从新冠疫情防控看信息技术创新应用促进经济社会高质量发展

新冠疫情发生以来,互联网、大数据、人工智能等新一代信息技术在服务疫情防控和经济社会发展中发挥了重要作用.通过梳理新一代信息技术创新应用在疫情防控、社会服务、复工复产、新动能培育等领域发挥的重要作用,从生产生活空间变化、技术产业增长、公共数据融通应用、数字化转型共识形成等4个方面剖析了新冠疫情为新一代信息技术创新带来的重要机遇,并在此基础上探讨新一代信息技术促进经济社会发展面临的制约因素,并提出相关对策建议.     

Constrained voting extreme learning machine and its application

Extreme learning machine(ELM)has been proved to be an effective pattern classification and regression learning mechanism by researchers.However,its good performance is based on a large number of hidden layer nodes.With the increase of the nodes in the hidden layers,the computation cost is greatly increased.In this paper,we propose a novel algorithm,named constrained voting extreme learning machine(CV-ELM).Compared with the traditional ELM,the CV-ELM determines the input weight and bias based on the differences of between-class samples.At the same time,to improve the accuracy of the proposed method,the voting selection is introduced.The proposed method is evaluated on public benchmark datasets.The experimental results show that the proposed algorithm is superior to the original ELM algorithm.Further,we apply the CV-ELM to the classification of superheat degree(SD)state in the aluminum electrolysis industry,and the recognition accuracy rate reaches87.4%,and the experimental results demonstrate that the proposed method is more robust than the existing state-of-the-art identification methods.     

MoS2的摩擦起电及充电特性研究

二维二硫化钼(2D MoS2)由于其优越的电学、光学和压电性能，使其在未来电子器件领域被广泛关注该文利用AFM接触模式和SKPM，在纳米尺度上，测试CVD 制备多层MoS2的隧道摩擦起电和充电特性通过AFM接触模式，在75 nN的法向载荷力下，用Pt导电探针摩擦MoS2样品的1 μm×1 μm正方形区域，同时使硅基底接地然后通过SKPM，对以摩擦区域为中心的5 μm×5 μm正方形区域的接触电势进行成像最后以上述方式，依次在A、B、C区域进行摩擦起电测试研究结果表明：MoS2不仅具有摩擦起电特性，而且表现出摩擦其他区域，可以对初始区域充电的现象因此，可以利用MoS2的摩擦起电特性和充电现象，指导构建一类新的基于2D MoS2纳米电子器件和摩擦电子学功能器件的应用     

A sparse algorithm for adaptive pruning least square support vector regression machine based on global representative point ranking

Least square support vector regression(LSSVR)is a method for function approximation,whose solutions are typically non-sparse,which limits its application especially in some occasions of fast prediction.In this paper,a sparse algorithm for adaptive pruning LSSVR algorithm based on global representative point ranking(GRPR-AP-LSSVR)is proposed.At first,the global representative point ranking(GRPR)algorithm is given,and relevant data analysis experiment is implemented which depicts the importance ranking of data points.Furthermore,the pruning strategy of removing two samples in the decremental learning procedure is designed to accelerate the training speed and ensure the sparsity.The removed data points are utilized to test the temporary learning model which ensures the regression accuracy.Finally,the proposed algorithm is verified on artificial datasets and UCI regression datasets,and experimental results indicate that,compared with several benchmark algorithms,the GRPR-AP-LSSVR algorithm has excellent sparsity and prediction speed without impairing the generalization performance.     

CaCl2的水解反应对固态SiO2显微特征的影响

利用扫描电镜(SEM)和能谱仪(EDS)等手段,并结合热力学理论计算,研究了浸泡在1173 K温度下脱水不完全的CaCl2熔盐中的固态SiO2圆柱样显微特征变化及其原因,初步分析了CaCl2盐的水解反应对固态SiO2电解特性的影响.结果表明,未严格脱水操作的CaCl2盐很容易高温水解,生成的CaO在熔体中的活度只要不低于0.001,即可与SiO2逐级形成CaO·SiO2(CS)、3CaO·2SiO2(C3 S2)和2CaO·SiO2(C2 S)等多种硅酸盐,导致圆柱体外表面的形貌、结构发生较大变化;圆柱体内部渗透进入的熔盐中CaO含量低,形貌变化较小.外表面硅酸盐层的存在使仅内置阴极集流体的固态Si O 2圆柱体电解还原速度减慢和难度增加.     

人类卫生健康共同体视域下的全球科技合作研究——以新冠肺炎疫情为背景

新冠疫情暴发以来,中国政府和人民秉持人类命运共同体理念,运用一系列先进技术,在较短时间内扭转了国内疫情的严峻形势.在此背景下,为了更好地发挥中国方案在全球抗疫中的作用,文章运用内容分析法和归纳分析法,首先梳理了从人类命运共同体到人类卫生健康共同体的理论逻辑,其次探究了人类卫生健康共同体、全球科技合作与疫情防控之间的结构关系,最后分析了中国疫情防控的有效举措,进而得出结论:中国疫情防控工作的成效离不开人类命运共同体理念的指引和全球科技合作的开展,而中国在平台建设、机制整合及合作网络等领域的方案共享将有助于遏制全球疫情的蔓延.     

团簇NiCo2S4的电子性质

依据密度泛函理论在B3LYP/Lan12dz水平下对团簇NiCo2S4进行优化计算,确定12种优化构型,并对其电子性质进行分析.团簇NiCo2S4整体呈电中性,其内部电子均从Ni、Co原子流入S原子,且从s轨道流向p、d轨道.Ni、Co原子均是电子供体,S原子是电子受体.构型1(3)电子流动性最强,构型1(1)电子流动性最弱,且三重态整体电子流动性强于单重态.Ni-4s轨道和Co-4s轨道对团簇NiCo2S4整体电子流动性贡献最大,而S原子三个轨道贡献均较小.稳定性与电子密度分布有关,电子密度分布对称性越好,且α电子云和β电子云重叠程度越高的构型越稳定.     

拥塞控制新算法:试算平衡

根据资源的需求和供给关系,提出了拥塞控制新算法——试算平衡法,旨在按一定比例抑制资源的需求,以实现系统资源的供需平衡,进而达到拥塞控制的目的.     

基于深度神经网络的高环境 适应性水声通信系统研究

深度神经网络中的自动编码器(Autoencoder,AE)通过收发端两个神经网络模块进行全局优化,利用端到端的训练方式以提高通信系统的可靠性.然而,现有对AE的研究未针对信道进行特殊设计,尤其对于时变的水声信道的多径效应,难以进行灵活调整,降低了该方法的实用性.本文提出一种提高水声通信系统信道环境适应性的Attention-Autoencoder网络模型,基于Attention网络可以高效地从大量信息中筛选出关键信息的特点,设计了一种针对水声信道的Attention机制,该机制能够增加网络提取水声信道特征的能力,使系统的适应性大大提高.仿真验证和湖试实验结果表明,基于Attention-Autoencoder网络模型的通信系统与基于文献中AE模型和没有引入神经网络的水声通信系统相比,具有更高的信道环境适应性.     

SAR image de-noising via grouping-based PCA and guided filter

A novel synthetic aperture radar(SAR)image de-noising method based on the local pixel grouping(LPG)principal component analysis(PCA)and guided filter is proposed.This method contains two steps.In the first step,we process the noisy image by coarse filters,which can suppress the speckle effectively.The original SAR image is transformed into the additive noise model by logarithmic transform with deviation correction.Then,we use the pixel and its nearest neighbors as a vector to select training samples from the local window by LPG based on the block similar matching.The LPG method ensures that only the similar sample patches are used in the local statistical calculation of PCA transform estimation,so that the local features of the image can be well preserved after coefficients shrinkage in the PCA domain.In the second step,we do the guided filtering which can effectively eliminate small artifacts left over from the coarse filtering.Experimental results of simulated and real SAR images show that the proposed method outstrips the state-of-the-art image de-noising methods in the peak signalto-noise ratio(PSNR),the structural similarity(SSIM)index and the equivalent number of looks(ENLs),and is of perceived image quality.