Search Results (257)

Accurate phase association and arrival time picking are pivotal for reliable microseismic event location and source characterization. However, the complexity of downhole microseismic wavefields, arising from heterogeneous subsurface structures, variable propagation paths, and ambient noise, poses significant challenges to conventional automatic picking methods, even when the signal-to-noise ratio (SNR) is moderate to high. Specifically, P-wave coda energy can obscure S-wave onsets analysis, and shear wave splitting can generate ambiguous arrivals. In this study, we propose a novel multi-channel arrival picking framework based on Constrained Dynamic Time Warping (CDTW) for phase identification and simultaneous P- and S-wave arrival estimation. The DTW algorithm aligns microseismic signals that may be out of sync due to differences in timing or wave velocity by warping the time axis to minimize cumulative distance. Time delay constraints are imposed to ensure physically plausible alignments and improve computational efficiency. Furthermore, we introduce a Multivariate CDTW approach to jointly process the three-component (3C) data, leveraging inter-component and inter-receiver arrival consistency across the entire downhole array. The method is validated against the Short-Term Average/Long-Term Average (STA/LTA) and waveform cross-correlation techniques using field data from a shale gas hydraulic fracturing. Results demonstrate that the proposed algorithm significantly enhances arrival time accuracy and inter-receiver consistency, particularly in scenarios involving P-wave coda interference and shear wave splitting. Full article

Wafer-level thin-film stress measurement is essential for reliable semiconductor fabrication. However, existing techniques present limitations in practice. Interferometry achieves high precision but at a cost that becomes prohibitive for large wafers. Meanwhile laser-scanning systems are more affordable but can only provide sparse data points. This work develops a phase-measuring deflectometry (PMD) system to bridge this gap and deliver a full-field solution for wafer stress mapping. The implementation addresses three key challenges in adapting PMD. First, screen positioning and orientation are refined using an inverse bundle-adjustment approach, which performs multi-parameter optimization without re-optimizing the camera model and simultaneously uses residuals to quantify screen deformation. Second, a backward-propagation ray-tracing framework benchmarks two iterative strategies to resolve the slope-height ambiguity which is a fundamental challenge in PMD caused by the absence of a fixed optical center on the source side. The reprojection constraint strategy is selected for its superior convergence precision. Third, this strategy is integrated with regional wavefront reconstruction based on Hermite interpolation to effectively eliminate edge artifacts. Experimental results demonstrate a peak-to-valley error in the reconstructed topography of 0.48 µm for a spherical mirror with a radius of 500 mm. The practical utility of the system is confirmed through curvature mapping of a 12-inch patterned wafer and further validated by stress measurements on an 8-inch bare wafer, which show less than 5% deviation from industry-standard instrumentation. These results validate the proposed PMD method as an accurate and cost-effective approach for production-scale thin-film stress inspection. Full article

Accurate co-registration between on-scalp Optically Pumped Magnetometer (OPM)–Magnetoencephalography (MEG) sensors and anatomical Magnetic Resonance Imaging (MRI) remains a critical bottleneck restricting the spatial fidelity of source localization. Optical Scanning Image (OSI) methods can provide high spatial accuracy but depend on surface visibility and cannot directly determine the internal sensitive point of each OPM sensor. Coil-based magnetic dipole localization, in contrast, targets the sensor’s internal sensitive volume and is robust to occlusion, yet its accuracy is affected by coil fabrication imperfections and the validity of the dipole approximation. To integrate the complementary advantages of both approaches, we propose a hybrid co-registration framework that combines Rigid Coil Structures (RCS), magnetic dipole-based sensor localization, and optical orientation constraints. A complete multi-stage co-registration pipeline is established through a unified mathematical formulation, including MRI–OSI alignment, OSI–RCS transformation, and final RCS–sensor localization. Systematic simulations are conducted to evaluate the accuracy of the magnetic dipole approximation for both cylindrical helical coils and planar single-turn coils. The results quantify how wire diameter, coil radius, and turn number influence dipole model fidelity and offer practical guidelines for coil design. Experiments using 18 coils and 11 single-axis OPMs demonstrate positional accuracy of a few millimeters, and optical orientation priors suppress dipole-only orientation ambiguity in unstable channels. To improve the stability of sensor orientation estimation, optical scanning of surface markers is incorporated as a soft constraint, yielding substantial improvements for channels that exhibit unstable results under dipole-only optimization. Overall, the proposed hybrid framework demonstrates the feasibility of combining magnetic and optical information for robust OPM–MEG co-registration. Full article

This study develops a tri-level adversarial robust optimization framework for cyber–physical scheduling in smart grids, addressing the intertwined challenges of coordinated cyberattacks, defensive resource allocation, and stochastic operational uncertainties. The upper level represents the attacker’s objective to maximize system disruption and conceal detection, the middle level models the defender’s optimization of detection and redundancy deployment under budgetary constraints, and the lower level performs economic dispatch given tampered data and uncertain renewable generation. The model integrates Distributionally Robust Optimization (DRO) based on a Wasserstein ambiguity set to safeguard against worst-case probability distributions, ensuring operational stability even under unobserved adversarial scenarios. A hierarchical reformulation using Karush–Kuhn–Tucker (KKT) conditions and Mixed-Integer Second-Order Cone Programming (MISOCP) transformation converts the nonconvex tri-level problem into a tractable bilevel surrogate solvable through alternating direction optimization. Numerical case studies on multi-node systems demonstrate that the proposed method reduces system loss by up to 36% compared to conventional stochastic scheduling, while maintaining 92% dispatch efficiency under high-severity attack scenarios. The results further reveal that adaptive defense allocation accelerates robustness convergence by over 50%, and that the risk–reward frontier stabilizes near a Pareto-optimal equilibrium between cost and resilience. This work provides a unified theoretical and computational foundation for adversarially resilient smart grid operation, bridging cyber-defense strategy, uncertainty quantification, and real-time economic scheduling into one coherent optimization paradigm. Full article

To address the uncertainty in power grid scheduling caused by the output variability of distributed energy resources (DERs) in microgrids, as well as the limitations of stochastic optimization relying on accurate probability distributions and the overly conservative nature of robust optimization leading to insufficient economic performance, this paper proposes a disseminated robust optimization method for microgrid operation that considers flexible demand-side resources. First, to address the uncertainty in the forecasting of wind and solar power scenarios, this paper launches a two-stage distributionally robust optimization (DRO) model based on a Kullback–Leibler (KL) divergence ambiguity set using a min–max–min framework. Then, the Column-and-Constraint Generation (C&CG) algorithm is employed to decouple the model for an iterative solution. Finally, simulation case studies are directed to validate the effectiveness of the proposed model. The demand response-based optimization model projected in the paper effectively enhances the flexibility of the Microgrid. Compared to robust optimization, this model reduces the daily operating cost by 2.86%. Although the cost is slightly higher (4.88%) than that of stochastic optimization, it achieves a balance between economy and robustness by optimizing the expected value under the worst-case probability distribution. Full article

Against the backdrop of the deepening “dual carbon” strategy and the globalization of ESG investment, China’s construction industry, an important key carbon-emitting sector, faces a “triple institutional dilemma”. It includes high carbon lock-in, human capital alienation, and an ambiguous governance structure. Current research on the practical paths of ESG compliance and its localized adaptation in this industry remains limited. Drawing on the green transformation theory, this study systematically explores the theoretical logic, realistic picture, and breakthrough path of ESG compliance in the industry. Firstly, it clarifies the connotation of ESG compliance and maps out the industry’s policy framework and practical patterns. Secondly, it analyzes core dilemmas from three dimensions: environmental constraints related to technical pathways, social conflicts between labor and community arising from institutional imbalances, and governance inefficiencies caused by irregular information disclosure and imperfect structure. Finally, it proposes a “three-dimensional collaborative” mitigation mechanism. This study provides localized, practical pathways for ESG compliance in the construction industry and offers a theoretical reference for the sector’s green transformation, thereby contributing to advancing Chinese-style modernization and ecological civilization construction. Full article

Realizing real-time dense 3D reconstruction on resource-limited mobile platforms remains a significant challenge, particularly in low-texture environments that demand robust multi-frame fusion to resolve matching ambiguities. However, the inherent tight coupling of pose estimation and mapping in traditional monolithic SLAM architectures imposes a severe restriction on integrating high-complexity fusion algorithms without compromising tracking stability. To overcome these limitations, this paper proposes MDR–SLAM, a modular and fully decoupled stereo framework. The system features a novel keyframe-driven temporal filter that synergizes efficient ELAS stereo matching with Kalman filtering to effectively accumulate geometric constraints, thereby enhancing reconstruction density in textureless areas. Furthermore, a confidence-based fusion backend is employed to incrementally maintain global map consistency and filter outliers. Quantitative evaluation on the NUFR-M3F indoor dataset demonstrates the effectiveness of the proposed method: compared to the standard single-frame baseline, MDR–SLAM reduces map RMSE by 83.3% (to 0.012 m) and global trajectory drift by 55.6%, while significantly improving map completeness. The system operates entirely on CPU resources with a stable 4.7 Hz mapping frequency, verifying its suitability for embedded mobile robotics. Full article

Sentiment analysis is essential for understanding consumer opinions, yet selecting the optimal models and embedding methods remains challenging, especially when handling ambiguous expressions, slang, or mismatched sentiment–rating pairs. This study provides a comprehensive comparative evaluation of sentiment classification models across three paradigms: traditional machine learning, pre-transformer deep learning, and transformer-based models. Using the Amazon Magazine Subscriptions 2023 dataset, we evaluate a range of embedding techniques, including static embeddings (GloVe, FastText) and contextual transformer embeddings (BERT, DistilBERT, etc.). To capture predictive confidence and model uncertainty, we include categorical cross-entropy as a key evaluation metric alongside accuracy, precision, recall, and F1-score. In addition to detailed quantitative comparisons, we conduct a systematic qualitative analysis of misclassified samples to reveal model-specific patterns of uncertainty. Our findings show that FastText consistently outperforms GloVe in both traditional and LSTM-based models, particularly in recall, due to its subword-level semantic richness. Transformer-based models demonstrate superior contextual understanding and achieve the highest accuracy (92%) and lowest cross-entropy loss (0.25) with DistilBERT, indicating well-calibrated predictions. To validate the generalisability of our results, we replicated our experiments on the Amazon Gift Card Reviews dataset, where similar trends were observed. We also adopt a resource-aware approach by reducing the dataset size from 25 K to 20 K to reflect real-world hardware constraints. This study contributes to both sentiment analysis and sustainable AI by offering a scalable, entropy-aware evaluation framework that supports informed, context-sensitive model selection for practical applications. Full article

This paper develops a general BN → LP framework for decision optimization under complex, structured uncertainty. A Bayesian network encodes causal dependencies among drivers and yields posterior joint probabilities; a linear program then reads expected coefficients directly from BN marginals to optimize the objective under operational constraints with explicit risk control via chance constraints or small ambiguity sets centered at the BN posterior. This mapping avoids explicit scenario enumeration and separates feasibility from credibility, so extreme but implausible cases are down-weighted rather than dictating decisions. A farm-planning case with interacting factors (weather → disease → yield; demand ↔ price; input costs) demonstrates practical feasibility. Under matched risk control, the BN → LP approach maintains the target violation rate while avoiding the over-conservatism of flat robust optimization and the optimism of independence-based stochastic programming; it also circumvents the inner minimax machinery typical of distributionally robust optimization. Tractability is governed by BN inference over the decision-relevant ancestor subgraph; empirical scaling shows that Markov-blanket pruning, mutual-information screening of weak parents, and structured/low-rank CPDs yield orders-of-magnitude savings with negligible impact on the objective. A standardized, data-and-expert construction (Dirichlet smoothing) and a systematic sensitivity analysis identifies high-leverage parameters, while a receding-horizon DBN → LP extension supports online updates. The method brings the largest benefits when uncertainty is high-dimensional and coupled, and it converges to classical allocations when drivers are few and essentially independent. Full article

This study examines the deconstruction of the Targum to Song of Songs, focusing on how binary oppositions and traditional interpretive frameworks are both challenged and reconfigured. While the targumists aim to prevent a potentially ‘sinful reading’ of the text, their interventions paradoxically recreate the possibility of a reading that could be considered ‘sinful’. They break down the boundaries between the spiritual and the physical, and question the identity, number, and gender of the participants in the love relationship. In the Targum, the classical narrative structure is also deconstructed. Chronology and causality yield to traditional memory and consciousness, producing a repetitive, fragmented, mosaic-like pattern unlike conventional narratives. Moreover, a semantic layer already shaped by context and in dialogue with the meaning of the textual antecedent enters the interpretive horizon of tradition, reinforcing the prominence of ambiguity—similarly to the base poetic text. The targumic deconstruction illuminates the relativity of concepts and meanings and highlights the flexible interpretive possibilities inherent in tradition. It not only liberates the conceptions within the text but also frees the reader from constraints imposed by binary hierarchies of value. The conceptual liberation leads to the realization that God and the relationship with God cannot be approached or described in earthly terms. Full article

Neural Architecture Search (NAS) is critical for developing efficient and robust perception models for UAV and drone-based applications, where real-time small object detection and computational constraints are major challenges. Existing NAS methods, including recent approaches leveraging large language models (LLMs), often suffer from static resource allocation and ambiguous architecture generation, limiting their effectiveness in dynamic aerial scenarios. In this study, we propose PhaseNAS, an adaptive LLM-driven NAS framework designed for drone perception tasks. PhaseNAS dynamically adjusts LLM capacity across exploration and refinement phases, and introduces a structured template language to bridge natural language prompts with executable model code. We also develop a zero-shot detection score for rapid screening of candidate YOLO-based architectures without full training. Experiments on NAS-Bench-Macro, CIFAR-10/100, COCO, and VisDrone2019 demonstrate that PhaseNAS consistently discovers superior architectures, reducing search time by up to 86% while improving accuracy and resource efficiency. On UAV detection benchmarks, PhaseNAS yields YOLOv8 variants with higher mAP and reduced computational cost, highlighting its suitability for real-time onboard deployment. These results indicate that PhaseNAS offers a practical and generalizable solution for autonomous AI model design in next-generation UAV systems. Full article

Conventional pixel-wise satellite-derived bathymetry (SDB) models face dual challenges: physical ambiguity from variable water quality and spatial incoherence from ignoring geographic context. This study addresses these limitations by proposing and validating OptiFusionStack, a novel two-stage physio-spatial synergistic framework that operates without in situ optical data for model calibration. The framework first generates diverse, physics-informed predictions by integrating Quasi-Analytical Algorithm (QAA)-derived inherent optical properties (IOPs) with multiple base learners. Critically, it then constructs a multi-scale spatial context by computing neighborhood statistics over an experimentally optimized 9 × 9-pixel window. These physical priors and spatial features are then effectively fused by a StackingMLP meta-learner. Validation in optically diverse environments demonstrates that OptiFusionStack significantly surpasses the performance plateau of pixel-wise methods, elevating inversion accuracy (e.g., R² elevated from 0.66 to >0.92 in optically complex inland waters). More importantly, the framework substantially reduces spatial artifacts, producing bathymetric maps with superior spatial coherence. A rigorous benchmark against several state-of-the-art, end-to-end deep learning models further confirms the superior performance of our proposed hierarchical fusion architecture in terms of accuracy. This research offers a robust and generalizable new approach for high-fidelity geospatial modeling, particularly under the common real-world constraint of having no in situ data for optical model calibration. Full article

Open-vocabulary semantic segmentation (OVSS) is of critical importance for unmanned aerial vehicle (UAV) imagery, as UAV scenes are highly dynamic and characterized by diverse, unpredictable object categories. Current OVSS approaches mainly rely on the zero-shot capabilities of vision–language models (VLMs), but their image-level pretraining objectives yield ambiguous spatial relationships and coarse-grained feature representations, resulting in suboptimal performance in UAV scenes. In this work, we propose a novel hybrid framework for OVSS in UAV imagery, named HOSU, which leverages the priors of vision foundation models to unleash the potential of vision–language models in representing complex spatial distributions and capturing fine-grained small-object details in UAV scenes. Specifically, we propose a distribution-aware fine-tuning method that aligns CLIP with DINOv2 across intra- and inter-region feature distributions, enhancing the capacity of CLIP to model complex scene semantics and capture fine-grained details critical for UAV imagery. Meanwhile, we propose a text-guided multi-level regularization mechanism that leverages the text embeddings of CLIP to impose semantic constraints on the visual features, preventing their drift from the original semantic space during fine-tuning and ensuring stable vision–language correspondence. Finally, to address the pervasive occlusion in UAV imagery, we propose a mask-based feature consistency strategy that enables the model to learn stable representations, remaining robust against viewpoint-induced occlusions. Extensive experiments across four training settings on six UAV datasets demonstrate that our approach consistently achieves state-of-the-art performance compared with previous methods, while comprehensive ablation studies and analyses further validate its effectiveness. Full article

Existing research has made significant strides in UAV formation control, particularly in active localization and certain passive methods. However, these approaches face substantial limitations in electromagnetically silent environments, often relying on strong assumptions such as fully known and stationary emitter positions. To overcome these challenges, this paper proposes a comprehensive framework for bearing-only passive localization and adjustment of UAV formations under strict electromagnetic silence constraints. We systematically develop three core models: (1) a geometric triangulation model for scenarios with three known emitters, enabling unique target positioning; (2) a hierarchical identification mechanism leveraging an angle database to resolve label ambiguity when some emitters are unknown; and (3) a cyclic cooperative strategy, Perceive-Explore-Judge-Execute (PEJE), optimized via an improved genetic algorithm with adaptive discrete neighborhood search (GA-IADNS), for dynamic formation adjustment. Extensive simulations demonstrate that our proposed methods exhibit strong robustness, rapid convergence, and high adjustment accuracy across varying initial deviations. Specifically, after adjustment, the maximum radial deviation of all UAVs from the desired position is less than 0.0001 m, and the maximum angular deviation is within 0.00013°; even for the $30\%R$ initial deviation scenario, the final positional error remains negligible. Furthermore, comparative experiments with a standard Genetic Algorithm (GA) confirm that GA-IADNS achieves superior performance: it reaches stable peak average fitness at the 6th generation (vs. no obvious convergence of GA even after 20 generations), reduces the convergence time by over $70\%$, and improves the final adjustment accuracy by more than $95\%$ relative to GA. These results significantly enhance the autonomous collaborative control capability of UAV formations in challenging electromagnetic conditions. Full article

This study addresses the common challenges in medical image segmentation and recognition, including boundary ambiguity, scale variation, and the difficulty of modeling long-range dependencies, by proposing a unified framework based on a hierarchical attention mechanism. The framework consists of a local detail attention module, a global context attention module, and a cross-scale consistency constraint module, which collectively enable adaptive weighting and collaborative optimization across different feature levels, thereby achieving a balance between detail preservation and global modeling. The framework was systematically validated on multiple public datasets, and the results demonstrated that the proposed method achieved Dice, IoU, Precision, Recall, and F1 scores of 0.886, 0.781, 0.898, 0.875, and 0.886, respectively, on the combined dataset, outperforming traditional models such as U-Net, Mask R-CNN, DeepLabV3+, SegNet, and TransUNet. On the BraTS dataset, the proposed method achieved a Dice score of 0.922, Precision of 0.930, and Recall of 0.915, exhibiting superior boundary modeling capability in complex brain MRI images. On the LIDC-IDRI dataset, the Dice score and Recall were improved from 0.751 and 0.732 to 0.822 and 0.807, respectively, effectively reducing the missed detection rate of small nodules compared to traditional convolutional models. On the ISIC dermoscopy dataset, the proposed framework achieved a Dice score of 0.914 and a Precision of 0.922, significantly improving the accuracy of skin lesion recognition. The ablation study further revealed that local detail attention significantly enhanced boundary and texture modeling, global context attention strengthened long-range dependency capture, and cross-scale consistency constraints ensured the stability and coherence of prediction results. From a medical economics perspective, the proposed framework has the potential to reduce diagnostic costs and improve healthcare efficiency by enabling faster and more accurate image-based clinical decision-making. In summary, the hierarchical attention mechanism presented in this work not only provides an innovative breakthrough in mathematical modeling but also demonstrates outstanding performance and generalization ability in experiments, offering new perspectives and technical pathways for intelligent segmentation and recognition in medical imaging. Full article