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.     

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。     

Electrocatalytic Reduction of CO2 to Ethylene by Molecular Cu‐Complex Immobilized on Graphitized Mesoporous Carbon

The electrochemical reduction of carbon dioxide (CO₂) to hydrocarbons is a challenging task because of the issues in controlling the efficiency and selectivity of the products. Among the various transition metals, copper has attracted attention as it yields more reduced and C2 products even while using mononuclear copper center as catalysts. In addition, it is found that reversible formation of copper nanoparticle acts as the real catalytically active site for the conversion of CO₂ to reduced products. Here, it is demonstrated that the dinuclear molecular copper complex immobilized over graphitized mesoporous carbon can act as catalysts for the conversion of CO₂ to hydrocarbons (methane and ethylene) up to 60%. Interestingly, high selectivity toward C2 product (40% faradaic efficiency) is achieved by a molecular complex based hybrid material from CO₂ in 0.1 m KCl. In addition, the role of local pH, porous structure, and carbon support in limiting the mass transport to achieve the highly reduced products is demonstrated. Although the spectroscopic analysis of the catalysts exhibits molecular nature of the complex after 2 h bulk electrolysis, morphological study reveals that the newly generated copper cluster is the real active site during the catalytic reactions.     

Collection efficiency of liquid‐based samplers for fungi in indoor air

This study assessed the collection efficiency (CE) of two popularly used sampling devices (BioSampler and Coriolis sampler) for fungal aerosols. Phosphate‐buffered saline (PBS) supplemented with or without surfactant (Tween‐20, Tween‐80, or Triton X‐100) and antifoam agent was prepared and used as collection liquids. The agar impactor (BioStage) was simultaneously operated with liquid‐based samplers to collect fungi from seven sites located at a university building, public library, and animal farming. Fungal concentrations determined by liquid samplers were divided by those by BioStage, and the ratio values represented CE. Results indicate that the CE of BioSampler was superior to that of Coriolis (P = 0.0001) and the PBS containing surfactant collected fungi better than that without surfactant (P < 0.0001), whereas antifoam agent showed no influence (P = 0.8). Moreover, fungal concentrations determined by BioSampler with surfactant‐added PBS were statistically indifferent from those by BioStage (P > 0.05) with a Spearman correlation coefficient of 0.81‐0.83 (P < 0.01). In addition to sampler and collection liquid, sampling location was also identified as a significant CE factor (P = 0.006), implying potential influences by fungal genera in the studied fields. Overall, BioSampler with surfactant‐supplemented PBS (eg, Triton X‐100) is recommended considering the great CE and compatibility with a variety of analytical assays.     

Predicting visual similarity between colour palettes

This work is concerned with the prediction of visual colour difference between pairs of palettes. In this study, the palettes contained five colours arranged in a horizontal row. A total of 95 pairs of palettes were rated for visual difference by 20 participants. The colour difference between the palettes was predicted using two algorithms, each based on one of six colour-difference formulae. The best performance (r² = 0.86 and STRESS = 16.9) was obtained using the minimum colour-difference algorithm (MICDM) using the CIEDE2000 equation with a lightness weighing of 2. There was some evidence that the order (or arrangement) of the colours in the palettes was a factor affecting the visual colour differences although the MICDM algorithm does not take order into account. Application of this algorithm is intended for digital design workflows where colour palettes are generated automatically using machine learning and for comparing palettes obtained from psychophysical studies to explore, for example, the effect of culture, age, or gender on colour associations.     

基于注意力机制与文本信息的用户关系抽取

随着社交媒体的发展,用户之间的关系网络对于社交媒体的分析有很大的帮助。因此,该文主要研究用户好友关系检测。以往的关于用户好友关系抽取的研究主要基于社交媒体上的结构化信息,比如其他好友关系,用户的不同属性等。但是,很多时候用户本身并没有大量的好友信息存在,同时也不一定有很多确定的属性。因此,我们希望基于用户发表的文本信息来对用户关系进行预测。不同于以往的潜在好友推荐算法,该文提出了一种基于注意力机制以及长短时记忆网络(long short-term memory,LSTM)的好友关系预测模型,将好友之间的评论分开处理,通过分析用户之间的评论来判断是否具备一定的好友关系。该模型将好友双方信息拼接后的结果作为输入,并将注意力机制应用于LSTM的输出。实验表明,用户之间的评论对于好友关系预测确实有较大的实际意义,该文提出的模型较之于多个基准系统的效果,取得了明显的提升。在不加入任何其它非文本特征的情况下,实验结果的准确率达到了77%。