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The anisotropic shear strength of rough joints is examined from both experiments and numerical simulations in this paper. The rough joints in the experiment are prepared by combining 3D laser scanning and 3D printing technology. The direct shear tests are conducted in eight shear directions under constant normal load condition (CNL) with the normal stress of 0.2, 0.5, and 1.0 MPa. The dramatic variation of shear strength in different shear directions is observed from experimental results. Meanwhile, the numerical simulation based on the three-dimensional discrete-element method (DEM) is conducted, in which, the method to generate the identical rough joint by connecting triangular walls is proposed. With the calibrated microparameters from the unconfined compression test and direct shear test, the anisotropic shear strength is also observed in the numerical simulation, which is consistent with the experimental result. Accordingly, the topography anisotropy of the rough surface is analyzed, and the statistical parameter, Z2θ , is found closely related to the anisotropic shear strength of rough joints. Based on this, the Barton model is modified to estimate the shear strength of artificial joints with an anisotropic effect involved. [ABSTRACT FROM AUTHOR]

LBSs have been prospering over recent years. Although many LPPMs are proposed to solve specific problems under certain settings, it is still hard to satisfy the different demands of users due to the diverse requirements for quality of service (QoS) and privacy preservation. In this article, we propose an adaptive scheme to achieve fine-grained QoS for privacy-aware users in LBSs. Specifically, we construct a Bayesian-based classification model to identify the QoS requirements of users under different circumstances. QoS and privacy degrees provided by LBSs are carefully defined based on the concept of rate-distortion theory, which plays an important role in bridging the LPPMs with users' requirements. When users request the location service from the built-in GPS module in smart devices, we first specify their circumstances and requirements, then reply with the modified locations. We also design an efficient algorithm to dynamically choose the appropriate privacy strategy according to the QoS requirements of users in different circumstances. Experimental results show that our scheme can identify QoS requirements accurately and protect location privacy adaptively. [ABSTRACT FROM AUTHOR]

In this paper, we propose a face perception model to mimic the biological mechanism of face perception and memory in human brain. We are mainly inspired by the fact that there are two functionally and neurologically distinct pathways after the early face perception and they both interact to process the changeable features of faces. Accordingly, our model consists of three perception parts, facial structure perception, facial expression perception and facial identity perception, which are all component-based. The structure perception has a feed-forward projection to the expression and identity perception, while the expression affects the identity perception with a modulation process. We embody the three parts referring to three bio-inspired computational models. For the facial structure perception, we utilize a cascaded-CNN (convolutional neural networks) approach to estimate the center locations of key facial components. For the facial expression perception, we propose a novel approach which exploits convolutional deep belief networks (CDBN) to spontaneously locate the places containing the most discriminative information and synchronously complete the feature learning and feature selection. For the facial identity perception, we propose an approach which adopts the hierarchical max-pooling (HMAX) model to encode notable characteristics of facial components and utilizes a new memory formation integrating the preliminary decision, expression modulation and final decision process. We evaluate our model through a series of experiments and the experimental results demonstrate its rationality and effectiveness. [ABSTRACT FROM AUTHOR]

Purpose – Human movement system is a Multi-DOF, redundant, complex and nonlinear system formed by coordinating combination of neural system, bones, muscles and joints, which is robust and has fast response and learning ability. Imitating human movement system can improve robustness, fast response and learning ability of the robots. Design/methodology/approach – In this paper, we propose a new motion model based on the human motion pathway, especially the information propagation mechanism between the cerebellum and spinal cord. Findings – The proposed motion model proves to have fast response and learning ability through experiments, which matches the features of human motion. Originality/value – The proposed model in this paper introduces the habitual theory in kinesiology and neuroscience into robot control, and improves robustness, fast response and learning ability of the robots. This paper proves that introduction of neuroscience has an important guiding significance for precise and adaptive robot control, such as assembly automation. [ABSTRACT FROM AUTHOR]

Reports on RASS Society activity in China. [ABSTRACT FROM PUBLISHER]

Two-phase fluid distributions in fractured porous media were studied using a single-component multiphase (SCMP) lattice Boltzmann method (LBM), which was selected among three commonly used numerical approaches through a comparison against the available results of micro X-ray computed tomography. The influence of the initial configuration and the periodic boundary conditions in the SCMP LBM for the fluid distribution analysis were investigated as well. It was revealed that regular porous media are sensitive to the initial distribution, whereas irregular porous media are insensitive. Moreover, to eliminate the influence of boundaries, the model's buffer size of an SCMP LBM simulation was suggested to be taken as approximately 12.5 times the average particle size. Then, the two-phase fluid distribution of a porous medium was numerically studied using the SCMP LBM. Both detailed distribution patterns and macroscopic morphology parameters were reasonably well captured. Finally, the two-phase fluid distributions in a fractured porous media were investigated. The influence of the degree of saturation, fracture length, and fracture width on the fluid distributions and migration was explored. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this note, the non-linear fluid flow through rough fracture is investigated experimentally. The rough surface of fracture is generated synthetically based on the fractal dimension (D) and standard deviation (σ). 3D printing technique is used to produce the fracture specimen, in which, different components of the specimen are printed and assembled so that the fracture roughness and aperture can be easily controlled. After that, the self-designed apparatus is used to conduct the fluid flow test on the fractured specimens under various pressure gradients. It is found that, the surface roughness imposes an important impact on the nonlinear characteristics of fluid flow through fracture; and the root-mean-square of the first derivative of profile, Z 2 , is found effective in characterizing the fracture roughness. Based on the test results and the derived parameters in Forchheimer equation, an empirical equation is proposed to relate the hydraulic aperture (e h) to Z 2 and mechanical aperture (e m). Furthermore, the correlation among the nonlinear coefficient β , e m and Z 2 is established. [ABSTRACT FROM AUTHOR]

The effects of joint roughness on the peak shear strength of fractured rock mass has been extensively investigated. Among these investigations, Barton's JRC-JCS model has been well recognized for its capability to characterize surface geometry. However, shear strength estimation is still a major challenge because of the difficulties involved in accurately calculating the joint roughness coefficient (JRC). Thus, alternative methods such as statistic methods based on fractal theory have been proposed to evaluate the impact of surface morphology on rock joints. Extensive evidences show that, the application of the fractal dimension alone cannot produce a satisfactory estimation of either the JRC or the peak shear strength. Therefore, this paper aims to investigate the peak shear strength of rock joints by adopting the fractal theory and via a numerical simulation. Gosford sandstone taken from Sydney Basin is adopted as the rock materials for both numerical validation and shear strength investigation. Along with numerical simulations on shear strength, a linear function has been proposed to describe the correlations among the root-mean-square of the first derivative of fracture profile (Z 2), fractal dimension and the standard deviation. The peak shear strength is then accurately interpreted as a function of joint roughness indices: fractal dimension (D), standard deviation (SD) and Z 2. Predictions by the proposed equation are compared with both the numerical simulation results and that reproduced by Barton's equation. Good agreement has been achieved even when the joint roughness coefficients (JRC) is varied which proves that the proposed equation is highly accurate and can be reused in other related applications. • The shear behavior of Gosford sandstone is investigated through PFC2D. • The relationship between fractal dimension and statistical parameters is explored. • The correlation between the shear strength and statistical parameters is studied. • An equation is proposed to predict the peak shear strength of jointed rock. [ABSTRACT FROM AUTHOR]

In this paper, the fluid flow through rough fractures was investigated via numerical simulation based on the lattice Boltzmann method (LBM). The accuracy of LBM was validated through the numerical simulation of the parallel plate model and the verification of the mass conservation of fluid flow through rough fracture. After that, the effect of roughness on fluid flow was numerically conducted, in which, the geometry of fractures was characterized by the joint roughness coefficient (JRC), fractal dimension (D) and standard deviation (σ). It was found that the JRC cannot reflect the realistic influence of roughness on the permeability of single fracture, in which, an increase in permeability with increasing JRC has been observed at the range of 8~12 and 14~16. The reason behind this was revealed through the calculation of the root mean square of the first derivative of profile (Z2), and an equation has been proposed to estimate the permeability based on the aperture and Z2 of the fracture. The numerical simulations were further conducted on fluid flow though synthetic fractures with a wide range of D and σ. In order to unify the parameter that characterizes the roughness, Z2 was obtained for each synthetic fracture, and the corresponding relationship between permeability, aperture and Z2 was analyzed. Meanwhile, it was found that the fluid flow behaves differently with different ranges of Z2 and the critical point was found to be Z2 = 0.5. Based on extensive study, it was concluded that Z2 is a generic parameter characterizing the roughness, and the proposed equation could be used to predict the permeability for fluid flow in fracture. [ABSTRACT FROM AUTHOR]