National University of Defense Technology

About: National University of Defense Technology is a education organization based out in Changsha, China. It is known for research contribution in the topics: Computer science & Radar. The organization has 39430 authors who have published 40181 publications receiving 358979 citations. The organization is also known as: Guófáng Kēxuéjìshù Dàxué & NUDT.

Self-powered soft robot in the Mariana Trench

Abstract: The deep sea remains the largest unknown territory on Earth because it is so difficult to explore1–4. Owing to the extremely high pressure in the deep sea, rigid vessels5–7 and pressure-compensation systems8–10 are typically required to protect mechatronic systems. However, deep-sea creatures that lack bulky or heavy pressure-tolerant systems can thrive at extreme depths11–17. Here, inspired by the structure of a deep-sea snailfish15, we develop an untethered soft robot for deep-sea exploration, with onboard power, control and actuation protected from pressure by integrating electronics in a silicone matrix. This self-powered robot eliminates the requirement for any rigid vessel. To reduce shear stress at the interfaces between electronic components, we decentralize the electronics by increasing the distance between components or separating them from the printed circuit board. Careful design of the dielectric elastomer material used for the robot’s flapping fins allowed the robot to be actuated successfully in a field test in the Mariana Trench down to a depth of 10,900 metres and to swim freely in the South China Sea at a depth of 3,224 metres. We validate the pressure resilience of the electronic components and soft actuators through systematic experiments and theoretical analyses. Our work highlights the potential of designing soft, lightweight devices for use in extreme conditions. A free-swimming soft robot inspired by deep-sea creatures, with artificial muscle, power and control electronics spread across a polymer matrix, successfully adapts to high pressure and operates in the deep ocean.

High-Level Semantic Feature Detection: A New Perspective for Pedestrian Detection

Abstract: Object detection generally requires sliding-window classifiers in tradition or anchor-based predictions in modern deep learning approaches. However, either of these approaches requires tedious configurations in windows or anchors. In this paper, taking pedestrian detection as an example, we provide a new perspective where detecting objects is motivated as a high-level semantic feature detection task. Like edges, corners, blobs and other feature detectors, the proposed detector scans for feature points all over the image, for which the convolution is naturally suited. However, unlike these traditional low-level features, the proposed detector goes for a higher-level abstraction, that is, we are looking for central points where there are pedestrians, and modern deep models are already capable of such a high-level semantic abstraction. Besides, like blob detection, we also predict the scales of the pedestrian points, which is also a straightforward convolution. Therefore, in this paper, pedestrian detection is simplified as a straightforward center and scale prediction task through convolutions. This way, the proposed method enjoys an anchor-free setting. Though structurally simple, it presents competitive accuracy and good speed on challenging pedestrian detection benchmarks, and hence leading to a new attractive pedestrian detector. Code and models will be available at https://github.com/liuwei16/CSP.

Surface transfer doping induced effective modulation on ambipolar characteristics of few-layer black phosphorus

Abstract: Black phosphorus, a fast emerging two-dimensional material, has been configured as field effect transistors, showing a hole-transport-dominated ambipolar characteristic. Here we report an effective modulation on ambipolar characteristics of few-layer black phosphorus transistors through in situ surface functionalization with caesium carbonate (Cs2CO3) and molybdenum trioxide (MoO3), respectively. Cs2CO3 is found to strongly electron dope black phosphorus. The electron mobility of black phosphorus is significantly enhanced to similar to 27 cm(2)V(-1) s(-1) after 10 nm Cs2CO3 modification, indicating a greatly improved electron-transport behaviour. In contrast, MoO3 decoration demonstrates a giant hole-doping effect. In situ photoelectron spectroscopy characterization reveals significant surface charge transfer occurring at the dopants/black phosphorus interfaces. Moreover, the surface-doped black phosphorus devices exhibit a largely enhanced photodetection behaviour. Our findings coupled with the tunable nature of the surface transfer doping scheme ensure black phosphorus as a promising candidate for further complementary logic electronics.

Fe/Fe3C@C nanoparticles encapsulated in N-doped graphene–CNTs framework as an efficient bifunctional oxygen electrocatalyst for robust rechargeable Zn–air batteries

Abstract: 3d transition metals or their derivatives encapsulated in nitrogen-doped nanocarbon show promising potential in non-precious metal oxygen electrocatalysts. Herein, we describe the simple construction of a bifunctional oxygen electrocatalyst with a “framework-active sites” structure, namely Fe/Fe3C@C (Fe@C) nanoparticles encapsulated in 3D N-doped graphene and bamboo-like CNTs (Fe@C–NG/NCNTs). The Fe@C structure provides additional electrons on the carbon surface, promoting the oxygen reduction reaction (ORR) on adjacent Fe–Nx active sites. The 3D NG hybrid with a bamboo-like CNTs framework facilitates fast reactant diffusion and rapid electron transfer. The optimized sample displays excellent ORR and oxygen evolution reaction (OER) activity, with a potential difference of only 0.84 V; this places it among the best bifunctional ORR/OER electrocatalysts. Most importantly, Zn–air batteries using Fe@C–NG/NCNTs as the cathode catalyst deliver a peak power density of 101.2 mW cm−2 and a specific capacity of 682.6 mA h g−1 (energy density of 764.5 W h kg−1). After 297 continuous cycle tests (99 h), the rechargeable batteries using Fe@C–NG/NCNTs show a voltage gap increase of only 0.13 V, almost half that of Pt/C + Ir/C (0.22 V) under the same conditions. This work provides new insight into advanced electrocatalysts utilizing the structural features of host nanocarbon materials and guest active species toward energy conversion.