Measurement最新文献_第2页

Transfer learning based on 1D-CNN for critical dimension Predication of HAR grating structures 基于1D-CNN的HAR光栅结构关键尺寸预测迁移学习

IF 5.6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2026-05-05 Epub Date: 2026-02-27 DOI: 10.1016/j.measurement.2026.120988

Pei-Lun Lan , Yu-Lung Lo , Pei-Hsien Wu

This study investigates the prediction of four critical dimension (CD) parameters—top space, bottom space, depth, and pitch—of high aspect ratio (HAR) structures using simulated deep ultraviolet (DUV) reflectance spectra based on transfer learning without collecting all necessary data. A large dataset was generated through COMSOL Multiphysics simulations and used to train a one-dimensional convolutional neural network (1D-CNN). Under a transfer-learning scheme in which the network was pre-trained on 5,000 ideal (smooth-sidewall) grating spectra and then fine-tuned with 1,200 scalloped (non-ideal) grating spectra, a deep learning model trained only with θ_i = 35° spectra achieved significant improvements with R² values of 0.982 (top space), 0.9556 (bottom space), 0.9877 (depth), and 0.9745 (pitch), respectively. The corresponding mean absolute errors (MAE) were 0.0053, 0.0082, 0.0223, and 0.0268, while the mean absolute percentage errors (MAPE) were 0.89%, 1.36%, 0.74%, and 1.07%. These results validate the effectiveness of the CNN-based approach for rapidly and precisely characterizing the dimensional properties of HAR structures. Importantly, these results confirm the value of transfer learning: fine-tuning significantly improves prediction performance for CD estimation in HAR grating structures while reducing the required number of non-ideal (scalloped) spectra for fine-tuning to 1,200 in this study. Additionally, uncertainties arising from the intended measurement configuration and practical implementation conditions can be systematically identified and characterized using data-driven approaches. Consequently, simulation-generated data can provide a distinctive and robust framework for advanced process monitoring and can be readily integrated with measurement data in future deployment. In summary, the proposed method requires significantly less training data than the three existing comparative approaches. This strategy greatly reduces the burden of data collection and labeling, enhancing modeling efficiency. Furthermore, to assess feasibility under fabrication-induced profile non-idealities, the forward surrogate spectral prediction model is trained on ideal structures and subsequently adapted to simulated Bosch-inspired scalloped sidewalls via transfer learning, thereby reducing the need for extensive non-ideal training data and lowering the data-collection burden.

本文研究了基于迁移学习的模拟深紫外（DUV）反射光谱在不收集所有必要数据的情况下，对高纵横比（HAR）结构的四个关键维度（CD）参数——顶部空间、底部空间、深度和俯仰进行预测。通过COMSOL Multiphysics模拟生成大型数据集，并用于训练一维卷积神经网络（1D-CNN）。在对5000个理想（光滑边墙）光栅光谱进行预训练，然后对1200个扇形（非理想）光栅光谱进行精细调整的迁移学习方案下，仅用θi = 35°光谱训练的深度学习模型的R2值分别为0.982（上空间）、0.9556（下空间）、0.9877（深度）和0.9745（间距），得到了显著的改进。平均绝对误差（MAE）分别为0.0053、0.0082、0.0223和0.0268，平均绝对百分比误差（MAPE）分别为0.89%、1.36%、0.74%和1.07%。这些结果验证了基于cnn的方法快速准确表征HAR结构尺寸特性的有效性。重要的是，这些结果证实了迁移学习的价值：微调显着提高了HAR光栅结构中CD估计的预测性能，同时将本研究中微调所需的非理想（扇形）光谱数量减少到1200个。此外，来自预期测量配置和实际实施条件的不确定性可以使用数据驱动的方法系统地识别和表征。因此，模拟生成的数据可以为高级过程监控提供独特而强大的框架，并且可以在未来的部署中很容易地与测量数据集成。总之，与现有的三种比较方法相比，所提出的方法所需的训练数据要少得多。该策略大大减轻了数据收集和标注的负担，提高了建模效率。此外，为了评估在制造引起的剖面非理想情况下的可行性，前向替代光谱预测模型在理想结构上进行训练，随后通过迁移学习适应模拟博世启发的扇贝侧壁，从而减少了对大量非理想训练数据的需求，降低了数据收集负担。

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引用次数: 0

Mobile mapping systems camera–LiDAR data registration for mitigating GNSS/INS trajectory perturbations 用于减轻GNSS/INS轨迹扰动的移动测绘系统摄像头-激光雷达数据配准

IF 5.6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2026-05-05 Epub Date: 2026-02-22 DOI: 10.1016/j.measurement.2026.120918

Mona Hodaei, Youssef Hany, Aser Eissa, Ayman Habib

Wheeled Mobile Mapping Systems (MMS), equipped with LiDAR, cameras, and integrated GNSS/INS units, are widely used in urban planning, map generation, and infrastructure monitoring. Accurate registration between wheeled MMS camera and LiDAR data, relying on precise system calibration and GNSS/INS trajectory, is crucial for effective data fusion to address the needs of these applications. However, environmental factors can degrade sensor calibration and GNSS/INS accuracy, leading to misalignment between imagery and LiDAR data. Calibration parameters, such as mounting parameters and sensors’ Interior Orientation Parameters (IOP), can be affected by sensor aging and environmental conditions, while data collection along transportation corridors may suffer from GNSS signal occlusions due to interference from traffic, bridges, and buildings. GNSS/INS trajectory errors are more frequent than calibration errors. This research addresses these trajectory issues by analyzing image-LiDAR misalignments and proposes a novel registration approach. The method establishes an appropriate transformation function, automatically extracts lane markings as common primitives, and develops a similarity measure tailored to these primitives. These elements are integrated into an automated optimization strategy that estimates transformation function parameters. The proposed learning-based algorithm is effective in both urban and highway environments, offering a robust solution for camera-LiDAR alignment. Additionally, an analysis of stereo camera poses before and after registration identifies misalignment causes, whether due to GNSS/INS errors or calibration inaccuracy. The proposed algorithm, evaluated using the mean of minimum Euclidean distances and Intersection over Union (IoU), demonstrates significant improvements, reducing misalignment to less than a few pixels and achieving IoU improvements exceeding 50%.

轮式移动测绘系统（MMS）配备了激光雷达、摄像头和集成GNSS/INS单元，广泛应用于城市规划、地图生成和基础设施监控。依靠精确的系统校准和GNSS/INS轨迹，轮式MMS相机和LiDAR数据之间的精确配准对于有效的数据融合至关重要，以满足这些应用的需求。然而，环境因素会降低传感器校准和GNSS/INS的精度，导致图像和LiDAR数据之间的不对准。校准参数，如安装参数和传感器的内部定向参数（IOP），可能会受到传感器老化和环境条件的影响，而沿交通走廊收集的数据可能会受到交通、桥梁和建筑物的干扰而受到GNSS信号遮挡。GNSS/INS轨迹误差比校准误差更常见。本研究通过分析图像-激光雷达的不对准来解决这些轨迹问题，并提出了一种新的配准方法。该方法建立适当的变换函数，自动提取车道标记作为公共原语，并开发适合这些原语的相似度度量。这些元素被集成到一个自动化的优化策略中，用于估计转换函数参数。所提出的基于学习的算法在城市和高速公路环境下都是有效的，为摄像头-激光雷达对准提供了一个强大的解决方案。此外，对配准前后立体相机姿势的分析确定了不对准的原因，无论是由于GNSS/INS错误还是校准不准确。使用最小欧几里得距离和交汇联距（Intersection over Union, IoU）的平均值对所提出的算法进行了评估，结果显示出显著的改进，将不对齐减少到几个像素以内，IoU改进超过50%。

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引用次数: 0

End-to-end single-shot fringe projection profilometry based on semi-supervised learning 基于半监督学习的端到端单镜头条纹投影轮廓测量

IF 5.6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2026-05-05 Epub Date: 2026-03-07 DOI: 10.1016/j.measurement.2026.121076

Huitao Wang , Lianpo Wang

Single-shot Fringe Projection Profilometry (SSFPP) enables three-dimensional (3D) reconstruction from a single captured fringe image and is widely applied to dynamic object measurement. Although deep learning has significantly improved the accuracy and robustness of SSFPP, most existing approaches remain fully supervised and rely heavily on labeled data that are difficult and costly to obtain. Existing self-supervised FPP methods circumvent the need for labeled data but typically rely on multiple input images to resolve the inherent

2 k π

phase ambiguity of a single fringe pattern. Consequently, they cannot achieve true single-shot FPP reconstruction. To enable self-supervised SSFPP, we propose a dual-domain self-supervised loss function. In the image domain, a Structural Similarity Index Measure (SSIM) loss is introduced to enforce physically meaningful consistency between the reprojected and input fringe images, thereby supporting end-to-end self-supervised learning. In the phase domain, an edge-aware self-smoothing loss is developed to suppress discontinuities caused by the

2 k π

phase ambiguity, enabling a unique and spatially continuous phase solution from a single frame. In addition, we design a dynamic dual-stream sampler that simultaneously samples labeled and unlabeled data and adaptively adjusts their proportions within each batch based on training progress, enabling progressive and synergistic optimization of supervised and self-supervised learning signals. Experimental results demonstrate that the proposed method, using only 50% of the labeled data, outperforms existing open-source supervised end-to-end SSFPP approaches on both synthetic and real-world datasets. This confirms its ability to substantially reduce annotation costs while maintaining high reconstruction accuracy.

单镜头条纹投影轮廓术（SSFPP）能够从单个捕获的条纹图像中进行三维（3D）重建，并广泛应用于动态物体测量。尽管深度学习显著提高了SSFPP的准确性和鲁棒性，但大多数现有方法仍然是完全监督的，并且严重依赖于难以获得且昂贵的标记数据。现有的自监督FPP方法绕过了对标记数据的需要，但通常依赖于多个输入图像来解决单个条纹图案固有的2kπ相位模糊。因此，它们无法实现真正的单次FPP重建。为了实现自监督SSFPP，我们提出了一个双域自监督损失函数。在图像域，引入了结构相似指数度量（SSIM）损失来强制重投影和输入条纹图像之间具有物理意义的一致性，从而支持端到端自监督学习。在相位域，开发了一种边缘感知的自平滑损失来抑制由2kπ相位模糊引起的不连续，从而实现了单帧的唯一且空间连续的相位解。此外，我们设计了一个动态双流采样器，可以同时对标记和未标记数据进行采样，并根据训练进度自适应调整其在每批中的比例，从而实现监督学习和自监督学习信号的渐进和协同优化。实验结果表明，该方法仅使用50%的标记数据，在合成数据集和真实数据集上都优于现有的开源监督端到端SSFPP方法。这证实了它能够在保持高重建精度的同时大幅降低注释成本。

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引用次数: 0

Radio propagation model for mobile network planning in the C-band c波段移动网络规划中的无线电传播模型

IF 5.6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2026-05-05 Epub Date: 2026-02-26 DOI: 10.1016/j.measurement.2026.120984

Dariusz P. Więcek, Igor Michalski, Daniil Ruban, Jacek Wroński

This paper proposes an enhanced radio propagation model derived from a tuned Standard Propagation Model (SPM) for efficient and optimal network planning of International Mobile Telecommunications (IMT) systems (like 5G and 6G) in the C-band (frequency range 3400–4200 MHz) for typical European cities. Based on a measurement campaign conducted by the authors, the model was analyzed and subsequently tuned using nonlinear regression, yielding results that more accurately estimate coverage areas compared to the standard. Error analysis demonstrated significant improvements in propagation modeling, resulting in reduced deviations between simulations and measurements.

本文提出了一种增强的无线电传播模型，该模型源自经过调谐的标准传播模型（SPM），用于典型欧洲城市c波段（频率范围3400-4200 MHz）的国际移动通信（IMT）系统（如5G和6G）的高效和最佳网络规划。基于作者进行的测量活动，模型被分析并随后使用非线性回归进行调整，产生比标准更准确地估计覆盖区域的结果。误差分析证明了传播建模的显著改进，从而减少了模拟和测量之间的偏差。

引用次数: 0

Intensity-enhanced LiDAR-inertial odometry with gradient flow sampling for degraded environments 强度增强激光雷达惯性里程计与梯度流采样退化环境

IF 5.6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2026-05-05 Epub Date: 2026-03-01 DOI: 10.1016/j.measurement.2026.121012

Zhenghui Xu, Jian Li, Ling Tang, Shimin Wei

High-precision localization is a fundamental requirement for autonomous robot navigation. However, in challenging LiDAR-degraded environments, sparse geometric structures, insufficient effective features, and interference from overlapping redundant points and cluttered noise often cause existing methods to drift severely, making accurate localization difficult. To address this, we propose a gradient flow sampled and intensity-enhanced LiDAR-Inertial Odometry (LIO) framework that improves matching efficiency and localization accuracy under geometric degeneracy. First, we propose a gradient flow-based point cloud sampling method that computes the distribution of point cloud gradient flows based on the observability of point cloud hyperplanes, minimizing sampling to suppress redundancy, and followed by a geometric consistency verification to reject noisy measurements. Second, to improve registration accuracy and robustness, we introduce an intensity-geometry fused point-pair association strategy that rates scan correspondences via intensity Kullback-Leibler (KL) divergence and geometric similarity to select the best match, integrates it into the point-to-plane iterative Extended Kalman Filter (iEKF) framework. Then, a dynamic factor during pose estimation adaptively balances geometric and photometric residuals across environments. Finally, extensive experiments on the Newer College, ENWIDE, DiTer++, and GEODE datasets show that the proposed algorithm outperforms the intensity-enhanced LIO algorithms on most sequences, with a 22.98% improvement in real-time performance compared to the baseline.

高精度定位是机器人自主导航的基本要求。然而，在具有挑战性的激光雷达退化环境中，几何结构稀疏、有效特征不足、重叠冗余点和杂波噪声的干扰往往导致现有方法漂移严重，难以准确定位。为了解决这个问题，我们提出了一个梯度流采样和强度增强的lidar -惯性测程（LIO）框架，该框架提高了几何退化下的匹配效率和定位精度。首先，我们提出了一种基于梯度流的点云采样方法，该方法基于点云超平面的可观测性计算点云梯度流的分布，最小化采样以抑制冗余，然后进行几何一致性验证以拒绝噪声测量。其次，为了提高配准精度和鲁棒性，我们引入了一种强度-几何融合点对关联策略，该策略通过强度Kullback-Leibler （KL）散度和几何相似性对扫描对应进行评分，选择最佳匹配，并将其集成到点对平面迭代扩展卡尔曼滤波（iEKF）框架中。然后，姿态估计过程中的动态因子自适应平衡不同环境下的几何残差和光度残差。最后，在Newer College、ENWIDE、DiTer++和GEODE数据集上进行的大量实验表明，该算法在大多数序列上优于强度增强的LIO算法，实时性比基线提高22.98%。

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引用次数: 0

Wildfire smoke detection based on enhanced YOLOv7 with video images 基于增强型YOLOv7视频图像的野火烟雾探测

IF 5.6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2026-05-05 Epub Date: 2026-03-06 DOI: 10.1016/j.measurement.2026.121045

Zezhong Zheng, Ruoliang Huang, Yibing Shang, Weishi Jin

An improved YOLOv7-I-CBAMI model (enhanced You Only Look Once version7 integrated with the Convolutional Block Attention Module) combined with ridge edge detection is proposed to detect wildfire smoke in mountainous areas. YOLO (You Only Look Once) reformulates object detection as a regression task, utilizing the entire image as input and generating bounding box coordinates and class labels through a single neural network, offering high detection accuracy and speed. However, limitations exist in detecting closely spaced and small objects, and distinguishing between clouds and wildfire smoke remains an unresolved issue. To address these challenges, a bidirectional feature pyramid network is introduced to improve detection accuracy, and an enhanced CBAM (Convolutional Block Attention Module) attention mechanism is incorporated to overcome YOLOv7′s limitations in detecting small targets and faint wildfire smoke features. Furthermore, ridge edge detection is integrated for secondary optimization, reducing the confusion between wildfire smoke and natural clouds. Experimental results on a wildfire-prone transmission corridor video dataset around Kunming, provided by Yunnan Power Grid, indicate that the YOLOv7-I-CBAMI network achieves superior performance in Precision, Recall, and F1-Score. By integrating ridge edge detection with wildfire smoke detection, Precision is improved and overall performance metrics are also enhanced, achieving final values of 0.83 for Precision, 0.82 for Recall, and 0.82 for F1-Score, with a detection speed of 21.20 FPS (Frames Per Second). These results validate the effectiveness of the proposed YOLOv7-I-CBAMI model with ridge detection for rapid and accurate detection of wildfire smoke in transmission corridors.

提出了一种改进的YOLOv7-I-CBAMI模型（与卷积块注意模块集成的增强版You Only Look Once version7）与山脊边缘检测相结合的山区野火烟雾检测方法。YOLO （You Only Look Once）将目标检测重新定义为回归任务，利用整个图像作为输入，通过单个神经网络生成边界框坐标和类标签，提供高检测精度和速度。然而，在探测近距离和小物体方面存在局限性，区分云和野火烟雾仍然是一个未解决的问题。为了解决这些挑战，YOLOv7引入了双向特征金字塔网络来提高检测精度，并引入了增强的CBAM（卷积块注意模块）注意机制来克服YOLOv7在检测小目标和微弱野火烟雾特征方面的局限性。此外，集成了山脊边缘检测进行二次优化，减少了野火烟雾和自然云之间的混淆。实验结果表明，YOLOv7-I-CBAMI网络在准确率（Precision）、召回率（Recall）和F1-Score等方面都取得了较好的效果。通过将山脊边缘检测与野火烟雾检测相结合，精度得到了提高，整体性能指标也得到了增强，最终精度值为0.83，召回率为0.82，F1-Score为0.82，检测速度为21.20 FPS（帧/秒）。这些结果验证了提出的带山脊检测的YOLOv7-I-CBAMI模型在输电走廊野火烟雾快速准确检测中的有效性。

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引用次数: 0

Bridging the gap between high-speed linear cascades and rotating turbine facilities 弥合高速线性级联和旋转涡轮设备之间的差距

IF 5.6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2026-05-05 Epub Date: 2026-03-01 DOI: 10.1016/j.measurement.2026.121017

Andrea Ruan , Hunter Douglas Nowak , Lukas Benjamin Inhestern , James Taylor , John P. Clark , Guillermo Paniagua

This paper presents the design, instrumentation, and commissioning of a novel measurement tool for testing turbomachinery components, specifically targeted to transonic rotors. This tool enables high-resolution investigation of complex 3D rotating geometries in the stationary frame, addressing the scarcity of relevant transonic facilities capable of precise aerodynamic diagnostics. The presented testing solution bridges the gap between the well-established linear cascades and rotating rigs enabling high-resolution measurements and optical access in an annular cascade comparable to what is standard practice in low-speed linear cascades. Conversely, high-speed rotating rigs are typically limited to torque and power measurements due to the small airfoil size and restricted access. A flow conditioning gauze with hundreds of radial and circumferential blades replicates the total pressure and whirl angle profiles experienced by the rotor in its relative frame at transonic conditions. Gauze placement was optimized using 3D Reynolds-Averaged Navier-Stokes simulations to ensure homogeneous inlet conditions. The final hardware was machined in stainless steel to withstand elevated temperatures. Inlet flow quality was verified with total pressure probes and static taps around the annulus. Downstream of the gauze, total pressure, whirl angle, and Mach number profiles were measured using Kiel and five-hole probe traverses, confirming agreement with design targets and validating this configuration for turbomachinery testing. The rig supports both sector and full-annular cascade configurations, including single and rainbow geometries. Tests can exceed 30 min, enabling high-resolution, full-annulus traverses. Exit Reynolds numbers range from 30,000 to 4,000,000, with independently adjustable pressure ratios allowing testing from subsonic to supersonic conditions.

本文介绍了一种新型测量工具的设计、仪器和调试，用于测试涡轮机械部件，特别是针对跨音速转子。该工具能够在固定框架中对复杂的3D旋转几何形状进行高分辨率研究，解决了能够进行精确空气动力学诊断的相关跨音速设施的短缺问题。该测试解决方案弥补了现有线性级联和旋转钻机之间的差距，在环形级联中实现高分辨率测量和光学接入，可与低速线性级联的标准做法相媲美。相反，高速旋转钻机通常限于扭矩和功率测量，由于小翼型尺寸和限制访问。一个由数百个径向和周向叶片组成的流动调节纱布可以复制转子在其相对框架中在跨音速条件下所经历的总压力和旋转角分布。利用三维reynolds - average Navier-Stokes模拟优化了纱布的放置，以确保均匀的入口条件。最后的硬件是用不锈钢加工的，以承受高温。用总压探头和环空周围的静态抽头验证了进口流动质量。使用Kiel和五孔探头测量了纱布下游的总压力、旋转角和马赫数分布，确认了与设计目标的一致，并验证了该配置用于涡轮机械测试。该钻机支持扇形和全环空级联配置，包括单面和彩虹几何形状。测试时间可超过30分钟，可实现高分辨率的全环空穿越。出口雷诺数范围从30,000到4,000,000，具有独立可调的压力比，允许从亚音速到超音速条件下进行测试。

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引用次数: 0

An open-set recognition method for ship radiated noise signals based on hybrid supervision 基于混合监督的船舶辐射噪声信号开集识别方法

IF 5.6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2026-05-05 Epub Date: 2026-03-04 DOI: 10.1016/j.measurement.2026.121067

Yichen Duan, Xuandi Sun, Yankun Chen, Tongmu Liu

Ship radiated noise signals are one of the most crucial sources of information for ship perception. With the intensification of human marine activities, the underwater acoustic environment has become increasingly complex, subjecting underwater acoustic perception systems to various interferences. In this paper, we construct a recognition scenario involving multiple target ship radiated noise signals and various types of interference. In this scenario, target signals comprise various ship radiated noise, whereas interference signals consist of both decoy and non-target ship noise. The objective of this study is to accurately recognize multiple target signals while mitigating the effects of interference. We formulate this as an open-set ship radiated noise recognition problem. We design an encoder–decoder architecture for processing time-domain ship radiated noise. Pre-trained on a closed-set dataset, this model effectively captures the distribution of closed-set data. Furthermore, we design a classifier integrating supervised and self-supervised learning, augmented with an attention mechanism to enhance its representation learning capability. We emulate decoy signals, and all experimental data are collected from real sea trials. Experimental results demonstrate that our method can accurately recognize multiple target ship radiated signals while remaining robust to interference.

舰船辐射噪声信号是舰船感知最重要的信息来源之一。随着人类海洋活动的加剧，水声环境日益复杂，水声感知系统受到各种干扰。本文构建了一个包含多目标舰船辐射噪声信号和多种干扰的识别场景。在这种情况下，目标信号包括各种舰船辐射噪声，而干扰信号包括诱饵和非目标舰船噪声。本研究的目的是准确识别多个目标信号，同时减轻干扰的影响。我们将其表述为一个开放式船舶辐射噪声识别问题。设计了一种用于时域船舶辐射噪声处理的编码器-解码器结构。该模型在封闭集数据集上进行预训练，有效地捕获了封闭集数据的分布。此外，我们设计了一个集成了监督学习和自监督学习的分类器，并添加了注意机制来增强其表示学习能力。我们模拟了诱饵信号，所有的实验数据都是从真实的海上试验中收集的。实验结果表明，该方法能够准确识别多目标舰船辐射信号，同时对干扰具有较强的鲁棒性。

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引用次数: 0

Low earth orbit satellite short-term orbit prediction using the LSTM-Transformer neural network model 基于LSTM-Transformer神经网络模型的近地轨道卫星短期轨道预测

IF 5.6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2026-05-05 Epub Date: 2026-03-05 DOI: 10.1016/j.measurement.2026.120973

Zhixin Yang , Yousi Zheng , Feifei Tang , Hui Liu , Bin Wang , Nanjie Li , Yongmao Zhao

The precise orbit of Low Earth Orbit (LEO) satellites is crucial for LEO-enhanced Global Navigation Satellite System (GNSS) precise positioning, and short-term orbit prediction is extremely necessary to compensate for the time delay caused by orbit determination. The traditional dynamical propagation method is susceptible to error accumulation, and single neural network models have limitations in effectively capturing temporal dependencies. In this study, we propose a hybrid neural network based on Long Short-Term Memory (LSTM) and Transformer architectures for LEO satellite orbit prediction, which combines the sequential processing capabilities of LSTM with the self-attention mechanism of the Transformer architecture. The orbit propagation errors are first calculated using traditional methods, and then the proposed hybrid model is employed to predict these errors for orbit correction. Four LEO satellites from different orbit altitudes, GRACE-C, SWARM-B, SENTINEL-3A, and SENTINEL-6A, are comprehensively evaluated to validate the prediction performance of the LSTM-Transformer model and corrected orbit accuracy. The results demonstrate that, under optimal length of sliding window (WL) parameter conditions, the prediction performance of propagation errors using the LSTM-Transformer model is improved by 40%-80% compared to the LSTM model. The corrected orbit accuracy by the predicted propagation errors is improved by 40%-95% and 5%-50% compared to the traditional method and the LSTM model, respectively, with no systematic bias present. Additionally, the LSTM-Transformer model also demonstrates strong generalization capabilities, with a 98% consistency compared with the standard models. During solar activity periods, the accuracy of orbit correction using this hybrid prediction model has also been improved by more than 30% compared with the traditional method.

低地球轨道卫星的精确轨道是实现低地球轨道增强型全球导航卫星系统（GNSS）精确定位的关键，短期轨道预测是弥补定轨时间延迟的必要手段。传统的动态传播方法容易产生误差积累，且单个神经网络模型在有效捕获时间依赖性方面存在局限性。在本研究中，我们提出了一种基于长短期记忆（LSTM）和Transformer架构的混合神经网络用于LEO卫星轨道预测，该网络将LSTM的顺序处理能力与Transformer架构的自关注机制相结合。首先采用传统方法计算轨道传播误差，然后采用混合模型对误差进行预测，进行轨道修正。对GRACE-C、SWARM-B、SENTINEL-3A和SENTINEL-6A四颗不同轨道高度的LEO卫星进行综合评估，验证LSTM-Transformer模型的预测性能和修正轨道精度。结果表明，在最佳滑动窗口长度参数条件下，LSTM- transformer模型对传播误差的预测性能比LSTM模型提高了40% ~ 80%。与传统方法和LSTM模型相比，预测传播误差修正的轨道精度分别提高了40% ~ 95%和5% ~ 50%，且不存在系统偏差。此外，LSTM-Transformer模型还显示出强大的泛化能力，与标准模型相比，一致性达到98%。在太阳活动期间，该混合预测模型的轨道校正精度也比传统方法提高了30%以上。

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引用次数: 0

Holistic framework to control ultrasonic cavitation in liquid media based on a systematic review 基于系统综述的液体介质超声空化控制的整体框架

IF 5.6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2026-05-05 Epub Date: 2026-03-03 DOI: 10.1016/j.measurement.2026.121040

José Fernandes , Hélder Puga , Stijn W.H. Van Hulle , Paulo J. Ramísio

The management of acoustic cavitation is crucial for enhancing the performance of optimized sonoreactors in various fields. With a growing interest in eco-friendly methods employing sono-reactors, the assessment of cavitation caused by ultrasonic devices is increasingly significant. Controlling cavitation is vital to maximize energy efficiency and its related impacts. Currently, traditional measurement methods include physical techniques (such as aluminium foil tests and calorimetry), optical methods (such as high-speed imaging, sonoluminescence, and particle image velocimetry), chemical approaches (including fluorescence and dosimetry), as well as acoustic methods (like hydrophones and active cavitation detectors). However, each of these methodologies possesses inherent limitations that can compromise measurement accuracy, lead to unnecessary costs, and result in a lack of methodological rigour. This review suggests an organized framework designed to assist in selecting the most suitable technique from the widely used methods, tailored to different application contexts. The framework includes application-specific questions derived from the review, helping to pinpoint methods that meet specific requirements.

声空化的控制是提高优化后的声反应器性能的关键。随着人们对采用声波反应器的环保方法的兴趣日益浓厚，超声装置引起的空化评估变得越来越重要。控制空化对于最大限度地提高能源效率及其相关影响至关重要。目前，传统的测量方法包括物理技术（如铝箔测试和量热法）、光学方法（如高速成像、声致发光和粒子图像测速法）、化学方法（包括荧光和剂量法）以及声学方法（如水听器和主动空化探测器）。然而，每种方法都具有固有的局限性，可能会损害测量精度，导致不必要的成本，并导致缺乏方法的严谨性。这篇综述提出了一个有组织的框架，旨在帮助从广泛使用的方法中选择最合适的技术，为不同的应用环境量身定制。该框架包括来自审查的特定于应用程序的问题，有助于确定满足特定需求的方法。

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引用次数: 0