Cloud Region Research Articles

BARRIER AND GUARDRAIL EXTRACTION AND CLASSIFICATION FROM POINT CLOUDS

Abstract. In the recent years, the modelling of infrastructures has been receiving increasingly attention due to the importance of transport infrastructures for global economy, traffic safety and for the generation of high definition maps, essential to autonomous vehicles. This paper presents a simple method for the segmentation and classification of concrete barriers and guardrails in road surroundings. First steps of the method are aimed to delimit the region of the point cloud outside the driving lanes in which barriers and guardrails are installed. The purpose is to significantly reduce the size of point clouds in order to improve further processing. Then, barrier segmentation and classification are designed as parameter-dependent processes because the geometric features of roads and barriers and guardrails are mostly regulated by norms and standards. Results show a good performance in terms of classification in comparison of other state of the art methods. Better results were obtained for guardrails than for concrete barriers. The method has been tested in a set of point clouds acquired with a Mobile Laser Scanner from conventional roads and highways.

On the conditions for winter lightning at the Eagle Nest Tower (2537 m asl) during the Cerdanya-2017 field experiment

In this paper, we analyze meteorological conditions, thundercloud structure, lightning activity and characteristics of the flashes that strike two towers separated by 1.3 km on Tosa d'Alp (2537 m asl) for two days (March 24 and 31) of the Cerdanya-2017 field campaign. Remote sensing products (cloud top temperature, lightning flash location, volumetric radar scans) and a set of sensors installed at Cerdanya station (electric field mill, microwave radiometer, vertically-pointing K-band Doppler radar, atmospheric soundings) provide the data for the analysis. A total of 20 flashes (72 strokes) have been detected on these towers with a large majority on March 24 (18 flashes and 66 strokes), despite of a lower convective activity in the study region. All these flashes are negative and most of them exhibit the features of upward flashes: large multiplicity (3.67 in average on March 24), strokes with low peak current (−10.6 kA in average on March 24), and short inter-stroke time interval (40 ms in average on March 24). Some flashes are supposed to be self-triggered on the towers because of the absence (the low number) of VHF sources before (after) the strokes. Compared to the instrumented “Eagle Nest Tower”, the “Cerdanya Tardia Antenna” collects a greater number of flashes (strokes) during the most prolific day with 14 out of 18 (57 out of 66). During this day, all flashes that radiate in VHF at less than 5 km from the towers strike them. We observe also that the cloud region around −15 °C favorable for charging process is located at lower altitude (1000 m above tower altitude) and produce strong radar reflectivity values on that day. Furthermore, the wind at the altitude of Tosa d'Alp stronger on March 24 could facilitate the inception from the towers by evacuating the corona ions.

UNDERSTANDING 3D POINT CLOUD DEEP NEURAL NETWORKS BY VISUALIZATION TECHNIQUES

Abstract. In the wake of the success of Deep Learning Networks (DLN) for image recognition, object detection, shape classification and semantic segmentation, this approach has proven to be both a major breakthrough and an excellent tool in point cloud classification. However, understanding how different types of DLN achieve still lacks. In several studies the output of segmentation/classification process is compared against benchmarks, but the network is treated as a “black-box” and intermediate steps are not deeply analysed. Specifically, here the following questions are discussed: (1) what exactly did DLN learn from a point cloud? (2) On the basis of what information do DLN make decisions? To conduct such a quantitative investigation of these DLN applied to point clouds, this paper investigates the visual interpretability for the decision-making process. Firstly, we introduce a reconstruction network able to reconstruct and visualise the learned features, in order to face with question (1). Then, we propose 3DCAM to indicate the discriminative point cloud regions used by these networks to identify that category, thus dealing with question (2). Through answering the above two questions, the paper would like to offer some initial solutions to better understand the application of DLN to point clouds.

Solar System Encounters with Open Star Clusters

The time and minimum distances of approaches were calculated more than 100 open star clusters (OSCs) near the Solar System over the past 5 Myr. It was shown that the closest proximity of the centers of the OSCs to the Solar System usually exceeded 60 pc. The Hyades cluster approached the Solar System at ~25 pc. The effect of such encounters on the comet motion in the outer regions of the Oort cloud is comparable and may even exceed the effect of encounters with individual stars. The celestial positions of the cluster antiapex were compared with the aphelia of long-period comets. Cases of its close positions were revealed for some comets and the interstellar asteroid 1I/2017 U1 (Oumuamua). The frequency of OSC passages in the vicinity of the Solar System was estimated. Encounters at a distance of 25 pc occur at a rate of ~20 times per billion years.

Anomalous Extinction toward NGC 1938

Abstract Intrigued by the extended red giant clump (RC) stretching across the color–magnitude diagram of the stars in a 50 × 50 pc2 region of the Large Magellanic Cloud (LMC) containing the clusters NGC 1938 and NGC 1939, we have studied the stellar populations to learn about the properties of the interstellar medium (ISM) in this area. The extended RC is caused by a large and uneven amount of extinction across the field. Its slope reveals anomalous extinction properties, with A V /E(B − V) ≃ 4.3, indicating the presence of an additional gray component in the optical contributing about 30% of the total extinction in the field and requiring big grains to be about twice as abundant as in the diffuse ISM. This appears to be consistent with the number of big grains injected into the surrounding ISM by the about 70 SN II explosions estimated to have occurred during the lifetime of the ∼120 Myr old NGC 1938. Although this cluster appears relatively small today and would be hard to detect beyond the distance of M31, with an estimated initial mass of ∼4800 M ⊙, NGC 1938 appears to have seriously altered the extinction properties in a wide area. This has important implications for the interpretation of luminosities and masses of star-forming galaxies both nearby and in the early universe.

The study of runaway candidate stars in the 30 Doradus region: Using Gaia DR2 data

We explore the Gaia DR2 relative proper motions of seven young, massive, OB type runaway candidate stars presented by Platais et al. (2018) in the 30 Doradus (30 Dor) region of the Large Magellanic Cloud (LMC). The stars are slow runaways in the tangential plane of the 30 Dor region with relative proper motions μ≤0.4masyr−1 that correspond to a tangential velocity of 100 kms−1 with respect to their subfields, and they have angular separation from the core of the 30 Dor less than the size of the 30 Dor region. We estimated the flight times of the target stars to the young, massive cluster R136 using angular separation from the cluster and their relative proper motion with respect to the cluster. VFTS 8 and VFTS 65 are inconsistent with even an early ejection from the cluster R136 because the flight time to R136 exceeds their ages significantly. The runaways VFTS 219, VFTS 285, and VFTS 290 could be runaways from R136, as they have flight times to R136 consistent with late ejection from it and have proper motions and positions consistent with the runaway scenario from R136. VFTS 406 and VFTS 838, have direction of motions inconsistent with the R136 runaway scenario, but kinematic ages consistent with late ejection from it.

An obstacle separation method for robotic picking of fruits in clusters

Selectively picking a target fruit surrounded by obstacles is one of the major challenges for fruit harvesting robots. Different from traditional obstacle avoidance methods, this paper presents an active obstacle separation strategy that combines push and drag motions based on 3D visual perception to separate obstacles from the target. We define a region of interest 3D point cloud with a number of sub-blocks around the target to determine the presence or absence of obstacles and generate the separation paths accordingly. A linear push is used to clear the obstacles from the area below the target, while a zig-zag push that contains several linear motions is proposed to push aside more dense obstacles. The zig-zag push can generate multi-directional pushes and the side-to-side motion can break the static contact force between the target and obstacles, thus helping the gripper to receive a target in more complex situations. Moreover, we propose a novel drag operation to address the issue of mis-capturing obstacles located above the target, in which the gripper drags the target to a place with fewer obstacles and then pushes back to move the obstacles aside for further detachment. Furthermore, an image processing pipeline consisting of color thresholding, object detection using deep learning and point cloud operation, is developed to implement the proposed method on a newly developed harvesting robot. Field tests show that the proposed method can improve the picking performance substantially. This method helps to enable complex clusters of fruits to be harvested with a higher success rate than conventional methods.

Mechanisms of vapor cloud explosion and its chain reaction induced by an explosion venting flame

This experimental study focused on vapor cloud explosion and its chain reaction induced by an explosion venting flame. Two vapor clouds were installed in front of the explosion venting container along the horizontal direction. Explosion flame evolution process was recorded by a high-speed camera and a schlieren system. Pressure sensors at different locations were used to acquire the pressure data. The temperature data were collected by a high-speed infrared thermal imager and three thermocouples. The changes in flame propagation characteristics, flame temperature, and pressure distribution were determined, and the mechanisms of the vapor cloud explosion and its chain reaction induced by the venting flame were deeply analyzed. Results indicate that the venting flame could induce vapor cloud explosion and set off a chain reaction, leading to the high explosion intensity. As for the ignition condition, the venting flame directly came in contact with the vapor cloud and provided the required energy for the ignition. The pressure first increased and then decreased as the distance from the venting port increased, and the highest pressure appeared in the central region of the vapor cloud. The temperature distribution also presented a similar phenomenon.

Numerical Simulation of Supercooled Water Droplets Impacting Ice with Rapid Crystal Growth Taken into Consideration.

The impact of a supercooled water droplet is considerably affected by the rapid crystal growth when the duration of the recalescence stage is comparable to the typical time of impact. However, the recalescence stage is generally neglected in the existing numerical simulations using the enthalpy-porosity method. We propose a subregion function method to deal with the rapid crystal growth during the impact of supercooled water droplets. A restricted region named the dendrite cloud region is defined in the method, and the phase change is enabled only in this dendrite cloud region while the evolution of this region is determined by the initial nucleation sites and the dendritic growth velocity of the ice. The impacts of supercooled water droplets on a smooth ice surface are simulated using a three-phase volume-of-fluid method coupled with the subregion function method. The calculated residual ice layer thickness at the impact center is consistent with previous experimental results. This subregion function method can also be extended to the numerical simulations of other types of fluid flows involving rapid solidification.

On the Effects of UV Photons/X-Rays on the Chemistry of the Sgr B2 Cloud

Abstract The lines of HOC+, HCO, and CO+ are considered good tracers of photon-dominated regions (PDRs) and X-ray-dominated regions. We study these tracers toward regions of the Sgr B2 cloud selected to be affected by different heating mechanisms. We find the lowest values of the column density ratios of HCO+ versus HOC+, HCO, and CO+ in dense H ii gas, where UV photons dominate the heating and chemistry of the gas. The HOC+, HCO, and CO+ abundances and the above ratios are compared with those of chemical modeling, finding that high-temperature chemistry, a cosmic-ray ionization rate of 10−16 s−1, and timescales >105.0 yr explain well the HOC+ abundances in quiescent Sgr B2 regions, while shocks are also needed to explain the highest HCO abundances derived for these regions. The CO+ is mainly formed in PDRs, since the highest CO+ abundances of ∼(6–10) × 10−10 are found in H ii regions with electron densities >540 cm−3 and CO+ emission is undetected in quiescent gas. Among the ratios, the HCO+/HCO ratio is sensitive to the electron density, as it shows different values in dense and diffuse H ii regions. We compare SiO J = 2–1 emission maps of Sgr B2 with X-ray maps from 2004 and 2012. One known spot shown on the 2012 X-ray map is likely associated with molecular gas at velocities of 15–25 km s−1. We also derive the X-ray ionization rate of ∼10−19 s−1 for Sgr B2 regions pervaded by X-rays in 2004, which is quite low to affect the chemistry of the molecular gas.

Refining understanding of corporate failure through a topological data analysis mapping of Altman’s Z-score model

Corporate failure resonates widely, leaving practitioners searching for understanding of default risk. Managers seek to steer away from trouble, credit providers to avoid risky loans and investors to mitigate losses. Applying Topological Data Analysis tools, this paper explores whether failing firms from the United States organise neatly along the five predictors of default proposed by the Z-score models. Each firm is represented as a point in a five-dimensional point cloud, each dimension being one of the five predictors. Visualising that cloud using Ball Mapper reveals failing firms are not always located in similar regions of the point cloud, that is they are not concentrated in an easily split out area of the space. As new modelling approaches vie to better predict firm failure, often using black boxes to deliver potentially over-fitting models, a timely reminder is sounded on the importance of evidencing the identification process. Value is added to the understanding of where in the parameter space failure occurs, and how firms might act to move away from financial distress. Further, lenders may find opportunity amongst subsets of firms that are traditionally considered to be in danger of bankruptcy, but which the Ball Mapper plots developed herein clarify actually sit in characteristic spaces where failure has not occurred.

An interstellar origin for high-inclination Centaurs

ABSTRACT We investigate the possible origins of real high-inclination Centaurs and trans-neptunian objects using a high-resolution statistical search for stable orbits that simulates their evolution back in time to the epoch when planet formation ended 4.5 billion years in the past. The simulation is a precise orbit determination method that does not involve ad hoc initial conditions or assumptions such as those found in planetesimal disc relaxation models upon which their conclusions depend. It can therefore be used to independently test origin theories based on relaxation models by examining the past orbits of specific real objects. Here, we examined 17 multiple-opposition high-inclination Centaurs and the two polar trans-neptunian objects 2008 KV42 and (471325) 2011 KT19. The statistical distributions show that their orbits were nearly polar 4.5 Gyr in the past, and were located in the scattered disc and inner Oort cloud regions. Early polar inclinations cannot be accounted for by current Solar system formation theory as the early planetesimal system must have been nearly flat in order to explain the low-inclination asteroid and Kuiper belts. Furthermore, the early scattered disc and inner Oort cloud regions are believed to have been devoid of Solar system material as the planetesimal disc could not have extended far beyond Neptune’s current orbit in order to halt the planet’s outward migration. The nearly polar orbits of high-inclination Centaurs 4.5 Gyr in the past therefore indicate their probable early capture from the interstellar medium.

Revisiting the dispersion safety factor (DSF) for vapor clouds of liquefied flammable gases (LNG and propane)

The concept of the Dispersion Safety Factor (DSF) was introduced by Vílchez et al. (2013, 2014) and has been revisited in this work. The DSF is defined as the ratio between the flammable region of the vapor cloud (set at a concentration equal to the low flammability level (LFL)) and the corresponding visible boundary of the cloud. We have used a computational fluid dynamics model (FLACS v.10.4) to simulate the dispersion of two liquefied flammable fuels (LNG and propane). DSF results have been analyzed using main effects and interaction plots, and a complementary metric (DSF50) has been introduced in order to establish more conservative threat areas in flammable vapor cloud scenarios. We have observed an interaction between relative humidity and wind velocity for DSF in the low-to-medium range of RH and wind velocity values. Four regression models have been proposed for the computation of DSF and DSF50 for LNG and propane dependent on ambient wind velocity and relative humidity. Contour plots have been prepared to be used as a practical tool, because through the reading of these plots the DSF (and DSF50) can be obtained immediately given wind velocity and relative humidity data.

Thunderstorm Cloud-Type Classification from Space-Based Lightning Imagers.

The organization and structure of thunderstorms determines the extent and severity of their hazards to the general public and their consequences for the Earth system. Distinguishing vigorous convective regions that produce heavy rain and hail from adjacent regions of stratiform or overhanging anvil clouds that produce light-to-no rainfall is valuable in operations and physical research. Cloud type algorithms that partition convection from stratiform regions have been developed for space-based radar, passive microwave, and now Geostationary Operational Environmental Satellites (GOES) Advanced Baseline Imager (ABI) multi-spectral products. However, there are limitations for each of these products including temporal availability, spatial coverage, and the degree to which they based on cloud microphysics. We have developed a cloud type algorithm for GOES Geostationary Lightning Mapper (GLM) observations that identifies convective / non-convective regions in thunderstorms based on signatures of interactions with non-convective charge structures in the lightning flash data. The GLM sensor permits a rapid (20-s) update cycle over the combined GOES-16 / GOES-17 domain across all hours of the day. Storm regions that do not produce lightning will not be classified by our algorithm, however. The GLM cloud type product is intended to provide situational awareness of electrified anvils and to complement other cloud type retrievals by providing a contemporary assessment tied to lightning physics. We propose that a future combined ABI / GLM cloud type algorithm would be a valuable product that could draw from the strengths of each instrument and approach.

SK-Net: Deep Learning on Point Cloud via End-to-End Discovery of Spatial Keypoints

Since the PointNet was proposed, deep learning on point cloud has been the concentration of intense 3D research. However, existing point-based methods usually are not adequate to extract the local features and the spatial pattern of a point cloud for further shape understanding. This paper presents an end-to-end framework, SK-Net, to jointly optimize the inference of spatial keypoint with the learning of feature representation of a point cloud for a specific point cloud task. One key process of SK-Net is the generation of spatial keypoints (Skeypoints). It is jointly conducted by two proposed regulating losses and a task objective function without knowledge of Skeypoint location annotations and proposals. Specifically, our Skeypoints are not sensitive to the location consistency but are acutely aware of shape. Another key process of SK-Net is the extraction of the local structure of Skeypoints (detail feature) and the local spatial pattern of normalized Skeypoints (pattern feature). This process generates a comprehensive representation, pattern-detail (PD) feature, which comprises the local detail information of a point cloud and reveals its spatial pattern through the part district reconstruction on normalized Skeypoints. Consequently, our network is prompted to effectively understand the correlation between different regions of a point cloud and integrate contextual information of the point cloud. In point cloud tasks, such as classification and segmentation, our proposed method performs better than or comparable with the state-of-the-art approaches. We also present an ablation study to demonstrate the advantages of SK-Net.

The VMC survey – XXXVI. Young stellar variability in the Large Magellanic Cloud

ABSTRACT Studies of young stellar objects (YSOs) in the Galaxy have found that a significant fraction exhibits photometric variability. However, no systematic investigation has been conducted on the variability of extragalactic YSOs. Here we present the first variability study of massive YSOs in an $\sim 1.5\, \mathrm{deg^2}$ region of the Large Magellanic Cloud (LMC). The aim is to investigate whether the different environmental conditions in the metal-poor LMC ($\sim 0.4\!-\!0.5\, \mathrm{Z_{\odot }}$) have an impact on the variability characteristics. Multi-epoch near-infrared (NIR) photometry was obtained from the VISTA Survey of the Magellanic Clouds (VMC) and our own monitoring campaign using the VISTA telescope. By applying a reduced χ2-analysis, stellar variability was identified. We found 3062 candidate variable stars from a population of 362 425 stars detected. Based on several Spitzer studies, we compiled a sample of high-reliability massive YSOs: a total of 173 massive YSOs have NIR counterparts (down to $K_s\sim 18.5\,$mag) in the VMC catalogue, of which 39 display significant (>3σ) variability. They have been classified as eruptive, fader, dipper, short-term variable, and long-period variable YSOs based mostly on the appearance of their Ks-band light curves. The majority of YSOs are aperiodic; only five YSOs exhibit periodic light curves. The observed amplitudes are comparable or smaller than those for Galactic YSOs (only two Magellanic YSOs exhibit $\Delta K_s\gt 1\,$mag), not what would have been expected from the typically larger mass accretion rates observed in the Magellanic Clouds.

RESEARCH ON IMPROVED REGION GROWING POINT CLOUD ALGORITHM

Abstract. The effective segmentation of point clouds is a prerequisite for surface reconstruction, blind spot repair, and so on. Among them, regional growth is widely used due to its simple and easy to implement algorithm. However, the traditional regional growth segmentation algorithm often causes problems such as over-segmentation or voiding of the segmentation result due to the instability of the local features of the point cloud or the unreasonable selection of the initial seed nodes. In view of the above shortcomings, this paper proposes an improved region growing point cloud algorithm. Firstly, by calculating the Gaussian curvature and the average curvature of the point cloud data and sorting them, and setting the minimum curvature point as the seed node, the total number of clusters is reduced, and the quality of the classification result is improved. Secondly, the growth of the point cloud region growth criterion is determined by combining the normal angles. Finally, according to the shape characteristics of the point cloud and the preliminary segmentation results, each threshold is adjusted and determined, and the segmentation result is optimized.The experimental results show that compared with the traditional regional growth segmentation algorithm, this method can not only reduce the total number of segmentation regions, but also segment the point cloud data quickly and effectively, and solve the segmentation result caused by the traditional region growth point cloud segmentation method. Problems such as stability improve the accuracy and stability of point cloud segmentation.

Corona-Australis DANCe

Context. Corona-Australis is one of the nearest regions to the Sun with recent and ongoing star formation, but the current picture of its stellar (and substellar) content is not complete yet. Aims. We take advantage of the second data release of the Gaia space mission to revisit the stellar census and search for additional members of the young stellar association in Corona-Australis. Methods. We applied a probabilistic method to infer membership probabilities based on a multidimensional astrometric and photometric data set over a field of 128 deg2 around the dark clouds of the region. Results. We identify 313 high-probability candidate members to the Corona-Australis association, 262 of which had never been reported as members before. Our sample of members covers the magnitude range between G ≳ 5 mag and G ≲ 20 mag, and it reveals the existence of two kinematically and spatially distinct subgroups. There is a distributed “off-cloud” population of stars located in the north of the dark clouds that is twice as numerous as the historically known “on-cloud” population that is concentrated around the densest cores. By comparing the location of the stars in the HR-diagram with evolutionary models, we show that these two populations are younger than 10 Myr. Based on their infrared excess emission, we identify 28 Class II and 215 Class III stars among the sources with available infrared photometry, and we conclude that the frequency of Class II stars (i.e. “disc-bearing” stars) in the on-cloud region is twice as large as compared to the off-cloud population. The distance derived for the Corona-Australis region based on this updated census is d = 149.4 +0.4−0.4 pc, which exceeds previous estimates by about 20 pc. Conclusions. In this paper we provide the most complete census of stars in Corona-Australis available to date that can be confirmed with Gaia data. Furthermore, we report on the discovery of an extended and more evolved population of young stars beyond the region of the dark clouds, which was extensively surveyed in the past.

Particle cloud dynamics in stagnant water

Laboratory experiments were conducted to study the dynamics of particle clouds released vertically downward into stagnant water. The importance of nozzle diameter, sand particle mass, and particle size were studied in the form of an aspect ratio and Stokes number. The axial and radial profiles of sand concentration and velocity of particle clouds were measured by an accurate and robust optical probe (PV6). Empirical formulations were developed to explain the variations in sand concentration and velocity profiles. It was found that the zone of jet development was smaller in particle clouds than in single-phase water jets and sand jets. Laboratory measurements also indicated that the centerline sand concentration in particle clouds decreased with a slower rate in comparison to single-phase buoyant and sand jets. Important parameters such as mass, momentum fluxes, and drag and entrainment coefficients were calculated inside particle clouds to better understand the evolution of particle clouds in stagnant water. The radial variation of drag coefficient indicated a particle grouping effect in the core region of particle clouds where the drag coefficients decreased from 0.4 to less than 0.1. The entrainment coefficient decreased non-linearly in the radial direction. A new mathematical correlation was also developed to calculate the distribution of entrainment coefficient inside particle clouds. It was observed that the aspect ratio can significantly alter the radial entrainment coefficient in transverse directions. The inter-particle collision of particle clouds was evaluated by calculating the Bagnold number in both axial and radial directions. It was found that the inter-particle collision occurred for x/do ≤ 10 for St = 0.74 and for St = 0.52, and the Bagnold number values were smaller than 45 for x/do ≥ 20, indicating that a micro viscose regime controls the flow in these particle cloud dynamics.

Structural and Dynamical Analysis of 0.1 pc Cores and Filaments in the 30 Doradus-10 Giant Molecular Cloud

Abstract High-resolution (<0.1 pc) Atacama Large Millimeter/submillimeter Array (ALMA) observations of the 30Dor-10 molecular cloud 15 pc north of R136 are presented. The 12CO 2–1 emission morphology contains clumps near the locations of known mid-IR massive protostars, as well as a series of parsec-long filaments oriented almost directly toward R136. There is elevated kinetic energy (line widths at a given size scale) in 30Dor-10 compared to other Large Magellanic Cloud and Galactic star formation regions, consistent with large-scale energy injection to the region. Analysis of the cloud substructures is performed by segmenting emission into disjoint approximately round “cores” using clumpfind, by considering the hierarchical structures defined by isointensity contours using dendrograms, and by segmenting into disjoint long thin “filaments” using Filfinder. Identified filaments have widths ∼0.1 pc. The inferred balance between gravity and kinematic motions depends on the segmentation method: entire objects identified with clumpfind are consistent with freefall collapse or virial equilibrium with moderate external pressure, whereas many dendrogram-identified parts of hierarchical structures have higher mass surface densities ΣLTE than if gravitational and kinetic energies were in balance. Filaments have line masses that vary widely compared to the critical line mass calculated assuming thermal and nonthermal support. Velocity gradients in the region do not show any strong evidence for accretion of mass along filaments. The upper end of the “core” mass distribution is consistent with a power law with the same slope as the stellar initial mass function.