Measurement最新文献_第2页

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。

{"title":"Holistic framework to control ultrasonic cavitation in liquid media based on a systematic review","authors":"José Fernandes , Hélder Puga , Stijn W.H. Van Hulle , Paulo J. Ramísio","doi":"10.1016/j.measurement.2026.121040","DOIUrl":"10.1016/j.measurement.2026.121040","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"272 ","pages":"Article 121040"},"PeriodicalIF":5.6,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The influence of rotor vibration frequency on measurement errors of the self-inductive displacement sensor 转子振动频率对自感位移传感器测量误差的影响

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.2026.121023

Yaoqi Feng , Hongwei Li , Xinwei Chen , Zihao Wang , Wentao Yu , Yinshu Ding

The self-inductive displacement sensor has been widely used to measure the displacement of maglev rotors and is a crucial component of the active magnetic bearing (AMB) system. However, some of the dynamic performance of this sensor is unclear and needs further research. Therefore, a dynamic output model under the rotor sinusoidal periodical vibration was proposed through theoretical analysis based on an accurate sensor coil’s impedance model established previously. Based on a sensor with a nominal air gap of 1.2 mm, this paper studied the sensor’s output voltages under an excitation frequency of 50 kHz, different rotor vibration frequency ranges, and a displacement range of [-0.6, 0.6] mm by using the dynamic output model, finite element method, and experiments respectively. Then, the measurement errors between the rotor’s forward and backward vibration strokes were calculated. The theoretical analysis, simulation and experimental results are consistent, and they all show that the higher the rotor vibration frequency, the more the stroke measurement errors are evident. The errors are more evident in the displacement range of [−0.3, 0.3] mm. The maximum stroke displacement measurement errors almost linearly increase from about 0.0049 mm to 0.0185 mm, i.e. 0.82% to 3.08% of the rotor vibration amplitude in theoretical analysis, as the rotor vibration frequency increases from 500 Hz to 2000 Hz. The simulation and experimental results have verified the accuracy of the proposed dynamic output model of the sensor. This paper will lay an essential theoretical basis for further studying the dynamic performance of the self-inductive displacement sensor.

自感式位移传感器已广泛应用于磁悬浮列车转子的位移测量，是主动磁轴承系统的重要组成部分。然而，该传感器的一些动态性能尚不清楚，需要进一步研究。因此，在前人建立的精确传感器线圈阻抗模型的基础上，通过理论分析，提出了转子正弦周期振动下的动态输出模型。本文以标称气隙为1.2 mm的传感器为研究对象，分别采用动态输出模型、有限元法和实验研究了该传感器在激励频率为50 kHz、不同转子振动频率范围和[-0.6,0.6]mm位移范围下的输出电压。然后，计算了转子前后振冲程的测量误差。理论分析、仿真和实验结果一致，均表明转子振动频率越高，冲程测量误差越大。在[−0.3,0.3]mm范围内误差更为明显，当转子振动频率从500 Hz增加到2000 Hz时，最大行程位移测量误差在约0.0049 mm至0.0185 mm范围内几乎呈线性增加，即转子振动幅值的0.82%至3.08%。仿真和实验结果验证了所提出的传感器动态输出模型的准确性。本文将为进一步研究自感位移传感器的动态性能奠定必要的理论基础。

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引用次数: 0

Parameter-decoupling discrete-time repetitive controller based on ADRC for PMLSM disturbance suppression 基于自抗扰控制器的参数解耦离散重复控制器抑制永磁同步电机扰动

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.2026.121047

Mingbo Xu , Degang Lv , Huijie Zhang

To address the critical challenges of thrust ripple and disturbance suppression in permanent magnet linear synchronous motor (PMLSM) drives, conventional active disturbance rejection control (ADRC), while effective against DC disturbances, exhibits limited capability in rejecting high-frequency periodic disturbances due to the restricted bandwidth of its extended state observer (ESO). Furthermore, the inherent coupling between the observer gain and the controller bandwidth complicates parameter tuning and compromises harmonic suppression. To overcome these limitations, this paper proposes a parameter-decoupling discrete-time repetitive ADRC (PDDTRC-ADRC) strategy. The proposed strategy first establishes a parameter decoupled current model, which integrates cross-coupling effects and parameter variations into lumped disturbance, thereby achieving dynamic decoupling. Subsequently, a discrete-time repetitive controller (DTRC) is embedded into the error observation channel of the ESO to selectively attenuate dominant periodic disturbances, Moreover, by redesigning the control structure, a dual decoupling architecture is realized, which separates the ESO gain from the ADRC bandwidth and simplifies the tuning of control parameters. Experimental results confirm that under various operating conditions, the proposed strategy achieves up to 87% suppression of the 6th current harmonic, reduces thrust ripple by over 76%, thereby significantly enhancing both the steady-state precision and dynamic robustness of the system.

为了解决永磁直线同步电机（PMLSM）驱动中推力脉动和干扰抑制的关键挑战，传统的自抗扰控制（ADRC）虽然对直流干扰有效，但由于其扩展状态观测器（ESO）的带宽有限，在抑制高频周期性干扰方面的能力有限。此外，观测器增益和控制器带宽之间的固有耦合使参数调谐变得复杂，并危及谐波抑制。为了克服这些限制，本文提出了一种参数解耦的离散时间重复自抗扰控制器（PDDTRC-ADRC）策略。该策略首先建立参数解耦电流模型，将交叉耦合效应和参数变化集成到集总扰动中，实现动态解耦。将离散时间重复控制器（DTRC）嵌入到ESO误差观测通道中，选择性地衰减主导周期扰动，并通过对控制结构的重新设计，实现了ESO增益与自抗扰带宽的双重解耦，简化了控制参数的整定。实验结果表明，在各种工况下，该策略对第六次电流谐波的抑制率高达87%，对推力脉动的抑制率超过76%，从而显著提高了系统的稳态精度和动态鲁棒性。

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引用次数: 0

Band-separated reconstruction for enhanced multiscale volumetric optoacoustic angiography 增强多尺度体积光声血管造影的带分离重建

IF 5.6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2026-05-05 Epub Date: 2026-02-24 DOI: 10.1016/j.measurement.2026.120860

Wei-Lian Ou , Jia-Yi Tsai , Xosé Luís Deán-Ben , Daniel Razanksy , Hsiao-Chun Amy Lin

Optoacoustic tomography offers unique capabilities for vascular imaging with high resolution and contrast. Its ability to resolve vessels across multiple scales is however hampered by limited detection bandwidth of piezoelectric sensors as well as frequency-dependent optical and acoustic attenuation. While band-separated reconstruction has shown promise in scanning optoacoustic mesoscopy and cross-sectional tomography, its potential for enhancing volumetric optoacoustic tomography (VOT) remains unexplored. This study proposes a band-separated processing workflow for VOT based on handheld spherical arrays with translational potential for clinical angiography. Complex volumetric phantoms were designed to assess the frequency-dependent enhancement in both small and large structures with arrays of 4, 7, and 10 MHz center frequencies. Additionally, imaging of healthy volunteers was further conducted to assess clinical viability. The stopband gain of the signal filter was optimized to balance resolution and contrast. In phantom studies, the proposed method improved the visibility of small deeply embedded targets, enhanced edge definition, and significantly increased the average signal intensity of small structures by more than 50-fold, while achieving a 5-fold increase for large structures despite their stronger optical attenuation. As expected, performance varied with imaging depth, shadowing, and transducer frequency: higher frequencies favored enhancement of small features, while lower frequencies better amplified larger structures. Angiographic finger images from healthy volunteers further confirmed these findings, demonstrating improved visualization of both fine and large vessels across all detectors. These results highlight the utility of band-separated processing for multiscale vascular imaging and expand the clinical potential of VOT for noninvasive vascular assessment.

光声断层成像为血管成像提供了独特的能力，具有高分辨率和对比度。然而，由于压电传感器的检测带宽有限以及与频率相关的光学和声学衰减，其在多个尺度上分辨船只的能力受到阻碍。虽然带分离重建在扫描光声介观镜和横断断层扫描中显示出前景，但其在增强体积光声断层扫描（VOT）方面的潜力仍未得到探索。本研究提出了一种基于手持式球面阵列的VOT波段分离处理流程，该流程具有临床血管造影术的转化潜力。设计了复杂的体积模型来评估大小结构在4、7和10 MHz中心频率阵列中的频率依赖性增强。此外，对健康志愿者进行影像学检查以评估临床生存能力。对信号滤波器的阻带增益进行了优化，以平衡分辨率和对比度。在体模研究中，该方法提高了深嵌小目标的可见性，增强了边缘清晰度，并将小结构的平均信号强度显著提高了50倍以上，而对于光学衰减更强的大型结构，该方法的平均信号强度提高了5倍。正如预期的那样，性能随成像深度、阴影和换能器频率的变化而变化：高频有利于增强小特征，而低频则能更好地放大较大的结构。来自健康志愿者的手指血管造影图像进一步证实了这些发现，表明所有探测器上细血管和大血管的可视化都得到了改善。这些结果突出了波段分离处理在多尺度血管成像中的应用，并扩大了VOT在无创血管评估中的临床潜力。

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引用次数: 0

Simultaneous estimation of fault type and severity in rolling bearings via multichannel measurement data fusion and a physics-informed measurement model 基于多通道测量数据融合和物理信息测量模型的滚动轴承故障类型和严重程度同时估计

IF 5.6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2026-05-05 Epub Date: 2026-03-09 DOI: 10.1016/j.measurement.2026.121095

Jin Zhou , Ruiqing Han

Existing research on rolling bearing condition monitoring has predominantly focused on identifying fault types, with significantly less attention given to quantifying different levels of fault severity. To tackle these challenges, this study introduces a physics-informed YOLO v8 (PI-YOLO v8) model for simultaneous estimation of bearing fault type and severity through two novel physics-informed constraint terms embedded in the loss function. The proposed framework is based on the YOLO v8 architecture, enhanced by incorporating two physics-informed constraint terms into the standard loss function. The first is a fault type classification constraint, designed from the vibration pattern characteristics of different faults, which compels the network to learn tri-axial energy distributions that align with physical principles. The second is a severity constraint, established on the fundamental measurement principle of the monotonically increasing relationship between defect size and vibration amplitude. This term identifies severity levels through a triple-layer constraint mechanism comprising ordinal boundary, consistency, and smoothness regularization constraints, thereby incorporating prior physical knowledge into the measurement process. Additionally, a novel Short-time Fourier Transform-based Signal-to-RGB Image Mapping (STFT-STRIM) method is proposed for fusing multi-sensor measurement data. This technique converts the X, Y, and Z-axis vibration signals into time–frequency images via STFT and maps them to the red, green, and blue channels of an RGB image. Under small-sample training conditions, which simulate challenging measurement scenarios with limited data, PI-YOLO v8 model achieved high estimation accuracies of 97.1 % and 92.9 % for two public datasets, representing substantial improvements of 10.8 % and 11.9 % over the baseline YOLO v8 model.

现有的滚动轴承状态监测研究主要集中在故障类型的识别上，而对不同级别故障严重程度的量化研究较少。为了应对这些挑战，本研究引入了一个物理通知YOLO v8 （PI-YOLO v8）模型，通过嵌入损失函数中的两个新的物理通知约束项来同时估计轴承故障类型和严重程度。所提出的框架基于YOLO v8架构，通过将两个物理信息约束项合并到标准损失函数中来增强。首先是根据不同故障的振动模式特征设计的故障类型分类约束，这迫使网络学习符合物理原理的三轴能量分布。二是基于缺陷尺寸与振动幅值单调递增关系的基本测量原理建立的严重程度约束。该术语通过包含有序边界、一致性和平滑正则化约束的三层约束机制确定严重性级别，从而将先前的物理知识纳入测量过程。此外，提出了一种新的基于短时傅里叶变换的信号到rgb图像映射（STFT-STRIM）方法，用于融合多传感器测量数据。该技术通过STFT将X、Y和z轴振动信号转换为时频图像，并将它们映射到RGB图像的红、绿、蓝通道。在小样本训练条件下，以有限的数据模拟具有挑战性的测量场景，PI-YOLO v8模型在两个公共数据集上获得了97.1%和92.9%的高估计精度，比基线YOLO v8模型大幅提高了10.8%和11.9%。

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引用次数: 0

Local oscillator laser power locked laser heterodyne radiometer with variable optical attenuator 可变光衰减器本振激光功率锁定激光外差辐射计

IF 5.6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2026-05-05 Epub Date: 2026-03-09 DOI: 10.1016/j.measurement.2026.121079

Zhao Chen , Nianna Fu , Jiaoxu Mei , Kun Liu , Xiaoming Gao , Guishi Wang

Laser heterodyne radiometer (LHR) has attracted considerable attention for atmospheric greenhouse gases (GHGs) monitoring due to its high spectral resolution and compact architecture. However, the local oscillator (LO) power fluctuation is a main limitation for improving LHR retrieval precision. To resolve this problem, a method for locking the LO power to a constant value is introduced. A variable optical attenuator (VOA) was employed for real-time locking of the LO power during scanning with a PID control algorithm. With this technique, the LO power was well locked with a fluctuation of less than 0.01%. This effectively reduced the mean relative intensity noise (RIN) with the maximum reduction reaching 2.44 dBc/Hz and increased the signal-to-noise ratio (SNR) by 83% compared with a conventional LHR without LO power locking. Atmospheric CO₂ transmission spectra were measured in Hefei, China, using the VOA-assisted LHR with LO power stabilization. The system achieved a measurement short-term repeatability of 0.2%, which was basically better than the conventional LHR. The proposed LO power locked technique provides an alternative solution for developing a high-accuracy LHR for atmospheric GHGs remote sensing.

激光外差辐射计（LHR）以其高光谱分辨率和紧凑的结构在大气温室气体（GHGs）监测中受到广泛关注。然而，本振功率波动是制约LHR检索精度提高的主要因素。为了解决这个问题，介绍了一种将LO功率锁定到一个恒定值的方法。采用可变光衰减器（VOA）和PID控制算法实现扫描过程中LO功率的实时锁定。使用这种技术，LO功率被很好地锁定，波动小于0.01%。这有效地降低了平均相对强度噪声（RIN），最大降幅达到2.44 dBc/Hz，与没有LO功率锁定的传统LHR相比，信噪比（SNR）提高了83%。利用低功率稳定的voa辅助LHR测量了合肥地区大气CO2的透射光谱。该系统的测量短期重复性为0.2%，基本优于传统的LHR。提出的低功率锁定技术为开发高精度大气温室气体遥感LHR提供了另一种解决方案。

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