Measurement最新文献

{"title":"Horn-integrated air-coupled PMUT rangefinder for simultaneous sensitivity and detection range enhancement","authors":"Yan Wang ,&nbsp;Peng Chen ,&nbsp;Junning Zhang ,&nbsp;Zihan Li ,&nbsp;Hongbin Yu","doi":"10.1016/j.measurement.2025.119243","DOIUrl":"10.1016/j.measurement.2025.119243","url":null,"abstract":"<div><div>Given the pulse-echo operation mode, the vibration amplitude and the duration time of the piezoelectric micromachined ultrasonic transducer (PMUT) worked under resonance are both positively correlated with the quality factor <em>Q</em>. Therefore, the PMUT-based self-transceiving rangefinders have to face performance trade-off dilemma between the maximum detection distance and the minimum detection blind area. To address this challenge, a specially designed horn-shaped acoustic package is integrated into a quasi-closed PMUT with inherently low <em>Q</em> factor. A lumped-parameter acoustic model and finite element modeling (FEM) method are used for analysis, through which effective enhancement of both of the transmitting and receiving sensitivities of the PMUT can be achieved by optimizing the horn configuration. At the same time, the low <em>Q</em> characteristic can be well maintained, enabling simultaneous detection distance extension and detection blind area reduction. From proof of concept experiment, it can be seen that after the integration of the horn structure, the emission sensitivity and the pulse-echo signal strength of the PMUT can be increased by factors of 2.65 and 7.68, respectively, with a slight decrease in <em>Q</em> factor. As a result, a large detection range covering from 142.9 mm to 6 m with a −6 dB divergence angle of 66° has been successfully demonstrated with a single PMUT driven by a pulsed sinusoidal signal with 72.9 kHz and 40 V_pp, despite its small effective device area of only 0.59 mm². Provided this competitive performance, the current method possesses excellent application perspective in air-coupled ultrasonic sensing applications.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119243"},"PeriodicalIF":5.6,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145270167","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 hybrid mechanism modeling and data-driven method for energy prediction and optimization for a class of industrial robots","authors":"Xiaolong Wang,&nbsp;Jianfu Cao,&nbsp;Ye Cao","doi":"10.1016/j.measurement.2025.119227","DOIUrl":"10.1016/j.measurement.2025.119227","url":null,"abstract":"<div><div>Predicting and optimizing the energy consumption of industrial robots (IRs) helps reduce their operating costs and achieve environmental protection. However, existing methods are often constrained by IR controller inputs or rely heavily on measured data, limiting their applicability to ordinary production-line IRs. To overcome these challenges, this paper proposes a hybrid robot energy model (HREM) for a class of commonly used elbow-type IRs, which integrates a robot simplified mechanism-based model (RSM) with a neural network for residual energy compensation. This approach combines the strengths of mechanism modeling and data-driven approaches. Unlike purely data-driven methods, HREM reduces dependence on training data and enables accurate prediction and optimization across untrained joint positions. In the mechanism modeling part, RSM parameters can be identified using only power-supply-side data, and an optimization algorithm combining gradient descent and genetic algorithms (GD-GA) is introduced to improve identification efficiency. Experimental results demonstrate that HREM achieves higher prediction accuracy and greater energy savings compared with data-driven methods, making it a practical solution for large-scale industrial deployment.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119227"},"PeriodicalIF":5.6,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268977","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":"Experimental evaluation of scour reduction around oblong collar pier structure: an integrative approach","authors":"Tania Sultana,&nbsp;Buddhadev Nandi,&nbsp;Subhasish Das","doi":"10.1016/j.measurement.2025.119241","DOIUrl":"10.1016/j.measurement.2025.119241","url":null,"abstract":"<div><div>Collar pier structure (CPS) effectively reduces the rate of scouring, highlighting the importance of temporal scour depth (<em>d_s</em>) estimation to enhance bridge safety. A thorough literature review is conducted to collect 696 data points and equations related to oblong CPS. The key parameters, including pier length × width (<em>L × D</em>), collar length × width × position (<em>L</em>* × <em>W</em> × <em>H</em>), sediment size, and velocity, are identified on <em>d_s</em> and their influences are evaluated. Performing new experiments varying <em>W</em> and <em>L</em>, another 174 data points are obtained, measuring <em>d_s</em> at five azimuthal positions (<em>ϕ</em>) in a flume of 11 m long, 0.81 m wide, and 0.60 m deep, with a uniform sediment bed thickness of 0.20 m. For a pier without a collar (PWOC), the location of maximum <em>d_s</em> is observed upstream (<em>ϕ</em> = 0°), whereas with a collar (PWC), the location shifts downstream (<em>ϕ</em> = 135°). A maximum 93.81 % scour reduction is achieved when <em>W</em>/<em>D</em> = 3.5 and <em>L</em>/<em>D</em> = 2. Conversely, the minimum scour reduction is only 10.74 % when <em>W</em>/<em>D</em> = 1.5 and <em>L</em>/<em>D</em> = 4. Limited literature provides formulas for calculating <em>d_s</em>, mainly relying on restricted data. The data from previous studies are rarely utilized for conducting comprehensive analyses. A new formula for <em>d_s</em> is developed by applying nonlinear regression analysis combined with the Gauss-Newton optimization technique. Its efficacy is confirmed through comparison with both experimental and literature datasets using statistical evaluation metrics. This new formula shows significantly improved accuracy by 10.8–54.2 % in terms of <em>P_in</em> when compared to the literature formula. This research offers engineers a reliable approach for predicting <em>d_s</em> across different scenarios, ultimately improving bridge safety.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119241"},"PeriodicalIF":5.6,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269684","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 multi-modal emotion recognition method considering the contribution and redundancy of channels and the correlation and heterogeneity of modalities","authors":"Yongxuan Wen,&nbsp;Wanzhong Chen","doi":"10.1016/j.measurement.2025.119247","DOIUrl":"10.1016/j.measurement.2025.119247","url":null,"abstract":"<div><div>Physiological signals could reflect individual true emotional state, and emotion recognition based on physiological signals is significant in the field of artificial intelligence. However, current multimodal emotion recognition methods used full channels, leading to data redundancy and hardware complexity, causing a waste of computing resources. In addition, existing feature fusion methods generally adopted a direct connection approach, lacking of mid-level alignment and interaction, which cannot effectively extract complementary features from multimodal information, thus affecting classification accuracy. To address the above-mentioned issues, this paper proposed a multimodal emotion recognition method based on both electroencephalogram signals (EEG) and peripheral physiological signals (PPS). First, we introduced a triple-weighted ReliefF-NMI channel selection (TWRNCS) to select channels for EEG signals where the triple weight of subject-feature-frequency band were considered, and the contribution and redundancy of EEG channels are screened in two stages. Secondly, we designed an adaptive feature extractor capable of automatically exacting features from multi-channel EEG and PPS. Additionally, we proposed a cross-modal hybrid attention module (CHAM) based on self-attention and cross-attention mechanisms, including intra-modality private pipelines and inter-modality common pipelines. The private pipelines used self-attention mechanisms to retain heterogeneous information of modalities, while the common pipelines used cross-attention and self-attention mechanisms to capture cross-modal correlations. Finally, the information from different modalities was fully integrated for classification. The experiments demonstrated that our model achieved accuracy of over 98% on the DEAP and MAHNOB-HCI datasets, which proved the superiority of this paper in emotion recognition tasks.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119247"},"PeriodicalIF":5.6,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247868","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":"AMPNet: An advanced lightweight defect detection network for tiny steel sheets inside mobile phone in industrial scenarios","authors":"Peng Shan ,&nbsp;Teng Liang ,&nbsp;Di He ,&nbsp;Guodong Pan ,&nbsp;Menghao Zhi ,&nbsp;Yuliang Zhao","doi":"10.1016/j.measurement.2025.119155","DOIUrl":"10.1016/j.measurement.2025.119155","url":null,"abstract":"<div><div>Mobile steel sheets, essential for smartphone structural reinforcement, often suffer from surface defects (irregular shapes/textures) caused by contaminants or manufacturing flaws. The Advanced Mobile Phone Steel Sheets Defect Detection Network (AMPNet) is a lightweight solution designed for real-time, high-accuracy defect detection in resource-limited industrial environments. AMPNet is structured around three main components specifically designed for tiny steel sheet defect detection. Firstly, contextual and spatial attention (CASA) merges strip convolution (capturing elongated defects) and a spatial attention module (SA Module) to prioritize critical spatial regions, enhancing defect feature extraction. Secondly, residual contextual and spatial (RCS) convolution integrates CASA with skip connections to mitigate gradient vanishing and improve multi-level feature fusion, boosting accuracy without computational overhead. Finally, the lightweight architecture utilizes the C2f-RVB backbone by replacing traditional convolutions with RepViTBlocks (RVB) and integrates the GhostHead detection head through a combination of GhostConv and coupled design, significantly slashing parameters and computational costs. Experimental results on the mobile phone steel sheet defect dataset demonstrate that AMPNet achieves 91.8% Average Precision at an Intersection over Union threshold of 0.5 (AP0.5) with only 3.0 million parameters (Params) and 5.8 billion floating point operations (FLOPs), outperforming a series of You Only Look Once (YOLO) models (e.g., YOLOv5 YOLO13), proving its efficacy and suitability for deployment on resource-limited embedded systems in industrial settings.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119155"},"PeriodicalIF":5.6,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269075","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":"Topology-aware dual-branch network via lightweight Mamba for crack segmentation and quantification","authors":"Jianming Zhang (张建明) ,&nbsp;Dianwen Li (李典稳) ,&nbsp;Shigen Zhang (张世根) ,&nbsp;Rui Zhang (张锐) ,&nbsp;Jin Zhang (张锦)","doi":"10.1016/j.measurement.2025.119180","DOIUrl":"10.1016/j.measurement.2025.119180","url":null,"abstract":"<div><div>Crack detection plays a crucial role in assessing the technical condition and facilitating the maintenance of pavements. Image segmentation is one of the most promising techniques for crack detection applications. However, crack segmentation is challenging due to complex pavement conditions. Existing methods either overlook the topological continuity of crack structures or exhibit limited capability in extracting semantic information. To address these shortcomings, a dual-branch crack segmentation network is proposed that emphasizes topology awareness and incorporates Mamba. First, a topology-aware module (TAM) based on dynamic snake convolution is proposed to extract topological information, which is used to construct the topology-aware branch. To reduce the high computational complexity of dynamic snake convolution, the TAM integrates horizontal convolution, vertical convolution, and the proposed direction selection module (DSM), which also improves the accuracy. Second, a lightweight vision state space module (LVSSM) is designed to construct the semantic branch, which reduces computational costs based on Mamba while effectively capturing long-distance dependencies. Third, an attention-based feature fusion module (AFFM) is proposed, augmented by a spatial enhancement module (SEM) designed to improve the spatial information within both branches. Features from both branches are dynamically fused layer by layer. Fourth, a segmentation-based crack length quantification method applicable to any width is proposed. This method can be combined with crack segmentation methods to achieve automated tasks such as crack measurement and pavement technical condition inspection. Finally, extensive experiments are conducted on three public datasets. The performance of the proposed model exceeds other state-of-the-art methods.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119180"},"PeriodicalIF":5.6,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145270173","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":"Impact of gas cell material and length on the performance of MEMS-based pulsed infrared emitters for gas sensing applications","authors":"Vinay Goyal ,&nbsp;Vishali Singh ,&nbsp;Ajay Kumar ,&nbsp;Rahul Prajesh","doi":"10.1016/j.measurement.2025.119075","DOIUrl":"10.1016/j.measurement.2025.119075","url":null,"abstract":"<div><div>This work presents a comprehensive study of MEMS-based pulsed infrared (IR) emitters and their integration with gas cell configurations for non-dispersive infrared (NDIR) CO₂ sensing. A dual-spiral platinum microheater was designed, simulated, and fabricated using standard MEMS processes, achieving efficient thermal performance with low power consumption and high modulation depth (100% up to 5 Hz). System-level evaluations examined the effects of optical path length and gas cell materials (Teflon, aluminum, and copper) on detector signal. Experimental results confirmed an exponential decay in signal with increased path length, consistent with the Beer–Lambert law, and demonstrated superior reflectivity and signal strength in copper-based gas cells. The sensor showed linear CO₂ detection up to 6% concentration, with saturation beyond this point, and sustained stable operation over 100,000 pulsing cycles. These findings highlight the importance of emitter–gas cell co-design in enhancing the sensitivity, efficiency, and miniaturization of NDIR gas sensing systems.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119075"},"PeriodicalIF":5.6,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269076","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":"Efficiency optimization of frequency-domain ultrasonic imaging by adaptive Var-MRLA sparse method","authors":"Xiao Li,&nbsp;Shijie Jin,&nbsp;Chengjun Di,&nbsp;Zhongbing Luo","doi":"10.1016/j.measurement.2025.119181","DOIUrl":"10.1016/j.measurement.2025.119181","url":null,"abstract":"<div><div>The time-domain total focusing method (TFM) in ultrasonic testing faces the issue of computational inefficiency, primarily due to the large-scale full matrix capture (FMC) datasets and the complex delay-and-sum (DAS) algorithm. In this paper, a new design scheme based on the frequency-domain TFM for sparse arrays is proposed by linking element selection with the FMC datasets to maintain stable imaging performance and improve imaging efficiency. A subset of transmitters containing critical information is dynamically selected through variance analysis, and the array arrangements are optimized according to the minimum redundancy principle. Experimental results demonstrate strong adaptability in detecting side-drilled holes (SDHs) with different spacings and positions in aluminum alloy specimens, and the beam directivity is maintained at a level comparable to the original array. The imaging time is reduced by 45.6 % at least, and the measurement errors of equivalent diameters are within 0.2 mm. Finally, simulation and experimental tests are conducted under different conditions, including steel material, planar defects, and double-layer media. The defect indications and quantitative results further confirm the great imaging performance of the proposed method compared to the original array and conventional sparse arrays.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119181"},"PeriodicalIF":5.6,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269083","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":"High-precision measurement of crack length and angle in structural materials using point-focused electromagnetic ultrasound","authors":"Jun Tu ,&nbsp;Xin Shen ,&nbsp;Hongjun Zhou ,&nbsp;Yini Song ,&nbsp;Zhiyang Deng ,&nbsp;Xiaochun Song","doi":"10.1016/j.measurement.2025.119190","DOIUrl":"10.1016/j.measurement.2025.119190","url":null,"abstract":"<div><div>Crack detection and quantitative evaluation in aircraft aluminum plate structures hold significant application value in aerospace engineering. However, existing non-contact inspection techniques face limitations in achieving high-precision measurements, particularly in terms of crack length and angle estimation. To address this challenge, this study focuses on fatigue cracks propagating from the surface into the interior of metallic plates and proposes a crack characterization method based on point-focused electromagnetic acoustic transducer (PF-EMAT) technology. The method utilizes point-focused surface waves generated by electromagnetic ultrasound, and accurately calculates the crack inclination by analyzing wave propagation characteristics and applying geometric principles. Meanwhile, the crack length is quantitatively measured using a relative sensitivity approach, enabling precise evaluation of crack geometry. Experimental results demonstrate that this method can effectively detect cracks with lengths of 10  mm or more and angles ranging from 0° to 90°, while maintaining length measurement errors within 0.2  mm and angle errors within 1°. The findings confirm the reliability of PF-EMAT technology in non-contact geometric crack characterization, offering a novel solution for the structural health monitoring of plate-like components.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119190"},"PeriodicalIF":5.6,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269077","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":"Measuring and correlating the concentration levels of biomarkers in exhaled breath","authors":"İsmail Bayrakli","doi":"10.1016/j.measurement.2025.119202","DOIUrl":"10.1016/j.measurement.2025.119202","url":null,"abstract":"<div><div>In this research, a sensor system was developed to quantify and correlate the concentration levels of biomarkers present in exhaled breath. To achieve this purpose, exhaled air samples were collected from a cohort of 300 individuals, and datasets comprising concentrations of oxygen (O<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>), carbon dioxide (CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>), nitrous oxide (N<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>O), carbon monoxide (CO), and water vapor (H<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>O) molecules were established. The analyses of CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> and N<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>O molecules were conducted utilizing our custom-built multiple-pass absorption spectroscopy (MuPAS) sensor, whereas the detection of CO and H<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>O molecules was carried out using our custom-built quartz-enhanced photoacoustic absorption spectroscopy (QEPAS) sensor. The oxygen molecule was examined utilizing a commercially available sensor. A novel methodology was established to correlate the concentration values of molecules and to derive the conversion equations between them. To the best of our knowledge, this study is the first to establish significant correlations between the breath molecules. Thus, it was demonstrated that the concentration values of other molecules can be determined based on the concentration value of a reference molecule. Consequently, breath analysis can be conducted in a more efficient, rapid, simple, and cost-effective manner.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119202"},"PeriodicalIF":5.6,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269148","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}