Search Results (307)

The rational placement of pollutant monitoring sensors has long been a prominent research focus in ocean environment science. Our method integrates an incremental Proper Orthogonal Decomposition with a mobility-constrained sensor selection strategy, enabling efficient monitoring and dynamic adaptation to spatio-temporal field changes. At each time step, the position of the sensors is updated based on the incoming measurements to minimize the reconstruction error while adhering to movement constraints. This online approach considers the need for mobility distance, making it suitable for long-term deployments in resource-limited scenarios. The proposed framework is validated in three scenarios: a linear advection–diffusion system with multiple moving pollution sources, the distribution of particulate matter with an aerodynamic diameter smaller than 2.5 μm (PM2.5) across the United States, and scalar transport in flows past side-by-side cylinder arrays in the ocean. The results demonstrate that the method achieves high reconstruction accuracy with significantly fewer sensors. This study conducts a comparative analysis of three typical mobility constraints and their respective effects on reconstruction accuracy. In addition, the proposed localized sensor mobility strategy effectively tracks evolving plume structures and maintains a low approximation error, providing a generalizable solution for sparse monitoring of the marine environment. Full article

Producing solid biofuels with high calorific value and high storage stability under limited energy consumption has become a crucial focus in the global energy field. Low temperature torrefaction below 300 °C is a common method for producing solid biofuels. However, this approach limits the carbon content and higher heating value (HHV) of the resulting biochar. Sodium percarbonate is a solid oxidant that can assist in the pyrolysis of organic molecules during the torrefaction to increase carbon content of biochar. Incorporating sodium percarbonate as a strategic additive presents a viable means to address the constraints associated with the torrefaction technologies. This study blended sodium percarbonate with spent coffee grounds (SCGs) to prepare torrefied SCG solid biofuels with high calorific value and high carbon content. Based on the Taguchi method with L9 orthogonal arrays, torrefaction temperature is identified as the most influential factor affecting higher heating value (HHV). Results from FTIR, water activity, hygroscopicity, and mold observation confirmed that torrefied SCGs blended with 0.5 wt% sodium percarbonate (0.5TSSCG) exhibited good storage stability. They were not prone to mold growth under ambient temperature and pressure. 0.5TSSCG with a carbon content of 61.88 wt% exhibited a maximum HHV of 29.42 MJ∙kg⁻¹. These findings indicate that sodium percarbonate contributes to increasing the carbon content and HHV of torrefied SCGs, enabling partial replacement of traditional coal consumption. Full article

The analysis of dynamic remote sensing scenes from unmanned aerial vehicles (UAVs) is shifting from reactive processing to proactive, predictive intelligence. Central to this evolution is video prediction—forecasting future imagery from past observations—which enables critical remote sensing applications like persistent environmental monitoring, occlusion-robust object tracking, and infrastructure anomaly detection under challenging aerial conditions. Yet, a systematic review of video prediction models tailored for the unique constraints of aerial remote sensing has been lacking. Existing taxonomies often obscure key design choices, especially for emerging operators like state-space models (SSMs). We address this gap by proposing a unified, multi-dimensional taxonomy with three orthogonal axes: (i) operator architecture; (ii) generative nature; and (iii) training/inference regime. Through this lens, we analyze recent methods, clarifying their trade-offs for deployment on UAV platforms that demand processing of high-resolution, long-horizon video streams under tight resource constraints. Our review assesses the utility of these models for key applications like proactive infrastructure inspection and wildlife tracking. We then identify open problems—from the scarcity of annotated aerial video data to evaluation beyond pixel-level metrics—and chart future directions. We highlight a convergence toward scalable dynamic world models for geospatial intelligence, which leverage physics-informed learning, multimodal fusion, and action-conditioning, powered by efficient operators like SSMs. Full article

This study addresses the challenge of cognitive waveform design for multiple-input–multiple-output (MIMO) radar systems operating in cluttered environments. It focuses on the key practical requirements for transmitting time-domain waveforms and proposes a novel approach. This method first determines the optimal frequency-domain waveform and then designs a time-domain waveform that closely approximates the frequency-domain solution. The primary objective is to enable MIMO radar systems to transmit orthogonal waveforms while accommodating various constraints. A frequency-domain waveform optimization model was initially developed using the principle of maximizing dual mutual information (DMI), and the energy spectral density (ESD) of the optimal waveform was derived using the water-filling method. Next, a time-domain waveform approximation model is constructed based on the minimum mean square error (MMSE) criterion, which incorporates constant modulus and peak-to-average power ratio (PAPR) constraints. To minimize the performance degradation of the waveform, an improved adaptive gradient descent genetic algorithm (GD-AGA) was proposed to synthesize multichannel orthogonal time-domain waveforms for MIMO radars. The simulation results demonstrate the effectiveness of the proposed model for enhancing the performance of MIMO radar. Compared with traditional genetic algorithms (GA) and two enhanced GA alternatives, the proposed algorithm achieves a lower ESD loss and better orthogonal performance. Full article

To address low training efficiency and poor reconstruction in traditional K Singular Value Decomposition (KSVD) for compressive sensing of shock wave signals, this study proposes an improved algorithm, DH-KSVD, integrating dynamic pruning and hybrid coding. The dynamic pruning mechanism eliminates redundant atoms according to their contributions and adaptive thresholds, while incorporating residual features to enhance dictionary compactness and training efficiency. The hybrid sparse constraint integrates the sparsity of ${\ell }_0$-Orthogonal Matching Pursuit (OMP) with the noise robustness of ${\ell }_1$-Least Absolute Shrinkage and Selection Operator (LASSO), dynamically adjusting their relative weights to enhance both coding quality and reconstruction stability. Experiments on typical shock wave datasets show that, compared with Discrete Cosine Transform (DCT), KSVD, and feature-based segmented dictionary methods (termed CC-KSVD), DH-KSVD reduces average training time by 46.4%, 31%, and 13.7%, respectively. At a Compression Ratio (CR) of 0.7, the Root Mean Square Error (RMSE) decreases by 67.1%, 65.7%, and 36.2%, while the Peak Signal-to-Noise Ratio (PSNR) increases by 35.5%, 39.8%, and 11.8%, respectively. The proposed algorithm markedly improves training efficiency and achieves lower RMSE and higher PSNR under high compression ratios, providing an effective solution for compressing long-duration, transient shock wave signals. Full article

A spacecraft is a typical rigid–flexible–thermal coupled multibody system, and the study of such rigid–flexible–thermal coupled systems has important engineering significance. The dissipation effect of material damping has a significant impact on the response of multibody system dynamics. Owing to the increasing multitude of computational dimensions, computational efficiency has remained a significant bottleneck hindering their practical applications in engineering. However, due to the fact that the stiffness matrix is a highly nonlinear function of generalized coordinates, traditional methods of modal truncation are difficult to apply directly. In this study, the absolute nodal coordinate formulation (ANCF) is used to uniformly describe the modeling of rigid–flexible–thermal coupled multibody systems with large-scale motion and deformation. The constant tangent stiffness matrix and damping matrix can be obtained by locally linearizing the dynamic equation and heat transfer equations, which are based on the Taylor expansion. The dynamic and heat transfer equations obtained by reducing the order of complex modes are transformed into a unified first-order equation, which is solved simultaneously. The orthogonal complement matrix of the constraint equation is proposed to eliminate the nonlinear constraints. A strategy based on energy preservation was proposed to update the reduced-order basis vectors, which improved the calculation accuracy and efficiency. Finally, a systematic method for rigid–flexible–thermal coupled viscoelastic multibody systems via modal truncation with complex global modes is developed. Full article

High-performance deep learning models require extensive computational resources and datasets, making their ownership protection a pressing concern. To address this challenge, we focus on advancing model security through robust watermarking mechanisms. In this work, we propose a novel deep neural network watermarking method that embeds ownership information directly within the image feature space. Unlike existing approaches that often suffer from low embedding success rates and significant performance degradation, our method leverages convolutional kernels with orthogonal preferences to extract multiperspective features, which are then linearly mapped at the output layer for watermark embedding. Furthermore, we introduce an orthogonal regularization constraint into the loss function to increase the watermark robustness. This constraint enforces orthogonality in both convolutional and fully connected layer weights, suppresses redundancy in hidden layer representations, and minimizes interference between the watermark and the model’s original feature space. Through these innovations, we significantly improve the embedding reliability and preserve model integrity. Experimental results obtained on ResNet-18 and ResNet-101 demonstrate a 100% watermark detection rate with less than 1% performance impact, underscoring the practical security value of our approach. Comparative analysis further validates that our method achieves superior harmlessness and effectiveness relative to state-of-the-art techniques. These contributions highlight the role of our work in strengthening intellectual property protection and the trustworthy deployment of deep learning models. Full article

Few-shot object detection methods perform well in natural scenes, where meta-learners can effectively extract target features from limited support data. However, PCB defect detection faces unique challenges: scarce defect samples, low-resolution targets, and severe overfitting risks. Additionally, PCBs’ dense circuitry creates low-contrast defects that blend into background noise, while traditional meta-learning struggles to generate realistic synthetic defects under actual manufacturing constraints. To overcome these limitations, we propose SM-FSOD, a second-order meta-learning model featuring a defect-aware prototype network. Unlike conventional approaches, it dynamically emphasizes critical defect features when constructing class prototypes from few-shot samples. Our extensive few-shot experiments on the DeepPCB and DsPCBSD+ datasets demonstrate the performance of the SM-FSOD model. Comparative tests on the DeepPCB dataset show that, compared with the strong Meta-RCNN baseline, our model achieves a maximum performance improvement of 14.0% under the challenging five-shot setting, while still attaining a 7.6% accuracy gain in the more relaxed 50-shot setting. Similarly, evaluation on the DsPCBSD+ dataset reveals that our proposed method maintains an average accuracy improvement of 2.3% to 9.6% compared to the competitive DeFRCN model in complex scenarios, indicating the strong adaptability of SM-FSOD across various application environments. Ablation studies further demonstrate that incorporating the improved MOMP and DGPT modules individually yields average accuracy gains of 3.6% and 4.5%, respectively, under the five-shot setting compared to the baseline, confirming that these enhancements can orthogonally improve the detection precision in few-shot PCB scenarios. Full article

Basalt fiber-reinforced polymer (BFRP) composites are increasingly utilized in photovoltaic mounting systems due to their excellent mechanical properties and durability. Bolted connections, valued for their simplicity, ease of installation, and effective load transfer, are widely employed for joining composite components. An orthogonal experimental design was adopted to investigate the effects of key parameters—including bolt end distance, number of bolts, bolt material, bolt diameter, preload, and connection length—on the load-bearing performance of three bolted BFRP plate configurations: lap joint (DJ), single lap joint (DP), and double lap joint (SP). Test results showed that the DJ connection exhibited the highest average tensile load capacity, exceeding those of the SP and DP connections by 45.3% and 50.2%, respectively. This superiority is attributed to the DJ specimen’s longer effective shear length and greater number of load-bearing bolts. Conversely, the SP connection demonstrated the largest average peak displacement, with increases of 29.7% and 52.9% compared to the DP and DJ connections. The double-sided constraint in the SP configuration promotes more uniform preload distribution and enhances shear deformation capacity. Orthogonal sensitivity analysis further revealed that the number of bolts and preload magnitude significantly influenced the ultimate tensile load capacity across all connection types. Finally, a calculation model for the tensile load capacity of bolted BFRP connections was established, incorporating a friction decay coefficient (α) and shear strength (τ). This model yields calculated errors under 15% and is applicable to shear slip-dominated failure modes, thereby providing a parametric basis for optimizing the tensile design of bolted BFRP joints. Full article

The extraction of architectural structural line features can simplify the 3D spatial representation of built environments, reduce the storage and processing burden of large-scale point clouds, and provide essential geometric primitives for downstream modeling tasks. However, existing 3D line extraction methods suffer from incomplete and fragmented contours, with missing or misaligned intersections. To overcome these limitations, this study proposes a patch-level framework for 3D line extraction and structural optimization from building point clouds. The proposed method first partitions point clouds into planar patches and establishes local image planes for each patch, enabling a structured 2D representation of unstructured 3D data. Then, graph-cut segmentation is proposed to extract compact boundary contours, which are vectorized into closed lines and back-projected into 3D space to form the initial line segments. To improve geometric consistency, regularized geometric constraints, including adjacency, collinearity, and orthogonality constraints, are further designed to merge homogeneous segments, refine topology, and strengthen structural outlines. Finally, we evaluated the approach on three indoor building environments and four outdoor scenes, and experimental results show that it reduces noise and redundancy while significantly improving the completeness, closure, and alignment of 3D line features in various complex architectural structures. Full article

Against the global backdrop of energy transition, the precise assessment of urban rooftop photovoltaic (PV) system capacity is recognized as crucial for optimizing the energy structure and enhancing the sustainable utilization efficiency of spatial resources. Publicly available aerial imagery is characterized by non-orthorectified issues; direct utilization is known to lead to geometric distortions in rooftop PV and errors in capacity prediction. To address this, a dual-optimization framework is proposed in this study, integrating monocular vision-based 3D reconstruction with a lightweight linear model. Leveraging the orthogonal characteristics of building structures, camera self-calibration and 3D reconstruction are achieved through geometric constraints imposed by vanishing points. Scale distortion is suppressed via the incorporation of a multi-dimensional geometric constraint error control strategy. Concurrently, a linear capacity-area model is constructed, thereby simplifying the complexity inherent in traditional multi-parameter fitting. Utilizing drone oblique photography and Google Earth public imagery, 3D reconstruction was performed for 20 PV-equipped buildings in Wuhan City. Two buildings possessing high-precision field survey data were selected as typical experimental subjects for validation. The results demonstrate that the 3D reconstruction method reduced the mean absolute percentage error (MAPE)—used here as an estimator of measurement uncertainty—of PV area identification from 10.58% (achieved by the 2D method) to 3.47%, while the coefficient of determination (R²) for the capacity model reached 0.9548. These results suggest that this methodology can provide effective technical support for low-cost, high-precision urban rooftop PV resource surveys. It has the potential to significantly enhance the reliability of energy planning data, thereby contributing to the efficient development of urban spatial resources and the achievement of sustainable energy transition goals. Full article

Addressing the inefficiency, subjective errors, and limited adaptability of existing methods for surveying complex ancient structures, this study presents an intelligent hierarchical algorithm for generating line drawings guided by structured architectural features. Leveraging point cloud data, our approach integrates prior semantic and structural knowledge of ancient buildings to establish a multi-granularity feature extraction framework encompassing local geometric features (normal vectors, curvature, Simplified Point Feature Histograms-SPFH), component-level semantic features (utilizing enhanced PointNet++ segmentation and geometric graph matching for specialized elements), and structural relationships (adjacency analysis, hierarchical support inference). This framework autonomously achieves intelligent layer assignment, line type/width selection based on component semantics, vectorization optimization via orthogonal and hierarchical topological constraints, and the intelligent generation of sectional views and symbolic annotations. We implemented an algorithmic toolchain using the AutoCAD Python API (pyautocad version 0.5.0) within the AutoCAD 2023 environment. Validation on point cloud datasets from two representative ancient structures—Guanchang No. 11 (Luoyuan County, Fujian) and Li Tianda’s Residence (Langxi County, Anhui)—demonstrates the method’s effectiveness in accurately identifying key components (e.g., columns, beams, Dougong brackets), generating engineering-standard line drawings with significantly enhanced efficiency over traditional approaches, and robustly handling complex architectural geometries. This research delivers an efficient, reliable, and intelligent solution for digital preservation, restoration design, and information archiving of ancient architectural heritage. Full article

ELM is an innovative learning algorithm that minimizes output error by only finding optimal output weights. Meta-learning is composed of base ELMs and exhibits good generalization. To improve its performance further by introducing orthogonal constraints into the base ELMs and “top” ELM, we propose a novel Meta-ELM with orthogonal constraints (Meta-QEC-ELM). Because of the particularity of the Meta-ELM, its orthogonal constraint problem is the quadratic equality constraint problem—that is, a one-column Procrustes problem—and it can preserve much more information from feature space to output subspace. The experimental results show that the Meta-QEC-ELM is both effective and feasible. Full article

Enhancing user access capacity in satellite beam-hopping systems remains challenging due to dynamic traffic and limited beam dwell times. Conventional Multi-Frequency Time-Division Multiple Access (MF-TDMA) proves highly inefficient under such constraints. To overcome this, we propose a novel scheme that integrates power-domain Non-Orthogonal Multiple Access (NOMA) with MF-TDMA, employing Successive Interference Cancelation (SIC) for multi-user signal separation. A bi-directional adaptive carrier synchronization method and optimized burst structure are introduced, which collectively reduce synchronization overhead by over 40% compared to MF-TDMA. Simulations demonstrate a dramatically improved frame error rate of 0.0005% at 4 dB SNR—30 times lower than the 0.016% achieved by MF-TDMA—and a transmission efficiency of 92–97%, significantly outperforming conventional MF-TDMA. These results validate the proposed method’s substantial gains in capacity and efficiency for next-generation satellite systems. Full article

Tool structure design methodologies predominantly rely on trial-and-error approaches or single-objective optimization but fail to achieve coordinated enhancement of multiple performance metrics while lacking thorough investigation into complex cutting coupling mechanisms. This study proposes a multi-objective optimization framework integrating joint simulation approaches. First, a finite element model for orthogonal turning was developed, incorporating the hyperbolic tangent (TANH) constitutive model and variable coefficient friction model. The cutting performance of four micro-groove configurations is comparatively analyzed. Subsequently, parametric modeling coupled with simulation–data interaction enables multi-objective optimization targeting minimized cutting force, reduced cutting temperature, and decreased wear rate. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) explores Pareto-optimized solutions for arc micro-groove geometric parameters. Finally, optimized tools manufactured via powder metallurgy undergo experimental validation. The results demonstrate that the optimized tool achieves significant improvements: a 19.3% reduction in cutting force, a 14.2% decrease in cutting temperature, and tool life extended by 33.3% compared to baseline tools. Enhanced chip control is evidenced by an 11.4% reduction in chip curl radius, accompanied by diminished oxidation/adhesive wear and superior surface finish. This multi-objective optimization methodology effectively overcomes the constraints of conventional single-parameter optimization, substantially improving comprehensive tool performance while establishing a reference paradigm for cutting tool design under complex operational conditions. Full article