A sensor graph network is a sensor network model organized according to graph network structure.Structural unit and signal propagation of core nodes are the basic characteristics of sensor graph networks.In sensor net...A sensor graph network is a sensor network model organized according to graph network structure.Structural unit and signal propagation of core nodes are the basic characteristics of sensor graph networks.In sensor networks,network structure recognition is the basis for accurate identification and effective prediction and control of node states.Aiming at the problems of difficult global structure identification and poor interpretability in complex sensor graph networks,based on the characteristics of sensor networks,a method is proposed to firstly unitize the graph network structure and then expand the unit based on the signal transmission path of the core node.This method which builds on unit patulousness and core node signal propagation(called p-law)can rapidly and effectively achieve the global structure identification of a sensor graph network.Different from the traditional graph network structure recognition algorithms such as modularity maximization and spectral clustering,the proposed method reveals the natural evolution process and law of graph network subgroup generation.Experimental results confirm the effectiveness,accuracy and rationality of the proposed method and suggest that our method can be a new approach for graph network global structure recognition.展开更多
Aiming at the dynamics and uncertainties of natural colors affected by the natural environment,a color P-law generation model based on the natural environment is proposed to develop algorithms and to provide a theoret...Aiming at the dynamics and uncertainties of natural colors affected by the natural environment,a color P-law generation model based on the natural environment is proposed to develop algorithms and to provide a theoretical basis for plant dynamic color simulation and color sensor data transmission.Based on the HSL(Hue,Saturation,Lightness)color solid,the proposed method uses the function P-set to provide a color P-law generation model and an algorithm of the Dynamic Colors System(DCS),establishing the DCS modeling theory of the natural environment and the color P-reasoning simulation based on the HSL color solid.The experimental results show that based on the color P-law,for the DCS of the natural environment,when the external factors change,the color of the plant changes,accordingly,verifying the effectiveness of the color P-law generation model and the algorithm of the DCS.In the dynamic color intel-ligent simulation system,when external factors change,the dynamic change of plant color generally conforms to the basic laws of the natural environment.This enables the effective extraction of color data from the Internet of Things(IoT)-based color sensors and provides an effective way to significantly reduce the data transmission bandwidth of the IoT network.展开更多
The longitudinal wave term within Faraday’s law of electromagnetic induction (Faraday’s law) underwent recovery to ensure its suitability for theoretical derivation of the equation governing longitudinal electromagn...The longitudinal wave term within Faraday’s law of electromagnetic induction (Faraday’s law) underwent recovery to ensure its suitability for theoretical derivation of the equation governing longitudinal electromagnetic (LEM) waves. The revised Maxwell’s equations include the crucial parameters being the attenuation time constants of magnetic vortex potential and electric vortex potential generated by external electromagnetic field within the propagation medium. Specific expressions for them are obtained through theoretical analysis. Subsequently, a model for propagating magnetic P-wave generated by the superposition of a left-handed photo and a right-handed photon in a vacuum is formulated based on reevaluated total current law and revised Faraday’s law, covering wave equations, energy equation, as well as propagation mode involving mutual induction and conversion between scalar magnetic field and vortex electric field. Furthermore, through theoretical derivations centered around magnetic P-wave, evidence was presented regarding its ability to absorb huge free energy through the entangled interaction between zero-point vacuum energy field and the torsion field produced by the vortex electric field.展开更多
P-集合(packet sets)是一个具有动态特征的、新的数学结构与数学模型;P-集合是由内P-集合XF珚(internal packet set XF珚)与外P-集合XF(outer packet set XF)构成的集合对;或者(XF珚,XF)是P-集合。P-集合是把动态特性引入有限普通集合X(...P-集合(packet sets)是一个具有动态特征的、新的数学结构与数学模型;P-集合是由内P-集合XF珚(internal packet set XF珚)与外P-集合XF(outer packet set XF)构成的集合对;或者(XF珚,XF)是P-集合。P-集合是把动态特性引入有限普通集合X(Cantor set X)内,改进有限普通集合X得到的。P-推理(packet reasoning)是由内P-推理(internal packet reasoning)与外P-推理(outer packet reasoning)共同构成的。利用P-集合、P-推理,研究风险投资亏损发现。给出规律、内P-规律、外P-规律、P-规律及其生成;给出规律属性定理、内P-规律、外P-规律的P-推理发现;介绍内P-推理在风险投资亏损估计中的应用。展开更多
基金This research is supported by the Natural Science Foundation Project of Fujian Provincial Department of Science and Technology(Grant No.2020J01385)Digital Fujian Industrial Energy Big Data Research Institute(Grant No.KB180045)Provincial Key Laboratory of Industrial Big Data Analysis and Application(Grant No.KB180029).
文摘A sensor graph network is a sensor network model organized according to graph network structure.Structural unit and signal propagation of core nodes are the basic characteristics of sensor graph networks.In sensor networks,network structure recognition is the basis for accurate identification and effective prediction and control of node states.Aiming at the problems of difficult global structure identification and poor interpretability in complex sensor graph networks,based on the characteristics of sensor networks,a method is proposed to firstly unitize the graph network structure and then expand the unit based on the signal transmission path of the core node.This method which builds on unit patulousness and core node signal propagation(called p-law)can rapidly and effectively achieve the global structure identification of a sensor graph network.Different from the traditional graph network structure recognition algorithms such as modularity maximization and spectral clustering,the proposed method reveals the natural evolution process and law of graph network subgroup generation.Experimental results confirm the effectiveness,accuracy and rationality of the proposed method and suggest that our method can be a new approach for graph network global structure recognition.
基金funded by the Natural Science Foundation Project of Fujian Provincial Department of science and technology,Grant No.:2020J01385Digital Fujian industrial energy big data research institute,Grant No.KB180045Provincial Key Laboratory of industrial big data analysis and Application,Grant No.KB180029,Sanming City 5G Innovation Laboratory,Grant No.:2020 MK18.
文摘Aiming at the dynamics and uncertainties of natural colors affected by the natural environment,a color P-law generation model based on the natural environment is proposed to develop algorithms and to provide a theoretical basis for plant dynamic color simulation and color sensor data transmission.Based on the HSL(Hue,Saturation,Lightness)color solid,the proposed method uses the function P-set to provide a color P-law generation model and an algorithm of the Dynamic Colors System(DCS),establishing the DCS modeling theory of the natural environment and the color P-reasoning simulation based on the HSL color solid.The experimental results show that based on the color P-law,for the DCS of the natural environment,when the external factors change,the color of the plant changes,accordingly,verifying the effectiveness of the color P-law generation model and the algorithm of the DCS.In the dynamic color intel-ligent simulation system,when external factors change,the dynamic change of plant color generally conforms to the basic laws of the natural environment.This enables the effective extraction of color data from the Internet of Things(IoT)-based color sensors and provides an effective way to significantly reduce the data transmission bandwidth of the IoT network.
文摘The longitudinal wave term within Faraday’s law of electromagnetic induction (Faraday’s law) underwent recovery to ensure its suitability for theoretical derivation of the equation governing longitudinal electromagnetic (LEM) waves. The revised Maxwell’s equations include the crucial parameters being the attenuation time constants of magnetic vortex potential and electric vortex potential generated by external electromagnetic field within the propagation medium. Specific expressions for them are obtained through theoretical analysis. Subsequently, a model for propagating magnetic P-wave generated by the superposition of a left-handed photo and a right-handed photon in a vacuum is formulated based on reevaluated total current law and revised Faraday’s law, covering wave equations, energy equation, as well as propagation mode involving mutual induction and conversion between scalar magnetic field and vortex electric field. Furthermore, through theoretical derivations centered around magnetic P-wave, evidence was presented regarding its ability to absorb huge free energy through the entangled interaction between zero-point vacuum energy field and the torsion field produced by the vortex electric field.
文摘P-集合(packet sets)是一个具有动态特征的、新的数学结构与数学模型;P-集合是由内P-集合XF珚(internal packet set XF珚)与外P-集合XF(outer packet set XF)构成的集合对;或者(XF珚,XF)是P-集合。P-集合是把动态特性引入有限普通集合X(Cantor set X)内,改进有限普通集合X得到的。P-推理(packet reasoning)是由内P-推理(internal packet reasoning)与外P-推理(outer packet reasoning)共同构成的。利用P-集合、P-推理,研究风险投资亏损发现。给出规律、内P-规律、外P-规律、P-规律及其生成;给出规律属性定理、内P-规律、外P-规律的P-推理发现;介绍内P-推理在风险投资亏损估计中的应用。
