期刊文献+
共找到648篇文章
< 1 2 33 >
每页显示 20 50 100
QPSO-optimized BP Neural Network to Predict Occurrence Quantity of Myzus persicae 被引量:1
1
作者 Qiu Jing Yang Yi +3 位作者 Qin Xiyun Li Kunlin Chen Keping Yin Jianli 《Plant Diseases and Pests》 CAS 2015年第1期1-3,14,共4页
In order to effectively predict occurrence quantity of Myzus persicae, BP neural network theory and method was used to establish prediction model for oc- currence quantity of M. persicae. Meanwhile, QPSO algorithm was... In order to effectively predict occurrence quantity of Myzus persicae, BP neural network theory and method was used to establish prediction model for oc- currence quantity of M. persicae. Meanwhile, QPSO algorithm was used to optimize connection weight and threshold value of BP neural network, so as to determine. the optimal connection weight and threshold value. The historical data of M. persica quantity in Hongta County, Yuxi City of Yunnan Province from 2003 to 2006 was adopted as training samples, and the occurrence quantities of M. persicae from 2007 to 2009 were predicted. The prediction accuracy was 99.35%, the mini- mum completion time was 30 s, the average completion time was 34.5 s, and the running times were 19. The prediction effect of the model was obviously superior to other prediction models. The experiment showed that this model was more effective and feasible, with faster convergence rate and stronger stability, and could solve the similar problems in prediction and clustering. The study provides a theoretical basis for comprehensive prevention and control against M. persicae. 展开更多
关键词 BP neural network QPSO algorithm Myzus persicae occurrence quantity Prediction model
下载PDF
A Metric Approach to Hot Topics in Biomedicine via Keyword Co-occurrence 被引量:1
2
作者 Jane H.Qin Jean J.Wang Fred Y.Ye 《Journal of Data and Information Science》 CSCD 2019年第4期13-25,共13页
Purpose:To reveal the research hotpots and relationship among three research hot topics in b iomedicine,namely CRISPR,iPS(induced Pluripotent Stem)cell and Synthetic biology.Design/methodology/approach:We set up their... Purpose:To reveal the research hotpots and relationship among three research hot topics in b iomedicine,namely CRISPR,iPS(induced Pluripotent Stem)cell and Synthetic biology.Design/methodology/approach:We set up their keyword co-occurrence networks with using three indicators and information visualization for metric analysis.Findings:The results reveal the main research hotspots in the three topics are different,but the overlapping keywords in the three topics indicate that they are mutually integrated and interacted each other.Research limitations:All analyses use keywords,without any other forms.Practical implications:We try to find the information distribution and structure of these three hot topics for revealing their research status and interactions,and for promoting biomedical developments.Originality/value:We chose the core keywords in three research hot topics in biomedicine by using h-index. 展开更多
关键词 Keyword co-occurrence network analysis Information visualization BIOMEDICINE Hot topics CRISPR-Cas iPS cell Synthetic biology
下载PDF
Co-occurrence prediction in a large location-based social network 被引量:11
3
作者 Rong-Hua LI Jianquan LIU +2 位作者 Jeffrey Xu YU Hanxiong CHEN Hiroyuki KITAGAWA 《Frontiers of Computer Science》 SCIE EI CSCD 2013年第2期185-194,共10页
Location-based social network (LBSN) is at the forefront of emerging trends in social network services (SNS) since the users in LBSN are allowed to "check-in" the places (locations) when they visit them. The a... Location-based social network (LBSN) is at the forefront of emerging trends in social network services (SNS) since the users in LBSN are allowed to "check-in" the places (locations) when they visit them. The accurate geographi- cal and temporal information of these check-in actions are provided by the end-user GPS-enabled mobile devices, and recorded by the LBSN system. In this paper, we analyze and mine a big LBSN data, Gowalla, collected by us. First, we investigate the relationship between the spatio-temporal co- occurrences and social ties, and the results show that the co- occurrences are strongly correlative with the social ties. Sec- ond, we present a study of predicting two users whether or not they will meet (co-occur) at a place in a given future time, by exploring their check-in habits. In particular, we first intro- duce two new concepts, bag-of-location and bag-of-time-lag, to characterize user's check-in habits. Based on such bag rep- resentations, we define a similarity metric called habits sim- ilarity to measure the similarity between two users' check-in habits. Then we propose a machine !earning formula for pre- dicting co-occurrence based on the social ties and habits sim- ilarities. Finally, we conduct extensive experiments on our dataset, and the results demonstrate the effectiveness of the proposed method. 展开更多
关键词 location-based social networks Gowalla co-occurrence
原文传递
Language clustering with word co-occurrence networks based on parallel texts 被引量:6
4
作者 LIU HaiTao CONG Jin 《Chinese Science Bulletin》 SCIE EI CAS 2013年第10期1139-1144,共6页
This study investigates the feasibility of applying complex networks to fine-grained language classification and of employing word co-occurrence networks based on parallel texts as a substitute for syntactic dependenc... This study investigates the feasibility of applying complex networks to fine-grained language classification and of employing word co-occurrence networks based on parallel texts as a substitute for syntactic dependency networks in complex-network-based language classification.14 word co-occurrence networks were constructed based on parallel texts of 12 Slavic languages and 2 non-Slavic languages,respectively.With appropriate combinations of major parameters of these networks,cluster analysis was able to distinguish the Slavic languages from the non-Slavic and correctly group the Slavic languages into their respective sub-branches.Moreover,the clustering could also capture the genetic relationships of some of these Slavic languages within their sub-branches.The results have shown that word co-occurrence networks based on parallel texts are applicable to fine-grained language classification and they constitute a more convenient substitute for syntactic dependency networks in complex-network-based language classification. 展开更多
关键词 网络构建 文本聚类 语言 平行 共生 聚类分析 遗传关系 复杂网络
原文传递
Seabed Classification Using BP Neural Network Based on GA 被引量:3
5
作者 Yang Fanlin1, Liu Jingnan2 1. GPS Engineering Research Center, Wuhan University, Wuhan 430079, China. 2. Presidential Secretariat, Wuhan University, Wuhan 430079, China 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2003年第4期523-531,共9页
Side scan sonar imaging is one of the advanced methods for seabed study. In order to be utilized in other projects, such as ocean engineering, the image needs to be classified according to the distributions of differe... Side scan sonar imaging is one of the advanced methods for seabed study. In order to be utilized in other projects, such as ocean engineering, the image needs to be classified according to the distributions of different classes of seabed materials. In this paper, seabed image is classified according to BP neural network, and. Genetic Algorithm is adopted in train network in this paper. The feature vectors are average intensity, six statistics of texture and two dimensions of fractal. It considers not only the spatial correlation between different pixels, but also the terrain coarseness. The texture is denoted by the statistics of the co-occurrence matrix. Double Blanket algorithm is used to calculate dimension. Because a uniform fractal may not be sufficient to describe a seafloor, two dimensions are calculated respectively by the upper blanket and the lower blanket. However, in sonar image, fractal has directivity, i. e. there are different dimensions in different direction. Dimensions are different in acrosstrack and alongtrack, so the average of four directions is used to solve this problem. Finally, the real data verify the algorithm. In this paper, one hidden layer including six nodes is adopted. The BP network is rapidly and accurately convergent through GA. Correct classification rate is 92.5 % in the result. 展开更多
关键词 BP network co-occurrence matrix FRACTAL CLASSIFICATION genetic algorithin
下载PDF
Content analysis of documents using neural networks: A study of Antarctic science research articles published in international journals
6
作者 DASTIDAR Prabir G JHA, Deepak Kumal 《Advances in Polar Science》 2012年第1期41-46,共6页
Content analysis of scientific papers emanating from Antarctic science research during the 25 years period (1980-- 2004) has been carried out using neural network based algorithm-CATPAC. A total of 10 942 research a... Content analysis of scientific papers emanating from Antarctic science research during the 25 years period (1980-- 2004) has been carried out using neural network based algorithm-CATPAC. A total of 10 942 research articles published in Science Citation Indexed (SCI) journals were used for the study. Normalized co-word matrix from 35 most-used significant words was used to study the semantic association between the words. Structural Equivalence blocks were constructed from these 35 most-used words. Four-block model solution was found to be optimum. The density table was dichotomized using the mean density of the table to derive the binary matrix, which was used to construct the network map. Network maps represent the thematic character of the blocks. The blocks showed preferred connection in establishing semantic relationship with the blocks, characterizing thematic composition of Antarctic science research. The analysis has provided an analytical framework for carrying out studies on the con- tent of scientific articles. The paper has shown the utility of co-word analysis in highlighting the important areas of research in Antarctic science. 展开更多
关键词 ANTARCTICA content analysis thematic analysis SCIENTOMETRICS neural network co-occurrence co-word social network analysis
下载PDF
Identification of Textile Defects Based on GLCM and Neural Networks
7
作者 Gamil Abdel Azim 《Journal of Computer and Communications》 2015年第12期1-8,共8页
In modern textile industry, Tissue online Automatic Inspection (TAI) is becoming an attractive alternative to Human Vision Inspection (HVI). HVI needs a high level of attention nevertheless leading to low performance ... In modern textile industry, Tissue online Automatic Inspection (TAI) is becoming an attractive alternative to Human Vision Inspection (HVI). HVI needs a high level of attention nevertheless leading to low performance in terms of tissue inspection. Based on the co-occurrence matrix and its statistical features, as an approach for defects textile identification in the digital image, TAI can potentially provide an objective and reliable evaluation on the fabric production quality. The goal of most TAI systems is to detect the presence of faults in textiles and accurately locate the position of the defects. The motivation behind the fabric defects identification is to enable an on-line quality control of the weaving process. In this paper, we proposed a method based on texture analysis and neural networks to identify the textile defects. A feature extractor is designed based on Gray Level Co-occurrence Matrix (GLCM). A neural network is used as a classifier to identify the textile defects. The numerical simulation showed that the error recognition rates were 100% for the training and 100%, 91% for the best and worst testing respectively. 展开更多
关键词 Image Processing NEURAL network Gray-Level co-occurrence MATRICES (GLCM)
下载PDF
油梨根际土壤微生物群落及其共生网络对根腐病的响应 被引量:1
8
作者 何应会 黄耀恒 +3 位作者 陆荣民 杨日升 韦燕妮 梁文汇 《中南林业科技大学学报》 CAS CSCD 北大核心 2024年第4期106-115,共10页
【目的】为研发根腐病绿色综合防控技术提供理论依据,有效推进油梨树种产业发展。【方法】本研究以百色市林业科学研究所的健康和根腐病发病油梨植株根际土壤为研究对象,并利用基于16S核糖体RNA(rRNA)和内部转录间隔区(ITS)扩增子高通... 【目的】为研发根腐病绿色综合防控技术提供理论依据,有效推进油梨树种产业发展。【方法】本研究以百色市林业科学研究所的健康和根腐病发病油梨植株根际土壤为研究对象,并利用基于16S核糖体RNA(rRNA)和内部转录间隔区(ITS)扩增子高通量测序技术,分析健康和染病植株根际细菌和真菌群落结构、组成及多样性差异,比较细菌和真菌群落结构及其相互作用,确定土壤病原菌和有益菌的变化。【结果】染病植株相对于健康植株,根际细菌和真菌群落结构和多样性均没有显著性变化。变形菌门Proteobacteria、酸杆菌门acidobacteria、放线菌门actinobacteria、拟杆菌门bacteroidetes和绿弯菌门chloroflexi是油梨根际土壤优势细菌类群,子囊菌门ascomycota和担子菌门basidiomycota是油梨根际土壤优势真菌类群。在门分类水平、纲分类水平以及属分类水平上,细菌和真菌群落组成有明显的变化,但随着分类水平降低,群落组成变化越明显。此外,健康和染病油梨根际细菌群落α多样性显著高于真菌群落,而健康与染病之间细菌群落结构差异小于真菌。细菌物种之间的相互作用比真菌网络物种间的相互作用更紧密,并且负连接百分比和关节类群数量更多,细菌网络的稳定性更高。同样,油梨根际土壤细菌群落相对于真菌群落表现出更高的生态位宽度和生态位重叠。对根腐病有反应的细菌和真菌,如芽孢杆菌Bacillus、假单胞杆菌pseudomonas和溶杆菌lysobacter和球囊菌纲glomeromycetes,由于其在染病油梨根际土壤中的相对丰度均高于健康土壤,可以被视为相关的生物防治菌。【结论】根腐病并不会使油梨根际细菌和真菌群落结构和多样性发生显著性变化,但会使得一些有益菌的相对丰度增加,并且细菌比真菌对根腐病具有更高的抵抗力。 展开更多
关键词 油梨 根腐病 高通量测序 根际细菌群落 根际真菌群落 共生网络
下载PDF
基于混合图神经网络的多模态相关性时尚服饰兼容度预测研究 被引量:1
9
作者 陈燕 吕梓民 +2 位作者 李云 陆星宇 井佩光 《北京服装学院学报(自然科学版)》 CAS 2024年第2期70-78,共9页
近年来,许多研究人员在多模态融合的研究中取得了显著效果。多模态比单模态具有更丰富的信息,然而,多模态融合过程中的类别共现频率偏差,使得服饰兼容度预测研究存在着巨大的挑战。因此,本研究提出了基于混合图神经网络的多模态相关性... 近年来,许多研究人员在多模态融合的研究中取得了显著效果。多模态比单模态具有更丰富的信息,然而,多模态融合过程中的类别共现频率偏差,使得服饰兼容度预测研究存在着巨大的挑战。因此,本研究提出了基于混合图神经网络的多模态相关性时尚服饰兼容度预测模型。该模型深度挖掘文本和视觉两个模态的相关性,并通过混合图神经网络解决多模态融合过程中类别共现频率偏差引起兼容度预测不准确的问题,提高服饰兼容度预测精度。该模型在Polyvore Outfits和Polyvore Outfits-D两个开源数据集上进行了服饰兼容度预测和填空任务的实验。结果显示,该模型在2个数据集中的服饰兼容度任务中分别取得了0.928和0.878的AUC值,在填空任务中分别取得了62.41%和56.83%的精确度,均优于比较的基准模型。 展开更多
关键词 多模态 动态图神经网络 共现频率偏差 服饰兼容度
下载PDF
Biological Neural Network Structure and Spike Activity Prediction Based on Multi-Neuron Spike Train Data
10
作者 Tielin Zhang Yi Zeng Bo Xu 《International Journal of Intelligence Science》 2015年第2期102-111,共10页
The micro-scale neural network structure for the brain is essential for the investigation on the brain and mind. Most of the previous studies typically acquired the neural network structure through brain slicing and r... The micro-scale neural network structure for the brain is essential for the investigation on the brain and mind. Most of the previous studies typically acquired the neural network structure through brain slicing and reconstruction via nanoscale imaging. Nevertheless, this method still cannot scale well, and the observation on the neural activities based on the reconstructed neural network is not possible. Neuron activities are based on the neural network of the brain. In this paper, we propose that multi-neuron spike train data can be used as an alternative source to predict the neural network structure. And two concrete strategies for neural network structure prediction based on such kind of data are introduced, namely, the time-ordered strategy and the spike co-occurrence strategy. The proposed methods can even be applied to in vivo studies since it only requires neural spike activities. Based on the predicted neural network structure and the spreading activation theory, we propose a spike prediction method. For neural network structure reconstruction, the experimental results reveal a significantly improved accuracy compared to previous network reconstruction strategies, such as Cross-correlation, Pearson, and the Spearman method. Experiments on the spikes prediction results show that the proposed spreading activation based strategy is potentially effective for predicting neural spikes in the biological neural network. The predictions on the neural network structure and the neuron activities serve as foundations for large scale brain simulation and explorations of human intelligence. 展开更多
关键词 Neural network Structure PREDICTION SPIKE PREDICTION Time-Order STRATEGY co-occurrence STRATEGY SPREADING ACTIVATION
下载PDF
基于LDA主题模型的“双一流”高校图书馆用户评论文本数据挖掘 被引量:3
11
作者 张文德 徐子杨 赵立红 《情报探索》 2024年第7期120-127,共8页
[目的/意义]图书馆用户评论中包含用户对图书馆服务和管理意见的重要信息,对用户评论文本进行深度挖掘,旨在探究用户关心主题及其情感态度、隐含诉求,为高校图书馆建设提供理论依据和数据支持。[方法/过程]以“双一流”高校图书馆为研... [目的/意义]图书馆用户评论中包含用户对图书馆服务和管理意见的重要信息,对用户评论文本进行深度挖掘,旨在探究用户关心主题及其情感态度、隐含诉求,为高校图书馆建设提供理论依据和数据支持。[方法/过程]以“双一流”高校图书馆为研究对象,收集用户在大众点评网上的评论数据,通过LDA主题建模,得到当前用户评论主要集中在信息资源建设、娱乐休闲服务、馆内设施环境三个维度,进而分别分析三个维度下的关键词共现网络和用户情感态度。[结果/结论]用户对高校图书馆的总体情感态度是积极正向的,但在纸质资源管理、社会化服务、馆内人性化服务等方面表现出负面情绪。 展开更多
关键词 高校图书馆 用户评论 LDA主题分析 共现网络 情感分析
下载PDF
信息茧房视域下在线问答社区用户健康信息需求对比研究 被引量:1
12
作者 邹筱 刘垣春 周欢 《衡阳师范学院学报》 2024年第3期59-67,共9页
信息获取途径的固化是促使信息茧房形成的重要原因之一,本研究旨在让社区了解用户的健康信息需求热点,并为社区信息治理和社区间信息整合提供参考,减少信息茧房对网络信息空间治理带来的负面影响。借助主题词提取和文本共现网络构建等... 信息获取途径的固化是促使信息茧房形成的重要原因之一,本研究旨在让社区了解用户的健康信息需求热点,并为社区信息治理和社区间信息整合提供参考,减少信息茧房对网络信息空间治理带来的负面影响。借助主题词提取和文本共现网络构建等自然语言处理技术,利用不同类型在线问答社区中真实的用户提问数据,分析用户健康信息需求热点,并对其需求特征的异同进行对比分析。实验结果表明:一方面,综合型与垂直型在线问答社区对于疾病的基础病理知识、药物信息以及饮食方面的关注度较高,且均具有适老化需求。另一方面,综合型在线问答社区对于疾病的预防关注度较高,而垂直型在线问答社区更加关注于疾病治疗,综合型在线问答社区用户存在情感支持需求,但总体上国内在线问答社区的情感支持需求均不高。 展开更多
关键词 信息茧房 在线健康问答社区 健康信息需求 主题提取 文本共现网络
下载PDF
全量化收集粪污厌氧发酵失稳过程中微生物群落动态变化及生态集群组装模式
13
作者 吁安 龙云 +4 位作者 陈莎莎 桂伦 吴强建 陈柳萌 龚贵金 《江西农业学报》 CAS 2024年第4期83-92,共10页
通过高通量测序技术分析了全量化粪污厌氧发酵在稳定、抑制和崩溃3个阶段的微生物菌群动态变化情况,结合微生物互作网络构建,识别互作网络中关键生态集群及组装模式,以期为全量化粪污沼气工程稳定运行提供理论依据和数据支撑。结果表明:... 通过高通量测序技术分析了全量化粪污厌氧发酵在稳定、抑制和崩溃3个阶段的微生物菌群动态变化情况,结合微生物互作网络构建,识别互作网络中关键生态集群及组装模式,以期为全量化粪污沼气工程稳定运行提供理论依据和数据支撑。结果表明:(1)在细菌门水平上,厚壁菌门(Firmicutes)、拟杆菌门(Bacteroidetes)和变形菌门(Proteobacteria)为优势菌门,在各样品中相对丰度合计占比为96.0%。(2)在细菌属水平上,相对丰度较高的菌属为瘤胃梭菌属(Ruminiclostridium)、狭义梭菌属类群I(Clostridium_sensu_stricto_1)等。(3)在古菌属水平上,甲烷短杆菌属(Methanobrevibacter)、甲烷球形菌属(Methanosphaera)和甲烷袋状菌属(Methanoculleus)为优势菌属,在各样品中相对丰度合计占比为96.5%。(4)微生物互作网络分析揭示,全量化粪污厌氧发酵过程中的功能集群主要划分为3个模块(模块1~模块3),微生物群落在厌氧发酵受到抑制时会产生应激性调整;在产气稳定期,模块1占据主导地位,厌氧发酵系统的水解酸化菌与产甲烷菌间的协同良好,无明显的小分子挥发酸累积现象,可稳定水解产酸;在产气崩溃期,模块2、模块3的相对丰度大幅提升,并占据了模块1的生态位;微生物通过增加乙酸、丙酸等挥发性有机酸氧化菌的相对丰度和嗜酸产甲烷能力,缓解小分子挥发酸和氨氮的胁迫。 展开更多
关键词 厌氧发酵 全量化收集粪污 微生物群落 共现互作网络 生态集群
下载PDF
基于词-主题-文本异质网络的短文本分类方法
14
作者 徐涛 赵星甲 卢敏 《计算机应用与软件》 北大核心 2024年第1期146-152,182,共8页
针对现有分类方法未考虑长距离词的语义相关性和文本间潜在主题共享的问题,提出一种基于词-主题-文本异质网络(WTDHN)的短文本分类方法。通过Word2vec训练词的上下文语义向量;构建词相关性矩阵以充足的词共现信息增强短文本各级别语义学... 针对现有分类方法未考虑长距离词的语义相关性和文本间潜在主题共享的问题,提出一种基于词-主题-文本异质网络(WTDHN)的短文本分类方法。通过Word2vec训练词的上下文语义向量;构建词相关性矩阵以充足的词共现信息增强短文本各级别语义学;构建以词、主题和文本为节点的异质网络,并采用图卷积学习节点之间的高阶邻域信息,丰富短文本语义。相较于基准分类模型,该方法在五个公开短文本数据集上的分类准确率平均提高1.56%。 展开更多
关键词 词-主题-文本异质网络 词共现 文本-主题分布 短文本分类
下载PDF
高斯混合模型与文本图卷积网络结合的虚假评论识别算法
15
作者 王星 刘贵娟 陈志豪 《计算机应用》 CSCD 北大核心 2024年第2期360-368,共9页
针对文本图卷积网络(Text GCN)窗口边权阈值策略不足的问题,为了更精准地挖掘相关的词关联结构、提高预测精度,提出一种高斯混合模型(GMM)与Text GCN结合的虚假评论识别算法F-Text GCN。首先,利用GMM分离噪声边权分布的特性,提高虚假评... 针对文本图卷积网络(Text GCN)窗口边权阈值策略不足的问题,为了更精准地挖掘相关的词关联结构、提高预测精度,提出一种高斯混合模型(GMM)与Text GCN结合的虚假评论识别算法F-Text GCN。首先,利用GMM分离噪声边权分布的特性,提高虚假评论在训练数据上相对正常评论数不足的边信号强度;然后,考虑到信源的多样性,综合文档、词汇和评论以及非文本特征构造邻接矩阵;最后,通过Text GCN的谱分解提取邻接矩阵的虚假评论关联结构实施预测。根据国内某大型电商平台采集的126086条实际中文评论数据开展实证研究,实验结果表明,F-Text GCN识别虚假评论的F1值达到82.92%,与预训练表征模型BERT和文本卷积神经网络相比分别提升了10.46%和11.60%,相较于只使用评论文本信源的Text GCN模型F1值提升了2.94%;研究了高仿虚假评论的预测错误率,在支持向量机(SVM)作用后难识别的评论样本上尝试二次识别,F-Text GCN整体预测准确率可达94.71%,相较于Text GCN和SVM,在识别准确率上分别提升了2.91%和14.54%。研究发现,虚假评论的二阶图邻居结构显示出较强的干预消费者决策的词汇,这表明所提算法特别适用于提取用于虚假评论检测的长程词语搭配结构和全局句子特征模式变化的场景。 展开更多
关键词 高斯混合模型 虚假评论识别 文本图卷积神经网络 邻接矩阵 词汇共现网络
下载PDF
储热技术研究展望
16
作者 高海涛 明智源 赵丹 《能源与环保》 2024年第8期134-139,共6页
近年来,储热技术被广泛认为是实现碳中和、碳达峰的一项关键技术备受关注。通过从CNKI、Web of Science等数据库中筛选储热技术相关文献,运用CiteSpace软件进行知识映射,展开系统分析、统计及可视化,绘制出储热技术研究力量合作网络图谱... 近年来,储热技术被广泛认为是实现碳中和、碳达峰的一项关键技术备受关注。通过从CNKI、Web of Science等数据库中筛选储热技术相关文献,运用CiteSpace软件进行知识映射,展开系统分析、统计及可视化,绘制出储热技术研究力量合作网络图谱,展示该技术研究力量的分布与科研合作情况。同时针对关键词进行分析,总结储热技术的研究热点、研究前沿及发展趋势,指出相变储热和混合储热模式是未来研究的重点。针对储热材料稳定性差、使用寿命短,有机相变材料成本高、安全性低,系统设备初始造价高、成本回收期长等储热技术现存问题,从政策干预和市场需求角度提出了改进建议。 展开更多
关键词 储热技术 文件计量 相变储热 关键词共现 合作网络
下载PDF
基于共现网络的HSK听力词汇教学模式构建--以HSK四级听力考试为例
17
作者 管延增 《国际中文教育(中英文)》 2024年第1期22-29,共8页
本研究针对HSK听力考试的特点及当前听力词汇教学中存在的问题,构建基于词汇共现网络的教学模式。该模式下的教学过程包含三个阶段:第一阶段聚焦基础词汇教学,主要利用词义网络组织教学;第二阶段聚焦主题词汇教学;第三阶段依托学生的学... 本研究针对HSK听力考试的特点及当前听力词汇教学中存在的问题,构建基于词汇共现网络的教学模式。该模式下的教学过程包含三个阶段:第一阶段聚焦基础词汇教学,主要利用词义网络组织教学;第二阶段聚焦主题词汇教学;第三阶段依托学生的学习数据分析进行有针对性的突破性练习。采用词云图、共现网络图等可视化辅助工具,实现听觉和视觉的双通道输入以提升词汇教学效果。研究结果表明,该模式有助于学生更好地记忆词汇,能够运用在HSK听力词汇教学中并取得积极效果。 展开更多
关键词 HSK听力 词汇教学 数据挖掘 主题聚类 共现网络
下载PDF
基于知识图谱的高校党建研究热点与演变分析
18
作者 陶丹 《高教学刊》 2024年第33期84-88,共5页
基于2000—2022年1180篇CSSCI期刊文献,对我国高校党建的研究进行知识图谱探析。研究发现,新世纪以来我国高校党建的研究可划分为党对高校的领导、学生主体向度、内容向度、路径方法及新时代发展等五个序列;研究关注度持续增加,热点演... 基于2000—2022年1180篇CSSCI期刊文献,对我国高校党建的研究进行知识图谱探析。研究发现,新世纪以来我国高校党建的研究可划分为党对高校的领导、学生主体向度、内容向度、路径方法及新时代发展等五个序列;研究关注度持续增加,热点演化与党的重要会议与文件颁布紧密相关,但也存在研究主题关联度不高,未形成有效合力的问题。未来可进一步加强和完善新时代高校党建理论体系,借鉴多学科工具方法与理论资源,进一步拓展高校党建研究的深度与广度。 展开更多
关键词 高校党建 知识图谱 关键词共现 聚类网络 演变分析
下载PDF
利用网络游记分析成都市旅游景区空间结构
19
作者 张红 李玥 +1 位作者 邓雯 王艺 《地理空间信息》 2024年第7期31-35,共5页
从携程网爬取了2010—2019年成都市游记数据,构建旅游景区关键词文本共现网络,采用数量统计、空间分析、复杂网络分析等方法,挖掘成都市旅游景区游客到访的时空分异特征。结果表明,成都市旅游景区到访频率呈长尾分布,空间上形成以青城... 从携程网爬取了2010—2019年成都市游记数据,构建旅游景区关键词文本共现网络,采用数量统计、空间分析、复杂网络分析等方法,挖掘成都市旅游景区游客到访的时空分异特征。结果表明,成都市旅游景区到访频率呈长尾分布,空间上形成以青城山—都江堰、金牛—武侯主城区为高热度中心的“双核摄动”格局;成都市旅游景区文本共现网络具有较高的集聚系数和较短的平均路径,等级圈层结构和马太效应较显著,热门景区对邻近热门景区的空间溢出效应显著,但对邻近低等级景区的带动效果有限;成都市旅游景区空间结构由条带状和团簇式向轴辐式和网络化方向转变,在区域旅游一体化发展中的作用日益显著。 展开更多
关键词 旅游景区 网络游记 空间结构 文本共现 复杂网络分析
下载PDF
基于知识图谱的公路气象灾害研究
20
作者 高建平 林德炀 +3 位作者 何恩怀 杨昌凤 何云勇 孙璐 《公路交通技术》 2024年第2期1-10,共10页
为更好掌握现有公路气象灾害研究的知识结构及发展进程,收集中国知网(CNKI)核心集1992—2022年和Web of Science核心集2000—2022年收录的1840篇论文,基于CiteSpace软件,从文献分布、共现网络、聚类分析、关键词突现等方面进行分析。结... 为更好掌握现有公路气象灾害研究的知识结构及发展进程,收集中国知网(CNKI)核心集1992—2022年和Web of Science核心集2000—2022年收录的1840篇论文,基于CiteSpace软件,从文献分布、共现网络、聚类分析、关键词突现等方面进行分析。结果表明:1)随着学科不断发展,公路气象灾害领域论文年发文量总体呈增长趋势;2)公路气象灾害研究具有多学科交叉性质,研究学者来自交通、气象及地质学等相关研究机构及院校;3)国内外研究热点主要有气象灾害对交通基础设施的破坏、气象灾害对交通运行及安全的影响、气象灾害模拟及风险评估、路网监测及交通管控措施等;4)公路边坡灾害及恶劣天气对公路正常运行的影响在多时期引起国内外学者的广泛关注;5)随着研究的不断深入,公路抗灾韧性、智慧交通管控及全寿命公路气象灾害评估等方向近几年引起研究学者关注。 展开更多
关键词 交通工程 气象灾害 公路基础设施 道路安全 聚类分析 共现网络
下载PDF
上一页 1 2 33 下一页 到第
使用帮助 返回顶部