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61.
Nano crystalline pure and Mg doped ceriaparticles were synthesized by simple chemical co-precipitation method using cerium nitrate hexahydrate as a source material and magnesium nitrate as doping precursor at room temperature. The effect of doping were investigated by X-ray diffraction pattern(XRD), FT-Raman,fourier transform infrared spectroscopy(FTIR), Ultraviolet spectroscopy(UV), photoluminescence spectroscopy(PL), field emission scanning electron microscope(FESEM) and high resolution transmission electron microscopy with energy dispersive spectroscopy (HRTEM &EDS). The X-ray diffraction pattern and FT-Raman studies showed that the prepared samples were nano particulates with cubic fluorite structure. The XRD pattern analysis showed that the size of the particles ranged from 13 to 20?nm, however 4?wt% Mg doping results in reduction of particle size compared with other doping concentrations. The effects of Mg concentration on various structural parameters of the prepared samples were also determined. The slight blue shift observed upon doping in UV–Vis absorption region around 330–360nmrecorded for reduction in particle size. The FTIR unveils the presence of Metal oxygen bonds below 700?cm?1in the prepared samples. All samples showed a broad emission band at 430?nm with linearly increasing intensity with respect to dopant concentrations. The Spherical morphology with weak agglomeration was identified through FESEM and HRTEM analysis. The elemental analysis of Ce, O and Mg were confirmed through EDS analysis.  相似文献   
62.
针对基于规则和统计的传统中文简历解析方法效率低、成本高、泛化能力差的缺点,提出一种基于特征融合的中文简历解析方法,即级联Word2Vec生成的词向量和用BLSTM(Bidirectional Long Short-Term Memory)建模字序列生成的词向量,然后再结合BLSTM和CRF(Conditional Random Fields)对中文简历进行解析(BLSTM-CRF)。为了提高中文简历解析的效率,级联包含字序列信息的词向量和用Word2Vec生成的词向量,融合成一个新的词向量表示;再由BLSTM强大的学习能力融合词的上下文信息,输出所有可能标签序列的分值给CRF层;再由CRF引入标签之间约束关系求解最优序列。利用梯度下降算法训练神经网络,使用预先训练的词向量和Dropout优化神经网络,最终完成对中文简历的解析工作。实验结果表明,所提的特征融合方法优于传统的简历解析方法。  相似文献   
63.
Interactions between financial time series are complex and changeable in both time and frequency domains. To reveal the evolution characteristics of the time-varying relations between bivariate time series from a multi-resolution perspective, this study introduces an approach combining wavelet analysis and complex networks. In addition, to reduce the influence the phase lag between the time series has on the correlations, we propose dynamic time-warping (DTW) correlation coefficients to reflect the correlation degree between bivariate time series. Unlike previous studies that symbolized the time series only based on the correlation strength, the second-level symbol is set according to the correlation length during the coarse-graining process. This study presents a novel method to analyze bivariate time series and provides more information for investors and decision makers when investing in the stock market. We choose the closing prices of two stocks in China’s market as the sample and explore the evolutionary behavior of correlation modes from different resolutions. Furthermore, we perform experiments to discover the critical correlation modes between the bull market and the bear market on the high-resolution scale, the clustering effect during the financial crisis on the middle-resolution scale, and the potential pseudo period on the low-resolution scale. The experimental results exactly match reality, which provides powerful evidence to prove that our method is effective in financial time series analysis.  相似文献   
64.
Cone-beam X-ray luminescence computed tomography (CB-XLCT) is an attractive hybrid imaging modality, and it has the potential of monitoring the metabolic processes of nanophosphors-based drugs in vivo. However, the XLCT imaging suffers from a severe ill-posed problem. In this work, a sparse nonconvex Lp (0?p?L1 regularization. Further, an iteratively reweighted split augmented lagrangian shrinkage algorithm (IRW_SALSA-Lp) was proposed to efficiently solve the non-convex Lp (0?p?p-values (1/16, 1/8, 1/4, 3/8, 1/2, 5/8, 3/4, 7/8) in both 3D digital mouse experiments and in vivo experiments. The results demonstrate that the proposed non-convex methods outperform L2 and L1 regularization in accurately recovering sparse targets in CB-XLCT. And among all the non-convex p-values, our Lp(1/4?p?相似文献   
65.
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%。  相似文献   
66.
Structurally stable β-Ca3(PO4)2/t-ZrO2 composite mixtures with the aid of Dy3+ stabilizer were accomplished at 1500°C. The precursors comprising Ca2+, P5+, Zr4+, and Dy3+ have been varied to obtain five different combinations. The results revealed the fact that complete phase transformation of calcium-deficient apatite to β-Ca3(PO4)2 occurred only at 1300°C, whereas the evidence of t-ZrO2 crystallization is obvious at 900°C. The dual occupancy of Dy3+ at β-Ca3(PO4)2 and t-ZrO2 structures was evident; however, Dy3+ initially prefers to occupy β-Ca3(PO4)2 lattice until its saturation limit and thereafter accommodates at the lattice site of ZrO2. The typical absorption and emission behavior of Dy3+ were noticed in all the systems and, moreover, the surrounding symmetry of Dy3+ domains has been determined from the luminescence study. All the systems ensured paramagnetic response that is generally contributed by the presence of Dy3+. A gradual increment in the phase content of t-ZrO2 in the composite mixtures ensured a significant improvement in the hardness and Young's modulus of the investigated compositions.  相似文献   
67.
ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.  相似文献   
68.
张娜  秦品乐  曾建潮  李启 《计算机应用》2019,39(6):1816-1823
针对在灰度图像着色领域中,传统算法信息提取率不高、着色效果不理想的问题,提出了基于密集神经网络的灰度图像着色算法,以实现改善着色效果,让人眼更好地观察图片信息的目的。利用密集神经网络的信息提取高效性,构建并训练了一个端到端的深度学习模型,对图像中的各类信息及特征进行提取。训练网络时与原图像进行对比,以逐渐减小网络输出结果的信息、分类等各类型的损失。训练完成后,只需向网络输入一张灰度图片,即可生成一张颜色饱满、鲜明逼真的彩色图片。实验结果表明,引入密集网络后,可有效改善着色过程中的漏色、细节信息损失、对比度低等问题,所提算法着色效果较基于VGG网络及U-Net、双流网络结构、残差网络(ResNet)等性能优异的先进着色算法而言取得了显著的改进。  相似文献   
69.
动态评价技术在塔河碳酸盐岩缝洞型油气藏中的应用   总被引:5,自引:2,他引:3  
塔河油田奥陶系油气藏是大型碳酸盐岩溶洞型油气藏,其储渗空间主要为大小不同的溶洞、裂缝带、溶蚀孔隙等组成,该油气藏具有极强的非均质性,单纯用静态资料来认识这类油气藏是非常困难。文章提出利用生产动态资料和信息进行该类型油气藏研究的新思路,利用人工神经网络技术在处理非线性相关参数预测方面的优势,并以渗流理论为基础,结合试井成果,选用已知油井的产量、油嘴、油压、含水率、气油比、原油密度等6个开发动态参数作为样品输入数据,推导出影响油气藏开发的重要参数(地层系数)与生产信息的关系,建立了人工神经网络预测储层参数的结构模型。通过塔里木盆地塔河油气田实例研究,说明了利用动态信息评价油气藏技术在碳酸盐岩缝洞型油气藏储层预测和非均质性分析等方面具有较高的实用价值。  相似文献   
70.
由于AD HOC网络自身的特点,其网络管理很多方面与固定网络不同,如移动性管理和体系结构管理等。讨论了AD HOC网络管理的各个方面,并列举了各个方面的设计思路和已有的相关算法的简介。  相似文献   
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