Search Results (4)

This study investigates the dynamic mechanical characteristics of strength-gradient composite rock masses under one-dimensional explosive plane waves by integrating digital image correlation (DIC) and Lagrangian inverse analysis. Using a one-dimensional explosive plane wave generator, high-spatiotemporal resolution displacement and strain data were acquired from specimen surfaces via an ultra-high-speed camera and DIC. The study compared the decay patterns of blast stress waves and deformation features of rock under two loading paths (forward and backward gradients) for three explosive charges, and employed Lagrangian inverse analysis to determine the strength-gradient distribution within the composite rock mass. The Lagrange inverse analysis method was employed to derive the constitutive relationship of the strength-gradient composite rock mass. The results indicate that in forward gradient rock masses, stress waves undergo stress jumps at joint surfaces, leading to increased wave amplitudes. In backward gradient rock masses, stress wave attenuation is more pronounced. In forward gradient coarse sandstone, stress attenuation rates are significantly higher than in the other two sandstone types. In backward gradient gray sandstone, attenuation rates are markedly greater than in the other two sandstones. However, under identical charge conditions, coarse sandstone exhibits a higher attenuation coefficient than gray sandstone. This indicates that stress waves decay more rapidly in the immediate vicinity of the explosion and that weaker media exhibit faster decay rates. The findings reveal the propagation patterns of explosive stress waves in layered gradient materials, providing a theoretical basis for engineering blasting in layered rock formations. Full article

This article explores the propagation behaviors of electromagnetic waves within a metamaterial structure composed of three distinct layers, nano, micro, and macro, arranged from the outermost to the innermost section. The outermost layer, which serves as the focus of this investigation, consists of carbon nanotubes. The second layer, positioned just behind the outermost coating, exhibits micro properties and features a graded structure in terms of nonlocal characteristics and material property parameters. Therefore, the analyses conducted in this micro layer are grounded in nonlocal theory. The nonlocal constant is set at values of $\eta$: 0.7, $\eta$: 0.5, and $\eta$: 0.25, with investigations carried out using a nano-graded approach. Additionally, this micro layer is configured in a material-graded manner concerning its property parameters, defined as $D$: 0.1, $D$: 0.3, and $D$: 0.7, respectively. In the micro layer, a nano-graded approach achieves the highest frequencies of electromagnetic wave propagation when the material property parameter $D$ is set at 0.5 and the nonlocal constant $\eta$ is 0.25. In contrast, the lowest frequencies of electromagnetic wave propagation are observed when the material property parameter $D$ is 0.1, and the nonlocal constant $\eta$ is 0.5. The innermost layer of the metamaterial structure is characterized by macro properties. Notably, unlike many other studies, this research specifically examines the behavior of backward electromagnetic waves, rather than traveling waves, within the context of the aforementioned metamaterial properties. The amplitude values of the reflected waves, particularly those corresponding to the backward electromagnetic waves delineated in this study, exhibit a reduction as they propagate through the metamaterial components. Full article

Action prediction is an important task in human activity analysis, which has many practical applications, such as human–robot interactions and autonomous driving. Action prediction often comprises two subtasks: action semantic prediction and future human motion prediction. Most of the existing works treat these subtasks separately, ignoring the correlations, leading to unsatisfying performance. By contrast, we jointly model these tasks and improve human motion predictions utilizing their action semantics. In terms of methodology, we propose a novel multi-task framework (Multi-head TrajectoryCNN) to simultaneously predict the action semantics and human motion of future human movements. Specifically, we first extract a general spatiotemporal representation of partial observations via two regression blocks. Then, we propose a regression head and a classification head for predicting future human motion and action semantics of human motion, respectively. For the regression head, another two stacked regression blocks and two convolutional layers are applied to predict future poses from the general representation learning. For the classification head, we propose a classification block and stack two regression blocks to predict action semantics from the general representation. In this way, the regression and classification heads are incorporated into a unified framework. During the backward propagation of the network, the human motion prediction and the semantic prediction may be enhanced by each other. NTU RGB+D is a widely used large-scale dataset for action recognition, which was collected by 40 different subjects from three views. Based on the official protocols, we use the skeletal modality and process action sequences with fixed lengths for the evaluation of our action prediction task. Experiments on NTU RGB+D show our model’s state-of-the-art performance. Furthermore, the experimental results also show that semantic information is of great help in predicting future human motion. Full article

This study used a backward propagation (BP) model to estimate the microhardness of Ni-TiN nanoplatings prepared using pulse electrodeposition. The influence of electroplating parameters on the microhardness of Ni-TiN nanoplatings was discussed. These parameters included the concentration of the TiN particle, pulse frequency, duty cycle, and current density. The surface morphology, microstructure, and microhardness of Ni-TiN nanoplatings were examined using white-light interfering profilometry, scanning electron microscopy, Rockwell hardness testing, and high-resolution transmission emission microscopy. The Ni-TiN thin film prepared by pulse electrodeposition had a surface roughness of about 0.122 µm, and the average size of the Ni and TiN grains on this film was 61.8 and 31.3 nm, respectively. The optimal process parameters were determined based on the maximum microhardness of the deposited Ni-TiN nanoplatings, which included an 8 g/L TiN particle concentration, a 5 A/dm² current density, an 80 Hz pulse frequency, and a 0.7 duty cycle. It could be concluded that the BP model would accurately forecast the microhardness of Ni-TiN nanoplatings, with a maximal error of about 1.04%. Full article