Search Results (154)

Modern photovoltaic (PV) systems face increasing cybersecurity threats due to their integration with smart grid infrastructure. While previous research has identified vulnerabilities, the lack of standardized datasets has hindered the development and evaluation of detection algorithms. Building upon our previously introduced Photo-Set dataset, this paper presents a benchmark evaluation of anomaly detection algorithms for PV cybersecurity applications. We evaluate three state-of-the-art algorithms (One-Class SVM, Isolation Forest, and Local Outlier Factor) across 12 attack scenarios, establishing performance baselines and identifying algorithm-specific strengths and limitations. Our experimental results reveal a clear detectability hierarchy. This work proposes a standardized benchmark for PV cybersecurity research and provides the industry with evidence-based guidance for security system deployment. Full article

The primary aim of this study is to develop an effective decision-support system for managing crises related to the release of hazardous airborne substances. Such incidents, which can arise from industrial accidents or intentional releases, necessitate the rapid identification of contaminant sources to enable timely response measures. This work focuses on a novel approach that integrates a modified Sandpile model with advection and employs the (1 + 1)-Evolution Strategy to solve the inverse problem of source localization. The initial section of this paper reviews existing methods for simulating atmospheric dispersion and reconstructing source locations. In the following sections, we describe the architecture of the proposed system, the modeling assumptions, and the experimental framework. A key feature of the method presented here is its reliance solely on concentration measurements obtained from a distributed network of sensors, eliminating the need for prior knowledge of the source location, release time, or emission strength. The system was validated through a two-stage process using synthetic data generated by a Gaussian dispersion model. Preliminary experiments were conducted to support model calibration and refinement, followed by formal tests to evaluate localization accuracy and robustness. Each test case was completed in under 20 min on a standard laptop, demonstrating the algorithm’s high computational efficiency. The results confirm that the proposed (1 + 1)-ES Sandpile model can effectively reconstruct source parameters, staying within the resolution limits of the sensor grid. The system’s speed, simplicity, and reliance exclusively on sensor data make it a promising solution for real-time environmental monitoring and emergency response applications. Full article

It is widely accepted that the lightweight design of a grid fin is closely related to its aerodynamic performance and structural integrity, while limited work seeks their balance. This study proposes a lightweight grid fin design method by taking the locally swept-back angle as a variable based on three-dimensional computational fluid dynamics and fluid–thermo–structure coupling analysis for Mach numbers ranging from 0.8 to 5. The effect of the swept-back angle on the relative aerodynamic efficiency profit, weight saving, and structural integrity (with a focus on static strength) was analyzed. The results showed that the locally swept-back configuration maintained structural integrity while enabling simultaneous aerodynamic performance improvement and weight saving across different Mach numbers through swept-back angle adjustment. At Mach 0.8, 1.5, and 2.0, the 20° swept-back configuration achieved a 13.2% weight saving and improved aerodynamic performance. At Mach 0.9, the 15° configuration delivered optimal aerodynamic enhancement with a 10% weight saving. Notably, the 15° configuration demonstrated excellent balance after evaluating all Mach number operating conditions. All these highlight a good attempt for the trade-off design of structures among weight saving, aerodynamic performance, and structural integrity. Full article

Demand response (DR) is increasingly recognized as a critical flexibility resource for modernizing power systems, enabling the large-scale integration of renewable energy and enhancing grid stability. While the field of general electricity load forecasting is supported by numerous systematic reviews, the specific subfield of DR potential forecasting has received comparatively less synthesized attention. This gap leaves a fragmented understanding of modeling techniques, practical implementation challenges, and future research problems for a function that is essential for market participation. To address this, this paper presents a PRISMA-2020-compliant systematic review of 172 studies to comprehensively analyze the state-of-the-art in DR potential estimation. We categorize and evaluate the evolution of forecasting methodologies, from foundational statistical models to advanced AI architectures. Furthermore, the study identifies key technological enablers and systematically maps the persistent technical, regulatory, and behavioral barriers that impede widespread DR deployment. Our analysis demonstrates a clear trend towards hybrid and ensemble models, which outperform standalone approaches by integrating the strengths of diverse techniques to capture complex, nonlinear consumer dynamics. The findings underscore that while technologies like Advanced Metering Infrastructure (AMI) and the Internet of Things (IoT) are critical enablers, the gap between theoretical potential and realized flexibility is primarily dictated by non-technical factors, including inaccurate baseline methodologies, restrictive market designs, and low consumer engagement. This synthesis brings much-needed structure to a fragmented research area, evaluating the current state of forecasting methods and identifying the critical research directions required to improve the operational effectiveness of DR programs. Full article

This study evaluated ten drought indices focusing on their ability to monitor drought events in Marathwada, a semi-arid region of India. High-resolution gridded monthly total precipitation data for 75 years (1950–2024) from the European Centre for Medium-Range Weather Forecasts (ECMWF) were used to evaluate the drought indices. These indices were computed across six timescales: 1, 3, 4, 6, 9, and 12 months. A Generalized Autoregressive Conditional Heteroscedastic (GARCH) model was employed to detect temporal volatility in precipitation, followed by a second-order geospatial autocorrelation eigenfunction eigendecomposition using Global Moran’s Index statistics to geolocate both aggregated and non-aggregated precipitation locations. The performance of drought indices was assessed using non-parametric Spearman’s correlation to identify the strength, direction, and similarity of regional-specific drought events. The temporal lag interdependence between meteorological and agricultural droughts was assessed using a non-parametric Spearman’s cross correlation function (SCCF). The findings revealed that the GARCH model with a skewed Student’s t distribution effectively captured conditional temporal volatility and asymptotic behavior in the precipitation series. The model’s sensitivity enabled the incorporation of temporal fluctuations related to droughts and extreme meteorological events. The Bhalme and Mooley Drought Index (BMDI-6) and Z-Score Index (ZSI-6) were the most applicable indices for drought monitoring. Spearman’s cross-correlation analysis revealed that meteorological droughts influenced agricultural droughts with a time lag of up to 4 months. Full article

Changes in the amount and distribution of rainfall highly impact agricultural production in predominantly rainfed farming systems in Africa. Reliable rainfall forecasts on a daily timescale are vital for in-season decision-making. This study evaluated the relative prediction abilities of the European Centre for Medium-Range Weather Forecasts Season 5.1 (ECMWFSv5.1) and the Climate Forecast System version 2 (CFSv2) gridded rainfall models across Africa and three sub-regions from 2012–2022. The results indicate that the performance of both models declines with increasing lead times and improves with aggregated or coarser temporal resolutions. ECMWFv5.1 consistently represented observed daily rainfall better than CFSv2 at all lead times, particularly in West Africa. On dekadal timescales, ECMWFv5.1 outperformed CFSv2 across all sub-regions. CFSv2 tended to overestimate low- and high-intensity rainfall events, whereas ECMWFv5.1 slightly underestimated low-intensity rainfall but accurately captured high-intensity events. While ECMWFv5.1 showed superior skill overall, model reliability was generally limited to West Africa; in contrast, both models performed poorly in East Africa. The high probability of detection (POD) indicates that the models are generally effective at identifying rainy days. However, their overall accuracy in forecasting rainfall across Africa varies depending on lead time, region, rainfall intensity, and elevation. While we did not apply bias-correction methods in this study, we recommend that such techniques be used in future work to improve the reliability of forecasts for operational and sectoral applications. This study therefore highlights both the strengths and the limitations of CFSv2 and ECMWFv5.1 for climate impact assessments, particularly in West Africa and low-elevation regions. Full article

The exponential growth of user-generated video content necessitates efficient summarization systems for improved accessibility, retrieval, and analysis. This study presents and benchmarks a multimodal video summarization framework that classifies segments as informative or non-informative using audio, visual, and fused features. Sixty hours of annotated video across ten diverse categories were analyzed. Audio features were extracted with pyAudioAnalysis, while visual features (colour histograms, optical flow, object detection, facial recognition) were derived using OpenCV. Six supervised classifiers—Naive Bayes, K-Nearest Neighbors, Logistic Regression, Decision Tree, Random Forest, and XGBoost—were evaluated, with hyperparameters optimized via grid search. Temporal coherence was enhanced using median filtering. Random Forest achieved the best performance, with 74% AUC on fused features and a 3% F1-score gain after post-processing. Spectral flux, grayscale histograms, and optical flow emerged as key discriminative features. The best model was deployed as a practical web service using TensorFlow and Flask, integrating informative segment detection with subtitle generation via beam search to ensure coherence and coverage. System-level evaluation demonstrated low latency and efficient resource utilization under load. Overall, the results confirm the strength of multimodal fusion and ensemble learning for video summarization and highlight their potential for real-world applications in surveillance, digital archiving, and online education. Full article

With the increasing penetration of renewable energy, the stochastic and intermittent nature of its generation increases operational uncertainty and vulnerability, posing significant challenges for grid stability. However, traditional algorithms typically identify critical nodes by focusing solely on the network topology or power flow, or by combining the two, which leads to the inaccurate and incomplete identification of essential nodes. To address this, we propose the Three-Dimensional Value-Based Gravity Model (3V-GM), which integrates structural and electrical–physical attributes across three layers. In the plane layer, we combine each node’s global topological position with its real-time supply–demand voltage state. In the line layer, we introduce an electrical coupling distance to quantify the strength of electromagnetic interactions between nodes. In the point layer, we apply eigenvector centrality to detect latent hub nodes whose influence is not immediately apparent. The performance of our proposed method was evaluated by examining the change in the load loss rate as nodes were sequentially removed. To assess the effectiveness of the 3V-GM approach, simulations were conducted on the IEEE 39 system, as well as six other benchmark networks. The simulations were performed using Python scripts, with operational parameters such as bus voltages, active and reactive power flows, and branch impedances obtained from standard test cases provided by MATPOWER v7.1. The results consistently show that removing the same number of nodes identified by 3V-GM leads to a greater load loss compared to the six baseline methods. This demonstrates the superior accuracy and stability of our approach. Additionally, an ablation experiment, which decomposed and recombined the three layers, further highlights the unique contribution of each component to the overall performance. Full article

With the widespread deployment of 5G technology, high-precision positioning in global navigation satellite system (GNSS)-denied environments is a critical yet challenging task for emerging 5G applications, enabling enhanced spatial resolution, real-time data acquisition, and more accurate geolocation services. Traditional methods relying on single-source measurements like received signal strength information (RSSI) or time of arrival (TOA) often fail in complex multipath conditions. To address this, the positional encoding multi-scale residual network (PE-MSRN) is proposed, a novel deep learning framework that enhances positioning accuracy by deeply mining spatial information from 5G channel state information (CSI). By designing spatial sampling with multigranular data and utilizing multi-source information in 5G CSI, a dataset covering a variety of positioning scenarios is proposed. The core of PE-MSRN is a multi-scale residual network (MSRN) augmented by a positional encoding (PE) mechanism. The positional encoding transforms raw angle of arrival (AOA) data into rich spatial features, which are then mapped into a 2D image, allowing the MSRN to effectively capture both fine-grained local patterns and large-scale spatial dependencies. Subsequently, the PE-MSRN algorithm that integrates ResNet residual networks and multi-scale feature extraction mechanisms is designed and compared with the baseline convolutional neural network (CNN) and other comparison methods. Extensive evaluations across various simulated scenarios, including indoor autonomous driving and smart factory tool tracking, demonstrate the superiority of our approach. Notably, PE-MSRN achieves a positioning accuracy of up to 20 cm, significantly outperforming baseline CNNs and other neural network algorithms in both accuracy and convergence speed, particularly under real measurement conditions with higher SNR and fine-grained grid division. Our work provides a robust and effective solution for developing high-fidelity 5G positioning systems. Full article

As the deployment of wind energy systems continues to rise globally, ensuring the reliability and efficiency of grid-connected Doubly Fed Induction Generator (DFIG) wind turbines has become increasingly critical. Two core challenges faced by these systems include fault diagnosis in power electronic converters and accurate prediction of wind conditions for adaptive power control. Recent advancements in artificial intelligence (AI) have introduced powerful tools for addressing these challenges. This study presents the first unified comparative performance analysis of two deep learning-based models: (i) a Convolutional Neural Network-Long Short-Term Memory CNN-LSTM with Variational Mode Decomposition for real-time Grid Side Converter (GSC) fault diagnosis, and (ii) an Incremental Generative Adversarial Network (IGAN) for wind attribute prediction and adaptive droop gain control, applied to grid-integrated DFIG wind turbines. Unlike prior studies that address fault diagnosis and wind forecasting separately, both models are evaluated within a common MATLAB/Simulink framework using identical wind profiles, disturbances, and system parameters, ensuring fair and reproducible benchmarking. Beyond accuracy, the analysis incorporates multi-dimensional performance metrics such as inference latency, robustness to disturbances, scalability, and computational efficiency, offering a more holistic assessment than prior work. The results reveal complementary strengths: the CNN-LSTM achieves 88% accuracy with 15 ms detection latency for converter faults, while the IGAN delivers more than 95% prediction accuracy and enhances frequency stability by 18%. Comparative analysis shows that while the CNN-LSTM model is highly suitable for rapid fault localization and maintenance planning, the IGAN model excels in predictive control and grid performance optimization. Unlike prior studies, this work establishes the first direct comparative framework for diagnostic and predictive AI models in DFIG systems, providing novel insights into their complementary strengths and practical deployment trade-offs. This dual evaluation lays the groundwork for hybrid two-tier AI frameworks in smart wind energy systems. By establishing a reproducible methodology and highlighting practical deployment trade-offs, this study offers valuable guidance for researchers and practitioners seeking explainable, adaptive, and computationally efficient AI solutions for next-generation renewable energy integration. Full article

Accurate prediction of shear strength at the interface between new and old concrete is vital for the structural performance of repaired and composite systems. However, the underlying shear transfer mechanism is highly nonlinear and influenced by multiple interdependent factors, which limit the applicability of conventional empirical models. To address this challenge, an interpretable machine-learning (ML) framework is proposed. The latest database of 247 push-off specimens was compiled from the recent literature, incorporating diverse interface types and design parameters. The hyperparameters of the adopted ML models were optimized via a grid search to ensure the predictive performance on the updated database. Among the evaluated algorithms, eXtreme Gradient Boosting (XGBoost) demonstrated the best predictive performance, with R² = 0.933, RMSE = 0.663, MAE = 0.486, and MAPE = 12.937% on the testing set, outperforming Support Vector Regression (SVR), Random Forest (RF), and adaptive boosting (AdaBoost). Compared with the best empirical model (AASHTO, R² = 0.939), XGBoost achieved significantly lower prediction errors (e.g., RMSE was reduced by 67.8%), enhanced robustness (COV = 0.176 vs. 0.384), and a more balanced mean ratio (1.054 vs. 1.514). The SHapley Additive exPlanations (SHAP) method was employed to interpret the model predictions, identifying the shear reinforcement ratio as the most influential factor, followed by interface type, interface width, and concrete strength. These results confirm the superior accuracy, generalizability, and explainability of XGBoost in modeling the shear behaviors of new–old concrete interfaces. Full article

To enhance the efficiency of renewable energy consumption and reduce reliance on fossil fuels, the study addresses the challenges of distributed photovoltaic and energy storage integration in distribution networks, such as voltage fluctuations, safety risks, and insufficient converter considerations to the distribution network. Through a four-dimensional comprehensive evaluation system including grid-strength quantification indicators like the generalized short-circuit ratio, a multi-objective mathematical model-based performance evaluation system using an analytic hierarchy process and criteria importance through the intercriteria correlation method has been established, and an optimization model for the configuration of photovoltaic and energy storage equipment is optimized. The study innovatively proposes a multi-type synchronous control framework enabling dynamic GFL/GFM converter selection at different nodes, overcoming traditional single-control limitations. The simulation results show that the proposed optimal configuration scheme can effectively improve the operating states and reduce the energy consumption of the distribution network. Full article

This paper addresses stability issues in modern power grids arising from extensive integration of power electronic converters, which introduce complex multi-time-scale interactions. A symbolic simplification method is proposed to accurately model grid-connected converter dynamics, significantly reducing computational complexity through transfer function approximations and yielding efficient reduced-order models. An impedance-based approach utilizing impedance ratio (IR) is developed for stability assessment under active-reactive (PQ) and active power-AC voltage (PV) control strategies. The impacts of Phase-Locked Loop (PLL) and proportional-integral (PI) controllers on system stability are analysed, with a particular focus on quantifying remote converter interactions and delineating stability boundaries across varying network strengths and configurations. Furthermore, time-scale separation effectively simplifies Multi-Voltage Source Converter (MVSC) systems by minimizing inner-loop dynamics. Validation is conducted through frequency response evaluations, IR characterizations, and eigenvalue analyses, demonstrating enhanced accuracy, particularly with the application of lead–lag compensators within the critical 50–250 Hz frequency band. Time-domain simulations further illustrate the adaptability of the proposed models and reduction methodology, providing an effective and computationally efficient tool for stability assessment in converter-dominated power networks. Full article

Accurate teeth segmentation and orientation are fundamental in modern oral healthcare, enabling precise diagnosis, treatment planning, and dental implant design. In this study, we present a comprehensive approach to teeth segmentation and orientation from panoramic X-ray images, leveraging deep-learning techniques. We built an end-to-end instance segmentation network that uses an encoder–decoder architecture reinforced with grid-aware attention gates along the skip connections. We introduce oriented bounding box (OBB) generation through principal component analysis (PCA) for precise tooth orientation estimation. Evaluating our approach on the publicly available DNS dataset, comprising 543 panoramic X-ray images, we achieve the highest Intersection-over-Union (IoU) score of 82.43% and a Dice Similarity Coefficient (DSC) score of 90.37% among compared models in teeth instance segmentation. In OBB analysis, we obtain the Rotated IoU (RIoU) score of 82.82%. We also conduct detailed analyses of individual tooth labels and categorical performance, shedding light on strengths and weaknesses. The proposed model’s accuracy and versatility offer promising prospects for improving dental diagnoses, treatment planning, and personalized healthcare in the oral domain. Full article

With the maturation of ultra-high-voltage direct current (UHVDC) technology, DC grids are taking on a more critical role in power systems. However, their impact on AC grids has become more pronounced, particularly in terms of frequency, short-circuit current level, and power flow control capabilities, which also affects the power supply reliability of the receiving-end grid. To comprehensively evaluate the balance between AC and DC strength at the receiving-end, this paper proposes a multidimensional assessment system that covers grid strength and operational security under various operating conditions. Furthermore, a rationality evaluation model for the AC/DC strong–weak balance relationship is developed based on the entropy weight method, forming a complete evaluation framework for assessing the AC/DC strong–weak balance in the receiving-end power grid. Finally, to address strength imbalances in grid, a structural optimization method for the receiving-end grid is designed by combining network decoupling techniques with modular multilevel converter-based HVDC (MMC–HVDC), serving as a strategy for enhancing grid stability. The proposed strategy is validated through simulations in a typical test system using PSD-BPA, demonstrating its effectiveness in optimizing power flow characteristics, improving system stability, reducing the risk of short-circuit current overloads and large-scale blackouts, and maintaining efficient system operation. Full article