Measurement最新文献

{"title":"A robust adaptive error state Kalman filter for MEMS IMU attitude estimation under dynamic acceleration","authors":"Xiaofeng Wei ,&nbsp;Shiwei Fan ,&nbsp;Ya Zhang ,&nbsp;Wei Gao ,&nbsp;Feng Shen ,&nbsp;Xie Ming ,&nbsp;Jian Yang","doi":"10.1016/j.measurement.2024.116097","DOIUrl":"10.1016/j.measurement.2024.116097","url":null,"abstract":"<div><div>Accurate estimation of attitude (pitch, roll) is a prerequisite for ensuring safe navigation during vehicle control execution. In dynamic environments, the presence of acceleration often degrades the accuracy and robustness of attitude estimation using traditional algorithms with Micro-Electro-Mechanical Systems (MEMS) Inertial Measurement Unit (IMU). This paper proposes a robust adaptive error state Kalman filter (RAESKF) algorithm for attitude estimation of MEMS IMU under dynamic acceleration conditions. The RAESKF algorithm decomposes the true attitude Direction Cosine Matrix (DCM) into nominal and error components. An error state Kalman filter is utilized to fuse real-time measurements from MEMS gyroscope and accelerometer, estimating the error component, which is then used to correct the nominal attitude DCM. Furthermore, within the error state Kalman filtering framework, a dynamic acceleration robust adaptive adjustment strategy is implemented. This strategy relies on real-time data monitoring, threshold-based decision-making, and switching mechanisms. It adaptively selects between dynamic acceleration model compensation and online adjustment of the measurement noise covariance matrix. The primary objective of this strategy is to mitigate the impact of disturbance induced by dynamic acceleration on attitude estimation, thereby enhancing the accuracy and robustness. Laboratory rotation experiments have demonstrated that the algorithm improved pitch and roll measurement accuracy by at least 8.81% and 11.69%, respectively, under rotational conditions. In dynamic acceleration conditions, pitch and roll measurement accuracy improved by at least 42.76% and 67.21%, respectively.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"242 ","pages":"Article 116097"},"PeriodicalIF":5.2,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651846","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":"Automatic measurement of rebar spacing based on 3D point cloud segmentation using Rebar-YOLOv8-seg and depth data","authors":"Jiayin Song ,&nbsp;Ting Liao ,&nbsp;Qinglin Zhu ,&nbsp;Jinlong Wang ,&nbsp;Liusong Yang ,&nbsp;Hongwei Zhou ,&nbsp;Teng Lu ,&nbsp;Zhuoyuan Jiang ,&nbsp;Wenlong Song","doi":"10.1016/j.measurement.2024.116111","DOIUrl":"10.1016/j.measurement.2024.116111","url":null,"abstract":"<div><div>In power transmission and transformation expansion projects, construction personnel must measure rebar spacing at construction sites to ensure the quality of concrete structures. Due to electrified equipment, high-precision steel rulers are prohibited. We introduced a new method for automatically measuring rebar spacing to improve construction safety and work efficiency. We developed the Rebar-YOLOv8-seg model to accurately extract the rebar image mask from complex backgrounds. Subsequently, the rebar mask was aligned with depth data to create a point cloud, which performed statistical filtering and principal component analysis. Finally, we used the Random Sample Consensus method to fit the point cloud centerline, then extracted key points and calculated rebar spacing. Experiments on 4 × 4 rebar measurements using the proposed method showed that, within a specific range of camera heights and shooting angles, the measured values of rebar spacing meet the engineering measurement requirements. The method achieves an average absolute error of 1.98 mm and an average relative error of 1.76 %. Additionally, multiple adjacent rebar spacings can be measured simultaneously and completed within 5 s, providing a new feasible approach for rebar spacing measurement.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"242 ","pages":"Article 116111"},"PeriodicalIF":5.2,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651687","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":"ADD-YOLO: An algorithm for detecting animals in outdoor environments based on unmanned aerial imagery","authors":"Qi Ye ,&nbsp;Mingtao Ma ,&nbsp;Xin Zhao ,&nbsp;Bichong Duan ,&nbsp;Lifen Wang ,&nbsp;Deyin Ma","doi":"10.1016/j.measurement.2024.116019","DOIUrl":"10.1016/j.measurement.2024.116019","url":null,"abstract":"<div><div>The breeding management of extensive livestock and scientific research surveys of animals in outdoor environments often require the utilization of UAVs due to their ability to efficiently cover large areas at a cost-effective rate. However, identifying small animal targets in aerial imagery from high-altitudes remains a significant challenge. This paper introduces an enhanced algorithm based on YOLOv8n, specifically designed for aerial animal detection. Firstly, we add a P2 small target detection layer on top of the original baseline model, while removing the P5 large target detection layer and 32x downsampling to enhance the detection of small animal targets and reduce the number of model parameters. Secondly, The improved N-SPPCSPC module replaces the spatial pyramid pooling structure in the baseline model to enhance the extraction capability for small targets. Thirdly, an improved DWRC2f module is adopted to enhance the extraction of multi-scale contextual information. Fourthly, the SEAM module is incorporated before the detection head to enhance the detection of occluded and overlapping animals. Finally, a combined NWD Loss function is implemented to address the scale sensitivity of IoU Loss, thereby improving the accuracy of small target detection. Compared to the baseline model, the improved model achieved an increase of 7.1% and 4.9% in mAP50 values and an increase of 4.0% and 1.3% in mAP50-95 values, respectively, across two datasets, while significantly reducing the number of parameters. Further comparisons with other single-stage object detection models demonstrate a better robustness of our model. Additionally, after quantization, when testing the inference speed of ADD-YOLO on performance-constrained edge devices, it was 1.72 times and 1.81 times faster than the baseline model. Therefore, this model provides a new and efficient monitoring tool for extensive pastoral management and wildlife surveys.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"242 ","pages":"Article 116019"},"PeriodicalIF":5.2,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142577798","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":"Automated sleep apnea detection from snoring and carotid pulse signals using an innovative neck wearable piezoelectric sensor","authors":"Yi-Ping Chao ,&nbsp;Hai-Hua Chuang ,&nbsp;Yu-Lun Lo ,&nbsp;Shu-Yi Huang ,&nbsp;Wan-Ting Zhan ,&nbsp;Guo-She Lee ,&nbsp;Hsueh-Yu Li ,&nbsp;Liang-Yu Shyu ,&nbsp;Li-Ang Lee","doi":"10.1016/j.measurement.2024.116102","DOIUrl":"10.1016/j.measurement.2024.116102","url":null,"abstract":"<div><div>This study introduces an innovative wearable neck piezoelectric sensor (NPS) that measures snoring vibrations and carotid pulsations, offering a significant advancement in sleep apnea syndrome (SAS) diagnosis. Utilizing advanced algorithms like discrete wavelet transform and dynamic thresholding, the NPS detects snoring events with 83% accuracy, comparable to polysomnography, and calculates key metrics such as the snoring index (SI) and normalized snoring vibration energy (SVE%). Unlike traditional methods, the SVE% from NPS directly correlates with subjective assessments of snoring severity. It also measures carotid pulsation metrics such as pulse rate and the standard deviation of normal-to-normal intervals, achieving 85% accuracy in sleep phase determination against polysomnography. Moreover, NPS surpasses traditional methods in SI and SVE% accuracy, closely aligning with clinical evaluations of SAS severity. This user-friendly technology automates the measurement of critical snoring metrics, transforming SAS diagnosis and treatment by enhancing accessibility and efficiency for healthcare providers and patients.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"242 ","pages":"Article 116102"},"PeriodicalIF":5.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572201","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}

{"title":"Polarization-maintaining fiber based macehead shaped interferometric sensor for accurate measurement of refractive index and temperature","authors":"Ashish Kumar,&nbsp;Abhishek Joshi,&nbsp;Hyoung Won Baac","doi":"10.1016/j.measurement.2024.116104","DOIUrl":"10.1016/j.measurement.2024.116104","url":null,"abstract":"<div><div>A macehead-shaped bent polarization-maintaining fiber-based interferometric sensing structure called MBPIS is described and experimentally demonstrated for precise temperature and refractive index measurement. The sensor’s working principle is explained by simulating the spatial distribution of the field intensity in straight and bending PANDA fibers. A maximum extinction ratio (∼21 dBm) for the interference dip wavelength (1527.825 nm) in the sensor’s output spectrum was optimized by manipulating the birefringence of propagating fiber modes by adjusting PMF’s bending diameter from 17 to 11 mm. The phase difference changes between these fiber modes due to temperature and RI-induced birefringence cause a shift in the interference spectrum. The sensor’s highest RI sensitivity has been seen at −259.32 nm/RIU for a wide range of analytes from 1.3333 to 1.3579. In contrast, the highest temperature sensitivities evaluated for the temperature range of 0 ∼ 100 ℃ are −220 pm/℃ and −0.139 dBm/℃, respectively.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"242 ","pages":"Article 116104"},"PeriodicalIF":5.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651635","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":"Research on tool wear and breakage state recognition of heavy milling 508III steel based on ResNet-CBAM","authors":"Yaonan Cheng ,&nbsp;Rui Guan ,&nbsp;Shilong Zhou ,&nbsp;Xingwei Zhou ,&nbsp;Jing Xue ,&nbsp;Wenjie Zhai","doi":"10.1016/j.measurement.2024.116105","DOIUrl":"10.1016/j.measurement.2024.116105","url":null,"abstract":"<div><div>Milling water chamber head material 508 III steel belongs to extreme manufacturing, and the tool failure is very serious in the process of machining. How to monitor and identify the tool wear damage state in time and effectively is a key problem to be solved urgently. Identifying chip morphology changes under machining conditions plays a very important role in characterizing tool wear and breakage. Tool wear has an important influence on the chip morphology during the process of milling 508III steel, and changes in chip morphology can also reflect the tool wear and breakage state. Therefore, this paper takes the chip morphology image as an important feature for tool wear and breakage recognition, and uses Gaussian fuzzy estimation morphology method to preprocess the chip morphology image. Build a ResNet network combined by convolutional block attention module (ResNet-CBAM) tool wear and breakage recognition model, and explore the selection of the model backbone network, determination of the ResNet backbone network depth, and fusion methods of different attention mechanisms. Through the ablation experiment and visual analysis of the attention mechanism module, it is verified that the ResNet-CBAM model proposed in this paper has an accuracy rate of 96.67% in tool wear and breakage state recognition, especially in the stage of severe tool wear and breakage. This study realizes the prediction and early warning of tool life, and provides effective guarantee for the efficient cutting of large parts of high-end equipment and the stable operation of manufacturing system.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"242 ","pages":"Article 116105"},"PeriodicalIF":5.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572200","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":"Progress in beamforming acoustic imaging based on phased microphone arrays: Algorithms and applications","authors":"Yong Wang ,&nbsp;Zhi Deng ,&nbsp;Jiaxi Zhao ,&nbsp;Victor Feliksovich Kopiev ,&nbsp;Donglai Gao ,&nbsp;Wen-Li Chen","doi":"10.1016/j.measurement.2024.116100","DOIUrl":"10.1016/j.measurement.2024.116100","url":null,"abstract":"<div><div>Beamforming acoustic imaging technology, utilizing phased microphone arrays, enables precise sound source localization and finds widespread application in aerodynamic wind tunnel testing, acoustic signal recognition, and mechanical fault diagnosis. This paper presents a comprehensive review of beamforming evolution, detailing its mathematical foundations and diverse applications in acoustic imaging. Various beamforming methodologies are critically analyzed using wind tunnel test data, and an overview of correction methods for external interferences and array optimization approaches is provided. Through this examination, the strengths and limitations of each method are highlighted, offering insights for future research. Additionally, potential future enhancements, including paradigm-shift approaches to advance beamforming capabilities, are explored, suggesting directions for further innovation. This review aims to establish a foundation for newcomers to the field, stimulate academic discussion, and drive ongoing research in acoustic imaging. By elucidating beamforming complexities, correction methods, and optimization techniques, this study seeks to enhance collective knowledge and support continued advancements in this technology.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"242 ","pages":"Article 116100"},"PeriodicalIF":5.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572204","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":"Schuler period oscillation error suppression for inertial navigation systems based on reverse navigation and wavelet transforms","authors":"Jincheng Peng ,&nbsp;Dongjie Wu ,&nbsp;Pengchao Yao ,&nbsp;Xiafu Peng ,&nbsp;Gongliu Yang ,&nbsp;Xiaoli Zhang","doi":"10.1016/j.measurement.2024.115862","DOIUrl":"10.1016/j.measurement.2024.115862","url":null,"abstract":"<div><div>In long-enduration pure inertial navigation of inertial navigation systems, the Schuler oscillation leads to a decrease in navigation accuracy. We propose a method based on reverse navigation and wavelet transform (RATF) to suppress Schuler oscillation errors. Reverse navigation solves the stored gyroscope and accelerometer data to predict the Schuler period in the forward navigation solution. Wavelet transform accurately identifies and extracts the instantaneous amplitude, frequency, and phase of the Schuler period predicted from the reverse navigation solution, thereby suppressing the navigation errors caused by the Schuler oscillations during the current forward navigation process. In the simulation and experimental data, we compare the RATF algorithm proposed in this paper with the pure inertial navigation algorithm and the internal damping network technology. The long-endurance positioning test results for vehicles and ships indicate that the RATF algorithm improves positioning accuracy by 50.77% and 67.28%, respectively, compared to the internal damping network technology. Therefore, the RATF algorithm not only better suppresses Schuler oscillation errors in the inertial navigation system, enhancing positioning accuracy during long-endurance navigation, but also does not rely on external sensor inputs, ensuring the autonomy of the inertial navigation system.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"242 ","pages":"Article 115862"},"PeriodicalIF":5.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586257","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":"Pose estimation of nonoverlapping FOV cameras for shield tunnel convergence measurement","authors":"Chenxi Yao ,&nbsp;Shuaimin He ,&nbsp;Hao Chen ,&nbsp;Xin Zhang ,&nbsp;Zhenyu Wang","doi":"10.1016/j.measurement.2024.116101","DOIUrl":"10.1016/j.measurement.2024.116101","url":null,"abstract":"<div><div>Shield tunnel convergence is a crucial indicator for the safety assessment of tunnel support structures and a key focus in routine tunnel inspections. Owing to limitations in efficiency and cost, traditional and existing advanced measurement methods are inadequate for the high-intensity demands of tunnel surveying tasks. This paper proposes a convergence measurement method using pose estimation of a pair of non-overlapping field of view (FOV) cameras. The method involves tracking the pose change of two targets fixed on both sides of a tunnel cross-section, which provides convergence information of the cross-section. The feasibility of the proposed method for measuring tunnel convergence in a single cross-section is validated through simulations and model experiments. The performance of several representative PnP algorithms is compared, confirming the superiority of the SQPnP algorithm. In addition, the calibration of non-overlapping FOV cameras is addressed by transforming it into a hand-eye calibration problem, tailored for tunnel scenarios. Besides, the application conditions of nonoverlapping FOV camera calibration are analyzed by adjusting different influencing factors, and the influence of calibration errors on convergence measurement accuracy is assessed. Finally, two model experiments are conducted to practically verify the measurement accuracy of the proposed method. The experimental results indicate that the maximum measurement errors of the proposed method are only 0.75 mm for the relative displacement in <em>z</em>-direction between two targets on both sides of the tunnel cross-section and 0.77 mm for the change of spatial distance between two targets on both sides of the tunnel cross-section, which is sufficient for the accuracy requirement of 1 mm for tunnel deformation monitoring. With high-speed cameras mounted on subway carriages acquiring clear target images, this method can achieve rapid convergency inspection across multiple tunnel sections during the subway operation period.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"242 ","pages":"Article 116101"},"PeriodicalIF":5.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586032","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":"Investigation of Lamb wave modes recognition and acoustic emission source localization for steel plate based on golden jackal optimization VMD parameters and CWT","authors":"Shishang Dong ,&nbsp;Jun You ,&nbsp;Mohamed El-attaouy ,&nbsp;Ming Li ,&nbsp;Li Guo ,&nbsp;Zian Cheng ,&nbsp;Xin Zhang ,&nbsp;Shi Gong ,&nbsp;Yong Wang","doi":"10.1016/j.measurement.2024.116103","DOIUrl":"10.1016/j.measurement.2024.116103","url":null,"abstract":"<div><div>Accurate identification of Lamb wave modes in acoustic emission(AE) signals propagating on steel plates is crucial for precise source localization. In this paper, we propose a novel method that optimizes variational mode decomposition(VMD) parameters using golden jackal optimization(GJO) and identifies Lamb wave modes on steel plates through continuous wavelet transform(CWT). The Hsu-Nielsen source(HNS) is employed as the AE source. The minimum permutation entropy of the AE signal is used as the optimization objective, with GJO adaptively determining the optimal mode number (K) and penalty factor (<em>α</em>) for VMD. The maximum correlation coefficient method is applied to reconstruct the AE waveform, and both A0 and S0 Lamb wave modes are identified in the wavelet time–frequency domain using CWT. Furthermore, an AE source localization algorithm based on the time difference of arrival and the geometric relationship between two sensors is developed, utilizing the group velocity of the S0 mode. The proposed method effectively identifies acoustic wave modes, achieving an average relative localization error of approximately 0.48% for HNS.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"242 ","pages":"Article 116103"},"PeriodicalIF":5.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586031","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}