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.     

Traffic identification method based on multiple probabilistic neural network model

Neural Computing and Applications - Traffic identification is currently an important challenge for network management and security. In this paper, we propose a novel application identification...     

Porous sunflower plate-like NiFe2O4/CoNi–S heterostructure as efficient electrocatalyst for overall water splitting

Constructing high-efficient and nonprecious electrocatalysts is of primary importance for improving the efficiency of water splitting. Herein, a novel sunflower plate-like NiFe₂O₄/CoNi–S nanosheet heterostructure was fabricated via facile hydrothermal and electrodeposition methods. The as-fabricated NiFe₂O₄/CoNi–S heterostructure array exhibits remarkable bifunctional catalytic activity and stability toward oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) in alkaline media. It presents a small overpotential of 219 mV and 149 mV for OER and HER, respectively, to produce a current density of 10 mA cm^?2. More significantly, when the obtained electrodes are used as both the cathode and anode in an electrolyzer, a voltage of 1.57 V is gained at 10 mA cm^?2, with superior stability for 72 h. Such outstanding properties are ascribed to: the 3D porous network structure, which exposes more active sites and accelerates mass transfer and gas bubble emission; the high conductivity of CoNi–S, which provides faster charge transport and thus promotes the electrocatalytic reaction of the composites; and the effective interface engineering between NiFe₂O₄ (excellent performance for OER) and CoNi–S (high activity for HER), which leads to a shorter transport pathway and thus expedites electron transfer. This work provides a new strategy for designing efficient and inexpensive electrocatalysts for water splitting.     

Bearing Failure Optimization of Composite Double-Lap Bolted Joints Based on a Three-Step Strategy Marked By Feasible Region Reduction and Model Decoupling

To minimize the mass and increase the bearing failure load of composite doublelap bolted joints, a three-step optimization strategy including feasible region reduction, optimization model decoupling and optimization was presented. In feasible region reduction, the dimensions of the feasible design region were reduced by selecting dominant design variables from numerous multilevel parameters by sensitivity analyses, and the feasible regions of variables were reduced by influence mechanism analyses. In model decoupling, the optimization model with a large number of variables was divided into various sub-models with fewer variables by variance analysis. In the third step, the optimization sub-models were solved one by one using a genetic algorithm, and the modified characteristic curve method was adopted as the failure prediction method. Based on the proposed optimization method, optimization of a double-lap single-bolt joint was performed using the ANSYS^® code. The results show that the bearing failure load increased by 13.5% and that the mass decreased by 8.7% compared with those of the initial design of the joint, which validated the effectiveness of the three-step optimization strategy.     

Flame Front Deformation Instabilities of Filtration Combustion for Initial Thermal Perturbation

The flame front deformation instability of low-velocity filtration combustion within an inert packed bed is studied based on the initial preheating non-uniformity. Based on the experimental phenomena, an initial thermal perturbation model is numerically proposed so as to predict the deformation behaviors of the flame front instabilities. The numerical prediction indicates that the assumption of an initial thermal perturbation is a feasible explanation as the cause of the flame front inclination instability. As the initial thermal perturbation increases, the phenomena of the flame front break and shrinking instabilities could easily occur at high filtration velocity or low equivalence ratio. Moreover, the evolutions of the flame front break rate and the shrinking rate are quantitatively analyzed.     

ADIC iPlatform智能磁带库体系结构

本文介绍了ADIC iPlatform智能磁带库体系结构。