Pub Date : 2025-02-21DOI: 10.1016/j.measurement.2025.117076
Emmanuel Resendiz-Ochoa , David Alejandro Elvira-Ortiz , Arturo Yosimar Jaen-Cuellar , Juan Jose Saucedo-Dorantes
This work proposes a new deep learning-based methodology which handles infrared thermography images for detecting and classifying different faults in both: induction motors and gearboxes, as elements of an electromechanical system. The plus advantages of this work are a deep learning scheme that integrates the Autoencoders in a Stacked network complimented with a heuristic technique, consisting of the Genetic Algorithms, for automatically adjusting the hyperparameters required by the deep learning technique, also the non-invasive diagnosis through the acquisition of thermal images and their characterization by estimating a meaningful set of statistical features. Subsequently, the estimated statistical features are modeled through the stacked autoencoder structure that works along with a Genetic Algorithm to perform a self-adapting procedure to extract a new set of features that characterize the most important fault-related patterns. Finally, the automatic detection and identification of faults are achieved by a single SoftMax layer. The results demonstrate the capability of this proposal to identify different faults in electromechanical systems under a non-intrusive approach achieving a global classification ratio of up to 99.0%.
{"title":"Non-invasive diagnosis methodology based on infrared thermography and deep feature learning for detecting multiple faults in electromechanical systems","authors":"Emmanuel Resendiz-Ochoa , David Alejandro Elvira-Ortiz , Arturo Yosimar Jaen-Cuellar , Juan Jose Saucedo-Dorantes","doi":"10.1016/j.measurement.2025.117076","DOIUrl":"10.1016/j.measurement.2025.117076","url":null,"abstract":"<div><div>This work proposes a new deep learning-based methodology which handles infrared thermography images for detecting and classifying different faults in both: induction motors and gearboxes, as elements of an electromechanical system. The plus advantages of this work are a deep learning scheme that integrates the Autoencoders in a Stacked network complimented with a heuristic technique, consisting of the Genetic Algorithms, for automatically adjusting the hyperparameters required by the deep learning technique, also the non-invasive diagnosis through the acquisition of thermal images and their characterization by estimating a meaningful set of statistical features. Subsequently, the estimated statistical features are modeled through the stacked autoencoder structure that works along with a Genetic Algorithm to perform a self-adapting procedure to extract a new set of features that characterize the most important fault-related patterns. Finally, the automatic detection and identification of faults are achieved by a single SoftMax layer. The results demonstrate the capability of this proposal to identify different faults in electromechanical systems under a non-intrusive approach achieving a global classification ratio of up to 99.0%.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"250 ","pages":"Article 117076"},"PeriodicalIF":5.2,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527141","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}
Pub Date : 2025-02-21DOI: 10.1016/j.measurement.2025.117064
Kenta Ichikawa , Nobuhiro Yoda , Wataru Hijikata
Bite force is a critical indicator of both oral and systemic health. However, traditional bite force sensors are limited by their inability to provide continuous and unrestricted measurements. In this study, we developed a wearable mouthguard-type sensor designed for continuous bite force monitoring. The sensor was fabricated by embedding a thin sensor sheet within a double-layered mouthguard. This sensor generates a voltage in response to the bite force applied to the mouthguard, eliminating the need for a battery. The bite force applied was estimated from the output voltage using a theoretical model based on the sensor’s electrical and mechanical characteristics. To achieve a sufficient dynamic range for effective bite force monitoring, we investigated three dielectric polymer materials for the sensor sheet. Because the dynamic range of the sensor is influenced by the mechanical properties of the material, we attained a dynamic range of 2.5 MPa, which is equivalent to a bite force of 1 kN in the whole dentition, by selecting an appropriate material, demonstrating promising results in vitro. With further in vivo experimental evaluation, the proposed mouthguard-type sensor shows potential for effective bite force monitoring.
{"title":"Self-powered mouthguard-type bite force sensor with wide dynamic range","authors":"Kenta Ichikawa , Nobuhiro Yoda , Wataru Hijikata","doi":"10.1016/j.measurement.2025.117064","DOIUrl":"10.1016/j.measurement.2025.117064","url":null,"abstract":"<div><div>Bite force is a critical indicator of both oral and systemic health. However, traditional bite force sensors are limited by their inability to provide continuous and unrestricted measurements. In this study, we developed a wearable mouthguard-type sensor designed for continuous bite force monitoring. The sensor was fabricated by embedding a thin sensor sheet within a double-layered mouthguard. This sensor generates a voltage in response to the bite force applied to the mouthguard, eliminating the need for a battery. The bite force applied was estimated from the output voltage using a theoretical model based on the sensor’s electrical and mechanical characteristics. To achieve a sufficient dynamic range for effective bite force monitoring, we investigated three dielectric polymer materials for the sensor sheet. Because the dynamic range of the sensor is influenced by the mechanical properties of the material, we attained a dynamic range of 2.5 MPa, which is equivalent to a bite force of 1 kN in the whole dentition, by selecting an appropriate material, demonstrating promising results in vitro. With further in vivo experimental evaluation, the proposed mouthguard-type sensor shows potential for effective bite force monitoring.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"249 ","pages":"Article 117064"},"PeriodicalIF":5.2,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-21DOI: 10.1016/j.measurement.2025.116978
Yuming Chen , Wei Li , YuXin Du
This study presents a robust adaptive Laplace Kalman filter (Robust-Laplace-KF) for enhancing the straightness measurement of scraper conveyors using SINS/UWB technology. By integrating the Laplace distribution into both process and measurement noise modeling, the Robust-Laplace-KF significantly improves robustness in heavy-tailed noise environments. The dual estimation of noise parameter scaling matrices and the one-step predictive probability density’s location vector facilitates comprehensive adaptive refinement of system state predictions. Extensive simulations and experimental analyses demonstrate the Robust-Laplace-KF’s superior performance under extreme conditions, including non-Gaussian noise and intense vibrations. The proposed method demonstrates higher accuracy and stability compared to existing techniques, with simulation results showing a reduction in measurement error from 55.482 cm to 3.613 cm under non-Gaussian noise conditions. Experimental validation using a scaled-down model highlights the method’s practical applicability, achieving a mean absolute error of 3.558 cm in straight configurations and 5.379 cm in oblique cutting configurations, representing a significant reduction in measurement errors. This research advances conveyor monitoring technologies, offering a promising solution for improving operational efficiency, reliability, and safety in mining and industrial applications.
{"title":"Straightness measurement of conveyors based on SINS/UWB with a Robust Laplace Kalman filter","authors":"Yuming Chen , Wei Li , YuXin Du","doi":"10.1016/j.measurement.2025.116978","DOIUrl":"10.1016/j.measurement.2025.116978","url":null,"abstract":"<div><div>This study presents a robust adaptive Laplace Kalman filter (Robust-Laplace-KF) for enhancing the straightness measurement of scraper conveyors using SINS/UWB technology. By integrating the Laplace distribution into both process and measurement noise modeling, the Robust-Laplace-KF significantly improves robustness in heavy-tailed noise environments. The dual estimation of noise parameter scaling matrices and the one-step predictive probability density’s location vector facilitates comprehensive adaptive refinement of system state predictions. Extensive simulations and experimental analyses demonstrate the Robust-Laplace-KF’s superior performance under extreme conditions, including non-Gaussian noise and intense vibrations. The proposed method demonstrates higher accuracy and stability compared to existing techniques, with simulation results showing a reduction in measurement error from 55.482 cm to 3.613 cm under non-Gaussian noise conditions. Experimental validation using a scaled-down model highlights the method’s practical applicability, achieving a mean absolute error of 3.558 cm in straight configurations and 5.379 cm in oblique cutting configurations, representing a significant reduction in measurement errors. This research advances conveyor monitoring technologies, offering a promising solution for improving operational efficiency, reliability, and safety in mining and industrial applications.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"249 ","pages":"Article 116978"},"PeriodicalIF":5.2,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487211","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}
Pub Date : 2025-02-21DOI: 10.1016/j.measurement.2025.117063
Xu Zhang , Qingzhou Mao , Yinhu Zhan , Hao Zhou , Chunlin Shi
The center of a spherical target is often used as a feature primitive when large-scene point clouds are scanned. In the measurement of large-scene point clouds, the number of points in spherical target point clouds is small and suffers from non-negligible measurement errors, which reduces the precision of the sphere center fit and decreases the layout distance of the targets. To overcome these difficulties, a spherical target point cloud correction (STPCC) model is proposed to correct the positions of the target point clouds, which facilitates obtaining a more precise sphere center. There are five key steps for establishing the STPCC: analyzing the spatial geometric relationship between the laser and the spherical target, rasterizing the laser spot, deriving a unified ranging equation for subspots, constructing a two-dimensional Gaussian weight function, and establishing a correction model. The feasibility of STPCC is analyzed from multiple perspectives, and the application flow is described in detail. The experimental conclusions are as follows: 1) Following the correction of the large-scene target point clouds by STPCC, the fitting radius error, the residual RMSE (Root mean square error) and the error between the target spacing and the target baseline decreased to at least 1/5, 1/3 and 1/5, respectively, of those before correction. 2) STPCC can effectively correct the coordinates of the target point clouds to the target surface. The various errors of fitting the sphere center via STPCC are better than those of existing sphere fitting methods. 3) STPCC can improve the precision of large-scene point cloud orientation, and the orientation error after processing was reduced by more than 70% compared to that before processing.
{"title":"STPCC: Spherical target point cloud correction model and its application to point cloud orientation of large scenes","authors":"Xu Zhang , Qingzhou Mao , Yinhu Zhan , Hao Zhou , Chunlin Shi","doi":"10.1016/j.measurement.2025.117063","DOIUrl":"10.1016/j.measurement.2025.117063","url":null,"abstract":"<div><div>The center of a spherical target is often used as a feature primitive when large-scene point clouds are scanned. In the measurement of large-scene point clouds, the number of points in spherical target point clouds is small and suffers from non-negligible measurement errors, which reduces the precision of the sphere center fit and decreases the layout distance of the targets. To overcome these difficulties, a spherical target point cloud correction (STPCC) model is proposed to correct the positions of the target point clouds, which facilitates obtaining a more precise sphere center. There are five key steps for establishing the STPCC: analyzing the spatial geometric relationship between the laser and the spherical target, rasterizing the laser spot, deriving a unified ranging equation for subspots, constructing a two-dimensional Gaussian weight function, and establishing a correction model. The feasibility of STPCC is analyzed from multiple perspectives, and the application flow is described in detail. The experimental conclusions are as follows: 1) Following the correction of the large-scene target point clouds by STPCC, the fitting radius error, the residual RMSE (Root mean square error) and the error between the target spacing and the target baseline decreased to at least 1/5, 1/3 and 1/5, respectively, of those before correction. 2) STPCC can effectively correct the coordinates of the target point clouds to the target surface. The various errors of fitting the sphere center via STPCC are better than those of existing sphere fitting methods. 3) STPCC can improve the precision of large-scene point cloud orientation, and the orientation error after processing was reduced by more than 70% compared to that before processing.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"250 ","pages":"Article 117063"},"PeriodicalIF":5.2,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527137","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}
Pub Date : 2025-02-21DOI: 10.1016/j.measurement.2025.117073
Teng-Chung Liu , Padmanabh Pundrikaksha Pancham , Ming-Tse Yuan , Yu-Wen Chen , Cheng-Han Tsai , Cheng-Yao Lo
A thermoresistive strain sensing system with a concentric-like pattern, a special thermal insulator (aerogel), and integrated graphical user interface (GUI) for strain visualization was proposed and demonstrated in this work. The temperature responses were resulted from the contribution of resistance change and the current that flowed through the sensor with Joule heating. The aerogel between the sensor and the demonstrators avoided misleading material-induced heat dispassion, leading to improved accuracy. The GUI was coded through Matlab that converted two-dimensional thermal images into three-dimensional (3D) profiles through an established temperature − profile database for operation simplicity. In addition, a stepwise signal processing on pixel patching further optimized the visual perception of the 3D profile for practicality. The proposed system not only decoupled the serial connection of tension and compression, but also correctly indicated arbitrary strain orientations without any blind spots. The maximum tolerance was found to be lower than 9.3 % and the converted 3D profiles showed remarkable reconstruction of the object of interest with a coefficient of determination of 0.961; demonstrating a novel, non-contact, and integrated profiling system with optimized signal processing for mechanical applications.
{"title":"A 3D profiling system using thermoresistive sensing and image processing with stepwise pixel patching","authors":"Teng-Chung Liu , Padmanabh Pundrikaksha Pancham , Ming-Tse Yuan , Yu-Wen Chen , Cheng-Han Tsai , Cheng-Yao Lo","doi":"10.1016/j.measurement.2025.117073","DOIUrl":"10.1016/j.measurement.2025.117073","url":null,"abstract":"<div><div>A thermoresistive strain sensing system with a concentric-like pattern, a special thermal insulator (aerogel), and integrated graphical user interface (GUI) for strain visualization was proposed and demonstrated in this work. The temperature responses were resulted from the contribution of resistance change and the current that flowed through the sensor with Joule heating. The aerogel between the sensor and the demonstrators avoided misleading material-induced heat dispassion, leading to improved accuracy. The GUI was coded through Matlab that converted two-dimensional thermal images into three-dimensional (3D) profiles through an established temperature − profile database for operation simplicity. In addition, a stepwise signal processing on pixel patching further optimized the visual perception of the 3D profile for practicality. The proposed system not only decoupled the serial connection of tension and compression, but also correctly indicated arbitrary strain orientations without any blind spots. The maximum tolerance was found to be lower than 9.3 % and the converted 3D profiles showed remarkable reconstruction of the object of interest with a coefficient of determination of 0.961; demonstrating a novel, non-contact, and integrated profiling system with optimized signal processing for mechanical applications.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"249 ","pages":"Article 117073"},"PeriodicalIF":5.2,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143479644","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}
Pub Date : 2025-02-21DOI: 10.1016/j.measurement.2025.117081
Islam Md Monirul , Li Qiu , Rukhsana Ruby , Inam Ullah , Amin Sharafian
Accurate state of charge (SOC) estimation is vital for optimizing lithium-ion battery (LIB) performance and capacity in modern battery management systems (BMS). This study introduces a precise SOC estimation method through the employment of an adaptive extended Kalman filter (AEKF) with a high-order electrical equivalent circuit model that incorporates two series resistor–capacitor (RC) pairs to effectively capture the dynamic characteristics of LIBs. The model parameters are mathematically derived by solving equations associated with two operational states, using experimental data from hybrid pulse power characterization and open-circuit voltage tests to parameterize the model, which is then simulated in Simulink/MATLAB. The AEKF algorithm enhances SOC estimation by dynamically adjusting parameters to mitigate statistical noise, thereby minimizing estimation errors. Comparative analysis with the standard extended Kalman filter (EKF) demonstrates that the AEKF achieves faster convergence, greater stability, and improved accuracy. Specifically, a comparative analysis with the standard EKF shows that the AEKF improves accuracy by reducing the maximum estimation error from 0.0813 to 0.0171. These results justify the effectiveness of the AEKF in achieving highly accurate SOC estimation for power LIBs, significantly enhancing battery pack performance and reliability.
{"title":"Accurate SOC estimation in power lithium-ion batteries using adaptive extended Kalman filter with a high-order electrical equivalent circuit model","authors":"Islam Md Monirul , Li Qiu , Rukhsana Ruby , Inam Ullah , Amin Sharafian","doi":"10.1016/j.measurement.2025.117081","DOIUrl":"10.1016/j.measurement.2025.117081","url":null,"abstract":"<div><div>Accurate state of charge (SOC) estimation is vital for optimizing lithium-ion battery (LIB) performance and capacity in modern battery management systems (BMS). This study introduces a precise SOC estimation method through the employment of an adaptive extended Kalman filter (AEKF) with a high-order electrical equivalent circuit model that incorporates two series resistor–capacitor (RC) pairs to effectively capture the dynamic characteristics of LIBs. The model parameters are mathematically derived by solving equations associated with two operational states, using experimental data from hybrid pulse power characterization and open-circuit voltage tests to parameterize the model, which is then simulated in Simulink/MATLAB. The AEKF algorithm enhances SOC estimation by dynamically adjusting parameters to mitigate statistical noise, thereby minimizing estimation errors. Comparative analysis with the standard extended Kalman filter (EKF) demonstrates that the AEKF achieves faster convergence, greater stability, and improved accuracy. Specifically, a comparative analysis with the standard EKF shows that the AEKF improves accuracy by reducing the maximum estimation error from 0.0813 to 0.0171. These results justify the effectiveness of the AEKF in achieving highly accurate SOC estimation for power LIBs, significantly enhancing battery pack performance and reliability.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"249 ","pages":"Article 117081"},"PeriodicalIF":5.2,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487098","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}
Pub Date : 2025-02-21DOI: 10.1016/j.measurement.2025.117036
Peng Ran , Yingbing Lai , Minchuan Li , Wei Liu , Zhuizhui Jiao , Ying Zhong , Daming Sun
To detect and analyze the mechanoelectrical coupling signals of esophageal tissue, this paper proposes a novel flexible balloon sensor structure. This structure consists of four internal piezoelectric sensor arrays and sixteen external impedance electrode arrays. To validate the performance of the sensor in detecting the structural and mechanical properties of esophageal tissue, various simulations and experiments were conducted using electrical impedance spectroscopy (EIS) and mechanical reconstruction analysis. The results demonstrate that the sensor can distinguish stressed segments and detect esophageal impedance. Additionally, selecting an 80% balloon inflation level and employing a remote excitation strategy for the impedance electrode array proved to be optimal choices. Overall, the coupling signals acquired by the sensor effectively reflect the mechanical and tissue characteristics of the esophagus.
{"title":"Development of a flexible balloon sensor for biomechanical-electrical coupling detection in the esophagus","authors":"Peng Ran , Yingbing Lai , Minchuan Li , Wei Liu , Zhuizhui Jiao , Ying Zhong , Daming Sun","doi":"10.1016/j.measurement.2025.117036","DOIUrl":"10.1016/j.measurement.2025.117036","url":null,"abstract":"<div><div>To detect and analyze the mechanoelectrical coupling signals of esophageal tissue, this paper proposes a novel flexible balloon sensor structure. This structure consists of four internal piezoelectric sensor arrays and sixteen external impedance electrode arrays. To validate the performance of the sensor in detecting the structural and mechanical properties of esophageal tissue, various simulations and experiments were conducted using electrical impedance spectroscopy (EIS) and mechanical reconstruction analysis. The results demonstrate that the sensor can distinguish stressed segments and detect esophageal impedance. Additionally, selecting an 80% balloon inflation level and employing a remote excitation strategy for the impedance electrode array proved to be optimal choices. Overall, the coupling signals acquired by the sensor effectively reflect the mechanical and tissue characteristics of the esophagus.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"249 ","pages":"Article 117036"},"PeriodicalIF":5.2,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143479630","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}
Pub Date : 2025-02-20DOI: 10.1016/j.measurement.2025.117065
Yuzhu Zhou , Hui Chen , Ying Chen , Qingqing Ma
Monitoring of tunnel cross-section measurements is crucial for construction safety and accurate engineering records. Terrestrial Laser Scanning offers an effective measurement solution with high-precision data. In this article, a novel method based on clustering is proposed for extracting the centerline of irregular tunnels and constructing their cross-sectional models, with the aim of measuring cross-sectional profile information to serve excavation assessment and engineering recordkeeping purposes. First, the point cloud data is simplified and projected onto a two-dimensional plane. Then, a novel method called uniform mean shift is proposed to extract the tunnel’s centerline. Last, we construct sectional contour models at any position on the central axis. In a tunnel excavation experiment, the proposed method efficiently extracts the irregular tunnel’s centerline with centimeter-scale errors. The results demonstrate the method’s potential to support excavated tunnel construction monitoring by providing reliable and timely cross-sectional data.
{"title":"Extraction and characterization of irregular tunnel cross-sections based on point clouds","authors":"Yuzhu Zhou , Hui Chen , Ying Chen , Qingqing Ma","doi":"10.1016/j.measurement.2025.117065","DOIUrl":"10.1016/j.measurement.2025.117065","url":null,"abstract":"<div><div>Monitoring of tunnel cross-section measurements is crucial for construction safety and accurate engineering records. Terrestrial Laser Scanning offers an effective measurement solution with high-precision data. In this article, a novel method based on clustering is proposed for extracting the centerline of irregular tunnels and constructing their cross-sectional models, with the aim of measuring cross-sectional profile information to serve excavation assessment and engineering recordkeeping purposes. First, the point cloud data is simplified and projected onto a two-dimensional plane. Then, a novel method called uniform mean shift is proposed to extract the tunnel’s centerline. Last, we construct sectional contour models at any position on the central axis. In a tunnel excavation experiment, the proposed method efficiently extracts the irregular tunnel’s centerline with centimeter-scale errors. The results demonstrate the method’s potential to support excavated tunnel construction monitoring by providing reliable and timely cross-sectional data.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"250 ","pages":"Article 117065"},"PeriodicalIF":5.2,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143528758","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}
Pub Date : 2025-02-20DOI: 10.1016/j.measurement.2025.117069
Lingkai Cui , Jie Yang , Mengqing Tan , Renhui Ding
Computational Fluid Dynamics (CFD) has been employed to study the effects of sensor structure, materials, and environmental factors on radiation-induced error and to explore their inherent variation patterns for correcting radiation-induced errors in radiosonde temperature data. Initially, CFD was used to assess the impact of different sensor structures, sizes, ascent speeds, altitudes, solar radiation intensity, and radiation directions on radiation-induced error. Subsequently, a four-wire sounding temperature sensor was designed. Simulation results indicate that the four-wire sensor exhibits excellent radiation thermal balance in three-dimensional space under various solar radiation directions. Therefore, the challenging factor of solar radiation direction can be disregarded in the radiation-induced error correction model. A multi-variable input radiation-induced error correction model for the four-wire sensor was developed using a neural network algorithm. Finally, experimental studies on the four-wire sensor and its radiation-induced error correction model were conducted using a low-pressure wind tunnel and a full-spectrum solar simulator.
{"title":"Development of a sounding temperature sensor with four wires for upper-air temperature measurements","authors":"Lingkai Cui , Jie Yang , Mengqing Tan , Renhui Ding","doi":"10.1016/j.measurement.2025.117069","DOIUrl":"10.1016/j.measurement.2025.117069","url":null,"abstract":"<div><div>Computational Fluid Dynamics (CFD) has been employed to study the effects of sensor structure, materials, and environmental factors on radiation-induced error and to explore their inherent variation patterns for correcting radiation-induced errors in radiosonde temperature data. Initially, CFD was used to assess the impact of different sensor structures, sizes, ascent speeds, altitudes, solar radiation intensity, and radiation directions on radiation-induced error. Subsequently, a four-wire sounding temperature sensor was designed. Simulation results indicate that the four-wire sensor exhibits excellent radiation thermal balance in three-dimensional space under various solar radiation directions. Therefore, the challenging factor of solar radiation direction can be disregarded in the radiation-induced error correction model. A multi-variable input radiation-induced error correction model for the four-wire sensor was developed using a neural network algorithm. Finally, experimental studies on the four-wire sensor and its radiation-induced error correction model were conducted using a low-pressure wind tunnel and a full-spectrum solar simulator.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"249 ","pages":"Article 117069"},"PeriodicalIF":5.2,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471586","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}
Pub Date : 2025-02-20DOI: 10.1016/j.measurement.2025.117074
Amir R. Ali , Dania Aljabari , Ahmed Elsherbiny , Farah Ezzeldin , Ramy Mahmoud , Menna Shalaby
This paper presents a novel approach for measuring flow rates in the artificial soft muscle of the humanoid robot leg, specifically focusing on GUCnoid 1.0. We integrate a rotameter and Linear Variable Differential Transformer (LVDT) sensor to accurately quantify airflow rates within the muscle. The experimental setup, calibration procedures, and data acquisition methods are comprehensively described. Our results demonstrate that this combined approach offers exceptional precision and reliability in measuring flow rates, with the LVDT exhibiting a sensitivity of 0.0634 V/cm and a standard deviation of 0.2502 V. The rotameter displays a sensitivity of −12.859 V/(L/s) and a resolution of 0.0184 L/s for flow rates from 0 to 0.036 L/s, while achieving a sensitivity of −0.1409 V/(L/s) and a resolution of 0.0248 L/s for flow rates from 0.036 to 0.079 L/s. These findings advance flow rate measurement systems for artificial soft muscles in humanoid robotics.
{"title":"Flow rate measurement system for humanoid robot artificial soft muscles using rotameter-LVDT sensor integration","authors":"Amir R. Ali , Dania Aljabari , Ahmed Elsherbiny , Farah Ezzeldin , Ramy Mahmoud , Menna Shalaby","doi":"10.1016/j.measurement.2025.117074","DOIUrl":"10.1016/j.measurement.2025.117074","url":null,"abstract":"<div><div>This paper presents a novel approach for measuring flow rates in the artificial soft muscle of the humanoid robot leg, specifically focusing on GUCnoid 1.0. We integrate a rotameter and Linear Variable Differential Transformer (LVDT) sensor to accurately quantify airflow rates within the muscle. The experimental setup, calibration procedures, and data acquisition methods are comprehensively described. Our results demonstrate that this combined approach offers exceptional precision and reliability in measuring flow rates, with the LVDT exhibiting a sensitivity of 0.0634 V/cm and a standard deviation of 0.2502 V. The rotameter displays a sensitivity of −12.859 V/(L/s) and a resolution of 0.0184 L/s for flow rates from 0 to 0.036 L/s, while achieving a sensitivity of −0.1409 V/(L/s) and a resolution of 0.0248 L/s for flow rates from 0.036 to 0.079 L/s. These findings advance flow rate measurement systems for artificial soft muscles in humanoid robotics.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"250 ","pages":"Article 117074"},"PeriodicalIF":5.2,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510141","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}
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