Pub Date : 2024-07-23DOI: 10.1088/1361-6501/ad6681
Frank J Van Kann, Alexey V Veryaskin
A novel room temperature capacitive sensor interface circuit is proposed and successfully tested, which uses a modified All-Pass filter architecture combined with a simple series resonant tank circuit with a moderate Q-factor. It is fashioned from a discrete inductor with small dissipation resonating with a grounded capacitor acting as the sensing element to obtain a resolution of ∆C ~ 2 zF in a capacitance range of 10 – 30 pF. The circuit converts the change in capacitance to the change in the phase of a carrier signal in a frequency range with a central frequency set up by the tank circuit’s resonant frequency and is configured to act as a close approximation of the ideal All-Pass filter. This cancels out the effects of amplitude modulation when the carrier signal is imperfectly tuned to the resonance. The proposed capacitive sensor interface has been specifically developed for use as a front-end constituent in ultra-precision mechanical displacement measurement systems, such as accelerometers, seismometers, gravimeters and gravity gradiometers, where moving plate grounded air gap capacitors are frequently used. Some other applications of the proposed circuit are possible including the measurement of the electric field, where the sensing capacitor depends on the applied electric field, and cost effective capacitive gas sensors. In addition, the circuit can be easily adapted to function with very small capacitance values (1 - 2 pF) as is typical in MEMS-based transducers.
{"title":"A Novel Capacitive Sensor Interface Based on a Simple Capacitance-to-Phase Converter","authors":"Frank J Van Kann, Alexey V Veryaskin","doi":"10.1088/1361-6501/ad6681","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6681","url":null,"abstract":"\u0000 A novel room temperature capacitive sensor interface circuit is proposed and successfully tested, which uses a modified All-Pass filter architecture combined with a simple series resonant tank circuit with a moderate Q-factor. It is fashioned from a discrete inductor with small dissipation resonating with a grounded capacitor acting as the sensing element to obtain a resolution of ∆C ~ 2 zF in a capacitance range of 10 – 30 pF. The circuit converts the change in capacitance to the change in the phase of a carrier signal in a frequency range with a central frequency set up by the tank circuit’s resonant frequency and is configured to act as a close approximation of the ideal All-Pass filter. This cancels out the effects of amplitude modulation when the carrier signal is imperfectly tuned to the resonance. The proposed capacitive sensor interface has been specifically developed for use as a front-end constituent in ultra-precision mechanical displacement measurement systems, such as accelerometers, seismometers, gravimeters and gravity gradiometers, where moving plate grounded air gap capacitors are frequently used. Some other applications of the proposed circuit are possible including the measurement of the electric field, where the sensing capacitor depends on the applied electric field, and cost effective capacitive gas sensors. In addition, the circuit can be easily adapted to function with very small capacitance values (1 - 2 pF) as is typical in MEMS-based transducers.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"117 36","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141811904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-22DOI: 10.1088/1361-6501/ad6625
Lai hu Peng, Yongchao Hu, Jianyi Zhang, Jianwei Lin
Natural gas pipelines are an essential part of the economy. Natural gas pipelines may leak after aging, strong vibration signals may be generated in the pipeline when leakage occurs, and vibration signals may be noisy. Traditional variational mode decomposition (VMD) noise reduction methods need to set parameters in advance, and so may not achieve the best decomposition effect. To solve this problem, this paper proposes a method for pipeline leakage location based on the sparrow search algorithm (SSA) optimization of VMD combined with generalized quadratic cross-correlation. The method first calculates the original signal-to-noise ratio, and if the signal-to-noise ratio is low, wavelet threshold denoising is used to process the signal. Then, SSA optimization is used to refine the two key parameters of VMD (penalty parameter α and mode decomposition number K) based on sample entropy. Subsequently, the signal undergoes decomposition into K intrinsic mode function (IMF) components through VMD according to the obtained analysis parameter combination. Then, the IMF components are screened to obtain the reconstructed signal. Finally, the noise reduction signal is obtained. The signal delay after noise reduction is obtained through a generalized quadratic cross-correlation and the accurate leakage position is obtained using the delay. Experiments showed that the minimum relative error of this method could reach 0.6%, which was more accurate than the traditional VMD method, and effectively improved the accuracy of noisy signals in pipeline leakage locations.
天然气管道是经济的重要组成部分。天然气管道老化后可能会发生泄漏，泄漏时管道内可能会产生强烈的振动信号，振动信号可能会产生噪声。传统的变模分解（VMD）降噪方法需要提前设置参数，因此可能无法达到最佳的分解效果。为解决这一问题，本文提出了一种基于麻雀搜索算法（SSA）优化 VMD 并结合广义二次交叉相关的管道泄漏定位方法。该方法首先计算原始信噪比，如果信噪比较低，则采用小波阈值去噪处理信号。然后，根据样本熵，使用 SSA 优化来完善 VMD 的两个关键参数（惩罚参数 α 和模式分解数 K）。随后，根据获得的分析参数组合，通过 VMD 将信号分解为 K 个本征模式函数（IMF）分量。然后，对 IMF 分量进行筛选，得到重建信号。最后得到降噪信号。通过广义二次交叉相关获得降噪后的信号延迟，并利用延迟获得准确的泄漏位置。实验表明，该方法的最小相对误差可达 0.6%，比传统的 VMD 方法更加精确，有效提高了噪声信号在管道泄漏位置中的准确性。
{"title":"Pipeline leak location method based on SSA-VMD with generalized quadratic cross-correlation","authors":"Lai hu Peng, Yongchao Hu, Jianyi Zhang, Jianwei Lin","doi":"10.1088/1361-6501/ad6625","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6625","url":null,"abstract":"\u0000 Natural gas pipelines are an essential part of the economy. Natural gas pipelines may leak after aging, strong vibration signals may be generated in the pipeline when leakage occurs, and vibration signals may be noisy. Traditional variational mode decomposition (VMD) noise reduction methods need to set parameters in advance, and so may not achieve the best decomposition effect. To solve this problem, this paper proposes a method for pipeline leakage location based on the sparrow search algorithm (SSA) optimization of VMD combined with generalized quadratic cross-correlation. The method first calculates the original signal-to-noise ratio, and if the signal-to-noise ratio is low, wavelet threshold denoising is used to process the signal. Then, SSA optimization is used to refine the two key parameters of VMD (penalty parameter α and mode decomposition number K) based on sample entropy. Subsequently, the signal undergoes decomposition into K intrinsic mode function (IMF) components through VMD according to the obtained analysis parameter combination. Then, the IMF components are screened to obtain the reconstructed signal. Finally, the noise reduction signal is obtained. The signal delay after noise reduction is obtained through a generalized quadratic cross-correlation and the accurate leakage position is obtained using the delay. Experiments showed that the minimum relative error of this method could reach 0.6%, which was more accurate than the traditional VMD method, and effectively improved the accuracy of noisy signals in pipeline leakage locations.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"36 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141815137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-22DOI: 10.1088/1361-6501/ad6622
A. K. Khambampati, Felipe Alberto Solano Sanchez, M. Jeon, Kyung Youn Kim
During pregnancy, it is important to monitor the health of the fetus and fetal movement count is one of the key parameters that can be used to check the health of the fetus. Consequently, there is growing interest in developing non-invasive passive methods for fetal monitoring techniques that can be used outside of clinical settings. This study introduces a home-use system based on electrical resistance tomography (ERT) that pregnant mothers can utilize for fetus monitoring. The setup utilizes a conductive fabric, functioning as electronic skin (e-skin), positioned on the mother's abdomen to detect alterations in the fabric's electrical characteristics caused by fetal movements. This method is validated through both numerical simulations and experimental investigations, which assess conductivity changes on the fabric's surface in reaction to localized pressure fluctuations, mimicking fetal motions.
{"title":"ERT-Based fetus monitoring system using wearable conductive fabrics","authors":"A. K. Khambampati, Felipe Alberto Solano Sanchez, M. Jeon, Kyung Youn Kim","doi":"10.1088/1361-6501/ad6622","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6622","url":null,"abstract":"\u0000 During pregnancy, it is important to monitor the health of the fetus and fetal movement count is one of the key parameters that can be used to check the health of the fetus. Consequently, there is growing interest in developing non-invasive passive methods for fetal monitoring techniques that can be used outside of clinical settings. This study introduces a home-use system based on electrical resistance tomography (ERT) that pregnant mothers can utilize for fetus monitoring. The setup utilizes a conductive fabric, functioning as electronic skin (e-skin), positioned on the mother's abdomen to detect alterations in the fabric's electrical characteristics caused by fetal movements. This method is validated through both numerical simulations and experimental investigations, which assess conductivity changes on the fabric's surface in reaction to localized pressure fluctuations, mimicking fetal motions.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"4 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141816593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-22DOI: 10.1088/1361-6501/ad6630
Lei Zhang, Chaoping Zang, Tong Jing
Continuous Scanning Laser Doppler Vibrometry (CSLDV) enables fast and full-field vibration measurement in an intact area of a structure. This paper further proposes a novel adaptive CSLDV method that can obtain the Operational Deflection Shapes (ODS) and mode shape of structures with holes. Firstly, an adaptive path planning method is applied to the surface of the tested structure, which can automatically adapt to the size and position of round and square holes, as well as the resolution requirements for the measurement. Secondly, based on the symmetry and other characteristics of the planned path, the time domain signal is processed by delay optimization and demodulation to obtain the ODS of each vibration mode. Finally, a modal test is conducted on a flat structure with a square and circle holes as an example. A validation experiment is also performed using SLDV. The results show that the mode shapes obtained from both methods are consistent and the Modal Assurance Criterion (MAC) between them are all above 0.96. The method can realize CSLDV of flat plate structures with arbitrary square and round holes, and has the advantages of high efficiency and dense measurement points, which enhances its engineering applicability.
{"title":"An adaptive continuous scanning laser Doppler vibrometry technique for measuring vibrational mode shapes of structures with holes","authors":"Lei Zhang, Chaoping Zang, Tong Jing","doi":"10.1088/1361-6501/ad6630","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6630","url":null,"abstract":"\u0000 Continuous Scanning Laser Doppler Vibrometry (CSLDV) enables fast and full-field vibration measurement in an intact area of a structure. This paper further proposes a novel adaptive CSLDV method that can obtain the Operational Deflection Shapes (ODS) and mode shape of structures with holes. Firstly, an adaptive path planning method is applied to the surface of the tested structure, which can automatically adapt to the size and position of round and square holes, as well as the resolution requirements for the measurement. Secondly, based on the symmetry and other characteristics of the planned path, the time domain signal is processed by delay optimization and demodulation to obtain the ODS of each vibration mode. Finally, a modal test is conducted on a flat structure with a square and circle holes as an example. A validation experiment is also performed using SLDV. The results show that the mode shapes obtained from both methods are consistent and the Modal Assurance Criterion (MAC) between them are all above 0.96. The method can realize CSLDV of flat plate structures with arbitrary square and round holes, and has the advantages of high efficiency and dense measurement points, which enhances its engineering applicability.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"28 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141815622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-22DOI: 10.1088/1361-6501/ad662d
Junwei Hu, Chao Xie
Real-time and accurate predictive maintenance of industrial equipment is fundamental for ensuring the safety and stability of advanced manufacturing processes. Current fault diagnosis methods based on data mining rely on a large number of labeled samples, and obtaining sufficient labeled data for diagnosing industrial equipment faults is challenging. Meta-learning can achieve the diagnosis of few-shot samples to a certain extent, but the effect is not ideal. Semi-supervision can effectively leverage a large number of unlabeled samples, which is of great practical significance for handling scenarios involving limited labeled samples. However, noise interference can occur when unlabeled samples appear that do not belong to known categories. Therefore, this study proposes adaptive semi-supervised meta-learning networks for noisy few-shot gearbox fault diagnosis. Firstly, a residual network with a Morlet Wavelet layer is used to extract signal features. Next, sample-level attention is defined to select unlabeled samples that are more similar to labeled sample prototypes, thereby reducing the influence of noisy samples. The adaptive metric is used to obtain the relational distance functions of labeled samples and unlabeled samples. Adaptive semi-supervised meta-learning networks uses unlabeled data to refine prototypes for better fault diagnosis. The effectiveness and anti-noise performance of the proposed method are verified by using two gearbox datasets with various few-shot noise scenarios.
{"title":"Semi-supervised adaptive anti-noise meta-learning for few-shot industrial gearbox fault diagnosis","authors":"Junwei Hu, Chao Xie","doi":"10.1088/1361-6501/ad662d","DOIUrl":"https://doi.org/10.1088/1361-6501/ad662d","url":null,"abstract":"\u0000 Real-time and accurate predictive maintenance of industrial equipment is fundamental for ensuring the safety and stability of advanced manufacturing processes. Current fault diagnosis methods based on data mining rely on a large number of labeled samples, and obtaining sufficient labeled data for diagnosing industrial equipment faults is challenging. Meta-learning can achieve the diagnosis of few-shot samples to a certain extent, but the effect is not ideal. Semi-supervision can effectively leverage a large number of unlabeled samples, which is of great practical significance for handling scenarios involving limited labeled samples. However, noise interference can occur when unlabeled samples appear that do not belong to known categories. Therefore, this study proposes adaptive semi-supervised meta-learning networks for noisy few-shot gearbox fault diagnosis. Firstly, a residual network with a Morlet Wavelet layer is used to extract signal features. Next, sample-level attention is defined to select unlabeled samples that are more similar to labeled sample prototypes, thereby reducing the influence of noisy samples. The adaptive metric is used to obtain the relational distance functions of labeled samples and unlabeled samples. Adaptive semi-supervised meta-learning networks uses unlabeled data to refine prototypes for better fault diagnosis. The effectiveness and anti-noise performance of the proposed method are verified by using two gearbox datasets with various few-shot noise scenarios.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"31 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141816955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-22DOI: 10.1088/1361-6501/ad6628
Yuebo Lai, Bing Liu
Efficient and precise identification of road pavement cracks contributes to better evaluation of road conditions. In practical road maintenance and safety assessment, traditional manual crack detection methods are time-consuming, physically demanding, and highly subjective. In addition, crack recognition based on image processing techniques lacks robustness. In this paper, a multi-branch feature fusion road crack segmentation network model (DTPC) based on deep convolution and transformer modules is proposed. The model is used for pixel-level segmentation of road crack images, which is a good solution to the existing needs and helps to repair dangerous cracks promptly in the follow-up work to prevent serious disasters due to crack breakage. Firstly, combine deep convolution with transformer modules to achieve precise local extraction and global contextual feature extraction. Secondly, a dual-channel attention mechanism is em-ployed to help the model better address information loss and positional offset issues. Finally, three-branch outputs are fused to obtain prediction maps that intuitively determine recognition results. The proposed model is tested for accuracy using a dedicated road pavement crack dataset. Results show that compared to mainstream models such as SegFormer, HRNet, PSPNet, and FCN, the DTPC model achieves the highest MIoU score (86.72%) and F1 score (92.49%).
高效、精确地识别路面裂缝有助于更好地评估道路状况。在实际道路维护和安全评估中，传统的人工裂缝检测方法耗时长、耗体力，而且主观性强。此外，基于图像处理技术的裂缝识别缺乏鲁棒性。本文提出了一种基于深度卷积和变压器模块的多分支特征融合道路裂缝分割网络模型（DTPC）。该模型用于道路裂缝图像的像素级分割，很好地解决了现有需求，有助于在后续工作中及时修复危险裂缝，防止因裂缝断裂造成严重灾害。首先，将深度卷积与变换器模块相结合，实现精确的局部提取和全局上下文特征提取。其次，采用双通道关注机制，帮助模型更好地处理信息丢失和位置偏移问题。最后，融合三分支输出以获得预测图，从而直观地确定识别结果。我们使用专用的路面裂缝数据集测试了所提出模型的准确性。结果表明，与 SegFormer、HRNet、PSPNet 和 FCN 等主流模型相比，DTPC 模型的 MIoU 得分（86.72%）和 F1 得分（92.49%）最高。
{"title":"Research on road crack segmentation based on deep convolution and transformer with multi-branch feature fusion.","authors":"Yuebo Lai, Bing Liu","doi":"10.1088/1361-6501/ad6628","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6628","url":null,"abstract":"\u0000 Efficient and precise identification of road pavement cracks contributes to better evaluation of road conditions. In practical road maintenance and safety assessment, traditional manual crack detection methods are time-consuming, physically demanding, and highly subjective. In addition, crack recognition based on image processing techniques lacks robustness. In this paper, a multi-branch feature fusion road crack segmentation network model (DTPC) based on deep convolution and transformer modules is proposed. The model is used for pixel-level segmentation of road crack images, which is a good solution to the existing needs and helps to repair dangerous cracks promptly in the follow-up work to prevent serious disasters due to crack breakage. Firstly, combine deep convolution with transformer modules to achieve precise local extraction and global contextual feature extraction. Secondly, a dual-channel attention mechanism is em-ployed to help the model better address information loss and positional offset issues. Finally, three-branch outputs are fused to obtain prediction maps that intuitively determine recognition results. The proposed model is tested for accuracy using a dedicated road pavement crack dataset. Results show that compared to mainstream models such as SegFormer, HRNet, PSPNet, and FCN, the DTPC model achieves the highest MIoU score (86.72%) and F1 score (92.49%).","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"25 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141816981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-22DOI: 10.1088/1361-6501/ad6624
Atharva Hans, Sayantan Bhattacharya, Kairui Hao, P. Vlachos, Ilias Bilionis
Measuring particles' three-dimensional (3D) positions using multi-camera images in fluid dynamics is critical for resolving spatiotemporally complex flows like turbulence and mixing. However, current methods are prone to errors due to camera noise, optical configuration and experimental setup limitations, and high seeding density, which compound to create fake measurements (ghost particles) and add noise and error to velocity estimations. We introduce a Bayesian Volumetric Reconstruction (BVR) method, addressing these challenges by using probability theory to estimate uncertainties in particle position predictions. Our method assumes a uniform distribution of particles within the reconstruction volume and employs a model mapping particle positions to observed camera images. We utilize variational inference with a modified loss function to determine the posterior distribution over particle positions. Key features include a penalty term to reduce ghost particles, provision of uncertainty bounds, and scalability through subsampling. In tests with synthetic data and four cameras, BVR achieved 95% accuracy with less than 3% ghost particles and an RMS error under 0.3 pixels at a density of 0.1 particles per pixel (ppp). This shows 57% to 97% less ghost particles and 1.3 to 2 times lower RMS error than standard MART and IPR reconstructions. In an experimental Poiseuille flow measurement, our method closely matched the theoretical solution. Additionally, in a complex cerebral aneurysm basilar tip geometry flow experiment, our reconstructions were dense and consistent with observed flow patterns. Our BVR method effectively reconstructs particle positions in complex 3D flows, particularly in situations with high particle image densities and camera distortions. It distinguishes itself by providing quantifiable uncertainty estimates and scaling efficiently for larger image dimensions, making it applicable across a range of fluid flow scenarios.
{"title":"Bayesian Reconstruction of 3D Particle Positions in High-Seeding Density Flows","authors":"Atharva Hans, Sayantan Bhattacharya, Kairui Hao, P. Vlachos, Ilias Bilionis","doi":"10.1088/1361-6501/ad6624","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6624","url":null,"abstract":"\u0000 Measuring particles' three-dimensional (3D) positions using multi-camera images in fluid dynamics is critical for resolving spatiotemporally complex flows like turbulence and mixing. However, current methods are prone to errors due to camera noise, optical configuration and experimental setup limitations, and high seeding density, which compound to create fake measurements (ghost particles) and add noise and error to velocity estimations. We introduce a Bayesian Volumetric Reconstruction (BVR) method, addressing these challenges by using probability theory to estimate uncertainties in particle position predictions. Our method assumes a uniform distribution of particles within the reconstruction volume and employs a model mapping particle positions to observed camera images. We utilize variational inference with a modified loss function to determine the posterior distribution over particle positions. Key features include a penalty term to reduce ghost particles, provision of uncertainty bounds, and scalability through subsampling. In tests with synthetic data and four cameras, BVR achieved 95% accuracy with less than 3% ghost particles and an RMS error under 0.3 pixels at a density of 0.1 particles per pixel (ppp). This shows 57% to 97% less ghost particles and 1.3 to 2 times lower RMS error than standard MART and IPR reconstructions. In an experimental Poiseuille flow measurement, our method closely matched the theoretical solution. Additionally, in a complex cerebral aneurysm basilar tip geometry flow experiment, our reconstructions were dense and consistent with observed flow patterns. Our BVR method effectively reconstructs particle positions in complex 3D flows, particularly in situations with high particle image densities and camera distortions. It distinguishes itself by providing quantifiable uncertainty estimates and scaling efficiently for larger image dimensions, making it applicable across a range of fluid flow scenarios.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"40 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141816893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-22DOI: 10.1088/1361-6501/ad662b
Meixuan Su, Zhipeng Yang, Kewei Cai, Zhiqiang Wang, Song Yang, Guofeng Li
As an efficient and environment-friendly method, electrostatic separation has gradually replaced flotation methods in the separation of magnesite in recent years. In the process of triboelectrostatic separation, the mineral particles are tribocharged driven by the air flow, then the trajectory is shifted under the action of the electric field, so as to realize the separation. The useful mineral in magnesite is MgCO3, but the theoretical research related to the charge characteristics of MgCO3 is not sufficient. Particle image velocimetry (PIV), as an indirect measurement technique, is able to obtain the velocity field of the fluids from images. However, the particles moving in the air have the issues such as excessive speed and small particle size, which make the traditional PIV has low accuracy in estimating the motion of particles. In this paper, a high-speed camera is used to capture the motion trajectory of tribocharged MgCO3 particles in a parallel electric field. A new optical flow method LFN-en-A network based on LiteFlowNet-en network is proposed to compute the particle motion trajectory by combining the deep learning method with the traditional PIV, which realizes the displacement estimation of particles moving in the air. It ultimately realizes the calculation of the charge-to-mass ratio on single particles. Analyzing the accuracy of the LFN-en-A network’s estimation in the experiments, the estimation of LiteFlowNet-en was compared. Changing the shooting frame rate analyzes the optimal one required by the LFN-en-A network. Combining the estimation results of LFN-en-A to calculate the particle charge-to-mass ratio (Q/m), the Q/m of MgCO3 particle was analyzed by changing the experimental conditions in the process of particles’ tribocharging, which provided a new method for particle-to-charge ratio measurement.
{"title":"Study on the Tribocharging Properties of MgCO3 Particles Based on LFN-en-A Model","authors":"Meixuan Su, Zhipeng Yang, Kewei Cai, Zhiqiang Wang, Song Yang, Guofeng Li","doi":"10.1088/1361-6501/ad662b","DOIUrl":"https://doi.org/10.1088/1361-6501/ad662b","url":null,"abstract":"\u0000 As an efficient and environment-friendly method, electrostatic separation has gradually replaced flotation methods in the separation of magnesite in recent years. In the process of triboelectrostatic separation, the mineral particles are tribocharged driven by the air flow, then the trajectory is shifted under the action of the electric field, so as to realize the separation. The useful mineral in magnesite is MgCO3, but the theoretical research related to the charge characteristics of MgCO3 is not sufficient. Particle image velocimetry (PIV), as an indirect measurement technique, is able to obtain the velocity field of the fluids from images. However, the particles moving in the air have the issues such as excessive speed and small particle size, which make the traditional PIV has low accuracy in estimating the motion of particles. In this paper, a high-speed camera is used to capture the motion trajectory of tribocharged MgCO3 particles in a parallel electric field. A new optical flow method LFN-en-A network based on LiteFlowNet-en network is proposed to compute the particle motion trajectory by combining the deep learning method with the traditional PIV, which realizes the displacement estimation of particles moving in the air. It ultimately realizes the calculation of the charge-to-mass ratio on single particles. Analyzing the accuracy of the LFN-en-A network’s estimation in the experiments, the estimation of LiteFlowNet-en was compared. Changing the shooting frame rate analyzes the optimal one required by the LFN-en-A network. Combining the estimation results of LFN-en-A to calculate the particle charge-to-mass ratio (Q/m), the Q/m of MgCO3 particle was analyzed by changing the experimental conditions in the process of particles’ tribocharging, which provided a new method for particle-to-charge ratio measurement.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141814833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-22DOI: 10.1088/1361-6501/ad662f
Yong Sun, Xiaofeng Yi, Cong Li, Zhiqin Yang, Jun Lin
The limited space within the tunnel constrains the size of the antenna for NMR detection, thereby significantly impacting the signal-to-noise ratio of NMR signals. Insufficient signal-to-noise ratio data poses substantial challenges to obtaining reliable NMR signals. The paper presents a novel approach to address the challenge of strong background noise in tunnel environments and low signal-to-noise ratio (SNR) data by incorporating the ground multi-channel remote reference denoising method into tunnel NMR advance detection. Specifically designed for narrow tunnels, a multichannel non-coaxial and non-coplanar remote reference denoising method is proposed. Firstly, the effectiveness of the non-coaxial, non-coplanar remote reference denoising method is verified in the laboratory environment. Secondly, the correlation between the detector antenna and the reference antenna is calculated theoretically to ensure the significant correlation between the detector antenna and the reference antenna. Finally, two processing methods of reference denoising and non-reference denoising are carried out respectively by combining the tunnel detection data. By comparing the inversion results and the engineering construction results, the effectiveness of non-coaxial and non-coplanar remote reference denoising methods in tunnel NMR detection is proved, which provides relevant research support for expanding the application of tunnel NMR detection technology.
{"title":"Research on remote reference denoising method based on non-coaxial and non-coplanar tunnel NMR detection","authors":"Yong Sun, Xiaofeng Yi, Cong Li, Zhiqin Yang, Jun Lin","doi":"10.1088/1361-6501/ad662f","DOIUrl":"https://doi.org/10.1088/1361-6501/ad662f","url":null,"abstract":"\u0000 The limited space within the tunnel constrains the size of the antenna for NMR detection, thereby significantly impacting the signal-to-noise ratio of NMR signals. Insufficient signal-to-noise ratio data poses substantial challenges to obtaining reliable NMR signals. The paper presents a novel approach to address the challenge of strong background noise in tunnel environments and low signal-to-noise ratio (SNR) data by incorporating the ground multi-channel remote reference denoising method into tunnel NMR advance detection. Specifically designed for narrow tunnels, a multichannel non-coaxial and non-coplanar remote reference denoising method is proposed. Firstly, the effectiveness of the non-coaxial, non-coplanar remote reference denoising method is verified in the laboratory environment. Secondly, the correlation between the detector antenna and the reference antenna is calculated theoretically to ensure the significant correlation between the detector antenna and the reference antenna. Finally, two processing methods of reference denoising and non-reference denoising are carried out respectively by combining the tunnel detection data. By comparing the inversion results and the engineering construction results, the effectiveness of non-coaxial and non-coplanar remote reference denoising methods in tunnel NMR detection is proved, which provides relevant research support for expanding the application of tunnel NMR detection technology.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"10 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141815694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-22DOI: 10.1088/1361-6501/ad6627
Jinbi Wei, Heng Deng, Jihong Wang, Liguo Zhang
In visual simultaneous localization and mapping (SLAM) systems, traditional methods often excel due to rigid environmental assumptions, but face challenges in dynamic environments. To address this, learning-based approaches have been introduced, but their expensive computing costs hinder real-time performance, especially on embedded mobile platforms. In this article, we propose a robust and real-time visual SLAM method towards dynamic environments using acceleration of feature extraction and object detection (AFO-SLAM). First, AFO-SLAM employs an independent object detection thread that utilizes YOLOv5 to extract semantic information and identify the bounding boxes of moving objects. To preserve the background points within these boxes, depth information is utilized to segment target foreground and background with only a single frame, with the points of the foreground area considered as dynamic points and then rejected. To optimize performance, CUDA program accelerates feature extraction preceding point removal. Finally, extensive evaluations are performed on both TUM RGB-D dataset and real scenes using a low-power embedded platform. Experimental results demonstrate that AFO-SLAM offers a balance between accuracy and real-time performance on embedded platforms, and enables the generation of dense point cloud maps in dynamic scenarios.
在视觉同步定位与映射（SLAM）系统中，传统方法往往因僵化的环境假设而表现出色，但在动态环境中却面临挑战。为解决这一问题，人们引入了基于学习的方法，但其昂贵的计算成本阻碍了实时性，尤其是在嵌入式移动平台上。在本文中，我们利用加速特征提取和目标检测（AFO-SLAM）提出了一种面向动态环境的稳健、实时视觉 SLAM 方法。首先，AFO-SLAM 采用独立的物体检测线程，利用 YOLOv5 提取语义信息并识别移动物体的边界框。为了保留这些框内的背景点，利用深度信息只需一帧就能分割目标前景和背景，前景区域的点被视为动态点，然后被剔除。为了优化性能，CUDA 程序在剔除点之前加速了特征提取。最后，使用低功耗嵌入式平台对 TUM RGB-D 数据集和真实场景进行了广泛评估。实验结果表明，AFO-SLAM 在嵌入式平台上实现了精度和实时性之间的平衡，并能在动态场景中生成密集的点云图。
{"title":"AFO-SLAM: an improved visual SLAM in dynamic scenes using acceleration of feature extraction and object detection","authors":"Jinbi Wei, Heng Deng, Jihong Wang, Liguo Zhang","doi":"10.1088/1361-6501/ad6627","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6627","url":null,"abstract":"\u0000 In visual simultaneous localization and mapping (SLAM) systems, traditional methods often excel due to rigid environmental assumptions, but face challenges in dynamic environments. To address this, learning-based approaches have been introduced, but their expensive computing costs hinder real-time performance, especially on embedded mobile platforms. In this article, we propose a robust and real-time visual SLAM method towards dynamic environments using acceleration of feature extraction and object detection (AFO-SLAM). First, AFO-SLAM employs an independent object detection thread that utilizes YOLOv5 to extract semantic information and identify the bounding boxes of moving objects. To preserve the background points within these boxes, depth information is utilized to segment target foreground and background with only a single frame, with the points of the foreground area considered as dynamic points and then rejected. To optimize performance, CUDA program accelerates feature extraction preceding point removal. Finally, extensive evaluations are performed on both TUM RGB-D dataset and real scenes using a low-power embedded platform. Experimental results demonstrate that AFO-SLAM offers a balance between accuracy and real-time performance on embedded platforms, and enables the generation of dense point cloud maps in dynamic scenarios.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"42 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141814877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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