Set Of Control Points Research Articles

Active learning-driven control point optimization method for efficient modeling of complex stiffened curved shells

The mesh-mapping technique has recently garnered significant attention due to its ability to model stiffened curved shells accurately. The precision of the modeling depends on the number of control points, particularly for complex stiffened curved shells. However, using too many control points results in a time-consuming process of model training and mapping, thereby impeding the structural optimization efficiency. To overcome this challenge, this study proposes an active learning (AL) function that considers the mechanical response and uncertainty of stiffened curved shells. This function is applied to select control points of the radial basis function (RBF) model. The control points with the most substantial AL function value are added to the control point set, and the process is iterated until convergence is achieved. Additionally, the proposed method is compared to other AL strategies to confirm its high efficiency. Furthermore, a data-driven optimization approach, based on the deep neural network method, is employed to minimize the weight of the complex stiffened curved shells. Finally, two engineering examples are presented to verify the effectiveness of the proposed method, which reduces the modeling time by 83.87% and 66.29%, respectively, in comparison to using all control points in mesh-mapping. Additionally, the total time of modeling and optimization is decreased by 42.63% and 59.35%, respectively. In conclusion, the proposed control point optimization method based on active learning-driven for complex stiffened curved shells has substantial potential in efficient modeling, significantly reducing the time of mesh-mapping modeling, and improving engineering’s optimization efficiency.

Direct Sampling for Spatially Variable Extreme Event Generation in Resampling‐Based Stochastic Weather Generators

AbstractResampling‐based weather generators simulate new time series of weather variables by reordering the observed values such that the statistics of the simulated data are consistent with the observed ones. These generators are fully data‐driven, easy to implement, and capable of reproducing the dynamics among weather variables. However, although the simulated time series is new, the weather fields produced at arbitrary time steps are replicas of those found in observations, limiting the spatial variability of simulations and preventing the generation of extreme weather fields beyond the range of observed values. To address these limitations, we propose the integration of the Direct Sampling algorithm—a data‐driven method for producing simulations—into resampling‐based weather generators. By incorporating Direct Sampling as a post‐processing step on the outputs of the weather generator, we enhance the spatial variability of the generated weather fields and enable the generation of extreme weather fields. We introduce an approach for generating out‐of‐sample extreme weather fields using Direct Sampling. This method involves utilizing a set of control points in conjunction with Direct Sampling, where the values of these control points are informed by return period analysis. The proposed approach is validated using precipitation, temperature, and cloud cover weather fields in a region of northwest India. The experimental results confirm that Direct Sampling enhances the spatial variability of the weather fields and facilitates the generation of out‐of‐sample precipitation fields that accurately adhere to the spatial statistics provided by return precipitation level maps, as well as the observed precipitation weather field employed in the analysis.

OptCtrlPoints: Finding the Optimal Control Points for Biharmonic 3D Shape Deformation

AbstractWe propose OptCtrlPoints, a data‐driven framework designed to identify the optimal sparse set of control points for reproducing target shapes using biharmonic 3D shape deformation. Control‐point‐based 3D deformation methods are widely utilized for interactive shape editing, and their usability is enhanced when the control points are sparse yet strategically distributed across the shape. With this objective in mind, we introduce a data‐driven approach that can determine the most suitable set of control points, assuming that we have a given set of possible shape variations. The challenges associated with this task primarily stem from the computationally demanding nature of the problem. Two main factors contribute to this complexity: solving a large linear system for the biharmonic weight computation and addressing the combinatorial problem of finding the optimal subset of mesh vertices. To overcome these challenges, we propose a reformulation of the biharmonic computation that reduces the matrix size, making it dependent on the number of control points rather than the number of vertices. Additionally, we present an efficient search algorithm that significantly reduces the time complexity while still delivering a nearly optimal solution. Experiments on SMPL, SMAL, and DeformingThings4D datasets demonstrate the efficacy of our method. Our control points achieve better template‐to‐target fit than FPS, random search, and neural‐network‐based prediction. We also highlight the significant reduction in computation time from days to approximately 3 minutes.

Data-driven hyperelasticity, Part I: A canonical isotropic formulation for rubberlike materials

Data-driven hyperelasticity shows great promise for modeling the mechanical response of rubberlike materials. It enables an automated linkage between experimental data and mechanical response without a priori knowledge of specific analytical expression for the strain energy density function or the stress expression. In this study, we propose a new data-driven approach with three distinct kinematic approaches; (i) invariant-based formulation, (ii) modified invariant-based approach, and (iii) principal stretch-based formulation to model the hyperelastic response of rubberlike materials. Within this context, we replace the partial derivatives of the strain energy density functions with appropriate B-spline interpolations, using a set of control points to implement various multiaxial loading scenarios, such as uniaxial tension, pure shear, and equibiaxial tension deformations. To ensure a polyconvex and stable constitutive response, we enforce the polyconvexity requirement into the B-spline interpolation along with appropriate normalization conditions. In order to obtain the control points of the B-splines, we train the proposed data-driven approach with respect to the Treloar and Kawabata datasets. On the numerical side, the stress and moduli expressions are derived for the finite element implementation. The predictive capabilities of the proposed approach are demonstrated through representative boundary value problems.

Research on blade statistics measurement registration planning technology

Blade has the characteristics of high machining quality of complex curved surface. If there is no benchmark before machining, it is impossible to judge whether the blades before machining are qualified. Therefore, it is necessary to analyze the blade measurement data. Due to the large measurement point error and disordered distribution, it is necessary to optimize the blade registration. Therefore, blade model registration and positioning is particularly important in blade shape detection and analysis. First, preregistration is carried out based on the six point optimization selection of the blade. After preregistration, the selection method of registration control point set based on theoretical model and statistical error is proposed, planning the registration datum point set on the blade model. The registration control point set is obtained through the registration operation between the measurement data and the registration reference point set. Finally, based on the stability and reliability of important sampling sensitivity and statistical error, obtain the probability density function of error normal distribution statistics samples and important samples. The selection of statistical control points and the rationality of the objective function were verified. The stability/reliability of the statistical alignment point selection is proved to be feasible. The statistical registration deviation is [0.015,0.026] mm, and the ICP registration deviation is [0.031,0.035] mm. The average deviation of statistics registration is about 0.013 mm smaller than the average deviation of ICP registration. The deviation of statistical sampling points is about 0.0214 mm, and that of traditional sampling points is about 0.0275 mm. The deviation of statistical sampling points is about 0.0061 mm smaller than that of traditional sampling points. It meets the requirements of rapid, high efficiency and high precision measurement for aeroengine blades.

Curve Fitting Using Genetic Algorithm and its Application in Craniofacial Reconstruction

A best-fit curve is required to reconstruct craniofacial fracture to ensure the preciseness of the reconstructed contour. A genetic algorithm (GA) is applied to get the best-fit curve in reconstructing the fractured part. This method provides a fast decision in obtaining suitable sets of control points to interpolate both boundary regions to form a reconstructed part without any try-and-error approach that requires altering the control points several times. The optimised sets of control points with different degrees of continuity are used to develop the reconstructed part using the quintic Bézier curve to generate a smooth curve. The best-fit curvature value of the quintic Bézier curve for each degree of continuity is compared, and the curve with the lowest absolute error of curvature is chosen as the inner and outer parts of the craniofacial fracture reconstruction.

Numerical solutions of Atangana-Baleanu time-fractional advection diffusion equation via an extended cubic B-spline technique

The B-spline function is made up of a set of smooth piecewise polynomials that are controlled by a set of control points. A linear combination of B-spline basis of a particular degree can be used to express any spline function of that degree. The spline functions and their derivatives are continuous and depending on the multiplication of knots. In this study, an extended cubic B-spline (ECBS) approximation is used for the numerical solution of time-fractional advection diffusion equation (TFADE) involving Atangana-Baleanu fractional derivative (ABFD). Initially, the non-singular kernel ABFD is discretized using finite difference method (FDM). The spatial derivatives are discretized using ECBS functions. Convergence and stability of the proposed scheme are studied. The results tabulated in tables that show how they accurate and desirable the outcomes are by comparing the obtained approximate results with the available exact solutions. To the authors’ knowledge, this work is the first time to use the ABFD for numerical solution of time-fractional advection diffusion equation using ECBS.

Semi-automatic 3D reconstruction of middle and inner ear structures using CBCT

ABSTRACT We present a semi-automatic reconstruction approach of middle and inner ear structures using generic 3D deformable surface models from Cone Beam CT (CBCT) examination. First, the user must position a set of control points in the CBCT volume for each of the 4 structures of the inner and middle ear. These points are used to position the deformable surface models and to customize them so that they are as close to the boundaries as possible. Finally, each mesh is refined iteratively segmenting the limits of the structure while taking into account neighbouring structures as boundary constraints. Our method is tested on left and right ears of 20 scans of patients analysed retrospectively. The results show the efficiency and reliability of this approach with an average Dice Similarity Coefficient of 91.8% for the inner ear model and 89.9% for the ossicular chain and a total reconstruction time of 5 minutes. The implementation of our method in a clinical setting could provide clinicians with distinct and accurate 3D models of the ear structures without requiring a tedious manual segmentation step, in order to give them a better understanding of the auditory system in vivo and help them in diagnosis and follow-up.

Automatic Satellite Images Orthorectification Using K–Means Based Cascaded Meta-Heuristic Algorithm

Orthorectification of high-resolution satellite images using a terrain- dependent rational function model (RFM) is a difficult task requiring a well-distributed set of ground control points (GCPs), which is often time-consuming and costly operation. Further, RFM is sensitive to over-parameterization due to its many coefficients, which have no physical meaning. Optimization-based meta-heuristic algorithms ap- pear to be an efficient solution to overcome these limitations. This pa- per presents a complete automated RFM terrain-dependent orthorec- tification for satellite images. The proposed method has two parts; the first part suggests automating the GCP extraction by combing Scale- Invariant Feature Transform and Speeded Up Robust Features algo- rithms; and the second part introduces the cascaded meta-heuristic al- gorithm using genetic algorithms and particle swarm optimization. In this stage, a modified K-means clustering selection technique was used to support the proposed algorithm for finding the best combinations of GCPs and RFM coefficients. The obtained results are promising in terms of accuracy and stability compared to other literature methods.

Computing the Riemannian center of mass on meshes

The Riemannian center of mass (a.k.a. Karcher mean or Fréchet mean) provides the equivalent to the Euclidean affine average on manifolds. In spite of its many potential applications in computer graphics and geometric modeling, there exist surprisingly few algorithms to compute it. We present a direct method for computing the Riemannian center of mass on a triangle mesh. Our method works in the polyhedral metric and uses a piecewise-linear interpolation of gradients of the distance fields from a set of control points. We present applications for tracing splines on a surface, comparing to other methods at the state of the art, and showing that we produce quality results while supporting user interaction.

Cooperative Nearest-Neighbor Control of Multi-Agent Systems: Consensus and Formation Control Problems

This letter studies the problem of cooperative nearest-neighbor control of multi-agent systems where each agent can only realize a finite set of control points. Under the assumption that the underlying graph representing the communication network between agents is connected and the interior of the convex hull of all finite actions of each agent contains the zero element, consensus or distance-based formation problems can practically be stabilized by means of nearest-neighbor control approach combined with the well-known consensus control or distributed formation control laws, respectively. Furthermore, we provide the convergence bound for each corresponding error vector which can be computed based on the information of individual agent’s finite control points. Finally, we show Monte Carlo numerical simulations that confirm our analysis.

Moving local mesh based on analysis-suitable T-splines and Bézier extraction for extended isogeometric finite element analysis - Application to two-dimensional crack propagation

We propose a moving local mesh for extended isogeometric analysis exploiting finite element data structures based on Bézier extraction of analysis-suitable T-splines. The local mesh, which is a refined subdomain, can be translocated over the physical space with no changes in its topological size and its polynomial functions, i.e. the number of elements, polynomial functions and their continuity remain the same for any potential change in the local mesh place. The idea is to consider the whole space as one or multiple domains of uniform rational B-splines, within which a subdomain is refined, in order to obtain two standardized local refinement and Bézier extraction operators. The local operators are compatible with every subdomain that has the same topological size. They are computed one single time and used for each iteration to find new refined control points of another specified subdomain. The computational time of isogeometric analysis is improved using extended sets of control points and parametric knots. These extended sets allow to use predetermined numbers in the connectivity arrays to describe the whole control net and physical mesh regardless of the local mesh place. It means there is no need to rebuild the element connectivity in each iteration for mapping the control points and physical nodes. A description for the implementation of this moving local mesh in isogeometric analysis is given, and three numerical examples in crack propagation analysis are studied. • Moving local mesh without knot reinsertion and element connectivity reconstruction. • A detailed description for the implementation + an updating algorithm. • Three numerical examples in crack propagation analysis for the validation.

Generation of Thermal Point Clouds From Uncalibrated Thermal Infrared Image Sequences and Mobile Laser Scans

Monitoring building efficiency is a hot topic for engineers and researchers. TIR (Thermal Infrared) images describe thermal attributes but require professional knowledge for analysis. Moreover, images can hardly describe the thermal attributes of the buildings in all aspects. Therefore, as-built thermal point clouds become the solution. In this case, geometric distortions of TIR images can not be neglected. To generate the thermal point clouds with TIR image sequences, we propose to use MLS (Mobile Laser Scanning) point clouds as control point sets for calibration and a workflow for the thermal point cloud generation. To begin with, intensity images are generated from the point clouds, and corresponding points are detected from the TIR image and intensity image frame. Moreover, the intrinsic parameters are estimated with corresponding points. Finally, the thermal point clouds are generated with calibrated TIR images and MLS point clouds. The result demonstrates that the automatic method can achieve comparable or even better calibration results than the manual method.

An efficient interactive segmentation framework for medical images without pre-training.

Accurate and efficient medical image segmentation plays an important role in subsequent clinical applications such as diagnosis and surgical planning. This paper proposes an efficient interactive framework based on a graph convolutional network (GCN) for medical imagesegmentation. The initial segmentation results showed that a set of boundary control points can be generated for further interactive segmentation. We presented an adaptive interactive manner that allows the user to click on the boundary for fast interaction or drag the erroneous predicted control points for accurate correction. Furthermore, we proposed an interactive segmentation network (referred to as IVIF-GCN) to learn user experience in the interactive process by transforming interactive cues into annotations. In IVIF-GCN, a module of information fusion of image features and vertex position features (IVIF) is proposed to learn the location relationship between the current vertex and the neighboring vertices. Finally, the locations of control points around the interaction point is predicted and updatedautomatically. The proposed method achieves mean Dice of 96.6% and 91.3% on PROMISE12 and our in-house nasopharyngeal carcinoma (NPC) test sets, respectively. The experimental results showed that the proposed method outperforms the state-of-the-art segmentationmethods. The proposed interactive medical image segmentation method can efficiently improve segmentation results for clinical applications in the absence of training data. The GUI tool based on our method is available at https://github.com/Tian-lab/IGMedSeg.

Using genetic algorithms for automating automated lane‐keeping system testing

AbstractIn this paper, we outline an approach for automatically generating challenging road networks for virtual testing of an automated lane‐keeping system. Based on a set of control points, we construct a parametric curve representing a road network, defining the dynamic driving task an automated lane‐keeping system‐equipped vehicle must perform. Changing control points has a global influence on the resulting road geometry. Our approach uses search to find control‐point sets that result in a challenging road, eventually forcing the vehicle to leave the intended path. We apply our approach in different search variants to evaluate their performance regarding test efficiency and the diversity of failing tests. In addition, we evaluate different genetic algorithm control parameter configurations to investigate the most influential parameters and if specific configurations can be seen asoptimal, leading to better results than others. For both studies, we consider another search‐based test method and two different random test generators as a baseline for comparison. The empirical results indicate that specific control parameter settings increase the overall performance for each search variant. While the population size is the most influential control parameter for all methods, the performance improvement when usingoptimalsettings is only significant for one method.

B-Spline Curve Fitting of Hungry Predation Optimization on Ship Line Design

The ship line often describes by the offset table of discrete data points, which leads to the problems that three view coordinates may not correspond, the fitting error is large and the fitted curve cannot be easily modified. This will seriously affect the subsequent ship performance evaluation and op-timization. To solve this problem, this paper develops a B-spline curve fitting of hunger predation optimization on ship line design (HPA), which contains knot guidance technology, hungry preda-tion optimization technology and adaptive adjustment of algorithm input parameters. HPA transforms the discrete ship line into a continuous B-spline curve description, which improves the accuracy and modifiability of the ship line design. Through the real-time feedback of the results of each round of iteration, the knot vector is adaptively adjusted towards a better fitness, and then the optimal control point set that satisfies the error threshold can be obtained. The effectiveness and superiority of HPA are verified by comparing with related research and engineering software.

New Tools for Urban Analysis: A SLAM-Based Research in Venice

This research proposes a detailed analysis of the potential of MMS (Mobile Mapping Systems), supported by SLAM (Simultaneous Localisation And Mapping) algorithms, performed on a multiscale test field in order to make a concrete contribution to the morphological study of cities. These systems, developed with the aim of acquiring a large number of points in a short time, are able to map the surrounding area and automatically localise themselves in real time in relation to a determined reference system. The analysed area, located in Venice, was divided into three different test fields characterised by typical elements potentially comparable to those of other urban realities. The data were acquired using the LiBackPack C50, Kaarta Stencil and Heron Lite systems and compared quantitatively and qualitatively with data obtained from more traditional surveying techniques. Specifically, the data obtained from TLS (Terrestrial Laser Scanning) surveys, supported by topographic measurements, were the most accurate basis on which to evaluate the accuracy and completeness of the three different MMS devices. The standard deviation values were initially analysed in the final 3D global models using the C2C (Cloud to Cloud) and C2M (Cloud to Mesh) distance calculation methods. Subsequently, the geometric differences were investigated through the extraction of horizontal profiles, and two more specific 2D analyses were carried out: the first inspecting the residual parameters calculated after the Helmert transformation from two sets of control points obtained from the profiles, followed by a local strain analysis. The study of the local deformation parameters allowed us to validate the results obtained and to identify the real limits of these survey instruments. The aim was to make a concrete contribution to the formalisation of an operative protocol for the morphological study of the city, exploiting the potential of these technologies to overcome the differences in scale and the gap between outdoor and indoor spaces.

Epistemic uncertainties on the estimation of minimum air gap for semi-submersible platforms

Different models are considered to estimate the minimum air gap of a semi-submersible accommodation unit in stormy conditions. The results are compared to quantify the epistemic uncertainties in the estimation of the minimum air gap. While both first- and second-order methods have been broadly considered in the literature to evaluate the air gap of semi-submersibles, the underlying hypotheses of these methods are considerably simplified, and conservative results depend on added margins of unknown degrees of confidence. Therefore, a systematic study is conducted to assess the uncertainties raised by some features of the state-of-the-art methods. First, the uncertainties resulting from the binned values of scatter diagrams and from the assumption that the highest significant wave height sea state is the critical sea state, are assessed. Then, the uncertainty implied in the finite set of control points is evaluated. The achievements are obtained considering First-Order Second-Moment methods, by performing searches over the environmental contours to find the actual critical sea states, and by performing Kriging interpolation over the whole underdeck of the platform. Moreover, the adopted uncertainty propagation tools allow characterising the minimum air gap in terms of its expected value and standard deviation. It is found that, before adding complexity to the wave-structure interaction model, air gap estimation is subject to large errors if rather few control points are assigned, or if a single critical sea state is assigned before performing any search for the actual critical sea state.

A Novel Flux Barrier Parametrization for Synchronous Reluctance Machines

This paper presents a novel parametrization for the flux barrier profiles of synchronous reluctance and permanent magnet assisted reluctance machines. In literature there are several methods used to design rotor flux barriers of various types, however the vast majority use only a few parameters to characterize their shape. These approaches are proven to be effective in terms of simplicity and computational burden required to achieve an optimal design. However, simplified parametrizations certainly decrease the degrees of freedom when designing the whole barrier shape. In this paper, an attempt to increase the degrees of freedom, introducing a novel rotor flux barrier parametrization, is presented. The method proposed uses natural splines, defined by the positions of a set of control points, to form the shape of the flux barriers. The spline and state-of-the-art barrier profiles are compared from both electromagnetic and mechanical perspectives. The results of this investigation show that by increasing the degrees of freedom it is possible to obtain better performance characteristics. The proposed parametrization is applied to a 6-pole synchronous reluctance motor and its permanent magnet assisted variant, optimized for a traction application. A prototype has been manufactured and tested to experimentally validate the design methodology.

Study on the Construction of 3D Geological Model of Quaternary Loose Sedimentary Strata Based on the Global Stratigraphic Discrete Points

Accurately depicting the spatial structure characteristics of Quaternary loose sedimentary strata is not only of great significance for the research of Quaternary geological evolution, but also for the analysis of spatial variation characteristics of the inner hydrogeological and engineering geological attributes of the strata. In this study, an approach for constructing a 3D geological model of Quaternary loose sedimentary strata is proposed based on global stratigraphical discrete points. The approach obtains the discrete control point set of each stratum by using limited borehole data for interpolation and encryption, and the contact relationships and intersection modes of adjacent strata can be determined via the analysis of stratigraphic sequence; finally, taking these as the professional basis, the construction of the 3D geological model of Quaternary loose sedimentary strata can be carried out. This application can not only accurately describe the three-dimensional spatial distribution characteristics of the Quaternary loose sedimentary strata, it can also be used to perform a layered simulation of the spatial variation characteristics of the inner geological properties of the Quaternary loose sedimentary strata, such as lithology, porosity, and water content, by taking the three-dimensional spatial framework of each stratum as the simulation boundary. Finally, this study takes the citizen center of Xiong’an new area as an example in order to verify the reliability and advancement of the 3D geological modeling scheme.