Specific emitter identification for satellite communication using probabilistic neural networks

Electromagnetic signal emitted by satellite communication (satcom) transmitters are used to identify specific individual uplink satcom terminals sharing the common transponder in real environment, which is known as specific emitter identification (SEI) that allows for early indications and warning (I&W) of the targets carrying satcom furnishment and furthermore the real time electromagnetic situation awareness in military operations. In this paper, the authors are the first to propose the identification of specific transmitters of satcom by using probabilistic neural networks (PNN) to reach the goal of target recognition. We have been devoted to the examination by exploring the feasibility of utilizing the Hilbert transform to signal preprocessing, applying the discrete wavelet transform to feature extraction, and employing the PNN to perform the classification of stationary signals. There are a total of 1000 sampling time series with binary phase shift keying (BPSK) modulation originated by five types of satcom transmitters in the test. The established PNNs classifier implements the data testing and finally yields satisfactory accuracy at 8 dB(±1 dB) carrier to noise ratio, which indicates the feasibility of our method, and even the keen insight of its application in military.     

Fabrication of direct Z-scheme FeIn2S4/Bi2WO6 hierarchical heterostructures with enhanced photocatalytic activity for tetracycline hydrochloride photodagradation

A series of direct Z-scheme FeIn₂S₄/Bi₂WO₆ hierarchical heterostructures with intimate interface contacts were synthesized by in-situ growth route and characterized by systematical analyses. All as-prepared FeIn₂S₄/Bi₂WO₆ nanocomposites showed significantly enhanced photocatalytic activity towards photodegradation for the removal of tetracycline hydrochloride (TCH) in comparison with individual FeIn₂S₄ and Bi₂WO₆. Meanwhile, the highest photocatalytic degradation activity can be achieved by modulating adding amount of FeIn₂S₄ in FeIn₂S₄/Bi₂WO₆ nanocomposites and the optimized component ratio of FeIn₂S₄ to Bi₂WO₆ is determined to be 10 wt%. The enhanced photocatalytic activity could be ascribed to efficient separation between photogenerated holes and electrons based on the construction of direct Z-scheme system. The high photocatalytic stability of resultant 10 wt% FeIn₂S₄/Bi₂WO₆ nanocomposites was revealed through six successive recycling reactions. The main intermediate generated during TCH photodegradation was explored by HPLC-MS. Besides, the direct Z-scheme photocatalytic mechanism was confirmed by band position analysis, electron spin resonance (ESR) and active species capture experiment.     

Combining molybdenum carbide with ceria overlayers to boost Mo/CeO2 catalyzed ammonia synthesis

The design of an efficient non-noble metal catalyst is of burgeoning interest for ammonia synthesis. Herein, we report a Mo₂C/CeO₂ catalyst that is superior in ammonia synthesis activity. In this catalyst, molybdenum carbide coexisted with the ceria overlayers which is from the ceria support as the strong metal–support interaction. There is a high proportion of low-valent Mo species, as well as high concentration of Ce³⁺ and surface oxygen species. The presence of Mo₂C and CeO₂ overlayers not only leads to enhancement of hydrogen and nitrogen adsorption, but also facilitates the desorption and exchange of adsorbed species with the gaseous reagents. Compared with the Mo/CeO₂ catalyst prepared without carbonization, the Mo₂C/CeO₂ catalyst is more than sevenfold higher in ammonia synthesis rate. This work not only presents an explicit example of designing Mo-based catalyst that is highly efficient for ammonia synthesis by tuning the adsorption and desorption properties of the reactant gases, but opens a perspective for other elements in ammonia synthesis.     

结合优化支持向量机与K-means++的工控系统入侵检测方法

针对工业控制系统传统单一检测算法模型对不同攻击类型检测率和检测速度不佳的问题，提出一种优化支持向量机和K-means++算法结合的入侵检测模型。首先利用主成分分析法（PCA）对原始数据集进行预处理，消除其相关性；其次在粒子群优化（PSO）算法的基础上加入自适应变异过程避免在训练的过程中陷入局部最优解；然后利用自适应变异粒子群优化（AMPSO）算法优化支持向量机的核函数和惩罚参数；最后利用密度中心法改进K-means算法与优化后的支持向量机组合成入侵检测模型，从而实现工业控制系统的异常检测。实验结果表明，所提方法在检测速度和对各类攻击的检测率上得到明显提升。     

Improved Input and State Estimation for Linear Stochastic Systems with Direct Feedthrough

This paper is concerned with the problem of joint input and state estimation for linear stochastic systems with direct feedthrough. Based on the fact that each unknown input between any two time steps is always bounded, a novel improved algorithm is proposed. Compared with existing results, this algorithm can effectively enhance estimation accuracy. Moreover, the stability of the algorithm is also discussed. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed approach.     

基于状态空间法的电动机故障诊断和识别

电动机的初期故障信号一般较弱,不易辨别,而状态空间法在小信号的处理中灵敏度很高。为达到对电动机故障的诊断和识别,试图把状态空间法应用到对电动机的故障诊断中,先通过推导,从数学理论层面分析了状态空间法对于电动机故障信号的可辨别性,再使用Matlab软件搭建状态空间的辨别模型,把实验台测试的三组数据导入到模型中进行测试。测试的结果有力地证明了此方法的可行性,并能克服快速傅里叶变换(FFT)中数据采集量较大、分辨率不高的缺点。     

A Dual Inhibitor of DYRK1A and GSK3β for β-Cell Proliferation: Aminopyrazine Derivative GNF4877

Loss of β-cell mass and function can lead to insufficient insulin levels and ultimately to hyperglycemia and diabetes mellitus. The mainstream treatment approach involves regulation of insulin levels; however, approaches intended to increase β-cell mass are less developed. Promoting β-cell proliferation with low-molecular-weight inhibitors of dual-specificity tyrosine-regulated kinase 1A (DYRK1A) offers the potential to treat diabetes with oral therapies by restoring β-cell mass, insulin content and glycemic control. GNF4877, a potent dual inhibitor of DYRK1A and glycogen synthase kinase 3β (GSK3β) was previously reported to induce primary human β-cell proliferation in vitro and in vivo. Herein, we describe the lead optimization that lead to the identification of GNF4877 from an aminopyrazine hit identified in a phenotypic high-throughput screening campaign measuring β-cell proliferation.