成型温度对PBX装药内部质量及性能的影响

针对压制成型的PBX炸药装药，选择CT无损检测、巴西实验和扫描电镜检测等技术，对比研究了室温和加热两种温度下压制成型的炸药装药内部质量、静态力学性能和细观破坏形式。结果表明，加热压制有利于改善炸药装药的内部质量，可避免产生初始损伤，且提高了装药的力学性能。细观尺度上，室温压制成型的装药主要发生界面脱黏破坏，加热压制成型装药的主要破坏形式是穿晶断裂。     

Mg gas infiltration for the fabrication of MgB2 pellets using nanosized and microsized B powders

MgB₂ superconductor pellets were synthesized through Mg gas infiltration method using nanosized- and microsized B powders. There was a marked difference in the superconducting properties of the two samples, particularly in the pinning force and dominant pinning mechanism. The microstructures of the samples were observed using HR-TEM and STEM-HAADF, and the results showed that the primary reason for the difference in the superconducting properties is the distribution of the nanosized second-phase particle MgO. Additionally, a feasible reaction model for the Mg gas infiltration method was established. Compared to the Mg liquid infiltration method, the gas infiltration showed better penetrability ability with a small amount of residual Mg. This study presents a novel synthesis process to fabricate an MgB₂ pellet with superior density and superconducting properties. This method can be used in multiple applications such as superconducting bearings, compact superconductor magnets, and magnetic shielding.     

A Review of Ultrasonic Reflectometry for the Physical Characterization of Lubricated Tribological Contacts: History,Methods, Devices,and Technological Trends

Tribology Letters - The tribological behavior of a nickel-based single crystal superalloy treated by shot-peening has been investigated. Friction tests under three normal loads and three...     

Decomposition and replacement: Spatial knowledge distillation for monocular depth estimation

Knowledge distillation has become a key technique for making smart and light-weight networks through model compression and transfer learning. Unlike previous methods that applied knowledge distillation to the classification task, we propose to exploit the decomposition-and-replacement based distillation scheme for depth estimation from a single RGB color image. To do this, Laplacian pyramid-based knowledge distillation is firstly presented in this paper. The key idea of the proposed method is to transfer the rich knowledge of the scene depth, which is well encoded through the teacher network, to the student network in a structured way by decomposing it into the global context and local details. This is fairly desirable for the student network to restore the depth layout more accurately with limited resources. Moreover, we also propose a new guidance concept for knowledge distillation, so-called ReplaceBlock, which replaces blocks randomly selected in the decoded feature of the student network with those of the teacher network. Our ReplaceBlock gives a smoothing effect in learning the feature distribution of the teacher network by considering the spatial contiguity in the feature space. This process is also helpful to clearly restore the depth layout without the significant computational cost. Based on various experimental results on benchmark datasets, the effectiveness of our distillation scheme for monocular depth estimation is demonstrated in details. The code and model are publicly available at : https://github.com/tjqansthd/Lap_Rep_KD_Depth.     

DFD-SS: Document Forgery Detection using Spectral – Spatial Features for Hyperspectral Images

In the present era of machines and edge-cutting technologies, still document frauds persist. They are done intuitively by using almost identical inks, that it becomes challenging to detect them—this demands an approach that efficiently investigates the document and leaves it intact. Hyperspectral imaging is one such a type of approach that captures the images from hundreds to thousands of spectral bands and analyzes the images through their spectral and spatial features, which is not possible by conventional imaging. Deep learning is an edge-cutting technology known for solving critical problems in various domains. Utilizing supervised learning imposes constraints on its usage in real scenarios, as the inks used in forgery are not known prior. Therefore, it is beneficial to use unsupervised learning. An unsupervised feature extraction through a Convolutional Autoencoder (CAE) followed by Logistic Regression (LR) for classification is proposed (CAE-LR). Feature extraction is evolved around spectral bands, spatial patches, and spectral-spatial patches. We inspected the impact of spectral, spatial, and spectral-spatial features by mixing inks in equal and unequal proportion using CAE-LR on the UWA writing ink hyperspectral images dataset for blue and black inks. Hyperspectral images are captured at multiple correlated spectral bands, resulting in information redundancy handled by restoring certain principal components. The proposed approach is compared with eight state-of-art approaches used by the researchers. The results depicted that by using the combination of spectral and spatial patches, the classification accuracy enhanced by 4.85% for black inks and 0.13% for blue inks compared to state-of-art results. In the present scenario, the primary area concern is to identify and detect the almost similar inks used in document forgery, are efficiently managed by the proposed approach.     

Special Issue on Artificial Intelligence and Technology: Trend,Challenges, and Opportunities

Mobile Networks and Applications -     

Research on slope reliability analysis using multi-kernel relevance vector machine and advanced first-order second-moment method

Engineering with Computers - To increase the efficiency and accuracy in slope stability analysis, a reliability analysis method based on machine learning and the advanced first-order second-moment...     

Sn-containing Si3N4-based composites for adaptive excellent friction and wear in a wide temperature range

Ceramic design based on reducing friction and wear-related failures in moving mechanical systems has gained tremendous attention due to increased demands for durability, reliability and energy conservation. However, only few materials can meet these requirements at high temperatures. Here, we designed and prepared a Sn-containing Si₃N₄-based composite, which displayed excellent tribological properties at high temperatures. The results showed that the friction coefficient and wear rate of the composites were reduced to 0.27 and 4.88 × 10^?6 mm³ N^?1 m^?1 in air at 800 °C. The wear mechanism of the sliding pairs at different temperatures was revealed via detailed analyses of the worn surfaces. In addition, the tribo-driven graphitization was detected on the wear surfaces and in the wear debris, and the carbon phase was identified by SEM, TEM, and Raman spectrum.