Complex network modeling characterizes system relationships and structures,while network visualization enables intuitive analysis and interpretation of these patterns.However,existing network visualization tools exhib...Complex network modeling characterizes system relationships and structures,while network visualization enables intuitive analysis and interpretation of these patterns.However,existing network visualization tools exhibit significant limitations in representing attributes of complex networks at various scales,particularly failing to provide advanced visual representations of specific nodes and edges,community affiliation attribution,and global scalability.These limitations substantially impede the intuitive analysis and interpretation of complex network patterns through visual representation.To address these limitations,we propose SFFSlib,a multi-scale network visualization framework incorporating novel methods to highlight attribute representation in diverse network scenarios and optimize structural feature visualization.Notably,we have enhanced the visualization of pivotal details at different scales across diverse network scenarios.The visualization algorithms proposed within SFFSlib were applied to real-world datasets and benchmarked against conventional layout algorithms.The experimental results reveal that SFFSlib significantly enhances the clarity of visualizations across different scales,offering a practical solution for the advancement of network attribute representation and the overall enhancement of visualization quality.展开更多
In IBVS (image based visual servoing), the error signal in image space should be transformed into the control signal in the input space quickly. To avoid the iterative adjustment and complicated inverse solution of im...In IBVS (image based visual servoing), the error signal in image space should be transformed into the control signal in the input space quickly. To avoid the iterative adjustment and complicated inverse solution of image Jacobian, CMAC (cerebellar model articulation controller) neural network is inserted into visual servo control loop to implement the nonlinear mapping. Two control schemes are used. Simulation results on two schemes are provided, which show a better tracking precision and stability can be achieved using scheme 2.展开更多
In the field of bioinformatics, the size of a biological network is usually very big. Without any help, it’s extremely hard to analyze the network. If it is shown as a visualized picture, things will be easier. So it...In the field of bioinformatics, the size of a biological network is usually very big. Without any help, it’s extremely hard to analyze the network. If it is shown as a visualized picture, things will be easier. So it’s very important to convert the biological network into a picture. However, there are a lot of software tools to be used to visualize the network. They use different file formats and do not support the transfer from one format to another. Sometimes it’s really hard to deal with it. So I analyzed three text file formats of them (“.dl”, “.net” and “.vna”) and developed a program to do this work automatically. The result of execution is very well and the efficiency is also impressive.展开更多
Most sensors or cameras discussed in the sensor network community are usually 3D homogeneous, even though their2 D coverage areas in the ground plane are heterogeneous. Meanwhile, observed objects of camera networks a...Most sensors or cameras discussed in the sensor network community are usually 3D homogeneous, even though their2 D coverage areas in the ground plane are heterogeneous. Meanwhile, observed objects of camera networks are usually simplified as 2D points in previous literature. However in actual application scenes, not only cameras are always heterogeneous with different height and action radiuses, but also the observed objects are with 3D features（i.e., height）. This paper presents a sensor planning formulation addressing the efficiency enhancement of visual tracking in 3D heterogeneous camera networks that track and detect people traversing a region. The problem of sensor planning consists of three issues：（i） how to model the 3D heterogeneous cameras;（ii） how to rank the visibility, which ensures that the object of interest is visible in a camera＇s field of view;（iii） how to reconfigure the 3D viewing orientations of the cameras. This paper studies the geometric properties of 3D heterogeneous camera networks and addresses an evaluation formulation to rank the visibility of observed objects. Then a sensor planning method is proposed to improve the efficiency of visual tracking. Finally, the numerical results show that the proposed method can improve the tracking performance of the system compared to the conventional strategies.展开更多
Pedestrian detection is one of the most important problems in the visual sensor network. Considering that the visual sensors have limited cap ability, we propose a pedestrian detection method with low energy consumpti...Pedestrian detection is one of the most important problems in the visual sensor network. Considering that the visual sensors have limited cap ability, we propose a pedestrian detection method with low energy consumption. Our method contains two parts: one is an Enhanced Self-Organizing Background Subtraction (ESOBS) based foreground segmentation module to obtain active areas in the observed region from the visual sensors; the other is an appearance model based detection module to detect the pedestrians from the foreground areas. Moreover, we create our own large pedestrian dataset according to the specific scene in the visual sensor network. Numerous experiments are conducted in both indoor and outdoor specific scenes. The experimental results show that our method is effective.展开更多
As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static images.Data from the camera is sent to energy efficient sink to extract ke...As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static images.Data from the camera is sent to energy efficient sink to extract key-information out of it.VWSN applications range from health care monitoring to military surveillance.In a network with VWSN,there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy,memory and I/O resources.In this case,Mobile Sinks(MS)can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head(CH),it also collects data from nearby nodes as well.The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system.However,making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account.We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe,learn and understand things from manual perspective.Proposed architecture is designed based on Mamdani’s fuzzy model.Following parameters are derived based on the model residual energy,node centrality,distance between the sink and current position,node centrality,node density,node history,and mobility of sink as input variables for decision making in CH selection.The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN.The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm(GA)and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules.Genetic algorithmbased machine learning optimizes the interpretability aspect of fuzzy system.Simulation results are obtained using MATLAB.The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy(LEACH)and LEACHExpected Residual Energy(LEACH-ERE).展开更多
Wireless visual sensor network (VSN) can be said to be a special class of wireless sensor network (WSN) with smart-cameras. Due to its visual sensing capability, it has become an effective tool for applications such a...Wireless visual sensor network (VSN) can be said to be a special class of wireless sensor network (WSN) with smart-cameras. Due to its visual sensing capability, it has become an effective tool for applications such as large area surveillance, environmental monitoring and objects tracking. Different from a conventional WSN, VSN typically includes relatively expensive camera sensors, enhanced flash memory and a powerful CPU. While energy consumption is dominated primarily by data transmission and reception, VSN consumes extra power onimage sensing, processing and storing operations. The well-known energy-hole problem of WSNs has a drastic impact on the lifetime of VSN, because of the additional energy consumption of a VSN. Most prior research on VSN energy issues are primarily focusedon a single device or a given specific scenario. In this paper, we propose a novel optimal two-tier deployment strategy for a large scale VSN. Our two-tier VSN architecture includes tier-1 sensing network with visual sensor nodes (VNs) and tier-2 network having only relay nodes (RNs). While sensing network mainly performs image data collection, relay network only for wards image data packets to the central sink node. We use uniform random distribution of VNs to minimize the cost of VSN and RNs are deployed following two dimensional Gaussian distribution so as to avoid energy-hole problem. Algorithms are also introduced that optimizes deployment parameters and are shown to enhance the lifetime of the VSN in a cost effective manner.展开更多
Political discussions are characterized by conflicts of interest, and decisions are made based on negotiations. In general, participants need to reinforce their opinions and influence other participants. In this conte...Political discussions are characterized by conflicts of interest, and decisions are made based on negotiations. In general, participants need to reinforce their opinions and influence other participants. In this context, it is important to know how allies and opponents are positioned, in order to understand the discussion dynamics and plan adequate actions. This paper suggests the use of social network visualizations to explicit oppositions and alliances in order to support the understanding and following of political discussions. A system which supports these visualizations was built. An experiment performed to test the proposed visualizations showed to which extent they can be more efficient in identifying information about clashes and alliances than an online discussion system can.展开更多
Recently a ubiquitous sensor network which collects our environmental information gets increasingly popular, a visualization application is necessary for users to manage complicated wireless networks, however, these a...Recently a ubiquitous sensor network which collects our environmental information gets increasingly popular, a visualization application is necessary for users to manage complicated wireless networks, however, these applications are developed individually for wireless communication standard or a type of wireless device. Therefore, users are forced to adopt and use the application individually according to the target of the wireless network. In this paper, we propose a visualization platform for wireless network environments using augmented reality technology, and evaluate the effectiveness of the platform. From the result of the evaluation, we have confirmed the proposed platform has availability for visualization and management of wireless networks.展开更多
A new visual servo control scheme for a robotic manipulator is presented in this paper, where a back propagation （BP） neural network is used to make a direct transition from image feature to joint angles without req...A new visual servo control scheme for a robotic manipulator is presented in this paper, where a back propagation （BP） neural network is used to make a direct transition from image feature to joint angles without requiring robot kinematics and camera calibration. To speed up the convergence and avoid local minimum of the neural network, this paper uses a genetic algorithm to find the optimal initial weights and thresholds and then uses the BP Mgorithm to train the neural network according to the data given. The proposed method can effectively combine the good global searching ability of genetic algorithms with the accurate local searching feature of BP neural network. The Simulink model for PUMA560 robot visual servo system based on the improved BP neural network is built with the Robotics Toolbox of Matlab. The simulation results indicate that the proposed method can accelerate convergence of the image errors and provide a simple and effective way of robot control.展开更多
The advent of the time of big data along with social networks makes the visualization and analysis of networks information become increasingly important in many fields. Based on the information from social networks, t...The advent of the time of big data along with social networks makes the visualization and analysis of networks information become increasingly important in many fields. Based on the information from social networks, the idea of information visualization and development of tools are presented. Popular social network micro-blog （＇Weibo＇） is chosen to realize the process of users＇ interest and communications data analysis. User interest visualization methods are discussed and chosen and programs are developed to collect users＇ interest and describe it by graph. The visualization results may be used to provide the commercial recommendation or social investigation application for decision makers.展开更多
茶多酚是一类从茶叶中提取的多酚类物质的总称,在医药、食品和农业等领域具有广阔的应用前景。基于CNKI和WOS数据库,采用文献计量学方法,应用CiteSpace和VOSviewer软件对1953-2023年茶多酚研究论文进行数据挖掘和定量分析。结果表明:①7...茶多酚是一类从茶叶中提取的多酚类物质的总称,在医药、食品和农业等领域具有广阔的应用前景。基于CNKI和WOS数据库,采用文献计量学方法,应用CiteSpace和VOSviewer软件对1953-2023年茶多酚研究论文进行数据挖掘和定量分析。结果表明:①70年来,CNKI和WOS数据库中茶多酚研究论文数量均呈逐渐增长趋势。CNKI数据库中,论文主要发表在《福建茶叶》《食品工业科技》《食品科学》等期刊上,轻工业手工业学科的发文最多,基金资助以国家自然科学基金、国家科技支撑计划、国家重点研发计划等为主;WOS数据库中,论文主要发表在Journal of Agricultural and Food Chemistry、Food Chemistry、International Journal of Biological Macromolecules等期刊上,属于Food Science Technology学科的发文最多,基金资助以National Natural Science Foundation of China、United States Department of Health and Human Services、National Institutes of Health等为主;②从国家间的合作来看,中国发文量最大,同United States以及Philippines、Sri Lanka、Singapore等亚洲国家合作研究紧密,与India合作较少;③CNKI数据库中,浙江大学、江南大学、西南大学等是主要的研究机构,杨贤强、王岳飞、沈生荣等团队是国内主要的研究团队,国内研究涉及茶多酚的提取、抗氧化特性及其药理活性等方面;WOS数据库中,Rutgers University Systerm、Chinese Academy of Sciences、Zhejiang University等是主要的研究机构,Hara Y和Mukhtar H是主要的研究团队,国际研究更加关注茶多酚在医疗健康领域中的应用;④CNKI数据库中,“抗氧化”“壳聚糖”“保鲜”等是出现频率较高的关键词,茶多酚在食品领域中的应用是国内研究的前沿领域;WOS数据库中,关键词“catechins”“epigallocatechin gallate”“inhibition”等出现频率较高,国际研究前沿聚焦茶多酚与人体健康间的互作关系。通过文献综合分析可以帮助科研人员掌握该领域的研究现状和热点动态,明确未来研究方向。展开更多
首次利用CiteSpace文献计量分析软件对Web of Science中2003—2023年高温结构材料用Laves相研究文献的关键词、文献共被引、作者和期刊共被引进行了定量和可视化网络图谱分析。关键词聚类时间线图反映了检索区间内Laves相研究的基本发...首次利用CiteSpace文献计量分析软件对Web of Science中2003—2023年高温结构材料用Laves相研究文献的关键词、文献共被引、作者和期刊共被引进行了定量和可视化网络图谱分析。关键词聚类时间线图反映了检索区间内Laves相研究的基本发展状况,揭示两大热点前沿主题分别是Laves相增强的多相高熵合金的室温脆性改善或强度-塑性协同提升问题,以及Laves相析出物对600~650℃蠕变服役条件下P92、12Cr和G115等新型耐热合金钢组织与性能的影响。关键词聚类时间线图与文献、作者共被引网络图具备一致性,共同反映Laves相研究的活跃性。未来,应关注可视化图谱挖掘的高质量成果产出的关键作者和期刊。展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.61773091 and 62476045)the LiaoNing Revitalization Talents Program(Grant No.XLYC1807106)the Program for the Outstanding Innovative Teams of Higher Learning Institutions of Liaoning(Grant No.LR2016070).
文摘Complex network modeling characterizes system relationships and structures,while network visualization enables intuitive analysis and interpretation of these patterns.However,existing network visualization tools exhibit significant limitations in representing attributes of complex networks at various scales,particularly failing to provide advanced visual representations of specific nodes and edges,community affiliation attribution,and global scalability.These limitations substantially impede the intuitive analysis and interpretation of complex network patterns through visual representation.To address these limitations,we propose SFFSlib,a multi-scale network visualization framework incorporating novel methods to highlight attribute representation in diverse network scenarios and optimize structural feature visualization.Notably,we have enhanced the visualization of pivotal details at different scales across diverse network scenarios.The visualization algorithms proposed within SFFSlib were applied to real-world datasets and benchmarked against conventional layout algorithms.The experimental results reveal that SFFSlib significantly enhances the clarity of visualizations across different scales,offering a practical solution for the advancement of network attribute representation and the overall enhancement of visualization quality.
基金This project is supported by National Natural Science Foundation of China (No.59990470).
文摘In IBVS (image based visual servoing), the error signal in image space should be transformed into the control signal in the input space quickly. To avoid the iterative adjustment and complicated inverse solution of image Jacobian, CMAC (cerebellar model articulation controller) neural network is inserted into visual servo control loop to implement the nonlinear mapping. Two control schemes are used. Simulation results on two schemes are provided, which show a better tracking precision and stability can be achieved using scheme 2.
文摘In the field of bioinformatics, the size of a biological network is usually very big. Without any help, it’s extremely hard to analyze the network. If it is shown as a visualized picture, things will be easier. So it’s very important to convert the biological network into a picture. However, there are a lot of software tools to be used to visualize the network. They use different file formats and do not support the transfer from one format to another. Sometimes it’s really hard to deal with it. So I analyzed three text file formats of them (“.dl”, “.net” and “.vna”) and developed a program to do this work automatically. The result of execution is very well and the efficiency is also impressive.
基金supported by the National Natural Science Foundationof China(61100207)the National Key Technology Research and Development Program of the Ministry of Science and Technology of China(2014BAK14B03)+1 种基金the Fundamental Research Funds for the Central Universities(2013PT132013XZ12)
文摘Most sensors or cameras discussed in the sensor network community are usually 3D homogeneous, even though their2 D coverage areas in the ground plane are heterogeneous. Meanwhile, observed objects of camera networks are usually simplified as 2D points in previous literature. However in actual application scenes, not only cameras are always heterogeneous with different height and action radiuses, but also the observed objects are with 3D features（i.e., height）. This paper presents a sensor planning formulation addressing the efficiency enhancement of visual tracking in 3D heterogeneous camera networks that track and detect people traversing a region. The problem of sensor planning consists of three issues：（i） how to model the 3D heterogeneous cameras;（ii） how to rank the visibility, which ensures that the object of interest is visible in a camera＇s field of view;（iii） how to reconfigure the 3D viewing orientations of the cameras. This paper studies the geometric properties of 3D heterogeneous camera networks and addresses an evaluation formulation to rank the visibility of observed objects. Then a sensor planning method is proposed to improve the efficiency of visual tracking. Finally, the numerical results show that the proposed method can improve the tracking performance of the system compared to the conventional strategies.
基金This paper was supported partially by the Natural Science Foundation of China under Grants No. 60833009, No. 61003280 the National Science Fund for Distinguished Young Scholars under Grant No. 60925010+1 种基金 the Funds for Creative Research Groups of China under Grant No.61121001 the Pro- gram for Changjiang Scholars and Innovative Research Team in University under Grant No. IRT1049.
文摘Pedestrian detection is one of the most important problems in the visual sensor network. Considering that the visual sensors have limited cap ability, we propose a pedestrian detection method with low energy consumption. Our method contains two parts: one is an Enhanced Self-Organizing Background Subtraction (ESOBS) based foreground segmentation module to obtain active areas in the observed region from the visual sensors; the other is an appearance model based detection module to detect the pedestrians from the foreground areas. Moreover, we create our own large pedestrian dataset according to the specific scene in the visual sensor network. Numerous experiments are conducted in both indoor and outdoor specific scenes. The experimental results show that our method is effective.
基金Dr.Deepak Dahiya would like to thank Deanship of Scientific Research at Majmaah University for supporting his work under Project No.(R-2022-96)。
文摘As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static images.Data from the camera is sent to energy efficient sink to extract key-information out of it.VWSN applications range from health care monitoring to military surveillance.In a network with VWSN,there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy,memory and I/O resources.In this case,Mobile Sinks(MS)can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head(CH),it also collects data from nearby nodes as well.The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system.However,making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account.We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe,learn and understand things from manual perspective.Proposed architecture is designed based on Mamdani’s fuzzy model.Following parameters are derived based on the model residual energy,node centrality,distance between the sink and current position,node centrality,node density,node history,and mobility of sink as input variables for decision making in CH selection.The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN.The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm(GA)and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules.Genetic algorithmbased machine learning optimizes the interpretability aspect of fuzzy system.Simulation results are obtained using MATLAB.The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy(LEACH)and LEACHExpected Residual Energy(LEACH-ERE).
文摘Wireless visual sensor network (VSN) can be said to be a special class of wireless sensor network (WSN) with smart-cameras. Due to its visual sensing capability, it has become an effective tool for applications such as large area surveillance, environmental monitoring and objects tracking. Different from a conventional WSN, VSN typically includes relatively expensive camera sensors, enhanced flash memory and a powerful CPU. While energy consumption is dominated primarily by data transmission and reception, VSN consumes extra power onimage sensing, processing and storing operations. The well-known energy-hole problem of WSNs has a drastic impact on the lifetime of VSN, because of the additional energy consumption of a VSN. Most prior research on VSN energy issues are primarily focusedon a single device or a given specific scenario. In this paper, we propose a novel optimal two-tier deployment strategy for a large scale VSN. Our two-tier VSN architecture includes tier-1 sensing network with visual sensor nodes (VNs) and tier-2 network having only relay nodes (RNs). While sensing network mainly performs image data collection, relay network only for wards image data packets to the central sink node. We use uniform random distribution of VNs to minimize the cost of VSN and RNs are deployed following two dimensional Gaussian distribution so as to avoid energy-hole problem. Algorithms are also introduced that optimizes deployment parameters and are shown to enhance the lifetime of the VSN in a cost effective manner.
文摘Political discussions are characterized by conflicts of interest, and decisions are made based on negotiations. In general, participants need to reinforce their opinions and influence other participants. In this context, it is important to know how allies and opponents are positioned, in order to understand the discussion dynamics and plan adequate actions. This paper suggests the use of social network visualizations to explicit oppositions and alliances in order to support the understanding and following of political discussions. A system which supports these visualizations was built. An experiment performed to test the proposed visualizations showed to which extent they can be more efficient in identifying information about clashes and alliances than an online discussion system can.
文摘Recently a ubiquitous sensor network which collects our environmental information gets increasingly popular, a visualization application is necessary for users to manage complicated wireless networks, however, these applications are developed individually for wireless communication standard or a type of wireless device. Therefore, users are forced to adopt and use the application individually according to the target of the wireless network. In this paper, we propose a visualization platform for wireless network environments using augmented reality technology, and evaluate the effectiveness of the platform. From the result of the evaluation, we have confirmed the proposed platform has availability for visualization and management of wireless networks.
文摘A new visual servo control scheme for a robotic manipulator is presented in this paper, where a back propagation （BP） neural network is used to make a direct transition from image feature to joint angles without requiring robot kinematics and camera calibration. To speed up the convergence and avoid local minimum of the neural network, this paper uses a genetic algorithm to find the optimal initial weights and thresholds and then uses the BP Mgorithm to train the neural network according to the data given. The proposed method can effectively combine the good global searching ability of genetic algorithms with the accurate local searching feature of BP neural network. The Simulink model for PUMA560 robot visual servo system based on the improved BP neural network is built with the Robotics Toolbox of Matlab. The simulation results indicate that the proposed method can accelerate convergence of the image errors and provide a simple and effective way of robot control.
文摘The advent of the time of big data along with social networks makes the visualization and analysis of networks information become increasingly important in many fields. Based on the information from social networks, the idea of information visualization and development of tools are presented. Popular social network micro-blog （＇Weibo＇） is chosen to realize the process of users＇ interest and communications data analysis. User interest visualization methods are discussed and chosen and programs are developed to collect users＇ interest and describe it by graph. The visualization results may be used to provide the commercial recommendation or social investigation application for decision makers.
文摘茶多酚是一类从茶叶中提取的多酚类物质的总称,在医药、食品和农业等领域具有广阔的应用前景。基于CNKI和WOS数据库,采用文献计量学方法,应用CiteSpace和VOSviewer软件对1953-2023年茶多酚研究论文进行数据挖掘和定量分析。结果表明:①70年来,CNKI和WOS数据库中茶多酚研究论文数量均呈逐渐增长趋势。CNKI数据库中,论文主要发表在《福建茶叶》《食品工业科技》《食品科学》等期刊上,轻工业手工业学科的发文最多,基金资助以国家自然科学基金、国家科技支撑计划、国家重点研发计划等为主;WOS数据库中,论文主要发表在Journal of Agricultural and Food Chemistry、Food Chemistry、International Journal of Biological Macromolecules等期刊上,属于Food Science Technology学科的发文最多,基金资助以National Natural Science Foundation of China、United States Department of Health and Human Services、National Institutes of Health等为主;②从国家间的合作来看,中国发文量最大,同United States以及Philippines、Sri Lanka、Singapore等亚洲国家合作研究紧密,与India合作较少;③CNKI数据库中,浙江大学、江南大学、西南大学等是主要的研究机构,杨贤强、王岳飞、沈生荣等团队是国内主要的研究团队,国内研究涉及茶多酚的提取、抗氧化特性及其药理活性等方面;WOS数据库中,Rutgers University Systerm、Chinese Academy of Sciences、Zhejiang University等是主要的研究机构,Hara Y和Mukhtar H是主要的研究团队,国际研究更加关注茶多酚在医疗健康领域中的应用;④CNKI数据库中,“抗氧化”“壳聚糖”“保鲜”等是出现频率较高的关键词,茶多酚在食品领域中的应用是国内研究的前沿领域;WOS数据库中,关键词“catechins”“epigallocatechin gallate”“inhibition”等出现频率较高,国际研究前沿聚焦茶多酚与人体健康间的互作关系。通过文献综合分析可以帮助科研人员掌握该领域的研究现状和热点动态,明确未来研究方向。
文摘首次利用CiteSpace文献计量分析软件对Web of Science中2003—2023年高温结构材料用Laves相研究文献的关键词、文献共被引、作者和期刊共被引进行了定量和可视化网络图谱分析。关键词聚类时间线图反映了检索区间内Laves相研究的基本发展状况,揭示两大热点前沿主题分别是Laves相增强的多相高熵合金的室温脆性改善或强度-塑性协同提升问题,以及Laves相析出物对600~650℃蠕变服役条件下P92、12Cr和G115等新型耐热合金钢组织与性能的影响。关键词聚类时间线图与文献、作者共被引网络图具备一致性,共同反映Laves相研究的活跃性。未来,应关注可视化图谱挖掘的高质量成果产出的关键作者和期刊。
