Calibration Process Research Articles

Beam Orbital Parameter Prediction Based on the Deployment of Cascaded Neural Networks at Edge Intelligence Acceleration Nodes

During the beam current calibration process, accurate guidance of the beam current to the metal target is a challenging issue for proton accelerators. To address this challenge, we propose the use of beam orbital parameters combined with reinforcement learning algorithms to achieve automatic beam calibration. This study introduces a system architecture that employs edge intelligent acceleration nodes based on deep learning acceleration techniques. We designed a system to predict BPM parameters using a cascaded backpropagation neural network (CBPNN) that is informed by the physical structure. This system serves as an environmental map for reinforcement learning, aiding beam current correction. The CBPNN was implemented on the acceleration node to hasten the forward inference process, leveraging sparsification, quantization algorithms, and pipelining techniques. Our experimental results demonstrated that the simulated inference speed reached 28 μs with FPGA hardware as the edge acceleration node, achieving forward inference speeds 35.66 and 12.66 times faster than those of the CPU and GPU. The energy efficiency ratio was 10.582 MOPS/W, which was 989 and 410 times that of the CPU and GPU, respectively. This confirms the designed architecture’s energy efficiency and low latency attributes.

Self-Calibrating Stress Measurement System Based on Multidirectional Barkhausen Noise Measurements

The system presented in this paper enables automatization of the two-dimensional calibration process (determination of Barkhausen noise (BN) intensity dependence on in-plane components of strain). Then, using dedicated software created by the authors in LabVIEW environment, and with the help of two dimensional calibration data one can effectively determine strain and stress distribution i.e. magnitude and orientation of main strain/stress components relative to measurement direction. BN signal measurements are performed using an advanced, multidirectional Barkhausen noise (BN) measuring sensor and a measurement system dedicated for cooperation with it. The system uses a robust algorithm for the strain components determination based on calibration surfaces, instead of usually applied curves, thus taking the influence of normal strain component directly into account instead of treating it as a correction factor (if not completely neglecting). The originality of the system arises also from the fact that this is the first BN measurement system that is self-calibrating (i.e. automatically loads the calibration sample in a pre-programmed way, performs BN signal measurements and calculates calibration planes), provided that the user possesses enough of the investigated material for calibration sample preparation.

Comparison of On-Sky Wavelength Calibration Methods for Integral Field Spectrograph

With advancements in technology, scientists are delving deeper in their explorations of the universe. Integral field spectrograph (IFS) play a significant role in investigating the physical properties of supermassive black holes at the centers of galaxies, the nuclei of galaxies, and the star formation processes within galaxies, including under extreme conditions such as those present in galaxy mergers, ultra-low-metallicity galaxies, and star-forming galaxies with strong feedback. IFS transform the spatial field into a linear field using an image slicer and obtain the spectra of targets in each spatial resolution element through a grating. Through scientific processing, two-dimensional images for each target band can be obtained. IFS use concave gratings as dispersion systems to decompose the polychromatic light emitted by celestial bodies into monochromatic light, arranged linearly according to wavelength. In this experiment, the working environment of a star was simulated in the laboratory to facilitate the wavelength calibration of the space integral field spectrometer. Tools necessary for the calibration process were also explored. A mercury–argon lamp was employed as the light source to extract characteristic information from each pixel in the detector, facilitating the wavelength calibration of the spatial IFS. The optimal peak-finding method was selected by contrasting the center of weight, polynomial fitting, and Gaussian fitting methods. Ultimately, employing the 4FFT-LMG algorithm to fit Gaussian curves enabled the determination of the spectral peak positions, yielding wavelength calibration coefficients for a spatial IFS within the range of 360 nm to 600 nm. The correlation of the fitting results between the detector pixel positions and corresponding wavelengths was >99.99%. The calibration accuracy during wavelength calibration was 0.0067 nm, reaching a very high level.

Feature Vector Effectiveness Evaluation for Pattern Selection in Computational Lithography

Pattern selection is crucial for optimizing the calibration process of optical proximity correction (OPC) models in computational lithography. However, it remains a challenge to achieve a balance between representative coverage and computational efficiency. This work presents a comprehensive evaluation of the feature vectors’ (FVs’) effectiveness in pattern selection for OPC model calibration, leveraging key performance indicators (KPIs) based on Kullback–Leibler divergence and distance ranking. Through the construction of autoencoder-based FVs and fast Fourier transform (FFT)-based FVs, we compare their efficacy in capturing critical pattern features. Validation experimental results indicate that autoencoder-based FVs, particularly augmented with the lithography domain knowledge, outperform FFT-based counterparts in identifying anomalies and enhancing lithography model performance. These results also underscore the importance of adaptive pattern representation methods in calibrating the OPC model with evolving complexities.

Low-Cost Thermal Point Clouds of Indoor Environments

Abstract. The integration of low-cost thermal sensors with Apple Smart Devices supports the generation of 3D point clouds that include temperature indicators. This generates a new perspective for the study of buildings, allowing for fast and reliable examination of physical building structures. To the best of our knowledge, this study is the first to demonstrate the use of affordable sensors for 3D thermal point cloud generation. The study involved capturing data from the LiDAR and thermal sensors, followed by an extrinsic calibration process to align the datasets. Subsequently, the point cloud was segmented based on different acquisition poses of the device and finally, the thermal data was projected onto the 3D model, integrating temperature information with spatial coordinates. Our results demonstrate the effectiveness of the approach for three-dimensional point cloud generation in indoor environments, highlighting significant thermal variations and enabling thermal mapping of building structures. Furthermore, our findings underscore the feasibility of employing low-cost sensors for generating detailed thermal models, opening possibilities for widespread adoption in various building analysis applications. This approach provides a comprehensive and cost-effective solution for building monitoring, democratizing access to advanced evaluation tools.

Validity and Reliability According to the Type of Examiners in the Process of Calibrating Dental Caries Experience Using the DMFT Index.

The process of examiner calibration is an essential step in all epidemiological research, as it aims to ensure uniform interpretation, understanding, and application of the instrument to be used. This ensures that the data collected will be valid and reliable. This study aimed to determine the differences in concordance in dental caries calibration across three dental specialties. The population consisted of 45 dentists, divided into three groups: 15 general dentists working in the public sector, 15 dentists specializing in Dental Public Health, and 15 dentists specializing in Restorative and Aesthetic Dentistry. The calibration process was carried out in three stages: theory, calibration using photographs, and calibration on natural teeth, performed by the gold standard. In the first validity process, a statistical difference was only found between the Kappa values of the inter-examiner calibration process using photographs. For the evaluation of teeth, in the second validity process, 33.33% (n = 15) of the participants achieved "almost perfect agreement." Finally, only 75.56% (n = 34) of the examiners were considered for the reliability report; of this group, 52.94% (n = 18) were in "almost perfect agreement," and 35.29% (n = 12) were in "substantial agreement." The validity and reliability of the dental caries experience calibration process did not present significant statistical differences between general dentists in the public sector, dentists specializing in Dental Public Health, and dentists specializing in Restorative and Aesthetic Dentistry.

Industrial robot calibration optimization method based on R-optimal criterion and improved IOOPS algorithm

Abstract In this paper, a novel kinematic parameter calibration method based on the R-optimal criterion and the improved iterative one-by-one pose search (IOOPS) algorithm is proposed to improve the end-effector positioning accuracy of industrial robots. First, a novel industrial robot error calibration model based on the product of exponential model and generalized error model is established, and the ideal pose transformation matrix from the robot base coordinate system to the laser tracker measurement coordinate system is obtained using the least-squares method, thereby obtaining the initial parameter vector of the robot calibration system. Second, various joint angle values at different positions within the robot’s workspace and the corresponding end-effector coordinate data, namely calibration sampling point data, are collected. Subsequently, the improved IOOPS algorithm combined with the observability index O R proposed by the R-optimal criterion is used to select the optimized calibration sampling points. Finally, the optimized sampling point data are combined with the Levenberg–Marquardt algorithm to optimize the parameters and obtain the optimal kinematic parameters. The experimental results show that the average end-effector error of the robot decreases from 0.5822 to 0.2492 mm after calibration using this method, demonstrating the effectiveness of the optimized sampling points in the calibration process achieved through the improved algorithm and the new observability index.

Engine Mass Flow Estimation through Neural Network Modeling in Semi-Transient Conditions: A New Calibration Approach

Nowadays, engine experimental research represents a very expensive field within the automotive industry, but it remains fundamental for engine and vehicle development. The present work aims to investigate a novel approach for engine control system calibration, by adopting machine learning techniques to model physical parameters of the engine starting from experimental data measured at the test bench. The main goal is to create a methodology which accelerates the calibration process without losing accuracy. A model that estimates air mass flow is created by adopting either a tree ensemble model or an artificial neural network trained on a small dataset, which was previously acquired at the test bench using a random calibration of the volumetric efficiency map. The model’s performance is first validated on a larger, random dataset. Then, the volumetric efficiency calculated from the air mass flow model estimation is used to calibrate the transfer function of the Engine Control Unit. Finally, the sensitivity of the model error correlated with the number of data points acquired is used in order to determine the best practice for a Design Of Experiment, which minimizes data acquisition. The methodology proposed can lead to reduced time and costs of the whole calibration process of the engine, without losing accuracy. The analysis was conducted on the entire vehicle, which is crucial for drivability, especially in motorcycles since they are highly sensitive to air-to-fuel ratio adjustments. This work demonstrates that machine learning models can be adopted for the fine-tuning of the calibration process, which is normally performed manually.

Circuit-noise-resilient virtual distillation

Quantum error mitigation (QEM) is vital for improving quantum algorithms’ accuracy on noisy near-term devices. A typical QEM method, called Virtual Distillation (VD), can suffer from imperfect implementation, potentially leading to worse outcomes than without mitigation. To address this, we introduce Circuit-Noise-Resilient Virtual Distillation (CNR-VD), which includes a calibration process using simple input states to enhance VD’s performance despite circuit noise, aiming to recover the results of an ideally conducted VD circuit. Simulations show that CNR-VD significantly mitigates noise-induced errors in VD circuits, boosting accuracy by up to tenfold over standard VD. It provides positive error mitigation even under high noise, where standard VD fails. Furthermore, our estimator’s versatility extends its utility beyond VD, enhancing outcomes in general Hadamard-Test circuits. The proposed CNR-VD significantly enhances the noise-resilience of VD, and thus is anticipated to elevate the performance of quantum algorithm implementations on near-term quantum devices.

A photonic sensor system for real‐time monitoring of turbidity changes in aquaculture

AbstractObjectiveThe objective of this study was to test the compatibility and performance of a developed photonic sensor system, which can serve as a dependable and practical device for continuous monitoring of turbidity changes in aquaculture tanks.MethodsThe fabricated photonic sensor system consisted of an integrated data logger and sensor probe. The sensor probe exhibited a precise emission of infrared light at a wavelength of 850 nm. Moreover, the sensor evaluates the ambient light across the red‐green‐blue spectrum. To ensure accuracy and reliability, the entire system underwent a thorough calibration process, referencing nephelometric turbidity unit values acquired through a specialized handheld turbidimeter. Rigorous trials were systematically conducted in 600‐L seawater tanks featuring tubular sea cucumber Holothuria tubulosa and Gilt‐head Sea Bream Sparus auratus to ensure the sensitivity and robustness of the photonic sensor system to the aquaculture environment.ResultA calibration curve revealed a significant correlation between the infrared channel values of the sensor (photon counts) and the turbidity values measured by the turbidimeter. The photonic sensor effectively captured turbidity changes in the aquaculture tanks, with significant differences observed between the tanks. The sensor performance was evaluated in trials with Gilt‐head Sea Bream, which showed sensitivity to high turbidity changes. The photonic sensor system accurately reflects turbidity changes continuously using its own active light source, independent of ambient light intensity, which is essential for turbid water conditions or for taking measurements in total darkness.ConclusionThe photonic sensor is a reliable tool for the continuous and accurate monitoring of turbidity changes in aquaculture systems. However, there are specific usage limitations under low‐turbidity conditions that can be improved in further studies.

Applying Low-Impact Development Techniques for Improved Water Management in Urban Areas

Worldwide, the increase in impervious surfaces due to urbanization has led to significant water cycle issues such as groundwater depletion, urban heat islands, and flooding. To address these challenges, Low-Impact Development (LID) techniques are increasingly being applied in stormwater management. This study focuses on Ulsan, designated as a water cycle model city in South Korea, with a particular emphasis on the highly urbanized Okgyo drainage watershed. Using the Stormwater Management Model (SWMM) version 5.1, long-term runoff simulations were conducted to evaluate the effects of LID implementation on water cycle change rates and recovery rates. The model incorporates detailed hydrological and hydraulic parameters, including inflow, runoff, infiltration, and evapotranspiration for six subcatchments within the watershed. The SWMM was calibrated and validated using 30 years of historical rainfall data (1987–2016) from the Ulsan weather station. Calibration and validation processes used the NRCS-CN (Curve Number) method to ensure accuracy in simulating runoff patterns and water balance. The study specifically evaluated the effectiveness of two LID techniques: bioretention and permeable pavements. Three scenarios were modeled: bioretention applied to 5% of the area, permeable pavements applied to 5% of the area, and a combined application of both techniques. The results showed that the combined scenario provided the best outcome, with a 7.80% reduction in surface runoff and a 14.56% improvement in water cycle health. The LID application scenario confirmed the potential to achieve the water cycle management target of handling 25.5 mm of rainfall. These findings demonstrate that the introduction of LID techniques in public spaces can significantly enhance water management. This research provides insights into effective water cycle management methods tailored to specific urban land uses, laying a foundation for future urban planning and sustainable development.

Assessment of OH femtosecond thermally assisted fluorescence thermometry for hydrogen non-premixed flames

In this work, we further developed the planar temperature measurement method based on thermally assisted laser-induced fluorescence (TALF) with a single femtosecond (fs) laser and investigated the temperature distribution in the nitrogen-diluted oxygen–hydrogen cross-jet non-premixed flames at 1 kHz. Fs-TALF thermometry was firstly calibrated and assessed in a series of unconfined methane/air laminar flames at atmospheric pressure. In the calibration process, although the calibrated energy difference between OH v′ = 0 and 1 energy level was different from the theoretical value, the measured fluorescence ratio accurately reflected the temperature change from 1600 K to 2200 K in different methane flames. By comparing the calculated OH fluorescence spectrum, we believed that the main reason was the overlap of the OH (1-1) and (0-0) fluorescence bands. After the calibration, experiments were conducted with changing hydrogen flow velocity from 60 to 100 m/s and constant oxidizer flow velocity, where the dilution ratio, D varies from 0.46 to 0.7, and the equivalence ratio, ϕ, varies from 0.5 to 0.9. It is found, within the calibration range, fs-TALF thermometry can correctly measure the flame temperature. However, with ϕ further increases, the difference between the measured and theoretical temperatures gradually increases. At the same time, it was noted that the error in the measured temperature rises gradually as D decreases. In conclusion, fs-TALF thermometry can measure turbulent non-premixed hydrogen flames accurately in a range of temperature with acceptable temporal and partial resolution, while the errors within 200 K and spatial resolution is 170μm. The main error source should be the collisional effect due to the different local concentration of N2 and H2O. The results of this work will contribute to the further development the temperature measurement method hydrogen turbulent flames.

Flat mirrors, virtual rear-view cameras, and camera-mirror calibration

Flat mirrors are useful to generate virtual cameras and capture a scene from multiple viewpoints. However, the resultant camera-mirror setup is cumbersome to calibrate because of the mirror image reversion. The typical calibration flaws are omitting the checkerboard symmetry and assuming that virtual cameras are front-view. This paper explains the rear-view abstraction and its usefulness in calibrating a camera-mirror setup to obtain the equivalent mirrorless configuration. The underlying theory is reviewed, including camera imaging, the calibration process, and the laws of reflection. The proposed approach is illustrated by calibrating a camera-mirror setup and then using the calibrated camera to generate mirror images synthetically. This work offers a practical insight into calibrating more sophisticated systems such as mirrored camera-projector profilometers.

An optimization-based method of calibrating load and resistance factors: Application to slope and breakwaters’ foundation stability

Monte Carlo simulation (MCS)-based calibrations can accurately determine probabilistic load and resistance factors (LRFs) needed in the limit state designs. However, most of the computing time and effort of the calibrations is for evaluating performance functions if they are defined implicitly, as in the case of slope stabilities. This study proposes a robust framework that combines the advantages of adaptive artificial neural networks (ANNs) in approximating implicit performance functions with an optimization process to establish the LRFs quickly and automatically. Furthermore, experiment data obtained in the preceding iterations of the optimizations are accumulated and reused in the subsequent trial. For illustration, three implicit problems, including a slope and two cases of breakwater foundation stability, are examined to demonstrate the efficiency and accuracy of the proposed procedure. The investigations show that the proposed framework can be accurately completed within 1 h instead of lasting for weeks of calculation when using basic MCSs. Remarkably, reusing experiment data helps decrease the necessary data by two-thirds compared to only using the adaptive ANN, facilitating a faster calibration process. Thus, this work contributes a practical method for calibrating LRFs needed in limit state designs of geotechnical engineering fields wherein limit state equations are defined in implicit fashions.

A pilot study: developing acoustic model for immersive environment

In the landscape of higher education in Malaysia, the adoption of immersive technologies still in its infancy, particularly within the field of building science education. This research endeavors to elevate the learning experiences of Indoor Environmental Quality (IEQ) concepts, with a specific focus on lighting and acoustics. The ongoing study seeks to revolutionize current learning methodologies by converting traditional modules into an immersive virtual environment, offering learners an interactive, and sensory-rich experience with the subject matter. To ensure the effectiveness of the acoustic immersive environment, this paper prioritizes the initial step of developing the acoustic simulation model. In an early pilot study, on-site measurements of sound pressure levels were recorded at various points within a basic classroom setting and an acoustic model will be developed subsequently. Consequently, the alignment between the developed acoustic model and real-world physical measurements establishes a solid foundation for the subsequent calibration process, during which the acoustic model will be tailored to meet VR-ready specifications delve into immersive environment model. This finding ensures a seamless transition of real-world acoustic conditions into the virtual realm, amplifying the authenticity and educational impact of the VR learning experience, thereby contributing to the advancement of immersive technologies in higher education.

Research on the calibration method of winding deformation tester

Abstract The focus of this paper is the calibration method and process for winding deformation tester, encompassing its working principle, metrological characteristics, calibration conditions and measurement methods. The calibration conditions are divided into environmental conditions and measurement standards and other equipments. The measurement methods are divided into indication error of scanning frequency and indication error of amplitude ratio. The key steps in the calibration process are elaborated, including the selection of standard equipments, error analysis, data processing. The method’s feasibility is verified through experiments.

Interpreting machine learning models based on SHAP values in predicting suspended sediment concentration

Machine learning (ML) has become a powerful tool for predicting suspended sediment concentration (SSC). Nonetheless, the ability to interpret the physical process is considered the main issue in applying most of ML approaches. In this regard, the current study presents a novel framework involving four standalone ML models (extra trees (ET), random forest (RF), categorical boosting (CatBoost), and extreme gradient boosting (XGBoost)) and their combination with genetic programming (GP). Three metrics (coefficient of correlation (r), root mean square error (RMSE), and Nash–Sutcliffe model-fit efficiency (NSE)) and a more advanced interpretation system SHapley Additive exPlanations (SHAP) are used to assess the performance of these models applied to hydro-climatic datasets for prediction of SSC. The calibration process was based on data from 2016 to 2020, and the validation was done for 2021 data. Further description and application of the framework are provided based on a case study of the Bouregreg watershed. The results revealed that all implemented models are efficient in SSC prediction with NSE, RMSE, and r varying from 0.53 to 0.86, 1.20 to 2.55 g/L, and 0.83 to 0.91 g/L respectively. Box plot diagrams confirm the enhanced performance of these combined models, and the best-performing ones for the four hydrological stations being the combined RF+GP model at the Aguibat Ziar station, the combined XGBoost+GP model at the Ain Loudah station, the CatBoost model at the Ras Fathia station, and the RF model at the Sidi Med Cherif station. The interpretability results showed that flow (Q) and seasonality (S) are the features most impacting SSC. These outcomes indicate that the applied models can extract accurate and detailed information from the interactions between the hydroclimatic factors and the generation of sediment by erosion (output). ML approaches illustrated the good reliability and transparency of the models developed for predicting SSC in a semi-arid setting, offered new perspectives for reducing ML models' "black box" character, and provided a useful source of information for assessing the consequences of SSC on water quality. The SHAP system and exploring other interpretable techniques are recommended to provide further information in future research. In addition, incorporating additional input data could enhance SSC predictions and deepen understanding of sediment transport dynamics.

Long-term investigation of the irrigation intervals and supplementary irrigation strategies effects on winter wheat in the U.S. Central High Plains based on a combination of crop modeling and field studies

Implementing optimized irrigation strategies to achieve acceptable wheat yield while conserving groundwater resources is critical for extensively irrigated regions. Field experiments regarding winter wheat irrigation management were conducted for two growing seasons (2013–2014 and 2014–2015) to calibrate and validate the AquaCrop model. To determine proper parameterization of the AquaCrop model for various data availability conditions, the model performance was tested for calibration based on three different datasets, including a) calibration based on a full irrigation condition (CA 1), b) calibration based on a dryland condition (CA 2), and c) calibration based on diverse irrigation conditions, which included full irrigation, different deficit irrigation levels, and dryland (CA 3). The CA 3 calibration scenario resulted in the model's highest accuracy in simulating biomass, grain yield, total soil water, and seasonal evapotranspiration during the calibration and validation process. However, the model performance was also convenient when calibration based on full irrigation conditions was pursued. The calibrated and validated AquaCrop model based on the CA3 was used to analyze long-term (36-year) irrigation management scenarios for identifying the optimized water-conservative winter wheat irrigation strategies. The long-term analysis emphasized that increasing irrigation intervals from 7 to 15 or 20 days could reduce groundwater withdrawal by 52–64 % while expecting a 14–19 % reduction in grain yield. By implementing one 25 mm irrigation after planting and two 25 mm depth irrigation events at jointing or leaf growth stages, 91 % of Kansas's average winter wheat yield (2.88 tons/ha) could be achieved. During wet years, the 1.2 ton/ha reduction in biomass and 0.27–0.62 ton/ha reduction in grain yield is expected irrespective of irrigation management strategies. Considerable uncertainty was detected in grain yield production during dry years under dryland (rainfed) conditions. The maximum winter wheat production could be achieved under normal years, establishing a balance between precipitation and heat units. Our agro-hydrological analysis revealed that the highest irrigation water use efficiency could be attained by the application of two 25 mm irrigation events at jointing or flag leaf growth stages during normal years, the application of two irrigation events during jointing or heading during wet years, and two 25 mm irrigations at heading or flowering during dry years. The results of this research could be used as a baseline for producers of the U.S. Central High Plains and semi-arid regions with similar climate characteristics to cope with water scarcity in winter wheat production.

AUTONOMOUS MONITORING ROBOT SYSTEM THAT MEASURES AIR POLLUTION

Air pollution poses a significant threat to human health, ecosystems, and climate stability, necessitating effective monitoring and control measures. Traditional air quality monitoring stations, while accurate, are static, expensive, and limited in coverage. The Autonomous Monitoring Robot System (AMRS) provides a dynamic solution, offering real-time air quality monitoring through mobile robotic platforms. Equipped with advanced sensors, these robots can measure various pollutants, such as particulate matter (PM), nitrogen dioxide (NO2), carbon monoxide (CO), and volatile organic compounds (VOCs), across large and complex areas. By employing AI-based navigation and mapping technologies, AMRS can autonomously traverse urban and industrial environments, collecting high-resolution pollution data and creating real-time air quality maps. This approach allows for better spatial coverage, cost-efficiency, and access to hard-to-reach locations compared to traditional methods. The system can be deployed in diverse use cases, including urban air quality monitoring, industrial pollution control, and disaster management. While challenges such as battery life, sensor calibration, and data processing remain, AMRS represents a promising technological advancement in environmental monitoring and pollution control.

Target control point position calibration based on coordinate measuring machine and global bundling optimization algorithm.

The target-based coordinate measurement system is primarily composed of a target, a camera, and a computer, relying on image processing to achieve coordinate measurement. It finds extensive applications in spatial positioning and precision measurement, such as industrial manufacturing, robot navigation, and shipbuilding. The accuracy of the target-based coordinate measurement system is highly dependent on the calibration precision of control point positions. A new method based on the calibration of control point positions using a traditional coordinate measuring machine is proposed in this paper. This method does not require the individual calculation of spatial positions for each control point; instead, it utilizes a global bundle algorithm to obtain the positions of all the control points within the field of view, thereby simplifying the calibration process. The simulation results indicate that the key factor influencing the accuracy of visual calibration is the motion range of the measured target within the field of view, and it is positively correlated with calibration accuracy. For a large-sized target, a method of stitching control points within a near-sighted distance is proposed, which achieves higher accuracy compared to full-field calibration without requiring all the control points to be present within the field of view. Experiments demonstrate that using the proposed calibration method, the standard deviations of measurement accuracy of the target-based coordinate measurement system in the x, y, and z directions are 0.01, 0.01, and 0.08mm, respectively.