Measurement最新文献_第7页

FMCW radar-based high-precision range estimation with generalized eigenvalue decomposition algorithm 基于广义特征值分解算法的FMCW雷达高精度距离估计

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-23 DOI: 10.1016/j.measurement.2025.117957

Wenju Zhao , Shijie Xing , Futao Ni , Yongding Tian , Qiang Liu

{"title":"FMCW radar-based high-precision range estimation with generalized eigenvalue decomposition algorithm","authors":"Wenju Zhao , Shijie Xing , Futao Ni , Yongding Tian , Qiang Liu","doi":"10.1016/j.measurement.2025.117957","DOIUrl":"10.1016/j.measurement.2025.117957","url":null,"abstract":"<div><div>Frequency-modulated Continuous Wave (FMCW) radar has been widely applied in defense systems, automotive collision avoidance, intelligent traffic monitoring, and precision level measurement due to its excellent long-range detection capabilities and improved range resolution. However, traditional FMCW radar techniques are inherently prone to spectral leakage artifacts and picket fence effects caused by non-integer period sampling and limited frequency resolution of discrete Fourier transform (DFT) processing, which seriously compromise ranging accuracy. To overcome these challenges, this paper proposes a novel high-precision range estimation algorithm based on generalized eigenvalue decomposition (GEVD). The key contributions of this study are: (1) the derivation of the analytical relationship between generalized eigenvalues and beat frequencies through formulating a generalized eigenvalue equation that includes both the beat frequency signal and its first-order derivative; and (2) the effective reduction of discretization errors and noise interference using frequency-shifting techniques combined with singular value decomposition (SVD)-based signal enhancement. A thorough parametric analysis has been performed to evaluate the impact of sampling frequency, matrix dimension, signal-to-noise ratio, and the number of targets on range precision. Extensive numerical simulations and controlled laboratory experiments validate the theoretical framework and operational effectiveness of the proposed methodology. Comparative results demonstrate that the GEVD-based method achieves superior resolution compared to conventional techniques, even in noisy environments. Field validation using single-target measurement trials confirms exceptional measurement stability, with empirical data showing maximum range deviation within 0.2 mm at sampling frequencies exceeding 400 kHz.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"255 ","pages":"Article 117957"},"PeriodicalIF":5.2,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144185464","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

UFS-YOLO: A real-time small fire target detection method incorporated hybrid attention in underground facilities UFS-YOLO：一种结合混合关注的地下设施小火源实时探测方法

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-23 DOI: 10.1016/j.measurement.2025.117948

Kefang Zhang, Binbin Mao, Hua Liu, Ying Zhang

{"title":"UFS-YOLO: A real-time small fire target detection method incorporated hybrid attention in underground facilities","authors":"Kefang Zhang, Binbin Mao, Hua Liu, Ying Zhang","doi":"10.1016/j.measurement.2025.117948","DOIUrl":"10.1016/j.measurement.2025.117948","url":null,"abstract":"<div><div>Timely fire detection is beneficial for improving the overall safety and reliability of underground facilities. However, the humid and spacious nature of underground environments poses challenges for traditional sensors. Traditional visual fire detection models based on simple classifiers often perform poorly in dimly lit underground environments. Considering the visual characteristics of fire in underground enclosed spaces, we designed a real-time small fire target image detection model named Underground Fire Scout based on YOLO (UFS-YOLO), which integrates a modified Convolutional Block Attention Module (CBAM) and a modified SIoU loss function. We built a fire image dataset by conducting experiments and collecting images from public datasets, including both original images and synthetic images after data augmentation, to implement transfer learning strategies. UFS-YOLO achieved a 5.4 % increase in accuracy and 4.9 % increase in precision on our self-built dataset compared to the original YOLOv5s, with a higher detection speed of 51.8 FPS. In our comparative experiments, some models confuse small fire targets with fire-like interferences, while others fail to extract small fire targets from the background. The proposed UFS-YOLO enhances the ability to distinguish small fire targets in the early stages of combustion in underground facilities.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"254 ","pages":"Article 117948"},"PeriodicalIF":5.2,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138295","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

ERViT System: Deep learning for high-accuracy OAM mode recognition and beam correction in adverse atmospheric conditions ERViT系统：用于恶劣大气条件下高精度OAM模式识别和光束校正的深度学习

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-22 DOI: 10.1016/j.measurement.2025.117947

Badreddine Merabet , Binbin Wang , Aleksandr Kireev , Syed A.A. Shah , Kai Guo , Sergey S. Stafeev , Elena S. Kozlova , Victor V. Kotlyar , Zhongyi Guo

{"title":"ERViT System: Deep learning for high-accuracy OAM mode recognition and beam correction in adverse atmospheric conditions","authors":"Badreddine Merabet , Binbin Wang , Aleksandr Kireev , Syed A.A. Shah , Kai Guo , Sergey S. Stafeev , Elena S. Kozlova , Victor V. Kotlyar , Zhongyi Guo","doi":"10.1016/j.measurement.2025.117947","DOIUrl":"10.1016/j.measurement.2025.117947","url":null,"abstract":"<div><div>Orbital Angular Momentum (OAM) beams offer a promising pathway to significantly increase the data capacity of Free Space Optical (FSO) communication systems due to their orthogonality and infinite dimensionality. However, atmospheric turbulence (AT) severely distorts both the intensity and phase profiles of OAM beams during propagation, leading to intermodal crosstalk, mode degradation, and increased bit error rate (BER). These effects pose critical challenges to reliable OAM-based FSO communication, especially under strong turbulence conditions. To address this issue, we propose the ERViT, a novel deep learning-based system that integrates three models: ENAT (Efficient-Net AT), ResRecNet, and Reinforced ViT (Vision Transformer), to perform AT detection, beam correction, and OAM mode classification, respectively. The ENAT model detects the presence of AT with an accuracy of 99.86 %. If the AT is detected, the ResRecNet autoencoder reconstructs the distorted beam using residual CNN blocks. Finally, the Reinforced ViT model classifies the corrected beam with high accuracy. Unlike existing approaches that require retraining for every AT’s levels, the ERViT system operates with a single training session and generalizes across varying levels of AT without additional training. Results show that under strong AT (<span><math><mrow><msubsup><mi>C</mi><mrow><mi>n</mi></mrow><mn>2</mn></msubsup><mo>=</mo><mn>3</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mrow><mo>-</mo><mn>14</mn></mrow></msup><msup><mrow><mi>m</mi></mrow><mrow><mo>-</mo><mn>2</mn><mo>/</mo><mn>3</mn></mrow></msup></mrow></math></span>), ERViT improves OAM mode recognition accuracy from 77.5 % to 99.6 %, demonstrating significant performance gains over current state-of-the-art methods.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"254 ","pages":"Article 117947"},"PeriodicalIF":5.2,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144154835","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

Lightweight bilateral network of Mura detection on micro-OLED displays 微型oled显示器上Mura检测的轻量级双边网络

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-22 DOI: 10.1016/j.measurement.2025.117937

Guobao Zhao , Yuhang Lin , Yijun Lu , Zhong Chen , Weijie Guo

引用次数: 0

Design and evaluation of a piezoelectric pressure sensor for mass detection with COMSOL and machine learning modeling 基于COMSOL和机器学习建模的质量检测压电压力传感器的设计与评价

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-22 DOI: 10.1016/j.measurement.2025.117945

Sabahudin Vrtagic , Mario Hoxha , Ahmed Abdelgalil , Ndricim Ferko , Mariam Abdallah , Albert Potams , Ardit Lushi , Halil Ibrahim Turan , Bachar Mourched

{"title":"Design and evaluation of a piezoelectric pressure sensor for mass detection with COMSOL and machine learning modeling","authors":"Sabahudin Vrtagic , Mario Hoxha , Ahmed Abdelgalil , Ndricim Ferko , Mariam Abdallah , Albert Potams , Ardit Lushi , Halil Ibrahim Turan , Bachar Mourched","doi":"10.1016/j.measurement.2025.117945","DOIUrl":"10.1016/j.measurement.2025.117945","url":null,"abstract":"<div><div>The need to monitor and manage the impact of heavy vehicles on infrastructure has led to the development of novel sensor technologies. This paper presents a prototype piezoelectric-based pressure sensor designed to detect vehicle weight through real-time mass detection, potentially enhancing transportation planning and infrastructure management. A COMSOL Multiphysics model was employed to simulate sensor response under load, and a machine learning (ML) framework, optimized using the BFGS algorithm, was implemented for accurate weight estimation. The model achieved high predictive performance, with a Mean Absolute Error (MAE) of 0.0677 and a Root Mean Squared Error (RMSE) of 0.1207, demonstrating strong agreement between predicted and actual values. While the R2 score on synthetic data was 0.99, real-world testing confirmed the model’s robustness by handling minor deviations caused by environmental and operational factors. The prototype was tested with weights up to 70 kg, with planned future studies aimed at scaling the sensor array for heavy-duty vehicle applications. Operating with a sampling interval of 5 ms, the system theoretically supports weight detection for moving loads at various speeds. However, achieving consistent performance under real-world high-speed conditions may require enhancements, such as faster or parallel data acquisition methods. This work highlights advancements in sensor design and mass detection, with future efforts focused on full-scale deployment in urban infrastructure for real-time traffic monitoring and enforcement.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"254 ","pages":"Article 117945"},"PeriodicalIF":5.2,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144154741","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 novel method for accurate modeling of a switched reluctance motor with low measurement effort 一种低测量量、精确建模开关磁阻电机的新方法

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-22 DOI: 10.1016/j.measurement.2025.117792

Linhao Sheng , Guofeng Wang , Yunsheng Fan , Jian Liu , Di Liu

{"title":"A novel method for accurate modeling of a switched reluctance motor with low measurement effort","authors":"Linhao Sheng , Guofeng Wang , Yunsheng Fan , Jian Liu , Di Liu","doi":"10.1016/j.measurement.2025.117792","DOIUrl":"10.1016/j.measurement.2025.117792","url":null,"abstract":"<div><div>This paper suggests a novel method for accurately determining the flux-linkage characteristics of a switched reluctance motor (SRM) with low measurement effort. The method integrates both static and dynamic measurements without rotor clamping devices and position sensors. The static flux-linkage characteristics at four torque-balanced positions are measured first, and an error compensation scheme is introduced. Through this compensation, the influence of magnetic coupling between phases is minimized. Then, a rotational measurement method is developed that greatly increases the flux-linkage data between balanced positions with low measurement effort. Finally, based on the obtained sample data, the complete flux-linkage characteristics are constructed by integrating the transfer learning method into a back-propagation (BP) neural network. The accuracy of the constructed model is verified by comparison with the measurement results of the rotor clamping method. Additionally, the advantages and feasibility of the proposed method are further verified through dynamic performance comparisons and experimental tests.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"254 ","pages":"Article 117792"},"PeriodicalIF":5.2,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138300","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

Research on sensing and identification of wave information via flexible array interconnection package triboelectric nanogenerators 柔性阵列互连封装摩擦电纳米发电机波浪信息传感与识别研究

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-22 DOI: 10.1016/j.measurement.2025.117934

Peng Yi , Ruixu Su , Yong Fan , Tongxin Lvu , Zhifu Zhang , Zhengru Han , Yingxue Wang , Song Gao

{"title":"Research on sensing and identification of wave information via flexible array interconnection package triboelectric nanogenerators","authors":"Peng Yi , Ruixu Su , Yong Fan , Tongxin Lvu , Zhifu Zhang , Zhengru Han , Yingxue Wang , Song Gao","doi":"10.1016/j.measurement.2025.117934","DOIUrl":"10.1016/j.measurement.2025.117934","url":null,"abstract":"<div><div>The detection of wave information is of profound significance, as they are a prevalent hydrodynamic phenomenon in the ocean. This paper suggests the development of a self-driven wave sensing unit that is based on the principles of triboelectric nanogenerator technology. This unit integrates coupled laser micro-texturing techniques with a triboelectric material fabrication approach and features a flexible array interconnection packaging structure with air-channel. Experimental results indicate that the triboelectric materials generated by four laser scanning templates exhibit a substantial improvement, which leads to a voltage increase of 3.19 times. Furthermore, the voltage is increased by 1.35 times by the flexible array interconnection package sensing unit. The output voltage of the unit demonstrates a gradient variation with frequency and a distinct linear correlation, which allows them to detect changes in the frequency and direction of waves. The results of fatigue strength test experiment provide strong support for the stability and reliability of the sensing unit in practical applications, further demonstrating its potential in the field of self-powered wave sensing. This development offers a valuable reference for the design of sensing unit that operate underwater.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"254 ","pages":"Article 117934"},"PeriodicalIF":5.2,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131372","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

Enhanced multi-band acoustic sensing from quasi-bound states in the continuum 连续介质中准束缚态的增强多波段声传感

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-22 DOI: 10.1016/j.measurement.2025.117949

Junrui Jiao, Dejie Yu

引用次数: 0

Optimizing process monitoring: Adaptive CUSUM control chart with hybrid score functions 优化过程监控：具有混合评分功能的自适应CUSUM控制图

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-21 DOI: 10.1016/j.measurement.2025.117847

Babar Zaman , Syed Zeeshan Mahfooz , Naveed Khan , Saddam Akber Abbasi

{"title":"Optimizing process monitoring: Adaptive CUSUM control chart with hybrid score functions","authors":"Babar Zaman , Syed Zeeshan Mahfooz , Naveed Khan , Saddam Akber Abbasi","doi":"10.1016/j.measurement.2025.117847","DOIUrl":"10.1016/j.measurement.2025.117847","url":null,"abstract":"<div><div>The Cumulative Sum (CUSUM) Control Chart (CC) is a powerful tool for detecting small to moderate shifts in process parameters. While the traditional CUSUM CC determines the shift magnitude, it may not effectively handle a wide range of shifts encountered in real-world applications. To address this, an adaptive CUSUM (ACUSUM) CC has been developed. This study introduces a novel ACUSUM CC by integrating Huber and Bi-square score functions, which possess unique attributes such as monotonicity and redescending properties. Despite their individual advantages, limited research has explored their combined use in ACUSUM CC design. By amalgamating these functions into a unified formulation, we propose an innovative ACUSUM CC capable of effectively monitoring shifts of varying magnitudes in the process location parameter. The proposed CC is assessed through Monte Carlo simulations under both zero-state and steady-state conditions. Key performance metrics, including Average Run Length (ARL), median run length (MDRL), and Standard Deviation of Run Length (SDRL), are used to evaluate the ability of the proposed CC to detect individual shifts. Additionally, relative ARL, extra quadratic loss, and the process capability index are utilized to measure efficiency across a range of shifts, ensuring a comprehensive performance evaluation. A comparative analysis based on performance metrics and visual representations highlights the superior performance of the proposed CC over conventional methods. Furthermore, its application in energy usage analysis demonstrates its effectiveness in detecting anomalies, supporting proactive decision-making for optimizing energy consumption and resource allocation. These findings confirm the practicality, reliability, and enhanced monitoring capabilities of the proposed ACUSUM CC in real-world industrial and environmental scenarios.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"254 ","pages":"Article 117847"},"PeriodicalIF":5.2,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144154832","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

Attenuation compensation for microwave imaging in highly lossy media 高损耗介质中微波成像的衰减补偿

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-21 DOI: 10.1016/j.measurement.2025.117918

Janghoon Jeong , Won-Kwang Park , Seong-Ho Son

引用次数: 0