The Peer-to-Peer(P2P)network traffic identification technology includes Transport Layer Identification(TLI)and Deep Packet Inspection(DPI)methods.By analyzing packets of the transport layer and the traffic characteris...The Peer-to-Peer(P2P)network traffic identification technology includes Transport Layer Identification(TLI)and Deep Packet Inspection(DPI)methods.By analyzing packets of the transport layer and the traffic characteristic in the P2P system,TLI can identify whether or not the network data flow belongs to the P2P system.The DPI method adopts protocol analysis technology and reverting technology.It picks up data from the P2P application layer and analyzes the characteristics of the payload to judge if the network traffic belongs to P2P applications.Due to its accuracy,robustness and classifying ability,DPI is the main method used to identify P2P traffic.Adopting the advantages of TLI and DPI,a precise and efficient technology for P2P network traffic identification can be designed.展开更多
This paper presents the method named acoustic holography which can be used to identify noise sources. A new formula of holography reconstruction is obtained, based on the Kirchhoff integral formula. Some simulating te...This paper presents the method named acoustic holography which can be used to identify noise sources. A new formula of holography reconstruction is obtained, based on the Kirchhoff integral formula. Some simulating tests are carried out using the new formula. The comparison with other reconstruction formulas proves that the new formula is more effective. By using acoustic holography method, some interesting results about the noise of a vehicle are shown. The results proves that acoustic holography is an effective method for the identification of the complex noise sources.展开更多
In order to support scientific evaluation and decision-making,this paper characterizes and identifies core researchers and their research topics by modeling the portrait of core researchers in technical fields.In this...In order to support scientific evaluation and decision-making,this paper characterizes and identifies core researchers and their research topics by modeling the portrait of core researchers in technical fields.In this paper,we constructed a portrait model of core researchers,explained the information attributes and their label data in each dimension of the portrait.Selected six identification indicators that can be measured,used the CRITIC method and gray correlation method to determine the weight coefficient and comprehensive score of the index,and designed quantitative identification methods for core researchers using Price's calculation formula and golden section coefficient.Took the field of artificial intelligence as an example for empirical analysis and result verification,selected representative core researchers to track the changes of research topics at the two levels of their teams and individuals,and characterize them based on the portrait model.展开更多
Objective To systematically characterize the developmental trajectory and interdisciplinary integration of intelligent diagnosis in traditional Chinese medicine(TCM)through quantitative topic evolution analysis,we add...Objective To systematically characterize the developmental trajectory and interdisciplinary integration of intelligent diagnosis in traditional Chinese medicine(TCM)through quantitative topic evolution analysis,we addressed the fragmentation of existing research and clarified the long-term research structure and evolutionary patterns of the field.Methods A topic evolution analysis was performed on Chinese-language literature pertaining to intelligent diagnosis in TCM.Publications were retrieved from the China National Knowledge Infrastructure(CNKI),Wanfang Data,and China Science and Technology Journal Database(VIP),covering the period from database inception to July 3,2025.A hybrid segmentation approach,based on cumulative publication growth trends and inflection point detection,was applied to divide the research timeline into distinct stages.Subsequently,the latent Dirichlet allocation(LDA)model was used to extract research topics,followed by alignment and evolutionary analysis of topics across different stages.Results A total of 3919 publications published between 2003 and 2025 were included,and the research trajectory was divided into five stages based on data-driven breakpoint detection.The field exhibited a clear evolutionary shift from early rule-based systems and tonguepulse image and signal analysis(2006–2010),to machine-learning-based syndrome and prescription modeling(2011–2015),followed by deep-learning-driven pattern recognition and formula association(2016–2020).Since 2021,research has increasingly emphasized knowledge-graph construction,multimodal integration,and intelligent clinical decision-support systems,with recent studies(2024–2025)showing the emergence of large language models and agent-based diagnostic frameworks.Topic evolution analysis further revealed sustained cross-stage continuity in syndrome modeling and prescription association analysis,alongside the progressive consolidation of integrated intelligent diagnostic platforms.Conclusion By identifying key technological transitions and persistent core research themes,our findings offer a structured reference framework for the design of intelligent diagnostic systems,the construction of knowledge-driven clinical decision-support tools,and the alignment of AI models with TCM diagnostic logic.Importantly,the stage-based evolutionary insights derived from this analysis can inform future methodological choices,improve model interpretability and clinical applicability,and support the translation of intelligent TCM diagnosis from experimental research to real-world clinical practice.展开更多
基金This work was funded by the National Natural Science Foundation of China under Grant60473090.
文摘The Peer-to-Peer(P2P)network traffic identification technology includes Transport Layer Identification(TLI)and Deep Packet Inspection(DPI)methods.By analyzing packets of the transport layer and the traffic characteristic in the P2P system,TLI can identify whether or not the network data flow belongs to the P2P system.The DPI method adopts protocol analysis technology and reverting technology.It picks up data from the P2P application layer and analyzes the characteristics of the payload to judge if the network traffic belongs to P2P applications.Due to its accuracy,robustness and classifying ability,DPI is the main method used to identify P2P traffic.Adopting the advantages of TLI and DPI,a precise and efficient technology for P2P network traffic identification can be designed.
基金This work wassupportedby the Natural Science Foundation of China(No.59775020).
文摘This paper presents the method named acoustic holography which can be used to identify noise sources. A new formula of holography reconstruction is obtained, based on the Kirchhoff integral formula. Some simulating tests are carried out using the new formula. The comparison with other reconstruction formulas proves that the new formula is more effective. By using acoustic holography method, some interesting results about the noise of a vehicle are shown. The results proves that acoustic holography is an effective method for the identification of the complex noise sources.
基金support from the National Social Science Fund of China(16ZDA224)。
文摘In order to support scientific evaluation and decision-making,this paper characterizes and identifies core researchers and their research topics by modeling the portrait of core researchers in technical fields.In this paper,we constructed a portrait model of core researchers,explained the information attributes and their label data in each dimension of the portrait.Selected six identification indicators that can be measured,used the CRITIC method and gray correlation method to determine the weight coefficient and comprehensive score of the index,and designed quantitative identification methods for core researchers using Price's calculation formula and golden section coefficient.Took the field of artificial intelligence as an example for empirical analysis and result verification,selected representative core researchers to track the changes of research topics at the two levels of their teams and individuals,and characterize them based on the portrait model.
基金Grants of National Natural Science Foundation of China(82274685).
文摘Objective To systematically characterize the developmental trajectory and interdisciplinary integration of intelligent diagnosis in traditional Chinese medicine(TCM)through quantitative topic evolution analysis,we addressed the fragmentation of existing research and clarified the long-term research structure and evolutionary patterns of the field.Methods A topic evolution analysis was performed on Chinese-language literature pertaining to intelligent diagnosis in TCM.Publications were retrieved from the China National Knowledge Infrastructure(CNKI),Wanfang Data,and China Science and Technology Journal Database(VIP),covering the period from database inception to July 3,2025.A hybrid segmentation approach,based on cumulative publication growth trends and inflection point detection,was applied to divide the research timeline into distinct stages.Subsequently,the latent Dirichlet allocation(LDA)model was used to extract research topics,followed by alignment and evolutionary analysis of topics across different stages.Results A total of 3919 publications published between 2003 and 2025 were included,and the research trajectory was divided into five stages based on data-driven breakpoint detection.The field exhibited a clear evolutionary shift from early rule-based systems and tonguepulse image and signal analysis(2006–2010),to machine-learning-based syndrome and prescription modeling(2011–2015),followed by deep-learning-driven pattern recognition and formula association(2016–2020).Since 2021,research has increasingly emphasized knowledge-graph construction,multimodal integration,and intelligent clinical decision-support systems,with recent studies(2024–2025)showing the emergence of large language models and agent-based diagnostic frameworks.Topic evolution analysis further revealed sustained cross-stage continuity in syndrome modeling and prescription association analysis,alongside the progressive consolidation of integrated intelligent diagnostic platforms.Conclusion By identifying key technological transitions and persistent core research themes,our findings offer a structured reference framework for the design of intelligent diagnostic systems,the construction of knowledge-driven clinical decision-support tools,and the alignment of AI models with TCM diagnostic logic.Importantly,the stage-based evolutionary insights derived from this analysis can inform future methodological choices,improve model interpretability and clinical applicability,and support the translation of intelligent TCM diagnosis from experimental research to real-world clinical practice.
