Hull Distance Research Articles

Prediction of thermodynamic properties oflanthanide/transition metal alloys by deep learning

The utilization of machine learning, especially deep learning, in solving materials science issues bring an opportunity to accelerate the development process of new materials and draw the attention of researchers all over the world. In this work, we present our study on applying deep neural networks to represent and predict thermodynamic quantities including formation energy, convex hull distance, and to recognize potential thermodynamical stabile materials. We employ our novel material descriptor, named orbital field matrix (OFM), to determine the feature vectors for materials. The OFM descriptors were developed based on the information of valence electron configuration and the Voronoi analysis of the atomic structures of materials. Our experiments show that deep neural networks can accurately predict formation energyand convex hull distancewith the mean absolute error around 0,124eV/atom and 0,105 eV/atom, respectively. In addition,the classification neural network can yield an accuracy of 92% in distinguishing the stable and unstable materials.

Analysis of the Hulls Floater Distance in the Manoeuvrability of the N219 Floatplane: An Experimental Approach in the Open Water Test

Floatplanes necessitate the utilization of floaters that possess the capacity to facilitate aircraft manoeuvring activities, hence safeguarding passengers against potential hazards associated with sea-based crashes. This paper is to analyse the impact of the manoeuvrability of the N219 floatplane on multiple variables, such as the distance between catamaran hulls, trim angle, and cruising speed. The selection of the open-free-running test method for the testing model is based on its advantages, including its costeffectiveness, simplicity, and demonstrated accuracy in yielding reliable outcomes. The floatplane model is outfitted with a remote-control system that is connected through a wireless communication system. The trajectory of the floatplane model is documented using a Global Positioning System (GPS) device, and the collected data is subsequently transformed into a trajectory line for the purpose of enabling analysis. The turning model test is used to find the best hull distance variation out of three options considering the parameters of tactical diameter (TD), advance (A), transfer (T), and radius (R). The tests are undertaken for three different combinations of trim angle and speed. The results show that S/L 0.5 is the ideal distances between the floater hulls while a trim angle of 0°, and a Froude number of 0.064. The condition defines the ratio of parameters and the length of the ship (Lpp), which includes the values of TD/Lpp, A/Lpp, T/Lpp, and R/Lpp as 2.49, 1.59, 1.60, and 1.26, respectively

EEDI ANALYSIS FOR TRIMARAN FEEDER AT MALUKU WATERS

The international maritime organization (IMO) has worked since 2009 to minimize harmful gas emissions from marine traffic. Due to its worldwide climate effect, IMO has implemented an energy efficiency design index (EEDI) to monitor and quantify CO2 emissions. Due to 1.0-meter wave height, Maluku waters remain quiet. For transporting from distant, outermost, and border places, feeder ships will work better. The three-body vessel (trimaran) has a vast deck surface and can efficiently feed tiny islands. This study analyzes trimaran vessel EEDI and resistance using Froude number Fr = 0.21 speed and transverse distance S/L = 0.2, 0.3, 0.4, and 0.5. Experimental calculations show a large fluctuation in hull distance. The trimaran ships with S/L=0.5 have the least resistance and essentially equal calculation results to independent trimarans. This shows that the trimaran with a 0.5 hull ratio has little hull interference. For trimaran ships, EEDI estimates suggest operating below Fr=0.21, which meets IMO criteria.

Viscous interference analysis of trimaran vessel using computational fluid dynamic

Multihull vessels such as trimaran have been well known to have better transverse stability and can provide wider deck area and have been found to be quite successful when operated as passenger carriers. Trimaran has also been known to produce lower resistance, and hence lower power and less fuel consumption, compared to monohulls. The current research investigates the viscous interference hull of a displacement trimaran at various spacing configuration (up to S/L = 0.5) and Froude numbers (up to 0.27). Due to viscous interference between trimaran hulls, the viscous form greatly affects trimaran ship resistance. The trimaran's hull form ratio affects viscous drag more than velocity or hull clearance. Increases in hull distance (S/L) minimize interference and enhance viscous resistance by 1.57%. This study will present the interference effect of trimaran ship models' viscous resistance components, which can be used directly in the resistance calculation used to determine trimarans' engine power at the design stage.

First-principles calculations to investigate electronic, magnetic and half-metallic ferromagnetic properties of full-Heusler Mn2OsSn

ABSTRACT The structural, electrical, magnetic, and elastic characteristics of the Mn2OsSn full-Heusler compound have all been studied using the full potential linearised augmented plane (FP-LAPW) method. The study's exchange and correlation potentials are calculated using the generalised gradient approach (GGA) developed by Burke, Perdew, and Ernzerhof; the GGA with the Tran-Blaha-modified Becke–Johnson approximations; and the GGA with the correlated Hubbard parameter (GGA +U). Our calculations show that the formation energy of the compound is negative for the two-type structure, which means the crystal may persist indefinitely. Our chemical has a convex hull distance at 0 K for cubic regular and inverse-type structures, indicating that it will likely be synthesised via equilibrium processing. The electronic band structures, densities of states, and 100 spin-polarisation at the Fermi level in the typical cubic AlCu2Mn-type structure show Mn2OsSn in its complete Heusler ferromagnetic state has a half-metallic feature with an indirect band gap in the minority spin. Alternatively, in the CuHg2Ti-type ferromagnetic state, with its inverse cubic structure, this material exhibits metallic ferromagnetic behaviour with a polarisation of 96,325. The half metallicity of the AlCu2Mn-type combination is preserved at 1 GPa of hydrostatic pressure. Thus, Mn2OsSn, with the appropriate correction option for the Hubbard-Coulomb parameter U, will be a promising contender for spintronic applications.

Feature Line Extraction from Building Façade Point Clouds by Exploring Spatial Topological Relationship

After using a terrestrial laser scanner to acquire building facade point clouds, the extraction of feature lines can simplify the expression of building objects, thereby contributing to the accurate construction of building facade geometric models. To address the problems of missing extraction and low accuracy in existing methods, this study proposes a feature line extraction method for building facade point clouds by exploring the different spatial topological relationship between feature and non‐feature points. The method comprises three steps: feature point extraction, feature line generation, and feature line merging and optimization. First, feature points are extracted using the convex hull distance value and relative angle, as defined in this study. Thereafter, feature points are clustered by combining the random sample consensus and region growing algorithms, and the feature lines are obtained by utilizing the iterative weighted least squares method based on the IGGIII weight function. Finally, the feature lines are merged and optimized using an endpoint search method to improve the discontinuity and missing common endpoints of the original feature lines. The experimental results obtained using simulated and measured point cloud data show that this method can accurately extract feature points, and the extracted feature lines obtained from building facade point clouds have better accuracy and completeness, which is more practical than the existing methods and can be used in many applications such as building facade measurement and urban three‐dimensional modeling.

Automatic body condition scoring for dairy cows based on efficient net and convex hull features of point clouds

Body condition score (BCS) is an important indicator to evaluate the nutritional and health status of perinatal dairy cows. The insufficient accuracy of the automatic body condition score is the core bottleneck restricting the popularization and application of this technology. To further improve the accuracy of automatic scoring of cow body condition, a 3D structure feature map based on convex hull distance of point clouds was constructed and used as the input of a two-level model based on EfficientNet network, which realized the improvement of recognition accuracy within the error of 0.25 in the automatic scoring of cow body condition. First, the depth image of the cow’s back was preprocessed to obtain the point cloud from the hook bone to the needle bone region containing the main body condition information. Second, the point cloud was voxelized and convex hulled, and the distance between each peripheral voxel and the nearest convex hull surface was calculated and projected to the X-Y plane to obtain the structural feature map. Then, a two-level classification model based on the EfficientNet network was constructed, and the whale optimization algorithm (WOA) was used to optimize the network scaling coefficient in the model. Finally, 5119 depth images of 77 cows were used to train and test the model, and the dataset ratio was 1:1. The results showed that when the BCS value was in the range of 2.25 to 3.75 and the two-class classification limit was 2.75, the proportions of images with recognition errors less than 0.25 and 0.50 were 91.21% and 97.60%, respectively, and the average recognition speed was 3.446 frames/s. The recognition effect was better than that of the MobileNet-V2, XceptionNet and LeNet-5 network models. This method could realize the noncontact assessment of individual body conditions of dairy cows in large-scale farms and had the characteristics of high accuracy, strong applicability and low cost.

CH-Bin: A convex hull based approach for binning metagenomic contigs

Metagenomics has enabled culture-independent analysis of micro-organisms present in environmental samples. Metagenomics binning, which involves the grouping of contigs into bins that represent different taxonomic groups, is an important step of a typical metagenomic workflow followed after assembly. The majority of the metagenomic binning tools represent the composition and coverage information of contigs as feature vectors consisting of a large number of dimensions. However, these tools use traditional Euclidean distance or Manhattan distance metrics which become unreliable in the high dimensional space. We propose CH-Bin, a binning approach that leverages the benefits of using convex hull distance for binning contigs represented by high dimensional feature vectors. We demonstrate using experimental evidence on simulated and real datasets that the use of high dimensional feature vectors to represent contigs can preserve additional information, and result in improved binning results. We further demonstrate that the convex hull distance based binning approach can be effectively utilized in binning such high dimensional data. To the best of our knowledge, this is the first time that composition information from oligonucleotides of multiple sizes has been used in representing the composition information of contigs and a convex hull distance based binning algorithm has been used to bin metagenomic contigs. The source code of CH-Bin is available at https://github.com/kdsuneraavinash/CH-Bin.

Half-metallic p0-d half-Heusler alloys with stable structure in ferromagnetic state

We investigate the structural, magnetic, thermodynamic, and mechanical stability of p0-d half-Heusler compounds composed of p0 = Li, Be, Na, Mg alkali and alkaline earth metals; 3d transition metals; and group V (pnicogens) & VI (chalcogens) sp atoms using the DFT based full-potential Wien2k package. Ferromagnetic, antiferromagnetic, and non-magnetic calculations are performed to ascertain the stable magnetic state. From 280 compounds under study, 16 of them show half-metallic properties in the minimum-energy half-Heusler phase at the optimized lattice constant. Calculations show that compounds are stable in the half-Heusler β-phase and the ferromagnetic state. The band structure and the density of states are obtained for both GGA and GGA + U exchange correlation approximations. Furthermore, in order to study the thermodynamic and the mechanical (dynamic) stability of the compounds, the formation energy, the hull distance, and the elastic constants of half-metallic compounds are calculated. The estimated coupling constants and Curie temperatures from Monte Carlo simulations show that these compounds preserve the ferromagnetic state and magnetic polarization at temperatures much higher than the room temperature and may well be suitable materials for spintronic applications. The phonon dispersion curve for NaVBi as the compound that has the highest Curie temperature among half-metallic compounds shows no imaginary frequencies and testifies to the dynamic stability of the compound in the ferromagnetic half-Heusler β-phase as expected from the elastic constants.

Structural configuration and phase stability in the quaternary Heusler compounds CoCrYSb (Y=Sc,Ti, V)

The structural configuration and phase stability of quaternary Heusler alloys CoCrScSb, CoCrTiSb and CoCrVSb have been systematically investigated. The results show that type-Ⅰ configuration in the cubic ferromagnetic phase has the lowest total energy, and the CoCrScSb and CoCrVSb exhibit half-metallic behavior under cubic ground state. The hull distance justify that the alloys CoCrScSb and CoCrVSb can be considered to be thermodynamically stable and suitable for experimental synthesis. The tetragonal phase transformation was investigated using the total energy change method. It was found that the deformation of CoCrScSb contributes to the energy reduction, with c/a = 1.25 being at the lowest value of energy. Under tetragonal phase the CoCrScSb exhibits metallic behavior. However, the deformation of the alloys CoCrTiSb and CoCrVSb did not reduce the total energy compared to the cubic phase. Elastic studies indicate that CoCrScSb, CoCrTiSb and CoCrVSb have stable mechanical properties, which provides the possibility for their engineering application.

High-Throughput Design of Interfacial Perpendicular Magnetic Anisotropy at Heusler/MgO Heterostructures.

The perpendicular magnetic anisotropy (PMA) at ferromagnet/insulator interfaces has important technological applications, such as in the fields of magnetic recording and sensing devices. The perpendicular magnetic tunnel junctions (p-MTJs) with strong PMA have recently attracted increasing interest because they offer high stability and device performance toward low energy consumption. Heusler alloys are a large family of compounds that offer promising magnetic properties for developing p-MTJs. However, it is challenging to select appropriate combinations of Heusler ferromagnets and insulators with the desired interfacial properties. Here, we report a systematic high-throughput screening approach to search for candidate Heusler/MgO material interfaces with strong PMA and other desired material properties for spintronic technologies. On the basis of the open quantum material repositories, we developed a series of material descriptors, including formation energy, convex hull distance, magnetic ordering, lattice misfit, magnetic anisotropy constant, cleavage energy, and tunnel magnetoresistance, to filter candidate Heusler/MgO interfaces among the possible 40 000 ternary Heusler compounds. After a comprehensive screening, five full-Heusler compounds, including Co2CrAl, Co2FeAl, Co2HfSn, Fe2IrGa, and Mn2IrGe, and two half-Heusler compounds, PtCrSb and PtMnAs, were found to be promising for designing p-MTJs. This work demonstrates a new way for the high-throughput design of functional material interfaces for spintronic applications via exploiting the open quantum material repositories and developing effective material descriptors along with the large-scale ab initio calculations for material interfaces.

Effect of Performance of Soil Cultivator with Different Surface Textures of Shovel Wing

Cultivating the soil is a necessary measure to ensure the growth of potatoes, and it has a significant impact on potato yield. In this study, a soil cultivator with a textured shovel wing was designed to address the problem that soil cultivators have poor working performance. Based on a combination of discrete element simulation and a digital soil trench verification test, the effects of the structure parameters of the surface textures on the traction resistance and soil fragmentation rate of the soil cultivator with a textured shovel wing were studied. These parameters were optimized to provide a basis for the design of the soil cultivator. The main research results of this paper are as follows. (1) The factors influencing the traction resistance of the soil cultivator were as follows: blade penetration angle > convex hull distance > convex hull diameter. The convex hull diameter was the main factor affecting the soil fragmentation rate. The traction resistance of the soil cultivator with a textured wing was reduced by 9.49%, and the soil fragmentation rate was increased by 10.67%, showing that the quality of soil cultivation was significantly improved. (2) The best parameters for the texture structure of the shovel wing were a blade penetration angle of 26°, a convex hull diameter of 34.4 mm, and a convex hull distance of 28.5 mm. (3) The relative errors between the simulation and the soil trench test for the traction resistance and the soil fragmentation rate were 2.60% and 13.97%, respectively. This study can provide technical support for the design of soil cultivators and is of great significance in improving the quality of soil cultivators.

The investigation of the half-metallic properties of half-Heusler KXP (X = Cr & Mo) compounds: A first-principles study

In this article, the structural phase stability of the KXP (X = Cr & Mo) compounds in the half-Heusler structure were investigated. The stability of the KXP compound was taken into account in the three phases α, β, and γ of the half-Heusler structure within various ferromagnetic (FM), antiferromagnetic (AFM), and nonmagnetic (NM) states. Total energy calculations, as well as cohesive energy calculations, indicated that for each KXP compound the β phase of the half-Heusler structure in the FM state has the lowest energy compared to the other phases α and γ. For all structural phases of the KMoP compound, the positive amount of the formation energy predicts that the compound cannot be grown in the hypothetical half-Heusler structure and prefers to decompose into stable phases of the constituent elements. The hull distance calculations justify that, unlike KMoP, the β and γ phases of the half-Heusler KCrP compound can be considered to be thermodynamically stable and suitable for experimental synthesis. Based on the elastic results, it can be argued that, unlike α phase, both β and γ phases of the KCrP compound in the FM state satisfy the conditions of mechanical stability. Moreover, γ phase of the KCrP compound is stiffer than β phase, while β phase has a more ductile nature compared to γ phase. In agreement with the elastic results, the phonon scattering calculations showed that both β and γ phases of the half-Heusler KCrP compound are dynamically stable and can be synthesized, while the α phase is regarded to be an unstable phase. The electronic calculations indicated that the KCrP compound in the different phases β and γ possess a half-metallic property with the highly desired Curie temperature, making it a potential candidate for use in spintronic devices.

Convex hull-based distance metric learning for image classification

Distance metric learning is an important topic in visual classification tasks. Learning an appropriate distance measure can greatly improve the performance of image recognitions. In this paper, we model an image set as a regularized convex hull and propose a distance metric learning method for image classification based on regularized convex hulls. In particular, a regularized point-to-convex hull distance metric (RPCHD) and a regularized convex hull-to-convex hull distance metric (RCHCHD) are introduced to measure the distance between the query image object and the existing image sets for image classification tasks. The coefficients in distance metric are solved by an approximate optimization strategy. Two SVM-like distance metric learning models are constructed and transformed into the standard support vector machines to learn distance metric matrix in RPCHD and RCHCHD. Positive and negative sample pairs are proposed to represent distance metric matrix. Experiments on three image databases show that our proposed RPCHD and RCHCHD can effectively improve the performance of image classification.

Convex hulls in hamming space enable efficient search for similarity and clustering of genomic sequences

BackgroundIn molecular epidemiology, comparison of intra-host viral variants among infected persons is frequently used for tracing transmissions in human population and detecting viral infection outbreaks. Application of Ultra-Deep Sequencing (UDS) immensely increases the sensitivity of transmission detection but brings considerable computational challenges when comparing all pairs of sequences. We developed a new population comparison method based on convex hulls in hamming space. We applied this method to a large set of UDS samples obtained from unrelated cases infected with hepatitis C virus (HCV) and compared its performance with three previously published methods.ResultsThe convex hull in hamming space is a data structure that provides information on: (1) average hamming distance within the set, (2) average hamming distance between two sets; (3) closeness centrality of each sequence; and (4) lower and upper bound of all the pairwise distances among the members of two sets. This filtering strategy rapidly and correctly removes 96.2% of all pairwise HCV sample comparisons, outperforming all previous methods. The convex hull distance (CHD) algorithm showed variable performance depending on sequence heterogeneity of the studied populations in real and simulated datasets, suggesting the possibility of using clustering methods to improve the performance. To address this issue, we developed a new clustering algorithm, k-hulls, that reduces heterogeneity of the convex hull. This efficient algorithm is an extension of the k-means algorithm and can be used with any type of categorical data. It is 6.8-times more accurate than k-mode, a previously developed clustering algorithm for categorical data.ConclusionsCHD is a fast and efficient filtering strategy for massively reducing the computational burden of pairwise comparison among large samples of sequences, and thus, aiding the calculation of transmission links among infected individuals using threshold-based methods. In addition, the convex hull efficiently obtains important summary metrics for intra-host viral populations.

Evolutionary construction of a formation-energy convex hull: Practical scheme and application to a carbon-hydrogen binary system

We present an evolutionary construction technique of formation-energy convex hull to search for thermodynamically stable compounds. In this technique, candidates with a wide variety of chemical compositions and crystal structures are created by systematically applying evolutionary operators, ``mating,'' ``mutation,'' and ``adaptive mutation,'' to two target compounds, and the convex hull is directly updated through the evolution. We applied the technique to carbon-hydrogen binary system at 10 GPa and obtained 15 hydrocarbons within the convex hull distance less than 0.5 mRy/atom: graphane, polybutadiene, polyethylene, butane, ethane, methane, three molecular compounds of ethane and methane, and six molecular compounds of methane and hydrogen. These results suggest that our evolutionary construction technique is useful for the exploration of stable phases under extreme conditions and the synthesis of new compounds.

Machine Learning Stability and Bandgaps of Lead‐Free Perovskites for Photovoltaics

AbstractCompositional engineering of perovskites has enabled the precise control of material properties required for their envisioned applications in photovoltaics. However, challenges remain to address efficiency, stability, and toxicity simultaneously. Mixed lead‐free and inorganic perovskites have recently demonstrated potential for resolving such issues but their composition space is gigantic, making it difficult to discover promising candidates even using high‐throughput methods. A machine learning approach employing a generalized element‐agnostic fingerprint is shown to rapidly and accurately predict key properties using a new database of 344 perovskites generated with density functional theory. Bandgap, formation energy, and convex hull distance are predicted using validation subsets to within 146 meV, 15 meV per atom, and 11 meV per atom, respectively. The resulting model is used to predict trends in entirely different chemical spaces, and perform rapid composition and configuration space sampling without the need for expensive ab initio simulations.

Video‐based person re‐identification based on regularised hull distance learning

Existing person re‐identification (re‐id) models mainly focus on still‐image‐based module, namely matching person images across non‐overlapping camera views. Since video sequence contains much more information than still images and can be easily achieved by tracking algorithms in practical applications, the video re‐id has attracted increasing attention in recent years. Distance learning is crucial for a re‐id system. However, the computed distances in traditional video‐based methods are easily distracted by the randomness of data distribution, especially with small sample size for training. To preferably distinguish different people, a novel regularised hull distance learning video‐based person re‐id method is proposed. It is advantageous in two aspects: robustness is guaranteed due to expanded video samples by regularised affine hull with limited ones, discriminability is ensured due to penalised hard negative samples more severely. Hence, the discriminability and robustness of the learnt metric are strengthened. Comparisons with the state‐of‐the‐art video‐based methods as well as related methods on PRID 2011, iLIDS‐VID and MARS datasets demonstrate the superiority of the authors’ method.

Nearest Neighbor Convex Hull Tensor Classification for Gear Intelligent Fault Diagnosis Based on Multi-Sensor Signals

For the feature tensor of multi-sensor signals classification problem in gear intelligent fault diagnosis, a new tensor classifier named nearest neighbor convex hull tensor classification (NNCHTC) is proposed in this paper. First, the convex hull distance from a test tensor sample to the convex hull is taken as the similarity measure for classification. Then, the convex hull distance calculation is transformed into the feature tensor inner product, and CANDECOMP/PARAFAC (CP) decomposition is applied to the calculation process to capture the intrinsic information of the feature tensor. Furthermore, the reduction factor is introduced into NNCHTC to enhance its robustness. Finally, feature tensors are obtained from multi-sensor signals by wavelet packet transform (WPT) and used to identify gear working condition by NNCHTC. The experimental results show that NNCHTC not only can be effectively applied to the gear intelligent fault diagnosis based on multi-sensor signals but also has better robustness.

D0-d half-Heusler alloys: A potential class of advanced spintronic materials

The possibility of ferromagnetic half-metallicity in lithium-based half-Heusler alloys, such as MnPLi, has been theoretically explored before [Damewood et al., Phys. Rev. B 91, 064409 (2015)], although at optimized lattice constants such lithiated manganese pnictides are predicted to be ordinary ferromagnets and therefore not suitable for spintronic applications. In the following, however, it is shown by first principles density functional calculations that half-Heusler alloys formed by 3d transition metals and d0 alkali or alkaline-earth metals, which are defined by the valence electronic configuration ns1,2,(n−1)d0, can produce all kinds of half-metallic behavior at optimized lattice constants including the elusive Dirac half-metallicity that is theoretically predicted for the first time in a three-dimensional prototype MnPK. Together with the predicted magnetic and mechanical stability, this could pave the way for massless and dissipationless spintronics of the future. Among other technologically important features of the prototype Dirac half-metal MnPK, are the maximal moment d-shell ferromagnetism, high Fermi velocity of Dirac fermions that is comparable with that in graphene, and a wide half-metallic gap. Furthermore, by considering the prototype conventional half-metal VSbSr, the introduction of d0 metals is shown to substantially reduce the hull distance and produce true metastability in the otherwise instable chemical structure of zinc-blende transition metal pnictides–in this case zinc-blende VSb–without altering the ‘p-d exchange’ that is mainly responsible for their half-metallicity, thus bringing their realization and potential applications in spintronics a step closer to reality.