Distribution Of Points Research Articles

Mechanistic insight into the coke formation tendency during upgradation of crude oil in slurry phase batch reactor using MoS2 and its various oil soluble catalysts

This work intends to provide a deep insight on how can the MoS2 and its oil soluble analogues synthesized using various organic acid ligands (oleate, palmitic acid, lauric acid, stearic acid) efficiently fits well into the landscape of refinery and petrochemical integrations. The slurry phase hydroprocessing of crude oil has been efficiently carried out at 420 °C temperature and 120 bar H2 pressure. The hydroprocessing of crude oil presents the prominent results by increasing the lighter and more valuable fractions in the crude oil, as obtained from SARA analysis result, boiling point distribution, viscosity, sulfur and MCR data. The oil soluble MoS2 catalyst (MLT) synthesized from sodium oleate presented better hydroprocessing activity (65 % VR conversion) than other catalysts due to the small crystallite size, greater hydrophobic interaction, low stacking number and slab length. In this study, the comprehensive analysis of the solid coke obtained from both thermal and catalytic reactions are conducted using the XRD to get various structural parameters (average layer distance between two aromatic sheets (dm), average inter-chain layer distance (dλ), average perpendicular height of stack of aromatic sheets (Lc), average diameter of aromatic sheet (La), and average number of stacked aromatic sheets (M)) to elucidate the coke formation mechanism. These findings highlight the crucial role of catalysts in activating supplied hydrogen gas and promoting the hydrocracking reactions.

Self-supervised rotation-equivariant spherical vector network for learning canonical 3D point cloud orientation

The perception of orientation in augmented reality, robot grasping, and 3D scene understanding is commonly addressed through the utilization of hand-crafted geometric features. However, machines can also learn the inherent orientation of 3D point clouds through experience, similar to humans. In this paper, we propose a self-supervised spherical vector network that is rotation-equivariant. Specifically, we use density-aware adaptive sampling to construct spherical signal samples to handle distorted point distributions in spherical space. Spherical convolutional vector layers and spherical routing layers are proposed to extract the rotation-equivariant vectors that represent the existence probability of the entity and orientations. Our method learns rotational representations from 3D point clouds through a self-supervised training process. We also provide theoretical proof that our proposed spherical vector networks are rotation-equivariant. Experiments on a variety of public datasets directly and indirectly demonstrate the effectiveness of the proposed method for canonical orientation estimation, even on unknown classes.

Storage stability of biocrude oil fractional distillates derived from the hydrothermal liquefaction of food waste

Biocrude oil produced via hydrothermal liquefaction (HTL) is a promising precursor for transportation fuels and biochemicals. Present studies have highlighted the inherent instability of the biocrude oil, which poses significant challenges for its subsequent storage, upgrading and transportation. On the other hand, biocrude oil distillates showed the potential to serve as a transportation fuel blendstock. To this end, this study aimed to investigate the influence of the distillation temperature (199–238 °C and 238–274 °C), storage atmosphere (air and nitrogen), temperature (25 °C and 55 °C), and time (0–16 weeks) on the physical, chemical, and boiling point distribution properties of the distillates. Results demonstrated that changes in the stored distillates due to different distillation temperature were drastically higher than that between different storage atmospheres and temperatures. Comparing with the raw distillate, the distribution of compounds with a molecular weight < 300 Da in low-temperature (199–238 °C) distillates (LD) and medium-temperature (238–274 °C) distillates (MD) was improved with the increasing storage time. In addition, storage led to a decrease in O:C ratio (21.6 % and 86.5 %), and an increase in the HHV (3.6 % and 12.3 %) in the LD and MD, respectively. Furthermore, only slight deviations were observed in the density (5.2 % and 7.4 %) and viscosity (5.2 % and 0.8 %) for LD and MD, respectively. In particular, the MD group exhibited comparable characteristics to transportation fuels with decreased acidity (1.8–2.8 mg KOH/g) and increased HHV (46.2–46.8 MJ/kg) after long-term storage. At last, the mechanism of superior distillates stability was discussed. This study indicated that distillation not only presents a potential approach for producing transportation fuel blendstock but also improves the stability of HTL biocrude oil.

Voronoi discretization to improve the meshless local Petrov–Galerkin method in 3D-computational fracture mechanics

Purpose1) The OE-MLPG penalty meshfree method is developed to solve cracked structure.(2) Smartening the numerical meshfree method by combining the particle swarm optimization (PSO) optimization algorithms and Voronoi computational geometric algorithm. (3). Selection of base functions, finding optimal penalty factor and distribution of appropriate nodal points to the accuracy of calculation in the meshless local Petrov–Galekrin (MLPG) meshless method.Design/methodology/approachUsing appropriate shape functions and distribution of nodal points in local domains and sub-domains and choosing an approximation or interpolation method has an effective role in the application of meshless methods for the analysis of computational fracture mechanics problems, especially problems with geometric discontinuity and cracks. In this research, computational geometry technique, based on the Voronoi diagram (VD) and Delaunay triangulation and PSO algorithm, are used to distribute nodal points in the sub-domain of analysis (crack line and around it on the crack plane).FindingsBy doing this process, the problems caused by too closeness of nodal points in computationally sensitive areas that exist in general methods of nodal point distribution are also solved. Comparing the effect of the number of sentences of basic functions and their order in the definition of shape functions, performing the mono-objective PSO algorithm to find the penalty factor, the coefficient, convergence, arrangement of nodal points during the three stages of VD implementation and the accuracy of the answers found indicates, the efficiency of V-E-MLPG method with Ns = 7 and ß = 0.0037–0.0075 to estimation of 3D-stress intensity factors (3D-SIFs) in computational fracture mechanics.Originality/valueThe present manuscript is a continuation of the studies (Ref. [33]) carried out by the authors, about; feasibility assessment, improvement and solution of challenges, introduction of more capacities and capabilities of the numerical MLPG method have been used. In order to validate the modeling and accuracy of calculations, the results have been compared with the findings of reference article [34] and [35].

Regional- to district-scale controls on thermal springs occurrence in South Africa: Insights from investigations of their spatial distribution and their spatial relationships with geological features

In the study presented here, the spatial distribution of thermal springs in South Africa was investigated using point distribution analysis, fractal analysis and Fry analysis, and the thermal springs’ spatial relationships with high-heat-producing granitoids (HHPGs), groundwater yield, and structural lineaments were investigated using distance distribution analysis. The purpose of these spatial investigations was to explore the regional- to district-scale geological controls on thermal springs occurrence in South Africa. The results show that regional- to district-scale geological controls on thermal springs occurrence in South Africa involve the presence of or proximity to HHPGs (as heat source control), groundwater yield of ∼2 L/s (as water source control) and intersections of structural lineaments with particular trends (as pathways control). In southern and eastern South Africa, the pathways controls are, respectively, intersections of ENE-trending lineaments with NNW- and NW-trending lineaments and intersections of ENE- and WNW-trending lineaments. This spatial information comprises a conceptual model that can be used as a framework for regional- to district-scale predictive modeling and mapping of the prospectivity (or likelihood) of areas where geothermal resources potentially exist in South Africa.

Solar Energy for Water Optimization: Advancing Clean Water Distribution at Universitas Samudra

The use of solar energy has become increasingly popular in recent years due to sustainability and environmental benefits. Universitas Samudra is designing the centralization of water distribution using solar energy as an energy resource. By using solar energy, universities can reduce their carbon footprint and energy costs while improving access to clean water for students, staff, and the surrounding community. This paper explores the benefits, challenges, design, and optimization of solar energy in pure water distribution, focusing on the technical and optimization of implementing such systems. The optimization is based on the water demand analysis and water source regarding the rainwater harvesting, then distribution points, including time patterns, peak demands, and seasonal fluctuation. The analyzing variables include the flow and residual head at each point distribution, pressure drop, pipe sizing, storage tank sizing, and water supply capacity input to each storage tank. The data is also based on the university's master plan and blueprint for the next 30 years of Development. Furthermore, the data will be analyzed and calculated to obtain optimum distribution pump capacity and pump working hours. The system was Analysed and calculated using Epanet hydraulic modeling software. The calculations result are that the distribution pump capacity is 16 L/s, the head is 30 M, the works for five hours a day, the power is 4.7 kW, the total energy is around 23.5 kWh per day, and The solar panel is eight kWp

The distribution of near point of convergence, near point of accommodation and their association with interpupillary distance in young adults- A cross sectional study

Vergence dysfunctions and accommodation anomalies are the main causes of asthenopic symptoms. These may be related to differences in the anthropometric measurements.The anthropometric measurements have a significance in understanding human physical and physiological variations. This study was carried out with the purpose of measuring the near point of convergence (NPC), near point of accommodation (NPA) and to determine their association with interpupillary distance (IPD) in a population of North-western India.This was a cross sectional study carried out on young adults of 18-22 years age group. The NPC and NPA were measured with a Royal Air Force (RAF) scale. Distance IPD was measured with a ruler and autorefractometer. The results were analysed statistically to study the relation of the variables with each other.The average IPD of the study population was 62.18 mm, IICD was 29.65, NPC was 6.55 cm, NPA was 9.42 cm. The correlation between IPD and IICD was 0.15 (weak). The correlation between IPD and NPC was found to be 0.18, correlation between IPD and NPA OD was 0.35 and the correlation between IPD and NPA OS was 0.30, which all were statistically weak.The anthropometric measurements of the study population were similar to those reported by other studies on the Indian population. The correlation between NPA and IPD and that between NPC and IPD was found to be weak.

A novel method for inverting coseismic 3D surface deformation using InSAR considering the weight influence of the spatial distribution of GNSS points

The combination of InSAR and GNSS data can provide more accurate measurements of coseismic 3D surface deformations, which are crucial in determining earthquake source parameters. Currently, the methods for inverting coseismic 3D surface deformations by integrating GNSS and InSAR data using the strain model (SM) (e.g., Simultaneous and integrated strain tensor estimation from geodetic measurements to obtain 3D displacement maps (SISTEM), SM-VCE) simply select GNSS points based on a certain number or distance, without considering the weight influence of the spatial distribution of G NSS points on the InSAR and GNSS data fusion results. To address this issue, this paper proposes a new method that uses Voronoi diagrams analyze the contribution of GNSS points to the deformation at target points. In this paper, GNSS points with high contribution can be used to construct observation value equations and optimize the internal weight of GNSS data. The proposed approach characterizes the contribution of GNSS data to target points by analyzing the changes in the Delaunay triangulation area formed before and after the target points are added to the GNSS data. Simulation experiments reveal that the proposed method outperforms conventional methods in accuracy and completeness. The proposed method is then applied to map the coseismic 3D surface deformations of the 2020 Mw6.5 Monte Cristo Range earthquake and find that the maximum deformation in the east–west, north–south, and vertical directions is about 13 cm, 11 cm, and 24 cm, respectively.

Landslide Analysis of Western Lombok Island Using Weight of Evidence (WoE) Method

Landslides are widely distributed in all parts of Indonesia. West Nusa Tenggara Province is one of Indonesia's provinces with a quite high landslide distribution. This research was conducted in the western part of Lombok Island, including West Lombok Regency, Mataram City and North Lombok Regency, which has a moderate to high landslide risk class based on the 2021 Indonesian Disaster Risk Index (IRBI) data, so it is necessary to mitigate landslides by making a landslide vulnerability map. This research aims to determine the main factors of landslides in western Lombok Island, create a landslide vulnerability level map and analyse the distribution of landslide-prone areas in western Lombok Island using the weight of evidence (WoE) method. The research methods used are the literature study method (collecting information about land movement events in the research location and other information about the theories needed and used during the research) and the ArcGIS-based WoE method. This research shows that the slope parameter is the main factor of ground movement based on the analysis result of the WoE method. The landslide vulnerability level of the WoE method and the distribution of landslide occurrence points are high vulnerability level with an area of 473.45 km2, medium with an area of 198.88 km2, low with an area of 210.58 km2 and very low with an area of 911.73 km2.

Accuracy and reproducibility of real and virtual occlusal contact points in implant-supported fixed prostheses.

To evaluate the accuracy and reproducibility of real and virtual occlusal contact points in implant-supported, fixed complete dentures. The study included 19 participants using mandibular interim complete-arch fixed prosthesis supported by 3 or 4 implants as opposed to conventional removable complete dentures. At installation, an examiner installed the prostheses and verified the occlusal contact points through 2 methods: recording the real contact points with carbon paper (RC) followed by occlusal photography and intraoral scanning (VC) to record the virtual contact points to obtain a screen print of the software. Then, the two images were randomized to determine the order to be inserted into Microsoft PowerPoint for blind and paired evaluation. The independent variables consisted of the distribution of occlusal contacts points (qualification through pre-defined scores based on the position of the contact points on the surfaces of the teeth) and the reproducibility of the methods by verifying the number of occlusal points. For this, a descriptive analysis was used to evaluate the distribution of occlusal contacts points and the Wilcoxon test for the reproducibility of the occlusal contact points between the methods (p<0.05). The methods had 100% and 73.6% real and virtual occlusal contact points, respectively, which is considered clinically excellent. There was no significant difference regarding the reproducibility of the methods by the number of occlusal contact points (RC: x̅13.32; VC: x̅13.68; p=0.715). The use of the tested intraoral scanner can be an easy and fast tool for studying and mapping the occlusion, and storing data for future treatment, with the conventional method being the preferred method for performing the occlusal adjustment.

Neural network Gaussian processes as efficient models of potential energy surfaces for polyatomic molecules

Kernel models of potential energy surfaces (PESs) for polyatomic molecules are often restricted by a specific choice of the kernel function. This can be avoided by optimizing the complexity of the kernel function. For regression problems with very expensive data, the functional form of the model kernels can be optimized in the Gaussian process (GP) setting through compositional function search guided by the Bayesian information criterion. However, the compositional kernel search is computationally demanding and relies on greedy strategies, which may yield sub-optimal kernels. An alternative strategy of increasing complexity of GP kernels treats a GP as a Bayesian neural network (NN) with a variable number of hidden layers, which yields NNGP models. Here, we present a direct comparison of GP models with composite kernels and NNGP models for applications aiming at the construction of global PES for polyatomic molecules. We show that NNGP models of PES can be trained much more efficiently and yield better generalization accuracy without relying on any specific form of the kernel function. We illustrate that NNGP models trained by distributions of energy points at low energies produce accurate predictions of PES at high energies. We also illustrate that NNGP models can extrapolate in the input variable space by building the free energy surface of the Heisenberg model trained in the paramagnetic phase and validated in the ferromagnetic phase. By construction, composite kernels yield more accurate models than kernels with a fixed functional form. Therefore, by illustrating that NNGP models outperform GP models with composite kernels, our work suggests that NNGP models should be a preferred choice of kernel models for PES.

Shipping route modelling of AIS maritime traffic data at the approach to ports

The complexity and diversity of ship traffic conditions have burdened maritime safety because of the increasing maritime transportation, especially in the waters around busy ports. A shipping route modelling approach around port areas is presented for maritime traffic identification and monitoring in areas characterized by the confluence of routes approaching the port entrance. In this study, a clustering-based approach is adopted, which involves identifying ship trajectories of different motion patterns corresponding to in-port and out-port ship routes based on Principal Component Analysis and K-mean clustering algorithms. Subsequently, the route centrelines are estimated for each ship route using Soft Dynamic Time Wrapping barycentre averaging algorithm from the near centre trajectory defined by Dynamic Time Wrapping. Afterwards, the route boundaries are generated with the optimal distribution of conjunction points at observation lines along the centrelines. Finally, a case study of ship traffic around Leixões port on the Portugal coast indicates that the proposed framework is practical and the ship route model facilitates the prudent selection of shipping routes for vessels, ensuring maritime traffic safety and promoting effective maritime supervision.

A mechanical analysis of variable angle-tow composite plates through variable kinematics models based on Carrera’s unified formulation

Variable Angle-Tow (VAT) laminates offer a promising alternative to straight fiber composites. By varying fibers orientation within the structure plane, ambitious design and performance goals can be achieved. However, the wider design space results in a more complex problem with more parameters to consider. Carrera’s Unified Formulation (CUF) has been used in previous works performing buckling, vibrational and stress analyses of VAT plates. Usually, one-dimensional (1D) CUF beam models are used, while two-dimensional (2D) plate models are obtained as a particular case of shells by considering a null curvature. In most cases, a linear law is considered to describe the variation of fibers orientation in the main plane of the structure. The purpose of this article is to extend the CUF 2D plate finite elements family to the mechanical analysis of composite laminated plate structures with curvilinear fibers. The main contribute consists in the development of a CUF FE model within the Reissner’s Mixed Variational Theorem (RMVT) context for an improved calculation of the out-of-plane stress components. Results show that RMVT can predict in-plane stresses and satisfy the though-the-thickness transverse stresses continuity due to inter-layer equilibrium. The accuracy of RMVT-based models is also investigated using two different approximation points distributions along the plates thickness.

Comparative analysis of the performance of humanitarian logistic structures using agent-based simulation

The post-disaster humanitarian logistic structure (PD-HL) used in a disaster determines the performance of the relief distribution, which impacts human suffering and loss of lives. This paper develops an Agent-Based simulation model to explore the performance of three different PD-HL structures: Agency Centric Efforts (ACEs), Partially Integrated Efforts (PIEs), and Collaborative Aid Networks (CANs). Using the proposed simulation framework, the authors analyze the role of information sharing and crowd movement in the distribution of relief supplies, and evaluate the ability of different PD-HL structures to respond to disasters of varying intensities. The performance of each PD-HL structure is measured using the social costs, i.e., the sum of logistic costs incurred by the relief organizations carrying out logistical activities, and the deprivation costs and walking costs of affected people. The study also discusses the humanitarian operations during the 2010 Port-au-Prince earthquake to demonstrate the real-world application of the simulation framework. The results suggest that sharing information can reduce the deprivation costs in general. However, as the level of information sharing increases, its marginal effect on deprivation costs decreases. Setting up temporary shelters to limit the movement of affected people can help reduce uncertainty in demands, thereby facilitating the distribution of relief supplies. The distribution capacity is the primary factor affecting the performance of the three PD-HL structures. The results indicate that CANs are particularly effective in responding to catastrophic disasters due to their high distribution capacity. However, ACEs and PIEs can also perform well in situations where their distribution capacities are adequate to ensure that available supplies reach the affected population. In addition, the waiting time of disaster-affected people at the points of distribution (PODs) significantly affects the distribution of relief supplies, while walking speed has no significant impact on the distribution of relief supplies.

Application of high-credible statistical results calculation scheme based on least squares Quasi-Monte Carlo method in multimodal stochastic problems

Solving stochastic problems based on multimodal distributions with high accuracy and efficiency is the focus of current research in the stochastic field. However, since exact statistical results in practical engineering problems are unknown, how to efficiently obtain statistical results with high credibility is of great importance. Therefore, a high-credible statistical results calculation scheme is proposed in this paper. First, the multimodal model is transformed into a weighted sum form of multiple unimodal models by Gaussian mixture model. Then, a high-credible statistical results calculation scheme is designed based on the reusability of low-discrepancy sequences in the quasi-Monte Carlo (QMC) method. The scheme calculates the coefficient of variation for the random response statistical results of the unimodal model across various sample ranges. When the coefficient of variation is less than the tolerance error, which indicates low fluctuation, the statistical results are deemed to have converged to the exact solution. In order to further accelerate the calculation efficiency of the proposed scheme, this paper proposes a least squares quasi-Monte Carlo (LSQMC) method to yield high accuracy statistical moments. Compared with the averageness weights in the QMC method, the non-averageness weights obtained by LSQMC method can more effectively reflect the numerical Characteristics of the random variables and the location distribution of the sampling points in the probability space. Finally, numerical examples have demonstrated that the high-credible statistical results calculation scheme based on the LSQMC method can obtain the high accuracy statistical moments with high efficiency in multimodal stochastic problems.

Consistent C-bar radial-basis Galerkin method for finite strain analysis

For finite-strain problems of quasi-incompressible materials, with hyperelastic and hyperelasto-plastic behavior, we develop a mixed polynomial-enriched radial-basis function (RBF) interpolation. It is characterized by decoupling quadrature and polynomials for the deviatoric and volumetric terms of the right Cauchy–Green tensor. With an enriched interpolation, first and second derivatives of the deformation gradient are continuous. A finite-element mesh is employed for quadrature purposes with the corresponding distribution of Gauss points. In terms of discretization, linear, quadratic and cubic polynomials are combined and function support is established from the number of pre-assigned nodes. Due to the adoption of a Lagrangian kernel, finite strain elasto-plastic constitutive developments are based on the Mandel stress. Combined with the C¯ technique, these developments are found to be convenient from an implementation perspective, as RBF formulations for finite strain plasticity have been limited in terms of generality and amplitude of deformations in the quasi-incompressibility case. Three benchmark tests are successfully solved, and it was found that a combination of reduced integration with a deviatoric cubic basis and volumetric quadratic basis provides superior results in the quasi-incompressible case. Numerical testing shows very good convergence behavior of the results. Besides absence of locking with selective interpolation, outstanding Newton convergence was observed regardless of strain levels.

Comparison of different pilot point parameterization strategies when measurements are unevenly distributed in space

The parameterization of spatially distributed hydraulic properties is one of the most crucial steps in groundwater modeling. A common approach is to estimate hydraulic properties at a set of pilot points and interpolate the values at each model cell. Despite the popularity of this method, several questions remain about the optimum number and distribution of pilot points, which are determining factors for the efficiency of the method. This study proposes a strategy for optimal pilot point parameterization that minimizes the number of parameters while maximizing the assimilation of an observed dataset unevenly distributed in space. The performance of different pilot point distributions has been compared with a synthetic groundwater model, considering regular grids of pilot points with different spacings and adaptive grids with different refinement criteria. This work considered both prior and iterative refinements, with a parameter estimation step between successive refinements. The parameter estimation was conducted with the Gauss–Levenberg–Marquardt algorithm, and the strategies were ranked according to the number of model calls to reach the target objective function. The strategy leading to the best fit with the measurement dataset at the minimum computational burden is an adaptive grid of pilot points with prior refinement based on measurement density. This strategy was successfully implemented on a regional, multilayered groundwater flow model in the south-western geological basin of France.

Assessment of Tracks of Resonance Frequencies of the Vocal Tract

A new method for estimating formant frequency tracks of the vocal tract for arbitrary speech segments is proposed. The method uses the ratio of two Fourier transforms of a speech signal with special exponential-type windows depending on some parameter. This ratio is used for specific points in time and is considered as a function of frequency and parameter. By analyzing, for several parameter values, the distribution of minimum points (in terms of frequency) for the phase of this ratio and/or a similar distribution of extreme points for its amplitude, it is possible to estimate formant frequencies from the peaks of these distributions. A mathematical study is presented that substantiates this approach. A series of numerical experiments were carried out on the processing of synthetic and real speech signals, which confirmed the performance capabilities of the proposed formant evaluation method. In particular, in experiments with synthesized vowels, it was found that the error in estimating their resonance frequencies is small and stable with respect to additive noise up to a signal-to-noise ratio of 5 dB. For real speech, the method makes it possible to calculate the formant frequency tracks for both sounds with vocal excitation and for voiceless fricatives, aspirated plosives, and whispered speech.

Application of unsupervised machine learning algorithms to credit classification methods for tobacco retailers

Abstract This paper uses a clustering algorithm to extract and classify credit rating features of tobacco retailers and evaluates whether the classification results are reasonable by combining clustering evaluation indexes. The distance between samples is calculated using the similarity measure. The natural domain method density and peak clustering method are used to analyze the distribution of sample points in the data set. Combining the cluster analysis creates the tobacco retail credit rating evaluation index. The results show that cluster analysis can effectively extract credit rating features from tobacco retailers. When the number of features is 25, the model has the best classification effect, with a classification accuracy rate of 91.1%, a recall rate of 91.5%, and an F1 value of 91.3%. The classification of tobacco retailers’ credit ratings can be improved effectively by the research in this paper.

Real-time Self-calibration Method of Vehicle Camera Attitude Based on ANN

The research of the monocular vision ranging method has great research value in the field of intelligent vehicles. Camera calibration is an important step in a monocular vision ranging model. The accuracy of camera calibration directly determines the accuracy of ranging. Because the camera attitude may change during vehicle driving, it will have a certain impact on the accuracy of the ranging algorithm. Therefore, based on a monocular ranging model and focal length calibration method based on geometric transformation, a real-time camera attitude self-calibration method based on the width measurement model and ANN model is proposed in this paper. Firstly, the method takes the wheel width of the known vehicle type as a fixed value, takes the contact points between the two wheels of the front vehicle and the ground as the feature points, and brings them into the width measurement model to form constraints. However, the single formula formed by a single pair of feature points cannot solve the camera inclination of multiple dimensions at the same time, and it is difficult to solve the analytical solution of the complex formula. In this paper, the ANN model is proposed to fit and solve the formula, and a large number of data sets are obtained through experimental point distribution to train and test the model. The results show that the ranging accuracy of using this method to calibrate the camera tilt angle is unchanged compared to the previous measurement accuracy using sensors, meeting the ranging requirements