Hull Method Research Articles

On-Line Feasible Wrench Polytope Evaluation Based on Human Musculoskeletal Models: An Iterative Convex Hull Method

Many recent human-robot collaboration strategies, such as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Assist-As-Needed</i> (AAN), are promoting human-centered robot control, where the robot continuously adapts its assistance level based on the real-time need of its human counterpart. One of the fundamental assumptions of these approaches is the ability to measure or estimate the physical capacity of humans in real-time. In this work, we propose an algorithm for the feasibility set analysis of a generic class of linear algebra problems. This novel iterative convex-hull method is applied to the determination of the feasible cartesian wrench polytope associated to a musculoskeletal model of the human upper limb. The method is capable of running in real-time and allows the user to define the desired estimation accuracy. The algorithm performance analysis shows that the execution time has near-linear relationship to the considered number of muscles, as opposed to the exponential relationship of the conventional methods. Finally, real-time robot control application of the algorithm is demonstrated in a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Collaborative carrying</i> experiment, where a human operator and a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Franka Emika Panda</i> robot jointly carry a 7 kg object. The robot is controlled in accordance to the AAN paradigm maintaining the load carried by the human operator at 30% of its carrying capacity.

Feasibility study on the estimation of the living vegetation volume of individual street trees using terrestrial laser scanning

As an important part of urban greening, the canopy of street trees has ecological benefits, such as oxygen production, noise reduction, and dust reduction. The living vegetation volume (LVV) can reflect the spatial structure of the canopy intuitively and enables the estimation of the ecological service value of street trees. Terrestrial laser scanning (TLS) has shown excellent performance for providing three-dimensional data of individual trees with high precision, enabling the accurate quantification of the LVV. In this study, we divided the LVV into the total living vegetation volume (tLVV) and the effective living vegetation volume (eLVV); the latter does not include branches. The eLVV of 40 ginkgo trees separated in two roads in Nanjing was calculated from TLS data. A novel method named LAIM for accurate eLVV calculation based on point cloud data was proposed. The point cloud data of individual tree was segmented along the Z-axis and image processing methods were used. With this, eLVV of each tree was obtained. The results were compared with data obtained from a clustered point cloud generated using convex hulls. The Bland-Altman analysis was used to investigate the consistency of the two methods. Furthermore, we used correlation analysis and all-subsets regression to choose the variables, and the eLVV was fitted using six models. Finally, we evaluated O2 production, CO2 and SO2 absorption by the street trees based on eLVV, the ecological benefits of street trees were quantified. The results showed the following: (1) The number of layers and the dilation size of the point cloud were crucial parameters in the LAIM. (2) For ginkgo trees, the mean difference between the eLVV obtained from the LAIM and the convex hull method was − 0.53–0.19 m3, indicating that the results were highly consistent for the two methods. (3) The eLVV fitting performance was better for the exponential function model (R2 =0.8523, RMSE=0.6838 m3) and linear model (R2 =0.8361, RMSE=0.7224 m3). The tree height and crown width significantly affected the eLVV estimation. (4) The evaluation about ecological benefits of Zhaoyang Road was better than Cuizhu Road. The quantified ecological benefits were conducive to road ecological evaluation. This study quantified the eLVV of individual trees using TLS, highlighting the importance of live vegetation in urban greening. The results can provide technical support for estimating the ecological service value of urban street trees.

Psychometric assessment of scales for measuring loneliness and social isolation: an analysis of the household, income and labour dynamics in Australia (HILDA) survey

BackgroundLoneliness and social isolation are increasingly recognised as global public health threats, meaning that reliable and valid measures are needed to monitor these conditions at a population level. We aimed to determine if robust and practical scales could be derived for conditions such as loneliness and social isolation using items from a national survey.MethodsWe conducted psychometric analyses of ten items in two waves of the Household, Income and Labour Dynamics in Australia Survey, which included over 15,000 participants. We used the Hull method, exploratory structural equation modelling, and multidimensional item response theory analysis in a calibration sample to determine the number of factors and items within each factor. We cross-validated the factor structure using confirmatory factor analysis in a validation sample. We assessed construct validity by comparing the resulting sub-scales with measures for psychological distress and mental well-being.ResultsCalibration and cross-validation consistently revealed a three-factor model, with sub-scales reflecting constructs of loneliness and social isolation. Sub-scales showed high reliability and measurement invariance across waves, gender, and age. Construct validity was supported by significant correlations between the sub-scales and measures of psychological distress and mental health. Individuals who met threshold criteria for loneliness and social isolation had consistently greater odds of being psychologically distressed and having poor mental health than those who did not.ConclusionsThese derived scales provide robust and practical measures of loneliness and social isolation for population-based research.

Tension distribution algorithm based on graphics with high computational efficiency and robust optimization for two-redundant cable-driven parallel robots

In this article, we present a novel tension distribution algorithm for cable-driven parallel robots (CDPRs) with two degrees of actuation redundancy. The algorithm consists of calculating the tension feasible region (TFR) and optimizing the tension distribution. The TFR has been innovatively calculated without relying on the convex hull method. The topological relationship of the vertices is maintained, so the calculation steps are greatly reduced. For optimizing TFR, various functions are proposed, especially the tension robustness index is defined. We prove the complexity and continuity of the algorithm and we study several examples. The results are compared with those obtained with the existing state-of-the-art method, and the proposed technique is shown to be more computationally efficient. The robustness of the optimal solution is also calculated. The proposed algorithm can be applied in some real-time systems with unmeasurable interference, such as interactive devices.

Fast prediction of phase equilibrium at varying temperatures for use in multi-component phase field models

A new method for temperature-dependent phase equilibrium prediction for use in multi-component phase field models of solidification is proposed. The method consists of two parts. First, the convex hull method is applied to predict the phase equilibrium at a single temperature. Second, a set of linear equations is developed to extend the equilibrium calculation over a range of temperatures. These linear equations are derived as an extension of the equation used for solidification of binary alloys in approximating the equilibrium state of multi-component systems. Phase field simulations of solidification of a Ti-Al-V-Fe alloy are performed to demonstrate the effectiveness of the present approach under isothermal and continuous cooling conditions. The results are compared against Thermo-Calc calculations, and indicate that a high accuracy of equilibrium prediction is achieved at a single and multiple temperatures, thus demonstrating that this approach can be successfully applied to the multi-component phase field models.

An Exploratory Analysis of the Internal Structure of Test Through a Multimethods Exploratory Approach of the ASQ:SE in Brazil

Background A wide range of exploratory methods is available in psychometrics as means of gathering insight on existing data and on the process of establishing the number and nature of an internal structure factor of a test. Exploratory factor analysis (EFA) and principal component analysis (PCA) remain well-established techniques despite their different theoretical perspectives. Network analysis (NA) has recently gained popularity together with such algorithms as the Next Eigenvalue Sufficiency Test. These analyses link statistics and psychology, but their results tend to vary, leading to an open methodological debate on statistical assumptions of psychometric analyses and the extent to which results that are generated with these analyses align with the theoretical basis that underlies an instrument. The current study uses a previously published data set from the Ages & Stages Questionnaires: Social-Emotional to explore, show, and discuss several exploratory analyses of its internal structure. To a lesser degree, this study furthers the ongoing debate on the interface between theoretical and methodological perspectives in psychometrics. Methods From a sample of 22,331 sixty-month-old children, 500 participants were randomly selected. Pearson and polychoric correlation matrices were compared and used as inputs in the psychometric analyses. The number of factors was determined via well-known rules of thumb, including the parallel analysis and the Hull method. Multidimensional solutions were rotated via oblique methods. R and Factor software were used, the codes for which are publicly available at https://luisfca.shinyapps.io/psychometrics_asq_se/ . Results Solutions from one to eight dimensions were suggested. Polychoric correlation overcame Pearson correlation, but nonconvergence issues were detected. The Hull method achieved a unidimensional structure. PCA and EFA achieved similar results. Conversely, six clusters were suggested via NA. Conclusion The statistical outcomes for determining the factor structure of an assessment diverged, varying from one to eight domains, which allowed for different interpretations of the results. Methodological implications are further discussed.

Adaptation and latent structure of the Brazilian version of the Ego Dissolution Inventory (EDI-BR): an exploratory study.

Existing scales that seek to measure alterations in self-experience were developed based on studies conducted in developed countries. Therefore, the aim of this study was to evaluate the psychometric properties of the Ego Dissolution Inventory (EDI) after translating and adapting it for the Brazilian context. The measure was translated by two translators fluent in both English and Portuguese, followed by back-translation into English to ensure there was no loss of meaning. The scale was used in an online survey exploring substance use. A total of 528 participants answered the full scale. We calculated the Kaiser-Meyer-Olkin (KMO) measure to evaluate sampling adequacy, then ran exploratory factor analysis (EFAs) to investigate the factor structure of the EDI. The scale showed acceptable psychometric properties, with excellent internal consistency and sampling adequacy for factor analysis. Kaiser-Guttman's criteria and Hull's method indicated a three-factor solution, while parallel analysis suggested a two-factor solution. All items showed salient loadings, with two items exhibiting cross-loading. Positive but weak correlations were found between EDI factors 1 and 2 and nature relatedness. The validated scale showed solid psychometric properties, with potential differences in factor structure in relation to the English version. Considering validation is an ongoing process, it is recommended that studies be conducted comparing ego dissolution scores across distinct substances and different regions of the country.

Environment Potential Field Modeling for Ship Automatic Collision Avoidance in Restricted Waters

The accurate environment potential field (EPF) modeling method and highly efficient collision avoidance (CA) approach are key technologies for maritime autonomous surface ships (MASS). A novel and accurate environment potential field (EPF) model is proposed using electronic navigation chart (ENC) face objects to describe different types of navigable and non-navigable areas, and an improved artificial potential field (APF) method is presented to realize collaborative CA and obstacle avoidance (OA). Implicit equations of complex-shaped face objects were constructed based on R-function theory, and the discrete-convex hull method was introduced to realize automatic EPF modeling. Collaborative CA and OA experiments in restricted waters were conducted on a ship handling simulator. The results show that the improved APF method can obtain a robust and deterministic collision-free path under different weather conditions and in restricted waters, and the track zone width remains within 0.1 nm. The proposed face object EPF model is efficient and accurate, even with numerous vertices and complex shapes, and can drive the ship apart at a relatively safe distance in accordance with the recommended CA parameter. We present a practical CA approach and an effective EPF modeling method for APF-based ship path planning.

3D Reconstruction Methods Purporting 3D Visualization and Volume Estimation of Brain Tumors

This work proposes the Crust algorithm for 3D reconstruction of brain tumor, an effective mechanism in the visualization of tumors for presurgical planning, radiation dose calculation. Despite the promising performance of Crust algorithm in reconstruction of Stanford models, it has not yet been considered in 3D reconstruction of brain tumor. Validation of the results is done using the comparison of the 3D models from two cutting edge techniques namely the Marching Cube and the Alpha shape algorithm. The obtained result shows that Crust algorithm provides the brain tumor model with an average quality of triangle meshes ranging from 0.85 to 0.95. Concerning the visual realism, the quality of Crust algorithm models is higher on comparison to the other models. Precision of tumor volume measurement by convex hull method is analysed by repeatability and reproducibility. The standard deviations of repeatability were between 2.03 % and 3.97 %. The experimental results show that Linear Crust algorithm produces high quality meshes with average quality of equilateral triangles close to 1.

Lifetime calculation of adhesively bonded joints under proportional and non-proportional multiaxial fatigue loading: a combined critical plane and critical distance approach

ABSTRACT This work presents an approach for lifetime calculation of adhesively bonded joints under proportional and non-proportional multiaxial fatigue loads. In-phase and out-of-phase fatigue data with varying stress ratio (R = 0.1 and −1.0) from several joint geometries (butt, scarf, thick adherend shear test and butt-bonded double pipe joints) was employed. The approach is based on the stress-life concept and the Theory of Critical Distances (TCD) considering point and line methods. Stresses were calculated using 2D-FEA with linear-elastic and elastoplastic adhesive behaviours. Non-proportional stresses were addressed by the Maximum Rectangular Hull method. Three failure criteria were investigated: Drucker-Prager (DPC): an invariant-based equivalent stress criterion; Findley (FC): a critical plane stress-based criterion; (c) Fatemi-Socie (FSC): a critical plane stress-strain-based criterion. The material parameter of each failure criterion was iteratively calibrated using fatigue data. The FSC had the worst prediction performance. The DPC presented good agreement with in-phase data, but less robust as the variation of its material parameter changed considerably the prediction accuracy. For both DPC and FC, the line method and linear-elastic models were capable of providing good predictions. The FC yielded the best predictions considering a normal sensitivity bF > 1.0 for different stress ratios, in-phase and out-of-phase loads.

A front‐fixing method for American option pricing on zero‐coupon bond under the Hull and White model

A new efficient numerical method is proposed for valuation of American option on zero‐coupon bond using Hull and White model. By applying the front‐fixing transformation suggested by Holmes and Yang, the original free boundary problem is transformed into a new fixed boundary partial differential equation (PDE) problem, where the optimal stopping boundary is one of the unknowns of the problem. The numerical finite difference scheme for the transformed problem is constructed. Stability and convergence rate is studied empirically. Numerical simulation of the computation of both the option price and the optimal stopping boundary are illustrated with examples and the comparison with the Hull and White tree method.

UAV-based individual shrub aboveground biomass estimation calibrated against terrestrial LiDAR in a shrub-encroached grassland

Shrub encroachment is an important ecological issue that is increasingly receiving global attention in arid and semiarid grasslands. Monitoring the spatial distribution of encroached shrub aboveground biomass (AGB) is critical for ecological conservation and adaptive ecosystem management. However, the low stature and fine spatial heterogeneity of encroached shrub communities increase difficulties for coarse spatial-resolution satellite images to adequately capture detailed characteristics of individual shrubs. Unmanned aerial vehicle (UAV) can acquire centimeter-level optical images or high-density LiDAR point cloud data, providing an effective means to map encroached shrub AGB spatially explicitly, even at the individual scale. In this study, we first extracted the individual shrubs based on thresholds in normalized difference vegetation index (NDVI) and canopy height model (CHM) using UAV-based multispectral and LiDAR data. For each shrub, we then derived and determined the dominant geometric, spectral, and textural features from the high-resolution multispectral image and the volumetric features from the LiDAR data as predictors of shrub AGB. Finally, we compared the capability of different data sources (UAV-based multispectral image, LiDAR, and their combination) and regression methods (multiple linear, random forest, and support vector regression) to estimate and map the individual shrub AGB in the study area. The volume-based approaches to individual shrub AGB, including global convex hull method, voxel method, and surface differencing method, were also employed using terrestrial laser scanning (TLS) to further calibrate the UAV-based estimation. Our results show that individual shrubs can be accurately extracted based on the threshold method with an overall classification accuracy of 91.8%. The UAV-based AGB estimation suggests that the textural feature, the sum of contrast metric within the individual shrub canopy, is the most important predictor of individual shrub AGB, followed by volumetric, geometric and spectral features. Moreover, the high-resolution multispectral image shows greater potential (R2 = 0.83, RMSE = 106.46 g) than LiDAR (R2 = 0.77, RMSE = 123.33 g) in the estimation of individual shrub AGB, and their combination can only slightly improve the estimation accuracy (R2 = 0.86, RMSE = 101.97 g). Our results also show that TLS-derived volume based on the surface differencing method obtained the best prediction accuracy of individual shrub AGB (R2 = 0.91, RMSE = 79.98 g), and can be used as an alternative of destructive harvesting. This study provides a new insight for quantifying and mapping individual shrub AGB using UAV-based optical sensors and TLS without destructive harvesting in arid and semiarid grasslands.

Dispositional Greed Scales

Abstract. In recent years, different scales have been developed to assess individual differences in dispositional greed. We report two studies ( N1 = 300, N2 = 1,000) on the comparative psychometric properties of these scales. We find that all scales are reliable and that they correlate highly, suggesting that all can be used to assess dispositional greed. Exploratory factor analyses, using the Empirical Kaiser Criterion, the Hull method, and Parallel Analysis as extraction methods, were done on the separate scales and all items together. These analyses reveal that there is quite some consistency in the scales, as in both studies a one-factor solution seems to describe the data best. These results imply that these different scales all assess dispositional greed, although the results also suggest that some items may be deleted from the scales.

A novel predictive model for multiaxial fatigue in carburized bevel gears

AbstractThis publication focuses on the numerical stress prediction in case‐carburized bevel gears and on their fatigue assessment. Four gear sets are analyzed for the common fatigue failure modes of pitting, tooth root breakage, and subsurface fatigue. The proposed algorithm, enabling the prediction of the dominant failure type and region, relies on the previously published material model for carburized CrNiMo steels. It utilizes a 2D plane strain simplification as only the mean cross‐section is analyzed and evaluates the shear mean and amplitude stresses through the maximum rectangular hull method. A novel multiaxial fatigue criterion is presented and validated.

Neural-Network Based Adaptive Self-Triggered Consensus of Nonlinear Multi-Agent Systems With Sensor Saturation

This paper aims to propose a self-triggered consensus control scheme for a class of nonlinear multi-agent systems with sensor saturation. Because of the existence of unknown nonlinear dynamics, this study borrows the approximation capability of neural networks to design the consensus control protocol. This paper adopts neural network to approximate the ideal controller, instead of using the combination of neural network and adaptive method to approximate the unknown system dynamics. Thus, the extended approximation property of neural network for event-based sampling can be beneficially introduced. Moreover, the designed controller only updates at discrete time, which enables that the system can be modeled as a hybrid system with impulsive dynamics. Thus, the stability theory of impulsive systems can be used to analyze the convergence of the system. It should be noted that this is the first time to propose an effective event-triggered consensus control algorithm based on neural network. Furthermore, this paper also considers a frequently encountered phenomenon of sensor saturation. The convex hull method is adopted to deal with sensor saturation problem, instead of the widely used sector condition method. Finally, the performance of the proposed neural-network based self-triggered consensus control algorithm is demonstrated by the numerical examples.

Comparison and Evaluation of Algorithms for LiDAR-Based Contour Estimation in Integrated Vehicle Safety

Many nations and organizations are committing to achieving the goal of ‘Vision Zero’ which aims to bring the number of road deaths close to zero by the year 2050. The core of the strategy is a safe transportation system with optimized vehicles and transportation routes. The industry continues to develop integrated safety systems to make vehicles safer, smarter, and more capable in safety-critical scenarios. Passive safety systems are now focusing on pre-crash deployment of restraint systems to better protect vehicle passengers. Current commonly used bounding box methods for the shape estimation of potential crash partners lack the fidelity required for edge case collision detection and advanced crash modeling of future pre-crash technologies. This has led to the development of novel algorithms for vehicle contour estimation in literature. With this work, we present a framework for assessing and comparing different contour estimation algorithms, including a simple bounding box, oriented bounding box, polynomial fit estimation, complemented convex hull, and three-arc fit. Tests on simulated virtual and experimental LiDAR measurements of a simplified vehicle contour have been conducted to determine performance at varying relative angles and distances. It has been concluded that the convex hull and the three-arc methods are the best performing of the studied algorithms, with each having different strengths. The three-arc algorithm offers higher accuracy estimations at low relative angles and near-mid distances, whereas the convex hull method requires low computation time and can provide accurate estimations even at extreme relative angles and distances.

Prototype Program Hand Gesture Recognize Using the Convex Hull Method and Convexity Defect on Android

One of the research topics of Human-Computer Interaction is the development of input devices and how users interact with computers. So far, the application of hand gestures is more often applied to desktop computers. Meanwhile, current technological developments have given rise to various forms of computers, one of which is a computer in the form of a smartphone whose users are increasing every year. Therefore, hand gestures need to be applied to smartphones to facilitate interaction between the user and the device. This study implements hand gestures on smartphones using the Android operating system. The algorithm used is convex hull and convexity defect for recognition of the network on the hand which is used as system input. Meanwhile, to ensure this technology runs well, testing was carried out with 3 scenarios involving variable lighting, background color, and indoor or outdoor conditions. The results of this study indicate that Hand gesture recognition using convex hull and convexity defect algorithms has been successfully implemented on smartphones with the Android operating system. Indoor or outdoor testing environment greatly affects the accuracy of hand gesture recognition. For outdoor use, a green background color with a light intensity of 1725 lux produces 76.7% accuracy, while for indoors, a red background color with a light intensity of 300 lux provides the greatest accuracy of 83.3%.

New Genome Sequence Detection via Natural Vector Convex Hull Method.

It remains challenging how to find existing but undiscovered genome sequence mutations or predict potential genome sequence mutations based on real sequence data. Motivated by this, we develop approaches to detect new, undiscovered genome sequences. Because discovering new genome sequences through biological experiments is resource-intensive, we want to achieve the new genome sequence detection task mathematically. However, little literature tells us how to detect new, undiscovered genome sequence mutations mathematically. We form a new framework based on natural vector convex hull method that conducts alignment-free sequence analysis. Our newly developed two approaches, Random-permutation Algorithm with Penalty (RAP) and Random-permutation Algorithm with Penalty and COstrained Search (RAPCOS), use the geometry properties captured by natural vectors. In our experiment, we discover a mathematically new human immunodeficiency virus (HIV) genome sequence using some real HIV genome sequences. Significantly, the proposed methods are applicable to solve the new genome sequence detection challenge and have many good properties, such as robustness, rapid convergence, and fast computation.

Balancing fit and parsimony to improve Q-matrix validation.

The Q-matrix identifies the subset of attributes measured by each item in the cognitive diagnosis modelling framework. Usually constructed by domain experts, the Q-matrix might contain some misspecifications, disrupting classification accuracy. Empirical Q-matrix validation methods such as the general discrimination index (GDI) and Wald have shown promising results in addressing this problem. However, a cut-off point is used in both methods, which might be suboptimal. To address this limitation, the Hull method is proposed and evaluated in the present study. This method aims to find the optimal balance between fit and parsimony, and it is flexible enough to be used either with a measure of item discrimination (the proportion of variance accounted for, PVAF) or a coefficient of determination (pseudo-R2 ). Results from a simulation study showed that the Hull method consistently showed the best performance and shortest computation time, especially when used with the PVAF. The Wald method also performed very well overall, while the GDI method obtained poor results when the number of attributes was high. The absence of a cut-off point provides greater flexibility to the Hull method, and it places it as a comprehensive solution to the Q-matrix specification problem in applied settings. This proposal is illustrated using real data.

ROC with Cost Pareto Frontier Feature Selection Using Search Methods

Cyber-physical systems (CPS) are finding increasing application in many domains. CPS are composed of sensors, actuators, a central decision-making unit, and a network connecting all of these components. The design of CPS involves the selection of these hardware and software components, and this design process could be limited by a cost constraint. This study assumes that the central decision-making unit is a binary classifier, and casts the design problem as a feature selection problem for the binary classifier where each feature has an associated cost. Receiver operating characteristic (ROC) curves are a useful tool for comparing and selecting binary classifiers; however, ROC curves only consider the misclassification cost of the classifier and ignore other costs such as the cost of the features. The authors previously proposed a method called ROC Convex Hull with Cost (ROCCHC) that is used to select ROC optimal classifiers when cost is a factor. ROCCHC extends the widely used ROC Convex Hull (ROCCH) method by combining it with the Pareto analysis for cost optimization. This paper proposes using the ROCCHC analysis as the evaluation function for feature selection search methods without requiring an exhaustive search over the feature space. This analysis is performed on 6 real-world data sets, including a diagnostic cyber-physical system for hydraulic actuators. The ROCCHC analysis is demonstrated using sequential forward and backward search. The results are compared with the ROCCH selection method and a popular Pareto selection method that uses classification accuracy and feature cost.