Spatial Points Research Articles

Behavioral Modeling of Drivers near Speed Control Cameras: A Dual Perspective from Micro and Macro Data

The present study investigates the efficacy of speed cameras in managing vehicle speeds and their influence on various driver demographics within the operational context of the cameras. The research, conducted as a case study in Iran, comprises both macroscopic and detailed analyses, quantifying vehicle speeds at different spatial points relative to the speed camera location—upstream, adjacent to, and downstream of the camera—using a manual laser speedometer. Furthermore, the study examines diverse driver behavior metrics, including vehicle type, speeds at different locations, lane changes, braking actions, and measures such as obscuring license plates. Statistical analyses show that speed cameras effectively reduce speeds across different user groups and lead to increased braking and lane-changing behaviors, particularly among light vehicle categories. Moreover, the study reveals distinct behavioral patterns between indigenous and non-indigenous drivers. In addition, a multi-layer perceptron neural network model successfully approximates user speed selection behaviors within the operational range of speed cameras. Overall, this research provides valuable insights into the effectiveness of speed cameras and their impact on diverse driver demographics, contributing to a deeper understanding of advanced speed control mechanisms.

Research on the role of multi-sensor system information fusion in improving hardware control accuracy of intelligent system

Abstract Multi-sensor management and control technology generally constructs a reasonable objective function to solve the optimal control command set to control a limited number of sensors to obtain higher quality measurement information, thus obtaining better target tracking performance. In the process of multi-sensor information fusion, there is not only the problem of information redundancy but also obvious time delay. A sensor fusion algorithm combined with global optimization algorithm is innovatively proposed. According to the key frames saved in the previous steps, feature points in local maps, sensor information, and loop information, a global optimization algorithm based on graph optimization model is constructed to optimize the position and pose of intelligent hardware system and the position of spatial feature points. Moreover, this work studies and experiments on multi-sensor fusion simultaneous localization and mapping (SLAM) comprehensively and systematically, and the experimental results show that the algorithm proposed in this work is superior to common open-source SLAM algorithm in positioning accuracy and mapping effect under special circumstances. Therefore, the method proposed in this work can be applied to intelligent driving of vehicles, vision-assisted movement of robots and intelligent control of unmanned aerial vehicles, thus effectively improving the hardware control accuracy of intelligent systems.

The Concept of Levels of Detail for 3D Niche Models in CityGML

Abstract. Buddhist niches in grottoes can be represented in three-dimensional (3D) for their detailed geometries on surfaces by using triangular meshes generated from point clouds. However, not all applications require 3D models with high geometric detail. The mesh models of niches have drawbacks such as large data volumes, lack of semantic information, and absence of spatial relationships between structural components and members within niches. Those limitations make mesh models suitable only for visualization and challenging to use directly in tasks like spatial analysis, simulation experiments, mechanical analysis, and disease investigation. To address this problem, this study defines four Levels of Detail (LoDs) for Buddhist niches, ranging from LoD0 to LoD3, drawing on the concept of LoDs for urban buildings in CityGML 3.0. As the LoD level increases, 3D models contain more detailed geometries, including spatial points, bounding boxes, niche structural components, and component members. Those 3D models at different LoDs can represent niches with varying degrees of abstraction, making them suitable for different applications and guiding the production of standardized 3D semantic models. To validate the feasibility of the LoDs definition for Buddha niches, this paper reconstructs 3D models of niches at different LoDs based on high-precision mesh models. Finally, a comparison is made between the original mesh model and models at different LoDs in terms of data size and potential application scenarios.

Stand-off hazardous materials identification based on near-infrared hyperspectral imaging combined with convolutional neural network

Near-infrared (NIR) hyperspectral imaging enables rapid, non-contact imaging of hazardous materials in a non-destructive manner, allowing for analysis based on spectral reflection information. However, using traditional methods, it is challenging to identify hazardous materials with less distinct spectral reflection features. This study utilizes a self-built NIR hyperspectral imaging system and proposes a new approach. Using a convolutional neural network (CNN), This allows for the rapid completion of high-throughput spectral screening, marking suspicious spectra at spatial points. we sophisticatedly classified six hazardous material types, generating impactful warning images. The optimized CNN demonstrated superior performance (accuracy 91.08 %, recall 91.15 %, specificity 91.62 %, precision 90.17 %, and 0.924 F1 score) compared to SVM and KNN methods. Our study included multitask validation tests, revealing a sensitive detection of 10 mg/cm2 for ammonium nitrate and trinitrotoluene, capable of identifying over 100 targets simultaneously. By simulating real-world scenarios, we successfully detected hazardous chemicals scattered on the ground and accurately identified these hazardous materials in glass and thin plastic products. Even in situations where clothing obstructed the view, we could still correctly identify hazardous chemicals and generate corresponding warning images. Our system demonstrated precise identification capabilities even amidst complex backgrounds. This method provides an accurate and rapid solution for identifying and locating hazardous chemicals, laying a strong foundation for the next steps in non-contact, long-distance quantitative determination of chemical concentrations. This study highlights the effective application of CNN in non-contact, long-distance classification, and recognition of hazardous materials, paving the way for further scientific and engineering applications.

Effects of OpenCL-Based Parallelization Methods on Explicit Numerical Methods to Solve the Heat Equation

In recent years, the need for high-performance computing solutions has increased due to the growing complexity of computational tasks. The use of parallel processing techniques has become essential to address this demand. In this study, an Open Computing Language (OpenCL)-based parallelization algorithm is implemented for the Constant Neighbors (CNe) and CNe with Predictor–Corrector (CpC) numerical methods, which are recently developed explicit and stable numerical algorithms to solve the heat conduction equation. The CPU time and error rate performance of these two methods are compared with the sequential implementation and Euler’s explicit method. The results demonstrate that the parallel version’s CPU time remains nearly constant under the examined circumstances, regardless of the number of spatial mesh points. This leads to a remarkable speed advantage over the sequential version for larger data point counts. Furthermore, the impact of the number of timesteps on the crossover point where the parallel version becomes faster than the sequential one is investigated.

A new spline method on graded mesh for fourth-order time-dependent PDEs: application to Kuramoto-Sivashinsky and extended Fisher-Kolmogorov equations

In this research, we introduce a two-tier non-polynomial spline approach with graded mesh discretization for addressing fourth-order time-dependent partial differential equations, which find applications in various physical scenarios like the nonlinear Kuramoto-Sivashinsky equation and extended Fisher-Kolmogorov equation. Our method involves considering three spatial points at each time step for scheme development, achieving spatial accuracy of three and temporal accuracy of two. Notably, our approach offers the advantage of directly tackling singular biharmonic problems without the need for discretizing boundary conditions or incorporating fictitious points. It demonstrates unconditional stability when tested on fourth-order problems and outperforms previous methods, as evidenced by comparative analyses. Additionally, we present 2D and 3D plots illustrating numerical solutions for different benchmark scenarios, including the depiction of the chaotic nature of the Kuramoto-Sivashinsky equation under Gaussian initial conditions at various time levels.

Robot Calibration Sampling Data Optimization Method Based on Improved Robot Observability Metrics and Binary Simulated Annealing Algorithm

As the societal demand for precision in industrial robot operations increases, calibration can enhance the end-effector positioning accuracy of robots. Sampling data optimization plays an important role in improving the calibration effect. In this study, a robot calibration sampling point optimization method based on improved robot observability metrics and a Binary Simulated Annealing Algorithm is proposed. Initially, a robot kinematic model based on the Product of Exponentials (POE) model and a generalized error model is established. By utilizing the least squares method, the ideal pose transformation relationship between the robot’s base coordinate system and the laser tracker measurement coordinate system is derived, resulting in an error calibration model based on spatial single points. An improved robot observability metric combined with the Binary Simulated Annealing Algorithm (BSAA) is introduced to optimize the selection of calibration sampling data. Finally, the robot’s parameter errors are obtained using a nonlinear least squares method. Experimental results demonstrate that the average end-effector positioning error of the robot after calibration can be reduced from 0.356 mm to 0.254 mm using this method.

How to visualize the spatial uncertainty of landmark representations in maps?

Landmark representations in maps play an important role in spatial tasks such as self-localization, spatial orientation, and navigation. By matching landmarks to their map representations, people can identify their current location and orientation within a map. However, spatial inaccuracies of landmark representations like these of pictograms in maps have been found to affect the ability to successfully match spatial environments to their map representations. These map matching difficulties could negatively affect the performance in spatial tasks. Due to the increasing number of data sources used in modern map services, for example provided by volunteers, map data quality has become more heterogeneous and uncertain. Therefore, solutions for counteracting the negative effects of spatially inaccurate landmark representations on map matching are required. Here, we report two studies that investigate whether visualizing spatial landmark uncertainty can improve map matching. Although suggestions for visualizing uncertainty exist, there is no empirical evidence whether they can intuitively communicate spatial uncertainty of landmark representations. If map users are made aware of potential spatial inaccuracies in a map, they may observe small or moderate spatial inaccuracies of landmark representations but still be able to match these to the represented landmarks. Our findings support this assumption. Using pictogram size, transparency, or circular uncertainty areas around pictograms to communicate spatial uncertainty increased the likelihood of a perceived match between 3D environments or 360° images and a corresponding map representation when landmark representations were spatially inaccurate. Furthermore, our findings indicate that, in addition to landmarks, people use spatial reference points such as road segments for map matching. Thus, if sufficient other spatial reference points are available both in the environment and its map representation, negative effects of spatially inaccurate landmark representations on map matching may be less severe.

A New 3D reconstruction method for pressure-sensitive paint measurements under limited optical access

Reconstruction of 3D pressure field is crucial for pressure-sensitive paint measurements in turbomachinery experiments. Existing methods impose high requirements on feature points in terms of quantity, spatial distribution, and positional accuracy, and is difficult to apply under limited optical access, such as in planar cascade wind tunnels. We propose a novel direct linear transformation method based on contour matching (CM-DLT). This method leverages model contours captured from a tilted camera perspective, which provides the basis for identifying the projection matrix linking image pixels to 3D spatial points. The subsequent optimization of the projection matrix significantly enhances the accuracy and robustness of 3D pressure field reconstruction. In a benchmark experiment, the efficiency and reprojection accuracy of 3D reconstruction are investigated. Subsequently, the CM-DLT method is successfully applied in the cascade wind tunnel experiment. The obtained 3D pressure fields of two surfaces of adjacent blades are analyzed.

Quantifying urban climate response to large-scale forcing modified by local boundary layer effects

Over the past two decades, the joint manifestation of global warming and rapid urbanization has significantly increased the occurrence of heatwaves and the formation of urban heat islands in temperate cities. Consequently, this synergy has amplified the frequency and duration of periods with tropical nights (TNs) in these urban areas. While the occurrences of such extreme events demonstrate irregular and nonlinear annual patterns, they consistently manifest a discernible rising decadal trend in local or regional climatic data. In urban regions situated amidst hilly or mountainous landscapes, changing wind directions—often associated with uphill or downhill thermal flows—profoundly impact the spread and dispersion of heat-related pollution, creating unique natural ventilation patterns. Using the Lausanne/Pully urban area in Switzerland as examples of hilly and lakeshore temperate cities, this study explores the influence of wind patterns and natural urban ventilation on the nonlinearity of recorded climatic data within an urban environment. This study integrates a mesoscale numerical weather prediction model (COSMO-1), a microscale Computational Fluid Dynamics (CFD) model, field observations, variational mode decomposition technique, and statistical analysis to investigate how wind speed and direction critically influence the nonlinearity of recorded long-term trends of extreme events, specifically focusing on the frequency and duration of TNs in lakeshore and hilly cities. The results strongly indicate a direct correlation between the frequency of TNs and the occurrence of specific moderate wind patterns. These wind patterns are exclusively captured by the microscale CFD model, unlike the mesoscale model, which neglects both urban morphology and complex hilly terrains. The impact of temporal and spatial variability of the wind field on long-term observations at fixed measurement stations suggests that caution should be exercised when relying on limited spatial measurement points to monitor and quantify long-term urban climate trends, particularly in cities located in complex terrains.

A fault detection and isolation method for independent metering control system based on adaptive robust observer

The independent metering control system (IMCS) demonstrates high control precision and energy efficiency for advanced electrohydraulic applications but tends to be faulty. Current model-based fault diagnosis methods face challenges in robustness and adaptability to high uncertainties and high nonlinearities of the IMCS. To enhance the safety and reliability of the IMCS, a novel fault detection and isolation method based on adaptive robust observer (ARO) is proposed. To reduce estimation errors, the designed ARO combines the advantages of the robust filter and parameter adaption, which can overcome the impact of uncertainties and nonlinearities. To detect the fault robustly and sensitively, three types of adaptive thresholds are designed according to the spool displacement residuals. To isolate the fault, a fault space is established, and spatial fault points are calculated. The verification experiments show that the proposed method can well estimate the spool displacement while accurately detecting and isolating faults of the IMCS.

Digital Surface Model Generation from Satellite Images Based on Double-Penalty Bundle Adjustment Optimization

Digital Surface Model (DSM) generation from high-resolution optical satellite images is an important topic of research in the remote sensing field. In optical satellite imaging systems, the attitude information of the cameras recorded by satellite sensors is often biased, which leads to errors in the Rational Polynomial Camera (RPC) model of satellite imaging. These errors in the RPC model can mislead the DSM generation. To solve the above problems, we propose an automatic DSM generation method from satellite images based on the Double-Penalty bundle adjustment (DPBA) optimization algorithm. In the proposed method, two penalty functions representing the camera’s attitude and the spatial 3D points, respectively, are added to the reprojection error model of the traditional bundle adjustment optimization algorithm. Instead of acting on images directly, the penalty functions are used to adjust the reprojection error model and improve the RPC parameters. We evaluate the performance of the proposed method using high-resolution satellite image pairs and multi-date satellite images. Through some experiments, we compare the accuracy and completeness of the DSM generated by the proposed method, the Satellite Stereo Pipeline (S2P) method, and the traditional bundle adjustment (BA) method. Compared to the S2P method, the experiment results of the satellite image pair indicate that the proposed method can significantly improve the accuracy and the completeness of the generated DSM by about 1–5 m and 20%–60% in most cases. Compared to the traditional BA method, the proposed method improves the accuracy and completeness of the generated DSM by about 0.01–0.05 m and 1%–3% in most cases. The experiment results can be a testament to the feasibility and effectiveness of the proposed method.

The Effect of Spatio‐Temporal Sampling and Biological Replication on the Detection of Kelp Forest Fish Communities Using eDNA Metabarcoding

ABSTRACTEnvironmental DNA (eDNA) metabarcoding surveys can support the acquisition of extensive biodiversity data to support ecosystem monitoring and conservation actions. However, the optimization of eDNA metabarcoding project design is essential to capture spatio‐temporal heterogeneity of eDNA signals and maximize diversity detection. In this study, we developed a system‐specific approach to detect fish communities in kelp forests, by analyzing fine‐scale spatio‐temporal patterns in eDNA signals at two sites along the South African coastline, as well as testing the effect of biological replication and pooling of replicates on species detection. At each site, samples were collected at two stations along the shoreline at two depth zones, and this was repeated at two time points (24 h apart). We detected 113 operational taxonomic units (OTUs) across 32 families, but fewer than 20% of OTUs could be assigned to species, indicating that barcode reference libraries need to be drastically improved. We detected significant differences in communities across small spatial scales (< 600 m) and time points, suggesting that to best capture a site's diversity patterns, samples should be collected at multiple points and times within at least 24 h. To detect ~80% of the fish community, including some low abundance species, a minimum of four samples appear sufficient. In addition, a higher number of OTUs (76 vs. 65) were found in individual replicates than in any of the pools. However, pooling samples prior to sequencing can still detect valuable broad‐scale biodiversity patterns for monitoring and can offset the decrease in data resolution with the benefit of accumulating comprehensive data from increased sampling efforts over time. As a pilot investigation into how best to maximize kelp forest‐associated fish communities, this study provides a basis for optimizing sampling design for coastal eDNA‐based surveys in southern Africa and strengthens the development of long‐term eDNA monitoring programs to better support conservation and management actions.

Design of a Thomson scattering diagnostic for the SMall Aspect Ratio Tokamak (SMART).

We describe the design of a Thomson scattering (TS) diagnostic to be used on the SMall Aspect Ratio Tokamak (SMART). SMART is a spherical tokamak being commissioned in Spain that aims to explore positive triangularity and negative triangularity plasma scenarios at a low aspect ratio. The SMART TS diagnostic is designed to operate at high spatial resolution, 6mm scattering length in the low-field side and 9mm in the high-field side regions, and a wide dynamic range, electron temperature from 1eV to 1keV and density from 5×1018m-3 to 1×1020m-3, to resolve large gradients formed at the plasma edge and in the scrape-off layer (SOL) under different triangularities and low aspect ratios. A 2J @1064 nm laser will be used that is capable of operating in the burst mode at 1, 2, and 4kHz to investigate fast phenomena and at 30Hz to study 1s (or more) long discharges. The scattered light will be collected over an angular range of 60° - 120° from 28 spatial points in the midplane covering the entire plasma width and the outer midplane SOL. Each scattering signal will be spectrally resolved on five wavelength channels of a polychromator to obtain the electron temperature measurement. We will also present a method to monitor in situ laser alignment in the core during calibrations and plasma operations.

Asteroid pairs: Survey of the inner main belt

Context. An asteroid pair forms when an asteroid splits into two unbound fragments because of collision, rotational fission, or binary system decay. The two components of the asteroid pair share similar physical properties and their orbits converge when integrated into the past. Currently, 268 asteroid pairs are known, and new pairs are discovered alongside the continuous discovery of new asteroids. Aims. We conducted a survey in the inner asteroid belt to find new asteroid pairs, estimated their age, and classified their physical properties. As presently no M-type asteroid pairs are known, we also conducted a specialized survey of them. Methods. We preselected asteroid pair candidates based on their distances in the five-dimensional space of osculating orbital elements. We created multiple clones within the uncertainties of their orbital elements and conducted their backtrack integration into the past. We searched for convergence of their clones at close spatial points with small relative velocities, the distribution of which determines the pair formation age. Results. We find 40 new asteroid pairs, thus increasing the total number of known pairs by 15%. One of the newly discovered pairs, 469759 - 2016 QZ 123, with an age of 2.6−0.2+0.7 kyr is now the third-youngest known asteroid pair. We studied the influence of the mutual gravitation of pair components on the process of their evolution and successfully observed the gravitational catching of the two pair members in the past. As a byproduct of pair search, we find eight asteroids connected in a cluster with an age of 76−25+15 kyr that belongs to the Phocaea family and incorporates one previously known asteroid pair. We confirm the convergence of ten asteroid pairs discovered in our previous research and improve their age estimates. We observed a deficiency of M-type asteroid pairs, and therefore conducted a dedicated search for M-type pairs, but found none.

Energy-efficient dynamic 3D metasurfaces via spatiotemporal jamming interleaved assemblies for tactile interfaces

Inspired by the natural shape-morphing abilities of biological organisms, we introduce a strategy for creating energy-efficient dynamic 3D metasurfaces through spatiotemporal jamming of interleaved assemblies. Our approach, diverging from traditional shape-morphing techniques reliant on continuous energy inputs, utilizes strategically jammed, paper-based interleaved assemblies. By rapidly altering their stiffness at various spatial points and temporal phases during the relaxation of the soft substrate through jamming, we enable the formation of refreshable, intricate 3D shapes with a desirable load-bearing capability. This process, which does not require ongoing energy consumption, ensures energy-efficient and lasting shape displays. Our theoretical model, linking buckling deformation to residual pre-strain, underpins the inverse design process for an array of interleaved assemblies, facilitating the creation of diverse 3D configurations. This metasurface holds notable potential for tactile displays, particularly for the visually impaired, heralding possibilities in visual impaired education, haptic feedback, and virtual/augmented reality applications.

Spatial solidarity among older adults in age-related housing

Ageing-in-place policies encourage older adults to live at home as long as possible; however, this challenges the abilities of both formal care and informal help. Utilizing the results of my research, I introduce the term spatial solidarity to describe the help that older individuals give each other in age-related housing. One starting point for solidarity is the ability to relate to others. In age-related housing people understand the challenges they face because of aging, although giving reciprocal help might not be possible due to a variety of challenges people face including illness. Helping may be exhausting for those who provide the help and furthermore may cause clashes between helpers and authorities. The results show that solidarity is connected to spatiality in many ways. I have introduced three spatial points that have an influence on solidarity and vice versa: relational space, everyday spatialities and affective qualities. In relational space, solidarity can change the spaces we live in but also the spaces can create solidarity. Furthermore, solidarity can change spaces both physically and through social relationships. Through everyday spatialities solidarity creates spatial patterns in everyday life and has an influence on everyday decisions. Solidarity is affective in the sense that it emerges in spaces where discussions are made about values and norms. Furthermore, when people show solidarity towards each other, it may influence others. Spatial solidarity amongst older individuals fills the gap between any inadequacy in the form of the home care and the needs of the residents. However, the spatial solidarity between older adults is precarious and may change due to the physical conditions of the people. Furthermore, the main responsibility for care of older individuals should not lie with other older people.

Deep Learning-Meshless Method for Inverse Potential Problems

This paper combines the deep learning method with the meshless method to propose a new numerical method, which is the deep learning-improved element-free Galerkin (DL-IEFG) method, for solving inverse potential problems. In this method, the unknown term in the governing equation of the inverse potential problem is represented by the feedforward neural network (FNN). By employing the improved element-free Galerkin (IEFG) method to solve the inverse potential problem, the solution equations are established to obtain numerical solutions. The training set is constructed on the valid values obtained from discretized observation spatial points. The predicted values at the training sample points are calculated by combining the numerical solutions with the approximation function built by the improved moving least-squares (MLS) approximation. Then, the FNN representing the unknown term is iterated using the loss function. The effectiveness of the DL-IEFG method for solving potential inverse problems is validated through numerical examples. In addition, the factors impacting the calculation accuracy and efficiency of the DL-IEFG method are investigated.

Construction of maritime traffic network based on DBSCAN

Abstract Trajectory data is essentially a sequence of spatial points ordered by timestamps, usually with some descriptive information in addition to basic spatiotemporal information. This paper investigates how publicly available Automatic Identification System (AIS) data can be used to analyze maritime traffic and transform it into directed graphs for estimating potential destination points of trajectories. In the maritime field, analyzing and modeling maritime traffic is crucial for vessel safety and efficiency, creating pathways with waypoints and segments, and further forming traffic networks. Our approach incorporates a detailed analysis of the distribution characteristics of different types of navigational points, leading to the adoption of tailored clustering parameters and methods. This differentiation allows for a nuanced understanding of stationary points at docks and anchorages, as well as navigational changes along shipping routes. Using the DBSCAN algorithm, we successfully cluster similar waypoints, considering cluster density and shape without needing to predefine the cluster count.

Pyneapple-G: Scalable Spatial Grouping Queries

This paper demonstrates Pynapple-G , an open-source library for scalable spatial grouping queries based on Apache Sedona (formerly known as GeoSpark). We demonstrate two modules, namely, SGPAC and DDCEL , that support grouping points, grouping lines, and polygon overlays. The SGPAC module provides a large-scale grouping of spatial points by highly complex polygon boundaries. The grouping results aggregate the number of spatial points within the boundaries of each polygon. The DDCEL module provides the first parallelized algorithm to group spatial lines into a DCEL data structure and discovers planar polygons from scattered line segments. Exploiting the scalable DCEL, we support scalable overlay operations over multiple polygon layers to compute the layers' intersection, union, or difference. To showcase Pyneapple-G , we have developed a frontend web application that enables users to interact with these modules, select their data layers or data points, and view results on an interactive map. We also provide interactive notebooks demonstrating the superiority and simplicity of Pyneapple-G to help social scientists and developers explore its full potential.