科技政策研究代表人物与核心文献可视化网络

引文计量方法常被用来确定某一研究领域的代表人物与核心文献。以国际科学技术政策研究权威期刊《科研政策》(Research Policy)的全部引文数据作为样本,通过作者共被引分析与文献共被引分析,确定了国际科技政策研究领域的代表人物与核心文献;并在此基础上,利用信息可视化技术,绘制出科技政策研究领域代表人物与核心文献的可视化网络,为科技政策研究者提供重要参考。

文学作品中的“小世界”——菲茨杰拉德小说人物关系网络的实证分析

文学之美在于映照现实。为检验这一观点,对菲茨杰拉德的四部小说采用自然语言处理技术提取小说人物角色,并构建人物共现关系网络;基于复杂网络拓扑测度,计算此网络的结构特性,结果表明:菲茨杰拉德四部小说中的人物关系网络均具有类似无尺度的度分布和小世界特征的结构特性,特别是《了不起的盖茨比》和《夜色温柔》中人物关系网络的直径与六度分离定理高度吻合,说明经典小说的魅力来自其描写的社会结构与现实社会高度相似,定量实证分析方法可为定性文学评论提供佐证。本研究为传统文学批评开启了新的视角,提供了新的工具,相关方法可用于对小说作品文学性的评判以及对现有小说定性评价的定量解释。

基于文本分析的网络人物观点识别研究

[目的]随着科学技术的不断发展,网络化发展的现象越来越受到人们的重视。如何在海量的网络信息中识别人物观点成为研究者关注的焦点,网络人物观点被视为网络文本表达的主要思想,是构成网络信息的"魂"。在海量的网络信息中快速识别网络人物观点对掌握网络信息主题具有重要作用。[方法]本文在前人研究的基础上理论与应用相结合,从文本分析的视角研究网络人物的观点。利用相应的算法对文本内容进行预处理,再通过文本句子中的词汇、词性标注和词汇之间的距离关系实现观点指示动词识别和观点持有者识别,从而实现网络人物的观点识别。[结果]通过网络人物观点算法识别的实证研究发现,通过对网络人物进行指代消解和观点持有者的扩展能有效地提高观点识别的准确率。

网络人物志的新闻主角书写——基于搜狐《今日主角》的分析

网络人物志,是一种专注于描述和分析新闻主角的系列网络新闻,其特点是叙述主角亦阐释基于社会职业分工的主角圈层人物关系。相较于其他网络新闻,网络人物志展现了更为立体的新闻人物以及清晰明确的人物关系,利于读者形成有关新闻主角的个性特征及其人际渊源的基础认知。基于对拉康镜像理论的拓展,认为网络人物志的功能在参照物方面有所突破,将原理论提出的人在镜中的呈现拓展为对主角及其所在圈层生态的描述,此类加入环境因素的主角书写更有助于读者立足特定圈层生态准确理解主角。

基于在线百科的大规模人物社会网络抽取与分析

在线百科词条中蕴含着海量的人物间关系信息,基于这些信息可以抽取出大规模社会网络,为数字人文和社会计算研究提供数据支撑。本研究以百度百科为例,首次对面向中文在线百科的大规模社会网络抽取进行探索,提出一种新的人物社会网络抽取方法。该方法利用排序学习综合多种特征计算人物关系权重,通过估计人物生存时空来发现人物间的时空耦合关系。由此,从百度百科中抽取出一个带权重的跨时空人物社会网络和一个时空耦合的人物网络。这两个人物网络具有良好的小世界和无标度特性,并存在清晰的社区结构。最后,通过可视化分析展示了百科人物网络在数字人文研究中的应用模式和应用价值。

基于复杂网络的人物关系建模研究——以《红楼梦》为例

经典的文学作品能够流传至今是因为其虚拟的社会能够反映出现实世界的许多特性。为了验证这个观点,将曹雪芹的小说《红楼梦》采用复杂网络方法对人物关系进行建模,利用NetworkX软件包计算网络指标分析网络的结构特性。结果表明该小说的人物关系网络直径符合六度分离定理,其社会关系结构和现实世界较为相似,具有小世界、社团结构、稀疏特性并且网络节点之间呈现异配情况。与传统文学作品分析方法相比,该实验使用的方法可定量对比小说世界和现实世界的特征。

人物关系网络在包装产品精准营销中的应用

在当今信息化社会中,人物关系网络研究具有十分重要的意义,它蕴含了巨大的商机,并可应用于不同的领域。在包装产品信息分析中,文本信息是重要的基础资源,将原始文本经过命名实体识别、人物实体关系抽取、指代消解等技术处理后,构建出人物社会关系网络来服务于包装产品的精准营销,是一种行之有效的方法。实验结果表明,人物关系网络构建方法具有领域可移植性,它便于在大规模的网络文本中对包装产品相关文本进行灵活分析,从而为包装产品生产经营者精准营销提供辅助决策功能,其方法具有较强的实用价值。

浅析戏剧《玩偶之家》人物关系设计

《玩偶之家》共织造了五个来自社会不同阶层的人物,娜拉、海尔茂、林丹太太、柯洛克斯泰、阮克医生。他们每人都有着一个神秘的心灵世界,都肩负着多种不同的角色,有着明暗不同的两重乃至多重关系,由此构成错综复杂而又多变的人物关系网络。

在线社交网络构建和结构挖掘系统

本系统基于复杂网络理论及方法,利用Python高级程序设计语言、Page Rank算法、Hits算法对在线社交网络的构建、结构进行了研究。系统主要实现的功能有三方面,即构建在线社交网络、分析在线社交网络的结构特性、挖掘在线社交网络的社团结构和网络核心人物。

Study on Artificial Neural Network Model for Crop Evapotranspiration

Based on potted plant experiment, BP-artifieial neural network was used to simulate crop evapotranspiration and 3 kinds of artificial neural network models were constructed as ET1 （meteorological factors）, ET2（ meteorological factors and sowing days） and ET3 （meteorological factors, sowing days and water content）. And the predicted result was compared with actual value ET that was obtained by weighing method. The results showed that the ET3 model had higher calculation precision and an optimum BP-artificial neural network model for calculating crop evapotranspiration.

特定事件意见领袖挖掘

为挖掘适用于特定事件的意见领袖,提出一种采用特定事件相关的新闻文本构建人物关系网络,结合社会网络分析方法挖掘意见领袖的方法。从特定事件相关的新闻文本中识别人名序列,采用滑动窗口和段落划分的方式确定人物之间的影响关系,计算影响关系的强弱,构建特定事件的人物关系网络。采用改进的LeaderRank算法计算人物关系网络中人物的影响力得分。实验结果表明,该方法能够有效识别特定事件的意见领袖,改进后的LeaderRank算法相比LeaderRank等算法能够更有效地识别意见领袖。

Kernel principal component analysis network for image classification

In order to classify nonlinear features with a linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network（ KPCANet） is proposed. First, the data is mapped into a higher-dimensional space with kernel principal component analysis to make the data linearly separable. Then a two-layer KPCANet is built to obtain the principal components of the image. Finally, the principal components are classified with a linear classifier. Experimental results showthat the proposed KPCANet is effective in face recognition, object recognition and handwritten digit recognition. It also outperforms principal component analysis network（ PCANet） generally. Besides, KPCANet is invariant to illumination and stable to occlusion and slight deformation.

Ratio of Fe-Al compound at interface of steel-backed Al-graphite semi-solid bonding plate

The ratio of Fe-Al compound at the bonding interface of solid steel plate to Al-7graphite slurry was used to characterize the interracial structure of steel-Al-7graphite semi-solid bonding plate quantitatively. The relationship between the ratio of Fe-Al compound at interface and bonding parameters （such as preheat temperature of steel plate, solid fraction of Al-7graphite slurry and rolling speed） was established by artificial neural networks perfectly. The results show that when the bonding parameters are 516 ℃ for preheat temperature of steel plate, 32.5% for solid fraction of Al-7graphite slurry and 12 mm/s for rolling speed, the reasonable ratio of Fe-Al compound corresponding to the largest interfacial shear strength of bonding plate is obtained to be 70.1%. This reasonable ratio of Fe-Al compound is a quantitative criterion of interracial embrittlement, namely, when the ratio of Fe-Al compound at interface is larger than 70.1%, interfacial embrittlement will occur.

Modified imperialist competitive algorithm-based neural network to determine shear strength of concrete beams reinforced with FRP

Fiber reinforced polymers (FRPs), unlike steel, are corrosion-resistant and therefore are of interest;however, their use is hindered because their brittle shear is formulated in most specifications using limited data available at the time. We aimed to predict the shear strength of concrete beams reinforced with FRP bars and without stirrups by compiling a relatively large database of 198 previously published test results (available in appendix). To model shear strength, an artificial neural network was trained by an ensemble of Levenberg-Marquardt and imperialist competitive algorithms. The results suggested superior accuracy of model compared to equations available in specifications and literature.

Prediction of the Performance of the Fabrics in Garment Manufacturing by Artificial Neural Network

An artificial neural network is used to predict the performance of fabrics in clothing manufacturing. The predictions are based on fabric mechanical properties measured on the FAST system. The influences of the different ANNs construct on the convergence speed and the prediction accuracy are investigated. The result indicates that the BP neural network is an efficiency technique and has a wide prospect in the application to garment processing.

Simulation of phytoplankton biomass in Quanzhou Bay using a back propagation network model and sensitivity analysis for environmental variables

Prediction and sensitivity models,to elucidate the response of phytoplankton biomass to environmental factors in Quanzhou Bay,Fujian,China,were developed using a back propagation(BP) network.The environmental indicators of coastal phytoplankton biomass were determined and monitoring data for the bay from 2008 was used to train,test and build a three-layer BP artificial neural network with multi-input and single-output.Ten water quality parameters were used to forecast phytoplankton biomass(measured as chlorophyll-a concentration).Correlation coefficient between biomass values predicted by the model and those observed was 0.964,whilst the average relative error of the network was-3.46% and average absolute error was 10.53%.The model thus has high level of accuracy and is suitable for analysis of the influence of aquatic environmental factors on phytoplankton biomass.A global sensitivity analysis was performed to determine the influence of different environmental indicators on phytoplankton biomass.Indicators were classified according to the sensitivity of response and its risk degree.The results indicate that the parameters most relevant to phytoplankton biomass are estuary-related and include pH,sea surface temperature,sea surface salinity,chemical oxygen demand and ammonium.

Neural Network Based on Quantum Chemistry for Predicting Melting Point of Organic Compounds

The melting points of organic compounds were estimated using a combined method that includes a backpropagation neural network and quantitative structure property relationship （QSPR） parameters in quantum chemistry. Eleven descriptors that reflect the intermolecular forces and molecular symmetry were used as input variables. QSPR parameters were calculated using molecular modeling and PM3 semi-empirical molecular orbital theories. A total of 260 compounds were used to train the network, which was developed using MatLab. Then, the melting points of 73 other compounds were predicted and results were compared to experimental data from the literature. The study shows that the chosen artificial neural network and the quantitative structure property relationships method present an excellent alternative for the estimation of the melting point of an organic compound, with average absolute deviation of 5%.

Application of Artificial Neural Network in Predicting Crop Yield： A Review

Agricultural system is very complex since it deals with large data situation which comes from a number of factors. A lot of techniques and approaches have been used to identify any interactions between factors that affecting yields with the crop performances. The application of neural network to the task of solving non-linear and complex systems is promising. This paper presents a review on the use of artificial neural network （ANN） in predicting crop yield using various crop performance factors. General overview on the application of ANN and the basic concept of neural network architecture are also presented. From the literature, it has been shown that ANN provides better interpretation of crop variability compared to the other methods.

Prediction of Flash Point Temperature of Organic Compounds Using a Hybrid Method of Group Contribution ＋ Neural Network ＋ Particle Swarm Optimization

The flash points of organic compounds were estimated using a hybrid method that includes a simple group contribution method (GCM) implemented in an artificial neural network (ANN) with particle swarm optimization (PSO). Different topologies of a multilayer neural network were studied and the optimum architecture was determined. Property data of 350 compounds were used for training the network. To discriminate different substances the molecular structures defined by the concept of the classical group contribution method were given as input variables. The capabilities of the network were tested with 155 substances not considered in the training step. The study shows that the proposed GCM+ANN+PSO method represent an excellent alternative for the estimation of flash points of organic compounds with acceptable accuracy (AARD = 1.8%; AAE = 6.2 K).

New hybrid model of proton exchange membrane fuel cell

Model and simulation are good tools for design optimization of fuel cell systems. This paper proposes a new hybrid model of proton exchange membrane fuel cell (PEMFC). The hybrid model includes physical component and black-box com-ponent. The physical component represents the well-known part of PEMFC, while artificial neural network (ANN) component estimates the poorly known part of PEMFC. The ANN model can compensate the performance of the physical model. This hybrid model is implemented on Matlab/Simulink software. The hybrid model shows better accuracy than that of the physical model and ANN model. Simulation results suggest that the hybrid model can be used as a suitable and accurate model for PEMFC.