Errors In Parameters Research Articles

Machine Learning-Based In Situ Identification of the High-Dimensional Parameter Space for an Equivalent Circuit Model

Abstract Accurate online identification of equivalent circuit model (ECM) parameters of lithium-ion battery electrochemical impedance spectroscopy (EIS) remains a challenge, particularly with high-dimensional parameter spaces. Here, 11-dimensional adapted Randles ECM (AR-ECM) is reduced to two low-dimensional models, and the EIS frequency ranges for each AR-ECM parameter were determined by distinguishing the frequency bands representing different electrochemical processes. A multi-step parameter in-situ identification methodology was developed to minimize onboard training costs by selecting an optimal training set for machine learning based on the Euclidean distance between the collected and generated EIS data. A Gaussian process regression model was constructed by correlating the AR-ECM parameters and EIS to estimate the AR-ECM parameters. Model performance was validated using 12 cells at different temperatures. Experimental results show that a simulated database covering the main EIS and AR-ECM characteristics can be established, whose scale is on the order of 1e^6, much smaller than the order of 1e^11 resulting from each AR-ECM parameter being varied among 10 values. The estimation errors of the key AR-ECM parameters are approximately 5% at different temperatures. The maximum estimation error of all parameters is as low as 9.03%, 13.96% lower than that based on the complex nonlinear least squares method.

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

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

Textural and geometric measures derived from digital tomosynthesis discriminate women with and without vertebral fracture.

Vertebral fractures are a common and debilitating consequence of osteoporosis. Bone mineral density (BMD), measured by dual energy x-ray absorptiometry (DXA), is the clinical standard for assessing overall bone quantity but falls short in accurately predicting vertebral fracture. Fracture risk prediction may be improved by incorporating metrics of microstructural organization from an appropriate imaging modality. Digital tomosynthesis (DTS)-derived textural and microstructural parameters have been previously correlated to vertebral bone strength in vitro, but the in vivo utility has not been explored. Therefore, the current study sought to establish the extent to which DTS-derived measurements of vertebral microstructure and size discriminate patients with and without vertebral fracture. In a cohort of 93 postmenopausal women with or without history of vertebral fracture, DTS-derived microstructural parameters and vertebral width were calculated for T12 and L1 vertebrae, as well as lumbar spine BMD and trabecular bone score (TBS) from DXA images. Fracture patients had lower BMD and TBS, while DTS-derived degree of anisotropy and vertebral width were higher, compared to nonfracture (p<0.02 to p<0.003) patients. The addition of DTS-derived parameters (fractal dimension, lacunarity, degree of anisotropy and vertebral width) improved discriminative capability for models of fracture status (AUC=0.79) compared to BMD alone (AUC=0.67). For twelve additional participants who were imaged twice, in vivo repeatability errors for DTS parameters were low (0.2% - 7.3%). The current results support the complementary use of DTS imaging for assessing bone quality and improving the accuracy of fracture risk assessment beyond that achievable by DXA alone.

Study on the dynamic load sharing characteristics of nutation face gear transmission

The nutation face gear transmission is a type of power splitting, and the effects of system parameters such as stiffness and transmission error on load distribution are not yet well understood. The Load Tooth Contact Analysis (LTCA) technique was used to calculate the time-varying meshing stiffness of the conjugate face gear pair. A 22-degree-of-freedom bending-torsional-axial coupled dynamic model of the system was established considering parameters such as support stiffness, time-varying meshing stiffness, meshing error, and installation error. A test bench for nutation face gear transmission system was set up, and experimental results shown that the dynamic model of the nutation face gear system had an accuracy of 2.32%. Based on the solution of the dynamic meshing force of the conjugate face gear pairs, the dynamic load sharing coefficient of the nutation face gear transmission system was defined. Through simulation calculations, the effects of support stiffness, comprehensive meshing error, and torsional stiffness of input shaft on the dynamic load characteristics of the system were analyzed. The research results indicate that the radial support stiffness of each face gear and the coupling stiffness between the oblique axis segment of the input shaft has the greatest impact on the load sharing coefficient. Reducing the radial support stiffness of the left branch, increasing the radial support stiffness of the right branch, and increasing the coupling stiffness between the oblique axis segment of the input shaft can improve the system's load-sharing performance by 76%. The asymmetry of the comprehensive meshing error parameters in the two power transmission branches can increase the system's load-sharing coefficient up to 1.8. These findings provide an important basis for the structural and stiffness parameter design of nutating face gear transmissions.

Kriging data with measurement error: A review and a generalized approach

Filtered kriging with parametric error (FKPE) is a mathematically sound method that generalizes and addresses the oversimplifications of previous kriging algorithms designed to filter error. The proposed approach is developed for handling grade-dependent (heteroscedastic), non-stationary, and spatially correlated sampling errors. The covariance between each pair of measurements or nodes is estimated from their model of errors and the spatial continuity of the underlying process. This research is driven by the fact that sampling and analytical errors are inherent in the samples used in the mining industry. In recent decades, data from quality control programs monitoring these errors have become widely available. FKPE handles more types of errors than other kriging algorithms to filter error and, for any number of subsets, it avoids jointly modelling the models of co-regionalization required by co-kriging methods. The precision gains of FKPE over other methods depend on how well the error model is fit to the data. Therefore, a detailed analysis of the error model and how to estimate its components is discussed. In a five-data toy example with high independent and grade-dependent error, kriging methods with oversimplified error models overweighted a high-grade datum by 50% compared to FKPE estimates. The performance of FKPE and other methods is illustrated by two synthetic examples.

It's about time: moving away from statistical analysis of ecotoxicity data.

Environmental risk assessment of chemicals (ERA) relies on single-species laboratory testing to establish the toxic properties of a compound. However, ERA is not concerned with toxicity under laboratory conditions: it needs to assess the impacts of the compound in the real world. Data-driven statistical analyses (e.g., hypothesis testing and interpolation) are the common approaches for analysing toxicity data, but such approaches are the wrong tool for the job at hand. ERA does not need a statistical description of the effects in the toxicity test (at the end of the standardised test duration), it needs to extrapolate from the laboratory test to longer and time-varying exposure. Such extrapolation requires mechanistic process models, providing a simplified representation of the mechanisms underlying toxicity. Any useful model for the toxicity process should explicitly consider both dose (e.g., exposure concentration) and time. In the history of effects analysis for ERA, the factor of time does not get as much attention as the dose, hence common use of the term 'dose-response analysis'. However, this is a historical oversight: time is a crucial factor for understanding toxicity and thereby essential for meaningful extrapolation from laboratory to field. Mechanistic models for ecotoxicity, considering both dose and time, have been around for quite some time and are classified as toxicokinetic-toxicodynamic (TKTD) models. TKTD models are starting to find their way into pesticide ERA in Europe, next to the classical statistical approaches. In this opinion paper, I argue that it is about time to leave statistical analysis of toxicity data behind us. Statistics remains important for ERA's effects assessment, but its role lies in the definition of appropriate 'error models', explaining the deviations between model output and observations, which is essential for parameter estimation, uncertainty quantification, and error propagation. The 'process model', essential for extrapolation, firmly belongs to TKTD modelling.

A new method for recognizing geometric parameters of industrial robots

Intelligent algorithms that are commonly used to obtain errors in the geometric parameters of industrial robots have a low accuracy, easily fall into the local optimal solution, and involve complicated coding such that they are unsuitable for use in engineering. In this study, we first apply the D-H method to establish a model of error in industrial robots, and then use the set of errors in their geometric parameters as the objective function. Following this, we improve the accuracy of global optimization of the particle swarm optimization (PSO) algorithm by drawing on the wandering behavior of the wolf pack algorithm and hybridization behavior of the genetic algorithm. We balance the convergence of the PSO algorithm by using a linearly diminishing weight. This leads to an improved PSO algorithm that can accurately determine errors in the geometric parameters of industrial robots. We compared our improve PSO algorithm with commonly used particle swarm algorithms, and the results showed that the former had a higher accuracy of convergence on average. Moreover, the errors in the geometric parameters obtained by the improved PSO algorithm can enhance the accuracy of localization of errors in industrial robots.

Alignment Method for Marine Propulsion Systems with Single Stern Tube Bearing Based on Fine-Tuning a Pre-Trained Model

This paper addresses the issue of insufficient accuracy and efficiency in existing methods for the alignment of marine propulsion systems with single stern tube bearing, caused by uncertainties in the actual parameters of propulsion systems and the scarcity of data, and a new alignment method based on fine-tuning a pre-trained model is proposed. First, a characterization method for the attitude of the main engine is proposed, taking into account the specific alignment requirements of marine propulsion systems with single stern tube bearing. Next, a pre-trained model is constructed based on large-scale samples from the design propulsion system and the fine-tuning of the pre-trained model is performed using small samples from the actual propulsion system’s alignment process to obtain the target model, which guides the practical alignment. Finally, the effectiveness and superiority of the proposed method are validated by applying actual measured data and by applying finite element simulations; in eight alignments, all verification parameter errors are much smaller than the maximum allowable error. The results show that the proposed method significantly improves the accuracy and efficiency of alignment of this type of propulsion system and provides a technical approach to the small-sample modeling problem in the alignment of propulsion systems.

Production of real signs but not pseudosigns affected by age of acquisition in American Sign Language.

Research shows that insufficient language access in early childhood significantly affects language processing. While the majority of this work focuses on syntax, phonology also appears to be affected, though it is unclear exactly how. Here we investigated phonological production across age of acquisition of American Sign Language (ASL). Participants were deaf adult signers who first learned ASL at ages ranging from birth to 14years and they performed both lexical decisions and repetitions of ASL signs and pseudosigns. Because phonological production has been understudied across age of acquisition, we were particularly interested in production accuracy for the sublexical phonological parameters of handshape, movement, and location. Lexical decision responses were slower and more accurate for impossible pseudosigns compared with possible pseudosigns, indicating participants were sensitive to ASL phonological structure regardless of age of acquisition. Despite this, age of acquisition affected repetition accuracy. Handshape errors were highest for those with earlier ages of acquisition, but movement errors were highest for those with later ages of acquisition, though this effect of age of acquisition was only seen for real ASL signs and not pseudosigns. The parameter error pattern for pseudosigns was not affected by age of acquisition. These results indicate that later age of acquisition does not inhibit the ability to produce ASL phonology but ultimately alters the processing of the phonological parameters when meaning and phonology are integrated.

Streaming quantum state purification

Quantum state purification is the task of recovering a nearly pure copy of an unknown pure quantum state using multiple noisy copies of the state. This basic task has applications to quantum communication over noisy channels and quantum computation with imperfect devices, but has only been studied previously for the case of qubits. We derive an efficient purification procedure based on the swap test for qudits of any dimension, starting with any initial error parameter. Treating the initial error parameter and the dimension as constants, we show that our procedure has sample complexity asymptotically optimal in the final error parameter. Our protocol has a simple recursive structure that can be applied when the states are provided one at a time in a streaming fashion, requiring only a small quantum memory to implement.

ERROR ANALYSIS OF THE MULTI-STAGE METHOD OF DETERMINING THE TRAJECTORY PARAMETERS OF LOW-ORBITAL SPACE OBJECTS DURING EXPERIMENTAL TESTING OF INFORMATION AND COMMUNICATION CONTROL SYSTEMS AND SPACE ENVIRONMENT ANALYSIS

The repulsion of large-scale Russian aggression and other existing threats in the sphere of national security and defense of Ukraine require the accelerated development of space information technologies in the state and determine the urgent need to improve the organization of the use of space technologies in the interests of defense, the creation of the necessary organizational structures, the deployment of modern information and communication systems with the aim of increasing space situational awareness. One of the main tasks of space situational awareness is the necessary level of knowledge about outer space, the characteristics of space objects of various origins, previous, current and projected knowledge about the parameters of their orbital motion. This task becomes particularly relevant due to the adversary's use of its space means of special and radio- technical intelligence and means of commercial organization, including microsatellites, as a result of which there is a need to control the routes of the passage of surveillance satellites with the subsequent development of plans for camouflage and other types of countermeasures and protection of troops. The article formulates the task of evaluating the effectiveness of information and communication systems that are being improved as part of conducting experimental tests as part of the System of Control and Analysis of the Space Situation, which are based on algorithms for processing the results of optical observations for maintaining a catalog of space objects. The value of the systematic errors of the initial determination of the motion parameters of a low-orbit circum-circular space object based on the results of optical observations using a multi-stage method based on the approximation of measurements by a circular model and consistent evaluation of planar and in-plane parameters of the orbit was evaluated. Methodological errors introduced when describing the circular motion model of a low-orbit space object along an elliptical trajectory with a small eccentricity are: according to the parameters of the orbit plane, up to 0,80; by period - up to 4 minutes; by the latitude argument – 0,2. In the case when the observation interval does not exceed 1 minute, the maximum errors in angular parameters do not exceed 0,1, which is quite acceptable for solving practical tasks of determining the orbits of small space objects. The systematic error of the period estimation can be compensated by joint processing of the results of optical observations on different turns of the flight.

Covariate Model Selection Approaches for Population Pharmacokinetics: A Systematic Review of Existing Methods, From SCM to AI.

A growing number of covariate modeling methods have been proposed in the field of popPK modeling, but limited information exists on how they all compare. The objective of this study was to perform a systematic review of all popPK covariate modeling methods, focusing on assessing the existing knowledge on their performances. For each method of each article included in this review, evaluation setting, performance metrics along with their associated values, and relative computational times were reported when available. Evaluation settings report was done for uncertainty assessment of communicated results. Results showed that EBEs-based ML methods stood out as the best covariate selection methods. AALASSO, a hybrid genetic algorithm, FREM with a clinical significance criterion and SCM+ with stagewise filtering were the best covariate model selection techniques-AALASSO being the very best one. Results also showed a lack of consensus on how to benchmark simulated datasets of different scenarios when evaluating method performances, but also on which metrics to use for method evaluation. We propose to systematically report TPR (sensitivity), FPR (Type I error), FNR (Type II error), TNR (specificity), covariate parameter error bias (MPE) and precision (RMSE), clinical relevance, and model fitness by means of BIC, concentration prediction error bias (MPE), and precision (RMSE) of new proposed methods and compare them with SCM. We propose to systematically combine covariate selection techniques to SCM or FFEM to allow for comparison with SCM. We also highlight the need for an open-source benchmark of simulated datasets on a representative set of scenarios.

Dimension Reduction of MUSIC Database for Tailored Clustering Using Lasso

The tailored clustering enabled regionalization (TCER) framework has shown that the prediction error of design parameters at a target site can be reduced by constructing a transformation model from a cluster that only contains sites with geotechnical properties similar to the target site. These sites are retrieved from a global/regional database. However, the similarity between any two sites will reduce when the number of properties increases. TCER will be ineffective if too many properties are included in the database (e.g., structural health monitoring and remote sensing databases). The current study presents a least absolute shrinkage and selection operator for databases with incomplete records (denoted by HMLasso; a dimension reduction method) to improve the performance of TCER in high dimensional databases. HMLasso is utilized to construct a dimensionally reduced database, which includes only the properties relevant to the design parameters, from a high-dimensional database. TCER is then adopted to retrieve a cluster from the dimensionally reduced database for inferring the design parameters at a target site. The capability of HMLasso to enhance the performance of TCER in terms of identifying clusters similar to the target site and reducing the prediction error of design parameters are demonstrated using real-world geotechnical databases.

ОБЕСПЕЧЕНИЕ ЭКСПЛУАТАЦИОННЫХ ПАРАМЕТРОВ ШПИНДЕЛЕЙ МЕТАЛЛОРЕЖУЩИХ СТАНКОВ НА ЭТАПАХ РЕМОНТА И ВОССТАНОВЛЕНИЯ

The study objective is to develop a model for determining the parameters of the spindle bearing assembly of metal-cutting machines, taking into account the minimum runout of the functional surface. The task to which the paper is devoted is to develop a technique for evaluating deviations from the alignment of the functional surface of a part (spindle) relative to the axis of the base necks. Research methods: modeling the influence of shape accuracy parameters and the relative position of the spindle support necks on the radial runout of the base cone using the theory of dimensional relationships. The novelty of the work: for the first time the effect of the accuracy parameters of the mating parts on the performance of the spindle assembly is analyzed and recommendations are given to reduce the radial runout of the functional surface. Research results: a model is developed to determine the parameters of the shape error and the error of the relative position of the surfaces and their effect on the radial runout of the spindle functional surface at the stage of the bearing assembly. Conclusions: in order to achieve the specified operational parameters of the spindle assembly at the stages of restoration and repair, it is necessary to develop a technique for performing control and assembly operations, taking into account the influence of shape accuracy parameters and the accuracy of the mutual positioning of the mating surfaces of all parts of the assembly unit.

Genetic Correlation Analysis of Calving Ease and Gestation Length of Korean Holstein Cattle.

To investigate genetic correlation between calving ease (CE) and gestation length (GL) traits of Korean Holstein cattle to understand genetic structures of these two traits and their potential implications. Records of progenies from first parity (P1, N=117,921) and second parity (P2, N=141,104) Holsteins cows were used for analysis. All phenotypes (CE and GL) were considered as calf traits. The CE was an ordered categorical trait. It was scored from 1 (normal calving) to 4 (difficult calving). GL observations were restricted between 260 and 305 days. Variance components and genetic parameters were estimated through a bivariate animal model with a correlated maternal effect using the BLUPF90+ software package. Heritability (h2) estimates of CE for direct and maternal effects were low (less than 0.01) in all parity calves. For GL, despite lower h2 of maternal effect (~0.03), the direct effect was moderately heritable (0.20 to 0.23) in this study. Direct and maternal effects of CE trait were weakly correlated (P1: 0.09 ± 16.60, P2: -0.04 ± 0.00). GL had similar correlation patterns (P1: 0.03 ± 0.00; P2: -0.15 ± 0.05) across parities. Direct genetic correlations of GL and CE were mostly weak (P1: 0.18 ± 0.31; P2: -0.01 ± 0.06), whereas maternal genetic correlations were moderate and positive (P1: 0.39 ± 0.95; P2: 0.46 ± 0.04). Although the genetic influence of GL on CE was not entirely clear due to large estimation errors for parameters, overall positive associations between direct effects and maternal effects essentially indicate a selection potential for GL as an indicator trait of CE. This is the first genetic correlation investigation of GL and CE in Korean Holstein cattle. It provides important insights into genetic architectures of GL and its future potential as an indicator trait for CE improvements in Korean Holsteins.

Nozzle Pressure- and Screw Position-Based CAE Scientific Process Parameter Setup for Injection Molding Process.

This study developed a scientific process parameter setup based on nozzle pressure and screw position, with the process parameter search sequence being injection speed, V/P switchover position, packing pressure, and packing time. Unlike previous studies, this study focuses on the scientific process parameter setup of experiments and simulations, as well as on the implementation of calibration. Experiments and simulations had the same trend of results in the scientific process parameter setup. Although the experiments and simulations had the same trend, the machine response caused parameter errors. After setting the time constant of the simulations, injection speed profiles from the experiments and simulations became closely aligned. The simulation results for the injection speed and V/P switchover position became closer to the experiment results than the results of the uncalibrated simulation. The error between the simulated and experimental injection speed was reduced from 20% to 6% after applying time constant calibration. The V/P switchover point error was also reduced from 11% to 5%, highlighting the effectiveness of the time constant to calibrate the simulation.

Skillful subseasonal ensemble predictions of heat wave onsets through better representation of land surface uncertainties

Uncertainties in land surface processes notably limit subseasonal heat wave (HW) onset predictions. A better representation of the uncertainties in land surface processes using ensemble prediction methods may be an important way to improve HW onset predictions. However, generating ensemble members that adequately represent land surface process uncertainties, particularly those related to land surface parameters, remains challenging. In this study, a conditional nonlinear optimal perturbation related to parameters (CNOP-P) approach was employed to generate ensemble members for representing the uncertainties in land surface processes resulting from parameters. Via six strong and long-lasting HW events over the middle and lower reaches of the Yangtze River (MLYR), HW onset ensemble forecast experiments were conducted with the Weather Research and Forecasting (WRF) model. The performance of the CNOP-P approach and the traditional random parameter perturbation ensemble prediction method was evaluated. The results demonstrate that the deterministic and probabilistic skills of HW onset predictions show greater excellence using the CNOP-P approach, leading to much better predictions of extreme air temperatures than those using the traditional method. This occurred because the ensemble members generated by the CNOP-P method better represented the uncertainties in important land physical processes determining HW onsets over the MLYR, notably vegetation process uncertainties, whereas the ensemble members generated by the random parameter perturbation method could not. This finding suggests that the CNOP-P method is suitable for producing ensemble members that more appropriately represent model uncertainties through more reasonable parameter error characterization.

High‐Frequency Instruments and Identification‐Robust Inference for Stochastic Volatility Models

ABSTRACTWe introduce a novel class of stochastic volatility models, which can utilize and relate many high‐frequency realized volatility (RV) measures to latent volatility. Instrumental variable methods provide a unified framework for estimation and testing. We study parameter inference problems in the proposed framework with nonstationary stochastic volatility and exogenous predictors in the latent volatility process. Identification‐robust methods are developed for a joint hypothesis involving the volatility persistence parameter and the autocorrelation parameter of the composite error (or the noise ratio). For inference about the volatility persistence parameter, projection techniques are applied. The proposed tests include Anderson‐Rubin‐type tests and their point‐optimal versions. For distributional theory, we provide finite‐sample tests and confidence sets for Gaussian errors, establish exact Monte Carlo test procedures for non‐Gaussian errors (possibly heavy‐tailed), and show asymptotic validity under weaker assumptions. Simulation results show that the proposed tests outperform the asymptotic test regarding size and exhibit excellent power in empirically realistic settings. The proposed inference methods are applied to IBM's price and option data (2009–2013). We consider 175 different instruments (IVs) spanning 22 classes and analyze their ability to describe the low‐frequency volatility. IVs are compared based on the average length of the proposed identification‐robust confidence intervals. The superior instrument set mostly comprises 5‐min HF realized measures, and these IVs produce confidence sets which show that the volatility process is nearly unit‐root. In addition, we find RVs with higher frequency yield wider confidence intervals than RVs with slightly lower frequency, indicating that these confidence intervals adjust to absorb market microstructure noise. Furthermore, when we consider irrelevant or weak IVs (jumps and signed jumps), the proposed tests produce unbounded confidence intervals. We also find that both RV and BV measures produce almost identical confidence intervals across all 14 subclasses, confirming that our methodology is robust in the presence of jumps. Finally, although jumps contain little information regarding the low‐frequency volatility, we find evidence that there may be a nonlinear relationship between jumps and low‐frequency volatility.

Reducing Computational Costs and Mitigating Measurement Errors in Parameters’ Estimation for the Single Diode PV Model

Reducing Computational Costs and Mitigating Measurement Errors in Parameters’ Estimation for the Single Diode PV Model

An Improved Multi-point Chord Reference Measurement Device and Error Correction Method for Corrugation Measuring in Sharp Curves

Background: The traditional chord reference method and its device have faced challenges matching the sharp curve track. Thus, it is of utmost importance to research and develop a measurement device and method that can obtain accurate and comprehensive information regarding rail running band corrugation. Aims: It provides a solution for the measurement of the multi-point chord reference method on sharply curved rails and, at the same time, meets the demand for data richness and accuracy in theoretical studies on the causes of rail corrugation and wheel-rail contact relationships. Objective: This paper aims to research and design a measurement device based on the multi-point chord reference method for the rail running band corrugation. Meanwhile, the error in measuring the sharp curve rail is corrected and verified by simulation. Methods: The lateral information of rail running band is obtained by using a line laser profile sensor; the rail corrugation measurement model and error correction model of the rail running band area are established based on multi-point reference system; the simulation of the system model is carried out using Matlab; and the mechanical structure is built for static testing. Results: The measurement model based on the profile sensor and multi-point chord reference system can accurately obtain the rail corrugation waveform of any longitudinal measurement line in the running band area. The measurement results can be accurately corrected for a radius of 200m- 400m, the error parameters can be effectively improved, and the mechanical mechanism of the measurement system runs stably. Conclusion: The results show that the measurement model can accurately measure the rail corrugation of the sharp curve section, and the multi-waveform in the rail running band area has good measurement performance, but the mechanical structure can be optimized and improved in the light weight.