Measurement最新文献_第3页

Analysis and elimination of GNSS carrier phase diffraction error in high occlusion environments 高遮挡环境下GNSS载波相位衍射误差分析与消除

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-10 DOI: 10.1016/j.measurement.2025.117809

Luming Han , Ruijie Xi , Qusen Chen , Yugang Xiao , Kaihua Wang , Dongsheng Xu , Weiping Jiang

{"title":"Analysis and elimination of GNSS carrier phase diffraction error in high occlusion environments","authors":"Luming Han , Ruijie Xi , Qusen Chen , Yugang Xiao , Kaihua Wang , Dongsheng Xu , Weiping Jiang","doi":"10.1016/j.measurement.2025.117809","DOIUrl":"10.1016/j.measurement.2025.117809","url":null,"abstract":"<div><div>In city canyons or natural valleys, carrier phase diffraction effect occurs when Global Navigation Satellite System (GNSS) signal approaches to the edge of buildings, trees and slopes etc., resulting in large diffraction errors, which is one of the important error sources for the low ambiguity fixing rate (AFR), the reduction of accuracy and frequent gross errors. In this study, the diffraction error estimation method was proposed, and the time-varying feature of the diffraction errors were comprehensively studied. It shows that the diffraction error estimated with double-differencing (DD) model generally increases or decrease monotonously, and according to the sign of the error, we can identify which station the diffraction effect occurs at. Large diffraction error usually corresponds to a low signal-to-noise ratio (SNR), with less than 40 dB-Hz, and the differenced SNR of the reference station and the monitoring station is generally greater than 5 dB-Hz. Based on this feature, we proposed to remove the diffraction error by a SNR mask and a differential SNR strategy. Based on an experiment of data processing in a high-occlusion complex environment, the SNR mask and differential SNR can effectively eliminate the large diffraction errors to achieve millimeter-level positioning accuracy and more than 90 % AFR, which is much better than the elevation- and SNR-based down weighting method, and the geographic cut-off elevation method. Meanwhile, the differential SNR strategy is more effective in practical monitoring applications.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117809"},"PeriodicalIF":5.2,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935446","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

Automated and highly precise surface wetting contact angle measurement with optical coherence tomography based on deep learning model 基于深度学习模型的光学相干断层扫描自动高精度表面润湿接触角测量

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-10 DOI: 10.1016/j.measurement.2025.117788

Ibrahim Akkaya , Ozkan Arslan , Jannick P. Rolland

{"title":"Automated and highly precise surface wetting contact angle measurement with optical coherence tomography based on deep learning model","authors":"Ibrahim Akkaya , Ozkan Arslan , Jannick P. Rolland","doi":"10.1016/j.measurement.2025.117788","DOIUrl":"10.1016/j.measurement.2025.117788","url":null,"abstract":"<div><div>Accurately determining the contact angle (CA) is critical for analyzing the wetting properties of materials and investigating solid–liquid interactions. This study presents a novel approach for predicting the CA of liquid droplets on three distinct material surfaces, High-Density Polyethylene (HDPE), Polystyrene (PS), and Polytetrafluoroethylene (PTFE), using Optical Coherence Tomography (OCT) due to providing high-resolution, non-contact, and three-dimensional structural imaging. We created a dataset from volumetric OCT images and then, developed and comprehensively evaluated machine learning and deep learning models, leveraging deep features extracted from five variations of the Next Generation of Convolutional Networks (ConvNeXt) architecture to enhance CA prediction accuracy. The extracted deep features were applied to both traditional machine learning (ML) models, such as Random Forest and Support Vector Regression, and advanced deep learning (DL) models, including Long Short-Term Memory (LSTM) and Bi-directional LSTM (Bi-LSTM). Results reveal that DL models, particularly the Bi-LSTM with ConvNeXt-Tiny features, consistently outperformed classical ML models across all material types. This model achieved the highest predictive accuracy, with superior R<sup>2</sup> values, reduced error rates, and strong consistency, as validated by regression fitting and Bland-Altman analyses. These findings highlight the robustness and versatility of the proposed study capturing volumetric OCT images and the DL framework for material-independent CA prediction, with potential implications for advancing surface wettability research and applications in a wide range such as coating technologies, material design, or biomedical surface analysis.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117788"},"PeriodicalIF":5.2,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935447","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

Data reconstruction leverages one-dimensional Convolutional Neural Networks (1DCNN) combined with Long Short-Term Memory (LSTM) networks for Structural Health Monitoring (SHM) 利用一维卷积神经网络（1DCNN）结合长短期记忆（LSTM）网络进行结构健康监测（SHM）的数据重建

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-10 DOI: 10.1016/j.measurement.2025.117810

T.Q Minh , Jose C. Matos , Helder S. Sousa , Son Dang Ngoc , Thuc Ngo Van , Huan X. Nguyen , Quyền Nguyễn

{"title":"Data reconstruction leverages one-dimensional Convolutional Neural Networks (1DCNN) combined with Long Short-Term Memory (LSTM) networks for Structural Health Monitoring (SHM)","authors":"T.Q Minh , Jose C. Matos , Helder S. Sousa , Son Dang Ngoc , Thuc Ngo Van , Huan X. Nguyen , Quyền Nguyễn","doi":"10.1016/j.measurement.2025.117810","DOIUrl":"10.1016/j.measurement.2025.117810","url":null,"abstract":"<div><div>SHM data collected in systems often face data loss due to transmission errors, sensor damage, or environmental impacts. Incomplete data can lead to erroneous assessments in evaluating structural safety in complex structures. Although data reconstruction has been studied, challenges are present in data reconstruction: (i) SHM data contains a large amount of noise; (ii) data structure is complex and doesn’t allow for simple linear or nonlinear formulation; (iii) reconstructed data needs to be accurate and reliable. This study proposes a hybrid deep learning approach combining the 1DCNN and LSTM network to reconstruct data within an SHM environment. The proposed model uniquely leverages 1DCNN for efficient spatial feature extraction and LSTM for capturing long-term temporal dependencies. Input data is strategically preprocessed through correlation-based sensor clustering and time-shift enhancement techniques. A hybrid model used the SHM data measurements before data loss to train models. The trained hybrid network can then reconstruct missing or erroneous data. The proposed method is validated on real datasets from different structures in various scenariosandcan be applied in practice, achieving better performance and accuracy compared to other neural network-based methods. Quantitative results show that the hybrid model reduces the Mean Absolute Error (MAE) by 10–15% and achieves Modal Assurance Criterion (MAC) values exceeding 0.95, outperforming other baseline neural network models. These results highlight the model’s practical applicability for accurate SHM data reconstruction under both single- and multi-channel sensor failures.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117810"},"PeriodicalIF":5.2,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143947145","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

Development of optical fiber sensors for high-alkaline pH monitoring based on silica fluorescent nanoparticles 基于二氧化硅荧光纳米粒子的高碱性pH监测光纤传感器的研制

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-10 DOI: 10.1016/j.measurement.2025.117803

Weiqiao Xu , Liang Fan , Zhenting Ma , Xia Zhao , Liangliang Huang , Jiguo Chen

引用次数: 0

A long-term vertical displacement prediction method of concrete bridges based on meteorological shared data and optimized GRU model 基于气象共享数据和优化GRU模型的混凝土桥梁长期竖向位移预测方法

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-10 DOI: 10.1016/j.measurement.2025.117811

Xu Wang , Guilin Xie , Wentao Liu , Hu Kong , Yang Gao

{"title":"A long-term vertical displacement prediction method of concrete bridges based on meteorological shared data and optimized GRU model","authors":"Xu Wang , Guilin Xie , Wentao Liu , Hu Kong , Yang Gao","doi":"10.1016/j.measurement.2025.117811","DOIUrl":"10.1016/j.measurement.2025.117811","url":null,"abstract":"<div><div>Based on data from the meteorological shared data platform, this study proposes a method for predicting the long-term vertical displacement (VD) of concrete bridges by integrating the Northern Goshawk Optimization (NGO) algorithm with the Gated Recurrent Unit (GRU) network. This method can be employed to safely assess concrete bridges and recover missing VD data. Specifically, it uses historical meteorological information and time information provided by the meteorological shared data platform (European Centre for Medium-Range Weather Forecasts) to generate the input parameters of the GRU model. It then employs the long-term VD data from the concrete bridge structural health monitoring system to produce the output parameters of the GRU model. Moreover, the hyperparameters for the GRU model training are optimized using the NGO algorithm. Four NGO-GRU models with different input conditions are proposed, taking the long-term VD prediction of a prestressed concrete bridge as a case study and considering the correlation between different meteorological factors and VD and the long-term time-dependent effects on concrete structures. Through a comparative analysis of the model’s prediction performance of multiple sensors under different conditions, it is found that the NGO-GRU model achieved the best prediction performance when using a combination of air temperature, time information, air pressure, solar radiation intensity, and wind speed as inputs, with a prediction error of less than 6.00%. Furthermore, compared with the benchmark models, the NGO-GRU model demonstrated the highest accuracy in VD prediction. Under the optimal input conditions, the prediction performance of the NGO-GRU model improved by 16.46% to 46.17% compared with the other models, validating the robustness and effectiveness of the proposed method.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117811"},"PeriodicalIF":5.2,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070846","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

A shadowgraph model for the thermal lens effect: a theoretical and experimental study 热透镜效应的阴影模型：理论与实验研究

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-10 DOI: 10.1016/j.measurement.2025.117815

J.B. Rojas-Trigos, J.L. Mejorada Sánchez, S. Alvarado, A. Bedoya, A. Calderón, E. Marín

引用次数: 0

Square-slotted metasurface optical sensor based on graphene material for efficient detection of brain tumor using machine learning 基于石墨烯材料的方槽超表面光学传感器，利用机器学习高效检测脑肿瘤

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-10 DOI: 10.1016/j.measurement.2025.117812

Jacob Wekalao , Osamah Alsalman , Shobhit K. Patel

引用次数: 0

A Two-Stage Space-Time image Velocimetry method based on deep learning 基于深度学习的两阶段时空图像测速方法

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-10 DOI: 10.1016/j.measurement.2025.117817

Lin Chen , Zhen Zhang , Hongyu Chen , Huibin Wang

引用次数: 0

A combined-observation pose correction method for enhancing tracking accuracy in visual measurement systems 一种提高视觉测量系统跟踪精度的组合观测位姿校正方法

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-10 DOI: 10.1016/j.measurement.2025.117708

Kang Yan , Jun Zheng , Shangjian Chen , Jiangfeng Wang , Fengxin Jin , Ming Dai , Weiyuan Liu , Weihao Tang , Yunbo Bi

{"title":"A combined-observation pose correction method for enhancing tracking accuracy in visual measurement systems","authors":"Kang Yan , Jun Zheng , Shangjian Chen , Jiangfeng Wang , Fengxin Jin , Ming Dai , Weiyuan Liu , Weihao Tang , Yunbo Bi","doi":"10.1016/j.measurement.2025.117708","DOIUrl":"10.1016/j.measurement.2025.117708","url":null,"abstract":"<div><div>Non-contact full-field 3D measurement and reconstruction using visual binocular tracking is crucial for many contemporary applications. However, accuracy limitations arise in long-range tracking due to low confidence in depth pose estimation. To address this, we propose a pose correction method using two binocular tracking units (TUs) in a perpendicular configuration. In the system, depth tracking from one unit is correlated to lateral tracking from the other unit through their relative pose. During fusion, lateral information takes precedence due to re-projection constraints, enabling the system to operate with high confidence in nearly any tracking direction. This approach not only improves tracking accuracy but also reduces spatial accuracy variations compared to conventional tracking systems. The proposed method is an optimization framework that leverages predictive models and corresponding loss functions. It estimates the combined tracking pose for each frame and the global relative poses between units using projection data from both TUs. Our system exhibits a maximum relative RMSE of 0.0074%, markedly lower than that of separate TU systems (0.0155–0.1153%). Furthermore, it achieves average reductions in spatial accuracy variation of 57.2% and 38.9% compared with individual TU systems.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117708"},"PeriodicalIF":5.2,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143934698","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

Time-optimised real-time time–frequency measurement technique for RoFSO applications 用于RoFSO应用的时间优化实时时频测量技术

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-09 DOI: 10.1016/j.measurement.2025.117698

Chandra Shekhar , A. Arockia Bazil Raj

{"title":"Time-optimised real-time time–frequency measurement technique for RoFSO applications","authors":"Chandra Shekhar , A. Arockia Bazil Raj","doi":"10.1016/j.measurement.2025.117698","DOIUrl":"10.1016/j.measurement.2025.117698","url":null,"abstract":"<div><div>Hardware-based embedded solution for the time–frequency (T–F) measurement/imaging is essential nowadays for numerous applications. Several solutions exists based on the software algorithms which are more time-consuming and hence not appropriate for real-time applications. In this work, an accurate T–F measurement/imaging algorithm based on the Wigner-Ville distribution (WVD) technique is proposed and implemented in a Xilinx Virtex-7 VC709 FPGA. A time/resource/space optimised pipelined-parallel digital architecture is designed for real-time applications. The proposed digital architecture is capable of completing (with seven advantages) all the computations and measuring the T–F values within <span><math><mrow><mn>99</mn><mo>.</mo><mn>38</mn><mspace></mspace><mi>μ</mi><mi>s</mi></mrow></math></span>. The designed architecture requires just 16 parallel modules, 26% of logic cores, 16% of DSP blocks, and 6% of memory. The computation performance & measurement accuracy of the same is experimentally validated with a data decoding scheme of an in-house-built radio over free space optical (RoFSO) communication system. The proposed T–F measurement algorithm, digital architecture design approach, construction of RoFSO communication test-bed, and processing time & device utilisation details are reported. The performance of the proposed T–F measurement technique and designed digital architecture are critically investigated in terms of absolute error (AE), mean absolute error (MAE), root mean square error (RMSE), & correlation-coefficient (R). The obtained AE (over 80 time-bins), MAE, RMSE & R (over 10 frame-bins) is <span><math><mo>±</mo></math></span>1.24E−4, 0.076, 0.23, & 0.73, respectively. The obtained bit error rate (BER) (over the 5000 frame-bins) is 3.33E−4.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117698"},"PeriodicalIF":5.2,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935449","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