Measurement最新文献

{"title":"An integrated deep learning model for predicting concrete dam deformation with multi-point spatiotemporal correlation","authors":"Huaipeng Wei ,&nbsp;Xingyang Liu ,&nbsp;Feng Wang ,&nbsp;Xingxing Ai","doi":"10.1016/j.measurement.2025.118546","DOIUrl":"10.1016/j.measurement.2025.118546","url":null,"abstract":"<div><div>Concrete dam structures exhibit complex, nonlinear responses to various influencing factors, making accurate deformation prediction of concrete dams both crucial and challenging. Most monitoring models for dam deformation prediction primarily emphasize temporal features and the correlation between environmental factors and dam deformation, often neglecting the spatial dependencies within the data. Even multi-point models that account for spatial coordinates still struggle to effectively capture the time-varying spatial correlations between different measurement points. This study presents an integrated deep learning model for deformation prediction that incorporates multi-point, time-varying spatiotemporal correlations to overcome the aforementioned challenges. The proposed model leverages a combination of long short-term memory (LSTM) neural network and Kalman filter (KF), further enhanced by K-means clustering and an attention mechanism. K-means clustering identifies each target point’s associated measurement points; Kalman filter-estimated deformation values from the associated measurement points serve as additional inputs to the LSTM model to capture their time-varying dynamic influences; and the attention mechanism enhances the interpretability of the LSTM model. The proposed model is subsequently employed to predict and analyze the deformation of the concrete dam, and its performance is compared against five other prediction models, including multiple linear regression, a standalone LSTM model and other LSTM-based models. Results from six measurement points show that incorporating spatiotemporal correlations increases <em>R</em>² of the proposed model by an average of 11.0 % over the standalone LSTM model and 7.3 % over the attention-based LSTM model, which did not account for spatial correlation. The proposed model reduced RMSE by 47.1 % relative to the standalone LSTM model and by 38.3 % relative to the attention-based LSTM model. The MAE decreased by 45.8 % versus the standalone LSTM model and 24.0 % versus the attention-based LSTM model. Moreover, the proposed model offers meaningful interpretability, making it a practical and forward-looking approach for structural health monitoring of concrete dams.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"256 ","pages":"Article 118546"},"PeriodicalIF":5.2,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714164","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":"Normal incident optical reflectance spectroscopy for thin-film thickness measurement with genetic algorithm","authors":"Jiao Bai ,&nbsp;Huaming Chen ,&nbsp;Junguang Chen ,&nbsp;Haowei Yang ,&nbsp;Xinghui Li ,&nbsp;Yan Shi ,&nbsp;Jiangfeng Song","doi":"10.1016/j.measurement.2025.118511","DOIUrl":"10.1016/j.measurement.2025.118511","url":null,"abstract":"<div><div>Thin films are widely used in applications such as corrosion prevention, semiconductor insulation, and photoelectric conversion, where precise thickness measurement is critical for quality control and performance optimization. Reflectance spectroscopy is attracting interest as a non-contact, non-destructive, and efficient method for industrial thin film thickness measurements. This paper proposed a normal incident optical reflectance spectroscopy to enhance the lateral resolution, signal-to-noise ratio, and decrease the incident angle range, thereby capturing a higher proportion of reflected light. The system integrates a white light lamp, fiber Y-bundles, a self-designed reflectance probe, and a spectrometer. Genetic algorithm (GA) is employed to extract the optimal film thickness by minimizing the discrepancy between the theoretical and measured reflectance spectra. Reflectance spectra of the silica film samples on silicon substrates were acquired to match well with those from the angular resolution spectrometer. The thickness deviations between the proposed system and an elliptical polarimeter were less than 2 nm, with a spectral fitting error below ±0.5 %. The system achieves a measurement stability of 0.018 nm, and a processing time of 0.37 s per point. Furthermore, the thickness distribution of a patterned film sample was successfully mapped, validating the system’s capability for in-situ thin film thickness measurements.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"256 ","pages":"Article 118511"},"PeriodicalIF":5.2,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703781","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":"All-metal packaged temperature compensation fiber optic Fabry-Pérot strain sensor for high-temperature liquid metal environments","authors":"Linlin Liu ,&nbsp;Feng Qin ,&nbsp;Yugen Xu ,&nbsp;Lei Sun ,&nbsp;Ning Wang ,&nbsp;Jie Zhang ,&nbsp;Kai Gong","doi":"10.1016/j.measurement.2025.118501","DOIUrl":"10.1016/j.measurement.2025.118501","url":null,"abstract":"<div><div>The erosion of critical in-core components by high-temperature flowing liquid metal leads to surface fatigue damage, and its reliable detection represents a significant challenge for nuclear reactor safety. This study introduces a novel approach that employs an all-metal encapsulated fiber-optic Fabry-Pérot (F-P) strain sensor, designed to endure high-temperature and high-pressure conditions, for in situ surface strain monitoring and fatigue damage assessment. The key contributions of this work are as follows: (1) the development of a comprehensive mechanical model that characterizes the strain transfer mechanism of the sensor; (2) the implementation of an innovative temperature self-compensation structure to mitigate cavity length variations under extreme thermal conditions; (3) the design of a composite-cavity configuration that enables simultaneous strain measurement and in situ temperature monitoring, ensuring accurate thermal compensation. The fabricated sensor was thoroughly evaluated through rigorous performance testing, including high-temperature calibration up to 500 °C and validation experiments in liquid metal environments. Experimental results demonstrate the sensor’s performance: stable operation at 500 °C with a strain sensitivity of 2.43 nm/με, accompanied by high measurement accuracy (SSE = 0.0761, Adj R2 = 0.9974, RMSE = 0.0617). These metrics confirm the sensor’s linearity, stability, and reliability under extreme operating conditions. The successful demonstration of this fiber-optic sensing technology in high-temperature liquid metal environments provides a viable solution for real-time fatigue damage monitoring of nuclear reactor components, offering significant potential to enhance reactor safety and operational lifetime.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"256 ","pages":"Article 118501"},"PeriodicalIF":5.2,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714165","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":"Multi-parameter acoustic emission analysis for fatigue crack evaluation in structural health monitoring","authors":"Jialin Cui ,&nbsp;Xianqiang Qu ,&nbsp;Chunwang Lv ,&nbsp;Jinbo Du ,&nbsp;Hanxu Wang","doi":"10.1016/j.measurement.2025.118529","DOIUrl":"10.1016/j.measurement.2025.118529","url":null,"abstract":"<div><div>Fatigue crack propagation poses a significant challenge to the service safety and reliability of steel structures. Acoustic Emission (AE) technology, as a real-time and highly sensitive non-destructive monitoring approach, holds great potential for tracking crack evolution. This study systematically examines AE signal evolution across different crack propagation stages through controlled experiments. A multi-parameter cross-correlation analysis is introduced to quantify the interdependencies among key AE parameters, offering a more comprehensive assessment than traditional single-parameter methods. The results reveal that AE amplitude, energy, event count, and duration exhibit distinct variations as cracks grow. Notably, energy, event count, and duration demonstrate strong positive correlations, making them robust indicators for crack propagation pattern recognition. In contrast, rise time and peak count show more scattered distributions, reflecting localized damage characteristics. Additionally, AE signals from surface cracks exhibit higher amplitude and energy than those from deep-embedded cracks, validating the spatial attenuation effect and providing a quantitative basis for crack depth estimation. This study presents a multi-parameter correlation-based AE signal analysis method, enhancing AE-based damage classification and monitoring accuracy. The proposed approach strengthens the theoretical foundation for structural health monitoring (SHM) and fatigue damage early warning, while also contributing to the optimization of non-destructive testing (NDT) techniques in engineering applications.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"256 ","pages":"Article 118529"},"PeriodicalIF":5.2,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703893","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":"Design of 3-D force-temperature tactile sensor and force decoupling method based on edge magnetic field","authors":"Shichao Zuo,&nbsp;Ling Weng,&nbsp;Xiaopeng Ji,&nbsp;Lanyang Hao,&nbsp;Xiaotao Du,&nbsp;Bowen Cui","doi":"10.1016/j.measurement.2025.118534","DOIUrl":"10.1016/j.measurement.2025.118534","url":null,"abstract":"<div><div>As an important part of human-robot interaction in robotics, the research direction of sensors is gradually moving towards multi-information detection. Research on three-dimensional (3-D) force-temperature bimodal sensors is important to obtain more dimensional information while grasping. A magnetic tactile sensor that can detect 3-D force and external temperature simultaneously is designed with the magnetic film, thermocouple, and graphene-silicone elastomer, as well as a 3-D force decoupling method based on the magnetic field at the edges of the square magnetic film is presented. We analyzed the sensor’s structural changes and spatial magnetic field distribution characteristics under stress, then further developed a mathematical model between the output voltage and the 3-D force. The experimental results show that the sensor has a highly linear output under the action of normal force in the range of 0–1.5 N and shear force in the range of maximum 0–0.16 N. The measurement sensitivity is 98.51 mV/N for normal force and a maximum of 880.52 mV/N for shear force. The sensor temperature measurement range is 25–55 °C while measuring 3-D force. The experiment of sliding and grasping based on this sensor was carried out. The tactile sensor can be used in applications relevant to robotic object grasping and object information recognition.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"256 ","pages":"Article 118534"},"PeriodicalIF":5.2,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704239","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 water level measurement based on visual foundation models","authors":"Zeheng Wu ,&nbsp;Yangbo Wen ,&nbsp;Kailin Huang ,&nbsp;Nie Zhou ,&nbsp;Hua Chen","doi":"10.1016/j.measurement.2025.118502","DOIUrl":"10.1016/j.measurement.2025.118502","url":null,"abstract":"<div><div>Image-based water level measurement offers low-cost and visualization advantages, making it suitable for high-frequency, multi-location data collection. Although deep learning-based methods achieve high accuracy, their transferability and robustness are constrained by the need for site-specific large-scale training data. This study proposes an intelligent water-level measurement method based on visual foundation models (VFMs) to address this limitation under nonsufficient data. First, Language-guided Generative Data Augmentation (LGDA) is proposed to generate high-fidelity and diverse training images by simulating weather conditions or lighting variations. Then, the Segment Anything Model (SAM) is guided by representative pixel points prompts sampled from Deeplabv3+ probability maps to perform few-shot water surface segmentation. Finally, photogrammetry techniques are used to convert pixel coordinates into water level elevations. Compared with traditional data augmentation methods and other semantic segmentation models, the proposed method achieves higher accuracy in water surface segmentation, with an Intersection over Union (IoU) of 0.904, representing a 9.7 % improvement over the best-performing Deeplabv3+ baseline (IoU = 0.824). Transferability is validated across three hydrological stations using fewer than 50 training images per site. The proposed method achieves more accurate water level estimation, with a mean absolute error (MAE) below 0.026 m and a coefficient of determination (R²) exceeding 0.9 across all stations during one-month monitoring, significantly outperforming the baseline (MAE up to 0.055 m, R² as low as 0.521). The proposed model demonstrates strong transferability under limited data and enables more accurate and reliable water level measurement.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"256 ","pages":"Article 118502"},"PeriodicalIF":5.2,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704251","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":"GFMS: An automatic system for gap and flush measurement in automobile assembly seams based on 3D vision","authors":"Xiang Wu ,&nbsp;Zikuan Li ,&nbsp;Anyi Huang ,&nbsp;Qiaoyun Wu ,&nbsp;Jun Wang ,&nbsp;Yuan Zhang","doi":"10.1016/j.measurement.2025.118432","DOIUrl":"10.1016/j.measurement.2025.118432","url":null,"abstract":"<div><div>The gap and flush (G&amp;F) of automobile assembly seams are important indicators that directly determine the exterior quality and performance. Traditional manual inspection methods fail to meet modern demands for efficiency and accuracy. Although point cloud-based G&amp;F detection technology demonstrates excellent accuracy characteristics, it faces challenges in complex assembly line environments caused by noise, redundant data, and inter-seam interference. To address these problems, we design an automatic G&amp;F measurement system (GFMS) that integrates point cloud data acquisition and analysis. Then, we propose an adaptive optimization method based on the spatial distribution of adjacent frame line point clouds, achieving adaptive segmentation of seam regions through spatial density and local geometric constraints, multi-level filtering for outlier removal and surface smoothing, and Gaussian-weighted filling for missing regions. Finally, we propose a G&amp;F analysis method based on interference suppression. A weighted voting mechanism with growing clustering is introduced to eliminate interference points between seams. A prior constrained circle fitting is adopted to reduce errors caused by missing fillet profiles. The final G&amp;F are obtained by averaging the calculation results of all frame point clouds. Experimental validation on standard blocks demonstrated maximum deviations of 0.006 mm (gap) and 0.004 mm (flush). The GFMS exhibited a maximum deviation of 0.02 mm in both G&amp;F measurements when compared with the ground truth (CMM) standards during automobile seams G&amp;F validation. Compared with traditional measurement methods, the proposed method greatly improves accuracy and efficiency.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"256 ","pages":"Article 118432"},"PeriodicalIF":5.2,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696448","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 deep learning-driven three-dimensional geological modeling method using sparse borehole sampling data","authors":"Zhengxiang He ,&nbsp;Xingliang Xu ,&nbsp;Pingan Peng ,&nbsp;Liguan Wang ,&nbsp;Suchuan Tian","doi":"10.1016/j.measurement.2025.118461","DOIUrl":"10.1016/j.measurement.2025.118461","url":null,"abstract":"<div><div>Constructing a three-dimensional (3D) geological model based on limited borehole sampling data is highly important for resource exploration and utilization. However, the sparsity of borehole sampling data is a key factor restricting the accuracy of geological modeling. Traditional explicit modeling still overly relies on expert experience, and the commonly used implicit modeling methods have high requirements for the modeling process, both of which limit the effect of geological modeling under sparse borehole sampling data. Therefore, this paper proposes a deep learning-driven 3D geological modeling method. A data self-organization method based on implicit modeling theory was innovatively developed to solve the problem of dataset construction. Moreover, an autoencoder was employed to eliminate the noise in the self-organized dataset and extract deep features. A ResCapsNet, which combines the advantages of the residual network and the capsule network, was proposed to predict the lithology of discrete units in the target area. We tested the proposed method via simulation experiments and field tests. In the simulation experiments, the method achieved an accuracy of 96.7 % in 3D geological modeling, outperforming 94.5 % of AE-ResNet, 93.6 % of ResCapsNet, 58.1 % of support vector machines, and 35 % of random forest. In the field tests, the accuracy of the constructed 3D geological model reached 99.1 %. Additionally, the influences of borehole sampling intervals and dataset volume on modeling accuracy were analyzed. The results show that the deep learning-driven 3D geological modeling method proposed in this paper can fully utilize sparse borehole sampling data to construct high-precision 3D geological models.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"256 ","pages":"Article 118461"},"PeriodicalIF":5.2,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703711","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":"Rapid subsea induced polarization detection using CSEM transmitter electrodes","authors":"Chentao Wang,&nbsp;Meng Wang,&nbsp;Ming Deng,&nbsp;Chenyu Rao,&nbsp;Sufeng Luo,&nbsp;Tailong Chen,&nbsp;Biao Wu","doi":"10.1016/j.measurement.2025.118505","DOIUrl":"10.1016/j.measurement.2025.118505","url":null,"abstract":"<div><div>The marine controlled-source electromagnetic method has become a pivotal tool in the exploration of subsea resources, including oil, gas, and metal sulfides. Recent advancements in both methodology and instrumentation have highlighted the benefits of signal amplitude analysis in near-source observations. Despite these developments, traditional exploration techniques continue to rely on a transmitter–receiver separation model, largely overlooking the potential responses at the transmitter site itself. This study breaks new ground by examining the relationship between the potential variations of the transmitter electrodes and the presence of anomalous targets. Introducing the innovative concept of a transceiver, this research leverages existing direct current (DC) theory to conduct COMSOL Multiphysics simulations. These simulations focus on DC potential anomalies related to resistivity and turnoff potential anomalies associated with induced polarization (IP) effects. By applying the Cole-Cole model within the complex resistivity theory framework, frequency-domain simulations were performed to extract frequency-domain IP signals from electrode potentials. The study further correlates these frequency-domain responses with time-domain turnoff responses using the fast cosine transform method. Laboratory water tank experiments utilizing the transceiver to observe graphite and polyvinyl chloride anomalies demonstrated a significant correlation between electrode potentials and anomalous bodies, aligning with theoretical predictions and simulation outcomes. Notably, the IP potential amplitude induced by the graphite anomaly obtained through this method demonstrates a 60 % relative increase compared to the background response. This method can serve as both an independent approach for detecting shallow seabed targets and shows promise as a complementary tool for conventional marine electromagnetic exploration.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"256 ","pages":"Article 118505"},"PeriodicalIF":5.2,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704131","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":"Quantification and assessment of steel pitted corrosion using optical frequency domain reflectometry (OFDR)-based distributed fiber optic sensors","authors":"Luyang Xu ,&nbsp;Shuomang Shi ,&nbsp;Ying Huang ,&nbsp;Fei Yan ,&nbsp;Xingyu Wang ,&nbsp;Rebekah Wilson ,&nbsp;Dawei Zhang","doi":"10.1016/j.measurement.2025.118519","DOIUrl":"10.1016/j.measurement.2025.118519","url":null,"abstract":"<div><div>Steel corrosion is a widespread issue affecting the integrity and serviceability of infrastructures, industrial equipment, and transportation systems. Distributed fiber optic sensors (DFOSs) based on optical frequency domain reflectometry (OFDR) offer high spatial resolution, distributed sensing ability, and environmental resistance, making them ideally suited for detecting steel corrosion, especially pitting corrosion. This study presents a real-time corrosion detection-based assessment methodology using OFDR-based DFOSs. A practical sensing model was proposed to derive corrosion severity from the strain measurements obtained from distributed sensors, enabling the estimation of pit depth, mass loss, and average corrosion rate. The corrosion conditions of steel specimens were monitored through accelerated corrosion tests using OFDR-based DFOSs, which was further validated against commonly used corrosion detection methods. The experimental results demonstrated an excellent matching in the location and depth of pitting corrosion, mass loss, and corrosion rate between the well-established detection techniques and the proposed methodology, indicating the accuracy and effectiveness of DFOSs in assessing corrosion. Specifically, the pit depths evaluated by DFOS and measured by microscopic scanning predominantly ranged from 13 μm to 22 μm, demonstrating excellent consistency between the two methods. The maximum pit depths evaluated by DFOSs for the three specimens were 27.23 μm, 28.10 μm, and 31.93 μm, respectively. In addition, the DFOS configurations were optimized by examining the effects of deployment spacing and gauge pitch of the DFOSs. This study highlights the potential of DFOSs in steel corrosion evaluation, paving the way for advanced design and practical application of DFOSs in structural health monitoring.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"256 ","pages":"Article 118519"},"PeriodicalIF":5.2,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704235","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}