37 research outputs found
Discrete element modeling of vibration compaction effect of the vibratory roller in roundtrips on gravels
This paper aims to study the vibration compaction mechanism of the vibratory roller on gravels using a two-dimensional discrete element method. The roadbed model was established by gravel particles with irregular shapes, which was closer to reality. The performance parameters of the vibratory roller, such as operating frequency and rolling velocity, were investigated to explore their influences on the operating efficiency of the vibratory roller in roundtrips. The frequencies of 15 Hz and 17 Hz were proved to be the optimal frequency and resonance frequency in the current simulations, respectively. The vibratory roller could achieve a better vibration compaction effect with less power consumption at the optimal frequency. In addition, the number of roundtrips and power consumption should be considered in the selection of the optimal rolling velocity. The movement direction and the contact force distribution of gravels were illustrated by the displacement field, velocity field, as well as the contact force chains. Our results provide a better understanding of the mechanical behavior of gravel particles and their interactions with the vibratory roller
Vertical Stress and Deformation Characteristics of Roadside Backfilling Body in Gob-Side Entry for Thick Coal Seams with Different Pre-Split Angles
Retained gob-side entry (RGE) is a significant improvement for fully-mechanized longwall mining. The environment of surrounding rock directly affects its stability. Roadside backfilling body (RBB), a man-made structure in RGE plays the most important role in successful application of the technology. In the field, however, the vertical deformation of RBB is large during the panel extraction, which leads to malfunction of the RGE. In order to solve the problem, roof pre-split is employed. According to geological conditions as well as the physical modeling of roof behavior and deformation of surrounding rock, the support resistance of RBB is calculated. The environment of surrounding rock, vertical stress and vertical deformation of the RBB in the RGE with different roof pre-split angles are analyzed using FLAC3D software. With the increase of roof pre-split angle, the vertical stresses both in the coal wall and RBB are minimum, and the vertical deformation of RBB also decreases from 110.51 mm to 6.1 mm. Therefore, based on the results of numerical modeling and field observation, roof pre-split angle of 90° is more beneficial to the maintenance of the RGE
Analysis of dynamic splitting tensile failure and energy evolution characteristics of Jinping marble
Splitting tensile failure is one of the main forms of instability failure of tunnel surrounding rock. At present, the mechanisms of rock crack propagation and energy evolution at the corresponding stages under dynamic splitting conditions have been rarely addressed. In this study, the splitting tests were carried out on Jinping marble samples using a split-Hopkinson pressure bar under different striking velocities. The dynamic damage processes of the samples were simulated with the ANSYS/LS-DYNA finite element software. From the perspectives of loboratory tests and numerical calculations, the mechanism of crack propagation and the characteristics of energy evolution during the splitting process of marble were comprehensively analyzed. The results show that the dynamic tensile strength of marble is linearly and positively related to the strain rate in the range of 5 sâ1 to 35 sâ1. The strain rate sensitivity of the Jinping marble is relatively low compared with the marbles of other regions. With the increase of the striking velocity, both the internal energy and kinetic energy of the system increase. At the moment of sample failure, the internal energy of the system drops to a minimum. Based on the calibrated parameters of Cowper-Symonds constitutive model, the final failure modes of the numerically simulated samples are basically consistent with the observed ones in the experiments. The research results of this study can provide guidance and reference for specific engineering applications
Visual sentiment prediction based on automatic discovery of affective regions
Automatic assessment of sentiment from visual
content has gained considerable attention with the increasing
tendency of expressing opinions via images and videos online.
This paper investigates the problem of visual sentiment analysis,
which involves a high-level abstraction in the recognition process.
While most of the current methods focus on improving holistic
representations, we aim to utilize the local information, which is
inspired by the observation that both the whole image and local
regions convey significant sentiment information. We propose
a framework to leverage affective regions, where we first use
an off-the-shelf objectness tool to generate the candidates, and
employ a candidate selection method to remove redundant and
noisy proposals. Then a convolutional neural network (CNN) is
connected with each candidate to compute the sentiment scores,
and the affective regions are automatically discovered, taking the
objectness score as well as the sentiment score into consideration.
Finally, the CNN outputs from local regions are aggregated with
the whole images to produce the final predictions. Our framework
only requires image-level labels, thereby significantly reducing
the annotation burden otherwise required for training. This is
especially important for sentiment analysis as sentiment can be
abstract, and labeling affective regions is too subjective and
labor-consuming. Extensive experiments show that the proposed
algorithm outperforms the state-of-the-art approaches on eight
popular benchmark datasets
Virtual histological staining of unlabeled autopsy tissue
Histological examination is a crucial step in an autopsy; however, the
traditional histochemical staining of post-mortem samples faces multiple
challenges, including the inferior staining quality due to autolysis caused by
delayed fixation of cadaver tissue, as well as the resource-intensive nature of
chemical staining procedures covering large tissue areas, which demand
substantial labor, cost, and time. These challenges can become more pronounced
during global health crises when the availability of histopathology services is
limited, resulting in further delays in tissue fixation and more severe
staining artifacts. Here, we report the first demonstration of virtual staining
of autopsy tissue and show that a trained neural network can rapidly transform
autofluorescence images of label-free autopsy tissue sections into brightfield
equivalent images that match hematoxylin and eosin (H&E) stained versions of
the same samples, eliminating autolysis-induced severe staining artifacts
inherent in traditional histochemical staining of autopsied tissue. Our virtual
H&E model was trained using >0.7 TB of image data and a data-efficient
collaboration scheme that integrates the virtual staining network with an image
registration network. The trained model effectively accentuated nuclear,
cytoplasmic and extracellular features in new autopsy tissue samples that
experienced severe autolysis, such as COVID-19 samples never seen before, where
the traditional histochemical staining failed to provide consistent staining
quality. This virtual autopsy staining technique can also be extended to
necrotic tissue, and can rapidly and cost-effectively generate artifact-free
H&E stains despite severe autolysis and cell death, also reducing labor, cost
and infrastructure requirements associated with the standard histochemical
staining.Comment: 24 Pages, 7 Figure
DiageneticâPorosity Evolution and Reservoir Evaluation in Multiprovenance Tight Sandstones: Insight from the Lower Shihezi Formation in Hangjinqi Area, Northern Ordos Basin
AbstractThe reservoir property of tight sandstones is closely related to the provenance and diagenesis, and multiprovenance system and complex diagenesis are developed in Hangjinqi area. However, the relationship between provenance, diagenesis, and physical characteristics of tight reservoirs in Hangjinqi area has not yet been reported. The Middle Permian Lower Shihezi Formation is one of the most important tight gas sandstone reservoirs in the Hangjinqi area of Ordos Basin. This research compared the diagenesis-porosity quantitative evolution mechanisms of Lower Shihezi Formation sandstones from various provenances in the Hangjinqi area using thin-section descriptions, cathodoluminescence imaging, X-ray diffraction (XRD), scanning electron microscopy (SEM), and homogenization temperature of fluid inclusions, along with general physical data and high-pressure mercury intrusion (HPMI) data. The sandstones mainly comprise quartzarenite, sublitharenite, and litharenite with low porosity and low permeability and display obvious zonation in the content of detrital components as a result of multiprovenance. Pore space of those sandstone mainly consists of primary pores, secondary pores, and microfractures, but their proportion varies in different provenances. According to HPMI, the order of the pore-throat radius from largest to smallest is central provenance, eastern provenance, and western provenance, which is consistent with the change tend of porosity (middle part>northern part>western part) in Hangjinqi region. The diagenetic evolution path of those sandstones is comparable, with compaction, cementation, dissolution, and fracture development. The central provenance has the best reservoir quality, followed by the eastern provenance and the western provenance, and this variation due to the diverse diagenesis (diagenetic stage and intensity) of different provenances. These findings reveal that the variations in detrital composition and structure caused by different provenances are the material basis of reservoir differentiation, and the main rationale for reservoir differentiation is varying degrees of diagenesis during burial process
DEM study of the shear behavior and formation of shear band in biaxial test
International audienc
A new installation technology of large diameter deeply-buried caissons: Practical application and observed performance
The development of installation technologies of open caissons has been lagging behind increasingly complex construction conditions. For such purpose, a new installation technology of large diameter deeply-buried (LDDB) open caissons has been developed and then used for construction of twin LDDB caissons into undrained ground with stiff soils in Zhenjiang, China. To assess the installation effects and filed performance, a monitoring program was presented to document the variations in total jacking forces provided by new shaft driven method, ground water level (GWL) around the caisson shaft, inclination angles of caisson shafts and radial displacements of surrounding soils as well as surface settlements of existing nearby facilities. It is observed that the monitoring data during the installation falls almost entirely within the design criteria, the reported new technology has limited impacts on the induced ground movements, depending on the variation in GWL, interaction between twin caissons and excavation-induced unloading effect. Moreover, the total jacking forces increase approximately in stepwise shape as the installation depth increases; the change law of surface settlements is highly similar to those of GWL, showing their close correlation; the larger inclination angles of caisson shafts are mainly encountered in the earlier installation phase, but well controllable. Further discussion on ground movements caused by various technologies confirms the feasibility of new installation technology. Both the observed and compared results give greater confidence on the use of such the technology in practice.</p
Dynamic trapping and manipulation of biological cells with optical tweezers
Current control techniques for optical tweezers work only when the cell is located in a small neighbourhood around the centroid
of the focused light beam. Therefore, the optical trapping fails when the cell is initially located far away from the laser beam
or escapes from the optical trap during manipulation. In addition, the position of the laser beam is treated as the control
input in existing optical tweezers systems and an open-loop controller is designed to move the laser source. In this paper,
we propose a new robotic manipulation technique for optical tweezers that integrates automatic trapping and manipulation
of biological cells into a single method. Instead of using open-loop control of the position of laser source as assumed in the
literature, a closed-loop dynamic control method is formulated and solved in this paper. We provide a theoretical framework
that bridges the gap between traditional robotic manipulation techniques and optical manipulation techniques of cells. The
proposed controller allows the transition from trapping to manipulation without any hard switching from one controller to
another. Simulation and experimental results are presented to illustrate the performance of the proposed controller.ASTAR (Agency for Sci., Tech. and Research, Sâpore)Accepted versio
Hybrid Robotic Grasping with a Soft Multimodal Gripper and a Deep Multistage Learning Scheme
Grasping has long been considered an important and practical task in robotic
manipulation. Yet achieving robust and efficient grasps of diverse objects is
challenging, since it involves gripper design, perception, control and
learning, etc. Recent learning-based approaches have shown excellent
performance in grasping a variety of novel objects. However, these methods
either are typically limited to one single grasping mode, or else more end
effectors are needed to grasp various objects. In addition, gripper design and
learning methods are commonly developed separately, which may not adequately
explore the ability of a multimodal gripper. In this paper, we present a deep
reinforcement learning (DRL) framework to achieve multistage hybrid robotic
grasping with a new soft multimodal gripper. A soft gripper with three grasping
modes (i.e., enveloping, sucking, and enveloping_then_sucking) can both deal
with objects of different shapes and grasp more than one object simultaneously.
We propose a novel hybrid grasping method integrated with the multimodal
gripper to optimize the number of grasping actions. We evaluate the DRL
framework under different scenarios (i.e., with different ratios of objects of
two grasp types). The proposed algorithm is shown to reduce the number of
grasping actions (i.e., enlarge the grasping efficiency, with maximum values of
161% in simulations and 154% in real-world experiments) compared to single
grasping modes