Direct observation of particle kinematics in biaxial shearing test

Biaxial shearing tests on dual-sized, 2d particle assemblies are conducted at several confining pressures. The effect of particle angularity, an important mesoscale shape descriptor, is investigated at the macro and micro levels. Macroscopically, it is observed that assemblies composed of angular particles exhibit higher strengths and dilations. The difference observed in bulk behavior due to particle angularity can be explained reasonably by considering particle-level mechanisms. A novel 2D image analysis technique is employed to estimate particle kinematics. Particle rotation results to be a key mechanism strongly influenced by particle shape determining the overall granular behavior. Unlike circular particles, angular ones are more resistant to rotations due to stronger interlocking and consequently exhibit higher strengths.

Multi-view dual attention network for 3D object recognition

The existing view-based 3D object classification and recognition methods ignore the inherent hierarchical correlation and distinguishability of views, making it difficult to further improve the classification accuracy. In order to solve this problem, this paper proposes an end-to-end multi-view dual attention network framework for high-precision recognition of 3D objects. On one hand, we obtain three feature layers of query, key, and value through the convolution layer. The spatial attention matrix is generated by the key-value pairs of query and key, and each feature in the value of the original feature space branch is assigned different importance, which clearly captures the prominent detail features in the view, generates the view space shape descriptor, and focuses on the detail part of the view with the feature of category discrimination. On the other hand, a channel attention vector is obtained by compressing the channel information in different views, and the attention weight of each view feature is scaled to find the correlation between the target views and focus on the view with important features in all views. Integrating the two feature descriptors together to generate global shape descriptors of the 3D model, which has a stronger response to the distinguishing features of the object model and can be used for high-precision 3D object recognition. The proposed method achieves an overall accuracy of 96.6% and an average accuracy of 95.5% on the open-source ModelNet40 dataset, compiled by Princeton University when using Resnet50 as the basic CNN model. Compared with the existing deep learning methods, the experimental results demonstrate that the proposed method achieves state-of-the-art performance in the 3D object classification accuracy.

Learning Grasp Configuration Through Object-Specific Hand Primitives for Posture Planning of Anthropomorphic Hands

The proposal of postural synergy theory has provided a new approach to solve the problem of controlling anthropomorphic hands with multiple degrees of freedom. However, generating the grasp configuration for new tasks in this context remains challenging. This study proposes a method to learn grasp configuration according to the shape of the object by using postural synergy theory. By referring to past research, an experimental paradigm is first designed that enables the grasping of 50 typical objects in grasping and operational tasks. The angles of the finger joints of 10 subjects were then recorded when performing these tasks. Following this, four hand primitives were extracted by using principal component analysis, and a low-dimensional synergy subspace was established. The problem of planning the trajectories of the joints was thus transformed into that of determining the synergy input for trajectory planning in low-dimensional space. The average synergy inputs for the trajectories of each task were obtained through the Gaussian mixture regression, and several Gaussian processes were trained to infer the inputs trajectories of a given shape descriptor for similar tasks. Finally, the feasibility of the proposed method was verified by simulations involving the generation of grasp configurations for a prosthetic hand control. The error in the reconstructed posture was compared with those obtained by using postural synergies in past work. The results show that the proposed method can realize movements similar to those of the human hand during grasping actions, and its range of use can be extended from simple grasping tasks to complex operational tasks.

Multiscale fractal dimension applied to tactical analysis in football: A novel approach to evaluate the shapes of team organization on the pitch

The aim of this study was to evaluate different shape descriptors applied to images of polygons that represent the organization of football teams on the pitch. The effectiveness of different shape descriptors (area/perimeter, fractal area, circularity, maximum fractal, rectangularity, multiscale fractal curve—MFC), and the concatenation of all shape descriptors (except MFC), denominated Alldescriptors (AllD)) was evaluated and applied to polygons corresponding to the shapes represented by the convex hull obtained from players’ 2D coordinates. A content-based image retrieval system (CBIR) was applied for 25 users (mean age of 31.9 ± 8.4 years) to evaluate the relevant images. Measures of effectiveness were used to evaluate the shape descriptors (P@n and R@n). The MFD (P@5, 0.46±0.37 and P@10, 0.40±0.31, p < 0.001; R@5, 0.14±0.13 and R@10, 0.24±0.19, p < 0.001) and AllD (P@5 = 0.43±0.36 and P@10 = 0.39±0.32, p < 0.001; R@5 = 0.13±0.11 and R@10 = 0.24±0.20, p < 0.001) descriptors presented higher values of effectiveness. As a practical demonstration, the best evaluated shape descriptor (MFC) was applied for tactical analysis of an official match. K-means clustering technique was applied, and different shapes of organization could be identified throughout the match. The MFC was the most effective shape descriptor in relation to all others, making it possible to apply this descriptor in the analysis of professional football matches.

Collection alteration and pristine structural properties of dust particles collected by MIDAS/Rosetta at comet 67P/Churyumov-Gerasimenko

&lt;p&gt;Comets are believed to have preserved pristine material from the early stages of the Solar System formation, thus providing unique information on intricate processes like dust growth mechanisms. The Rosetta mission gave us the best opportunity to investigate nearly pristine cometary dust particles of comet 67P/Churyumov&amp;#8211;Gerasimenko. Among the three in-situ dust instruments, the MIDAS (Micro-Imaging Dust Analysis System) atomic force microscope collected cometary dust particles with sizes from hundreds of nanometres to tens of micrometres and recorded their 3D topography, size, shape, morphology, and related parameters [1].&lt;/p&gt; &lt;p&gt;MIDAS collected dust emitted from comet 67P on dedicated targets. Particles fell through the entry funnel and collided with the collection targets [2] causing an unknown degree of particle alteration. To understand which structural properties of the dust remained pristine and can be used to understand comets and early Solar System processes it is important to understand the collection alteration. Dedicated laboratory experiments were carried out by previous studies [3, 4]. They found that the degree of alteration upon collection is strongly determined by the particle size, strength, and the collection velocity. They indicate that particles in the MIDAS size range deposited with moderate velocities about less than a few metres per second can stick on a target without major alteration.&lt;/p&gt; &lt;p&gt;We aim to determine the structurally least altered MIDAS particles and investigate their properties. As database we use an improved version of the MIDAS particle catalogue [5]. Selecting all particles suitable for our analysis (e.g., cometary origin, sufficiently high image quality) grants us topographic data of over 600 nano- to micrometre-sized dust particles of comet 67P. We create dust coverage maps showing the distribution of the selected dust particles on the collection targets. As first, simple classification we divide the particles into those detected in clusters, suggested to be fragments originating in a shattering event of one large parent particle, and those remote from others that are potentially individually collected particles. Finally, we&amp;#160;use a shape descriptor to categorise the particles according to their characteristics, e.g., shape and size, and compare to previous results from COSIMA [6] and simulation/laboratory studies [3, 7].&lt;/p&gt; &lt;p&gt;&amp;#160;&lt;/p&gt; &lt;p&gt;[1] Bentley, M.S., Schmied, R., Mannel, T., et al. 2016, Nature, 537&lt;/p&gt; &lt;p&gt;[2] Bentley, M. S., Arends, H., Butler, B., et al. 2016, Acta Astronautica, 125, 11&lt;/p&gt; &lt;p&gt;[3] Ellerbroek, L. E., Gundlach, B., Landeck, A., et al. 2017, MNRAS, 469, S204&lt;/p&gt; &lt;p&gt;[4] Ellerbroek, L. E., Gundlach, B., Landeck, A., et al. 2019, MNRAS, 486, S3755&lt;/p&gt; &lt;p&gt;[5] Boakes, P., and the MIDAS team, 2018. &amp;#8216;MIDAS Particle Catalogue&amp;#8217;. ESA Planetary Science Archive&amp;#160;&amp;#160;Dataset: RO-C-MIDAS-5-PRL-TO-EXT3-V2.0. Product ID: RO-C-MIDAS-5-PRL-TO-EXT3-V2.0&lt;/p&gt; &lt;p&gt;[6] Langevin, Y., Hilchenbach, M., Ligier, N., et al. 2016, Icarus, 271, 76&lt;/p&gt; &lt;p&gt;[7] Lasue, J., Maroger, I., Botet, R., et al. 2019, A&amp;A, 630,&lt;/p&gt;