Convex Hull Algorithm Research Articles

3DDA: A Novel Python Toolkit to Analyze 3D‐Dynamic Contact Angles from Molecular Dynamics Simulations

Obtaining fast and reliable contact angles in molecular dynamics simulations is crucial for screening surface wettability. Traditional methods rely on bidimensional density profile analysis to localize the liquid/vapor interface, which may not be optimal at the nanoscale, especially for nonsymmetrical droplets. Herein, 3DDA, a Python‐based code that uses the 3D droplet geometry to determine contact angles and analyze dynamic changes during spreading processes at the nanoscale, is presented. The 3D density profile of liquid molecules is enveloped using the Euclidean 3D convex hull algorithm to locate the contact line at the solid‐, liquid‐, and vapor‐phase boundaries. By fitting a general ellipsoidal function and optimizing it with the limited‐memory Broyden–Fletcher–Goldfarb–Shanno method, the best candidate function is analytically obtained, describing the interface. The angle derived from the ellipsoidal function and the solid boundary is used to gather a collection of contant angles, which are then fed into a kernel density estimator to determine the most probable angle along the contact line during the spreading process. This methodology is tested on systems such as liquid water and metals, demonstrating its efficacy and reliability. Herein, valuable insights are provided into the behavior of liquids, considering droplet asphericity induced by liquid/solid interactions.

A cardiac audio classification method based on image expression of multidimensional features.

Heart sound auscultation plays a crucial role in the early diagnosis of cardiovascular diseases. In recent years, great achievements have been made in the automatic classification of heart sounds, but most methods are based on segmentation features and traditional classifiers and do not fully exploit existing deep networks. This paper proposes a cardiac audio classification method based on image expression of multidimensional features (CACIEMDF). First, a 102-dimensional feature vector is designed by combining the characteristics of heart sound data in the time domain, frequency domain and statistical domain. Based on the feature vector, a two-dimensional feature projection space is constructed by PCA dimensionality reduction and the convex hull algorithm, and 102 pairs of coordinate representations of the feature vector in the two-dimensional space are calculated. Each one-dimensional component of the feature vector corresponds to a pair of 2D coordinate representations. Finally, the one-dimensional feature component value and its divergence into categories are used to fill the three channels of a color image, and a Gaussian model is used to dye the image to enrich its content. The color image is sent to a deep network such as ResNet50 for classification. In this paper, three public heart sound datasets are fused, and experiments are conducted using the above methods. The results show that for the two-classification/five-classification task of heart sounds, the method in this paper can achieve a classification accuracy of 95.68%/94.53% when combined with the current deep network.

A star selection strategy based on MDS and fast convex hull algorithm in COSPAS-SARSAT system

Abstract The space segment of the current COSPAS-SARSAT system is mainly supported by the Galileo system, and the positioning technology used is generally three-star time-difference positioning. Most of the antennas equipped in the Mission Control Centre (MCC) are not omnidirectional. In the case of Source of Search and Rescue (SAR) in a heavily shaded environment, it is necessary to use the different orbit time-sharing single satellite geolocations using TDOA, and the time-sharing will generate more satellite position data. In order to achieve the higher positioning accuracy of the solution, it is necessary to select the appropriate time and optimal geometrical distribution from the satellite positions. In this paper, we propose a satellite selection strategy based on Multiple Dimensional scaling (MDS) data degradation and a fast convex hull algorithm. The three-dimensional satellite data are downscaled to two-dimensional while maintaining their relative positional relationships, and the two-dimensional convex hull algorithm is used for Geometric Dilution of Precision (GDOP) minimum search star selection. By simulating the satellite position data in different orbits and time-sharing to complete the localization solution, the results show that compared with the traditional satellite selection method, the satellite selection strategy proposed in this paper is able to achieve better localization accuracy in a shorter period of time.

A geometric solution to political gerrymandering: A modified convex hull approach in ghana's democracy

In the realm of Ghanaian politics, gerrymandering is a practice that involves political leaders strategically manipulating the boundaries of voting constituencies to serve their vested interests. This research seeks to address the challenge of gerrymandering in Ghana by introducing a novel mathematical approach, a modified Convex Hull algorithm. The data were collected from the Asunafo North constituency, and the study aims to provide a practical and effective solution to promote electoral fairness, transparency, and democratic integrity in the Ghanaian political system. We develop and implement a modified Convex Hull algorithm, which provides a more accurate and relevant measure of compactness than traditional compactness metrics. The evaluated electoral constituency exhibited reduced susceptibility to gerrymandering, resulting in more equitable representation and a fairer political landscape.

A Model to Analyze Industrial Clusters to Measure Land Use Efficiency in China

An understanding of how land use efficiency and industrial clusters interact helps one to make informed decisions that balance economic benefits with sustainable urban development. The emergence of industrial clusters is a result of market behavior, while the determination of administrative boundaries is a result of government behavior. When these two are not consistent, it can lead to distortions in the allocation of land resources. However, current research on industrial development and land use efficiency is based on agglomeration within administrative regions rather than on industrial clusters. This study addresses this gap by identifying industrial clusters based on the spatial distribution of enterprises and analyzing their impact on land use efficiency. This study uses the density-based spatial clustering of applications with noise (DBSCAN) algorithm to identify industrial clusters, the convex hull algorithm to study their morphology, and spatial econometrics to measure the relationship between land use efficiency and the scale of industrial clusters. The results indicate the following: (1) the density of manufacturing industry (MI) clusters is significantly higher than that of information technology industry (ITI) clusters, and larger industrial clusters tend to be more circular in shape; (2) there is a positive correlation between the scale of industrial clusters and land use efficiency, and industrial clusters with varying levels of land use efficiency are interspersed throughout; (3) significant differences exist between the boundaries of industrial clusters and administrative regions, which could lead to biases when analyzing land use efficiency based on administrative regions. This study provides theoretical support for government policies on improving land use efficiency in China.

Hybrid A-Star Path Planning Method Based on Hierarchical Clustering and Trichotomy

Aiming to improve on the poor smoothness and longer paths generated by the traditional Hybrid A-star algorithm in unstructured environments with multiple obstacles, especially in confined areas for autonomous vehicles, a Hybrid A-star path planning method based on hierarchical clustering and trichotomy is proposed. This method first utilizes the Prewitt compass gradient operator (Prewitt operator) to identify obstacle boundaries and discretize boundaries. Then, it employs a single linkage hierarchical clustering algorithm to cluster obstacles based on boundaries. Subsequently, the clustered points are enveloped using a convex hull algorithm, considering collision safety for vehicle expansion. This fundamentally addresses the ineffective expansion issue of the traditional Hybrid A-star algorithm in U-shaped obstacle clusters. Finally, the expansion strategy of Hybrid A-star algorithm nodes is improved based on the trichotomy method. Simulation results demonstrate that the improved algorithm can search for a shorter and smoother path without significantly increasing the computational time.

Satellite Image Cloud Automatic Annotator with Uncertainty Estimation

In satellite imagery, clouds obstruct the ground information, directly impacting various downstream applications. Thus, cloud annotation/cloud detection serves as the initial preprocessing step in remote sensing image analysis. Recently, deep learning methods have significantly improved in the field of cloud detection, but training these methods necessitates abundant annotated data, which requires experts with professional domain knowledge. Moreover, the influx of remote sensing data from new satellites has further led to an increase in the cost of cloud annotation. To address the dependence on labeled datasets and professional domain knowledge, this paper proposes an automatic cloud annotation method for satellite remote sensing images, CloudAUE. Unlike traditional approaches, CloudAUE does not rely on labeled training datasets and can be operated by users without domain expertise. To handle the irregular shapes of clouds, CloudAUE firstly employs a convex hull algorithm for selecting cloud and non-cloud regions by polygons. When selecting convex hulls, the cloud region is first selected, and points at the edges of the cloud region are sequentially selected as polygon vertices to form a polygon that includes the cloud region. Then, the same selection is performed on non-cloud regions. Subsequently, the fast KD-Tree algorithm is used for pixel classification. Finally, an uncertainty method is proposed to evaluate the quality of annotation. When the confidence value of the image exceeds a preset threshold, the annotation process terminates and achieves satisfactory results. When the value falls below the threshold, the image needs to undergo a subsequent round of annotation. Through experiments on two labeled datasets, HRC and Landsat 8, CloudAUE demonstrates comparable or superior accuracy to deep learning algorithms, and requires only one to two annotations to obtain ideal results. An unlabeled self-built Google Earth dataset is utilized to validate the effectiveness and generalizability of CloudAUE. To show the extension capabilities in various fields, CloudAUE also achieves desirable results on a forest fire dataset. Finally, some suggestions are provided to improve annotation performance and reduce the number of annotations.

Fast, Nondestructive and Precise Biomass Measurements Are Possible Using Lidar-Based Convex Hull and Voxelization Algorithms

Light detection and ranging (lidar) scanning tools are available that can make rapid digital estimations of biomass. Voxelization and convex hull are two algorithms used to calculate the volume of the scanned plant canopy, which is correlated with biomass, often the primary trait of interest. Voxelization splits the scans into regular-sized cubes, or voxels, whereas the convex hull algorithm creates a polygon mesh around the outermost points of the point cloud and calculates the volume within that mesh. In this study, digital estimates of biomass were correlated against hand-harvested biomass for field-grown corn, broom corn, and energy sorghum. Voxelization (r = 0.92) and convex hull (r = 0.95) both correlated well with plant dry biomass. Lidar data were also collected in a large breeding trial with nearly 900 genotypes of energy sorghum. In contrast to the manual harvest studies, digital biomass estimations correlated poorly with yield collected from a forage harvester for both voxel count (r = 0.32) and convex hull volume (r = 0.39). However, further analysis showed that the coefficient of variation (CV, a measure of variability) for harvester-based estimates of biomass was greater than the CV of the voxel and convex-hull-based biomass estimates, indicating that poor correlation was due to harvester imprecision, not digital estimations. Overall, results indicate that the lidar-based digital biomass estimates presented here are comparable or more precise than current approaches.

A novel algorithm for finding convex hull of a generic polygon with simulation of progressively supporting elastic lines

A novel algorithm for finding convex hull of a generic polygon with simulation of progressively supporting elastic lines

Non-destructive evaluation of a composite honeycomb sandwich panel with in-plane waviness damage using ultrasonic guided waves

ABSTRACT Composite honeycomb sandwich structures (CHSS) are often manufactured using an autoclave co-cure process, which can introduce in-plane waviness (IW) damage due to fibre misalignment. Such fibre waviness, whether in-plane or out-of-plane, significantly weakens the strength of these structures. Therefore, developing reliable non-destructive evaluation (NDE) techniques is essential for detecting IW. This study presents an NDE technique utilising ultrasonic guided waves (GW) to identify IW damage in CHSS panels. A numerical model was created using COMSOL Multiphysics, where IW damage is simulated through curvilinear coordinate physics, followed by a time-dependent study of GW propagation. This model simulates local fibre orientation changes due to IW damage, enhancing our understanding of GW interaction with IW. Experimental investigations are performed using contact-type transducers to corroborate the numerical observations. The presence of in-plane waviness causes a reduction in the group velocity of the A0 mode, a characteristic that can be exploited for detecting in-plane fibre waviness in CHSS. Also, parametric studies were performed to examine the effects of IW severity (aspect ratio) and increased IW area on GW characteristics. Finally, a technique for IW damage localisation is demonstrated using a convex hull algorithm based on changes in correlation coefficient values of the A0 mode.

An efficient method for modeling and evaluating the bench terrain of open-pit mines

In order to quantitatively analyze the roughness of the bench floor during open-pit mine blasting, this study proposes a real-time measuring method for the three-dimensional terrain of the bench floor during the excavation process. Real-time monitoring is conducted at the boundary and discrete internal points of the workbench floor during electric shovel operation, utilizing real-time kinematic global navigation satellite system (RTK-GNSS) positioning technology. An improved convex hull algorithm is introduced to automatically extract the optimal boundary of discrete point clouds based on their spatial distribution characteristics. This study establishes a digital elevation model (DEM) using five interpolation algorithms for 3D terrain visualization simulation. Through cross-validation, a comparative analysis of the DEM accuracy, the simulation results of the ordinary kriging interpolation algorithm were found to be optimized. The optimized interpolation algorithm is applied to simulate the 3D terrain in the Dexing open-pit copper mine, and the relevant terrain parameters were calculated. This dataset can serve as a precise foundation for the real-time path planning of elevation blasting design and ground leveling operations.

Collaborative orchard pesticide spraying routing problem with multi-vehicles supported multi-UAVs

The application of UAVs in orchard management presents a substantial opportunity to enhance efficiency and promote environmentally friendly agricultural production. While individual UAVs encounter limitations, such as endurance constraints, these challenges can be mitigated through collaborative efforts involving multiple vehicles and UAVs. This study addresses the issue of multi-UAV cooperative orchard path planning with multiple vehicles by formulating a mathematical optimization model aimed at minimizing the maximum UAV operation time. In the proposed model, the vehicle solely provides batteries and supplies to the UAV. To efficiently solve this model, we propose an improved Simulated Annealing-Lin Kernighan Helsgaun (SA-LKH) algorithm. The proposed algorithm utilizes the K-means and convex hull algorithm to generate the initial solution. Subsequently, the paths of each group of UAVs and vehicles are optimized based on the LKH algorithm. Additionally, we construct a directed perturbation operator that perturbs the maximum and minimum time-consuming paths to achieve fast convergence of the algorithm. The results from four different cases demonstrate that, compared to the general variable neighborhood search algorithm, the improved Ant Colony Algorithm and Improved Genetic Algorithm, the proposed algorithm has demonstrated an average improvement in solution quality of 3.41%, 5.72% and 9.67%, respectively. The proposed method effectively enhances the solution quality, which can provide valuable insights into green cleaning management of orchards.

MEDAVET: Traffic Vehicle Anomaly Detection Mechanism based on spatial and temporal structures in vehicle traffic

Road traffic anomaly detection is vital for reducing the number of accidents and ensuring a more efficient and safer transportation system. In highways, where traffic volume and speed limits are high, anomaly detection is not only essential but also considerably more challenging, given the multitude of fast-moving vehicles, often observed from extended distances and diverse angles, occluded by other objects, and subjected to variations in illumination and adverse weather conditions. This complexity has meant that human error often limits anomaly detection, making the role of computer vision systems integral to its success. In light of these challenges, this paper introduces MEDAVET - a sophisticated computer vision system engineered with an innovative mechanism that leverages spatial and temporal structures for high-precision traffic anomaly detection on highways. MEDAVET is assessed in its object tracking and anomaly detection efficacy using the UA-DETRAC and Track 4 benchmarks and has its performance compared with that of an array of state-of-the-art systems. The results have shown that, when MEDAVET’s ability to delimit relevant areas of the highway, through a bipartite graph and the Convex Hull algorithm, is paired with its QuadTree-based spatial and temporal approaches for detecting occluded and stationary vehicles, it emerges as superior in precision, compared to its counterparts, and with a competitive computational efficiency.

Estimation of plant's morphological parameters using terrestrial laser scanning-based three-dimensional point cloud data

The yield of crop is very important for sustainable agriculture to meet continuously growing demand of food. The morphological parameters, such as plant height (PH), projected leaf area (PLA), and plant volume (PV) are important metrics to estimate crop yield. In this paper, a multi-step methodology is proposed to estimate parameters PH, PLA and PLA using state-of-the-art three-dimensional (3D) mapping technique: terrestrial laser scanning (TLS). The proposed methodology consists of three well-designed steps: pre-processing, ground filtering and clustering, and estimation of plant's morphological parameters. In pre-processing TLS-derived laser scans acquired from multiple scan stations to cover the selected agricultural test site registered and coordinate transformation is performed to convert the TLS point cloud into total station (TS)-based locally defined coordinate system. The second step transformed point cloud data is processed to generate ground and non-ground points, where subsequently Euclidean clustering is employed to generate clusters of non-ground points. The clustered data is cleaned based on geometrical constraints and cabbage plant's clusters are extracted, which are processed using height-based analysis and convex hull algorithm to estimate PH, PLA and PLA of each cluster. The proposed methodology performance was evaluated using TLS point cloud data of selected agricultural field of cabbage plants, and PH, PLA and PLA estimation was performed at RMSE of 0.005 m, 0.012 m2 and 0.0063 m3 respectively. The proposed methodology is efficient and easy to implement step-wise approach from data collection to processing for plant's geometrical analysis, thus it has potential to be used as one of the industrial tools for precision agriculture.

Efficient IoT Traffic Inference: From Multi-view Classification to Progressive Monitoring

Machine learning-based techniques have proven to be effective in Internet-of-Things (IoT) network behavioral inference. Existing works developed data-driven models based on features from network packets and/or flows, but mainly in a static and ad-hoc manner, without adequately quantifying their gains versus costs. In this article, we develop a generic architecture that comprises two distinct inference modules in tandem, which begins with IoT network behavior classification followed by continuous monitoring. In contrast to prior relevant works, our generic architecture flexibly accounts for various traffic features, modeling algorithms, and inference strategies. We argue quantitative metrics are required to systematically compare and efficiently select various traffic features for IoT traffic inference. This article 1 makes three contributions: (1) For IoT behavior classification, we identify four metrics, namely, cost, accuracy, availability, and frequency, that allow us to characterize and quantify the efficacy of seven sets of packet-based and flow-based traffic features, each resulting in a specialized model. By experimenting with traffic traces of 25 IoT devices collected from our testbed, we demonstrate that specialized-view models can be superior to a single combined-view model trained on a plurality of features by accuracy and cost. We also develop an optimization problem that selects the best set of specialized models for a multi-view classification. (2) For monitoring the expected IoT behaviors, we develop a progressive system consisting of one-class clustering models (per IoT class) at three levels of granularity. We develop an outlier detection technique on top of the convex hull algorithm to form custom-shape boundaries for the one-class models. We show how progression helps with computing costs and the explainability of detecting anomalies. (3) We evaluate the efficacy of our optimally selected classifiers versus the superset of specialized classifiers by applying them to our IoT traffic traces. We demonstrate how the optimal set can reduce the processing cost by a factor of six with insignificant impacts on the classification accuracy. Also, we apply our monitoring models to a public IoT dataset of benign and attack traces and show they yield an average true-positive rate of 94% and a false-positive rate of 5%. Finally, we publicly release our data (training and testing instances of classification and monitoring tasks) and code for convex hull-based one-class models.

PH-shape: an adaptive persistent homology-based approach for building outline extraction from ALS point cloud data

ABSTRACT Building outline extraction from segmented point clouds is a critical step of building footprint generation. Existing methods for this task are often based on the convex hull and α-shape algorithm. There are also some methods using grids and Delaunay triangulation. The common challenge of these methods is the determination of proper parameters. While deep learning-based methods have shown promise in reducing the impact and dependence on parameter selection, their reliance on datasets with ground truth information limits the generalization of these methods. In this study, a novel unsupervised approach, called PH-shape, is proposed to address the aforementioned challenge. The methods of Persistence Homology (PH) and Fourier descriptor are introduced into the task of building outline extraction. The PH from the theory of topological data analysis supports the automatic and adaptive determination of proper buffer radius, thus enabling the parameter-adaptive extraction of building outlines through buffering and “inverse” buffering. The quantitative and qualitative experiment results on two datasets with different point densities demonstrate the effectiveness of the proposed approach in the face of various building types, interior boundaries, and the density variation in the point cloud data of one building. The PH-supported parameter adaptivity helps the proposed approach overcome the challenge of parameter determination and data variations and achieve reliable extraction of building outlines.

Visual Navigation and Obstacle Avoidance Control for Agricultural Robots via LiDAR and Camera

Obstacle avoidance control and navigation in unstructured agricultural environments are key to the safe operation of autonomous robots, especially for agricultural machinery, where cost and stability should be taken into account. In this paper, we designed a navigation and obstacle avoidance system for agricultural robots based on LiDAR and a vision camera. The improved clustering algorithm is used to quickly and accurately analyze the obstacle information collected by LiDAR in real time. Also, the convex hull algorithm is combined with the rotating calipers algorithm to obtain the maximum diameter of the convex polygon of the clustered data. Obstacle avoidance paths and course control methods are developed based on the danger zones of obstacles. Moreover, by performing color space analysis and feature analysis on the complex orchard environment images, the optimal H-component of HSV color space is selected to obtain the ideal vision-guided trajectory images based on mean filtering and corrosion treatment. Finally, the proposed algorithm is integrated into the Three-Wheeled Mobile Differential Robot (TWMDR) platform to carry out obstacle avoidance experiments, and the results show the effectiveness and robustness of the proposed algorithm. The research conclusion can achieve satisfactory results in precise obstacle avoidance and intelligent navigation control of agricultural robots.

Numerical generation of omnistrain failure envelopes

Traditional failure criteria for composites are usually formulated in material coordinates and depend on all three inplane stresses, hence failure evaluation depends on the ply angle. The omnistrain failure envelope describes the most critical failure envelope in strain space irrespective of ply orientation. This independence of ply orientation leads to an isotropic failure criterion that depends only on the principal strains. Omnistrain envelopes greatly simplify the task of design and optimisation of composite laminates. This paper proposes a numerical technique to generate omnistrain failure envelopes for different composite failure criteria. The failure index, describing how far a point in strain space is from the failure boundary, is used to describe the failure surface. Assuming convexity of the failure surface, a set of points is generated on the surface, and the convex hull algorithm is used to generate a polygonal approximation of the failure surface. Representing strains in terms of principal strains and the angle between the principal and material coordinates, allows us to eliminate the angle analytically by considering the worst case condition. The omnistrain envelope is thus directly generated from the approximate three-dimensional failure surface. The proposed algorithm does not require analytic expressions of the failure surface. An adaptive algorithm is proposed to generate the omnistrain envelope with relatively small number of points. As demonstration of the proposed algorithm, the omnistrain envelopes for various composite materials are generated for a number of composite failure criteria. The omnistrain envelopes generated for the Tsai-Wu criteria accurately match to existing analytic expressions.

Long-term trends in functional diversity of exploited marine fish in the Azores’ archipelago: past and present

IntroductionEffective fisheries management requires monitoring and quantifying changes in exploited fish communities. Concerns about global fisheries sustainability have led to innovative approaches. Functional diversity, rooted in ecological theory, offers valuable insights into fishery activities and ecosystem processes. A trait-based approach was used to investigate the functional diversity of landed fish species in the Azores archipelago from 1980 to 2021.MethodsLandings data of exploited Actinopterygii and Elasmobranchii were provided by the Azores Fisheries Auction Services (LOTAÇOR/OKEANOS-UAc Fisheries Database). A trait matrix was built, incorporating 12 functional traits assigned to each species, capturing their importance in marine ecological processes. The Quickhull algorithm for convex hull was employed to calculate the volume occupied by the species in the four-dimensional functional space. Functional diversity (FD) was measured using three indices: functional richness (FRic), functional evenness (FEve), and functional divergence (FDiv). Trends in FD indices over the past 42 years were visualized using Generalized Additive Models (GAM) with interaction terms.Results and discussionGAM analysis revealed significant variations in the functional space and FD metrics over time. FRic exhibited peaks in the 1980s and 2010s, declining in the 1990s and from the 2010s onwards, indicating diversification in target species. The recent decrease in FRic can be attributed to the absence of catches of species with unique traits. The distribution of landings and trait combinations showed higher regularity in the functional space during the 1980s and 1990s (high FEve). Actinopterygii species targeted in the 1980s and 1990s had lower trait divergence (low FDiv) compared to those targeted from the 2000s onwards (high FDiv). Variability in FD can be linked to changes in fishing practices, species availability, market demand, environmental factors, and local regulations. This study underscores the importance of considering FD metrics alongside species richness and abundance when assessing the potential effects of fisheries on marine ecosystems and sustainable use of fishery resources.

Receiver Operating Characteristic Optimization Based on Convex Hull and Evolutionary Algorithm

Classification is basically a Multi-objective problem. The efficiency of classification vastly depends on performance of a classifier which can be evaluated on the basis of Receiver Operating Characteristic (ROC) graph, Area under Curve (AUC), and selection of different threshold values are generally used as a tool. In machine learning, generally, 2-D classifiers are available that deal with bi-objective problems where overlapping of class may occur i.e. sensitivity and specificity may overlap. Recently, multi-class classification in which classes are mutually exclusive is in research trends along with the evolutionary algorithm. The application of ROC graph is extended to evaluate multi-dimensional classification as it is cost sensitive learning. The goal of this paper is to gather recent achievements in the field at one place to analyze the classifier performance for multi-dimensional classification problems using convex hull and evolutionary algorithms. In this paper, we tried to cover all the existing recent advance techniques in maximization of ROC and proposed a convex hull and evolutionary algorithm based new model for ROC maximization.