Measurement最新文献_第3页

Research on qualitative identification of equivalent wave based on underwater micro-triboelectric sensing method 基于水下微三电传感方法的等效波定性识别研究

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-24 DOI: 10.1016/j.measurement.2024.116309

Peng Yi , Qingbing Wei , Yong Fan , Baoping Cai , Chao Wang , Xueliang Pei , Zhe Li

Ocean exploration and underwater monitoring are becoming increasingly vital due to the abundant biological, mineral, energy, and other resources in the ocean. In this paper, a triboelectric nanogenerator (TENG) based on laser processing technology is proposed for underwater monitoring of mechanical motion/triggering as well as signal screening. A self-powered wave sensing unit with a stable electrical output is fabricated by means of a new performance enhancement method for dielectric friction materials combined with an external flexible waterproof encapsulated housing. The unit can accurately respond to a variety of external mechanical stimuli. More precisely, it can analyze three basic elements of waves: frequency, amplitude and waveform. We construct a wave equivalent testing platform and perform equivalent wave tests on the sensing unit to realize sensing and analysis of wave information. We also explore the response time of the sensing unit to changes in the wave conditions. The developed self-powered sensing unit based on the TENG technology is not only low-cost and has a simple structure, but is also accurate and stable. Finally, the proposed work provides new ideas for future seabed exploration and ocean monitoring.

由于海洋中蕴藏着丰富的生物、矿物、能源和其他资源，海洋勘探和水下监测正变得越来越重要。本文提出了一种基于激光加工技术的三电纳米发电机（TENG），用于水下监测机械运动/触发以及信号筛选。通过对介电摩擦材料采用新的性能增强方法，并结合外部柔性防水封装外壳，制造出了具有稳定电输出的自供电波传感装置。该装置可对各种外部机械刺激做出准确响应。更准确地说，它可以分析波的三个基本要素：频率、振幅和波形。我们建造了一个等效波测试平台，并在传感单元上进行等效波测试，以实现对波信息的传感和分析。我们还探讨了传感单元对波浪条件变化的响应时间。基于 TENG 技术开发的自供电传感单元不仅成本低、结构简单，而且精确稳定。最后，所提出的工作为未来的海底勘探和海洋监测提供了新思路。

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引用次数: 0

Training techniques and enhanced kinematic modeling for extended range twisted string actuators 大量程扭弦执行器的训练技术和增强的运动学建模

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-24 DOI: 10.1016/j.measurement.2024.116286

Junyeong Lee , Jinuk Kwon , Joowon Park , Gyumin Kim , Hyeondo Kim , Sukho Park

As a linear actuator, the twisted string actuator (TSA) offers ease of miniaturization, flexibility, and the capability to exert a strong actuation force, due to its large gear ratio. Owing to these characteristics, TSA has recently gained attention as a suitable small actuator for wearable devices, and surgical robots. However, the operating range relative to its size has been reported as limited. This study aims to overcome this limitation by extending the operating range of TSA using the coiling phase, following the twisting phase. To achieve this, we first analyze the training process to effectively mitigate the irregular overlapping phenomena in the coiling stage of TSA, considering the hysteresis and the training effect of TSA under different loads. Second, we address the limitations of conventional TSA kinematic models and propose an improved kinematic model for the extended operating range of TSA. Specifically, this study presents the precise transition points between the twisting and coiling phases of TSA and proposes enhanced analytical models for each phase. Finally, through various experiments, we validate the proposed training process and kinematic model for extended TSA operation. It is expected that the proposed training process and kinematic model for TSA will enable precise actuation within the extended operating range, facilitating a wider array of applications.

作为一种线性致动器，扭弦致动器（TSA）提供易于小型化，灵活性，并能够施加强大的致动力，由于其大传动比。由于这些特点，TSA最近作为一种适用于可穿戴设备和手术机器人的小型驱动器而受到关注。然而，据报道，相对于其大小的操作范围是有限的。本研究旨在克服这一限制，通过扩大TSA的工作范围，使用卷曲阶段，在扭转阶段之后。为此，我们首先分析了训练过程，以有效缓解TSA卷绕阶段的不规则重叠现象，同时考虑了不同载荷下TSA的滞后和训练效果。其次，我们解决了传统的TSA运动学模型的局限性，并提出了一个改进的运动学模型，以扩大TSA的工作范围。具体而言，本研究提出了TSA扭转和盘绕阶段之间的精确过渡点，并提出了每个阶段的增强分析模型。最后，通过各种实验，我们验证了所提出的训练过程和运动学模型用于扩展TSA操作。预计提出的TSA训练过程和运动学模型将在更大的操作范围内实现精确驱动，促进更广泛的应用。

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引用次数: 0

Design and application of a new high-performance flexible six-axis force/torque sensor for massage therapy 用于按摩治疗的新型高性能柔性六轴力/扭矩传感器的设计与应用

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-24 DOI: 10.1016/j.measurement.2024.116312

Lu Liu , Xiaoli Jia , Qingyu Yang , Jinglong Zhang , Liaoliang Ke , Jie Yang , Sritawat Kitipornchai

Precise sensing of force magnitude and direction in massage therapy can significantly enhance treatment effectiveness. Despite advancements in flexible multidimensional force sensors, achieving comprehensive spatial force sensing with soft materials remains challenging. This study presents a novel flexible six-axis force/torque sensor, calibrated using a deep neural network. The calibrated sensor exhibits a maximum class I error of 0.603%F.S. and a maximum class II error of 0.751%F.S., indicating excellent measurement performance. Demonstrated in massage physiotherapy, the sensor effectively distinguishes between various techniques and accurately detects subtle force variations within the same technique. The present work introduces a novel strategy for designing flexible six-axis force/torque sensors, thereby laying the groundwork for advancements in intelligent robotics, human–computer interfaces, as well as in the fields of rehabilitation and medicine.

在按摩治疗中，准确感知力的大小和方向，可以显著提高治疗效果。尽管柔性多维力传感器取得了进步，但用软材料实现全面的空间力传感仍然具有挑战性。本研究提出了一种新型柔性六轴力/扭矩传感器，使用深度神经网络进行校准。校准后的传感器显示最大I类误差为0.603%F.S。最大II类误差为0.751%F.S。，表明测量性能优异。在按摩理疗中，该传感器有效地区分了各种技术，并准确地检测到同一技术中细微的力变化。本文介绍了一种设计柔性六轴力/扭矩传感器的新策略，从而为智能机器人、人机界面以及康复和医学领域的进步奠定了基础。

引用次数: 0

Embedded YOLO v8: Real-time detection of sugarcane nodes in complex natural environments by rapid structural pruning method 嵌入式YOLO v8：通过快速结构修剪方法实时检测复杂自然环境中的甘蔗节点

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-23 DOI: 10.1016/j.measurement.2024.116291

Shanshan Hu , Guoxin Tang , Kang Yu , Wen Chen , Zhiwei Wang

In the exploring of sugarcane intelligent harvesting technology, the target detection task of sugarcane node faces serious challenges on low detection accuracy affected by complex natural environment and the time-consuming task to lightweight algorithm structure. In addition, the classical target detection model, such as YOLO (You Only Look Once), with requirement of huge computationally ability puts huge computing pressure to the embedded device for sugarcane nodes detection. In order to solve these problems, this study proposed an improved YOLOv8n-ghost, which adopted Ghost module to build lightweight network and reduce model redundancy while ensuring performance. The dataset was extended and diversified by data classification and enhancement to increasing the robustness and generalization ability of the model. The structured pruning method based on DepGraph (Dependency Graph) was adopted to compress the optimized model, which greatly reduced the complexity of the model. To reduce experimentation time and computational cost, a rapid method for the optimal pruning rate was proposed by combination of big-small step size to quickly achieve the optimal pruning rate with shorter experiment time and satisfied accuracy. By comparison of different pruning methods and lightweight methods, the AP (Average Precision) of model Pruned 65 %-YOLOv8n-ghost was > 90 % with only 0.53 M parameters and 2.2G FLOPs. Finally, the model was accelerated with TensorRT and tested in the embedded device. The real-time speed of the improved model approached 30 frames per second, which was 200 % higher than before and met the requirements of real-time detection of sugarcane node.

在甘蔗智能收获技术的探索中，甘蔗节点的目标检测任务面临着受复杂自然环境影响检测精度低、算法结构轻量化耗时长的严峻挑战。此外，YOLO （You Only Look Once）等经典目标检测模型对计算能力要求很高，对甘蔗节点检测的嵌入式设备带来了巨大的计算压力。为了解决这些问题，本研究提出了一种改进的YOLOv8n-ghost，采用Ghost模块构建轻量级网络，在保证性能的同时减少模型冗余。通过数据分类和增强对数据集进行扩展和多样化，提高模型的鲁棒性和泛化能力。采用基于依赖图（DepGraph）的结构化剪枝方法对优化后的模型进行压缩，大大降低了模型的复杂度。为了减少实验时间和计算成本，提出了一种大小步长相结合的快速最优剪枝率方法，以更短的实验时间和满意的精度快速获得最优剪枝率。通过对不同剪枝方法和轻量化方法的比较，剪枝65% -YOLOv8n-ghost模型的AP （Average Precision）为>；90%，参数为0.53 M， FLOPs为2.2G。最后，利用TensorRT对模型进行加速，并在嵌入式设备上进行测试。改进模型的实时速度接近30帧/秒，比以前提高了200%，满足了甘蔗节点实时检测的要求。

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引用次数: 0

Multi-architecture optimization of pipeline inner wall defect detection algorithm based on YOLOv8 基于 YOLOv8 的管道内壁缺陷检测算法的多架构优化

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-23 DOI: 10.1016/j.measurement.2024.116305

Qian Zhao, Gaojuan Wang

As traditional computer vision technology struggles to meet the demands for accurate detection of modern supply pipes. A detection model based on an improved YOLOv8 network has been proposed. First, the Inverted Residual Mobile Block (iRMB) is integrated into the backbone network. This effectively enhances the feature representation capability for extracting image defects. Next, a shift-wise operator is introduced to simulate the effects of using a large convolutional kernel at a lower computational cost while improving performance. Finally, GSConv replaces the Conv layer in the neck network, and the VoVGSCSP module substitutes the C2f module in the neck network to further enhance the algorithm’s detection accuracy for corroded regions. Experimental results demonstrate that the mean Average Precision (mAP) of the improved algorithm reaches 95.0 % on the dataset of pipeline inner wall corrosion defects. This provides an accurate method for the intelligent identification of corrosion defects on the pipeline dataset.

传统的计算机视觉技术难以满足现代供水管道精确检测的需求。我们提出了一种基于改进型 YOLOv8 网络的检测模型。首先，在骨干网络中集成了反向残留移动块（iRMB）。这有效增强了提取图像缺陷的特征表示能力。其次，引入了移位算子，以模拟使用大卷积核的效果，在提高性能的同时降低了计算成本。最后，GSConv 取代了颈部网络中的 Conv 层，VoVGSCSP 模块取代了颈部网络中的 C2f 模块，进一步提高了算法对腐蚀区域的检测精度。实验结果表明，在管道内壁腐蚀缺陷数据集上，改进算法的平均精度（mAP）达到了 95.0%。这为智能识别管道数据集上的腐蚀缺陷提供了一种准确的方法。

引用次数: 0

Low-temperature-crosstalk humidity fiber sensor based on a thermal expansion-resistant Fabry-Perot interferometer 基于耐热膨胀法布里-珀罗干涉仪的低温串扰湿度光纤传感器

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-23 DOI: 10.1016/j.measurement.2024.116302

Haiming Qiu , Mengxuan Zhu , Shuji Mo , Ying Gao , Yong Yao , Yongkang Dong , Jiajun Tian

A low-temperature-crosstalk fiber-optic humidity sensor based on a thermal expansion-resistant Fabry-Pérot interferometer (FPI) has been proposed. This sensor is constructed by cascading a single-mode fiber, a hollow core fiber, an air-clad photonic crystal fiber (APCF), and a polyimide microrod. The two mirrors of FPI are composed of the end faces of single-mode optical fiber and polyimide microrod (located on the end face of APCF). Thus, hollow core fiber thermal expansion will offset the change in FPI length caused by polyimide microrod thermal expansion, thereby achieving low temperature sensitivity. In contrast, an increase in humidity causes the polyimide microrod to absorb moisture and swell, resulting in a shortening of the FPI. Experimentally, the sensor’s humidity sensitivity between 30 % RH and 80 % RH and temperature sensitivity between 20 °C and 200 °C are 203.1 pm/% RH and 0.95 pm/°C, respectively. With a temperature cross-sensitivity as low as 0.0047 % RH/°C. This article provided a novel solution for achieving low temperature cross sensitivity in humidity sensors.

我们提出了一种基于耐热膨胀法布里-佩罗干涉仪（FPI）的低温串扰光纤湿度传感器。该传感器由单模光纤、中空纤芯光纤、空气包覆光子晶体光纤（APCF）和聚酰亚胺微棒级联而成。FPI 的两个反射镜由单模光纤的端面和聚酰亚胺微镜（位于 APCF 的端面）组成。因此，中空芯光纤的热膨胀将抵消聚酰亚胺微棒热膨胀引起的 FPI 长度变化，从而实现低温度灵敏度。相反，湿度增加会导致聚酰亚胺微晶块吸湿膨胀，从而缩短 FPI。实验结果表明，传感器在 30%RH 和 80%RH 之间的湿度灵敏度以及在 20 °C 和 200 °C 之间的温度灵敏度分别为 203.1 pm/%RH 和 0.95 pm/°C。温度交叉灵敏度低至 0.0047 % RH/°C。这篇文章为实现湿度传感器的低温交叉灵敏度提供了一种新颖的解决方案。

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引用次数: 0

An enhanced manifold alignment method for fault diagnosis of liquid-cooled plate based on manifold distance features 基于流形距离特征的液冷板故障诊断改进流形对准方法

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-23 DOI: 10.1016/j.measurement.2024.116303

Xiaoping Liu , Chen Shang , Wei Wang , Mingmin Wu , Hong Bao

The application of data-driven fault diagnosis in structural health monitoring (SHM) encounters several challenges. One major obstacle is obtaining a large quantity of accurate fault samples in certain scenarios. Additionally, the complexity of working environments exacerbates the difficulty of diagnostics due to significant distribution disparities among samples from different environments. This paper proposes a manifold alignment method based on manifold distance features (MDFMA), aiming to address the issue of insufficient fault samples during the fault diagnosis process. Initially, a simulated physical model of the monitoring object is established to generate a large number of simulated fault samples. These are supplemented with a small number of actual fault samples and then merged into a training dataset. Subsequently, the training samples undergo reconstruction employing the shortest manifold distance feature. Then the cosine similarity (COS) measuring method is applied to calculate the similarity between simulated and actual samples, thereby facilitating cross-domain feature alignment. Finally, all sample points are projected into the labeled space, and the manifold alignment (MA) method is employed to predict the state of the monitoring subject. The experiments conducted on the liquid-cooled plate confirmed the effectiveness and feasibility of the proposed method. The results demonstrate that this approach exhibits superior robustness and applicability in practical applications compared to other manifold alignment methods.

数据驱动故障诊断在结构健康监测中的应用面临着诸多挑战。一个主要的障碍是在某些情况下获得大量准确的故障样本。此外，由于来自不同环境的样本之间存在显著的分布差异，工作环境的复杂性加剧了诊断的难度。针对故障诊断过程中故障样本不足的问题，提出了一种基于流形距离特征（MDFMA）的流形对齐方法。首先建立监测对象的模拟物理模型，生成大量的模拟故障样本。这些数据与少量实际故障样本相补充，然后合并成一个训练数据集。随后，利用最短流形距离特征对训练样本进行重构。然后采用余弦相似度（COS）测量方法计算模拟样本与实际样本之间的相似度，从而便于跨域特征对齐。最后，将所有样本点投影到标记空间中，并采用流形对齐（MA）方法预测监测对象的状态。在液冷板上进行的实验验证了该方法的有效性和可行性。结果表明，该方法在实际应用中具有较好的鲁棒性和适用性。

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引用次数: 0

mL-level low gas consumption PAS sensor for dual gases CH4/C2H2 detection based on an optimized TT-type resonator 基于优化 TT 型谐振器的毫升级低气耗 PAS 传感器，用于检测 CH4/C2H2 两种气体

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-23 DOI: 10.1016/j.measurement.2024.116288

Fupeng Wang , Liyan Fu , Jianguo Zhang , Jiachen Sun , Ze Han , Shuo Pang , Qingsheng Xue , Diansheng Cao , Qian Li , Qiang Wang

The miniaturization of photoacoustic (PA) cell is an important and practical direction for the area of photoacoustic spectroscopy (PAS) trace gas detection to save sample consumption. In this study, a CH₄/C₂H₂ PAS sensor with gas consumption of only 1.1 mL is developed for the purpose of transformer oil diagnosis. A new TT-type resonator is proposed and optimized to design the PA cell with characteristics of easy integration, low gas consumption, differential detection, and photoacoustic enhancement. The special design of TT-type resonator allows us to install the microphones at the resonant center of the acoustic field, which avoids the probing loss when inserting the microphones as in conventional Helmholtz cell. Based on the experimental results, a good linearity of R² better than 0.999 is achieved for both CH₄ and C₂H₂ in a full concentration range of 0–5000 ppm. The noise equivalent sensitivity is evaluated to be 0.25 ppm and 77 ppb for CH₄ and C₂H₂ respectively. In addition, the noise source, gas exchanging efficiency and improvement potential of the TT-type resonator are also analyzed in this study.

光声（PA）电池的微型化是光声光谱（PAS）痕量气体检测领域节省样品消耗的一个重要而实用的方向。本研究开发了一种耗气量仅为 1.1 mL 的 CH4/C2H2 PAS 传感器，用于变压器油诊断。提出并优化了一种新型 TT 型谐振器，用于设计 PA 单元，该单元具有易于集成、气体消耗量低、差分检测和光声增强等特点。TT 型谐振器的特殊设计使我们可以将传声器安装在声场的谐振中心，从而避免了传统亥姆霍兹电池在插入传声器时的探测损耗。根据实验结果，在 0-5000 ppm 的全浓度范围内，CH4 和 C2H2 的线性度 R2 均优于 0.999。经评估，CH4 和 C2H2 的噪声等效灵敏度分别为 0.25 ppm 和 77 ppb。此外，本研究还分析了 TT 型谐振器的噪声源、气体交换效率和改进潜力。

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引用次数: 0

Analysis on identifying thin-plate void parameters in concrete based on vibro-acoustic method 基于振动声学方法的混凝土薄板空隙参数识别分析

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-23 DOI: 10.1016/j.measurement.2024.116281

Yong Yang, Weigang Zhao

Thin-plate voids are common defects found in concrete structures, such as tunnel vaults. Parameter identification for concrete thin plates using the vibro-acoustic method is a crucial approach for rapid and accurate detection in specific locations. In this study, a fixed-support rectangular thin-plate vibration model was developed, and the vibro-acoustic echo components were derived under external excitation. Based on this model, a method for identifying horizontal dimensions was proposed, utilizing the power spectrum frequency distribution. Subsequently, a digital filter based on Wavelet Synchronous Squeezing Transform (WSST) was constructed to obtain the modal vibration echo. A fitting method for the vibro-acoustic echo attenuation coefficient and a correction for thin-plate thickness were implemented. Finally, the accuracy and applicability of identifying the size parameters of thin plates were verified using square specimens with thicknesses of 12 mm, 21 mm, and 30 mm, as well as circular specimens with a thickness of 15 mm.

薄板空洞是隧道拱顶等混凝土结构中常见的缺陷。使用振动声学方法对混凝土薄板进行参数识别是快速准确检测特定位置的重要方法。本研究建立了一个固定支撑矩形薄板振动模型，并推导了外部激励下的振声回波分量。在此模型的基础上，提出了一种利用功率谱频率分布识别水平尺寸的方法。随后，构建了基于小波同步挤压变换（WSST）的数字滤波器，以获得模态振动回声。此外，还采用了振动回声衰减系数拟合方法和薄板厚度修正方法。最后，使用厚度为 12 毫米、21 毫米和 30 毫米的方形试样以及厚度为 15 毫米的圆形试样验证了识别薄板尺寸参数的准确性和适用性。

引用次数: 0

Signal flux and time-of-flight estimation based on waveform optimization for single-photon LiDAR 基于单光子激光雷达波形优化的信号通量和飞行时间估算

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-22 DOI: 10.1016/j.measurement.2024.116239

Linjie Lyu , Duan Li , Tengfei Wu , Qinggai Mi , Yanhong Jiang , Lijun Xu

Single-photon LiDAR is widely used for target detection and 3D imaging. Traditional flux inversion methods, which count the echo photons of the signal, struggle with high signal fluxes due to dead time and a detection probability close to 100 %. This paper presents a new approach that uses the temporal information of the echo photons to estimate LiDAR fluxes in the range [0.5, 21] photons instead of simply counting the echo signal photons. The calibration of the laser pulse shape is performed using the GMM (Gaussian Mixture Model) method, which allows for the optimal signal flux value to be identified for describing the measured histogram. The waveform optimization is used to compensate for walking errors and improve range accuracy. Simulation results show that the method achieves a mean absolute error (MRE) within 9.55 % and a distance estimation accuracy of 12.01 mm. Changing the noise from 10 kHz to 100 kHz did not significantly degrade the algorithm performance. In the experiment, the depth precision can be better than 15.07 mm. Under the condition of different signal fluxes, the inversion values of signal flux and distance of the proposed method will converge to the ground truth with higher accuracy, which will more suitable for high dynamic range scene ranging and imaging.

单光子激光雷达被广泛用于目标探测和三维成像。传统的通量反演方法对信号的回波光子进行计数，但由于死区时间和接近 100 % 的检测概率，这种方法在高信号通量下很难奏效。本文提出了一种新方法，利用回波光子的时间信息来估算[0.5, 21]光子范围内的激光雷达通量，而不是简单地计算回波信号光子。采用 GMM（高斯混杂模型）方法对激光脉冲形状进行校准，从而确定描述测量直方图的最佳信号通量值。波形优化用于补偿行走误差，提高测距精度。模拟结果表明，该方法的平均绝对误差（MRE）不超过 9.55%，距离估计精度为 12.01 毫米。将噪声从 10 kHz 改为 100 kHz 并没有明显降低算法性能。在实验中，深度精度可优于 15.07 毫米。在不同的信号通量条件下，所提方法的信号通量和距离反演值将以更高的精度收敛到地面实况，更适用于高动态范围场景测距和成像。

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引用次数: 0