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.

Rational Construction of Fe 2 N@C Yolk-Shell Nanoboxes as Multifunctional Hosts for Ultralong Lithium-Sulfur Batteries

Abstract: Rationally constructing inexpensive sulfur hosts that have high electronic conductivity, large void space for sulfur, strong chemisorption, and rapid redox kinetics to polysulfides is critically important for their practical use in lithium-sulfur (Li-S) batteries. Herein, we have designed a multifunctional sulfur host based on yolk-shelled Fe2N@C nanoboxes (Fe2N@C NBs) through a strategy of etching combined with nitridation for high-rate and ultralong Li-S batteries. The highly conductive carbon shell physically confines the active material and provides efficient pathways for fast electron/ion transport. Meanwhile, the polar Fe2N core provides strong chemical bonding and effective catalytic activity for polysulfides, which is proved by density functional theory calculations and electrochemical analysis techniques. Benefiting from these merits, the S/Fe2N@C NBs electrode with a high sulfur content manifests a high specific capacity, superior rate capability, and long-term cycling stability. Specifically, even after 600 cycles at 1 C, a capacity of 881 mAh g-1 with an average fading rate of only 0.036% can be retained, which is among the best cycling performances reported. The strategy in this study provides an approach to the design and construction of yolk-shelled iron-based compounds@carbon nanoarchitectures as inexpensive and efficient sulfur hosts for realizing practically usable Li-S batteries.

The DESI Experiment Part II: Instrument Design

Abstract: DESI (Dark Energy Spectropic Instrument) is a Stage IV ground-based dark energy experiment that will study baryon acoustic oscillations and the growth of structure through redshift-space distortions with a wide-area galaxy and quasar redshift survey. The DESI instrument is a robotically-actuated, fiber-fed spectrograph capable of taking up to 5,000 simultaneous spectra over a wavelength range from 360 nm to 980 nm. The fibers feed ten three-arm spectrographs with resolution $R= \lambda/\Delta\lambda$ between 2000 and 5500, depending on wavelength. The DESI instrument will be used to conduct a five-year survey designed to cover 14,000 deg$^2$. This powerful instrument will be installed at prime focus on the 4-m Mayall telescope in Kitt Peak, Arizona, along with a new optical corrector, which will provide a three-degree diameter field of view. The DESI collaboration will also deliver a spectroscopic pipeline and data management system to reduce and archive all data for eventual public use.

Efficient reinforcement learning using recursive least-squares methods

Abstract: The recursive least-squares (RLS) algorithm is one of the most well-known algorithms used in adaptive filtering, system identification and adaptive control. Its popularity is mainly due to its fast convergence speed, which is considered to be optimal in practice. In this paper, RLS methods are used to solve reinforcement learning problems, where two new reinforcement learning algorithms using linear value function approximators are proposed and analyzed. The two algorithms are called RLS-TD(λ) and Fast-AHC (Fast Adaptive Heuristic Critic), respectively. RLS-TD(λ) can be viewed as the extension of RLS-TD(0) from λ =0 to general 0≤ λ ≤1, so it is a multi-step temporal-difference (TD) learning algorithm using RLS methods. The convergence with probability one and the limit of convergence of RLS-TD(λ) are proved for ergodic Markov chains. Compared to the existing LS-TD(λ) algorithm, RLS-TD(λ) has advantages in computation and is more suitable for online learning. The effectiveness of RLS-TD(λ) is analyzed and verified by learning prediction experiments of Markov chains with a wide range of parameter settings. The Fast-AHC algorithm is derived by applying the proposed RLS-TD(λ) algorithm in the critic network of the adaptive heuristic critic method. Unlike conventional AHC algorithm, Fast-AHC makes use of RLS methods to improve the learning-prediction efficiency in the critic. Learning control experiments of the cart-pole balancing and the acrobot swing-up problems are conducted to compare the data efficiency of Fast-AHC with conventional AHC. From the experimental results, it is shown that the data efficiency of learning control can also be improved by using RLS methods in the learning-prediction process of the critic. The performance of Fast-AHC is also compared with that of the AHC method using LS-TD(λ). Furthermore, it is demonstrated in the experiments that different initial values of the variance matrix in RLS-TD(λ) are required to get better performance not only in learning prediction but also in learning control. The experimental results are analyzed based on the existing theoretical work on the transient phase of forgetting factor RLS methods.

Gray matter density reduction in the insula in fire survivors with posttraumatic stress disorder: A voxel-based morphometric study

Abstract: Voxel-based morphometry (VBM) is an objective whole-brain technique for characterizing regional cerebral volume and tissue concentration differences in structural magnetic resonance images. In the current study, we used VBM to examine possible cerebral gray matter abnormalities in patients with posttraumatic stress disorder (PTSD) due to fire. The subjects included 12 victims of a fire disaster with PTSD and 12 matched victims of the same fire without PTSD. Magnetic resonance images were obtained on a 1.5-Tesla General Electric scanner at Central South University, and an entire brain volume of 248 contiguous slices was obtained for each subject. Then, gray matter density in patients with PTSD and control groups was compared by using a VBM approach in SPM2. Group analysis was thresholded at P<0.001, uncorrected, at the voxel level. The following three regions of reduced gray matter volume were found in patients with PTSD compared with controls: left hippocampus, left anterior cingulate cortex (ACC), and bilateral insular cortex. It was concluded that there are structural abnormalities of the hippocampus, the ACC and the insular cortex in patients with PTSD due to fire.

Towards Real-Time Path Planning through Deep Reinforcement Learning for a UAV in Dynamic Environments

Abstract: Path planning remains a challenge for Unmanned Aerial Vehicles (UAVs) in dynamic environments with potential threats. In this paper, we have proposed a Deep Reinforcement Learning (DRL) approach for UAV path planning based on the global situation information. We have chosen the STAGE Scenario software to provide the simulation environment where a situation assessment model is developed with consideration of the UAV survival probability under enemy radar detection and missile attack. We have employed the dueling double deep Q-networks (D3QN) algorithm that takes a set of situation maps as input to approximate the Q-values corresponding to all candidate actions. In addition, the e-greedy strategy is combined with heuristic search rules to select an action. We have demonstrated the performance of the proposed method under both static and dynamic task settings.