Traditional Measurement Methods Research Articles

Rapid measurement method for key dimensions of train wheelset based on improved image processing algorithm

The key dimensions of train wheelsets change with the increase of running time. To ensure the safe operation of the train, the key dimensions should meet the relevant technical requirements. Maintenance of wheelsets and measurement of key dimensions are important part of the daily maintenance work. A rapid measurement method based on image processing algorithms for key dimensions of hand-held wheelsets integrating multiple parameter measurements is studied. The general framework of the system is examined and the roles of the modules are described. The optical center for the initial position of the continuous line spot is searched. The continuous line spot center extraction algorithm during tread measurement is optimized. A three-dimensional model is reconstructed using point cloud data, then key dimensions are measured and calculated from the model. In order to verify the feasibility of the method, traditional measurement methods are used for comparison tests. The results show that the method is effective in the daily maintenance of trains.

Research on online monitoring method for time-varying tension in transmission lines based on operational modal response

The cumulative overload of conductor tension under severe weather conditions is an important cause of accelerated fatigue fracture of transmission lines. Traditional tension measurement methods require the replacement of ball head hanging rings, which poses safety risks. In this paper, a method for monitoring conductor tension based on acceleration data under operating conditions is proposed. Firstly, a modal order extraction based method for identifying the modal frequencies of conductor operation is proposed, and then the time-varying tension of the conductor is estimated based on the instantaneous modal frequencies. Since this method directly installs sensors on the conductor, there is a certain error in the obtained intrinsic characteristic data of the conductor. Therefore, a modal correction method is used to remove the influence of the sensors. The accuracy of modal identification, modal correction, and tension identification methods is verified through finite element models. Based on the above methods, a monitoring system for conductor tension status is designed, and the feasibility of this system is verified through experiments. Finally, the vibration data obtained from the field engineering pilot test is successfully used for conductor tension analysis. The results show that the proposed method can effectively identify time-varying tension and provide a new approach for monitoring the status of transmission line conductors.

KBMeasure: small sample in-situ damage automatic measurement method based on the combination of keypoints detection and binocular 3D reconstruction for aero-engine blades

Size is one of the important bases for the level assessment of aero-engine blade damage and the disposal method selection for damaged blades. Therefore, research on in-situ damage measurement of aero-engine blades is conducted in this paper. We break the inherent pipeline of "3D reconstruction and manual annotation of keypoints" in traditional damage measurement methods, and propose an in-situ damage automatic measurement method (KBMeasure) based on the combination of damage keypoints intelligent detection and binocular 3D reconstruction. KBMeasure replaces the manual annotation of damage keypoints, improves the damage measurement efficiency, and reduces the dependence on professional inspectors. The proposed method also overcomes the problem of high computational cost and low efficiency caused by redundant 3D reconstruction of the entire damaged area. For the characteristics of large changes in damage scale, low image resolution, the requirement of high-precision keypoints positioning, limited annotated data, and lightweight deployment in aero-enginge blade damage measurement task, a novel blade damage keypoints detection model (DKeyDet) with top-down framework is designed by introducing coordinate classification, semi-supervised learning, and knowledge distillation. Then, intersecting optical axis binocular model is used to estimate the spatial coordinates of the detected keypoints and compute the size of damage. The keypoints detection average precision (AP) and average recall (AR) of our method are 87.6 and 91.3, and the damage measurement size error (SE) is 0.08, which is superior to existing methods. This research provides a new theoretical support for in-situ damage automatic measurement for aero-engine in service, and provides what we believe is a novel idea for damage measurement of industrial components in other fields.

Organ Segmentation and Phenotypic Trait Extraction of Cotton Seedling Point Clouds Based on a 3D Lightweight Network

Cotton is an important economic crop; therefore, enhancing cotton yield and cultivating superior varieties are key research priorities. The seedling stage, a critical phase in cotton production, significantly influences the subsequent growth and yield of the crop. Therefore, breeding experts often choose to measure phenotypic parameters during this period to make breeding decisions. Traditional methods of phenotypic parameter measurement require manual processes, which are not only tedious and inefficient but can also damage the plants. To effectively, rapidly, and accurately extract three-dimensional phenotypic parameters of cotton seedlings, precise segmentation of phenotypic organs must first be achieved. This paper proposes a neural network-based segmentation algorithm for cotton seedling organs, which, compared to the average precision of 75.4% in traditional unsupervised learning, achieves an average precision of 96.67%, demonstrating excellent segmentation performance. The segmented leaf and stem point clouds are used for the calculation of phenotypic parameters such as stem length, leaf length, leaf width, and leaf area. Comparisons with actual measurements yield coefficients of determination R2 of 91.97%, 90.97%, 92.72%, and 95.44%, respectively. The results indicate that the algorithm proposed in this paper can achieve precise segmentation of stem and leaf organs, and can efficiently and accurately extract three-dimensional phenotypic structural information of cotton seedlings. In summary, this study not only made significant progress in the precise segmentation of cotton seedling organs and the extraction of three-dimensional phenotypic structural information, but the algorithm also demonstrates strong applicability to different varieties of cotton seedlings. This provides new perspectives and methods for plant researchers and breeding experts, contributing to the advancement of the plant phenotypic computation field and bringing new breakthroughs and opportunities to the field of plant science research.

Optimizing Accumulator Performance in Hydraulic Systems through Support Vector Regression and Rotational Factors

The piston-type accumulator is an energy storage device in hydraulic–pneumatic systems, playing a significant role in industries such as petrochemicals, heavy machinery, and steel metallurgy. The displacement parameters of the piston-type accumulator are vitally important for fault diagnosis and early warning in hydraulic systems. Traditional displacement measurement methods cannot meet the requirements of the internal testing environment of the accumulator. Therefore, this paper proposes an accumulator piston displacement signal compensation method based on rotational factors and support vector regression. Firstly, empirical mode decomposition is utilized to denoise the signal. Then, rotational factors are used to generate a delay compensation module to compensate for the signal attenuation and time delay caused by metallic reflection and scattering within the cylinder of the radar signal. The support vector regression model is improved based on a hash table to enhance its computational efficiency and achieve radar displacement signal compensation. Finally, a simulation experiment is designed to verify the effectiveness of the proposed method.

A three-dimensional algorithm for precise measurement of human auricle parameters

Measurement of auricle parameters for planning and post-operative evaluation presents substantial challenges due to the complex 3D structure of the human auricle. Traditional measurement methods rely on manual techniques, resulting in limited precision. This study introduces a novel automated surface-based three-dimensional measurement method for quantifying human auricle parameters. The method was applied to virtual auricles reconstructed from Computed Tomography (CT) scans of a cadaver head and subsequent measurement of important clinically relevant aesthetical auricular parameters (length, width, protrusion, position, auriculocephalic angle, and inclination angle). Reference measurements were done manually (using a caliper and using a 3D landmarking method) and measurement precision was compared to the automated method. The CT scans were performed using both a contemporary high-end and a low-end CT scanner. Scans were conducted at a standard scanning dose, and at half the dose. The automatic method demonstrated significantly higher precision in measuring auricle parameters compared to manual methods. Compared to traditional manual measurements, precision improved for auricle length (9×), width (5×), protrusion (5×), Auriculocephalic Angle (5–54×) and posteroanterior position (23×). Concerning parameters without comparison with a manual method, the precision level of supero-inferior position was 0.489 mm; and the precisions of the inclination angle measurements were 1.365 mm and 0.237 mm for the two automated methods investigated. Improved precision of measuring auricle parameters was associated with using the high-end scanner. A higher dose was only associated with a higher precision for the left auricle length. The findings of this study emphasize the advantage of automated surface-based auricle measurements, showcasing improved precision compared to traditional methods. This novel algorithm has the potential to enhance auricle reconstruction and other applications in plastic surgery, offering a promising avenue for future research and clinical application.

Industrial technology network security measurement in international trade under discrete hopfield neural network

As global economic integration continues to advance, international trade has become increasingly vital for the economic development and growth of nations. This research aims to assess the trends in industrial technology security within China’s international trade and provide practical guidance for policy-making, corporate strategies, and international cooperation. The significance of the rising trend in security within China’s international trade industry lies in its establishment of a robust foundation for the long-term development of China’s international trade, contributing to its cooperation and competitiveness with other countries. In addressing the limitations of traditional measurement methods and providing a more comprehensive and accurate assessment of industrial technology security, this research presents an approach based on a discrete Hopfield Neural Network (HNN) for evaluating industrial technology security in international trade. This method integrates multiple indicators, including technology gap rates, to construct the Superior Quality Engineering (SQE) comprehensive evaluation model. The research employs a combination model of “entropy-grey relational-Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS)-discrete HNN” to assess industrial technology security. This research evaluates international trade industry technology security using patent data from 2015 to 2022 as samples. The results indicate an overall upward trend in security in China’s international trade industry. Within this trend, the research observes a stepwise increase in scale components, leading to continuous improvement in security. In terms of quality components, although security develops relatively slowly overall, it exhibits a trend of initial gradual decline followed by rapid growth. Regarding efficiency components, there is overall slow growth with periodic fluctuations. This research outcome provides substantial support for the research of industrial technology in international trade. The proposed method can assist businesses in evaluating their technological security in international trade and offer robust support for international trade decision-making.

Influence of Leaf Area Index Inversion and the Light Transmittance Mechanism in the Apple Tree Canopy

Light plays a crucial role in the growth of fruit trees, influencing not only nutrient absorption but also fruit appearance. Therefore, understanding fruit tree canopy light transmittance is essential for agricultural and forestry practices. However, traditional measurement methods, such as using a canopy analyzer, are time-consuming, labor-intensive, and susceptible to external influences, lacking convenience and automation. To address this issue, we propose a novel method based on point clouds to estimate light transmittance, with the Leaf Area Index (LAI) serving as the central link. Focusing on apple trees, we utilized handheld LiDAR for three-dimensional scanning of the canopy, acquiring point cloud data. Determining the optimal voxel size at 0.015 m via standardized point cloud mean spacing, we applied the Voxel-based Canopy Profile method (VCP) to estimate LAI. Subsequently, we established a function model between LAI and canopy light transmittance using a deep neural network (DNN), achieving an overall correlation coefficient R2 of 0.94. This model was then employed to estimate canopy light transmittance in dwarfed and densely planted apple trees. This approach not only provides an evaluation standard for pruning effects in apple trees but also represents a critical step towards visualizing and intelligentizing light transmittance.

Design of a Transformer Oil Viscosity, Density, and Dielectric Constant Simultaneous Measurement System Based on a Quartz Tuning Fork.

Transformer oil, crucial for transformer and power system safety, demands effective monitoring. Aiming to address the problems of expensive and bulky equipment, poor real-time performance, and single parameter detection of traditional measurement methods, this study proposes a quartz tuning fork-based simultaneous measurement system for online monitoring of the density, viscosity, and dielectric constant of transformer oil. Based on the Butterworth-Van Dyke quartz tuning fork equivalent circuit model, a working mechanism of transformer oil density, viscosity, and dielectric constant was analyzed, and a measurement model for oil samples was obtained. A miniaturized simultaneous measurement system was designed based on a dedicated chip for vector current-voltage impedance analysis for data acquisition and a Savitzky-Golay filter for data filtering. A transformer oil test platform was built to verify the simultaneous measurement system. The results showed that the system has good repeatability, and the measurement errors of density, viscosity, and dielectric constant are lower than 2.00%, 5.50%, and 3.20%, respectively. The online and offline results showed that the system meets the requirements of the condition maintenance system for online monitoring accuracy and real-time detection.

Tree parameter extraction method based on new remote sensing technology and terrestrial laser scanning technology

Ground LiDAR is a terrestrial LiDAR system that is often used for terrain and geomorphic mapping. Ground-based LiDAR can be used to collect more local and short-range data, making it ideal for mapping smaller areas with high precision. In order to solve the rapid extraction of tree parameters in the national public welfare forest survey, the ground-based LIDAR was used to obtain the point cloud of trees, and the point cloud data was registered, denoised, normalized, sliced, parameter extracted, etc., and the parameters of individual trees in the forest were obtained. The Bland-Altman consistency test is used to test whether the method of extracting tree parameters from point clouds is consistent with the traditional measurement method. The experimental results show that the point cloud data obtained by the ground-based LIDAR can quickly, conveniently and accurately extract the tree parameters, which is consistent with the traditional tree parameter extraction method, and has the advantages than the traditional tree parameter measurement, such as point cloud, image and traceability. It has a unique advantage in establishing a tree database. It is suggested that LIDAR should be used for forest survey in the future.

Cattle Body Intelligent Measurement based on Improved CenterNet

Traditional cattle body measurement methods have limitations such as manual contact measurement and low efficiency. To improve the efficiency of measuring cattle bodies and reduce labor costs, an improved intelligent measurement network based on CenterNet was proposed. In this study, we used the DenseNet-100 to replace the ResNet-101 to alleviate the vanishing gradient problem. We also improved the cattle body size measurement efficiency by reducing the network parameters. In addition, we conducted a comparative experiment on the cattle posture dataset collected by ourselves to verify the feasibility of the network. The experiment confirmed that the proposed intelligent measurement based on improved CenterNet outperforms the traditional networks and other advanced object measurement networks in accuracy and measurement efficiency, and can effectively solve most of the error problems caused by manual measurement. Besides, our network has good applicability and strong stability, which can meet the requirements of the evaluation of cattle body size measurement indicators.

Unlocking Patient Voices: Advancing Physical Therapist Practice with Discrete Choice Experiments.

As health care moves away from volume-based to value-based delivery, the role of the patient in determining value in health care is now paramount. Thus, health care should be aligned with what matters most to patients. Ascertaining patient preferences is therefore critical if we are to provide patients with care that is meaningful to them. However, preferences are difficult to measure and traditional methods of preference measurement in physical therapy face challenges when attempting to measure such. This perspective makes a case for greater use of the discrete choice experiment (DCE) in physical therapy as a research method to measure patient preferences. The DCE is a research method used to elicit preferences for services or goods. This article addresses the importance of eliciting patient preferences as part of person-centered care in the value-based space, the challenges faced in preference measurement in physical therapy, and how the discrete choice experiment can alleviate some of those challenges. It also provides examples of the discrete choice experiment in health care and suggests ways in which it can be effectively used in physical therapist practice to improve the delivery of meaningful rehabilitation services to patients. Implementing greater use of the DCE in physical therapy can improve person-centered physical therapist service delivery and inform policy development that creates cost-effective care which is meaningful to patients. It can further help to highlight the value of physical therapy to population health, and to policy makers as health care moves towards more value-based models.

Impact of Strategic Audit on Enhancing the Efficiency of the Internal Control System: A Study of Industrial Companies in Iraq

The study aims to provide a general and brief introduction to the audit process, demonstrate traditional and modern performance measurement tools and methods that are compatible with strategic auditing, and provide a conceptual framework for strategic auditing and the internal control system. And the relationship between them. It helps the concerned establishments increase their performance and achieve their goals in light of the results of the study. The research sample consisted of the General Company for Textile Industries in Iraq, which is the Baghdad Laboratory, the Nineveh Laboratory, the Diwaniyah Laboratory, and the Babylon Laboratory (according to the SPSS statistical program). The purpose of the study was to know the extent to which industrial companies in Iraq apply strategic audit mechanisms, what their impact is on the internal control system in companies, and how to benefit from the application of these strategies in the audit process. The study found a relationship between the efficiency and effectiveness of strategic auditing and improving the efficiency of the internal control system. Increasing the efficiency of the internal control system using the strategic audit method. The study recommended implementing the strategic review system effectively to benefit from its advantages in increasing the efficiency of the internal control system in companies in general and industrial companies in particular and trying to use advanced techniques to measure the internal control system.

Real‐Time Measurements of Photonic Microchips with Femtometer‐Scale Spectral Precision and Ultrahigh Sensitivity

AbstractPhotonic integrated circuits (PICs) are enabling breakthroughs in several areas, including quantum computing, neuromorphic processors, wearable devices, and more. Nevertheless, existing PIC measurement methods lack the spectral precision, speed, and sensitivity required for refining current applications and exploring new frontiers such as point‐of‐care or wearable biosensors. Here, the “sweeping optical frequency mixing method (SOHO)” is presented, surpassing traditional PIC measurement methods with real‐time operation, 30 dB higher sensitivity, and over 100 times better spectral resolution. Leveraging the frequency mixing process with a sweeping laser, SOHO excels in simplicity, eliminating the need for advanced optical components and additional calibration procedures. Its superior performance is demonstrated on ultrahigh‐quality factor (Q) fiber‐loop resonators (Q = 46 × 106), as well as microresonators, realized on a new optical waveguide platform. An experimental spectral resolution of 19.1 femtometers is demonstrated using an 85‐meter‐long unbalanced fiber Mach Zehnder Interferometer, constrained by noise resulting from the extended fiber length, while the theoretical resolution is calculated to be 6.2 femtometers, limited by the linewidth of the reference laser. With its excellent performance metrics, SOHO has the potential to become a vital measurement tool in photonics, excelling in high‐speed and high‐resolution measurements of weak optical signals.

Reverse Model for Curved Bridge Measurement Based on 3D Laser Scanning Technology

Receiving inspection plays a crucial role in ensuring construction quality after the completion of engineering projects. Traditional inspection measurement methods, such as manual observation means and optical equipment measurement methods, have limitations in terms of the number of measurement sites and the range of measurements. These traditional methods fail to provide accurate curve parameters and continuous spatial morphology information for large-span curved bridge structures. This paper proposes a reverse model measurement method to address this issue. The reverse model is built based on point cloud data acquired by 3D laser scanning technology. Finally, take the Taizicheng No. 1 Bridge as an example, the validity of the proposed method is verified.

Calibration of visual measurement system for excavator manipulator pose

The automatic control of excavator operation trajectories is a pivotal technology for autonomous excavators, with the essential prerequisite being the real-time measurement of manipulator poses. Given the complexity of the operating environment, traditional sensor-based measurement methods face limitations, whereas visual measurement emerges as a promising technique. Accurately measuring excavator manipulator poses involves a crucial aspect: mapping the relationship between image information and poses. First, to address the significant errors in pose prediction encountered with machine learning techniques like artificial neural networks, this work introduces a mathematical model for mapping this relationship, referred to as the pose mapping mathematical model, which includes calibrating model parameters. Second, to address the sensitivity of initial values in the calibration process, we propose a residual-guided initialization algorithm. This algorithm aims to ensure that initial values closely approximate the ground truth values, thus preventing matrix singularity at the source and avoiding parameter estimation divergence. Third, to tackle challenges such as unstable lighting conditions and discrepancies between the dataset and the mathematical model, we introduce the random sample consensus-driven Levenberg–Marquardt parameter optimization algorithm to enhance parameter estimation accuracy. Experiments with static and dynamic online measurement demonstrate that our method reduces pose measurement errors compared to existing methods. This research lays a solid foundation for developing visual measurement techniques for excavators and automated manipulator control based on visual measurements, also serving as a valuable reference for research on mechanical arms.

The Application Strategy of Oral Digital Technology in Clinical and Teaching of Oral Restoration

<p>Digital oral technology is currently the main development direction of oral restoration, which can improve treatment efficiency and enhance the accuracy of dental models. This article focuses on the application of digital oral technology. Firstly, it describes the advantages of digital technology in obtaining oral data, and discusses the methods of obtaining dental models and oral data. The obtained data is statistically analyzed and compared with traditional measurement methods. The comparison results show the advantages of digital oral technology, and further elucidate the application strategies of oral digitization. Then, regarding the teaching issues of oral digital technology, a survey questionnaire was used to explore the advantages and roles of oral digital technology in the classroom, as well as the attitudes of students towards new courses. Based on the current teaching situation, strategies were formulated for teaching application, mainly clarifying the content planning and future development direction of the course, which has good guiding significance for the clinical and teaching implementation of oral digital technology.</p> <p> </p>

Optical fiber-based acoustic intensity microphone for high-intensity airborne ultrasound measurement

The increasing use of airborne ultrasonic waves in daily life, driven by advances in parametric and phased arrays, has led to innovative applications like highly directional speakers, non-contact tactile feedback, 3D acoustic levitation, and medical therapies. These advancements necessitate accurate measurement of high-intensity ultrasonic waves, exceeding the capability of traditional microphones limited to around 160 dB, and highlight the growing importance of measuring the sound field not merely as scalar (sound pressure) but as vector (acoustic intensity) to accommodate future technological developments. This paper introduces an acoustic intensity microphone using optical fibers as probes to overcome these limitations. The proposed method replaces the two ordinary microphones used in the traditional acoustic intensity measurement method with thin optical fibers, minimizing sound field disturbance. Experimental validation and the structure of a practical acoustic intensity microphone are discussed, building upon foundational work presented at USE2023 with added verification and insights.

Morphological Differences and Contour Visualization of Statoliths in Different Geographic Populations of Purpleback Flying Squid (Sthenoteuthis oualaniensis)

Statoliths are important hard tissues in cephalopods. Significant differences are found in the external morphology of statoliths in different groups or species. In this study, stepwise discriminant analysis was used to investigate the external morphological differences in purpleback flying squid statoliths in three different marine regions, comprising the East Indian Ocean (5° S–2° N, 82°–92° E), Central East Pacific Ocean (02°37′ S–0°59′ N, 99°44′ W–114°19′ W), and Northwest Indian Ocean (17°04′ N–17°18′ N, 61°05′ E–61°32′ E). The contours of statoliths were reconstructed visually by using Fourier analysis and the landmark method. The results obtained by stepwise discriminant analysis showed that the accuracy of identification was 84.4% for the traditional measurement method, 82.9% for the Fourier analysis method, and 87.3% for the landmark method. The contour visualization results showed that the purpleback flying squid statoliths were small in the Central East Pacific Ocean, and the curvature of the side region was the most obvious. The radian differentiation of statoliths was most gentle in the East Indian Ocean. In the Northwest Indian Ocean, the rostral region of statoliths was shorter and the dorsal region was smoother. The reconstruction results detected significant differences in the outer morphology of statoliths in different marine regions. The results obtained in this study show that all three methods are effective for identifying populations, but the landmark method is better than the traditional measurement method. The reconstruction of statolith contours using the Fourier transform and landmark methods provides an important scientific basis for conducting taxonomy, according to statolith morphology.

A New Inversion Method for Obtaining Underwater Spatial Information of Subsidence Waterlogging Based on InSAR Technology and Subsidence Prediction

Surface waterlogging disasters due to underground mining and geological status have caused the abandonment of fertile land, seriously damaged the ecological environment, and have influenced the sustainable development of coal resource-based cities, which has become a problem that some mining areas need to face. However, the traditional underwater terrain measurement method using sonar encompasses a time-consuming and labor-intensive process. Thus, an inversion method for obtaining the underwater spatial information of subsidence waterlogging in coal mining subsidence waterlogging areas is proposed, based on differential interferometric synthetic aperture radar (D-InSAR) and the probability integral prediction method. First, subsidence values are obtained in the marginal area of the subsidence basin using D-InSAR technology. Then, the subsidence prediction parameters of the probability integral method (PIM) are inverted by a genetic algorithm (GA) based on the subsidence values. Finally, the underwater spatial information of subsidence waterlogging is calculated on the basis of the prediction parameters. The subsidence waterlogging area in the Wugou coal mine was adopted as the study area, and the underwater spatial information of subsidence waterlogging was inverted by the proposed method. The results show that this method can effectively provide the underwater spatial information of subsidence waterlogging, including the maximum subsidence value, waterlogging volume, subsidence waterlogging area, and underwater terrain in the subsidence waterlogging area. Compared with field-measured data from the same period, the RMSE of water depth is 99 mm, and the relative error is 9.9%, which proves that this inversion method is accurate and can meet engineering precision requirements.