IF 5.2 2区工程技术

Measurement Pub Date : 2025-04-28 DOI: 10.1016/j.measurement.2025.117696

Cyrine Lamloum , Oumaïma Gharbi , Laurent Belliard , Vincent Vivier , Kieu Ngo

引用次数: 0

Bayesian neural network-driven accelerometer-based type intelligent tire force measurement system 基于贝叶斯神经网络驱动的加速度计型智能轮胎力测量系统

IF 5.2 2区工程技术

Measurement Pub Date : 2025-04-28 DOI: 10.1016/j.measurement.2025.117699

Xiaoqiang Sun , Tianli Gu , Zhenqiang Quan , Yingfeng Cai , Houzhong Zhang , Bo Li

{"title":"Bayesian neural network-driven accelerometer-based type intelligent tire force measurement system","authors":"Xiaoqiang Sun , Tianli Gu , Zhenqiang Quan , Yingfeng Cai , Houzhong Zhang , Bo Li","doi":"10.1016/j.measurement.2025.117699","DOIUrl":"10.1016/j.measurement.2025.117699","url":null,"abstract":"<div><div>Accurate real-time measurement of tire forces is crucial for vehicle dynamics control. However, the current cost of direct tire force measurement is very high. In this study, we propose a cost-effective tire force measurement method based on in-tire acceleration measuring information and Bayesian Neural Network (BNN). To develop this measurement system, we designed three key components: (1) Fourier Amplitude Sensitivity Test (FAST) was used to determine the optimal accelerometer arrangement for improving this system measurement accuracy; (2) Signal preprocessing algorithms was designed to extract promising acceleration signal features of tire force; (3) Bayesian Neural Network was used to achieve precise tire force estimation. Experimental results indicate that the estimated tire force of this system has a good agreement with the reference tire force under varying conditions, including load, slip ratio, slip angle, tire pressure, vehicle speed and road friction coefficient. The offline Mean Absolute Percentage Errors for tire longitudinal, lateral, and vertical forces based on the BNN were 2.64%, 2.44%, and 0.55%, respectively. The online estimation also demonstrated good validation results. Additionally, under high-friction conditions, the BNN tire force estimation produced smaller confidence intervals, indicating greater stability.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117699"},"PeriodicalIF":5.2,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886013","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

Ultrasonic detection of porosity in composites based on wavelet packet transform and convolutional neural network 基于小波包变换和卷积神经网络的复合材料孔隙度超声检测

IF 5.2 2区工程技术

Measurement Pub Date : 2025-04-27 DOI: 10.1016/j.measurement.2025.117695

Xiaoying Cheng , Xiangfei Wu , Zhenyu Wu , Kehong Zheng , Hongjun Li , Xudong Hu

{"title":"Ultrasonic detection of porosity in composites based on wavelet packet transform and convolutional neural network","authors":"Xiaoying Cheng , Xiangfei Wu , Zhenyu Wu , Kehong Zheng , Hongjun Li , Xudong Hu","doi":"10.1016/j.measurement.2025.117695","DOIUrl":"10.1016/j.measurement.2025.117695","url":null,"abstract":"<div><div>Carbon fiber reinforced polymer composites (CFRPs) are widely used in many applications, while the pores have a significant influence on mechanical performance as a critical defect. In this work, wavelet packet transform (WPT) method is utilized as an effective feature extraction method to capture the information about pore defects in ultrasonic A-scan signals. Given the large amount of A-scan data and the spatial distribution of pores within CFRPs, A-scan signals are randomly extracted from multiple regions within the specimen to ensure a comprehensive representation of the material’s porosity. A multi-scale features obtained by this method not only compress the data volume but also reflect the details and variations of the pore’s distribution. These features are used as inputs to a convolutional neural network (CNN) for porosity classification. The experimental results showed that the method based on the combination of WPT and CNN can effectively distinguish the samples with different porosities with an accuracy as high as 98%. The results showed a promising application for determining the porosity of composites.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117695"},"PeriodicalIF":5.2,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886139","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

Deformation characteristics and angular error compensation of taper-mounted circular grating optical encoders 锥形圆光栅光学编码器的变形特性及角误差补偿

IF 5.2 2区工程技术

Measurement Pub Date : 2025-04-27 DOI: 10.1016/j.measurement.2025.117694

Guangzhen Li , Yiming He , Zhaoliang Gong , Xiangyu Zhao , Lei Zhang

{"title":"Deformation characteristics and angular error compensation of taper-mounted circular grating optical encoders","authors":"Guangzhen Li , Yiming He , Zhaoliang Gong , Xiangyu Zhao , Lei Zhang","doi":"10.1016/j.measurement.2025.117694","DOIUrl":"10.1016/j.measurement.2025.117694","url":null,"abstract":"<div><div>In recent years, taper mounting has become the predominant mounting configuration for circular grating optical encoders, owing to its superior mechanical stability and facile adjustability. Nevertheless, the installation deformation induced by screw tightening can result in significant angular errors. In this paper, the characteristics and mechanisms of deformation errors were analyzed for the first time. By employing a finite element method rather than a full mathematical modelling approach, the analysis denotes that the angular error resulting from installation deformation can be expressed in terms of a harmonic function with a period of 2π/<em>m</em>, where <em>m</em> represents the quantity of installation screws. Subsequently, leveraging “<em>m</em> cycle” fluctuation, a comprehensive model for angular error compensation that considers all installation errors, including deformation, eccentricity and inclination, is put forward. The experimental results with an uncertainty of 0.3″ indicate that the amplitude of the deformation error is 0.63″, deviating from the theoretical value of 0.68″ by merely 7.4%. After being compensated by the proposed comprehensive model, the angular error is diminished by 87.6% and attains a value of 2.58″, which is even better than the manufacturing accuracy of 3.5″. The results demonstrate that deformation error represents a significant error factor in precision circular grating optical encoder systems, and corroborate the validity of the proposed deformation error mechanism and the effectiveness of the comprehensive compensation model.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117694"},"PeriodicalIF":5.2,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883183","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

Artificial intelligence approach to predict microfibril angle of cellulose in wood cell walls by wide-angle X-ray diffraction 利用广角x射线衍射预测木材细胞壁纤维素微纤维角的人工智能方法

IF 5.2 2区工程技术

Measurement Pub Date : 2025-04-26 DOI: 10.1016/j.measurement.2025.117402

Ricardo Baettig , Ben Ingram

{"title":"Artificial intelligence approach to predict microfibril angle of cellulose in wood cell walls by wide-angle X-ray diffraction","authors":"Ricardo Baettig , Ben Ingram","doi":"10.1016/j.measurement.2025.117402","DOIUrl":"10.1016/j.measurement.2025.117402","url":null,"abstract":"<div><div>In the cell wall of cellulose-based fibers such as wood, the microfibril angle (MFA) in the S2 layer plays a crucial role in determining anisotropic properties. Current Wide-angle X-ray diffraction (WAXD) methods for MFA prediction rely on empirical equations, lacking clear predictive capabilities and remaining stagnant for decades. This study presents a novel approach to predict MFA and its variability using a generalized diffraction equation, Monte Carlo simulations of diffraction patterns, and Machine Learning models, including Random Forest (RF), k-Nearest Neighbors (kNN), and Artificial Neural Networks (ANNs). Results show that the commonly used Variance Approach generates inaccurate predictions (RMSE=2.61°, MAE=2.12°), while the proposed AI models demonstrate significantly higher accuracy (RF: RMSE=0.72°, MAE=0.29°; kNN: RMSE=0.87°, MAE=0.40°; ANN: RMSE=0.47°, MAE=0.24°). Furthermore, the AI models suggest that empirical cross-section shape data is not required for accurate MFA prediction. This innovative approach, leveraging advanced computational methods and AI, addresses long-standing challenges in MFA prediction using WAXD.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117402"},"PeriodicalIF":5.2,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883014","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

Coal resources under carbon peak: Integrating LOAM Livox with laser point cloud for coal mine working face environment three-dimensional perception technology 碳峰下的煤炭资源：LOAM Livox与激光点云集成的煤矿工作面环境三维感知技术

IF 5.2 2区工程技术

Measurement Pub Date : 2025-04-26 DOI: 10.1016/j.measurement.2025.117704

Zhizhong Xing , Shuanfeng Zhao , Wei Guo , Fanyuan Meng , Xiaojun Guo , Shenquan Wang , Lin Yang , Haitao He

{"title":"Coal resources under carbon peak: Integrating LOAM Livox with laser point cloud for coal mine working face environment three-dimensional perception technology","authors":"Zhizhong Xing , Shuanfeng Zhao , Wei Guo , Fanyuan Meng , Xiaojun Guo , Shenquan Wang , Lin Yang , Haitao He","doi":"10.1016/j.measurement.2025.117704","DOIUrl":"10.1016/j.measurement.2025.117704","url":null,"abstract":"<div><div>Under the goal of peaking carbon emissions, achieving safe and unmanned mining in coal mines is crucial for the sustainable development of the coal industry, and underground environmental perception is a key link in achieving this goal. This study proposes integrating Lidar Odometry and Mapping Livox (LOAM Livox) with a large number of laser point clouds to reconstruct the coal mine working face scene. In terms of research methods, this study first conducted point cloud registration for coal mine working face to achieve alignment of point cloud data. Then the odometer information of the coal mine working face was outputted in order to provide the motion trajectory for subsequent three-dimensional reconstruction. Finally, a point cloud model of the coal mine working face was successfully constructed. We validated the accurate three-dimensional modeling of the coal mine working face using the diameter of the hydraulic support column, the width of the hydraulic support top beam, and the height of the coal wall as objects. This study has improved the engineering technology means of coal mine working face environment perception, which not only provides strong technical support for the safety and efficient production of coal mines, but also lays a solid foundation for further research in related fields, and is expected to promote greater progress in the direction of intelligent mining in the coal industry.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117704"},"PeriodicalIF":5.2,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886025","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

Enhancing tracking performance of motion control based on a novel fuzzy adaptive finite time extended state observer subject to uncertainties and measurement noises 基于模糊自适应有限时间扩展状态观测器的不确定性和测量噪声增强运动控制的跟踪性能

IF 5.2 2区工程技术

Measurement Pub Date : 2025-04-26 DOI: 10.1016/j.measurement.2025.117624

Ba-Hoa Thai, Wonkeun Youn

引用次数: 0

A low-cost bidirectional laser communication system for pH and temperature monitoring of Teluk Seruo Lake, Indonesia 一种低成本的双向激光通信系统，用于印尼泰鲁克塞罗湖的pH和温度监测

IF 5.2 2区工程技术

Measurement Pub Date : 2025-04-26 DOI: 10.1016/j.measurement.2025.117679

Assaidah , Rendy M. Wahid , Ilham Affandy , Saifudin Juri , Menik Ariani , Khairul Saleh , Yulia Fitri , Khaeriah Dahlan , Feriska Handayani Irka

{"title":"A low-cost bidirectional laser communication system for pH and temperature monitoring of Teluk Seruo Lake, Indonesia","authors":"Assaidah , Rendy M. Wahid , Ilham Affandy , Saifudin Juri , Menik Ariani , Khairul Saleh , Yulia Fitri , Khaeriah Dahlan , Feriska Handayani Irka","doi":"10.1016/j.measurement.2025.117679","DOIUrl":"10.1016/j.measurement.2025.117679","url":null,"abstract":"<div><div>There has been limited infrastructure to run the automatic monitoring of environmental qualities in Indonesia. Therefore, a low-cost prototype of bidirectional communication based on visible light (laser) using an Arduino board had been designed for telemonitoring the pH and temperature of the underwater environment. The monitoring was made upon a user’s request to save battery power and digital memory capacity. By installing the sensors to the transceivers, the collected data was modulated by red laser frequency using 1 and 2-pulse-width-modulation (PWM) schemes. The laser beam was captured by solar cells that have dual functions i.e. photo-detector and power-generator. Thus, it became a self-powered modem. A user can start the sensor measurement anytime by pushing the button on the website. Then the instruction was sent to Modem I which was located on the lakeside via the radio frequency (RF) signal. Modem I passed the order to Modem II −which was floating in the middle of the lake, through a red laser beam. Modem II asked sensors to measure pH and temperature at the requested time. After seconds, the sensor data were delivered back to the user and stored in the website database. The design had been tested to monitor the pH and temperature of Teluk Seruo Lake’s water. The data shown on the website was similar to the conventional pH- and thermo-meter showed i.e. 4.29 and 30.1 °C, respectively.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117679"},"PeriodicalIF":5.2,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143878941","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 pressure-based intelligent seat systems: A novel approach to driver behavior recognition 基于压力的智能座椅系统：一种驾驶员行为识别的新方法

IF 5.2 2区工程技术

Measurement Pub Date : 2025-04-25 DOI: 10.1016/j.measurement.2025.117588

Jiayi Lu, Boao Zhang, Rui Cao, Bin Sun, Rui Wang, Zhaowen Pang, Zexiang Tong, Shichun Yang, Yaoguang Cao

{"title":"A pressure-based intelligent seat systems: A novel approach to driver behavior recognition","authors":"Jiayi Lu, Boao Zhang, Rui Cao, Bin Sun, Rui Wang, Zhaowen Pang, Zexiang Tong, Shichun Yang, Yaoguang Cao","doi":"10.1016/j.measurement.2025.117588","DOIUrl":"10.1016/j.measurement.2025.117588","url":null,"abstract":"<div><div>As intelligent vehicles become increasingly prevalent, ensuring their operational safety has emerged as a paramount challenge. Vehicles equipped with advanced intelligent systems offer the potential to enhance safety by continuously monitoring and analyzing the driver’s condition and behavior in real time, thereby aligning vehicle control more closely with the driver’s intentions. While visual detection methods within the cockpit are often impeded by obstructions such as other passengers or interior elements, the driver’s seat provides a unique advantage due to its constant, unobstructed contact with the driver throughout the driving process. This positioning enables the seat to detect subtle changes in its morphology, offering a reliable means to map the driver’s state with minimal interference. This paper introduces a novel pressure-based intelligent seat system integrated with an array of polyvinylidene fluoride (PVDF) sensors and proposes a tailored Pressure Mat Vision Transformer (ViT) model to accurately classify the driver’s operational behaviors. The system demonstrates the capability to predict the precise depth of throttle, brake, and clutch pedal engagement, as well as the steering wheel’s control state. Validation of the model’s performance was conducted using a custom-built dataset. Furthermore, the feasibility of the pressure recognition technology and the efficacy of the fine-tuned architecture for real-time application were substantiated through engineering deployment on an operational platform. The development of this intelligent seat represents a significant advancement in addressing safety concerns related to human–machine interactions and establishes an innovative framework for leveraging data in the evolution of next-generation intelligent vehicles.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117588"},"PeriodicalIF":5.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876914","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

Electromagnetic radiation detection and monitoring in high-voltage transmission lines using machine learning techniques 利用机器学习技术对高压输电线路进行电磁辐射检测与监测

IF 5.2 2区工程技术

Measurement Pub Date : 2025-04-25 DOI: 10.1016/j.measurement.2025.117645

N. Anand , M. Balasingh Moses

{"title":"Electromagnetic radiation detection and monitoring in high-voltage transmission lines using machine learning techniques","authors":"N. Anand , M. Balasingh Moses","doi":"10.1016/j.measurement.2025.117645","DOIUrl":"10.1016/j.measurement.2025.117645","url":null,"abstract":"<div><div>Electromagnetic radiation (EMR) from high-voltage transmission lines (HVTL) poses significant risks to both human health and electrical infrastructure. Accurate detection and monitoring of EMR are essential for assessing its impact, severity, and potential mitigation strategies. This study investigates EMR data collected from transmission lines operating at 400 kV, 230 kV, 110 kV, 22 kV, and 11 kV at multiple locations, leveraging Machine Learning (ML) techniques based on Artificial Intelligence (AI) for classification and regression analysis. The dataset comprises electric and magnetic field measurements as input features, while transmission line voltage, EMR impact, and severity serve as target variables. To achieve precise classification and prediction, multiple ML models, including Random Forest (RF), Decision Trees (DT), Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), Ensemble methods, and Artificial Neural Networks (ANN), were employed. A comparative performance analysis demonstrated that the Ensemble Bagged Trees algorithm outperformed other models in terms of accuracy, sensitivity, specificity, false positive rate (FPR), and F1 score. The model achieved an impressive accuracy of 90.1 % in classifying transmission line voltage levels and 99.4 % in predicting EMR severity, making it a highly effective tool for real-time monitoring. By integrating ML-based classification and prediction frameworks, this research provides a robust and scalable approach to real-time EMR assessment, enhancing power grid reliability and electromagnetic safety. The findings contribute to improved safety protocols for power line workers, UAV operations, and proactive fault detection in power systems.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117645"},"PeriodicalIF":5.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876916","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

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