磺酸化CNTs在钙钛矿太阳能电池电极中的应用

为解决钙钛矿太阳能电池(perovskite solar cells,PSCs)中采用贵金属作为对电极(counter electrode,CE)而导致的成本高昂问题,对碳纳米管(CNTs)进行磺酸化处理,并将磺酸化CNTs(s-CNTs)作为对电极材料应用到PSCs中.利用稳态荧光(PL)、电化学阻抗(EI)等方法对PSCs的电荷传输过程进行研究,结果如下:与CNTs对电极相比,s-CNTs对电极具有更好的导电性、更优异的空穴提取和传输能力,在s-CNTs基PSC上获得6.18％的能量转换效率.当喷涂量为1 mL/块,退火温度为100℃,退火时间为30 min时,s-CNTs基PSC的各项性能最优,并具有较好的重复性.研究结果为改善碳基PSCs的光电性能提供了新思路.     

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.     

CAE分析在汽车引擎盖优化设计中的应用

本文以某皮卡车发生引擎盖开裂问题为例,探讨CAE分析在汽车引擎盖优化设计中的应用。首先,对某皮卡车发生引擎盖开裂的问题进行描述,然后从建立模型、模态分析、强度分析、优化设计等几个方面出发,探讨CAE分析在实际问题中的应用。     

复掺配合比对自密实高流态混凝土性能影响

为了优化自密实高流态混凝土配合比,提高混凝土工作和力学性能,分别以胶凝材料、砂率和外加剂组分为关键变量,采用单一控制变量法,通过改变材料组分掺合量探究对自密实高流态混凝土性能影响.结果 表明:当胶凝材料在500～530kg/m3时,有较高流动性;超过530kg/m3时,间隙通过性效果好;低于530kg/m3时,可降低离析率.砂率为48％～50％时,拌合物流动性、间隙通过性较稳定,继续增大,抗离析性效果欠佳.当外加剂增大,流动性增加而抗离析性降低,外加剂为1.0％～1.2％时,对间隙通过性影响较小,与流动性、抗离析性较统一.随胶凝材料、砂率和外加剂各自增大,胶凝材料对3d、7d和28d抗压强度均有影响,砂率对3d抗压强度影响显著,外加剂对28d抗压强度影响明显.     

人丝氨酸蛋白酶抑制剂B3基因的生物信息学分析

人丝氨酸蛋白酶抑制剂B3 SERPINB3能抑制半胱氨酸蛋白酶,具有抗细胞调亡、促进细胞增殖和迁移作用,且与许多肿瘤的发生与发展密切相关.通过基因电子克隆(in silico cloning)获得SERPINB3基因全长cDNA序列(1779 bp),编码由390个氨基酸组成的蛋白质.核酸序列分析表明:SERPINB3基因位于人体18号染色体长臂(18q21.33),基因组跨越80373285 bp,含有个16外显子,15个内含子,其cDNA序列上总共有45个酶切位点.SERPINB基因核酸序列同源性分析显示,人类与黑猩猩的亲缘关系较近,与白眉猴和猕猴的亲缘关系是最远.SERPINB3蛋白序列同源性分析显示,人类和黑猩猩亲缘关系较近,和苍疣猴亲缘关系较远.SERPINB3相互作用蛋白的生物信息学分析表明:与SERPINB3相互作用的蛋白中,以丝氨酸蛋白酶家族成员居多,且大多在调节炎症反应、细胞增殖分化以及凋亡等方面起着关键作用.本论文通过对SERPINB3基因的生物信息学分析,为SERPINB3的功能和分子进化研究提供更多的生物信息学参考.     

基于共现分析的分类器链标签序列优化方法

针对分类器链模型采用随机生成方式确定标签序列会极大影响分类器链性能的问题。通过共现分析技术深入挖掘标签间的潜在关系, 提出一种基于贪心算法和n-gram模型的两种标签序列优化策略以提升分类器链模型性能。基于贪心算法的策略通过计算和排序标签之间共现率来生成优化的分类器链标签序列, 而基于n-gram模型的策略则通过最大化标签之间条件概率来生成优化的分类器链标签序列。最后通过多个多标签基准数据集进行实验验证, 实验结果表明, 与当前流行的各种分类器链模型相比, 所提的两种策略很有竞争力, 可以明显提升多标签分类效果。     

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.     

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.     

考虑楼板作用的RC框架压膜机制 抗倒塌承载力分析

为研究考虑楼板作用后钢筋混凝土框架结构的压膜机制抗连续倒塌性能,利用SAP2000建立了框架梁板子结构连续倒塌有限元分析模型,通过已有的12个抗连续倒塌试验验证了分析模型的准确性和合理性.在此基础上,通过定义考虑楼板作用后压膜机制抗连续倒塌承载力提高系数,研究了混凝土强度、楼板厚度及配筋率等参数对承载力提高系数的影响规律.结果表明:考虑楼板作用后,结构的抗倒塌承载力将显著提高,增大靠近边柱梁端的压拱效应,减小承载力再次上升时的位移.承载力提高系数随着混凝土强度等级、板的长宽比、梁底配筋率、梁高的增大而显著增加;承载力提高系数随着楼板厚度、板顶配筋率、板底配筋率、梁顶配筋率的增加而减小.     

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.