IEEE Transactions on Instrumentation and Measurement最新文献_第9页

Comprehensive System for Noninvasive Glucose Monitoring Utilizing a Rectangular Dielectric Resonator Microwave Sensor

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-02-21 DOI: 10.1109/TIM.2025.3544356

Hala M. Marzouk;Anwer S. Abd El-Hameed;Ahmed Allam;Ramesh K. Pokharel;Adel B. Abdel-Rahman

{"title":"Comprehensive System for Noninvasive Glucose Monitoring Utilizing a Rectangular Dielectric Resonator Microwave Sensor","authors":"Hala M. Marzouk;Anwer S. Abd El-Hameed;Ahmed Allam;Ramesh K. Pokharel;Adel B. Abdel-Rahman","doi":"10.1109/TIM.2025.3544356","DOIUrl":"https://doi.org/10.1109/TIM.2025.3544356","url":null,"abstract":"This study introduces a comprehensive system for glucose level measurement using a compact two-port rectangular dielectric resonator (RDR) to validate the system’s accuracy against traditional invasive glucometer measurements. The system features a voltage-controlled oscillator (VCO) that generates a 2.47-GHz frequency. A power detector converts the RF signal to dc voltage levels, and a multimeter facilitates the interpretation. The primary sensor component is energized via a rectangular aperture-coupling mechanism in the ground plane. The RDR acts as a sensor because of the varying dielectric permittivity linked to different glucose concentrations, leading to unique resonance frequencies and magnitude shifts. The bare sensor’s resonance frequency was designed to be at 3.28 GHz, shifting to 2.47 GHz when loaded with a human finger. The Cole-Cole method modeled the human thumb with the blood layer in simulation. An electrical prototype enhanced detection, providing a 39E−02 MHz/mg/dL resolution at 2.47 GHz. The RDR sensors’ S-parameters highly correlated with laboratory-based testing, achieving 92.68% accuracy. Compared to home-based invasive glucometer measurements, the proposed continuous glucose monitoring (CGM) system with a two-port RDR sensor measures diabetes value changes with 92.08% precision.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583223","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

Separation and Parameter Measurement of Compound Intermittent Sampling Repeater Jamming Based on AE-YOLOv8

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-02-21 DOI: 10.1109/TIM.2025.3544369

Yaojun Wu;Liang Huang;Zhixing Liu;Minghui Sha;Liming Zhou;Yinghui Quan

{"title":"Separation and Parameter Measurement of Compound Intermittent Sampling Repeater Jamming Based on AE-YOLOv8","authors":"Yaojun Wu;Liang Huang;Zhixing Liu;Minghui Sha;Liming Zhou;Yinghui Quan","doi":"10.1109/TIM.2025.3544369","DOIUrl":"https://doi.org/10.1109/TIM.2025.3544369","url":null,"abstract":"When measuring in the complex electromagnetic environment, radar systems need to design the signal waveform based on the jamming parameters in the environment to enhance the target measurement performance. Therefore, accurately measuring jamming parameters is of great significance for improving radar performance in jamming scenarios. To address the challenges of measuring jamming parameters under compound intermittent sampling repeater jamming (ISRJ) conditions, this article proposes a method for separating compound ISRJs and measuring their parameters, based on the attention-embedded YOLOv8 (AE-YOLOv8) cascaded with a multiple-difference-based parameter measurement (MDBPM) algorithm. First, this article analytically examines the time–frequency distribution (TFD) characteristics of ISRJ. Then, based on these characteristics, a data annotation method is designed to generate an instance segmentation dataset. Finally, AE-YOLOv8 is introduced to achieve the separation of compound ISRJs, and the MDBPM method, leveraging the separation results, is presented to measure the parameters of ISRJs. The proposed method can measure parameters such as sampling duration, forwarding duration, jamming repeat forwarding times, jamming amplitude, and arrival time in scenarios involving three overlapping ISRJs. The number of ISRJs that can be measured and the range of parameters measured are significantly superior to those of similar algorithms. Extensive simulated and real experiments validate the effectiveness of the AE-YOLOv8 cascaded with the MDBPM algorithm.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-15"},"PeriodicalIF":5.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583280","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

Quality-Driven Orthogonal Kernel Subspace Analysis Method for Fault Detection of Nonlinear Industrial Processes

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-02-21 DOI: 10.1109/TIM.2025.3544378

Hao Ma;Yan Wang;Xiang Liu;Jie Yuan;Wentao Liu

{"title":"Quality-Driven Orthogonal Kernel Subspace Analysis Method for Fault Detection of Nonlinear Industrial Processes","authors":"Hao Ma;Yan Wang;Xiang Liu;Jie Yuan;Wentao Liu","doi":"10.1109/TIM.2025.3544378","DOIUrl":"https://doi.org/10.1109/TIM.2025.3544378","url":null,"abstract":"Kernel theory-based methods are widely used in modeling and fault detection for nonlinear systems. Among these, kernel partial least squares (KPLS) and kernel canonical correlation analysis (KCCA) are two commonly used methods for quality-oriented fault detection. However, subsequent studies have revealed limitations in both KPLS and KCCA when applied to quality-oriented fault detection, leading to unsatisfactory results. To address these limitations, this study proposes a quality-driven orthogonal kernel subspace analysis (QOKSA) approach. This approach analyzes the relationship between process and quality variables in depth, dividing them into three mutually orthogonal subspaces: the quality-related subspace, the quality-unrelated subspace, and the process-unrelated subspace. A theoretical analysis of the mutual orthogonality of these subspaces is also provided. By establishing detection indicators in these subspaces and formulating corresponding detection logic, the proposed method can not only detect faults but also determine whether they are quality-related or quality-unrelated. In the online detection phase, the method can still be applied even when quality variables are missing. Furthermore, the proposed method uses the cumulative percent variance approach for selecting the latent variable space, based on kernel principal component analysis (PCA) and singular value decomposition (SVD). This approach is more direct and efficient than the KPLS method, which uses cross validation to determine the number of principal components. Finally, the effectiveness and superiority of the proposed method are verified through two industrial case studies.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553356","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 Multisource-Domain Adaptation Framework for Bearing Fault Diagnosis Based on Adversarial Network and Feature Enhancement

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-02-21 DOI: 10.1109/TIM.2025.3544359

Zhixin Li;Shiyi Shen;Zhijun Liu;Ying Chen

{"title":"A Novel Multisource-Domain Adaptation Framework for Bearing Fault Diagnosis Based on Adversarial Network and Feature Enhancement","authors":"Zhixin Li;Shiyi Shen;Zhijun Liu;Ying Chen","doi":"10.1109/TIM.2025.3544359","DOIUrl":"https://doi.org/10.1109/TIM.2025.3544359","url":null,"abstract":"Data-driven fault diagnosis methods have received extensive attention, and the existing diagnostic methods usually require sufficient supervised data. However, developing effective diagnostic methods with insufficient training data remains challenging, which is highly demanding in real industrial scenarios since collecting high-quality fault data is often difficult and expensive. Considering the potential similarity of rotating machinery, collecting bearing fault data from different but related equipment may benefit the diagnostic performance of the target machinery. This article proposes a novel multisource-domain adaptation framework for bearing fault diagnosis methods based on adversarial network and feature enhancement. This method reduces the feature distribution discrepancy between the target domain and each source domain through domain adversarial training and transfers the fault diagnosis knowledge learned from multiple labeled source domains to a single unlabeled target domain. Considering the problem of scarce fault data, this method uses multilinear mapping to fuse the class information (Class Inf.) with high-level features and introduces AlignMixup to enhance the real fault signal features. To evaluate the model, experimental validation was performed on the Case Western Reserve University (CWRU) and KAT datasets. The results show that the proposed method is promising to address the bearing fault diagnosis tasks from different places of machines, further improving the applicability of data-driven methods in real industries.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553452","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 Comprehensive Hardware Platform Leveraging Impedimetric nanoMIP Sensors for Fast Evaluation of Trypsin in Artificial Saliva

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-02-21 DOI: 10.1109/TIM.2025.3544294

Antonio Vincenzo Radogna;Marco Costa;Sabrina Di Masi;Giuseppe Egidio De Benedetto;Cosimino Malitesta;Giuseppe Grassi

{"title":"A Comprehensive Hardware Platform Leveraging Impedimetric nanoMIP Sensors for Fast Evaluation of Trypsin in Artificial Saliva","authors":"Antonio Vincenzo Radogna;Marco Costa;Sabrina Di Masi;Giuseppe Egidio De Benedetto;Cosimino Malitesta;Giuseppe Grassi","doi":"10.1109/TIM.2025.3544294","DOIUrl":"https://doi.org/10.1109/TIM.2025.3544294","url":null,"abstract":"Pancreatic cancer is a leading cause of death worldwide, primarily due to late-stage diagnoses and limited treatment options. With the aim of introducing new screening strategies, research has intensified around trypsin, a promising biomarker exhibiting altered expression in pancreatic cancer patients. Conventional trypsin detection methods in biofluids show problems related to the need for trained personnel, bulky instrumentation, and high measurement time. The latter should be a key feature to accelerate the training of robust regression models with large datasets, gain prompt predictions in clinical environments, and reduce measurement errors due to intrinsic variations and drift of the sensors. Moreover, despite advancements in sensing technology, a gap remains in deploying high-performance sensors to point-of-care devices. This article addresses these issues by presenting a comprehensive hardware platform for fast, noninvasive trypsin evaluation in saliva aimed to pancreatic cancer screening. The key finding of the work regards the adoption of a fast electrochemical impedance spectroscopy (EIS) time-based read-out approach, applied to nanoMIP technology, to drastically reduce the detection time of trypsin in artificial saliva samples. The detection is possible thanks to the adoption of a neural network (NN). The root-mean-squared error (RMSE), obtained by testing the system with trypsin concentrations from 0 to 100 ng<inline-formula> <tex-math>$cdot $ </tex-math></inline-formula>mL−1 in artificial saliva, is equal to 5.9 and 5.4 for training and test, respectively. The results prove the efficacy of the system as a portable, user-friendly device for rapid trypsin evaluation.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553504","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

Time-Varying Threshold and Signed-Rectified Regressor Design of Diagnosis Observer for Analog Circuits With Intermittent Fault

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-02-21 DOI: 10.1109/TIM.2025.3544289

Shigen Gao;Kaibo Zhao;Wenxiao Si;Chenglin Wen

引用次数: 0

Improved Frequency Detection Capability of MEMS Bionic Vector Hydrophone in Low Signal-to-Noise Ratio Environment

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-02-21 DOI: 10.1109/TIM.2025.3544358

Li Jia;Wenshu Dai;Guojun Zhang;Yanan Geng;Zhengyu Bai;Wenqing Zhang;Zican Chang;Mengfan Wang;Wendong Zhang

{"title":"Improved Frequency Detection Capability of MEMS Bionic Vector Hydrophone in Low Signal-to-Noise Ratio Environment","authors":"Li Jia;Wenshu Dai;Guojun Zhang;Yanan Geng;Zhengyu Bai;Wenqing Zhang;Zican Chang;Mengfan Wang;Wendong Zhang","doi":"10.1109/TIM.2025.3544358","DOIUrl":"https://doi.org/10.1109/TIM.2025.3544358","url":null,"abstract":"With the development of underwater target noise reduction technologies, the detection capabilities of microelectromechanical system (MEMS) bionic vector hydrophones (BVHs) are encountering significant challenges in environments characterized by a low signal-to-noise ratio (SNR). Thus, this article proposes an innovative time-reversal convolution (TRC) processing method for both sound pressure and velocity based on the output characteristics of MEMS BVHs. This methodology capitalizes on the distinctions between signal and noise postconvolution processing, employing a subsequent stage of adaptive line enhancement (ALE) technology to adeptly mitigate ambient interference, which, in turn, markedly augments the detection capabilities within low SNR. In addition, addressing the challenge of broad main lobes in the directional pattern and the reduced precision in bearing estimation for single vector sensors during the processing of pressure and velocity signals, an innovative direction-of-arrival (DOA) estimation technique has been introduced. This technique employs adaptive cancellation of the outputs from TRC processing, enhancing the accuracy and reliability of the bearing estimation. Utilizing Monte Carlo simulations, this study meticulously examined the gain fluctuations in response to varying input SNRs and then meticulously contrasted them with those of existing integrated methodologies to evaluate their comparative effectiveness. The simulation results demonstrate that the proposed methodology is capable of markedly amplifying the detection efficacy for low-intensity underwater targets within environments of diminished SNR. The potency of this approach in bolstering the operational prowess of MEMS BVHs is corroborated through empirical validation with actual test data.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570871","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 Class-Incremental Learning Method for PCB Defect Detection

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-02-21 DOI: 10.1109/TIM.2025.3544321

Quanbo Ge;Ruilin Wu;Yupei Wu;Huaping Liu

{"title":"A Class-Incremental Learning Method for PCB Defect Detection","authors":"Quanbo Ge;Ruilin Wu;Yupei Wu;Huaping Liu","doi":"10.1109/TIM.2025.3544321","DOIUrl":"https://doi.org/10.1109/TIM.2025.3544321","url":null,"abstract":"Defect detection of printed circuit boards (PCBs), as a critical step in the manufacturing process, has achieved significant improvement with the help of deep learning techniques. However, existing research has focused only on the closed static detection scenario. This study aims to transfer the PCB defect detection task to the more practical incremental detection scenario. First, to cope with the performance requirements of industrial quality inspection, this article proposes a PCB-YOLOX detector for PCB defect detection by optimizing based on YOLOX-S. Specifically, a feature enhancement module (FEM) is designed to improve the feature representation of the model for small targets of defects, while an attention feature fusion module (AFFM) is designed to facilitate the efficient fusion of features at different scales. Then, the PCB-YOLOX is combined with an incremental learning method, elastic response distillation (ERD), to propose a class-incremental PCB defect detection method. Experimental results in the static detection scenario show that PCB-YOLOX achieves competitive performance in terms of detection accuracy compared to several state-of-the-art detectors, with 96.5% (mAP0.5) and 51.9% (mAPs), respectively. The model parameters, detection speed, model size, and computation of PCB-YOLOX are 12.8 M, 50.5 frames/s, 49.1 M, and 35.6 G, respectively, which can meet the needs of industrial inspection. In addition, the experimental results in the incremental detection scenario show that the method proposed in this article can effectively alleviate the catastrophic forgetting problem in the incremental learning process.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-15"},"PeriodicalIF":5.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564097","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

Background-Weaken Generalization Network for Few-Shot Industrial Metal Defect Segmentation

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-02-21 DOI: 10.1109/TIM.2025.3544335

Ruiyun Yu;Haoyuan Li;Bingyang Guo;Ziming Zhao

{"title":"Background-Weaken Generalization Network for Few-Shot Industrial Metal Defect Segmentation","authors":"Ruiyun Yu;Haoyuan Li;Bingyang Guo;Ziming Zhao","doi":"10.1109/TIM.2025.3544335","DOIUrl":"https://doi.org/10.1109/TIM.2025.3544335","url":null,"abstract":"Identifying surface defects in industrial metal fabrication is a vital quality control task that is essential for maintaining product quality and safety. Traditional surface defect identification algorithms depend on a substantial quantity of labeled data, which typically necessitates considerable time and human resources. Additionally, these methods often require retraining when dealing with new types or specific metal defects. This research suggests a new background-weaken generalization network (BGNet) to address these challenges by diminishing the impact of background and improving generalization in the few-shot segmentation of industrial metal defects. BGNet introduces the compressed strengthening (CS) module, center memory (CM) feature fusion module, and cross embedding (CE) module. The CS module consists of a compressed sensing block and a multilevel feature strengthening block, which reduces background interference and enhances the defect foreground through dimensionality reduction and feature enhancement with different receptive fields. The CM feature fusion module activates clear object features by utilizing fuzzy memory features, strengthening the mapping relationship between sets. The CE module mines the relationships between images in different sets through cross-guidance operations, enabling the model to better generalize to new defect classes. The results of the experiments on different datasets show that BGNet delivers the best performance currently available.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564219","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

Identification of an Incipient Snubber Failure in Inverter Employed in Solid Oxide Fuel Cell (SOFC) Fed Microgrid

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-02-21 DOI: 10.1109/TIM.2025.3544391

Sankha Subhra Ghosh;Surajit Chattopadhyay;Arabinda Das

{"title":"Identification of an Incipient Snubber Failure in Inverter Employed in Solid Oxide Fuel Cell (SOFC) Fed Microgrid","authors":"Sankha Subhra Ghosh;Surajit Chattopadhyay;Arabinda Das","doi":"10.1109/TIM.2025.3544391","DOIUrl":"https://doi.org/10.1109/TIM.2025.3544391","url":null,"abstract":"Production of electricity in a manner that is clean, efficient, and ecologically friendly is one of the primary challenges today. For producing clean electrical energy, solid oxide fuel cells (SOFCs), are regarded as an intriguing technology. When it comes to microgrid (MG) applications, SOFCs are a great choice because of their efficiency, environmental advantages, fuel flexibility, and dependability. In MG inverters (MGIs), snubber circuits are essential for enhancing the power electronic system’s performance, efficiency, and dependability. The MGI is an extremely significant component of an integral grid-tied system. This article proposes a 3-phase inverter’s (3PHIs) incipient snubber circuit fault (ISCF) recognition approach for insulated gate bipolar transistor (IGBT) inverters coupled to SOFCs employed in microgrid systems. Investigative work has been carried out on the inverter’s current output using discrete wavelet transform (DWT) to identify snubber defects. During the analysis, wavelet coefficients of the output current of the inverter are taken into account together with their kurtosis and skewness values. A comparative analysis has been conducted to find the optimum specific variables for the identification of ISCF in IGBT-based MGIs. For detecting ISCF, a defect detection algorithm has been developed. Furthermore, this investigation’s comparative evaluation and distinctive contribution have been evidenced.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564233","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