China Academy of Space Technology

About: China Academy of Space Technology is a government organization based out in Beijing, China. It is known for research contribution in the topics: Spacecraft & Computer science. The organization has 4917 authors who have published 3837 publications receiving 22029 citations.

Visual Domain Adaptation with Manifold Embedded Distribution Alignment

Abstract: Visual domain adaptation aims to learn robust classifiers for the target domain by leveraging knowledge from a source domain. Existing methods either attempt to align the cross-domain distributions, or perform manifold subspace learning. However, there are two significant challenges: (1) degenerated feature transformation, which means that distribution alignment is often performed in the original feature space, where feature distortions are hard to overcome. On the other hand, subspace learning is not sufficient to reduce the distribution divergence. (2) unevaluated distribution alignment, which means that existing distribution alignment methods only align the marginal and conditional distributions with equal importance, while they fail to evaluate the different importance of these two distributions in real applications. In this paper, we propose a Manifold Embedded Distribution Alignment (MEDA) approach to address these challenges. MEDA learns a domain-invariant classifier in Grassmann manifold with structural risk minimization, while performing dynamic distribution alignment to quantitatively account for the relative importance of marginal and conditional distributions. To the best of our knowledge, MEDA is the first attempt to perform dynamic distribution alignment for manifold domain adaptation. Extensive experiments demonstrate that MEDA shows significant improvements in classification accuracy compared to state-of-the-art traditional and deep methods.

From IoT to 5G I-IoT: The Next Generation IoT-Based Intelligent Algorithms and 5G Technologies

Abstract: The Internet of Things is a novel paradigm with access to wireless communication systems and artificial intelligence technologies, which is considered to be applicable to a variety of promising fields and applications. Meanwhile, the development of the fifth-generation cellular network technologies creates the possibility to deploy enormous sensors in the framework of the IoT and to process massive data, challenging the technologies of communications and data mining. In this article, we propose a novel paradigm, 5G Intelligent Internet of Things (5G I-IoT), to process big data intelligently and optimize communication channels. First, we articulate the concept of the 5G I-IoT and introduce three major components of the 5G I-IoT. Then we expound the interaction among these components and introduce the key methods and techniques based on our proposed paradigm, including big data mining, deep learning, and reinforcement learning. In addition, an experimental result evaluates the performance of 5G I-IoT, and the effective utilization of channels and QoS have been greatly improved. Finally, several application fields and open issues are discussed.

Many-body Effect, Carrier Mobility, and Device Performance of Hexagonal Arsenene and Antimonene

Abstract: Two-dimensional (2D) semiconductors are very promising channel materials in next-generation field effect transistors (FETs) due to the enhanced gate electrostatics and smooth surface. Two new 2D materials, arsenene and antimonene (As and Sb analogues of graphene), have been fabricated very recently. Here, we provide the first investigation of the many-body effect, carrier mobility, and device performance of monolayer (ML) hexagonal arsenene and antimonene based on accurate ab initio methods. The quasi-particle band gaps of ML arsenene and antimonene by using the GW approximation are 2.47 and 2.38 eV, respectively. The optical band gaps of ML arsenene and antimonene from the GW-Bethe–Salpeter equation are 1.6 and 1.5 eV, with exciton binding energies of 0.9 and 0.8 eV, respectively. The carrier mobility is found to be considerably low in ML arsenene (21/66 cm2/V·s for electron/hole) and moderate in ML antimonene (150/510 cm2/V·s for electron/hole). In terms of the ab initio quantum transport simulations, t...

Tunable piezoresistivity of nanographene films for strain sensing.

Abstract: Graphene-based strain sensors have attracted much attention recently. Usually, there is a trade-off between the sensitivity and resistance of such devices, while larger resistance devices have higher energy consumption. In this paper, we report a tuning of both sensitivity and resistance of graphene strain sensing devices by tailoring graphene nanostructures. For a typical piezoresistive nanographene film with a sheet resistance of ∼100 KΩ/□, a gauge factor of more than 600 can be achieved, which is 50× larger than those in previous studies. These films with high sensitivity and low resistivity were also transferred on flexible substrates for device integration for force mapping. Each device shows a high gauge factor of more than 500, a long lifetime of more than 104 cycles, and a fast response time of less than 4 ms, suggesting a great potential in electronic skin applications.

Fast Adaptive Thermal Camouflage Based on Flexible VO2/Graphene/CNT Thin Films

Abstract: Adaptive camouflage in thermal imaging, a form of cloaking technology capable of blending naturally into the surrounding environment, has been a great challenge in the past decades. Emissivity engineering for thermal camouflage is regarded as a more promising way compared to merely temperature controlling that has to dissipate a large amount of excessive heat. However, practical devices with an active modulation of emissivity have yet to be well explored. In this letter we demonstrate an active cloaking device capable of efficient thermal radiance control, which consists of a vanadium dioxide (VO2) layer, with a negative differential thermal emissivity, coated on a graphene/carbon nanotube (CNT) thin film. A slight joule heating drastically changes the emissivity of the device, achieving rapid switchable thermal camouflage with a low power consumption and excellent reliability. It is believed that this device will find wide applications not only in artificial systems for infrared camouflage or cloaking bu...