Measurement Accuracy Research Articles

Deviations in evapotranspiration measurements between enclosed- and open-path eddy covariance systems

The evapotranspiration (ET) measured by a closed-path eddy covariance system (CPEC), even with tube attenuation correction considering relative humidity (RH), frequently underestimated the ET measured by an open-path eddy covariance system (OPEC). An enclosed-path eddy covariance system (EPEC), which combines the advantages of both CPEC and OPEC, shows superior performance in many aspects, but doubts remain regarding its ET measurement accuracy. In this technical note, the comparative data between EPEC and OPEC were collected from various ecosystems and gas analyzers in the Korea Flux Network (KoFlux) and analyzed. Despite careful application of time lag compensation and frequency response correction using EddyPro, a widely used open-source data processing software in the flux science community, significant underestimation (6–8%) of ET measured by EPEC compared to OPEC remained consistently in all the three intercomparison experiments. Such deviations in ET measurements must be considered when synthesizing ET data measured by various EC systems.

Interpreting Pediatric Laryngeal Ultrasonography: A Training Protocol for Novice Examiners.

Laryngeal ultrasonography (LUS) is a noninvasive alternative to nasal endoscopy for diagnosing vocal fold pathologies in the pediatric population. Inducing less discomfort and physiological impact, LUS is more well tolerated by young patients. Despite its advantages, interpreting ultrasound images is highly subjective, potentially undermining diagnostic accuracy. To address the limitation, this research aims to evaluate the effect of training on novice examiners' LUS interpretation proficiency and, secondly, whether examiners' interpretation confidence increases after receiving the training. Thirty-eight novice examiners were randomly assigned to the experimental and control group where the former received training. A stimulus-response-feedback-stimulus paradigm was employed in the training. Qualitatively, the presence of vocal fold lesions and vocal fold motion impairment was examined. Quantitatively, the left and right vocal fold-arytenoid angles were measured. Results showed that training significantly improved diagnostic accuracy in qualitative measurements. Quantitatively, statistically significant effects were found posttraining with enhanced intrarater agreement and reduced interrater variability. A substantial increase in interpretation confidence was observed following training. In conclusion, there is an overall significant training effect on novice examiners' proficiency in LUS image interpretation. For future directions, it is recommended to investigate the training effect on the proficiency from ultrasound image acquisition to interpretation.

Precise Camera Calibration Method Based on Eccentricity Correction of Circular Target

Abstract Acquiring high-precision and robust calibration methods for determining the intrinsic and extrinsic parameters of cameras is essential to ensuring the accuracy of visual measurements and three-dimensional reconstruction. This paper addresses the challenge of reduced calibration accuracy in camera systems caused by eccentricity errors in the extraction of circular target feature centers. We propose a precise calibration method that corrects these eccentricity errors associated with circular targets. The method begins by solving for the homography matrix of each calibration image using the coordinates of both image points and target points. Subsequently, the homography matrix is used to generate the frontal view of the circular target, enabling the precise extraction and back-mapping of the circular target's center based on this frontal view. Finally, using the corrected coordinates of the circle center and the calibration method proposed by Zhang in the paper "A Flexible New Technique for Camera Calibration", we accurately determine the intrinsic and extrinsic parameters of the camera. Simulation experiments show the proposed calibration method aligns perfectly with ground truth without noise. In practical tests, it outperformed the checkerboard and traditional circular target methods, reducing reprojection errors by 0.0295 and 0.0176 pixels, respectively. Additionally, the cross-ratio experiment showed a 0.076% reduction in error for corrected circle center points. These results highlight significant improvements in calibration accuracy, meeting high-precision needs in visual sensing.

Opportunistic osteoporosis screening in intraoperative CT can accurately identify patients with low volumetric bone mineral density and osteoporosis during spine surgery.

To evaluate the accuracy of opportunistic measurements of volumetric bone mineral density (vBMD) in intraoperative multi-detector CT (MDCT) scans, using preoperative MDCT as the reference. This retrospective, single-center study included 105 patients (mean age: 73 ± 12.6 years, 53 women) who underwent spine surgery for various indications. All patients had preoperative MDCT with/without intravenous contrast and unenhanced intraoperative scans. VBMD of thoracolumbar vertebrae was automatically extracted using a convolutional neural network (CNN)-based framework with asynchronous calibration and contrast-phase correction. Vertebrae affected by artifacts, fractures, or severe degenerations were excluded. Root-mean-square errors (RMSEs) for associations between pair-wise vertebrae from preoperative and intraoperative vBMD values were calculated in linear regression models. Mean bias and 95%-limits of agreement (LOA) were calculated in Bland-Altman plots. Strong associations between preoperative and intraoperative vBMD values were observed in the thoracic (R2 = 0.94) and lumbar spine (R2 = 0.96). Intraoperative vBMD values showed high accuracy in reference to preoperative measurements with a mean bias of -1.3mg/cm3 for the thoracic spine (LOA: -18.7 to 16.1mg/cm3) and - 3.0mg/cm3 for the lumbar spine (LOA: -17.4 to 11.3mg/cm3). RMSEs between preoperative and intraoperative vBMD values slightly increased for contrast-enhanced scans (RMSEthoracic: 8.42 vs. 10.1mg/cm3; RMSElumbar: 7.75 vs. 8.87mg/cm3). Opportunistic osteoporosis screening with the presented approach is feasible and demonstrates high accuracy in reference to preoperative MDCT scans. This could enable the identification of patients with low bone mass during surgery, allowing surgeons to take measures (e.g., adapted techniques) that prevent postoperative complications and improve patient outcomes.

IoT-Integrated Smart Energy Meter for Industrial Energy Monitoring

- Electric energy monitoring in industrial loads like pressure die casting is extremely vital especially due to their energy handling requirements. This work details the conception and implementation of an Internet of Things based Smart Energy Meter designed specifically for Industrial purposes. The system can measure three-phase voltage, current (up to 100A) and furnace temperatures with the aid of Resistance Temperature detectors. Using the ESP32 microcontroller and the Blynk Internet of things platform, the meter enables the collection, storage, and displaying of the data on the Cloud in real time. In order to enhance accuracy, the hardware architecture combines ZMPT101B voltage sensors, SCT-013-000 current sensors and a MAX31865 Resistance Temperature detector module. The software part of the system uses the Open-Energy Monitor library which contains various algorithms for energy calculations and transfers data over Serial peripheral interface/Wi-Fi protocols. The system has been tested in an industrial setting and the results have shown an accuracy of measurement within a tolerance of 1% for electrical and thermal characteristics plus a normal operation for variable conditions. The presented results prove the possibility of intra- system energy optimization and the industrial prospects of its application. Promising research directions include the addition of predictive maintenance features based on machine learning as well as more efficient scaling to support many different industrial applications. Key Words: Smart Energy Meter, IoT, ESP32, RTD Sensors, Energy Monitoring, Industrial Automation

Overview of development and challenges of attitude determination for rotary wing UAVs based on GNSS

Attitude determination of rotary-wing unmanned aerial vehicles (RUAVs) is crucial for grasping their motion state and is a necessary condition to ensure the correct execution of flight missions. With the continuous development and the constant enhancement of measurement accuracy related to the Global Navigation Satellite System (GNSS), attitude determination based on GNSS have become the mainstream high-precision attitude measurement approach. This paper mainly discusses the relevant theories of using GNSS for RUAV’s attitude determination, and introduces the relevent key aspects that determine attitude accuracy in the attitude resolution process, such as integer ambiguity fixing, attitude solution algorithms, and integrated attitude measurement. It especially elaborates on the challenges that faced to be solved for current RUAVs to use the GNSS system for real-time and guarded attitude measurement.

An IoT-based quality inspection robot for measuring conductor sag and detecting defects in ultra-high voltage overhead transmission lines

Abstract Aiming to solve the urgent problem of sag measurement in constructing ultra-high voltage 
(UHV)power transmission lines non-line-of-sight conditions, we have developed an IoT-based quality inspection robot for UHV overhead transmission line conductors to assist in construction. This robot not only improves the accuracy of sag measurement but also possesses the function of detecting and locating line defects. Utilizing sensors, it monitors sag, environment, and operating conditions in real-time and uploads the results to a cloud platform. The proposed lightweight YOLOv5s-SGSW algorithm, deployed on NVIDIA Jetson TX2, detects transmission line defects with 81.9% precision at 35.62 FPS. The integration of autonomous positioning algorithms and defect detection locates the defects on the transmission line.The relative errors are 0.25% for the X-coordinate and 2.07% for the Y-coordinate of the defect localization. A new tension-based sag compensation method is proposed to compensate for the sag error caused by the robot's weight, with experimental results showing an error of 1.3%. This indicates that the robot can meet the expected design goals and satisfy the practical engineering requirements.



Design and Calibration of a Slit Light Source for Infrared Deflectometry

Infrared deflectometry is an efficient and accurate measuring method for curved surfaces fabricated via grinding or finish milling. The emitting properties and geometrical configurations of the infrared light source is a core component governing the measurement performance. In this paper, an infrared slit light source is designed based on the cavity structure of a polyimide heating film. This design ensures good stability and uniformity of the light source whilst effectively reducing background noise. Additionally, the light source can be applied as a calibration board for calibrating infrared cameras. The light source is aligned using a theodolite and cubic prism to control the positional deviations during scanning. Experimental results demonstrate that the proposed slit light source and calibration method can achieve a measurement accuracy of 1 µm RMS, which can meet the needs of rapid measurement in grinding. This approach provides a reliable, cost-effective, and efficient tool for surface quality assessments in optical workshops and has a broad application potential.

International co-validation of glycated hemoglobin A1c certified reference materials.

Accurate quantification of glycated hemoglobin A1c (HbA1c) is essential for the diagnosis and glycemic control in diabetic patients. The validation of HbA1c certified reference material (CRM) enhances the accuracy and comparability of HbA1c measurements. An international co-validation of HbA1c CRMs was conducted among the National Institute of Metrology, P.R. China (NIM), the Korea Research Institute of Standards and Science (KRISS), and the National Metrology Institute of Japan (NMIJ). Candidate reference materials (RMs) of VHLTPE, glycated VHLTPE, and two levels of HbA1c were developed and supplied by NIM. NIM investigated the homogeneity and stability of these materials, finding no inhomogeneity and confirming stability throughout the co-validation period. Isotope dilution mass spectrometry was employed for the quantitation of both hexapeptides in solution and HbA1c in amatrix. All three national metrology institutes (NMIs) were involved in the quantification of hexapeptides in solution, whereas only NIM and KRISS were involved in the quantification of HbA1c in thematrix. En values for both hexapeptides and HbA1c in the matrix were calculated, all being less than 1, indicating good agreement with the certified values. The successful co-validation of these CRMs demonstrates the reliability of the certified values, thereby benefiting the accuracy and comparability of HbA1c measurements.

High-resolution deep learning reconstruction for coronary CTA: compared efficacy of stenosis evaluation with other methods at in vitro and in vivo studies.

To directly compare coronary arterial stenosis evaluations by hybrid-type iterative reconstruction (IR), model-based IR (MBIR), deep learning reconstruction (DLR), and high-resolution deep learning reconstruction (HR-DLR) on coronary computed tomography angiography (CCTA) in both in vitro and in vivo studies. For the in vitro study, a total of three-vessel tube phantoms with diameters of 3 mm, 4 mm, and 5 mm and with simulated non-calcified stepped stenosis plaques with degrees of 0%, 25%, 50%, and 75% stenosis were scanned with area-detector CT (ADCT) and ultra-high-resolution CT (UHR-CT). Then, ADCT data were reconstructed using all methods, although UHR-CT data were reconstructed with hybrid-type IR, MBIR, and DLR. For the in vivo study, patients who had undergone CCTA at ADCT were retrospectively selected, and each CCTA data set was reconstructed with all methods. To compare the image noise and measurement accuracy at each of the stenosis levels, image noise, and inner diameter were evaluated and statistically compared. To determine the effect of HR-DLR on CAD-RADS evaluation accuracy, the accuracy of CAD-RADS categorization of all CCTAs was compared by using McNemar's test. The image noise of HR-DLR was significantly lower than that of others on ADCT and UHR-CT (p < 0.0001). At a 50% and 75% stenosis level for each phantom, hybrid-type IR showed a significantly larger mean difference on ADCT than did others (p < 0.05). At in vivo study, 31 patients were included. Accuracy on HR-DLR was significantly higher than that on hybrid-type IR, MBIR, or DLR (p < 0.0001). HR-DLR is potentially superior for coronary arterial stenosis evaluations to hybrid-type IR, MBIR, or DLR shown on CCTA. Question How do coronary arterial stenosis evaluations by hybrid-type IR, MBIR, DLR, and HR-DLR compare to coronary CT angiography? Findings HR-DLR showed significantly lower image noise and more accurate coronary artery disease reporting and data system (CAD-RADS) evaluation than others. Clinical relevance HR-DLR is potentially superior to other reconstruction methods for coronary arterial stenosis evaluations, as demonstrated by coronary CT angiography results on ADCT and as shown in both in vitro and in vivo studies.

Перспективы развития волоконно-оптических систем измерений

Статья посвящена исследованиям в области распределенных и точечных волоконно-оптических измерений (РВОИ и ТВОИ). Представлены результаты этих исследований. Показаны направления разрешения проблем, связанных с одновременным использованием различных возможностей волоконно-оптической техники (ВОТ) для поиска наиболее продуктивных методов и технологий комплексирования антагонистических функций передачи диагностической информации на основе волоконно-оптических линий связи. The article is devoted to research and obtained results related to the field of distributed and point-based fiber-optic measurements based on coherent reflectometry. The necessity of overcoming the following main obstacle is shown: the high sensitivity of the fiber-optic cable laid along the railway track and used as the basis for building a distributed fiber-optic sensor for monitoring train movement parameters to vibroacoustic and seismoacoustic influences exerted during the interaction of wheelsets and rails during the passage of the train, which is the basis for improving the accuracy of optical measurements of train movement parameters, it also becomes an obstacle when transmitting ordinary information packets over other optical fibers of the cable in the mode of transmitting information over an optical communication line. Because of this, various requirements for laying an optical cable also appear: in the first case, as close as possible to the source of vibroacoustic and seismoacoustic effects to increase the sensitivity of distributed fiber-optic sensors, in the second – as far as possible to reduce interference when transmitting information in the optical communication line mode. To solve the existing problems, a method is proposed based on connecting optical coils assembled from optical fiber loops to certain optical fibers of the main optical cable at specified locations. At the same time, to ensure that the results of optical measurements on a distributed sensor are linked to a digital train movement map, the placement of coils is chosen combined with elements of the railway infrastructure that have an accurate geodetic reference. A distinctive feature of the innovations proposed in the article is the possibility of their joint use, which ensures a significant increase in the efficiency of optical measurements.

Machine learning-based pH quantification from test strip images using multiple color spaces

Abstract Recent advances in colorimetric analysis have focused on improving measurement accuracy through the integration of smartphone cameras and machine learning. Particular focus has been placed on enhancing predictive accuracy by combining variables from multiple color spaces. In this study, we developed a model incorporating multiple color spaces using images of universal pH test strips. For model selection, we focused on localized errors and used the mean absolute error for each pH value as an evaluation metric. As a result, by emphasizing localized errors, we were able to build a model with better generalization performance than the model selected on the basis of overall error. We implemented this model in a real-time pH measurement demo web application.

Ion suppression correction and normalization for non-targeted metabolomics

Ion suppression is a major problem in mass spectrometry (MS)-based metabolomics; it can dramatically decrease measurement accuracy, precision, and sensitivity. Here we report a method, the IROA TruQuant Workflow, that uses a stable isotope-labeled internal standard (IROA-IS) library plus companion algorithms to: 1) measure and correct for ion suppression, and 2) perform Dual MSTUS normalization of MS metabolomic data. We evaluate the method across ion chromatography (IC), hydrophilic interaction liquid chromatography (HILIC), and reversed-phase liquid chromatography (RPLC)-MS systems in both positive and negative ionization modes, with clean and unclean ion sources, and across different biological matrices. Across the broad range of conditions tested, all detected metabolites exhibit ion suppression ranging from 1% to >90% and coefficients of variation ranging from 1% to 20%, but the Workflow and companion algorithms are highly effective at nulling out that suppression and error. To demonstrate a routine application of the Workflow, we employ the Workflow to study ovarian cancer cell response to the enzyme-drug L-asparaginase (ASNase). The IROA-normalized data reveal significant alterations in peptide metabolism, which have not been reported previously. Overall, the Workflow corrects ion suppression across diverse analytical conditions and produces robust normalization of non-targeted metabolomic data.

Enhancing Gas-Liquid Two-Phase Stratified Flow Imaging with EIGEN-NET: A Fusion of Deep Learning and Measurement Matrices

Abstract Gas-liquid two-phase stratified flow is a common occurrence in various industries, demanding real-time monitoring and precise measurement for safety and operational efficiency. Current methods primarily rely on sensors or limited imaging technologies, leaving room for improvement. In this paper, we introduce the EIGEN-Net framework, specifically designed for real-time monitoring of gas-liquid&amp;#xD;stratified flow within pipelines, utilizing Electrical Impedance Tomography(EIT). EIT, a noninvasive imaging method, shows potential despite challenges from gas affecting voltage stability in image reconstruction. EIGEN-Net capitalizes on measurement matrices and deep learning to enhance measurement accuracy. It extracts crucial physical properties characterizing liquid fractions and employs acquired voltage data to pre-reconstruct a tensor. By fusing these tensors with prior information, it significantly improves imaging outcomes, effectively addressing data insufficiency issues. The incorporation of adaptive information into deep learning networks eliminates the necessity for data classification. Moreover, this approach accommodates scenarios of continuous flow variation, enhancing imaging resolution. EIGEN-Net’s versatility extends to other two-phase flow scenarios and sensor measurement matrices, holding promise for applications in various industrial inspection domains. &amp;#xD;EIGEN-Net: Deep Learning Fusion Network Guided by Leading Eigenvalues of Measurement Matrix

Nonlinearity Harmonic Error Compensation Method Based on Intelligent Identification for Rate Integrating Resonator Gyroscope

This paper presents an improved intelligent optimizing algorithm based on parameter identification and drift compensation for the rate-integrating resonator gyroscope (RIRG). Besides damping and frequency imperfections, the RIRG measurement accuracy still suffers from limitations due to nonlinear error. Therefore, the dynamic nonlinear error model for RIRG has been established to reveal the relationship between the pattern angle and harmonic drifts. Based on this, a dynamic analysis of the gyroscope operating state is carried out using nonlinear motion equations. The optimal harmonic error parameters are then identified by a particle swarm optimization (PSO) algorithm for the drift error compensation. To further improve the measurement accuracy, chaotic technology is integrated with PSO, leading to more precise identification. Subsequently, the harmonic parameters of the bias drifts are efficiently compensated. Experimental results demonstrate that the bias drift is reduced by over 90% after harmonic error compensation, demonstrating the validity of the proposed method in enhancing the measurement accuracy of RIRGs.

Integration of the XY-MD02 Module in an IoT-Based Humidity and Temperature Monitoring System with Graphic Display on Nextion LCD

Abstract—Temperature and humidity monitoring is crucial in various industrial sectors such as agriculture, manufacturing and health. Integrating the latest technology, such as IoT (Internet of Things), opens up new opportunities to increase the efficiency and accuracy of monitoring systems. This research focuses on integrating the XY-MD02 module into an IoT-based monitoring system to measure and monitor humidity and temperature levels accurately. The system uses the XY-MD02 module to collect data, which is transmitted wirelessly to the server using the MQTT protocol. The collected data is processed and displayed in real-time on the Nextion LCD, providing an intuitive graphical representation of environmental conditions. Integrating the XY-MD02 module and Nextion LCD in an IoT-based monitoring system demonstrates practical and reliable humidity and temperature measurements. The average value of the measured temperature is 31.58 with a standard deviation of 0.17, indicating high accuracy in temperature measurement with low variation. Meanwhile, for humidity, the average is 62.34, with a standard deviation of 1.01. The system's compatibility with the MQTT protocol ensures smooth communication and data exchange between devices. Integrating the XY-MD02 module in an IoT-based monitoring system has proven successful in providing accurate and real-time monitoring of humidity and temperature, offering an effective solution in environmental monitoring by keeping up with the latest technological developments.

A New Calibration Method for Broadband Complex Resistivity Measurement System of Rocks and Ores

The complex resistivity (CR) measurement constitutes a practical methodology for investigating the internal structures of rocks and ores alongside their mineralogical compositions and the chemical properties of fluids. However, during complex resistivity testing, particularly at high frequencies, the leakage current caused by the distributed capacitance of the instrument’s acquisition channels reduces the measurement accuracy. Additionally, the contact impedance of the measuring devices and the electromagnetic coupling effects of the measurement cables further affect the complex resistivity test results of samples. To accurately characterize samples’ intrinsic induced polarization (IP) properties, we developed a broadband complex resistivity measurement system (1 mHz–100 kHz) for rocks and ores, comprising a complex resistivity analyzer and a sample holder, employing the four-electrode method. In this study, we establish a circuit model for the measurement system to analyze the influence of the distributed capacitance of the acquisition channels on the test results at elevated frequencies. We derive the error terms inherent in the instrument’s measurements across various circuit design configurations and propose a novel method for calculating the distributed capacitance of the instrument’s acquisition channels, the parasitic capacitance of the sampling resistor, and for calibrating data by reversing the polarity of the excitation signal. Furthermore, we investigate the effect of contact impedance within the measurement setup on test results and design two sample-testing devices. Through extensive testing on multiple circuit models and samples, the system achieves an accuracy of up to 1% within the 10 MΩ range. Its overall performance surpasses that of the Solartron 1260A impedance analyzer and traditional signal source forward connection calibration methods. This advancement holds significant implications for complex resistivity measurements and the study of rock physical properties.

Research on accuracy testing of close-range photogrammetry for geometric feature measurement in particle accelerator

To meet the needs of the Hefei Advanced Light Facility project and reasonably use the close-range photogrammetric system in future alignment work. The research on the method and accuracy of the Aicon DPA close-range photogrammetry system for geometric element measurement was carried out. Using the external consistency and the internal consistency as evaluation indexes, the systems are designed and built with the laser interferometer (XL80), laser tracker (AT 960), standard tetrahedral and marble guide to test the point coordinates, distance, flatness measurement accuracy and measurement repeatability for geometric element measurement accuracy. The experimental results show that Aicon DPA point coordinates, distance, and flatness measurements have high accuracy in close-range photogrammetry. The point coordinate and flatness measurements are 9.5 μm and 5.4 μm, respectively, equal to or even better than the laser tracker's measurement results. Furthermore, the measurement repeatability is better than 5 μm. The results of the simulated engineering site show that the Aicon DPA has an accuracy of 19.8 μm, maintaining high accuracy and good immunity to environmental interference. With the expansion of the photogrammetry control range, when the length reference is shorter than the measured object, the measurement accuracy will continue to decrease. But when using the length data of the interferometer as a reference, the accuracy within 8 meters can be better than 30 μm. The accuracy testing process and conclusion of this article have reference values for the use of close-range photogrammetry systems in particle accelerators. The close-range photogrammetry system will be fully utilized in alignment work such as geometric element measurement, deformation monitoring and data fusion with laser trackers in the Hefei Advanced Light Facility.

Influence of Sample Surface Oil on the Accuracy of Space Charge Measurement in Oil-Paper

Influence of Sample Surface Oil on the Accuracy of Space Charge Measurement in Oil-Paper

A Novel Model for Noninvasive Haemoglobin Detection Based on Visibility Network and Clustering Network for Multi-Wavelength PPG Signals

Non-invasive haemoglobin (Hb) testing devices enable large-scale haemoglobin screening, but their accuracy is not comparable to traditional blood tests. To this end, this paper aims to design a non-invasive haemoglobin testing device and propose a classification-regression prediction framework for non-invasive testing of haemoglobin using visibility graphs (VG) with network clustering of multi-sample pulse-wave-weighted undirected graphs as the features to optimize the detection accuracy of non-invasive haemoglobin measurements. Different prediction methods were compared by analyzing 608 segments of multiwavelength fingertip PPG signal data from 152 volunteers and analyzing and comparing the data and methods. The results showed that the classification using NVG with complex network clustering as features in the interval classification model was the best, with its classification accuracy (acc) of 93.35% and model accuracy of 88.28%. Among the regression models, the classification regression stack: SVM-Light Gradient Boosting Machine (LGBM) was the most effective, with a Mean Absolute Error (MAE) of 6.67 g/L, a Root Mean Square Error (RMSE) of 8.21 g/L, and an R-Square (R2) of 0.64. The results of this study indicate that the use of complex network technology in non-invasive haemoglobin detection can effectively improve its accuracy, and the detector designed in this study is promising to carry out a more accurate large-scale haemoglobin screening.