Measurement最新文献_第2页

Exploratory study on sound quality evaluation and prediction for engineering machinery cabins 工程机械舱室音质评价与预测的探索性研究

IF 5.2 2区工程技术

Measurement Pub Date : 2025-04-25 DOI: 10.1016/j.measurement.2025.117684

Ruxue Dai , Jian Zhao , Weidong Zhao , Weiping Ding , Haibo Huang

{"title":"Exploratory study on sound quality evaluation and prediction for engineering machinery cabins","authors":"Ruxue Dai , Jian Zhao , Weidong Zhao , Weiping Ding , Haibo Huang","doi":"10.1016/j.measurement.2025.117684","DOIUrl":"10.1016/j.measurement.2025.117684","url":null,"abstract":"<div><div>The noise produced by engineering machinery in harsh environments poses risks to operators’ health and reduces driving comfort, making sound quality a critical concern. However, most of the current research on sound quality evaluation of engineering machinery is based on the automotive field. However, due to the significant differences in the external environment and cab structure between engineering machinery and automobiles, the evaluation methods in the field of direct transplantation of automobiles have great limitations. To address this issue, a five-level subjective evaluation system was developed, combining the ranking scale method with semantic segmentation. Standardized processing was used to minimize variations caused by inconsistencies in evaluators’ scoring. A dual-ear synchronized measurement technique was applied to collect noise data, addressing the asymmetry of sound sources inside the cabin. Correlation analysis between subjective scores and extracted objective parameters identified key factors affecting cabin sound quality. An optimal parameter combination was determined, and a prediction model based on particle swarm optimization-based random forest (PSO-RF) was proposed. Compared to random forest, support vector regression, and genetic algorithm optimization-based random forest models, the PSO-RF model showed superior accuracy (root mean square error = 0.51) and generalization (mean relative error = 6.61 %). This study introduces an effective method for evaluating and predicting sound quality in engineering machinery cabins. The approach can be applied to other products, supporting the improvement of equipment comfort and market competitiveness.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117684"},"PeriodicalIF":5.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886141","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

PEBU-Net: A lightweight segmentation network for blueberry bruising based on Unet3+ using hyperspectral transmission imaging PEBU-Net：基于Unet3+使用高光谱透射成像的蓝莓瘀伤轻量化分割网络

IF 5.2 2区工程技术

Measurement Pub Date : 2025-04-25 DOI: 10.1016/j.measurement.2025.117700

Jingyuan Dai, Guozheng Wang, Mianqing Yang, Dayang Liu

{"title":"PEBU-Net: A lightweight segmentation network for blueberry bruising based on Unet3+ using hyperspectral transmission imaging","authors":"Jingyuan Dai, Guozheng Wang, Mianqing Yang, Dayang Liu","doi":"10.1016/j.measurement.2025.117700","DOIUrl":"10.1016/j.measurement.2025.117700","url":null,"abstract":"<div><div>Quantitative segmentation of blueberry bruising is essential for farmers and processors to effectively manage blueberries of varying quality, thereby improving economic returns. This study proposed a lightweight semantic segmentation model called PEBU-Net, which accurately detects internal bruising in blueberries resulting from mechanical damage using hyperspectral transmittance images (HSTIs). The model is based on the UNet3 + architecture, utilizing fewer filters to create a lightweight framework. Subsequently, a partial self-attention module is used to reduce the impact of redundant information. Then, a novel encoder module is proposed to retain key features during the downsampling process. In addition, using atrous spatial pyramid pooling, which incorporates an efficient multi-scale attention mechanism, enhances the model’s ability to extract high-level features at different scales. Finally, the performance of the proposed model was validated by ablation experiments, comparison experiments, and visualization images of the segmentation result. The results showed that the accuracy, dice coefficients, and mean intersection over union of PEBU-Net for blueberry bruise segmentation were 95.1 %, 93.4 %, and 84.8 %, respectively. These results demonstrated improvements of 1.4 %, 2 %, and 4.4 % over the baseline model. Compared to other semantic segmentation models, the proposed PEBU-Net achieved optimal segmentation performance. Concurrently, the model size of just 7.6 MB significantly reduces the computational cost and memory usage in practical applications. The proposed lightweight segmentation model based on HSTIs achieves high-precision segmentation of bruised blueberries, which provides a technical reference for bruise segmentation of other fruits.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117700"},"PeriodicalIF":5.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892248","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

Robust state of charge prediction for lithium-ion batteries in diverse operating environments via machine learning 基于机器学习的不同操作环境下锂离子电池的鲁棒充电状态预测

IF 5.2 2区工程技术

Measurement Pub Date : 2025-04-25 DOI: 10.1016/j.measurement.2025.117595

Basak Gok, Firat Can Yilmaz, Mustafa Fazıl Serincan, Recep Onler

{"title":"Robust state of charge prediction for lithium-ion batteries in diverse operating environments via machine learning","authors":"Basak Gok, Firat Can Yilmaz, Mustafa Fazıl Serincan, Recep Onler","doi":"10.1016/j.measurement.2025.117595","DOIUrl":"10.1016/j.measurement.2025.117595","url":null,"abstract":"<div><div>Accurate estimation of the state of charge (SOC) in lithium-ion batteries is critical for optimizing performance and ensuring safety, particularly under dynamic and sub-zero conditions, where capacity utilization and internal resistance vary significantly. A key gap in existing literature was addressed by comprehensively investigating temperature profiles (25 °C to -10 °C) in conjunction with varying discharge rates (0.2C to 2C). Two machine learning (ML) techniques – Neural Networks (NN) and Gaussian Process Regression (GPR) – were applied to predict SOC using a feature set that includes C-rate, measured ambient temperature, battery surface temperatures from five different locations, and voltage. Real-world scenarios, including the New European Driving Cycle (NEDC), were replicated to capture simultaneous changes in temperature and current. Experimental results show that both ML models consistently achieve high coefficients of determination (<span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>)—ranging from 0.98 to nearly 1.00—across all tested conditions. In simpler scenarios, NN achieved slightly higher accuracy and reduced computational time by up to %30, making it suitable for real-time applications such as battery management systems (BMS). Conversely, GPR excelled in more complex conditions, accurately modeling nonlinear interactions among temperature, C-rate, and SOC. Furthermore, up to an %18.51 reduction in discharged energy capacity was observed under sub-zero temperatures combined with elevated C-rates, underscoring the severity of cold-temperature operation. Consequently, these results highlight the effectiveness of ML-based approaches for refining SOC estimation and guiding energy management decisions in demanding real-world environments.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117595"},"PeriodicalIF":5.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883082","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

An explainable missing data imputation method and its application in soft sensing 一种可解释缺失数据的输入方法及其在软测量中的应用

IF 5.2 2区工程技术

Measurement Pub Date : 2025-04-25 DOI: 10.1016/j.measurement.2025.117692

Dongnian Jiang, Shuai Zhang

引用次数: 0

Research on the directional bounding box algorithm of YOLO11 in tailings pond identification YOLO11在尾矿库识别中的定向包围盒算法研究

IF 5.2 2区工程技术

Measurement Pub Date : 2025-04-25 DOI: 10.1016/j.measurement.2025.117674

Lu-hao He, Yong-zhang Zhou, Lei Liu, Yu-qing Zhang, Jian-hua Ma

{"title":"Research on the directional bounding box algorithm of YOLO11 in tailings pond identification","authors":"Lu-hao He, Yong-zhang Zhou, Lei Liu, Yu-qing Zhang, Jian-hua Ma","doi":"10.1016/j.measurement.2025.117674","DOIUrl":"10.1016/j.measurement.2025.117674","url":null,"abstract":"<div><div>Tailings ponds are crucial components of mining operations. However, their safety monitoring and management present significant challenges. Loss of control may result in ecological harm, economic repercussions, and risks to human safety. This study applies deep learning object detection technology for the automated identification of tailings ponds, utilizing the YOLO11 algorithm in conjunction with oriented bounding boxes (OBBs) to develop an efficient object detection model that improves detection accuracy and monitoring efficiency. After 391 training iterations, the model achieved exceptional detection performance. The findings revealed that Box_Loss, Cls_Loss, and DFL_Loss were 0.4577, 0.4063, and 1.3909, respectively, demonstrating effective convergence. In the evaluation metrics, the precision, recall, mAP50, and mAP50-95 reached 0.9256, 0.8258, 0.9190, and 0.8054, respectively. Notably, [email protected] reached a value of 0.919, indicating the model’s efficiency and stability in recognizing tailings pond targets. In the validation set, 90 % of the samples exhibited confidence levels exceeding 92 %, further confirming the model’s accuracy. In practical applications, the average confidence level for single targets is 0.80, whereas it decreases to 0.65 in multitarget scenarios, illustrating the challenges faced by the model in complex environments. Despite these challenges, the model effectively identifies targets in tailings ponds, establishing a technical basis for the timely detection of potential risks. In conclusion, the YOLO11-obb-based recognition model for tailings ponds has significant application potential, enhances monitoring accuracy and efficiency, provides essential support for mine management and environmental monitoring, and advances the automation of tailings pond surveillance.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117674"},"PeriodicalIF":5.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883012","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

Study on noise reduction method for bridge temperature signal using adaptive parameter selection and improved wavelet threshold function 基于自适应参数选择和改进小波阈值函数的桥梁温度信号降噪方法研究

IF 5.2 2区工程技术

Measurement Pub Date : 2025-04-24 DOI: 10.1016/j.measurement.2025.117683

Zhongchu Tian , Jiangyan Wu , Zujun Zhang , Ye Dai , Wei Zhang , Shiyao Wang

{"title":"Study on noise reduction method for bridge temperature signal using adaptive parameter selection and improved wavelet threshold function","authors":"Zhongchu Tian , Jiangyan Wu , Zujun Zhang , Ye Dai , Wei Zhang , Shiyao Wang","doi":"10.1016/j.measurement.2025.117683","DOIUrl":"10.1016/j.measurement.2025.117683","url":null,"abstract":"<div><div>To address the persistent challenge of environmental noise interference in bridge structural health monitoring, this study proposes a denoising method combining an improved wavelet threshold function with adaptive parameter selection. First, a Coati Optimization Algorithm-Variational Mode Decomposition (COA-VMD) collaborative optimization framework is established, marking the first application of the COA in bridge monitoring. This framework achieves adaptive precision matching of variational mode decomposition parameters (k, α), overcoming inherent limitations of traditional empirical parameter selection in complex environmental signal processing. Second, a novel modal selection theory based on harmonic parameter confidence intervals is proposed. A comprehensive criterion parameter P is constructed through the fusion of approximate entropy and permutation entropy, enabling adaptive inference of noise modes and establishing new standards for modal separation of nonlinear non-stationary signals. Subsequently, an improved wavelet threshold function is designed to resolve the technical bottleneck of effective signal distortion in traditional threshold processing. Validation through both simulated signals and real bridge temperature monitoring data demonstrates: In 15 dB simulation experiments, the signal-to-noise ratio (SNR) improves by 48.7 % with 37.9 % reduction in mean square error (MSE); In practical applications, the noise mode (NM) reaches optimal values while maintaining signal energy ratio (SER) over 99.7 %. This methodology achieves deep integration of bio-inspired algorithms with bridge signal decomposition theory, establishing a new paradigm of “self-optimizing parameters-precise modal identification-gradual noise filtration” for bridge monitoring data denoising.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117683"},"PeriodicalIF":5.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886140","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 data-driven feedforward control combining feedforward tuning and cascaded iterative learning control 结合前馈调谐和级联迭代学习控制的数据驱动前馈控制

IF 5.2 2区工程技术

Measurement Pub Date : 2025-04-24 DOI: 10.1016/j.measurement.2025.117414

Zongwei Tian , Siyang Yu , Qingrong Chen , Fan Yang , Jian Wang , Jixiao Liu , Jupu Yang , Fanxing Li , Wei Yan

{"title":"A data-driven feedforward control combining feedforward tuning and cascaded iterative learning control","authors":"Zongwei Tian , Siyang Yu , Qingrong Chen , Fan Yang , Jian Wang , Jixiao Liu , Jupu Yang , Fanxing Li , Wei Yan","doi":"10.1016/j.measurement.2025.117414","DOIUrl":"10.1016/j.measurement.2025.117414","url":null,"abstract":"<div><div>Feedforward control can effectively enhance the accuracy of control systems. This paper introduces a data-driven feedforward control method that combines iterative feedforward parameter tuning with cascaded iterative learning control (CILC). The proposed approach employs a feedforward parameterization technique with an input shaping filter (CFT) to obtain an optimal feedforward controller, effectively eliminating errors induced by the reference trajectory and significantly enhancing extrapolation capability for trajectory variations. CILC builds upon standard iterative learning control (ILC) by incorporating an external iteration loop, which more fully utilizes the ability of ILC to suppress repetitive disturbances in the system, and further improve control accuracy. The proposed method integrates the flexibility of iterative feedforward tuning with the high tracking accuracy of CILC and is validated through theoretical analysis and simulation. Additionally, experiments conducted on a wafer stage confirm the effectiveness and practical value of this approach.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117414"},"PeriodicalIF":5.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143878924","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 survey on machine learning algorithms in autonomous multiple unmanned aerial vehicles (UAVs) in wireless networks 无线网络中自主多无人机的机器学习算法研究

IF 5.2 2区工程技术

Measurement Pub Date : 2025-04-24 DOI: 10.1016/j.measurement.2025.117677

Arslan Ahmed Amin , Muazzam Ghafoor , Muhammad Irfan , Saba Waseem

{"title":"A survey on machine learning algorithms in autonomous multiple unmanned aerial vehicles (UAVs) in wireless networks","authors":"Arslan Ahmed Amin , Muazzam Ghafoor , Muhammad Irfan , Saba Waseem","doi":"10.1016/j.measurement.2025.117677","DOIUrl":"10.1016/j.measurement.2025.117677","url":null,"abstract":"<div><div>Wireless networks powered by unmanned aerial vehicles (UAVs) are becoming increasingly popular and are finding applications in many different social spheres. Networks of UAVs are in high demand because of the growing complexity of UAV applications, including environmental monitoring, security, and crisis management. Integration with various communication-oriented applications is anticipated to enhance coverage and spectrum efficiency in comparison to conventional ground-based solutions. More autonomy for the network, however, will inevitably lead to other issues. A new development that might address this problem is the proliferation of multi-UAV wireless networks. These networks enable several UAVs to collaborate on missions and share resources more efficiently. For maximum performance, particularly in jobs involving goal attainment and decision-making in diverse environmental conditions, a multi-UAV wireless network that is more intelligent and autonomous is necessary. This paper aims to address this gap by presenting a comprehensive survey of machine learning (ML) algorithms in the context of multi-UAV wireless networks by providing an analysis of nearly 300 scholarly articles published in the years 2015 to 2024. We intend to give a thorough overview of the multi-UAV architecture’s current state of research and practical applications in this study. Researchers now have solid evidence from this work to anticipate future applications of ML algorithms in multi-UAV systems. Furthermore, the authors have thoroughly examined a handful of unanswered concerns and challenges that stem from the knowledge we have acquired from our study.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117677"},"PeriodicalIF":5.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876915","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

An improved u-net for image segmentation of iron roughnecks on offshore oil drilling platforms 一种用于海上石油钻井平台铁钻工图像分割的改进u-net

IF 5.2 2区工程技术

Measurement Pub Date : 2025-04-24 DOI: 10.1016/j.measurement.2025.117689

Qingfeng Zhang , Hua Zhu , Yan Lv , Dezhi Jiang , Zhixiong Li , Sumika Chauhan , Govind Vashishtha

{"title":"An improved u-net for image segmentation of iron roughnecks on offshore oil drilling platforms","authors":"Qingfeng Zhang , Hua Zhu , Yan Lv , Dezhi Jiang , Zhixiong Li , Sumika Chauhan , Govind Vashishtha","doi":"10.1016/j.measurement.2025.117689","DOIUrl":"10.1016/j.measurement.2025.117689","url":null,"abstract":"<div><div>With the rapid advancement of modern drilling technology, the demand for automation and intelligence in oil-drilling platform equipment continues to grow. Iron roughneck is a key component for connecting drill pipes during drilling operations. However, positioning the iron roughneck still depends on manual alignment, limiting automation and inducing operational risks. To bridge this research gap, the integration of light detection and ranging (LiDAR) and cameras is proposed to detect the iron rough-neck position in real-world applications. By extracting point cloud data from segmented images of the iron roughneck, the three-dimensional (3D) coordinates of the connecting seam between the drill pipe and iron roughneck can be determined. To ensure accurate image segmentation, an enhanced Seg-UNet model was developed using the Segformer backbone and transformer to improve the understanding of global information in the images. The decoder was further enhanced by utilizing the CBAM (convolutional block attention module) attention mechanism for feature fusion and detail recovery. A new attention loss function was introduced to mitigate the category imbalance caused by the background regions. In this study, a drill pipe image dataset was created and the proposed Seg-UNet model was evaluated. The experimental results demonstrate that the Seg-UNet possesses better segmentation performance with a mean intersection ratio union (MIoU) of 84.5% compared to the existing popular U-Net, TransUNet, SETR, Next-ViT and Segformer methods. Thus, a better detection accuracy of 91.0% was achieved for the iron rough-neck position.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117689"},"PeriodicalIF":5.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892249","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

Multimode visible optical force microscopy for characterization of two-dimensional materials 二维材料表征的多模可见光学力显微镜

IF 5.2 2区工程技术

Measurement Pub Date : 2025-04-24 DOI: 10.1016/j.measurement.2025.117682

Mingtan Li, Xinfeng Tan, Dan Guo

{"title":"Multimode visible optical force microscopy for characterization of two-dimensional materials","authors":"Mingtan Li, Xinfeng Tan, Dan Guo","doi":"10.1016/j.measurement.2025.117682","DOIUrl":"10.1016/j.measurement.2025.117682","url":null,"abstract":"<div><div>With the rapid development of two-dimensional (2D) materials and their heterostructures in advanced electronic and optoelectronic applications, there is an increasing need for precise characterization of 2D materials. In this work, we developed multimode visible optical force microscopy (MV OFM) to address this demand, offering a convenient and powerful tool for the nanoscale characterization of 2D materials. The MV OFM utilizes a visible 405 nm semiconductor laser and an oscillator within a lock-in amplifier for analog modulation, simplifying the system by eliminating the need for an expensive laser, a chopper or an acousto-optic modulator (AOM). It operates in three distinct modes, contact, tapping (homodyne), and tapping (heterodyne) mode, enabling precise detection of optical force signals using a silicon tip in the ambient atmosphere. The phase signal allows for qualitative analysis of optical forces, revealing disparities in material properties. The MV OFM can achieve a spatial resolution of ∼10 nm, offering high sensitivity and contrast by using the optical force phase. By adjusting laser power, setpoint, material thickness, and material type, MV OFM can selectively tune dominant optical forces. Additionally, MV OFM is compatible with the transparent substrate (glass), extending its applicability. With these capabilities, we demonstrated the differentiation of materials components in hexagonal boron nitride/molybdenum disulfide (h-BN/MoS<sub>2</sub>) heterostructure and the precise characterization of the subsurface boundary, which is crucial for optimizing the performance of two-dimensional materials heterostructures in electronic and optoelectronic applications.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117682"},"PeriodicalIF":5.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143895240","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