High-precision Method Research Articles

GSBBO: a high-precision method for stress tensor inversion and its application at the great wall station in Antarctica

GSBBO: a high-precision method for stress tensor inversion and its application at the great wall station in Antarctica

High-Precision Measurement Method for Small Angles Based on the Defect Spot Mode of the Position-Sensitive Detector

The paper proposes and verifies a small-angle measurement method based on the defect spot mode of the position-sensitive detector (PSD). With the output characteristics of the PSD in the defect spot mode and the size transformation properties of a focused beam, the measurement sensitivity can be significantly improved. Calibration experiments with the piezoelectric transducer (PZT) indicate that compared with the current PSD-based autocollimation method, the proposed method can improve the sensitivity of small-angle measurement by 57 times, and the measurement sensitivity of the proposed method can be further improved by optimizing the system parameters, while the proposed method has the advantages of a simple system and high real-time performance. Therefore, the proposed method is expected to be used in high-precision motion error detection, as well as in shape and position measurement.

A high-precision two-parameter road roughness grading and identification methodology across multiple frequency intervals

Based on a dual track road shape meter, various road roughness was measured. It was discovered that the road grading method in ISO 8608 assumes a constant waviness value of W = 2 with a one-straight line approximation, but it could not accurately fit and grade the measured road roughness. A high-precision piecewise fitting function for the low-frequency and high-frequency parts of the road roughness power spectral density (PSD) is established. A high-precision two-parameter grade method in multiple frequency bands for road roughness is proposed ( G q( n0), the geometric average of the road roughness index at n0, W, the waviness, is the slope of the road roughness PSD fitting expression in the double logarithmic coordinate system). According to the vehicle driving performance obtained by simulation, the grading method is verified. For Paved and Undulating roads, compared to the road roughness classification method in ISO 8608, the performance simulated based on the grade results of the two parameters grading method are closer to the performance simulated based on the measured road roughness. The grading method proposed in this paper can more accurately fit and classify the measured road roughness. The influence of these parameters at each frequency interval on vehicle driving performance is studied. A two-parameter identification method for road roughness PSD at variable intervals over a large frequency range is proposed. In the double logarithmic coordinate system, this method divides the selected spatial frequency into three sub-intervals along the abscissa, in each sub-interval, the parameters G q( n0) and W are identified separately. This method can provide effective control inputs for the vehicle’s active or semi-active suspension system.

High-Precision Time-Domain Analysis Method Based on the Superposition Principle for CLLC Converter in Above-Resonant-Frequency Mode

High-Precision Time-Domain Analysis Method Based on the Superposition Principle for CLLC Converter in Above-Resonant-Frequency Mode

Experimental study on high-precision measurement of surface temperature in friction stir welding aluminium alloy 2219 based on infrared thermometry

In friction stir welding (FSW) of aluminium alloy 2219, the welding temperature critically influences the weld quality. Accurate measurement of the welding temperature is essential for effective welding process control. However, the conventional approach of non-contact infrared thermometry faces significant challenges in FSW due to the inherently low and variable emissivity of aluminium alloys, making it susceptible to environmental radiation interference. This paper proposes a novel, high-precision method for measuring the surface temperature of FSW aluminium alloy 2219 based on infrared thermometry principles. Initially, the study delves into these principles, analysing factors that affect the accuracy of infrared thermometry in the context of FSW in industrial settings. To enhance the surface emissivity of the weldments, a high-emissivity coating is applied to the aluminium alloy surface. Additionally, strategic adjustments in the positioning of the thermal imager and careful selection of the measuring area effectively mitigate interference from the FSW tool’s radiation. Conventional methods report a temperature measurement error greater than 2%, while the proposed method reduces this error to 1.2%. The effectiveness and practical utility of the method are validated through FSW experiments on flat plates and rocket tank rings, corroborated by comparative analyses with surface thermocouple temperature measurements. This confirms the viability of the proposed method for high-precision temperature measurement in aluminium alloy FSW applications.

High-precision dynamic axial clearance measurement method based on an all-fiber heterodyne microwave-AMCW with an all-phase tracking algorithm

Rotor-stator axial clearance is critical to the safety and efficiency of major rotating machinery. However, factors such as high-speed rotation, narrow space, high temperature, and vibration present significant challenges for high-precision dynamic measurement of axial clearance. This paper proposes an axial clearance measurement method based on an all-fiber heterodyne microwave amplitude-modulated continuous wave (microwave-AMCW) system combined with an all-phase tracking algorithm, characterized by high precision, wide bandwidth, and a large measurement range. To mitigate environmental influences, a heterodyne all-fiber microwave-AMCW optical path structure is developed, and a compact dual-core fiber sensor probe is designed. The all-phase tracking algorithm is introduced to enhance dynamic precision and expand bandwidth. Additionally, what we believe to be a novel bandwidth test method based on time division multiplexing is proposed to evaluate the system's wide-bandwidth performance. The proposed system's performance is validated through simulations and experiments. The results demonstrate that the system exhibits excellent resistance to environmental interference, with a measurement range up to 24.5 mm and a static precision better than 4.5µm. Dynamic experiments further confirm the algorithm's effectiveness, achieving a precision better than 5.3µm at 100kHz bandwidth. Compared to other clearance measurement algorithms including the Hilbert transform and FFT, the proposed method reduces dynamic error by over 74%.

CVW-Etr: A High-Precision Method for Estimating the Severity Level of Cotton Verticillium Wilt Disease.

Cotton verticillium wilt significantly impacts both cotton quality and yield. Selecting disease-resistant varieties and using their resistance genes in breeding is an effective and economical control measure. Accurate severity estimation of this disease is crucial for breeding resistant cotton varieties. However, current methods fall short, slowing the breeding process. To address these challenges, this paper introduces CVW-Etr, a high-precision method for estimating the severity of cotton verticillium wilt. CVW-Etr classifies severity into six levels (L0 to L5) based on the proportion of segmented diseased leaves to lesions. Upon integrating YOLOv8-Seg with MobileSAM, CVW-Etr demonstrates excellent performance and efficiency with limited samples in complex field conditions. It incorporates the RFCBAMConv, C2f-RFCBAMConv, AWDownSample-Lite, and GSegment modules to handle blurry transitions between healthy and diseased regions and variations in angle and distance during image collection, and to optimize the model's parameter size and computational complexity. Our experimental results show that CVW-Etr effectively segments diseased leaves and lesions, achieving a mean average precision (mAP) of 92.90% and an average severity estimation accuracy of 92.92% with only 2.6M parameters and 10.1G FLOPS. Through experiments, CVW-Etr proves robust in estimating cotton verticillium wilt severity, offering valuable insights for disease-resistant cotton breeding applications.

Exploiting high-precision AoA estimation method using CSI from a single WiFi station

The accuracy of estimating the angle of arrival (AoA) using wireless fidelity (WiFi) channel state information (CSI) has been a topic of intense interest in the fields of the Internet of Things, location-based services, etc. We propose a high-precision method of AoA estimation of the direct path (DP) using WiFi CSI from a single station. It contains three stages: data preprocessing, AoA-time of flight (ToF) joint estimation for all paths, and the DP's AoA estimation. Firstly, phase calibration, linear transform, and multiple-layer filtering are accordingly conducted after CSI collection in the preprocessing stage to output the denoised CSI. Then, the AoA and ToF values for all paths are simultaneously obtained utilizing a spatial smoothing multiple signal classification (MUSIC) algorithm. Finally, the density-based spatial clustering for noise applications (DBSCAN) algorithm divides all the AoA and ToF values into several clusters. The target cluster that meets the requirements of maximum counts and minimum mean ToF is subsequently selected. The weighted centroid AoA value of the target cluster is regarded as the AoA of the DP. AoA estimation experiments using different sampling packets are conducted in a small conference room with an Intel 5300 network interface card along a straight line. The proposed method could recognize the DP with a rate of 100 percent and estimate the AoA of the DP with a mean absolute error of 2° and root mean square error of 2.82° Compared with SpotFi and hierarchical clustering–logistic regression systems, the proposed method improves AoA estimation accuracy by at least 75 %. Therefore, the proposed method could achieve a high-precision estimation of the AoA of the DP in the case 26 of different short distances.

A Candy Defect Detection Method Based on StyleGAN2 and Improved YOLOv7 for Imbalanced Data.

Quality management in the candy industry is a vital part of food quality management. Defective candies significantly affect subsequent packaging and consumption, impacting the efficiency of candy manufacturers and the consumer experience. However, challenges exist in candy defect detection on food production lines due to the small size of the targets and defects, as well as the difficulty of batch sampling defects from automated production lines. A high-precision candy defect detection method based on deep learning is proposed in this paper. Initially, pseudo-defective candy images are generated based on Style Generative Adversarial Network-v2 (StyleGAN2), thereby enhancing the authenticity of these synthetic defect images. Following the separation of the background based on the color characteristics of the defective candies on the conveyor belt, a GAN is utilized for negative sample data enhancement. This effectively reduces the impact of data imbalance between complete and defective candies on the model's detection performance. Secondly, considering the challenges brought by the small size and random shape of candy defects to target detection, the efficient target detection method YOLOv7 is improved. The Spatial Pyramid Pooling Fast Cross Stage Partial Connection (SPPFCSPC) module, the C3C2 module, and the global attention mechanism are introduced to enhance feature extraction precision. The improved model achieves a 3.0% increase in recognition accuracy and a 3.7% increase in recall rate while supporting real-time recognition scenery. This method not only enhances the efficiency of food quality management but also promotes the application of computer vision and deep learning in industrial production.

High-precision spatial measurement method of accelerator pre-alignment based on multiple total stations

Abstract With the development of accelerator technology, the scale of accelerators is becoming larger, ranging from hundreds of meters to several kilometers. For stable operation of accelerators, high-precision alignment, positioning, and installation are crucial. Installing all equipment inside the tunnel poses safety risks as personnel would be in a closed environment with potential radiation exposure for prolonged periods. To address the challenges of long adjustment and maintenance periods inside the tunnel due to the installation of equipment for large-scale accelerators, most accelerator devices under construction or in research have pre-alignment assemblies. Each assembly consists of a certain number of magnets distributed on girders. The magnets in one unit are pre-aligned with high precision in the laboratory and then transported to the tunnel. Aligning the entire magnet girder can significantly improve installation efficiency inside the tunnel. To meet the pre-alignment accuracy requirement of 10 μm in the horizontal and vertical directions for the magnet units in the high energy photon source (HEPS) storage ring, a system for high-precision pre-alignment of accelerator units using four total stations for angle observation has been designed in this paper. By employing different instrument layout configurations and incorporating reliable distance benchmarks, high-precision pre-alignment of the magnet are achieved. By arranging targets and utilizing recognition for automatic targeting, real-time point calculations during pre-alignment enhance efficiency. Subsequently, based on this system, pre-alignment simulation calculations and experimental verification of eight magnet focusing–defocusing units in the HEPS storage ring are conducted and ultimately realizing the 10 μm transverse and vertical pre-alignment measuring error within the units. This method, based on high-precision measurements in a small-scale space, reduces the period required for personnel on-site and improves pre-alignment efficiency. It also provides a reference for pre-alignment of multiple magnet units in large accelerators such as the Southern Advanced Photon Source and Circular Electron Positron Collider.

A high-precision visual detection method for rail profile based on Scheimpflug condition

Rail profile is an important indicator of rail service status, which should be always kept in good condition. How to efficiently collect rail profiles with high precision is a critical issue for their condition-based maintenance. This paper focuses on the method for accurate visual detection of rail full profile (RFP), including optical modeling, visual calibration, and error compensation. The proposed optical unit, featuring double line structured-light sensors, introduces an innovative method under the Scheimpflug condition to avoid the limitation of depth of field in RFP measurement. Subsequently, double visual sensors are calibrated together using the common reference target to extract the internal and external parameters respectively. The dynamic errors resulting from the impact of vehicle vibrations are ultimately investigated to rectify the distorted profiles to the normal. Both laboratory and field tests are performed to verify the effectiveness of the proposed system in terms of precision and efficiency.

YOLOv8-CDD: an improved concrete defect detection method combined CNN with transformer

Abstract Concrete defect detection is a critical task to ensure the safe and stable operation of concrete infrastructure. In order to effectively solve the problems of low efficiency, high cost and poor performance of existing methods, a high-precision concrete defect detection method YOLOv8-CDD (Concrete Defect Detection) combining convolutional neural network and transformer is proposed in this paper. Firstly, based on the features with a large span of concrete defect features, a bot-transformer module that can effectively extract the global information of defect features is proposed to improve the network’s ability to extract global features. Secondly, in order to further strengthen the interaction between defect feature channels and spatial information, a convolutional triplet attention module is introduced into the feature enhancement network to effectively integrate the information of different dimensions of defect features and improve the model detection accuracy. Additionally, in order to enhance the learning of samples with different degrees of difficulty, the introduction of Focaler-CIoU instead of the original boundary regression loss function can optimize the model training process. Finally, the dataset was collected and organized in concrete scenarios from bridge towers, dams, and tunnel corridors, and our method achieved 0.898 average precision, 0.893 average recall, 0.031 average FPR, 0.895 average F1 score, 0.929 mAP50, and 0.731 mAP50:95 on the dataset. The experimental results show that the proposed method achieves the best performance in concrete defect detection.

Hybrid Refractive and Diffractive Testing Method for Free-Form Convex Mirror in High-Resolution Remote-Sensing Cameras

The development of high-resolution and large field of view remote-sensing cameras is inextricably linked to the application of free-form mirrors. The free-form mirror offers higher design of freedom and is more effective at correcting aberrations in optical systems. The surface shape error of a free-form mirror directly affects the imaging quality of remote-sensing cameras. Consequently, a high-precision free-form mirror detection method is of paramount importance. For the convex free-form surface mirror with a large aperture, a hybrid refractive and diffractive testing method combining computer-generated holography (CGH) and spherical mirrors for high-precision null test is proposed in this paper. When comparing the effect of error and the detection sensitivity of different designs, the results showed that the influence of the system error is reduced by about 42% and the sensitivity is increased by more than 2.6 times. The proposed method can achieve higher testing accuracy and represents an effective and feasible approach for the surface shape detection method.

Ultrasonic Array-Based Multi-Source Fusion Indoor Positioning Technology

Underground mining involves numerous risks, such as collapses, gas leaks, and explosions, posing significant threats to worker safety. In this work, we develop an indoor localization system that uses Bluetooth for coarse positioning and ultrasonic arrays for precision calibration. This system is particularly useful for automated mining operations in underground environments where satellite positioning signals are unavailable. The indoor localization system consists of ultrasonic receiver arrays and an improved multi-transmitter-multi-receiver algorithm, enabling accurate localization within the mining environment. Geometric Dilution of Precision (GDOP) analysis is incorporated to optimize the network layout, and an inertial navigation module is integrated to track the posture of moving objects, enabling precise trajectory determination over large areas, such as coal mines. In the experiment, three traditional methods were compared, and the proposed tracking approach demonstrated a positioning accuracy within 10 cm, reducing error by 20% compared to conventional techniques. This high-precision indoor localization method proves beneficial for underground mining applications.

A high-precision data-driven radar modeling method for autonomous driving simulations testing

Autonomous driving represents the future of transportation, and simulation testing is a critical technology for ensuring the safety and reliability of autonomous driving. In simulation systems, a real-time and accurate radar model is crucial for enhancing the confidence of autonomous driving simulation systems. Existing radar models are typically simplistic and cannot accurately reflect the detection results of real radars. This paper introduces a data-driven radar modeling method that analyzes radar detection mechanisms to identify the model’s input parameters. Furthermore, this method decouples the model’s output parameters based on the differing mechanisms of radar output parameters and tailors model structures to align with the mechanistic characteristics of each parameter. Subsequently, considering the varied influences among input parameters, a segmented model structure is proposed that reduces input dimensionality while retaining critical information. Lastly, the model outputs are optimized using Kalman filtering. This study collects radar data from real vehicles to train the radar model. Testing has demonstrated that the radar model developed by this paper’s method yields outputs very close to actual radar predictions, with a significant improvement in prediction accuracy compared to traditional radar models. The model operates in approximately 0.8 ms, markedly faster than the real radar’s 72-millisecond detection cycle. This demonstrates that the radar model developed using this modeling method can predict radar detection results accurately and in real-time.

A simple, effective and high-precision boundary meshfree method for solving 2D anisotropic heat conduction problems with complex boundaries

A simple, effective and high-precision boundary meshfree method called virtual boundary meshfree Galerkin method (VBMGM) is used to tackle 2D anisotropic heat conduction problems with complex boundaries. Temperature and heat flux are expressed by virtual boundary element method. The virtual source function is constructed through the utilization of radial basis function interpolation. Calculation model diagram and discrete model diagram of real boundaries, and schematic diagram of VBMGM are demonstrated. Using Galekin method and considering boundary conditions, the integral equation and the discrete formula of VBMGM are given in detail. The benefits of the Galerkin, meshfree, and boundary element methods are all presented in VBMGM. Seven numerical examples of general anisotropic heat conduction problems (including three numerical examples with complex boundaries and four numerical examples with mixed boundary conditions) are computed and contrasted with precise solutions and different numerical methods. The computation time of each example is given. The number of degrees of freedom used in many examples is half or less than that of the numerical method being compared. The suggested method has been demonstrated to be effective and high-precision for solving the 2D anisotropic heat conduction problems with complex boundaries.

High-precision optical modeling method for galvanometer-driven dual-camera systems.

To build a galvanometer-driven dual-camera sensing system, it is important to accurately correlate the wide-view image coordinates with the pan-tilt mirror angles for adjusting the incident light path of the zoom-in camera. Existing optical modeling methods assume sufficient target distance and simplify dual-camera optical centers as coincident. However, this simplification is not valid in many practical cases and might cause severe system malfunctions, such as complete loss tracking of important targets. To address this problem, we propose a novel approach, to the best of our knowledge, to facilitate high-precision optical modeling and calibration of galvanometer-driven dual-camera systems. The proposed method takes into consideration the dual-camera optical center misalignment issue and builds a model for accurate estimation and rectification of target localization errors under various optical configurations. Qualitative and quantitative experimental results demonstrate the superiority of our method, improving the performance of galvanometer-driven dual-camera systems for high-precision optical sensing applications.

Ultra-compact and high-precision differential detection method based on liquid crystal polarization grating for miniature atomic magnetometer

Ultra-compact and high-precision differential detection method based on liquid crystal polarization grating for miniature atomic magnetometer

High-Precision Photonics-Assisted Two-Step Microwave Frequency Measurement Combining Time and Power Mapping Method.

Photonics-assisted methods for microwave frequency measurement (MFM) show great potential for overcoming electronic bottlenecks and offer promising applications in radar and communication due to their wide bandwidth and immunity to electromagnetic interference. In common photonics-assisted MFM methods, the frequency-to-time mapping (FTTM) method has the capability to measure various types of signals, but with a trade-off between measurement error, measurement range, and real-time performance, while the frequency-to-power mapping (FTPM) method offers low measurement error but faces great difficulty in measuring signal types other than single-tone signals. In this paper, a two-step high-precision MFM method based on the combination of FTTM and FTPM is proposed, which balances real-time performance with measurement precision and resolution compared with other similar works based on the FTTM method. By utilizing high-speed optical sweeping and an optical filter based on stimulated Brillouin scattering (SBS), FTTM is accomplished, enabling the rough identification of multiple different signals. Next, based on the results from the previous step, more precise measurement results can be calculated from several additional sampling points according to the FTPM principle. The demonstration system can perform optical sweeping at a speed of 20 GHz/μs in the measurement range of 1-18 GHz, with a measurement error of less than 10 MHz and a frequency resolution of 40 MHz.

Damage mechanics coupled with a transfer learning approach for the fatigue life prediction of bronze/steel diffusion welded bimetallic material

The bronze/steel diffusion welded (BSDW) bimetallic material is often applied in the rotors of piston pumps to withstand complex alternating loads under high-speed operating conditions. Although diffusion welding is a type of solid-phase welding method to achieve high-quality material connections, the fatigue problems still deserve our attention, especially the very high cycle fatigue (VHCF) and high cycle fatigue (HCF) problems. However, due to the high cost of obtaining data, it is necessary to find an efficient and high-precision fatigue life prediction method for diffusion welded materials with a small sample size. In this study, a novel method continuum damage mechanics − transfer learning method (CDM-TLM) for fatigue life prediction of BSDW material is proposed based on the transfer learning (TL) and continuum damage mechanics − finite element method (CDM-FEM). In comparison with the test results, the predicted values of BSDW material fatigue life all fall within the twice error band of the median values of the test life. The influence of frozen layers during TL and training samples in source and target models on the prediction performance is further discussed. CDM-TLM is an effective life prediction method for high-precision life prediction of BSDW material with a small sample size.