Fitting Method Research Articles

Calibration and Uncertainty Analysis for Isovel Contours-based Stage-discharge Rating Curve by Sequential Uncertainty Fitting (SUFI-2) Method

Calibration and Uncertainty Analysis for Isovel Contours-based Stage-discharge Rating Curve by Sequential Uncertainty Fitting (SUFI-2) Method

An experimental comparative study on condensing supersonic steam jets in water tank: Square, rectangular, and circular cross-sectional shapes

The direct contact condensation phenomenon has gained significant attraction among researchers due to positive aspects such as low driving potential and rapid heat transfer rates in its industrial uses. The present study explores the effect of the cross-sectional shape of converging-diverging on steam-submerged jet condensation behavior. The influence of three cross-sections (circular, square, and rectangular) on steam jet shapes, jet length, average heat transfer coefficient, and maximum expansion ratio were studied. The dimensionless steam jet length for circular, square, and rectangular nozzles has been found in the range of 2.2–7.7, 2.43–8.41, and 2.22–8.35, respectively. The maximum expansion ratio for circular, square, rectangular major axis plane, and rectangular minor axis plane has been found in the range of 1–1.71, 1.14–2.15, 1.22–2.82, and 1.06–1.45. Results show that the maximum expansion ratio and dimensionless jet length rise with steam pressure and water temperature rise. Dimensionless jet length decreases in the order of nozzle with cross-section: square > rectangular > circular. Correlation for dimensional jet length of both circular and non-circular nozzles is presented using the analytical and curve fitting methods. The maximum expansion ratio decreases in order: major axis plane > square > circular > minor axis plane. The average heat transfer coefficient (MW/m2 oC) for circular, square, and rectangular nozzles has been found in the range of 1.16–4.88, 0.89–3.14, and 1.01–3.49. Results show that the average heat transfer coefficient falls with the rise in steam pressure and water temperature. The average heat transfer coefficient decreases in order: circular > rectangular > square. Condensation regime maps are proposed for circular, square, and rectangular nozzles, including diagonal plane effects in square and rectangular nozzle.

Estimation of Remaining Useful Life (RUL) for Pneumatic Actuator without apriori RUL History: A Hybrid Prognostic Approach

Predicting the Remaining Useful Life (RUL) of an industrial pneumatic actuator is crucial for enhancing maintenance strategies, reducing downtime and optimizing resource allocation. However, estimation becomes challenging when no historical RUL data is available for modeling. In this paper, a novel hybrid prognostic approach that combines Dynamic Time Warping (DTW), Exponential Degradation Model (EDM) and Random Forest Regressor (RFR) is proposed to estimate the RUL of pneumatic actuators under the absence of apriori RUL history. The DTW technique is employed to identify the onset of potential degradation. By aligning the healthy and faulty data, DTW provides a robust measure of distance and time at the point of deviation as the threshold value. Subsequently, the EDM is introduced to capture the degradation pattern in the actuator behavior. The EDM accounts for the relationship between threshold value, operating conditions, degradation rate and exponential coefficients through curve fitting methods. To further enhance prediction accuracy, RFR is employed to predict the RUL based on input features of aligned data from DTW and the derived degradation rates from EDM. In the simulation studies, the proposed methodology is applied to a synthetic dataset and benchmark DAMADICS dataset of the industrial pneumatic actuator in sugar processing unit to estimate RUL. The estimated RUL for each health indicator is quantified and the severity of each fault is discussed. The proposed method is implemented on a real time laboratory setup. The results are also validated on the benchmark NASA turbo-engine dataset by comparing the actual and estimated RULs, achieving 82.5% range-based accuracy.

Adaptive Spline Surface Fitting With Arbitrary Topological Control Mesh.

Reconstructing a spline surface from a given arbitrary topological triangle mesh is a fundamental and challenging problem in computer-aided design and engineering. This article introduces a novel surface fitting method utilizing G-NURBS capable of handling control meshes with arbitrary topologies. This method employs adaptive control point adjustment, guided by the geometric attributes of the input model, ensuring precise representation of sharp features such as edges and corners. Two primary strategies are employed: A parameter correspondence approach designed for sharp features and a control mesh iterative refinement technique that incorporates geometrical feature information. The proposed method has been tested and evaluated on various CAD models to demonstrate its effectiveness. This method can achieve higher fitting accuracy while faithfully preserving the geometrical features with fewer control points.

Temperature extraction from Brillouin sensing based on temporal convolutional networks

The Brillouin optical time-domain sensing system has become a hot spot of research due to its ability to seamlessly monitor the temperature and strain variations in optical fibers along the line. Given its current limitations of low accuracy and inadequate real-time performance in long-distance monitoring, the Brillouin gain extraction temperature method based on temporal convolutional networks is proposed. On this basis, we established a Brillouin optical time-domain experimental system where comprehensive simulations and tests were conducted to assess the temperature extraction performance under different conditions. Besides, a comparison was made between the system and traditional methods like Lorentz fitting method and extreme learning machine method. The results have suggested that the temporal convolutional network exhibits remarkable measurement accuracy, even in scenarios with low signal-to-noise ratios and large sweep frequency steps.

PyAMARES, an Open-Source Python Library for Fitting Magnetic Resonance Spectroscopy Data

Background/Objectives: Magnetic resonance spectroscopy (MRS) is a valuable tool for studying metabolic processes in vivo. While numerous quantification methods exist, the advanced method for accurate, robust, and efficient spectral fitting (AMARES) is among the most used. This study introduces pyAMARES, an open-source Python implementation of AMARES, addressing the need for a flexible, user-friendly, and versatile MRS quantification tool within the Python ecosystem. Methods: PyAMARES was developed as a Python library, implementing the AMARES algorithm with additional features such as multiprocessing capabilities and customizable objective functions. The software was validated against established AMARES implementations (OXSA and jMRUI) using both simulated and in vivo MRS data. Monte Carlo simulations were conducted to assess robustness and accuracy across various signal-to-noise ratios and parameter perturbations. Results: PyAMARES utilizes spreadsheet-based prior knowledge and fitting parameter settings, enhancing flexibility and ease of use. It demonstrated comparable performance to existing software in terms of accuracy, precision, and computational efficiency. In addition to conventional AMARES fitting, pyAMARES supports fitting without prior knowledge, frequency-selective AMARES, and metabolite residual removal from mobile macromolecule (MM) spectra. Utilizing multiple CPU cores significantly enhances the performance of pyAMARES. Conclusions: PyAMARES offers a robust, flexible, and user-friendly solution for MRS quantification within the Python ecosystem. Its open-source nature, comprehensive documentation, and integration with popular data science tools enhance reproducibility and collaboration in MRS research. PyAMARES bridges the gap between traditional MRS fitting methods and modern machine learning frameworks, potentially accelerating advancements in metabolic studies and clinical applications.

Numerical analysis of critical support stiffness in ring ribs of a sandwich composite pressure shell

ABSTRACT To study the hydro-static pressure carrying capability of a sandwich composite pressure shell with ring ribs, and explore a structure design method, we obtain an empirical formula of the ring rib segments critical support stiffness, which is defined as Dcr , via finite element simulation and theoretical analysis method. It was shown that, Dcr is of great significance in designing the sandwich composite pressure shell structure. On this basis, the parameters were dimensionless and the empirical formula was obtained by fitting serious independent simulated results. The fitting error of the empirical formula is within 2%. This fitting method has a certain theoretical value for the theoretical research of sandwich composite pressure shell structure. The obtained results are useful for desinging the bearing capacity of novel structure.

A Novel Time-Varying P-III Distribution Curve Fitting Model to Estimate Design Floods in Three Gorges Reservoir Operation Period

Design floods are traditionally estimated based on the at-site annual maximum flood series, including historical information of hydraulic structures. Nevertheless, the construction and operation of upstream reservoirs undermine the assumption of stationarity in the downstream flood data series. This paper investigates non-stationary design flood estimation considering historical information from the Three Gorges Reservoir (TGR) in the Yangtze River. Based on the property that the distribution function of a continuous random variable increases monotonically, we proposed a novel time-varying P-III distribution coupled with the curve fitting method (referred to as the Tv-P3/CF model) to estimate design floods in the TGR operation period, and we comparatively studied the reservoir indices and parameter estimation methods. The results indicate that: (1) The modified reservoir index used as a covariate can effectively capture the non-stationary characteristics of the flood series; (2) The Tv-P3/CF model emphasizes the fitness of historical information, yielding superior results compared to time-varying P-III distribution estimated by the maximum likelihood method; (3) Compared to the original design values, the 1000-year design peak discharge Qm and 3-day and 7-day flood volumes in the TGR operation period are reduced by approximately 20%, while the 15-day and 30-day flood volumes are reduced by about 16%; (4) The flood-limited water level of the TGR can be raised from 145 m to 154 m, which can annually generate 0.32 billion kW h more hydropower (or increase by 6.8%) during flood season without increasing flood prevention risks.

Impact of Sport and Physical Activity on Urinary Incontinence: Does Exercise Act as a Protective Factor or Increase Risk?

Introduction: Urinary incontinence is a health problem which affects women more often. The condition manifests itself as uncontrolled urine leakage. A basic distinction is made between stress urinary incontinence (SUI), urgency urinary incontinence (UUI), and mixed urinary incontinence (MUI). Physical activity is commonly used in the treatment of UI. Interestingly, numerous studies show that athletes and especially female athletes are more likely to develop UI. In this study, we will try to answer the question of whether sport and physical activity have a protective effect or are a risk factor for the development of UI. In this review, we will focus on sports and physical activity that does not directly target pelvic floor muscle training.Aim of the study: A review of the literature on the impact of sport and physical activity on the prevalence of urinary incontinence in both sexes in different age groups and different health and fitness statusesMaterials and methods: A review of the recent literature available in PubMed and Google Scholar databases was conducted, using keywords such as ‘urinary incontinence’, ‘stress urinary incontinence’, ‘prostatectomy”. This review was enriched by urological guidelines on urinary incontinence.Conclusion: Urinary incontinence is a common ailment, especially among women. Particularly at risk are those who participate in high-intensity sports and do prolonged training, performing exercises such as jumping and sprinting. On the other hand, limited physical mobility is also a risk factor for incontinence. Keeping active in seniors can reduce symptoms. Exercises with positive effects include Pilates, which has very good efficacy in men after total prostatectomy surgery. Physical activity may also help to reduce body weight, as obesity is a recognised risk factor for urinary incontinence. Further research into the therapeutic use of exercises such as yoga and Pilates is warranted and may help treat patients with incontinence.

Color Characterization Model Using Subspace Polynomial Fitting Method for Modern Displays

ABSTRACT The prevailing color characterization model was established on the assumption of channel independence. However, modern displays did not typically exhibit good channel independence on color reproduction. With the emergence of modern displays, the computing performance of the existing characterization models is insufficient. In this study, we propose a subspace polynomial fitting model, constructing polynomial fitting relationships across one, two, and three channels respectively, based on the channel independence of the display to determine the division criterion of subspace (0, M, N, 255), which can form 64 combinations of training samples. The model outperformed existing models, with the maximum ∆E 00 color differences of the 777 tested combinations between the model predictions and the measurements across the seven displays ranging from 1.27 to 5.73. In addition, based on the subspace polynomial regression model, 4913 data sets of multilayer perceptron (MLP) neural networks can be constructed for reverse color characterization. Similarly, 777 tested combinations were calculated for seven displays, resulting in maximum color differences of 2.35 to 3.83 ∆E 00.

Analysis on identifying thin-plate void parameters in concrete based on vibro-acoustic method

Thin-plate voids are common defects found in concrete structures, such as tunnel vaults. Parameter identification for concrete thin plates using the vibro-acoustic method is a crucial approach for rapid and accurate detection in specific locations. In this study, a fixed-support rectangular thin-plate vibration model was developed, and the vibro-acoustic echo components were derived under external excitation. Based on this model, a method for identifying horizontal dimensions was proposed, utilizing the power spectrum frequency distribution. Subsequently, a digital filter based on Wavelet Synchronous Squeezing Transform (WSST) was constructed to obtain the modal vibration echo. A fitting method for the vibro-acoustic echo attenuation coefficient and a correction for thin-plate thickness were implemented. Finally, the accuracy and applicability of identifying the size parameters of thin plates were verified using square specimens with thicknesses of 12 mm, 21 mm, and 30 mm, as well as circular specimens with a thickness of 15 mm.

The Properties of an Edge-On Low Surface Brightness Galaxies Sample

We analyzed the properties of a sample of edge-on low-surface brightness galaxies, which are referred to as Cao23 ELSBGs. Cao23 ELSBGs exhibit a wide range of luminosities (−22 < Mr < −13) with a mean scale length of 3.19 ± 1.48 kpc. Compared to HI-rich dwarf ELSBGs, Cao23 ELSBGs display more extended disk structures and redder (g-r) colors. They are also, on average, more massive than HI-rich dwarf ELSBGs. Star formation rates (SFRs) were calculated using WISE 12 μm luminosity conversions and spectral energy distribution (SED) fitting methods, respectively. Cao23 ELSBGs fall below the main sequence with specific star formation rates (sSFRs) primarily in the range of 0.01–0.1 Gyr−1. More massive Cao23 LSBGs tend to have lower sSFRs. Additionally, we derived the non-parametric star formation histories (SFHs) of Cao23 ELSBGs by SED fitting, dividing the SFHs into seven look back time bins with constant SFRs assumed for each bin. Our analysis indicates that high-mass (M∗ > 109.0M⊙) Cao23 ELSBGs assembled their mass earlier than their lower-mass counterparts, supporting a downsizing trend for LSBGs.

Hall probe calibration in high-precision magnetic field mapping system of superconducting cyclotron

Abstract Introduction Superconducting cyclotron can generate high-energy proton beams and are mainly used for radiation therapy of tumors and cancers. In the superconducting cyclotron SC200, the maximum magnetic induction intensity can typically reach up to 4.6 T, and the magnetic field accuracy is 1e-4. Hall probes are commonly used tools for measuring high-intensity magnetic fields. Objective Through comprehensive consideration, this study selects the SENIS Low-Noise Teslameter 3MH5 and Hall probe C to measure the magnetic field. When the magnetic field exceeds the range of 2 T, the measurement accuracy of the Hall probe is less than 1e-4, and the Hall probe needs to be calibrated to improve its measurement accuracy. Methods The Hall probes are calibrated using Swiss METROLAB PT2025 nuclear magnetic resonance (NMR) Tesla instrument and 1062 probe. Based on the calibration principle, a calibration system platform was built, test data were collected, and calibration curves were obtained. At the same time, the calibration data were analyzed through cross-validation experiments using the cubic polynomial fitting method. Results The results indicate that the test deviation range is from −0.1 g to 0.1 g, and the measurement accuracy can reach 1e-4. Conclusion In summary, the Hall probe can accurately measure the magnetic field distribution of the superconducting cyclotron. It can provide accurate and important data for the calculation and analysis of particle beam dynamics.

Sports Injuries in Germany – A Pilot Study

Problem: Sports accidents are omnipresent in all areas of sport. However, due to a lack of data, conclusions cannot be drawn about how many sports accidents occur in Germany each year. This study aims to provide an initial overview of sports injuries and the types of sports involved, serving as a foundation for a larger survey. Methods: From September 12th-16th, 2022, a representative survey was conducted via telephone (forsa.omniTel®), interviewing 2002 individuals in Germany aged 14 and older about their sports activities and injuries in the past 12 months. Results were analyzed using 95% confidence intervals (CI) and logistic regression to assess the impact of age and gender on injury probability (α=0.05). The distribution of injuries across sports was calculated using an iterative proportional fitting method and Bonferroni correction for significance levels. The original document was internally reviewed, contains no personal data, and is approved for publication, with no further ethics committee review required, as forsa, a member of ADM, complies with all legal and ethical standards, including GDPR. Results: It is estimated that 12.2% (CI=10.5 to 14.0%; CIadj=9.8 to 14.7%) of athletes in Germany have been injured during sport in the past 12 months, requiring medical treatment or a break from sport. Younger and male athletes were more likely to be injured than older and female athletes (age: p<0.001, padj<0.001; gender: p<0.001, padj<0.01). Most injuries occurred during running/walking/hiking (16%) and cycling (14%). Soccer (11%) and tennis (4%) had the highest injury rates among sports games. Conclusion: This pilot study offers crucial insights into sports injury prevalence in Germany. Future studies should include detailed injury types, severity and exposure times for more individualized preventive programs.

Biomechanical analysis of balance control in the elderly By Ba Duan Jin

This study aims to evaluate the biomechanical effects of Ba Duan Jin on balance control in the elderly, seeking effective fitness methods to enhance their balance capabilities and reduce the risk of falls. Methods: Participants were randomly assigned to an experimental group (EG) and a control group (CG), with 25 individuals in each. The Berg Balance Scale (BBS) and mechanical measurements were utilized to evaluate the participants’ balance abilities and biomechanical performance. Statistical analyses were performed to compare the outcomes between the EG and CG, ensuring a comprehensive assessment of the differences. Results: The experimental group demonstrated a significant improvement in the Berg Balance Scale (BBS) scores (p < 0.01), with a notable increase in the eyes-closed standing task (BBS6), which reached significance (p < 0.05), indicating a consistent advantage for the experimental group in this area. Biomechanical measurements revealed that the experimental group exhibited significantly higher parameters compared to the control group, including stability index (EG: 0.7 ± 0.1/2.2 ± 0.4, CG: 0.6 ± 0.1/1.9 ± 0.3), power (EG: 3.2 ± 0.5/3.8 ± 0.6, CG: 2.9 ± 0.4/3.5 ± 0.5), energy expenditure (EG: 20.1 ± 3.8/25.0 ± 4.3, CG: 16.5 ± 3.2/20.3 ± 3.8), and step frequency (EG: 95.0 ± 5.5/105.0 ± 6.0, CG: 85.0 ± 5.0/100.0 ± 5.5). Additionally, peak force (EG: 345.2 ± 30.1/412.6 ± 35.8, CG: 310.4 ± 28.5/310.4 ± 28.5), impact force (EG: 68.3 ± 7.2/85.7 ± 8.9, CG: 55.8 ± 6.7/72.4 ± 8.1), average force (EG: 280.5 ± 25.6/320.7 ± 30.2, CG: 245.3 ± 22.8/280.1 ± 26.4), and direction of force (EG: 10.0 ± 2.0/15.0 ± 2.5, CG: 8.5 ± 1.5/12.0 ± 2.0) also exhibited significant differences (p < 0.05). Notably, the static single-leg stance (EG: 3.38 ± 0.39, CG: 2.38 ± 0.50), dynamic sit-to-stand (EG: 3.77 ± 0.34, CG: 2.85 ± 0.37), turning movements (EG: 3.88 ± 0.27, CG: 2.58 ± 0.56), and double-leg step-ups (EG: 4.00 ± 0.02, CG: 2.69 ± 0.49) displayed extremely significant differences (p < 0.01). These results indicate that Ba Duan Jin training effectively enhances balance control and reduces the risk of falls among the elderly. Conclusion: As a traditional form of physical exercise, Ba Duan Jin effectively enhances balance control and reduces the risk of falls among older adults, providing valuable practical evidence for health management in this population. Future research should focus on conducting more long-term studies with larger sample sizes to verify the applicability and long-term effects of Ba Duan Jin across various age groups and health conditions in older individuals.

A Multi-Parameter Optimization Method for Electromagnetic Characteristics Fitting Based on Deep Learning

Electromagnetic technology is widely applied in numerous fields, and precise electromagnetic characteristic fitting technology has become a crucial part for enhancing system performance and optimizing design. However, it faces challenges such as high computational complexity and the difficulty in balancing the accuracy and generalization ability of the model. For example, the Radar Cross Section (RCS) distribution characteristics of a single corner reflector model or Luneberg lens provide a relatively stable RCS value within a certain airspace range, which to some extent reduces the difficulty of radar target detection and fails to truly evaluate the radar performance. This paper aims to propose an innovative multi-parameter optimization method for electromagnetic characteristic fitting based on deep learning. By selecting common targets such as reflectors and Luneberg lens reflectors as optimization variables, a deep neural network model is constructed and trained with a large amount of electromagnetic data to achieve high-precision fitting of the target electromagnetic characteristics. Meanwhile, an advanced genetic optimization algorithm is introduced to optimize the multiple parameters of the model to meet the error index requirements of radar target detection. In this paper, by combining specific optimization variables such as corner reflectors and Luneberg lenses with the deep learning model and genetic algorithm, the deficiencies of traditional methods in handling electromagnetic characteristic fitting are effectively addressed. The experimental results show that the 60° corner reflector successfully realizes the simulation of multiple peak characteristics of the target, and the Luneberg lens reflector achieves the simulation of a relatively small RCS average value with certain fluctuations in a large space range, which strongly proves that this method has significant advantages in improving the fitting accuracy and optimization efficiency, opening up new avenues for research and application in the electromagnetic field.

Dual-Wavelength Confocal Laser Speckle Contrast Imaging Using a Deep Learning Approach

This study developed a novel dual-wavelength confocal laser speckle imaging platform. The system includes both visible and near-infrared lasers and two imaging modes: confocal and wide-field laser speckle contrast imaging. The experimental results confirm that the proposed system can be used to measure not only blood flow but also blood oxygen saturation. Additionally, we proposed a blood flow perfusion imaging method called BlingNet (a blood flow imaging CNN) based on the laser speckle contrast imaging technique and deep learning approach. Compared to the traditional nonlinear fitting method, this method has superior accuracy and robustness with higher imaging speed, making real-time blood flow imaging possible.

A trajectory tracking control method for the discharge arm of the self-propelled forage harvester

The cooperative operation between the self-propelled forage harvester and the trailer aims to achieve precise and automatic forage unloading. The discharge arm serves as the core structure for conveying forage, and the precision and speed of its motion control are crucial factors influencing the efficiency of forage harvesting. In this context, we proposed a trajectory tracking control method for the discharge arm based on an improved particle swarm optimization (IPSO)-PID controller. Firstly, we utilized the Denavit-Hartenberg (D-H) model of the discharge arm for a positive kinematics solution and the geometric resolution and linear fitting method for the inverse kinematics solution. Secondly, we employed the polynomial interpolation method for trajectory planning on the joint space of the discharge arm, and the PSO algorithm for time-optimal trajectory planning. Then, we designed an IPSO-PID trajectory tracking control algorithm and built an Amesim-Simulink co-simulation model for multiple simulation experiments. Finally, we conducted several performance tests of the discharge arm automatic control system in the workstation and the field, respectively. The experiment results indicate that the performance of the IPSO-PID controller exceeds that of all other controllers, which can meet the discharge arm’s motion control accuracy and speed requirements. Our research results are of great significance for improving the productivity and automation process of the self-propelled forage harvester and provide valuable references for research on automatic and precise control of material loading in other agricultural cooperative harvesting.

Improving power law fitting of root tensile strength–diameter relationships

Abstract Purpose The relationship between root tensile strengths and diameters is often fitted using power law curves. More accurate fitting methodologies were developed, investigating a) the validity of least-squares regression assumptions underlying existing methods, b) how to best quantify intra-diameter variation, and c) whether to fit in terms of tensile strength or tensile force at failure. Methods Regression and maximum likelihood estimation were used to fit various power law models. 6461 tensile strength measurements from 153 existing datasets, covering 103 different plant species, were used to compare models. Results The intra-diameter variation in root strength is proportional to the average strength at each diameter, and is best described using a gamma distribution. When using linear regression on log-transformed measurements, a mathematical correction must be used to avoid underestimating the actual strength (18% on average). Compared to fitting tensile strengths, fitting in terms of root forces at failure was less reliable; the extra emphasis this method places on the effect of large diameters roots was not appropriate because of the typical abundance of thin roots in the field relative to those tested in tension. Average power law fits were proposed for broadleaved trees, conifers, shrubs, grasses and forbs. Conclusion Power law curves should be fitted in terms of root strength rather than root forces at failure, using the newly developed fitting methods that simultaneously fit both the inter-diameter (power law) and intra-diameter variation and can account for fitting bias. This will increase the reliability of future root reinforcement predictions.

Continuous Curvature Parking Path Planning for Unmanned Mining Trucks Using Transition Curves and Model Predictive Control

<div>Geometric methods based on Reeds–Shepp (RS) curves offer a practical approach for the parking path planning of unmanned mining truck, but discontinuous curvature can cause tire wear and road damage. To address this issue in mine scenario, a continuous curvature parking path planning method based on transition curve and model predictive control (MPC) is proposed for mine scenarios. Initially, according to the shovel position information issued by the cloud dispatching platform, a reference line is planned using RS curves. In order to mitigate the wear and tear of the tires and the damage to unstructured roads due to the in situ steering caused by the sudden change of the curvature, a transition curve consisting of clothoid–arc–clothoid that satisfies the kinematics of continuous vehicle steering is designed on the basis of RS curves to achieve the continuity of road curvature, which will contribute to the economy of tire and handling performance. The calculation of Fresnel integral involved by clothoid is simplified by using Chebyshev polynomial fitting method. Moreover, MPC is employed to re-plan an obstacle-avoidance path based on the reference line by designing a rational cost function. Finally, a simulation test platform is built considering the typical parking scenarios for unmanned mining trucks. The simulation results verify the effectiveness of the planning algorithm proposed in this article, and it shows the potential to reduce maintenance costs and improve mining efficiency.</div>