Search Results (272)

Accurate localization of voltage sag sources is crucial for maintaining reliable and stable operation in microgrids with high penetration of distributed generation (DG). However, the complex topology, bidirectional and time-varying power flows, and measurement uncertainty make it difficult for these conventional model-based approaches to achieve high accuracy. To address these challenges, this paper proposes a two-stage voltage sag source localization method that integrates a data-driven spatio-temporal learning model with a model-based binary search refinement. In the first stage, an improved spatial-temporal graph convolutional network (STGCN) is developed to extract temporal and spatial correlations among voltage and current measurements, enabling section-level localization of sag sources. In the second stage, a binary search–based refinement strategy is applied within the candidate section to iteratively converge on the exact fault location with high precision and robustness. Simulations are conducted on a modified IEEE 33-node system with diverse PV output scenarios, covering combinations of fault types and locations. The results demonstrate that the proposed method maintains stable localization performance under high DG penetration and achieves high accuracy despite multiple fault types and noise interference. Full article

This study was conducted to address the demand for interpretable intelligent recognition of fruit tree diseases in smart horticultural environments. A KAD-Former framework integrating an agricultural knowledge graph with a visual Transformer was proposed and systematically validated through extensive cross-regional, multi-variety, and multi-disease experiments. The primary objective of this work was to overcome the limitations of conventional deep models, including insufficient interpretability, unstable recognition of weak disease features, and poor cross-regional generalization. In the experimental evaluation, the model achieved significant advantages across multiple representative tasks: in the overall performance comparison, KAD-Former reached an accuracy of $0.946$, an F1-score of $0.933$, and a mAP of $0.938$, outperforming classical models such as ResNet50, EfficientNet, and Swin-T. In the cross-regional generalization assessment, a DGS of $0.933$ was obtained, notably surpassing competing models. In terms of explainability consistency, a Consistency@5 score of $0.826$ indicated strong alignment between the model’s attention regions and expert annotations. The ablation experiments further demonstrated that the three core modules—AKG (agricultural knowledge graph), SAM (semantic alignment module), and KGA (knowledge-guided attention)—each contributed substantially to final performance, with the complete model exhibiting the best results. These findings collectively demonstrate the comprehensive advantages of KAD-Former in disease classification, symptom localization, model interpretability, and cross-domain transfer. The proposed method not only achieved state-of-the-art performance in pure visual tasks but also advanced knowledge-enhanced and interpretable reasoning by emulating the diagnostic logic employed by agricultural experts in real orchard scenarios. Through the integration of the agricultural knowledge graph, semantic alignment, and knowledge-guided attention, the model maintained stable performance under challenging conditions such as complex illumination, background noise, and weak lesion features, while exhibiting strong robustness in cross-region and cross-variety transfer tests. Furthermore, the experimental results indicated that the approach enhanced fine-grained recognition capabilities for various fruit tree diseases, including apple ring rot, brown spot, powdery mildew, and downy mildew. Full article

This paper presents a compact four-port multiple-input multiple-output (MIMO) antenna operating in the Ku-band around 13 GHz, targeting early-phase 6G upper-midband front-end applications. The proposed antenna employs orthogonally arranged short-ended square patch elements combined with a modified defected ground structure (DGS) to achieve high port isolation and compact footprint. A prototype fabricated on Rogers RO4350 substrate demonstrates good agreement between simulated and measured results. The antenna achieves $|S_{11}|$ < −10 dB over 12.9–13.1 GHz band, inter-port isolation exceeding 25 dB, and an envelope correlation coefficient (ECC) below $0.01$. The measured realized gain reaches 7.02 dBi with a radiation efficiency above 80%. Compared with recent Ku-band MIMO antennas, the proposed design provides a 45% size reduction while maintaining high isolation at a close element spacing of 0.25${\lambda }_0$. The proposed antenna intentionally adopts a narrowband operating characteristic, reflecting a design trade-off that prioritizes compact size, high isolation, and low spatial correlation over wideband performance. These features make the antenna well suited for early-stage 6G-oriented front-end modules, fixed wireless access, backhaul links, and short-range sensing systems operating in the upper-midband frequency range. Full article

A multistage coordinated expansion-planning framework for distribution systems is developed to jointly optimize investments in the network, distributed generation (DG), and energy storage systems (ESS). Network reinforcements select from multiple feeder and transformer candidates, while DG installations consider conventional and photovoltaic (PV) options. In this study, a set of candidate buses are considered for the installation of PVs and energy storage systems. Therefore, the expansion plan can determine the optimal installation locations and timing of these candidate assets. The objective minimizes total cost in net-present-value terms, covering investment, maintenance, generation, and operating components. Representative hourly load profiles are incorporated to capture ESS dispatch behavior and PV output variability; operating costs are modeled via piecewise linearization. To preserve connectivity and preclude islanding in the presence of DG and ESS, modified radiality constraints are imposed. The formulation is a mixed-integer linear program solvable efficiently by commercial optimizers, and numerical studies confirm the method’s effectiveness. Full article

In response to the challenges of low computational efficiency, insufficient detail restoration, and dependence on multiple GPUs in 3D Gaussian Splatting for large-scale UAV scene reconstruction, this study introduces an improved 3D Gaussian Splatting framework. It primarily targets two aspects: optimization of the partitioning strategy and enhancement of adaptive density control. Specifically, an adaptive partitioning strategy guided by scene complexity is designed to ensure more balanced computational workloads across spatial blocks. To preserve scene integrity, auxiliary point clouds are integrated during partition optimization. Furthermore, a pixel weight-scaling mechanism is employed to regulate the average gradient in adaptive density control, thereby mitigating excessive densification of Gaussians. This design accelerates the training process while maintaining high-fidelity rendering quality. Additionally, a task-scheduling algorithm based on frequency-domain analysis is incorporated to further improve computational resource utilization. Extensive experiments on multiple large-scale UAV datasets demonstrate that the proposed framework can be trained efficiently on a single RTX 3090 GPU, achieving more than a 50% reduction in average optimization time while maintaining PSNR, SSIM and LPIPS values that are comparable to or better than representative 3DGS-based methods; on the MatrixCity-S dataset (>6000 images), it attains the highest PSNR among 3DGS-based approaches and completes training on a single 24 GB GPU in less than 60% of the training time of DOGS. Nevertheless, the current framework still requires several hours of optimization for city-scale scenes and has so far only been evaluated on static UAV imagery with a fixed camera model, which may limit its applicability to dynamic scenes or heterogeneous sensor configurations. Full article

The compact and wideband patch antennas applied to WiFi 7 multiple-input multiple-output (MIMO) antenna systems are presented. The MIMO antenna structure consists of four multi-branch radiating patches fed by coupled microstrip lines, which occupies a size of $32\times 32\times 1 {\mathrm{m}\mathrm{m}}^3$. By exploiting multiple resonant modes, an impedance bandwidth of 37% (5.07–7.37 GHz) achieves a reflection coefficient of less than −10 dB and fully encompasses both WiFi 7 high-frequency ranges. To alleviate mutual coupling, two decoupling structures, named complementary split-ring resonators (CSRRs), are employed between the MIMO elements to interact with the undesirable surface current; furthermore, the proposed orthogonal placement of four elements further minimizes radiation coupling. Consequently, the proposed array achieves measured isolations greater than 14.5 dB and 11 dB at 5 GHz and 6 GHz bands, respectively. The prototype of the proposed MIMO antenna has been manufactured. It has also been measured and the results show similarity with the simulations. The measured radiation pattern and the diversity performance, including the envelope correlation coefficient (ECC), diversity gain (DG), and multiplexing efficiency, are calculated, and they verify the outstanding diversity characteristics of the proposed MIMO antenna. This makes it a promising solution for emerging WiFi 7 wideband applications. Full article

In dense visual simultaneous localization and mapping VSLAM (VSLAM), a fundamental challenge lies in the inability of existing loss functions to dynamically balance luminance, contrast, and structural fidelity under photometric variations, while their underlying mechanisms, particularly the conventional Gaussian kernel in SSIM, suffer from limited receptive fields due to rapid exponential decay, preventing the capture of long-range dependencies essential for global consistency. To address this, we propose a fractional Gaussian field (FGF) that synergizes Caputo derivatives with Gaussian weighting, creating a hybrid kernel that couples power-law decay for long-range memory with local smoothness. This foundational kernel serves as the core component of FGF-SSIM, a novel loss function that adaptively recalibrates luminance, contrast, and structure using fractional-order statistics. The proposed FGF-SSIM is further integrated into a complete 3D Gaussian Splatting (3DGS)-based SLAM system, named FGF-SLAM, where it is employed across both tracking and mapping modules to enhance performance. Extensive evaluations demonstrate state-of-the-art performance across multiple benchmarks. Comprehensive analysis confirms the superior long-range dependency of the fractional kernel, dedicated illumination robustness tests validate the enhanced invariance of FGF-SSIM, and quantitative results on TUM and Replica datasets show significant improvements in reconstruction quality and trajectory estimation. Ablation studies further substantiate the contribution of each proposed component. Full article

The transition to electric vehicles (EVs) is pivotal for decarbonizing transport, yet the siting of EV charging stations (EVCSs) can load radial distribution networks with higher losses and more pronounced voltage drops. This study formulates the joint siting and sizing of EVCSs and distributed generators (DGs) as a constrained optimization that minimizes real and reactive losses and voltage deviation with integer bus location decisions. A novel version of the Artificial Bee Colony (ABC) algorithm known as GBest–Lévy Adaptive Differential ABC (GLAD-ABC) is introduced, combining global best guidance, differential perturbations, adaptive step sizes, Lévy-flight scouting, and periodic local refinement for finding the global optimum solution and avoiding local optima. The optimizer is coupled with a backward–forward sweep load flow and a EVCS power demand model. Validation on the IEEE-33 and IEEE-69 feeders across multiple scenarios shows that EVCS-only deployment degrades network performance, whereas optimizing EVCS and DG allocation via GLAD-ABC markedly improves voltage profiles and reduces both real and reactive losses. The proposed optimizer shows superior performance compared with other optimization algorithms reported in the literature, delivering consistently lower active losses alongside fast, stable convergence, indicating strong suitability for utility planning in EV-rich grids. Full article

Multiple-Input Multiple-Output (MIMO) antenna systems are key to improving wireless channel capacity and reliability. Yet, their inherent need for compact configurations introduces a significant challenge: electromagnetic coupling between closely placed radiating elements. This undesirable phenomenon diminishes efficiency, increases signal correlation, and compromises electromagnetic isolation. To mitigate these issues, researchers have proposed diverse isolation techniques, such as Defected Ground Structures (DGS), metamaterials, fractal geometries, and neutralization lines. These techniques are crucial for boosting isolation and facilitating antenna miniaturization without compromising overall electromagnetic performance, making them indispensable for modern compact communication systems. This article provides a comprehensive review of these techniques, dissecting their fundamental operating principles and analyzing the electromagnetic isolation results previously documented in the literature. Furthermore, experimental findings derived from the fabrication and characterization of prototypes, aiming to confirm the practical efficacy of these isolation methods, are presented. Full article

This study introduces a versatile seed 3D reconstruction method that is applicable to multiple crops—including maize, wheat, and rice—and designed to overcome the inefficiency and subjectivity of manual measurements and the high costs of laser-based phenotyping. A panoramic video of the seed is captured and processed through frame sampling to extract multi-view images. Structure-from-Motion (SFM) is employed for sparse reconstruction and camera pose estimation, while 3D Gaussian Splatting (3DGS) is utilized for high-fidelity dense reconstruction, generating detailed point cloud models. The subsequent point cloud preprocessing, filtering, and segmentation enable the extraction of key phenotypic parameters, including length, width, height, surface area, and volume. The experimental evaluations demonstrated a high measurement accuracy, with coefficients of determination (R²) for length, width, and height reaching 0.9361, 0.8889, and 0.946, respectively. Moreover, the reconstructed models exhibit superior image quality, with peak signal-to-noise ratio (PSNR) values consistently ranging from 35 to 37 dB, underscoring the robustness of 3DGS in preserving fine structural details. Compared to conventional multi-view stereo (MVS) techniques, the proposed method can achieve significantly improved reconstruction accuracy and visual fidelity. The key outcomes of this study confirm that the 3DGS-based pipeline provides a highly accurate, efficient, and scalable solution for digital phenotyping, establishing a robust foundation for its application across diverse crop species. Full article

A hybrid energy system (HES) integrates various energy resources to attain synchronized energy output. However, HES faces significant challenges due to rising energy consumption, the expenses of using multiple sources, increased emissions due to non-renewable energy resources, etc. This study aims to develop an energy management strategy for distribution grids (DGs) by incorporating a hydrogen storage system (HSS) and demand-side management strategy (DSM), through the design of a multi-objective optimization technique. The primary focus is on optimizing operational costs and reducing pollution. These are approached as minimization problems, while also addressing the challenge of achieving a high penetration of renewable energy resources, framed as a maximization problem. The third objective function is introduced through the implementation of the demand-side management strategy, aiming to minimize the energy gap between initial demand and consumption. This DSM strategy is designed around consumers with three types of loads: sheddable loads, non-sheddable loads, and shiftable loads. To establish a bidirectional communication link between the grid and consumers by utilizing a distribution grid operator (DGO). Additionally, the uncertain behavior of wind, solar, and demand is modeled using probability distribution functions: Weibull for wind, PDF beta for solar, and Gaussian PDF for demand. To tackle this tri-objective optimization problem, this work proposes a hybrid approach that combines well-known techniques, namely, the non-dominated sorting genetic algorithm II and multi-objective particle swarm optimization (Hybrid-NSGA-II-MOPSO). Simulation results demonstrate the effectiveness of the proposed model in optimizing the tri-objective problem while considering various constraints. Full article

Mesenchymal stem cells (MSCs) exhibit therapeutic properties, which have been attributed to their secretome, the set of secreted factors comprising cytokines, growth factors, and extracellular vesicles (EVs). In particular, small extracellular vesicles (sEVs) or exosomes, ranging between 30 nm and 120 nm in diameter, can target specific tissues to deliver molecular payloads, thus lending themselves as promising platform for cell-free therapies. In this study, sEVs were purified from the conditioned medium (CM) harvested from human bone marrow-derived MSC culture and purified using size-exclusion chromatography (SEC) or density gradient ultracentrifugation (DG-UC). Then sEVs were fully characterized for identity and integrity using multiple analytical methods, including single-particle, transcriptomic and proteomic analyses. Different in vitro cell-based assays were established to evaluate the biological effects of the purified sEVs. Specifically, scratch wound healing and tube formation assays using human umbilical vein endothelial cells (HUVECs) were used to evaluate the regenerative properties of MSC-sEVs. Our findings demonstrated that the in vitro functional properties of MSC-sEVs are correlated with sEVs’ purity levels obtained by different purification methods. Full article

Recent advances in 3D Gaussian Splatting (3DGS) have achieved remarkable performance in scene reconstruction and novel view synthesis on benchmark datasets. However, real-world images are frequently affected by degradations such as camera shake, object motion, and lens defocus, which not only compromise image quality but also severely hinder the accuracy of 3D reconstruction—particularly in fine details. While existing deblurring approaches have made progress, most are limited to addressing a single type of blur, rendering them inadequate for complex scenarios involving multiple blur sources and resolution degradation. To address these challenges, we propose Gaussian Splatting with Multi-Scale Deblurring and Resolution Enhancement (GS-MSDR), a novel framework that seamlessly integrates multi-scale deblurring and resolution enhancement. At its core, our Multi-scale Adaptive Attention Network (MAAN) fuses multi-scale features to enhance image information, while the Multi-modal Context Adapter (MCA) and adaptive spatial pooling modules further refine feature representation, facilitating the recovery of fine details in degraded regions. Additionally, our Hierarchical Progressive Kernel Optimization (HPKO) method mitigates ambiguity and ensures precise detail reconstruction through layer-wise optimization. Extensive experiments demonstrate that GS-MSDR consistently outperforms state-of-the-art methods under diverse degraded scenarios, achieving superior deblurring, accurate 3D reconstruction, and efficient rendering within the 3DGS framework. Full article

In the era of big data and rapid information growth, investors encounter a complex financial environment characterized by extensive data, conflicting investment objectives, and markets that are unpredictable due to economic and policy fluctuations. Hence, asset selection is vital for both investors and researchers. Multi-objective optimization algorithms balance multiple objectives to find optimal solutions and are widely used in engineering, economics, etc. This paper proposes a multi-objective decomposition optimization algorithm integrated with differential grouping (DG-MOEA/D). Initially, the algorithm employs the recursive spectral clustering differential grouping (RDGSC) technique to identify dependencies among variables, grouping them to reduce interactions between the variables. It then uses MOEA/D-UTEA to optimize each group, with an external archive for storing and updating solutions. Experimental results on the DTLZ and LSMOP test functions show that the DG-MOEA/D algorithm greatly outperforms the other seven comparison algorithms. When used in real-world scenarios like stock and bond asset allocation, the algorithm continues to outperform other methods, demonstrating its significant advantages in practical applications. Full article

A compact fork-shaped four-element multiple-input multiple-output (MIMO) antenna system with wide bandwidth for 5G millimeter-wave (mmWave) applications is presented. The antenna elements are arranged orthogonally to achieve a compact footprint of $20\times 26{\mathrm{mm}}^2$. To enhance the gain, a frequency selective surface (FSS) is placed above the MIMO system, providing an average gain improvement of 1.5 dB across the entire operating band and achieving a peak gain of 7.5 dB at 41 GHz. The proposed design operates in the Ka-band (22–46 GHz), making it well suited for 5G communications. The antenna exhibits an isolation greater than 20 dB and radiation efficiency exceeding 80% across the band. Moreover, key MIMO performance metrics, including diversity gain (DG ≈ 10) and envelope correlation coefficient (ECC < 0.05), meet the required standards. A prototype of the proposed system was fabricated and measured, with the experimental results showing good agreement with simulations. Full article