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
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
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
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
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
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
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
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
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
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
基金Supported by Science and Technology Project of China National Tobacco Corporation(2009YN005&2010YN18&2010YN19)
文摘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.
基金the National Natural Science Foundation of China Grant 71673131 for financial support
文摘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.
文摘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.
基金supported by the National Social Science Foundation of China (09BYY024 and 11&ZD188)
文摘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.
文摘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.
文摘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.
文摘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.
文摘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.
文摘近年来,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。
文摘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。