Adjacent Points Research Articles

Single-Shot, Monochrome, Spatial Pixel-Encoded, Structured Light System for Determining Surface Orientations

This study introduces a technique for determining surface orientations by projecting a monochrome, spatial pixel-encoded pattern and calculating the surface normals from single-shot measurement. Our method differs from traditional methods, such as shape from shading and shape from texture, in that it does not require relating the local surface orientations of adjacent points. We propose a multi-resolution system incorporating symbols varying in sizes from 8 × 8, 10 × 10, 12 × 12, 14 × 14, and 16 × 16 pixels. Compared to previous methods, we have achieved a denser reconstruction and obtained a 5.2 mm resolution using an 8 × 8 pattern at a depth of 110 cm. Unlike previous methods, which used local point orientations of grid intersection and multiple colors, we have used the monochrome pattern and deterministic centroid positions to compute the unit vector or direction vector between the neighboring symbols. The light plane intersections are used to calculate the tangent vectors on the surface. Surface normals are determined by the cross-product of two tangent vectors on the surface. A real experiment was conducted to measure simple plane surfaces, circular surfaces, and complex sculptures. The results show that the process of calculating surface normals is fast and reliable, and we have computed 1654 surface normals in 29.4 milliseconds for complex surfaces such as sculptures.

Vehicle trajectory extraction with interacting multiple model for low-channel roadside LiDAR

High-precision and consistent vehicle trajectories encompass microscopic traffic parameters, mesoscopic traffic flow characteristics, and macroscopic traffic flow features, which is the cornerstone of innovation in data-driven traffic management and control applications. However, occlusion and trajectory interruption remain challenging in multivehicle tracking under complex traffic environments using low-channel roadside LiDAR. To address the challenge, a novel framework for vehicle trajectory extraction using low-channel roadside LiDAR was proposed. First, the geometric features of the cluster and its L-shape bounding box were used to address the over-segmentation in vehicle detection arising from occlusion and point cloud sparse. Then, objects within adjacent point cloud frames were associated by developing an improved Hungarian algorithm integrated with an adaptive distance threshold to solve the mismatching problem caused by objects entrancing and exiting in a new point cloud frame. Finally, an improved interacting multiple model by considering vehicle driving patterns was deployed to predict the location of missing vehicles and connect the interrupted trajectories. Experimental results showed that the proposed methods achieve 98.76 % of vehicle detection accuracy and 97.40 % of data association precision. The mean absolute error (MAE) and mean square error (MSE) of the vehicle position estimation are 0.2252 m and 0.0729 m2, respectively. The trajectory extraction precision outperforms most of the state-of-the-art algorithms.

Research on registration method for enface image using multi-feature fusion

Objective.The purpose of this work is to accurately and quickly register the Optical coherence tomography (OCT) projection (enface) images at adjacent time points, and to solve the problem of interference caused by CNV lesions on the registration features.Approach.In this work, a multi-feature registration strategy was proposed, in which a combined feature (com-feature) containing 3D information, intersection information and SURF feature was designed. Firstly, the coordinates of all feature points were extracted as combined features, and then these feature coordinates were added to the initial vascular coordinate set simplified by the Douglas-Peucker algorithm as the point set for registration. Finally, the coherent point drift registration algorithm was used to register the enface coordinate point sets of adjacent time series.Main results.The newly designed features significantly improve the success rate of global registration of vascular networks in enface images, while the simplification step greatly improves the registration speed on the basis of preserving vascular features. The MSE, DSC and time complexity of the proposed method are 0.07993, 0.9693 and 42.7016 s, respectively.Significance.CNV is a serious retinal disease in ophthalmology. The registration of OCT enface images at adjacent time points can timely monitor the progress of the disease and assist doctors in making diagnoses. The proposed method not only improves the accuracy of OCT enface image registration, but also significantly reduces the time complexity. It has good registration results in clinical routine and provides a more efficient method for clinical diagnosis and treatment.

Improving Interpolating Accuracy of Weighted Mean Temperature by Using a Novel Lapse Rate Model in Compact VMF1 Product

AbstractIn GNSS (Global Navigation Satellite Systems) meteorology, the accuracy of precipitable water vapor (PWV) retrieved from the tropospheric delay of GNSS signals is affected by the conversion factor. Compact VMF1 product (known as GGOS Atmosphere data) provides high‐accuracy global grid‐wise weighted mean temperature (Tm) values, which can be utilized to calculate the conversion factor. However, the Tm provided in the compact VMF1 data are solely ground surface values. To enhance the performance of compact VMF1 product, a new Tm lapse rate model for each grid point was developed for the purpose of reducing its surface Tm to the elevation of the GNSS site. Then the reduced Tm values over the neighboring grid points together with horizontal interpolation were used to obtain the interpolated Tm for the GNSS station. The sample data for the development of the new model were the Tm profiles obtained from ERA5 monthly averaged data spanning 2009–2018. To assess the model's performance, global radiosonde data at 504 radiosonde stations spanning 2019–2021 were employed. Results demonstrated that implementing the Tm lapse rate model significantly enhanced the accuracy of interpolating Tm values for GNSS stations with substantial height disparities from adjacent grid points, thereby improving PWV conversion accuracy. This indicates that employing the new Tm lapse rate model to adjust surface Tm data in the compact VMF1 product holds promise for enhancing its utility in GNSS meteorology.

A Hybrid Parametrization Method for B‐Spline Curve Interpolation via Supervised Learning

AbstractB‐spline curve interpolation is a fundamental algorithm in computer‐aided geometric design. Determining suitable parameters based on data points distribution has always been an important issue for high‐quality interpolation curves generation. Various parameterization methods have been proposed. However, there is no universally satisfactory method that is applicable to data points with diverse distributions. In this work, a hybrid parametrization method is proposed to overcome the problem. For a given set of data points, a classifier via supervised learning identifies an optimal local parameterization method based on the local geometric distribution of four adjacent data points, and the optimal local parameters are computed using the selected optimal local parameterization method for the four adjacent data points. Then a merging method is employed to calculate global parameters which align closely with the local parameters. Experiments demonstrate that the proposed hybrid parameterization method well adapts the different distributions of data points statistically. The proposed method has a flexible and scalable framework, which can includes current and potential new parameterization methods as its components.

An Efficient Attentive-GRU Structure for Uncertainty Modelling of Crowdsourced Human Trajectories Under Building-obscured Urban Scenes

Abstract. Uncertainty modelling is regarded as one of the core components in the field of human mobility analysis and urban navigation, that can affect the performance of human behaviour modelling and location information acquisition. Existing uncertainty modelling algorithms towards the human movement trajectory are subjected to random and highly dynamic human motion characteristics and sampling and observation errors of Global Navigation Satellite System (GNSS) signals caused by the occlusion of buildings in urban scenes, which lead to the insufficient spatiotemporal correlation and poor accuracy of uncertainty modelling. To fill in this gap, this paper proposes an efficient attentive-GRU structure for uncertainty modelling of crowdsourced human trajectories under building-obscured urban scenes, that takes into account both temporal correlation and spatial correlation of human-originated GNSS trajectories and related motion features. A period of human motion data is modelled instead of only one or adjacent location points to avoid interference factors caused by the obstruction of urban buildings, and time-varying measurement and sampling errors are further estimated and combined with comprehensive human motion features to improve the accuracy of final uncertainty modelling. Comprehensive experiments indicate that compared with existing uncertainty modelling methods including physical models and deep-learning models, the proposed attentive-GRU structure realizes much better performance under different accuracy indexes.

An Enhanced Seamless Localization Framework Using Spatial-temporal Uncertainty Predictor Under Obscured Indoor and Outdoor Scenes

Abstract. Uncertainty modelling is regarded as one of the core components in the field of urban navigation, that can affect the performance of indoor and outdoor location information acquisition, especially under obscured scenarios. Existing multi-source fusion-based seamless positioning algorithms are subjected to random and highly dynamic human motion characteristics and changeable observation errors caused by the dynamic occlusions of human and buildings under urban scenes, which lead to the insufficient spatiotemporal correlation and poor accuracy of final multi-source fusion structure. To fill in this gap, this paper proposes an enhanced seamless localization framework using spatial-temporal uncertainty predictor under obscured indoor and outdoor scenes (ESL-STUP), that takes into account both temporal correlation and spatial correlation of trajectories provided by Wi-Fi, GNSS, and sensor-originated motion information. An iPDR-based trajectory estimation structure is proposed, using the integration of INS/PDR mechanizations, magnetic observations, and deep-learning based speed estimation to enhance the performance of traditional PDR algorithm. A period of human motion features extracted from hybrid location sources are modelled instead of only one or adjacent location points to realize time-varying measured uncertainty errors prediction, and the predicted uncertainty errors of different indoor and outdoor location sources are integrated with iPDR to realize robust seamless positioning performance. Comprehensive experiments indicate that compared with existing multi-source fusion-based seamless positioning structure, the proposed ESL-STUP realizes much better performance under different scenes.

A precise registration method for large-scale urban point clouds based on phased and spatial geometric features

Abstract The dense high-rise buildings and multipath effects in urban areas significantly reduce the positioning signal accuracy of laser scanning systems, leading to layering and offset issues in the collected point cloud data on the same road. In order to acquire comprehensive and consistent three-dimensional information on the objects, thereby providing field inspection data for large-scale road traffic network scenarios, in this paper, an improved point cloud registration method is proposed to divide the registration process into two stages: elevation registration and plane registration. Elevation registration takes the ground point cloud as the registration primitive, reduces the number of point clouds through curvature down-sampling, and constrains the feature point sequence with a fixed range to provide a good initial pose for fine registration. The plane registration first inherits the elevation registration parameters, combining the dynamic distance parameters of spherical region step based on the median, using robust multi-scale loss functions to address residual points, effective adjacent point pairs are selected to obtain the spatial transformation matrix, and realizes accurate registration. Experimental results with multiple sets of urban point cloud data show that the root mean square error of point cloud registration can be controlled within 0.06 m, achieving a relatively superior registration accuracy, it can provide detailed prior data for measurement information analysis.

Forest management with fire simulation

In this work, we address a forest management problem for timber production with fire concerns, employing a novel simulation-based optimization approach wherein forest management is iteratively guided by the feedback from fire spread simulations.The forest management problem involves selecting an alternative prescription for each stand, subject to various restrictions (e.g. bounds on ecosystems services), to maximize the net present value. For each stand, prescriptions involve projecting forest conditions and outcomes using species-specific growth and yield models, combined with different fuel treatment scenarios. In each iteration, the optimization problem is solved. Fire travel times between adjacent points in a grid representing the forest are calculated, based on fuel models associated with the selected prescriptions and other conditions as wind and slopes. Fire spread is simulated for all potential ignitions. Fire paths with a rate of spread greater than a given threshold are identified and constraints are added to the forestry problem to exclude their associated prescriptions to be jointly selected. This problem is re-optimized and the process is repeated until there are no such paths.We describe computational experiments in a Portuguese forest showing how trade-offs between the net present value and the maximum fire rate of spread can be obtained. When too restrictive conditions are imposed on fire, the approach suggests a set of stands to become fire breaks. We also conducted experiments to demonstrate how the impact of the forest surroundings, as well as bounds on ecosystem services, can be evaluated with respect to these trade-offs.

Simulation of Motion Nonlinear Error Compensation of CNC Machine Tools With Multi-Axis Linkage

In order to solve the problem of nonlinear error for a dual rotary table five-axis CNC machine tool due to the linkage of rotary and translational axes, the simulation of motion nonlinear error compensation for a multi-axis linkage CNC machine tool is proposed. The adjacent points in the tool position file are selected as the tool position points for building the model, and then the nonlinear error model resolved by the harmonic function is established according to the error distribution in the classical post-processing. The nonlinear error between the two tool position points is quickly predicted by the analytical expression of this model, and the real-time error compensation of the intermediate interpolation points is realized. Finally, MALTLAB simulation analysis is performed on the tool position file of an impeller part machining to verify the effectiveness of the proposed algorithm. The experimental results show that it can be seen from the distribution curve of the nonlinear error that it is about 10% after compensation as before compensation, thus verifying the effectiveness of the nonlinear error compensation mechanism. The correctness of the nonlinear error analysis and compensation method and the effectiveness of post-processing are verified.

A new higher-order super compact finite difference scheme to study three-dimensional non-Newtonian flows

This work introduces a new higher-order accurate super compact (HOSC) finite difference scheme for solving complex unsteady three-dimensional (3D) non-Newtonian fluid flow problems. As per the author's knowledge, the proposed scheme is the first ever developed higher-order compact finite difference scheme to solve 3D non-Newtonian flow problems. The proposed scheme is fourth-order accurate in space and second-order accurate in time, utilizing only seven adjacent grid points at the (n+1)th time level for the finite difference discretization. A time-marching methodology is employed with pressure calculated via a pressure-correction strategy based on the modified artificial compressibility method. Using the power-law viscosity model, we tackle the benchmark problem of a 3D lid-driven cavity, systematically analyzing the varied rheological behavior of shear-thinning (n = 0.5), shear-thickening (n = 1.5), and Newtonian (n = 1.0) fluids across different Reynolds numbers (Re=1,50,100,200). Both Newtonian and non-Newtonian results are carefully investigated in terms of streamlines, velocity variation, pressure distributions, and viscosity contours, and the computed results are validated with the existing benchmark results. The findings demonstrate excellent agreement with the existing results. It is found that for shear-thinning fluid (n = 0.5), u velocity is higher near the top moving wall than the case of Newtonian (n = 1.0) and shear-thickening fluid (n = 1.5) for all Re values. This extensive analysis, using the new HOSC scheme, not only increases our understanding of non-Newtonian fluid behavior but also provides a robust foundation for future research and practical applications.

A temperature-sensitive points selection method for machine tool based on rough set and multi-objective adaptive hybrid evolutionary algorithm

Reasonable deployment of temperature sensors is the key to accurately monitoring the temperature field of machine tools and improving the accuracy of thermal error prediction and compensation models. To determine the optimal deployment location of sensors, this paper proposes a temperature-sensitive points selection method tightly coupled with rough set and multi-objective optimization. Firstly, the importance of each temperature measurement point to the thermal error is calculated based on the rough set, and information entropy is introduced to amplify the importance difference among adjacent measurement points at the same heat source. Then, with the temperature measurement points groups as the variables, the number of temperature measurement points in the group, and the information importance of the group as the objectives, a multi-objective attribute reduction model is established, which transforms the temperature-sensitive points selection problem into a discrete multi-objective optimization problem. Finally, a multi-objective adaptive hybrid evolutionary algorithm is proposed, which designs a population initialization method based on mutual information and interval probability, and dynamic adaptive evolutionary parameters to achieve optimal temperature-sensitive points selection. Experiments on the high-speed dry hobbing machine verify the superiority and effectiveness of the proposed method.

The human lower leg muscle pump functions as a flow diverter pump, maintaining low ambulatory venous pressures during locomotion

ObjectiveAmbulatory venous pressure (AVP) is the drop of pressure observed in the superficial veins of the lower leg during movement. This phenomenon has been linked to the function of the calf muscle pump (CMP) and the competence of venous valves. Nevertheless, the concept of the CMP function remains controversial. This study aimed to elucidate the association between lower leg muscles activity, changes in pressure in distinct venous segments, and lower extremity arterial blood supply in healthy subjects during various types and intensities of exercise. MethodsTwelve legs of nine healthy volunteers were enrolled in the study. Continuous pressure (intramuscular vein [IV] and three great saphenous vein [GSV] points) and surface electromyography data (gastrocnemius and anterior tibial [ATM] muscles) were recorded during treadmill walking, running, and plantar flexion exercises. The pressure gradient (ΔP, mmHg) between adjacent points of measurement was calculated. Minute unit power of muscle pump ejection and suction (NE, and NS, MPa/min) were calculated and compared with the arterial blood supply of the lower extremity (LBF, L/min). ResultsΔP demonstrated a consistent pattern of changes during walking and running. In GSV, the ΔP was observed to be directed from the thigh to the mid-calf (retrogradely) and from the ankle to the mid-calf (anterogradely) throughout the entire stride cycle. However, its value decreased with increasing stride cycle frequency. The dynamics of ΔP between the IV and GSV were as follows: It was directed from the IV to GSV during gastrocnemius contraction and was reversed during anterior tibial muscle contraction and gastrocnemius relaxation (swing phase). LBF, NE, and NS demonstrated similar exponential growth with increasing stride frequency during walking and running. ConclusionsDuring natural locomotion, the muscle pump acts as a flow diverter pump, redirecting the flow of blood from the superficial veins to the intramuscular veins via the perforating veins. During ambulation, the pressure in the superficial venous network depends upon the capacity of the muscle pump to provide output that matches the changes in arterial blood flow.

TimeNorm: a novel normalization method for time course microbiome data.

Metagenomic time-course studies provide valuable insights into the dynamics of microbial systems and have become increasingly popular alongside the reduction in costs of next-generation sequencing technologies. Normalization is a common but critical preprocessing step before proceeding with downstream analysis. To the best of our knowledge, currently there is no reported method to appropriately normalize microbial time-series data. We propose TimeNorm, a novel normalization method that considers the compositional property and time dependency in time-course microbiome data. It is the first method designed for normalizing time-series data within the same time point (intra-time normalization) and across time points (bridge normalization), separately. Intra-time normalization normalizes microbial samples under the same condition based on common dominant features. Bridge normalization detects and utilizes a group of most stable features across two adjacent time points for normalization. Through comprehensive simulation studies and application to a real study, we demonstrate that TimeNorm outperforms existing normalization methods and boosts the power of downstream differential abundance analysis.

A simple method to automatically remove artificial terrain from airborne LiDAR DTMs in plain areas

Alluvial plains are highly vulnerable to floods and ground disasters. Recent rapid urbanization and climate change have heightened disaster risks in urban areas. Geomorphological maps, crucial for estimating disaster risks, delineate landform boundaries based on patterns of concave breaks of slope and micro-landforms. However, enhancing the accuracy of such maps requires data extraction methods capable of capturing these features with greater precision. Traditional topographic measurements derived from adjacent elevation points in airborne light detection and ranging (LiDAR) digital terrain models (DTMs) fail to accurately represent slope variations on low ground surfaces due to the inclusion of numerous noise-like artificial terrain features. Consequently, analyzing natural terrain becomes challenging. To address this issue, our study devised a method to automatically identify and eliminate noise-like artificial terrain from LiDAR DTMs. We achieved this by removing major artificial terrain features from land-use vector data, creating an edge-preserving smooth DTM, and selectively removing and interpolating only those areas where were large differences between the smooth DTM and the LiDAR DTM. This method minimizes the interpolation of artificial terrain and quotes the LiDAR DTM for other areas, thereby minimizing the data quality loss. It is possible to identify and demarcate topographic boundaries in plains with a longitudinal gradient of approximately 1% or less at a high resolution, which can be used to investigate the relationship between flood and ground characteristics and landform volume in the plains. This method can be easily processed using only QGIS and free open data. This approach enhances the precision of disaster risk estimation and facilitates more effective urban planning in vulnerable areas.

Spatio-temporal variability of soil water content across plot scales within a vineyard

Understanding spatio-temporal variability of soil water content (SWC) at different scales is of great importance for designing monitoring networks and assimilating observed data in hydrological modeling. The objective of the study was to examine spatio-temporal variability of SWC for different soil layers at different extent scales within an agricultural field. Spatio-temporal variability of SWC and the relationship between variability and mean were investigated at three extent scales for different soil layers of 0–100 cm in different years within an intensively irrigated vineyard. Nested sampling grids were used, and at each scale, there was a total of 16 points distributed regularly at the grids. The distance between any two adjacent sampling points was 25-m, 50-m, and 75-m for the three scales respectively. Overall, there was no consistent pattern regarding the change of mean, standard deviation (SD), and coefficient of variation (CV) with scales during the three years. For the surface soil layer, mean SWC was similar between scales, while SD was closely related to soil clay content at the sampling locations of each scale. For subsurface soils, the overall magnitude of variability of SWC was greater, whereas the difference in the variability between scales was lower than that of the soil texture of the corresponding soil layer. The proportional decrease of CV with increasing mean was found to be largely consistent between different years for all soil layers excluding 20–40 cm, but variable between scales and soil layers. For subsurface soil layers, the CV vs. mean relationship differed between relatively wet and dry years, indicating that factors controlling SWC dynamics for the layers below the surface layer were different depending on climatic conditions. Variance partitioning analysis showed that spatial variance accounted for at least 57 % of total variance for all cases, and the temporal variance at different scales fluctuated within a narrow range for subsurface soils for the three years. The results could benefit soil water content monitoring network design, calibration of soil moisture related parameters and assimilation of observed soil moisture data into field soil water modeling.

ANALYSIS OF RESOLVING EFFICIENT DOMINATING SET AND ITS APPLICATION SCHEME IN SOLVING ETLE PROBLEMS

This research focuses on the analysis of Resolving Efficient Dominating Set (REDS) and its application in solving Electronic Traffic Law Enforcement (ETLE) problems using the Spatial Temporal Graph Neural Network (STGNN). Resolving Efficient Dominating Set (REDS) is a concept in graph theory that studies a set of points in a graph that efficiently monitors other points. It involves ensuring that each point v ∈ V (G) - D is dominated by exactly one point in D, with no adjacent points in D, and the representation of point v ∈ V (G) concerning D is not the same, which is termed as a resolving efficient dominating set. In the context of Electronic Traffic Law Enforcement (ETLE), the analysis of REDS has a significant impact. The theorem resulting from the analysis of REDS enables the determination of the number of traffic violation sensors required. Furthermore, by taking simulation data from road points, violation forecasting can be performed. The accurate predictions from this forecasting can assist authorities in anticipating and addressing traffic violation issues more effectively.

Research on a Train Safety Driving Method Based on Fusion of an Incremental Clustering Algorithm and Lightweight Shared Convolution.

This paper addresses the challenge of detecting unknown or unforeseen obstacles in railway track transportation, proposing an innovative detection strategy that integrates an incremental clustering algorithm with lightweight segmentation techniques. In the detection phase, the paper innovatively employs the incremental clustering algorithm as a core method, combined with dilation and erosion theories, to expand the boundaries of point cloud clusters, merging adjacent point cloud elements into unified clusters. This method effectively identifies and connects spatially adjacent point cloud clusters while efficiently eliminating noise from target object point clouds, thereby achieving more precise recognition of unknown obstacles on the track. Furthermore, the effective integration of this algorithm with lightweight shared convolutional semantic segmentation algorithms enables accurate localization of obstacles. Experimental results using two combined public datasets demonstrate that the obstacle detection average recall rate of the proposed method reaches 90.3%, significantly enhancing system reliability. These findings indicate that the proposed detection strategy effectively improves the accuracy and real-time performance of obstacle recognition, thereby presenting important practical application value for ensuring the safe operation of railway tracks.

Fault diagnosis method for rotating machinery based on SEDenseNet and Gramian Angular Field

The fault diagnosis in rotating machinery is crucial for ensuring the safe and dependable operation of intricate mechanical systems. Addressing the limitations inherent in traditional deep learning approaches concerning extended time sequence encoding and subpar generalization capability is paramount. The study utilizes the Gramian Angular Field (GAF) and Squeeze and Excitation (SE) attention mechanisms to alleviate these constraints. GAF enhances feature extraction by emphasizing the angular relationships among adjacent signal points to uncover latent fault characteristics. Simultaneously, through the integration of SE with DenseNet architecture, the network facilitates global information exchange and improves multi-scale fusion, thereby enhancing the precise identification of fault type and location within the signal. Experiments conducted on two datasets achieved accuracies of 100% and 99.85%, respectively, outperforming other methods and models, thereby validating the effectiveness of this study.

Contour error control for the biaxial system based on active disturbance rejection generalised prediction

ABSTRACT For the purpose of contour error control in two-axis systems, a cross-coupled controller utilising auto-disturbance rejection generalised predictive control and nonlinear proportional differentiation is proposed. The contour error components along each axis are estimated by utilising the information from the nearest reference point and its adjacent reference points. The combination of ADRC and GPC is utilised for uniaxial tracking control. The LESO is utilised for estimating and compensating for the total disturbance of the system while simplifying it into two cascaded integrators. Because GPC is designed only for the series form of the integrator, the dependence on mathematical models is reduced. A nonlinear cross-coupled controller is designed utilising the fal function, with compensation control quantities for each axis directly obtained from their respective contour error components. Finally, the results obtained from the simulation demonstrate that the control strategy can availably enhance the performance of single-axis tracking and contour processing.