Measurement最新文献

{"title":"Feature gene screening and diagnosis of breast cancer based on the minimum classification error rate criterion","authors":"Jingze Song ,&nbsp;Chang Cai ,&nbsp;Kedong Zhang ,&nbsp;Tongshun Liu","doi":"10.1016/j.measurement.2025.117787","DOIUrl":"10.1016/j.measurement.2025.117787","url":null,"abstract":"<div><div>Breast cancer is currently the most common type of malignant cancer in women worldwide, accounting for 31 % of cancers in women, and has been on the rise in terms of both morbidity and mortality. Feature gene screening is essential for diagnosing, prognosis, and timely treatment. This study proposed a breast cancer feature genes screening method based on the minimum classification error rate criterion for accurate breast cancer diagnosis. Firstly, the overlapping area between the two distribution curves of cancer and normal gene expression data, namely, the statistically minimum classification error rate was calculated, and the breast cancer feature genes were then pre-screened from The Cancer Genome Atlas (TCGA) data with the minimum classification error rate criterion. Secondly, the feature genes were further screened based on the Weighted Gene Co-expression Network Analysis (WGCNA) and Protein-Protein Interaction (PPI) network analysis, and the Bayesian network for diagnosing breast cancer was constructed based on the screened genes. Finally, the effectiveness of the genetic screening method was validated using TCGA data within the Bayesian network diagnostic model. Experimental results showed that the method proposed in this paper had an accuracy of 96.67%, precision of 100%, recall of 93.1%, and F1 score of 0.9643, which were improved by 5%, 7.14%, 3.44%, and 5.7% compared to the conventional cancer gene screening methods with differential expression analysis.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117787"},"PeriodicalIF":5.2,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935443","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}

{"title":"Multipath mitigation methods for challenging environments caused by snowy weather in BDS/GPS deformation monitoring","authors":"Wei Zhan,&nbsp;Xiufeng He,&nbsp;Hao Yang,&nbsp;Haijun Yuan,&nbsp;Dongzhen Jia,&nbsp;Guowang Huang","doi":"10.1016/j.measurement.2025.117804","DOIUrl":"10.1016/j.measurement.2025.117804","url":null,"abstract":"<div><div>Snowy weather makes the multipath effects of receivers in BDS/GPS short baseline applications more complex. Traditional multipath hemispherical map (MHM) relies on stable observation environments, while the multipath period of the BDS MEO constellation is as long as seven days. Random walk multipath (RWM) utilizes multipath-corrected GPS observations to estimate multipath errors of other systems, providing advantages in challenging environments. We evaluated the performance of MHM and RWM in the BDS/GPS combined system, and the results showed that only the RWM meets the application requirements. Moreover, adopting multi-period modeling data may lead to a decrease in positioning accuracy. Further experiments showed that GPS satellites exhibited higher multipath correlation coefficients than BDS satellites. Considering the limited modeling data, three different RWM schemes are designed and evaluated. Positioning results from two consecutive weeks at Jurong Power Station indicate that the scheme of using GPS to assist all BDS satellites (RWM-G) is more recommended. This was further validated with a week’s data from another reservoir. In addition, a detailed evaluation of RWM-G revealed significant suppression of low-frequency multipath and some suppression but limited of high-frequency ones, with an average residual reduction rate of about 45 % for all satellites. During the initial snowfall period with better observation environments, the improvement of daily solutions in the vertical direction is more significant. In the snow accumulation stage with poor observation environments, the RWM-G can correct positioning offsets, ensuring accurate and reliable results.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117804"},"PeriodicalIF":5.2,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942413","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}

{"title":"Calibration-free method of recovering the time-varying absorbance profile in TDLAS by the Fourier reconstructed spectroscopy","authors":"Youquan Yang ,&nbsp;Zhimin Peng ,&nbsp;Dao Zheng ,&nbsp;Yanjun Ding ,&nbsp;Yanjun Du","doi":"10.1016/j.measurement.2025.117771","DOIUrl":"10.1016/j.measurement.2025.117771","url":null,"abstract":"<div><div>Traditional scan-direct absorption spectroscopy (scan-DAS) is susceptible to 1/f noise, limiting its ability to realize high signal-to-noise ratio (SNR) measurements. This study proposes a calibration-free and noise-immune method, named fast scan–Fourier reconstructed spectroscopy (FC-FRS), capable of improving the SNR of the single-shot absorbance spectrum and reconstructing the time-varying absorbance without decreasing the time resolution. Two validation experiments with CO₂ absorbance varying over time were designed using a shock tube device within the millisecond time duration. In the first validation experiment, CO₂ was gradually released during the combustion of hydrocarbons, resulting in the slowly increased CO₂ absorbance. The average spectral SNR of FC-FRS reconstructed absorbance at 2100 K reached 73.51, which increased by 22.83 % compared to the original scan-DAS spectrum. The CO₂ generation profile extracted from the recovered absorbance agreed well with the prediction of kinetic mechanisms. In the second experiment where the CO₂ absorbance was stable behind the reflected shock at 1312 K, the average spectral SNR of the recovered absorbance line behind the reflected shock was 433.81, which is 44 % higher than that of the scan-DAS spectrum. The averaged fitted line strength from the recovered absorbance curve had a relative error of only 1.07 % compared to the HITAN2020 database. Finally, the proposed method was compared with the 2f/1f method of scan-WMS in a static CO measurement experiment, the FC-FRS showed the similar SNR but better stability over time.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117771"},"PeriodicalIF":5.2,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070221","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}

{"title":"A novel operational transfer path analysis based on the complex-valued crosstalk elimination method","authors":"Lingchen Kong,&nbsp;Xuan Zhao,&nbsp;Xiaolei Yuan,&nbsp;Qiang Yu,&nbsp;Chenyu Zhou,&nbsp;Yuzhou Yang,&nbsp;Meiying Li","doi":"10.1016/j.measurement.2025.117813","DOIUrl":"10.1016/j.measurement.2025.117813","url":null,"abstract":"<div><div>Operational transfer path analysis (OTPA) can identify significant excitation sources and transfer paths using only operational responses. However, the signal crosstalk between reference points reduces the accuracy of OTPA, and significantly limits practical applications. Existing crosstalk elimination methods always ignore complex-valued information about frequency vibration signals, which causes undesirable analysis results under noncircular complex-valued conditions. In this paper, the transcendental complex maximization of non-Gaussianity theory (TCMN) is introduced for the first time to extend the crosstalk elimination into the complex-valued domain, and the OTPA-TCMN model is constructed to analyze the vibration transmission of the vehicle motor controller unit (MCU) by coupling actual vehicle structures and driving conditions. In the proposed OTPA-TCMN model, the time–frequency calculation module based on the welch method is used to acquire accurate auto and cross power spectrums. To cancel strong crosstalk, the TCMN method is introduced in the crosstalk elimination module to obtain more accurate transmissibility functions, and the contribution calculation module is used to calculate the reconstructed target signal and the contribution of each transfer path. The estimated MCU signal of the proposed OTPA-TCMN and the OTPA based on the complex independent component analysis (OTPA-cICA) and truncated singular value (OTPA-SVD) models are compared to the measured value to validate the proposed model by utilizing actual vehicle experiments. The MCU signal estimated by the proposed model is closer to the measured value, with the errors reduced by more than 20.0% and 5.4% regarding the RMSE and peak values, respectively. Therefore, the proposed OTPA-TCMN model can further improve crosstalk elimination and acquire more accurate analysis results, which can be used as an elegant tool to resolve vibration transfer problems in complex mechanical systems.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117813"},"PeriodicalIF":5.2,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143934695","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}

{"title":"Intelligent monitoring of impact damage within concrete through deep learning-empowered electromechanical impedance technique","authors":"Qixiang Yan ,&nbsp;Yifan Yang ,&nbsp;Chuan Zhang ,&nbsp;Zhengyu Xiong ,&nbsp;Haojia Zhong ,&nbsp;Yajun Xu ,&nbsp;Wenbo Yang","doi":"10.1016/j.measurement.2025.117642","DOIUrl":"10.1016/j.measurement.2025.117642","url":null,"abstract":"<div><div>As fundamental elements in civil infrastructures, concrete structures may experience impact loads during service life, which poses threat to the structural integrity and serviceability. Accurate detection and assessment of internal damage are critical to ensuring post-impact safety and guiding reinforcement strategies. The electromechanical impedance (EMI) technique has proven to be a reliable non-destructive approach for detecting concrete damage. However, traditional EMI approaches rely on manual feature extraction and statistical analysis, hindering real-time and intelligent applications. To address this limitation, this paper developed a fused deep learning framework named KoCG-Net, which integrates convolutional neural networks (CNNs), gated recurrent units (GRUs), and Kolmogorov-Arnold networks (KAN) to automate the EMI-based damage detection. KoCG-Net directly processed raw conductance signals from repetitive drop weight impact tests, achieving accurate prediction of impact damage. The results demonstrated its superior performance, with R² values of 0.9937 for C30 dataset and 0.9985 for C50 dataset. Moreover, the framework outperformed five benchmark models in prediction accuracy, noise immunity, and efficiency under limited training data, manifesting its substantial potentials for real-time and intelligent monitoring of impact damage within concrete.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117642"},"PeriodicalIF":5.2,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143934702","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}

{"title":"Stress prediction model of oil and gas pipeline based on magnetic-force coupling and machine learning","authors":"Guangyuan Weng,&nbsp;Xinlei Xing,&nbsp;Zhaoyang Han,&nbsp;Bo Wang,&nbsp;Xiyu Zhu,&nbsp;Jie Zheng","doi":"10.1016/j.measurement.2025.117818","DOIUrl":"10.1016/j.measurement.2025.117818","url":null,"abstract":"<div><div>This study proposes a novel machine learning-based model for accurate stress monitoring in gas pipelines, essential for ensuring safe and efficient operations. The model leverages data on stress and magnetic flux density derived from parameters such as pipe external diameter, wall thickness, elastic modulus, relative permeability, and structural characteristics, combined with internal pressure loads and environmental magnetic fields, obtained via a magnetic simulation platform. So, the data mainly comes from the magnetic simulation platform, but also includes some model tests and the accumulation of measured data of the study team. Data preprocessing included missing value imputation, outlier processing, and normalization. A 3D parallel computing machine learning model was developed to predict stress, incorporating pipeline structural parameters, internal pressure loads, and magnetic parameters. Model parameters were optimized using a grid search method with 50 % cross-validation, and performance was evaluated using R², RMSE, and MAE metrics. Among RF, SVR, CART, and ANN algorithms, Random Forest (RF) performed best, achieving R² = 0.87, RMSE = 0.045, MAE = 0.01 for stress prediction, and R² = 0.97, RMSE = 0.05, MAE = 0.02 for magnetic flux density prediction. Comparisons with finite element method calculations across 12 pipeline parameter sets showed a maximum accuracy value error is within 6 %. The model’s robustness allows accurate predictions even with incomplete data, enabling non-excavation stress assessment using design data, field surveys, and tests. This provides valuable insights for pipeline lifecycle management and preventive maintenance, offering effective technical support for stress monitoring in oil and gas pipelines.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117818"},"PeriodicalIF":5.2,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143931540","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}

{"title":"Detecting defects in rising mains using the acoustic fluid velocity","authors":"Joanna Watts,&nbsp;Kirill Horoshenkov","doi":"10.1016/j.measurement.2025.117702","DOIUrl":"10.1016/j.measurement.2025.117702","url":null,"abstract":"<div><div>It is a serious challenge to detect wall damage in live rising mains that transport wastewater along flat or elevated sections of the sewer pipe network. This work proposes a novel method that uses the acoustic velocity vector in the fluid to detect the onset of wall defects in a ductile iron rising main. Numerical simulations are performed to show that this acoustic velocity vector is more sensitive to the presence of a wall defect than the acoustic pressure or wall acceleration traditionally measured in fluid-filled pipes. The method can detect internal and external wall loss and small (0.020–0.025 m) wall perforations. An adapted triaxial accelerometer is used to demonstrate experimentally the method on an exhumed section of a 0.31 m diameter ductile iron pipe. It is shown that the radial and horizontal components of the acoustic velocity vector are particularly sensitive to the presence of small wall perforations. The proposed acoustic velocity sensor can be easily deployed on a mobile pipe inspection robot with a collocated or remote source of sound.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117702"},"PeriodicalIF":5.2,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143947185","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}

{"title":"Liquid holdup measurement of permittivity-changing gas-liquid two-phase flow by multimode microwave resonant cavity","authors":"Xiaokai Zhang ,&nbsp;Jiangtao Sun ,&nbsp;Peng Suo ,&nbsp;Xiaolin Li ,&nbsp;Shijie Sun ,&nbsp;Lijun Xu","doi":"10.1016/j.measurement.2025.117816","DOIUrl":"10.1016/j.measurement.2025.117816","url":null,"abstract":"<div><div>The accurate measurement of gas–liquid two-phase flow has been a key issue in multiple engineering fields but remains challenging. During the long-term monitoring of the liquid holdup in gas–liquid two-phase flow, the permittivity of the flow may change frequently due to inevitable environmental fluctuations, and the measurement errors can accumulate and become significant. In this work, a multimode resonant cavity based on the perturbation method is developed to achieve the accurate measurement of liquid holdup in long-term monitoring despite permittivity changes. The detailed design process, focusing on sensitivity optimization and waveguide characterization, is described. By simultaneously considering the resonant frequency and quality factor under multiple resonant modes, a compensation method for holdup measurement is proposed that adaptively mitigates the adverse impact of property changes without the need of frequent calibrations. Experiments covering the full holdup range are conducted using white oil and air. By mixing #68 white oil with #10 white oil in different ratios, the permittivity changes of the liquid phase are simulated. Results show that the full-range measurement errors are less than 0.8% with the permittivity unchanged, with a rooted mean standard error (RMSE) of 0.31%. When the permittivity of the liquid change by up to 3.18%, the errors are reduced from up to 9.84% to less than 2.05%, with an RMSE of 0.67%, by applying the compensation method. This manifests that the proposed method can effectively mitigate cumulative measurement errors induced by environmental changes, making it viable for long-term monitoring without frequent off-line calibrations.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117816"},"PeriodicalIF":5.2,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143934699","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}

{"title":"Method for hybrid materials diagnosis based on ultrasonic testing signal analysis through Dynamic Time Warping and machine learning combination","authors":"Adam Janek,&nbsp;Patryk Jakubczak","doi":"10.1016/j.measurement.2025.117779","DOIUrl":"10.1016/j.measurement.2025.117779","url":null,"abstract":"<div><div>Ultrasonic testing is one of the most commonly used non-destructive testing (NDT) techniques due to its low cost and wide applicability. Automation and artificial intelligence (AI) are utilised to enhance performance and efficiency, often in high-tech solutions or specifically for monolithic materials. Consequently, a new method for testing fibre-metal laminates (FML) using A-scans is proposed. The approach employs AI-supported signal analysis to compare measurements with those from undamaged areas. The XGBoost library was used to develop the model, and Dynamic Time Warping (DTW) was employed to assess signal similarity, including shape-based analysis (DTW_z). The method was tested on undamaged samples, increased gain scenarios, delamination, and bottom recess. Chosen threshold values were not exceeded in healthy cases. In the increased gain scenario, despite DTW exceeding the threshold fourfold, the signal shape confirmed structural integrity. For delamination and holes, DTW thresholds were exceeded by up to 21%, while for DTW_z, were exceeded by 3% and 7%, respectively. Additional distance matrices can also visualise the changes reflected in the shape of optimal alignment paths. When focusing on the most variable signal regions, DTW reached 140% of the threshold value, while DTW_z attained 136% and 175% of their thresholds for delamination and cutouts. Furthermore, applying constraints improved detection accuracy and reduced processing time, increasing average DTW values from 28% to 36% for delamination and from 60% to 88% for recess, while the average DTW_z increased from 19.3% to 20.8% and from 26.1% to 29.6%, respectively.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117779"},"PeriodicalIF":5.2,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942290","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}

{"title":"Thin and flexible pressure sensors for classifying body signals enhanced by tiny machine learning algorithms","authors":"Chi Cuong Vu,&nbsp;Tuan Nghia Nguyen,&nbsp;Manh Hung Nguyen","doi":"10.1016/j.measurement.2025.117799","DOIUrl":"10.1016/j.measurement.2025.117799","url":null,"abstract":"<div><div>Flexible pressure sensors are a trend that has attracted many scientists in recent years. Among them, pressure sensors made from fabrics or fabric components are widely used due to their accessibility and diversity in structure. However, current studies face many challenges in fabrication and signal processing to obtain helpful information and build a complete wearable system for e-healthcare. To solve the above problems, we present a system consisting of soft pressure sensors from conductive fabrics and tiny machine-learning (tiny ML) algorithms for body signal monitoring applications. The fabricated sensor achieves a small thickness of 0.75 mm, a good sensitivity of 0.16 kPa⁻¹, and a fast response/recovery time of 70 ms. Besides, the flexible sensor’s capabilities are optimized thanks to intelligent algorithms that help remove noises and track body signals in real-time. In the pharyngeal movement classification scenario, the accuracy of the machine learning models is multilayer perceptron (98.43 %), k-nearest neighbors (98.43 %), and decision tree (95.28 %), respectively. When the actions are increased, the accuracy of the multilayer perceptron model still reaches 96.78 %, and the classification time is speedy at only 24 ms. The support of tiny ML algorithms has helped improve the accuracy and feasibility of the system. The ML models are built and described in detail with a small size (&lt;1 Mb), ensuring they can be deployed on small embedded boards with limited resources. The most important contributions of the paper include two aspects: the soft pressure sensor with good performance/easy manufacturing process and the details of intelligent embedded machine learning models that improve the sensor performance in practical tasks. The research represents a new trend in flexible healthcare technology development when data is processed at the network’s edge − directly on endpoint devices. We expect the work to become a highlight reference for more complete studies close to industrial production or commercial products.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117799"},"PeriodicalIF":5.2,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143947147","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}