Search Results (139)

This study proposes a hybrid forecasting framework that integrates the Auto-Regressive Integrated Moving Average (ARIMA) model with multiple variations of the Ata model, using a performance-based model selection strategy to enhance forecasting accuracy on the M3 and M4 competition datasets. For each time series, seven versions of the Ata model are generated by adjusting level and trend parameters, and the version with the lowest in-sample symmetric mean absolute percentage error (sMAPE) is selected. To improve robustness and prevent overfitting, the median-performing Ata model is also included. These selected models’ forecasts are then combined with ARIMA outputs through optimized weighting schemes tailored to the characteristics of each series. Given the varying frequencies (e.g., yearly, quarterly, monthly, weekly, daily, and hourly) and diverse lengths of time series, a grid search algorithm is employed to determine the best hybrid combination for each frequency group. The model is applied in a series-specific manner, allowing it to adapt to different seasonal, trend, and irregular patterns. Extensive empirical results demonstrate that the hybrid model outperforms its individual components and traditional benchmarks across all frequency categories. It ranked first in the M3 competition and achieved second place in the M4 competition based on the official error metric, the sMAPE and Overall Weighted Average (OWA), respectively. The results highlight the framework’s adaptability and scalability for complex, heterogeneous time series environments. Full article

This study introduces a memristive Hopfield neural network (M-HNN) model to investigate electromagnetic radiation impacts on neural dynamics in complex electromagnetic environments. The proposed framework integrates a magnetic flux-controlled memristor into a three-neuron Hopfield architecture, revealing significant alterations in network dynamics through comprehensive nonlinear analysis. Numerical investigations demonstrate that memristor-induced electromagnetic effects induce distinctive phenomena, including coexisting attractors, transient chaotic states, symmetric bifurcation diagrams and attractor structures, and constant chaos. The proposed system can generate more than 12 different attractors and extends the chaotic region. Compared with the chaotic range of the baseline Hopfield neural network (HNN), the expansion amplitude reaches 933%. Dynamic characteristics are systematically examined using phase trajectory analysis, bifurcation mapping, and Lyapunov exponent quantification. Experimental validation via a DSP-based hardware implementation confirms the model’s operational feasibility and consistency with numerical predictions, establishing a reliable platform for electromagnetic–neural interaction studies. Full article

Natural ventilation could be established as an effective passive design strategy for increasing air changes per hour in a built environment. Modern air conditioning systems often fail to provide sufficient fresh air, potentially causing health issues for occupants. In contrast, natural ventilation offers an effective alternative for maintaining sufficient indoor air quality in buildings. This study explores the application of grouped airfoil arrays on building façades as an innovative passive design to enhance the air change rate. Numerical simulations were conducted to analyze various airfoil configurations, determining the most effective design for building a façade. Three groups, including symmetrical, semi-symmetrical, and flat-bottomed grouped airfoils, were selected according to their aerodynamic properties and potential impacts on airflow dynamics. For this purpose, a typical high-rise residential building was selected as a case study for field measurement and CFD simulation. The results indicated that symmetrical airfoil arrays could increase the air changes per hour (ACH) up to 23 times per hour with a wind velocity of 0.37 m/s at 10 m above ground, whereas their bidirectional performance ensured stable airflow regardless of wind direction. Although semi-symmetrical airfoil arrays maximize air capture and induce beneficial turbulence, the ACH within a residential unit was boosted up to 16 times per hour under the same outdoor wind velocity conditions. The ACH was 14 times per hour for the flat-bottom airfoils, serving as a comparative baseline and providing insights into the performance advantages of more complex designs. Full article

Vertical slot fishways are a crucial measure to mitigate the blockage of fish migration caused by hydraulic engineering infrastructures, but their passage efficiency is often hindered by the complex interactions between fish behavior and hydrodynamic conditions. This study combines computational fluid dynamics (CFD) simulations with behavioral laboratory experiments to identify the hydrodynamic characteristics and swimming strategies of three types of fishways—Central Orifice Vertical Slot (COVS), Standard Vertical Slot (SVS), and L-shaped Vertical Slot (LVS)—using the endangered species Schizothorax prenanti from the upper Yangtze River as the study subject. The results revealed that (1) a symmetric and stable flow field was formed in the COVS structure, yet the passage ratio was the lowest (50%); in the SVS structure, high turbulent kinetic energy (peak of 0.03 m²/s²) was generated, leading to a significant increase in the fish’s tail-beat angle and amplitude (p < 0.01), with the passage time extending to 10.2 s. (2) The LVS structure induced a controlled vortex formation and created a reflux zone with low turbulent kinetic energy, facilitating a “wait-and-surge” strategy, which resulted in the highest passage ratio (70%) and the shortest passage time (6.1 s). (3) Correlation analysis revealed that flow velocity was significantly positively correlated with absolute swimming speed (r = 0.80), turbulent kinetic energy, and tail-beat parameters (r > 0.68). The LVS structure achieved the highest passage ratio and shortest transit time for Schizothorax prenanti, demonstrating its superior functionality for upstream migration. This design balances hydrodynamic complexity with low-turbulence refuge zones, providing a practical solution for eco-friendly fishways. Full article

Anti-resonant hollow-core fibers have exhibited excellent performance in applications such as high-power pulse transmission, network communication, space exploration, and precise sensing. Employing anti-resonant hollow-core fibers instead of light guiding arms for transmitting laser energy at the 2.79 μm band can significantly enhance the flexibility of medical laser handles, reduce system complexity, and increase laser transmission efficiency. Nevertheless, common anti-resonant hollow-core fibers do not have the ability to maintain the polarization state of light during laser transmission, which greatly affects their practical applications. In this paper, we propose a polarization-maintaining anti-resonant hollow-core fiber applicable for transmission at the mid-infrared 2.79 μm band. This fiber features a symmetrical geometric structure and an asymmetric refractive index cladding composed of quartz and a type of mid-infrared glass with a higher refractive index. Through optimizing the fiber structure at the wavelength scale, single-polarization transmission can be achieved at the 2.79 μm wavelength, with a polarization extinction ratio exceeding 1.01 × 10⁵, indicating its stable polarization-maintaining performance. Simultaneously, it possesses low-loss transmission characteristics, with the loss in the x-polarized fundamental mode being less than 9.8 × 10⁻³ dB/m at the 2.79 µm wavelength. This polarization-maintaining anti-resonant hollow-core fiber provides a more reliable option for the light guiding system of the 2.79 μm Er; Cr: YSGG laser therapy device. Full article

Fire detection primarily relies on sensors such as smoke detectors, heat detectors, and flame detectors. However, due to cost constraints, it is impractical to deploy such a large number of sensors for fire detection in outdoor gardens and landscapes. To address this challenge and aiming to enhance fire detection accuracy in gardens while achieving lightweight design, this paper proposes an improved symmetry SSS-YOLOv8 model for lightweight fire detection in garden video surveillance. Firstly, the SPDConv layer from ShuffleNetV2 is used to preserve flame or smoke information, combined with the Conv_Maxpool layer to reduce computational complexity. Subsequently, the SE module is introduced into the backbone feature extraction network to enhance features specific to fire and smoke. ShuffleNetV2 and the SE module are configured into a symmetric local network structure to enhance the extraction of flame or smoke features. Finally, WIoU is introduced as the bounding box regression loss function to further ensure the detection performance of the symmetry SSS-YOLOv8 model. Experimental results demonstrate that the improved symmetry SSS-YOLOv8 model achieves precision and recall rates for garden flame and smoke detection both exceeding 0.70. Compared to the YOLOv8n model, it exhibits a 2.1 percentage point increase in mAP, while its parameter is only 1.99 M, reduced to 65.7% of the original model. The proposed model demonstrates superior detection accuracy for garden fires compared to other YOLO series models of the same type, as well as different types of SSD and Faster R-CNN models. Full article

Conventional image encryption schemes struggle to meet the high security demands of medical images due to their large data volume, strong pixel correlation, and structural redundancy. To address these challenges, we propose a grayscale medical image encryption algorithm based on a novel 5-D memristor chaotic system. The algorithm integrates a Symmetric L-type Josephus Spiral Scrambling (SLJSS) module and a Dynamic Codon-based Multi-RNA Diffusion (DCMRD) module to enhance spatial decorrelation and diffusion complexity. Simulation results demonstrate that the proposed method achieves near-ideal entropy (e.g., 7.9992), low correlation (e.g., 0.0043), and high robustness (e.g., NPCR: 99.62%, UACI: 33.45%) with time complexity of $\mathcal{O}\left(11MN\right)$, confirming its effectiveness and efficiency for medical image protection. Full article

Aiming to enable intelligent vehicles to achieve autonomous charging under low-battery conditions, this paper presents a navigation system for autonomous charging that integrates an improved bidirectional A* algorithm for path planning and an optimized YOLOv11n model for visual recognition. The system utilizes the improved bidirectional A* algorithm to generate collision-free paths from the starting point to the charging area, dynamically adjusting the heuristic function by combining node–target distance and search iterations to optimize bidirectional search weights, pruning expanded nodes via a greedy strategy and smoothing paths into cubic Bézier curves for practical vehicle motion. For precise localization of charging areas and piles, the YOLOv11n model is enhanced with a CAFMFusion mechanism to bridge semantic gaps between shallow and deep features, enabling effective local–global feature fusion and improving detection accuracy. Experimental evaluations in long corridors and complex indoor environments showed that the improved bidirectional A* algorithm outperforms the traditional improved A* algorithm in all metrics, particularly in that it reduces computation time significantly while maintaining robustness in symmetric/non-symmetric and dynamic/non-dynamic scenarios. The optimized YOLOv11n model achieves state-of-the-art precision (P) and mAP@0.5 compared to YOLOv5, YOLOv8n, and the baseline model, with a minor 0.9% recall (R) deficit compared to YOLOv5 but more balanced overall performance and superior capability for small-object detection. By fusing the two improved modules, the proposed system successfully realizes autonomous charging navigation, providing an efficient solution for energy management in intelligent vehicles in real-world environments. Full article

Ship detection in complex environments presents challenges such as sea surface reflections, wave interference, variations in illumination, and a range of target scales. The interaction between symmetric ship structures and wave patterns challenges conventional algorithms, particularly in maritime wireless networks. This study presents YOLO-StarLS (You Only Look Once with Star-topology Lightweight Ship detection), a detection framework leveraging wavelet transforms and multi-scale feature extraction through three core modules. We developed a Wavelet Multi-scale Feature Extraction Network (WMFEN) utilizing adaptive Haar wavelet decomposition with star-topology extraction to preserve multi-frequency information while minimizing detail loss. We introduced a Cross-axis Spatial Attention Refinement module (CSAR), which integrates star structures with cross-axis attention mechanisms to enhance spatial perception. We constructed an Efficient Detail-Preserving Detection head (EDPD) combining differential and shared convolutions to enhance edge detection while reducing computational complexity. Evaluation on the SeaShips dataset demonstrated YOLO-StarLS achieved superior performance for both mAP50 and mAP50–95 metrics, improving by 2.21% and 2.42% over the baseline YOLO11. The approach achieved significant efficiency, with a 36% reduction in the number of parameters to 1.67 M, a 34% decrease in complexity to 4.3 GFLOPs, and an inference speed of 162.0 FPS. Comparative analysis against eight algorithms confirmed the superiority in symmetric target detection. This work enhances real-time ship detection and provides foundations for maritime wireless surveillance networks. Full article

Accurate settlement prediction for high-speed railway (HSR) soft foundations remains challenging due to the irregular and dynamic nature of real-world monitoring data, often represented as non-equidistant and non-stationary time series (NENSTS). Existing empirical models lack clear applicability criteria under such conditions, resulting in subjective model selection. This study introduces a Monte Carlo-based evaluation framework that integrates data-driven simulation with geotechnical principles, embedding the concept of symmetry across both modeling and assessment stages. Equivalent permeability coefficients (EPCs) are used to normalize soil consolidation behavior, enabling the generation of a large, statistically robust dataset. Four empirical settlement prediction models—Hyperbolic, Exponential, Asaoka, and Hoshino—are systematically analyzed for sensitivity to temporal features and resistance to stochastic noise. A symmetry-aware comprehensive evaluation index (CEI), constructed via a robust entropy weight method (REWM), balances multiple performance metrics to ensure objective comparison. Results reveal that while settlement behavior evolves asymmetrically with respect to EPCs over time, a symmetrical structure emerges in model suitability across distinct EPC intervals: the Asaoka method performs best under low-permeability conditions (EPC ≤ 0.03 m/d), Hoshino excels in intermediate ranges (0.03 < EPC ≤ 0.7 m/d), and the Exponential model dominates in highly permeable soils (EPC > 0.7 m/d). This framework not only quantifies model robustness under complex data conditions but also formalizes the notion of symmetrical applicability, offering a structured path toward intelligent, adaptive settlement prediction in HSR subgrade engineering. Full article

Energy-aware scheduling plays a critical role in modern computing and manufacturing systems, where energy consumption often increases with job execution order or resource usage intensity. This study investigates a scheduling problem in which a sequence of heterogeneous jobs—classified as either heavy or light—must be assigned to multiple identical machines with monotonically increasing slot costs. While the machines are structurally symmetric, the fixed job order and cost asymmetry introduce significant challenges for optimal job allocation. We formulate the problem as an integer linear program and simplify the objective by isolating the cumulative cost of heavy jobs, thereby reducing the search for optimality to a position-based assignment problem. To address this challenge, we propose a structured assignment model termed monotonic machine assignment, which enforces index-based job distribution rules and restores a form of functional symmetry across machines. We prove that any feasible assignment can be transformed into a monotonic one without increasing the total energy cost, ensuring that the global optimum lies within this reduced search space. Building on this framework, we first present a general dynamic programming algorithm with complexity $O\left(n^2{m}^2\right)$. More importantly, by introducing a structural correction scheme based on misaligned assignments, we design an iterative refinement algorithm that achieves global optimality in only $O\left(n{m}^2\right)$ time, offering significant scalability for large instances. Our results contribute both structural insight and practical methods for optimal, position-sensitive, energy-aware scheduling, with potential applications in embedded systems, pipelined computation, and real-time operations. Full article

To address the common issues of high small object miss rates, frequent false positives, and poor real-time performance in PCB defect detection, this paper proposes a multi-scale fusion algorithm based on the YOLOv12 framework. This algorithm integrates the Global Attention Mechanism (GAM) into the redesigned A2C2f module to enhance feature response strength of complex objects in symmetric regions through global context modeling, replacing conventional convolutions with hybrid weighted downsampling (HWD) modules that preserve copper foil textures in PCB images via hierarchical weight allocation. A bidirectional feature pyramid network (BiFPN) is constructed to reduce bounding box regression errors for micro-defects by fusing shallow localization and deep semantic features, employing a parallel perception attention (PPA) detection head combining dense anchor distribution and context-aware mechanisms to accurately identify tiny defects in high-density areas, and optimizing bounding box regression using a normalized Wasserstein distance (NWD) loss function to enhance overall detection accuracy. The experimental results on the public PCB dataset with symmetrically transformed samples demonstrate 85.3% recall rate and 90.4% mAP@50, with AP values for subtle defects like short circuit and spurious copper reaching 96.2% and 90.8%, respectively. Compared to the YOLOv12n, it shows an 8.7% enhancement in recall, a 5.8% increase in mAP@50, and gains of 16.7% and 11.5% in AP for the short circuit and spurious copper categories. Moreover, with an FPS of 72.8, it outperforms YOLOv5s, YOLOv8s, and YOLOv11n by 12.5%, 22.8%, and 5.7%, respectively, in speed. The improved algorithm meets the requirements for high-precision and real-time detection of multi-category PCB defects and provides an efficient solution for automated PCB quality inspection scenarios. Full article

Pollen allergy has emerged as a critical global health challenge. Proactive pollen monitoring is imperative for safeguarding susceptible populations through timely preventive interventions. Current manual detection methods suffer from inherent limitations: notably, suboptimal accuracy and delayed response times, which hinder effective allergy management. Therefore, we present an automated pollen concentration detection system integrated with a novel GGD-YOLOv8n model (Ghost-generalized-FPN-DualConv-YOLOv8), which was specifically designed for allergenic pollen species identification. The methodological advancements comprise three components: (1) combining the C2f convolution in Backbone with the G-Ghost module, this module generates features through half-convolution operations and half-symmetric linear operations, enhancing the extraction and expression capabilities of detailed feature information. (2) The conventional neck network is replaced with a GFPN architecture, facilitating cross-scale feature aggregation and refinement. (3) Standard convolutional layers are substituted with DualConv, thereby reducing model complexity by 22.6% (parameters) and 22% GFLOPs (computational load) while maintaining competitive detection accuracy. This systematic optimization enables efficient deployment on edge computing platforms with stringent resource constraints. The experimental validation substantiates that the proposed methodology outperforms the baseline YOLOv8n model, attaining a 5.4% increase in classification accuracy accompanied by a 4.7% enhancement in mAP@50 metrics. When implemented on Jetson Nano embedded platforms, the system demonstrates computational efficiency with an inference latency of 364.9 ms per image frame, equating to a 22.5% reduction in processing time compared to conventional implementations. The empirical results conclusively validate the dual superiority in detecting precision and operational efficacy when executing microscopic pollen image analysis on resource-constrained edge computing devices; they establish a feasible algorithm framework for automated pollen concentration monitoring systems. Full article

Existing remote sensing scene classification (RSSC) models mainly rely on convolutional neural networks (CNNs) to extract high-level features from remote sensing images, while neglecting the importance of low-level features. To address this, we propose a novel RSSC framework, LGNet, a lightweight model with a symmetric dual-branch architecture that combines Lie group-based feature extraction with an innovative multi-dimensional cross-attention mechanism. By utilizing the Lie group feature covariance matrix in one branch, the model captures the low-level features of the image while extracting the high-level semantic information using CNNs in the parallel branch. The dynamic fusion of these multi-scale features using the attention mechanism optimizes the classification accuracy and computational efficiency. Extensive experiments on three standard datasets (AID, UC Merced, and NWPU-RESISC45) show that LGNet outperforms current state-of-the-art models, providing superior classification performance with significantly fewer parameters. Verified on publicly available challenging datasets, LGNet achieves a classification accuracy of 96.50% with 4.71M parameters on AID. Compared with other state-of-the-art models, it has certain advantages regarding classification accuracy and parameter count. These results highlight the efficiency and effectiveness of LGNet in complex remote sensing scenarios, making it a promising solution for large-scale high-resolution remote sensing images (HRSSI) tasks. Full article

Detecting aluminum defects in industrial environments presents significant challenges related to low-resolution images, subtle damage features, and an imbalance between easy and difficult samples. The You Only Look Once–Aluminum (YOLO-AL) algorithm proposed in this paper addresses these challenges. Firstly, to enhance the model’s performance on low-resolution images and small object detection, as well as to improve its flexibility and adaptability, C2f-US replaces the first two CSP bottleneck with 2 Convolutions (C2f) layers in the original Backbone network. Secondly, to boost multi-scale context capture and strip defect detection, a CPMSCA mechanism with a class-symmetric structure is proposed and integrated at the end of the Backbone network. Thirdly, to efficiently capture both high-level semantics and low-level spatial details, and improve detection of complex aluminum surface defects, ODE-RepGFPN is introduced to replace the entire Neck network. Finally, to address the imbalance between hard and easy samples, Focaler-WIoU is proposed. Extensive experiments conducted on the publicly available AliCloud dataset (APDDD) demonstrate that YOLO-AL achieves 86.5%, 77.8%, and 81.5% for Precision, Recall, and mAP@0.5, respectively, surpassing both the baseline model and other state-of-the-art methods. The model can be integrated with an industrial camera system for the automated inspection of aluminum profiles in a production environment. Full article