Distribution Of Points Research Articles

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

Конкуренция в процессе обучения в образовательных организациях высшего образования

Rivalry is present in various spheres of human activity, for example, rivalry (competition) for a workplace, for a position, for the necessary resources, for a free place in transport and for points/grades in the educational process. The article is devoted to the issues of competition among students in educational activities for grades, including in the implementation of the point-rating system of assessment in educational institutions of higher education. According to various studies and based on the experience of the author’s professional activity, the use of a point-rating evaluation system in educational organizations has both its advantages and disadvantages. So, one of the indisputable advantages is considered to be an increase in students’ motivation for learning activities (to complete the task better than classmates), and one of the disadvantages is considered to be competition (sometimes unfair) for the assessment necessary for the student (often, this is a tough comparison with the scores received by classmates, and dissatisfaction (not corresponding to the real state of affairs) earned assessment, etc.). The significance of these points is enhanced due to the subjectivity of the teacher’s assessment of students, as well as the peculiarities of the distribution of points (individually for each teacher) and the evaluation of various creative educational works. Based on the study of the interaction of competing parties in educational activities for points, some methods are proposed, the use of which contributes to conscientious rivalry among students (joint gain, gain due to advantages), which indicates the practical significance of the article. The article shows that even with a detailed description and justification of the assessment (drawing up a scale and its criteria), various controversial issues will still arise in the assessment of students, leading to a certain form of rivalry in educational activities.

An evaluation of GPU filters for accelerating the 2D convex hull

The Convex Hull is one of the most relevant structures in computational geometry, with many applications such as in computer graphics, robotics, and data mining. Despite the advances in the new algorithms in this area, it is often needed to improve the performance to solve more significant problems quickly or in real-time processing. This work presents an experimental evaluation of GPU filters to reduce the cost of computing the 2D convex hull. The techniques first perform a preprocessing of the input set, filtering all points within an eight-vertex polygon to obtain a reduced set of candidate points. We use parallel computation and the use of the Manhattan distance as a metric to find the vertices of the polygon and perform the point filtering. For the filtering stage we study different approaches; from custom CUDA kernels to libraries such as Thrust and Cub. Four types of point distributions are tested: a normal distribution (favorable case), uniform (favorable case), circumference (the worst case), and a case where points are shifted randomly from the circumference (intermediate case). The experimental evaluation shows that the GPU filtering algorithm can be up to 17.5× faster than a sequential CPU implementation, and the whole convex hull computation can be up to 160× faster than the fastest implementation provided by the CGAL library for a uniform distribution and 23× for a normal distribution.

Impact of urban tunnel sidewall effect on drivers: Examining visual characteristics and driving behavior across different lanes

Urban tunnels have been widely used as special transportation infrastructure. This study investigated the impact of the urban tunnel sidewall effect on drivers, focusing on the drivers’ visual characteristics and driving behavior as the primary research objectives. In this study, numerous real vehicle tests were conducted in the middle section of an urban tunnel, focusing on different lanes. The drivers’ visual characteristics were examined through the distribution of gaze points and fixation duration, and driving behavior was assessed using the vehicles’ lateral offset and acceleration. The drivers’ visual characteristics and driving behavior were analysed together to investigate the impact of the sidewall effect on the relationship between the visual information input and driving behavior output. A questionnaire was administered after the experiment to assess the subjective experiences of each participant. The results indicated significant variations in the impact of the sidewall effect on driver attention distribution and vehicle stability across different lanes. Drivers in the left lane were the most affected and had the highest driving risk, followed by those in the right lane; those in the middle lane had the least driving risk. Meanwhile, when drivers in the left or right lanes focus their gaze on the adjacent sidewall or shortly afterwards, the vehicle experiences a higher lateral acceleration away from the sidewall compared to other areas.

Influence of quantity, quality, horizontal and vertical distribution of ground control points on the positional accuracy of UAV survey

Influence of quantity, quality, horizontal and vertical distribution of ground control points on the positional accuracy of UAV survey

ALGD-ORB: An improved image feature extraction algorithm with adaptive threshold and local gray difference.

Simultaneous Localization and Mapping (SLAM) technology is crucial for achieving spatial localization and autonomous navigation. Finding image features that are representative presents a key challenge in visual SLAM systems. The widely used ORB (Oriented FAST and Rotating BRIEF) algorithm achieves rapid image feature extraction. However, traditional ORB algorithms face issues such as dense, overlapping feature points, and imbalanced distribution, resulting in mismatches and redundancies. This paper introduces an image feature extraction algorithm called Adaptive Threshold and Local Gray Difference-ORB(ALGD-ORB) to address these limitations. Specifically, an adaptive threshold is employed to enhance feature point detection, and an improved quadtree method is used to homogenize feature point distribution. This method combines feature descriptors generated from both gray size and gray difference to enhance feature descriptor distinctiveness. By fusing these descriptors, their effectiveness is improved. Experimental results demonstrate that the ALGD-ORB algorithm significantly enhances the uniformity of feature point distribution compared to other algorithms, while maintaining accuracy and real-time performance.

Identification of Waterlogging-Prone Areas in Nanning from the Perspective of Urban Expansion

The objective of this study was to discern the spatial and temporal patterns of areas in Nanning that are susceptible to waterlogging, particularly during various phases of urban expansion. Furthermore, this study presents a proposal outlining strategies aimed at preventing and controlling waterlogging. These strategies are based on the integration of the concepts of sponge city and resilient city construction. This study employed remote sensing (RS) and geographic information system (GIS) techniques to provide technical support. The supervised classification method and normalized difference index method were utilized to compare and extract impervious surfaces in Nanning from 2013 to 2020. The present investigation utilized the acquired impervious surfaces to compute the fractal dimension as a weighting factor, incorporating a digital elevation model (DEM) for the purpose of conducting a hydrological analysis in ArcGIS. Based on the findings of the study, several conclusions can be derived. The following conclusions can be drawn from the study: (1) The fractal dimension of Nanning varied over the study period, with values of 1.32, 1.41, and 1.58 in 2013, 2017, and 2020, respectively. The distribution of impervious surfaces showed a decreasing trend from the city center to the periphery. Urban planning and construction activities have significantly influenced the distribution of impervious surfaces, resulting in a progressively more complex and unstable structure. (2) From 2013 to 2020, the urban expansion fractal dimension increased from 1.32 to 1.58, indicating a decrease in the stability of impervious surfaces. The areas with higher concentrations of impervious surfaces coincided with frequent waterlogging-prone areas. Furthermore, the distribution of waterlogging-prone points transformed from a concentrated pattern to a scattered one. (3) In terms of waterlogging prevention and control strategies, the old urban areas are recommended to be transformed into sponge city projects, and the new development areas are planned, designed and implemented with the concept of “resilience”.

Orientation and distribution relative to the polarity dividing line of two types of coronal bright points

Based on synoptic maps, the orientation and localization (distribution) of coronal bright points (CBP) relative to the polarity line (PL) on the Sun is studied. Preliminary results show that the slope of the CBP angles of the quiet Sun is oriented, in most cases, parallel to the solar equator, while for the CBP of the active Sun it is more randomly distributed. The question of the localization of both types of CBP with respect to active formations and PL is not so clear and requires a more detailed study.

Supply and demand of higher vocational education in China: Comprehensive evaluation and geographical representation from the perspective of educational equality.

The imbalanced regional development of higher vocational education, particularly the disparity between the supply and demand of educational resources, has emerged as the primary factor impeding the provision of high-quality higher education in China during the establishment of a universal education system. Based on the 1,482 higher vocational education institutions recognized by the Ministry of Education of China in 2021 as the research objects, the development of higher vocational education in China was explored from the perspective of supply and demand using the entropy weight TOPSIS method and coupling coordination degree model. It was found that China's higher vocational institutions were mainly located in provincial capitals, representing a point distribution pattern. From a comprehensive evaluation of the supply level, areas such as the Beijing-Tianjin-Hebei region, Yangtze River Delta, and central Henan Province have become the catchment areas for the development of higher vocational education, laying the foundation for regional network cooperation. From the perspective of educational equality, the higher vocational education in China was found to be sufficient to match the supply and demand, and a balance between supply and demand was apparent in provincial capitals. The coupling degree between supply and demand exhibited an "olive-type" spatial structure pattern, indicating that the development of higher vocational education in most cities in China is still in the transformation stage. The results provide a scientific basis for optimizing resources in the provision of higher vocational education.

Boiling point modeling for petroleum speciation data

An empirical model is presented for generating the boiling curve of a petroleum sample based on parameters that are experimentally available using typical speciation techniques. The approach estimates the boiling point distribution of the detected components and allows a comparison with the simulated distillation profile of the sample. The model is shown to predict the boiling behavior of the speciation-derived average components in crude oil distillation fractions, which match the measured average distillation temperature of the fractions. The model is then applied to generate boiling distributions, based on mass spectrometric and gas chromatographic speciation data, which match the measured simulated-distillation curves with less than 20 °C difference. The model is also applied to the isolated fraction of saturated compounds, where the modeled boiling profile matches well with the measured distillation profile of the entire sample. This confirms that the model, although built using Fourier transform ion cyclotron resonance mass spectrometry data of aromatic compounds, extends to data obtained using comprehensive 2-dimensional gas chromatography, and also describes the boiling behavior of saturated compounds speciated by field desorption time of flight mass spectrometry. Consequently, the modeled boiling distribution enables an assessment of how well a speciation measurement describes the sample, and provides an estimation of lower and higher-boiling portions that have been omitted in the data set.

Extraction of Urban Road Boundary Points from Mobile Laser Scanning Data Based on Cuboid Voxel

The accuracy of point cloud processing results is greatly dependent on the determination of the voxel size and shape during the point cloud voxelization process. Previous studies predominantly set voxel sizes based on point cloud density or the size of ground objects. Voxels are mostly considered square in shape by default. However, conventional square voxels are not applicable to all surfaces. This study proposes a method of using cuboid voxels to extract urban road boundary points using curb points as road boundary points. In comparison with conventional cubic voxels, cuboid voxels reduce the probability of mixed voxels at the road curb, highlight two geometric features of road curb voxels (i.e., normal vector and distribution dimension), and improve the accuracy of road curb point extraction. In this study, ground points were obtained using cloth simulation filtering. First, the cuboid-based voxelization of ground points was performed. Then, taking the voxel as a unit, two geometric features, namely, the normal vector of the voxel and the linear dimension of the point distribution in the voxel, were calculated. According to these geometric features, the voxels that met the conditions were regarded as candidate road curb voxels, and the points in them as candidate road curb points. Afterward, filtering was applied using the intensity value to eliminate the bottom points of fences, street trees, and other ground objects in the candidate road curb points. Finally, noise points were eliminated according to the clustering results of the density based spatial clustering of applications with noise (DBSCAN) algorithm. In this study, point cloud data obtained by the SSW vehicle-mounted mobile mapping system and three-point cloud datasets in the IQmulus & TerraMobilita competition dataset were used to experimentally extract road curbs. Results showed that this method could effectively extract road curb points as the precision of the four groups of data results was over 90% and the quality coefficient reached over 75%.

A back propagation neural network-based adaptive sampling strategy for uncertainty surfaces

Owing to the lack of prior knowledge, the accurate reconstruction of surfaces with high uncertainty is dependent on the reasonable real-time selection of the next best point (NBP) during the sampling process. In this study, a new informative criterion called the MaxCWVar weighting shape effect is proposed for NBP selection. The responses to the geometric features of the candidate locations are predicted by a back propagation neural network (BPNN), which is then used in combination with the jackknife method to estimate the candidate uncertainty. The blade cross-section sampling case is considered to validate the flexibility and effectiveness of the proposed method. A comparison with other adaptive sampling strategies shows that BPNN-based response prediction is well-suited for allocating sample points. In contrast to other NBP selection criteria, the sample point distribution recommended by the MaxCWVar criterion is preferable as it improves the reconstruction accuracy and modeling efficiency. This study promotes the exploration of metrological methods for the fast and intelligent reconstruction of complex surfaces with high uncertainty.