peak with each change in the reference voltage, accompanied by relatively small chattering. The simulation and experimental results demonstrate that adaptive L-SMC achieves accurate voltage regulation, a rapid transient response, and reduces chattering, and the simulation and experimental testing show that the proposed controller has a significantly lower steady-state error, which ensures precise and stable voltage regulation with time. Additionally, the system converges faster for the proposed controller at conversion and is stabilized quickly to the adaptation reference state after the drastic and dynamic change in either the input voltage or load, thus minimizing the settling time. The proposed control approach also contributes to saving energy for the application at hand, all in consideration of minimizing losses. Full article

With the widespread integration of renewable energy sources such as wind and solar power into power systems, their inherent unpredictability and fluctuations present significant challenges to grid stability and security. To address these issues, Battery Energy Storage Systems (BESSs) offer an effective means of enhancing renewable energy absorption and improving the overall system efficiency. This study proposes a coordinated planning method based on the improved bat algorithm (IBA) to tackle the challenges associated with integrating renewable energy into distribution networks. A bi-level optimization framework is introduced to coordinate the planning and operation of the distributed generation (DG) and BESS. The upper-level model focuses on selecting optimal sites and determining the capacity of wind turbines, photovoltaic arrays, and storage systems from an economic perspective. The lower-level model optimizes the curtailment of wind and solar energy and minimizes network losses based on the upper-level planning outcomes. Additionally, the lower-level model also coordinates the dispatch between renewable energy generation and storage systems to ensure the reliable operation of the system. To effectively solve this bi-level optimization model, we have improved the conventional bat algorithm. Simulation results show that the improved bat algorithm not only significantly enhances the convergence speed but also improves the voltage stability, with the photovoltaic utilization rate reaching 90.27% and the wind energy utilization rate reaching 92.18%. These results highlight the practical advantages and success of the proposed method in optimizing renewable energy configurations. Full article

Accurate real-time detection of hawthorn by vision systems is a fundamental prerequisite for automated harvesting. This study addresses the challenges in hawthorn orchards—including target overlap, leaf occlusion, and environmental variations—which lead to compromised detection accuracy, high computational resource demands, and poor real-time performance in existing methods. To overcome these limitations, we propose YOLO-DCL (group shuffling convolution and coordinate attention integrated with a lightweight head based on YOLOv8n), a novel lightweight hawthorn detection model. The backbone network employs dynamic group shuffling convolution (DGCST) for efficient and effective feature extraction. Within the neck network, coordinate attention (CA) is integrated into the feature pyramid network (FPN), forming an enhanced multi-scale feature pyramid network (HSPFN); this integration further optimizes the C2f structure. The detection head is designed utilizing shared convolution and batch normalization to streamline computation. Additionally, the PIoUv2 (powerful intersection over union version 2) loss function is introduced to significantly reduce model complexity. Experimental validation demonstrates that YOLO-DCL achieves a precision of 91.6%, recall of 90.1%, and mean average precision (mAP) of 95.6%, while simultaneously reducing the model size to 2.46 MB with only 1.2 million parameters and 4.8 GFLOPs computational cost. To rigorously assess real-world applicability, we developed and deployed a detection system based on the PySide6 framework on an NVIDIA Jetson Xavier NX edge device. Field testing validated the model’s robustness, high accuracy, and real-time performance, confirming its suitability for integration into harvesting robots operating in practical orchard environments. Full article

Background: Systematic studies providing differentiated insight into the contribution of immunity directed against conserved and non-conserved epitopes of SARS-CoV-2 Spike on long-term protection are rare and insufficiently guide future pan-variant vaccine research. The present observational cohort study aimed to evaluate the correlation of neutralizing antibody levels and cellular immunity against the Spike protein with symptomatic Omicron breakthrough infection. Methods: Neutralizing antibody levels against multiple (sub)variants were analyzed 6 months following the second wild-type mRNA vaccination and 6 months after booster in 107 subjects using a multiplex surrogate virus neutralization assay. To assess cellular immunity, cytokine mRNA expression levels were determined after peptide pool stimulation in whole blood samples of a study subgroup. Results: Neutralizing antibody titers were found to serve as a reasonably reliable correlate of protection prior to booster immunization. However, the predictive power of neutralizing antibody titers was diminished after boosting. This loss appears to be due to a critical remodeling of the antibody repertoire—a process that was dose-dependent on pre-boost humoral immunity. Vaccination against Omicron infection was most effective when a balanced immune response to both conserved and non-conserved epitopes of the viral Spike protein was induced. While neutralizing antibodies against receptor-binding domain epitopes affected by mutations were specifically associated with protection from symptomatic variant infection, cellular immunity was most effective when targeting conserved Spike epitopes. Conclusions: Optimal long-term protection against Omicron infection requires balanced immunity to both conserved and non-conserved epitopes of the viral Spike protein. The limited availability of cross-neutralizing antibodies targeting non-conserved epitopes and their inherently lower efficacy renders them a limiting factor as humoral immunity wanes over time. Future pan-SARS-CoV-2 variant vaccines that primarily target conserved epitopes may therefore provide less effective long-term protection against symptomatic variant infection than vaccines targeting a broader epitope spectrum including both conserved and non-conserved epitopes. Full article