Pub Date : 2026-05-05Epub Date: 2026-02-28DOI: 10.1016/j.measurement.2026.120968
D. Mishra , V. Chopra , D. Mishra , A. Baral
A non-invasive and quantitative methodology for assessing the health of oil–paper insulation in power transformers is presented in this paper. Paper moisture (moisture in cellulosic part of insulation) (pm) and dissipation factor (tan δ) are widely recognized as critical indicators of insulation aging and overall system reliability, yet their accurate determination often requires intrusive or time-consuming procedures. To overcome these limitations, this work introduces an Aging Factor (AF), computed directly from the complete depolarization current profile by estimating the branch parameters of a Debye-based insulation model and evaluating the ratio of charge contributions associated with the two branches having the largest time constants. This approach utilizes the full dielectric relaxation behaviour of the insulation without relying on extensive curve fitting or lengthy frequency-domain measurements. The method was validated using ten laboratory-prepared oil-impregnated insulation samples and subsequently applied to ten in-service power transformers. The AF showed strong correlation with both %pm and %tan δ, enabling their prediction with accuracies of 2.47% and 5.0%, respectively, with all deviations confined within ± 5%. By providing a robust, non-invasive, and time-efficient means to estimate key insulation-health indicators, the proposed AF-based technique supports condition-based maintenance and enhances diagnostic reliability for power transformer insulation systems.
{"title":"A non-invasive measurement-based modeling for assessing power transformer aging and reliability","authors":"D. Mishra , V. Chopra , D. Mishra , A. Baral","doi":"10.1016/j.measurement.2026.120968","DOIUrl":"10.1016/j.measurement.2026.120968","url":null,"abstract":"<div><div>A non-invasive and quantitative methodology for assessing the health of oil–paper insulation in power transformers is presented in this paper. Paper moisture (moisture in cellulosic part of insulation) (<em>pm</em>) and dissipation factor (tan <em>δ</em>) are widely recognized as critical indicators of insulation aging and overall system reliability, yet their accurate determination often requires intrusive or time-consuming procedures. To overcome these limitations, this work introduces an Aging Factor (<em>A<sub>F</sub></em>), computed directly from the complete depolarization current profile by estimating the branch parameters of a Debye-based insulation model and evaluating the ratio of charge contributions associated with the two branches having the largest time constants. This approach utilizes the full dielectric relaxation behaviour of the insulation without relying on extensive curve fitting or lengthy frequency-domain measurements. The method was validated using ten laboratory-prepared oil-impregnated insulation samples and subsequently applied to ten in-service power transformers. The AF showed strong correlation with both %pm and %tan δ, enabling their prediction with accuracies of 2.47% and 5.0%, respectively, with all deviations confined within ± 5%. By providing a robust, non-invasive, and time-efficient means to estimate key insulation-health indicators, the proposed AF-based technique supports condition-based maintenance and enhances diagnostic reliability for power transformer insulation systems.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"272 ","pages":"Article 120968"},"PeriodicalIF":5.6,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147388295","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}
Pub Date : 2026-05-05Epub Date: 2026-03-03DOI: 10.1016/j.measurement.2026.121034
Ying Gu , Min Tu , Tingyong Liu , Songbo Ren , Chao Kong
Accurate evaluation of the effective prestress in existing prestressed concrete (PC) structures is crucial for assessing their in-service performance. This study presents an improved stress release technique, the single-groove method, for measuring the effective prestress in PC simply supported beams. The method involves cutting a shallow arc-shaped groove (depth < 35 mm) on the concrete surface and determining the stress from the released strain. Experimental validation was conducted on two PC beams. The results demonstrated a monotonic relationship between the stress on the top slab and the effective prestress, providing the theoretical basis for the method. A comparative analysis showed a close agreement between the stresses obtained from the single-groove method and the benchmark stresses, with a maximum absolute deviation of 0.29 MPa and relative deviations below 7.5%. Compared to traditional deep-cutting methods requiring grooves of 50 mm or more, the single-groove method significantly reduces damage to the concrete structure. The study confirms that the single-groove method is a reliable technique for evaluating effective prestress in simply supported beams. The applicability of the method to continuous beams remains a subject for future investigation.
{"title":"Assessment of effective prestress in simply supported prestressedconcrete beams using the single-groove method","authors":"Ying Gu , Min Tu , Tingyong Liu , Songbo Ren , Chao Kong","doi":"10.1016/j.measurement.2026.121034","DOIUrl":"10.1016/j.measurement.2026.121034","url":null,"abstract":"<div><div>Accurate evaluation of the effective prestress in existing prestressed concrete (PC) structures is crucial for assessing their in-service performance. This study presents an improved stress release technique, the single-groove method, for measuring the effective prestress in PC simply supported beams. The method involves cutting a shallow arc-shaped groove (depth < 35 mm) on the concrete surface and determining the stress from the released strain. Experimental validation was conducted on two PC beams. The results demonstrated a monotonic relationship between the stress on the top slab and the effective prestress, providing the theoretical basis for the method. A comparative analysis showed a close agreement between the stresses obtained from the single-groove method and the benchmark stresses, with a maximum absolute deviation of 0.29 MPa and relative deviations below 7.5%. Compared to traditional deep-cutting methods requiring grooves of 50 mm or more, the single-groove method significantly reduces damage to the concrete structure. The study confirms that the single-groove method is a reliable technique for evaluating effective prestress in simply supported beams. The applicability of the method to continuous beams remains a subject for future investigation.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"272 ","pages":"Article 121034"},"PeriodicalIF":5.6,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147388297","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}
Pub Date : 2026-05-05Epub Date: 2026-03-03DOI: 10.1016/j.measurement.2026.121032
Xiaochen Liu , Meng Yuan , Huijun Zhao , Chong Shen , Chenguang Wang
In the field of visual attitude estimation for unmanned aerial vehicles, precise and reliable horizontal pitch/roll angle measurement is crucial for applications such as autonomous navigation, environmental monitoring, and search-and-rescue missions. However, relying on a single visual information often results in susceptibility to interference and limited accuracy. To address these challenges, we propose the cascaded spatiotemporal fusion attitude estimation architecture. Initial pitch/roll angle estimates are independently obtained through horizon detection and optical flow estimation. Subsequently, a forward–backward optical flow optimization algorithm based on gradient descent is proposed to address the problem of degraded accuracy in conventional optical flow at object boundaries. Finally, in light of the disparities in the noise characteristics of the two types of observation data, an adaptive sequential Kalman filtering algorithm is proposed. This algorithm incorporates a two-stage updating mechanism and dynamically adjusts the measurement noise covariance matrix through an adaptive factor to efficiently fuse the results of horizon and optical flow. The experimental results demonstrate that the suggested method significantly enhances the accuracy of pitch/roll angle measurements compared to the single horizon or optical flow methods and meets the requirements for the stability and reliability criteria for vision-based UAV attitude estimation.
{"title":"High-precision pure vision horizontal attitude measurement method for UAVs","authors":"Xiaochen Liu , Meng Yuan , Huijun Zhao , Chong Shen , Chenguang Wang","doi":"10.1016/j.measurement.2026.121032","DOIUrl":"10.1016/j.measurement.2026.121032","url":null,"abstract":"<div><div>In the field of visual attitude estimation for unmanned aerial vehicles, precise and reliable horizontal pitch/roll angle measurement is crucial for applications such as autonomous navigation, environmental monitoring, and search-and-rescue missions. However, relying on a single visual information often results in susceptibility to interference and limited accuracy. To address these challenges, we propose the cascaded spatiotemporal fusion attitude estimation architecture. Initial pitch/roll angle estimates are independently obtained through horizon detection and optical flow estimation. Subsequently, a forward–backward optical flow optimization algorithm based on gradient descent is proposed to address the problem of degraded accuracy in conventional optical flow at object boundaries. Finally, in light of the disparities in the noise characteristics of the two types of observation data, an adaptive sequential Kalman filtering algorithm is proposed. This algorithm incorporates a two-stage updating mechanism and dynamically adjusts the measurement noise covariance matrix through an adaptive factor to efficiently fuse the results of horizon and optical flow. The experimental results demonstrate that the suggested method significantly enhances the accuracy of pitch/roll angle measurements compared to the single horizon or optical flow methods and meets the requirements for the stability and reliability criteria for vision-based UAV attitude estimation.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"272 ","pages":"Article 121032"},"PeriodicalIF":5.6,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147388300","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}
Pub Date : 2026-05-05Epub Date: 2026-03-06DOI: 10.1016/j.measurement.2026.121075
Zhuang Cao, Kai Li, Sheng Liu, Xiantuo Tang
Modern electronic systems impose strict requirements on clock accuracy and stability. To address this, this paper proposes a high-precision clock generation and retention method based on GNSS timing and all-digital control logic. Taking GNSS second pulse (1PPS) as a high-precision reference, a fully digital closed-loop control system based on heterogeneous computing platforms was constructed to achieve highly integrated hardware and flexible deployment of algorithms. In the control strategy, a Model Reference Adaptive Control (MRAC) algorithm was designed to estimate and compensate the nonlinear and time-varying characteristics of OCXO tuning sensitivity in real time, thereby enhancing the frequency control accuracy. In view of the loss of external reference time, a semi-parametric aging prediction model combining physical prior and lightweight Multilayer Perceptron (MLP) was used to actively predict the frequency drift and compensate the voltage, so as to prolong the high-precision retention time of the clock. Experimental results show that the proposed system can improve the long-term stability of the OCXO output clock to the order of , and maintain the clock accuracy in the sub-ppb (part per billion) accuracy range for several hours after GNSS loss, which effectively enhances the reliability and adaptability of the clock system under the GNSS signal interruption. This study provides an integrated and intelligent solution for the design of high-precision clock sources, which is suitable for application scenarios with stringent time synchronization requirements in communication, navigation, finance and other fields.
{"title":"MMC: GNSS-based high-precision clock generation and holdover system with MRAC control and MLP aging prediction","authors":"Zhuang Cao, Kai Li, Sheng Liu, Xiantuo Tang","doi":"10.1016/j.measurement.2026.121075","DOIUrl":"10.1016/j.measurement.2026.121075","url":null,"abstract":"<div><div>Modern electronic systems impose strict requirements on clock accuracy and stability. To address this, this paper proposes a high-precision clock generation and retention method based on GNSS timing and all-digital control logic. Taking GNSS second pulse (1PPS) as a high-precision reference, a fully digital closed-loop control system based on heterogeneous computing platforms was constructed to achieve highly integrated hardware and flexible deployment of algorithms. In the control strategy, a Model Reference Adaptive Control (MRAC) algorithm was designed to estimate and compensate the nonlinear and time-varying characteristics of OCXO tuning sensitivity in real time, thereby enhancing the frequency control accuracy. In view of the loss of external reference time, a semi-parametric aging prediction model combining physical prior and lightweight Multilayer Perceptron (MLP) was used to actively predict the frequency drift and compensate the voltage, so as to prolong the high-precision retention time of the clock. Experimental results show that the proposed system can improve the long-term stability of the OCXO output clock to the order of <span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>13</mn></mrow></msup></mrow></math></span>, and maintain the clock accuracy in the sub-ppb (part per billion) accuracy range for several hours after GNSS loss, which effectively enhances the reliability and adaptability of the clock system under the GNSS signal interruption. This study provides an integrated and intelligent solution for the design of high-precision clock sources, which is suitable for application scenarios with stringent time synchronization requirements in communication, navigation, finance and other fields.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"272 ","pages":"Article 121075"},"PeriodicalIF":5.6,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147388106","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}
Pub Date : 2026-05-05Epub Date: 2026-03-10DOI: 10.1016/j.measurement.2026.121124
Lifei Chen , Qiyin Lin , Mingjun Qiu , Chen Wang , Tao Wang , Hao Guan , Jun Hong
To address the current lack of comprehensive evaluation and optimization methods for contact interface characteristics in mechanical assemblies, this paper proposes a novel comprehensive evaluation and optimization method by integrating information entropy theory and harmonic mean analysis. The proposed method discretizes the assembly contact interface into a set and modifies the surface topography by adjusting the spatial coordinates of contact nodes. Iterative optimization is achieved using a novel evaluation metric and a node modification strategy based on target contact stress. As a case study, the flange interface between the first and second discs of an aeroengine is analyzed. By combining two critical characteristics uniformity of the contact stress distribution (quantified using information entropy) and the effective contact area, an optimization experiment was conducted. The results show a significant improvement, with a 89.13% increase in contact performance. Furthermore, comprehensive comparative experiments were conducted with existing evaluation and optimization methods to confirm the superior effectiveness of the proposed method. The proposed method offers a scalable solution for enhancing connection reliability and extending the service life of mechanical assemblies.
{"title":"A novel evaluation and optimization method of assembly interface contact characteristics","authors":"Lifei Chen , Qiyin Lin , Mingjun Qiu , Chen Wang , Tao Wang , Hao Guan , Jun Hong","doi":"10.1016/j.measurement.2026.121124","DOIUrl":"10.1016/j.measurement.2026.121124","url":null,"abstract":"<div><div>To address the current lack of comprehensive evaluation and optimization methods for contact interface characteristics in mechanical assemblies, this paper proposes a novel comprehensive evaluation and optimization method by integrating information entropy theory and harmonic mean analysis. The proposed method discretizes the assembly contact interface into a set and modifies the surface topography by adjusting the spatial coordinates of contact nodes. Iterative optimization is achieved using a novel evaluation metric and a node modification strategy based on target contact stress. As a case study, the flange interface between the first and second discs of an aeroengine is analyzed. By combining two critical characteristics uniformity of the contact stress distribution (quantified using information entropy) and the effective contact area, an optimization experiment was conducted. The results show a significant improvement, with a 89.13% increase in contact performance. Furthermore, comprehensive comparative experiments were conducted with existing evaluation and optimization methods to confirm the superior effectiveness of the proposed method. The proposed method offers a scalable solution for enhancing connection reliability and extending the service life of mechanical assemblies.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"272 ","pages":"Article 121124"},"PeriodicalIF":5.6,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147388163","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}
Pub Date : 2026-05-05Epub Date: 2026-02-27DOI: 10.1016/j.measurement.2026.120988
Pei-Lun Lan , Yu-Lung Lo , Pei-Hsien Wu
This study investigates the prediction of four critical dimension (CD) parameters—top space, bottom space, depth, and pitch—of high aspect ratio (HAR) structures using simulated deep ultraviolet (DUV) reflectance spectra based on transfer learning without collecting all necessary data. A large dataset was generated through COMSOL Multiphysics simulations and used to train a one-dimensional convolutional neural network (1D-CNN). Under a transfer-learning scheme in which the network was pre-trained on 5,000 ideal (smooth-sidewall) grating spectra and then fine-tuned with 1,200 scalloped (non-ideal) grating spectra, a deep learning model trained only with θi = 35° spectra achieved significant improvements with R2 values of 0.982 (top space), 0.9556 (bottom space), 0.9877 (depth), and 0.9745 (pitch), respectively. The corresponding mean absolute errors (MAE) were 0.0053, 0.0082, 0.0223, and 0.0268, while the mean absolute percentage errors (MAPE) were 0.89%, 1.36%, 0.74%, and 1.07%. These results validate the effectiveness of the CNN-based approach for rapidly and precisely characterizing the dimensional properties of HAR structures. Importantly, these results confirm the value of transfer learning: fine-tuning significantly improves prediction performance for CD estimation in HAR grating structures while reducing the required number of non-ideal (scalloped) spectra for fine-tuning to 1,200 in this study. Additionally, uncertainties arising from the intended measurement configuration and practical implementation conditions can be systematically identified and characterized using data-driven approaches. Consequently, simulation-generated data can provide a distinctive and robust framework for advanced process monitoring and can be readily integrated with measurement data in future deployment. In summary, the proposed method requires significantly less training data than the three existing comparative approaches. This strategy greatly reduces the burden of data collection and labeling, enhancing modeling efficiency. Furthermore, to assess feasibility under fabrication-induced profile non-idealities, the forward surrogate spectral prediction model is trained on ideal structures and subsequently adapted to simulated Bosch-inspired scalloped sidewalls via transfer learning, thereby reducing the need for extensive non-ideal training data and lowering the data-collection burden.
{"title":"Transfer learning based on 1D-CNN for critical dimension Predication of HAR grating structures","authors":"Pei-Lun Lan , Yu-Lung Lo , Pei-Hsien Wu","doi":"10.1016/j.measurement.2026.120988","DOIUrl":"10.1016/j.measurement.2026.120988","url":null,"abstract":"<div><div>This study investigates the prediction of four critical dimension (CD) parameters—top space, bottom space, depth, and pitch—of high aspect ratio (HAR) structures using simulated deep ultraviolet (DUV) reflectance spectra based on transfer learning without collecting all necessary data. A large dataset was generated through COMSOL Multiphysics simulations and used to train a one-dimensional convolutional neural network (1D-CNN). Under a transfer-learning scheme in which the network was pre-trained on 5,000 ideal (smooth-sidewall) grating spectra and then fine-tuned with 1,200 scalloped (non-ideal) grating spectra, a deep learning model trained only with θ<sub>i</sub> = 35° spectra achieved significant improvements with R<sup>2</sup> values of 0.982 (top space), 0.9556 (bottom space), 0.9877 (depth), and 0.9745 (pitch), respectively. The corresponding mean absolute errors (MAE) were 0.0053, 0.0082, 0.0223, and 0.0268, while the mean absolute percentage errors (MAPE) were 0.89%, 1.36%, 0.74%, and 1.07%. These results validate the effectiveness of the CNN-based approach for rapidly and precisely characterizing the dimensional properties of HAR structures. Importantly, these results confirm the value of transfer learning: fine-tuning significantly improves prediction performance for CD estimation in HAR grating structures while reducing the required number of non-ideal (scalloped) spectra for fine-tuning to 1,200 in this study. Additionally, uncertainties arising from the intended measurement configuration and practical implementation conditions can be systematically identified and characterized using data-driven approaches. Consequently, simulation-generated data can provide a distinctive and robust framework for advanced process monitoring and can be readily integrated with measurement data in future deployment. In summary, the proposed method requires significantly less training data than the three existing comparative approaches. This strategy greatly reduces the burden of data collection and labeling, enhancing modeling efficiency. Furthermore, to assess feasibility under fabrication-induced profile non-idealities, the forward surrogate spectral prediction model is trained on ideal structures and subsequently adapted to simulated Bosch-inspired scalloped sidewalls via transfer learning, thereby reducing the need for extensive non-ideal training data and lowering the data-collection burden.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"272 ","pages":"Article 120988"},"PeriodicalIF":5.6,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387977","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}
Wheeled Mobile Mapping Systems (MMS), equipped with LiDAR, cameras, and integrated GNSS/INS units, are widely used in urban planning, map generation, and infrastructure monitoring. Accurate registration between wheeled MMS camera and LiDAR data, relying on precise system calibration and GNSS/INS trajectory, is crucial for effective data fusion to address the needs of these applications. However, environmental factors can degrade sensor calibration and GNSS/INS accuracy, leading to misalignment between imagery and LiDAR data. Calibration parameters, such as mounting parameters and sensors’ Interior Orientation Parameters (IOP), can be affected by sensor aging and environmental conditions, while data collection along transportation corridors may suffer from GNSS signal occlusions due to interference from traffic, bridges, and buildings. GNSS/INS trajectory errors are more frequent than calibration errors. This research addresses these trajectory issues by analyzing image-LiDAR misalignments and proposes a novel registration approach. The method establishes an appropriate transformation function, automatically extracts lane markings as common primitives, and develops a similarity measure tailored to these primitives. These elements are integrated into an automated optimization strategy that estimates transformation function parameters. The proposed learning-based algorithm is effective in both urban and highway environments, offering a robust solution for camera-LiDAR alignment. Additionally, an analysis of stereo camera poses before and after registration identifies misalignment causes, whether due to GNSS/INS errors or calibration inaccuracy. The proposed algorithm, evaluated using the mean of minimum Euclidean distances and Intersection over Union (IoU), demonstrates significant improvements, reducing misalignment to less than a few pixels and achieving IoU improvements exceeding 50%.
轮式移动测绘系统（MMS）配备了激光雷达、摄像头和集成GNSS/INS单元，广泛应用于城市规划、地图生成和基础设施监控。依靠精确的系统校准和GNSS/INS轨迹，轮式MMS相机和LiDAR数据之间的精确配准对于有效的数据融合至关重要，以满足这些应用的需求。然而，环境因素会降低传感器校准和GNSS/INS的精度，导致图像和LiDAR数据之间的不对准。校准参数，如安装参数和传感器的内部定向参数（IOP），可能会受到传感器老化和环境条件的影响，而沿交通走廊收集的数据可能会受到交通、桥梁和建筑物的干扰而受到GNSS信号遮挡。GNSS/INS轨迹误差比校准误差更常见。本研究通过分析图像-激光雷达的不对准来解决这些轨迹问题，并提出了一种新的配准方法。该方法建立适当的变换函数，自动提取车道标记作为公共原语，并开发适合这些原语的相似度度量。这些元素被集成到一个自动化的优化策略中，用于估计转换函数参数。所提出的基于学习的算法在城市和高速公路环境下都是有效的，为摄像头-激光雷达对准提供了一个强大的解决方案。此外，对配准前后立体相机姿势的分析确定了不对准的原因，无论是由于GNSS/INS错误还是校准不准确。使用最小欧几里得距离和交汇联距（Intersection over Union, IoU）的平均值对所提出的算法进行了评估，结果显示出显著的改进，将不对齐减少到几个像素以内，IoU改进超过50%。
{"title":"Mobile mapping systems camera–LiDAR data registration for mitigating GNSS/INS trajectory perturbations","authors":"Mona Hodaei, Youssef Hany, Aser Eissa, Ayman Habib","doi":"10.1016/j.measurement.2026.120918","DOIUrl":"10.1016/j.measurement.2026.120918","url":null,"abstract":"<div><div>Wheeled Mobile Mapping Systems (MMS), equipped with LiDAR, cameras, and integrated GNSS/INS units, are widely used in urban planning, map generation, and infrastructure monitoring. Accurate registration between wheeled MMS camera and LiDAR data, relying on precise system calibration and GNSS/INS trajectory, is crucial for effective data fusion to address the needs of these applications. However, environmental factors can degrade sensor calibration and GNSS/INS accuracy, leading to misalignment between imagery and LiDAR data. Calibration parameters, such as mounting parameters and sensors’ Interior Orientation Parameters (IOP), can be affected by sensor aging and environmental conditions, while data collection along transportation corridors may suffer from GNSS signal occlusions due to interference from traffic, bridges, and buildings. GNSS/INS trajectory errors are more frequent than calibration errors. This research addresses these trajectory issues by analyzing image-LiDAR misalignments and proposes a novel registration approach. The method establishes an appropriate transformation function, automatically extracts lane markings as common primitives, and develops a similarity measure tailored to these primitives. These elements are integrated into an automated optimization strategy that estimates transformation function parameters. The proposed learning-based algorithm is effective in both urban and highway environments, offering a robust solution for camera-LiDAR alignment. Additionally, an analysis of stereo camera poses before and after registration identifies misalignment causes, whether due to GNSS/INS errors or calibration inaccuracy. The proposed algorithm, evaluated using the mean of minimum Euclidean distances and Intersection over Union (IoU), demonstrates significant improvements, reducing misalignment to less than a few pixels and achieving IoU improvements exceeding 50%.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"272 ","pages":"Article 120918"},"PeriodicalIF":5.6,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387985","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}
Pub Date : 2026-05-05Epub Date: 2026-03-07DOI: 10.1016/j.measurement.2026.121076
Huitao Wang , Lianpo Wang
Single-shot Fringe Projection Profilometry (SSFPP) enables three-dimensional (3D) reconstruction from a single captured fringe image and is widely applied to dynamic object measurement. Although deep learning has significantly improved the accuracy and robustness of SSFPP, most existing approaches remain fully supervised and rely heavily on labeled data that are difficult and costly to obtain. Existing self-supervised FPP methods circumvent the need for labeled data but typically rely on multiple input images to resolve the inherent phase ambiguity of a single fringe pattern. Consequently, they cannot achieve true single-shot FPP reconstruction. To enable self-supervised SSFPP, we propose a dual-domain self-supervised loss function. In the image domain, a Structural Similarity Index Measure (SSIM) loss is introduced to enforce physically meaningful consistency between the reprojected and input fringe images, thereby supporting end-to-end self-supervised learning. In the phase domain, an edge-aware self-smoothing loss is developed to suppress discontinuities caused by the phase ambiguity, enabling a unique and spatially continuous phase solution from a single frame. In addition, we design a dynamic dual-stream sampler that simultaneously samples labeled and unlabeled data and adaptively adjusts their proportions within each batch based on training progress, enabling progressive and synergistic optimization of supervised and self-supervised learning signals. Experimental results demonstrate that the proposed method, using only 50% of the labeled data, outperforms existing open-source supervised end-to-end SSFPP approaches on both synthetic and real-world datasets. This confirms its ability to substantially reduce annotation costs while maintaining high reconstruction accuracy.
{"title":"End-to-end single-shot fringe projection profilometry based on semi-supervised learning","authors":"Huitao Wang , Lianpo Wang","doi":"10.1016/j.measurement.2026.121076","DOIUrl":"10.1016/j.measurement.2026.121076","url":null,"abstract":"<div><div>Single-shot Fringe Projection Profilometry (SSFPP) enables three-dimensional (3D) reconstruction from a single captured fringe image and is widely applied to dynamic object measurement. Although deep learning has significantly improved the accuracy and robustness of SSFPP, most existing approaches remain fully supervised and rely heavily on labeled data that are difficult and costly to obtain. Existing self-supervised FPP methods circumvent the need for labeled data but typically rely on multiple input images to resolve the inherent <span><math><mrow><mn>2</mn><mi>k</mi><mi>π</mi></mrow></math></span> phase ambiguity of a single fringe pattern. Consequently, they cannot achieve true single-shot FPP reconstruction. To enable self-supervised SSFPP, we propose a dual-domain self-supervised loss function. In the image domain, a Structural Similarity Index Measure (SSIM) loss is introduced to enforce physically meaningful consistency between the reprojected and input fringe images, thereby supporting end-to-end self-supervised learning. In the phase domain, an edge-aware self-smoothing loss is developed to suppress discontinuities caused by the <span><math><mrow><mn>2</mn><mi>k</mi><mi>π</mi></mrow></math></span> phase ambiguity, enabling a unique and spatially continuous phase solution from a single frame. In addition, we design a dynamic dual-stream sampler that simultaneously samples labeled and unlabeled data and adaptively adjusts their proportions within each batch based on training progress, enabling progressive and synergistic optimization of supervised and self-supervised learning signals. Experimental results demonstrate that the proposed method, using only 50% of the labeled data, outperforms existing open-source supervised end-to-end SSFPP approaches on both synthetic and real-world datasets. This confirms its ability to substantially reduce annotation costs while maintaining high reconstruction accuracy.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"272 ","pages":"Article 121076"},"PeriodicalIF":5.6,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147388339","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}
Pub Date : 2026-05-05Epub Date: 2026-02-26DOI: 10.1016/j.measurement.2026.120984
Dariusz P. Więcek, Igor Michalski, Daniil Ruban, Jacek Wroński
This paper proposes an enhanced radio propagation model derived from a tuned Standard Propagation Model (SPM) for efficient and optimal network planning of International Mobile Telecommunications (IMT) systems (like 5G and 6G) in the C-band (frequency range 3400–4200 MHz) for typical European cities. Based on a measurement campaign conducted by the authors, the model was analyzed and subsequently tuned using nonlinear regression, yielding results that more accurately estimate coverage areas compared to the standard. Error analysis demonstrated significant improvements in propagation modeling, resulting in reduced deviations between simulations and measurements.
{"title":"Radio propagation model for mobile network planning in the C-band","authors":"Dariusz P. Więcek, Igor Michalski, Daniil Ruban, Jacek Wroński","doi":"10.1016/j.measurement.2026.120984","DOIUrl":"10.1016/j.measurement.2026.120984","url":null,"abstract":"<div><div>This paper proposes an enhanced radio propagation model derived from a tuned Standard Propagation Model (SPM) for efficient and optimal network planning of International Mobile Telecommunications (IMT) systems (like 5G and 6G) in the C-band (frequency range 3400–4200 MHz) for typical European cities. Based on a measurement campaign conducted by the authors, the model was analyzed and subsequently tuned using nonlinear regression, yielding results that more accurately estimate coverage areas compared to the standard. Error analysis demonstrated significant improvements in propagation modeling, resulting in reduced deviations between simulations and measurements.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"272 ","pages":"Article 120984"},"PeriodicalIF":5.6,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387976","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}
Pub Date : 2026-05-05Epub Date: 2026-03-01DOI: 10.1016/j.measurement.2026.121012
Zhenghui Xu, Jian Li, Ling Tang, Shimin Wei
High-precision localization is a fundamental requirement for autonomous robot navigation. However, in challenging LiDAR-degraded environments, sparse geometric structures, insufficient effective features, and interference from overlapping redundant points and cluttered noise often cause existing methods to drift severely, making accurate localization difficult. To address this, we propose a gradient flow sampled and intensity-enhanced LiDAR-Inertial Odometry (LIO) framework that improves matching efficiency and localization accuracy under geometric degeneracy. First, we propose a gradient flow-based point cloud sampling method that computes the distribution of point cloud gradient flows based on the observability of point cloud hyperplanes, minimizing sampling to suppress redundancy, and followed by a geometric consistency verification to reject noisy measurements. Second, to improve registration accuracy and robustness, we introduce an intensity-geometry fused point-pair association strategy that rates scan correspondences via intensity Kullback-Leibler (KL) divergence and geometric similarity to select the best match, integrates it into the point-to-plane iterative Extended Kalman Filter (iEKF) framework. Then, a dynamic factor during pose estimation adaptively balances geometric and photometric residuals across environments. Finally, extensive experiments on the Newer College, ENWIDE, DiTer++, and GEODE datasets show that the proposed algorithm outperforms the intensity-enhanced LIO algorithms on most sequences, with a 22.98% improvement in real-time performance compared to the baseline.
{"title":"Intensity-enhanced LiDAR-inertial odometry with gradient flow sampling for degraded environments","authors":"Zhenghui Xu, Jian Li, Ling Tang, Shimin Wei","doi":"10.1016/j.measurement.2026.121012","DOIUrl":"10.1016/j.measurement.2026.121012","url":null,"abstract":"<div><div>High-precision localization is a fundamental requirement for autonomous robot navigation. However, in challenging LiDAR-degraded environments, sparse geometric structures, insufficient effective features, and interference from overlapping redundant points and cluttered noise often cause existing methods to drift severely, making accurate localization difficult. To address this, we propose a gradient flow sampled and intensity-enhanced LiDAR-Inertial Odometry (LIO) framework that improves matching efficiency and localization accuracy under geometric degeneracy. First, we propose a gradient flow-based point cloud sampling method that computes the distribution of point cloud gradient flows based on the observability of point cloud hyperplanes, minimizing sampling to suppress redundancy, and followed by a geometric consistency verification to reject noisy measurements. Second, to improve registration accuracy and robustness, we introduce an intensity-geometry fused point-pair association strategy that rates scan correspondences via intensity Kullback-Leibler (KL) divergence and geometric similarity to select the best match, integrates it into the point-to-plane iterative Extended Kalman Filter (iEKF) framework. Then, a dynamic factor during pose estimation adaptively balances geometric and photometric residuals across environments. Finally, extensive experiments on the Newer College, ENWIDE, DiTer++, and GEODE datasets show that the proposed algorithm outperforms the intensity-enhanced LIO algorithms on most sequences, with a 22.98% improvement in real-time performance compared to the baseline.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"272 ","pages":"Article 121012"},"PeriodicalIF":5.6,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387982","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}
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