The existence of time delay in complex industrial processes or dynamical systems is a common phenomenon and is a difficult problem to deal with in industrial control systems,as well as in the textile field.Accurate id...The existence of time delay in complex industrial processes or dynamical systems is a common phenomenon and is a difficult problem to deal with in industrial control systems,as well as in the textile field.Accurate identification of the time delay can greatly improve the efficiency of the design of industrial process control systems.The time delay identification methods based on mathematical modeling require prior knowledge of the structural information of the model,especially for nonlinear systems.The neural network-based identification method can predict the time delay of the system,but cannot accurately obtain the specific parameters of the time delay.Benefit from the interpretability of machine learning,a novel method for delay identification based on an interpretable regression decision tree is proposed.Utilizing the self-explanatory analysis of the decision tree model,the parameters with the highest feature importance are obtained to identify the time delay of the system.Excellent results are gained by the simulation data of linear and nonlinear control systems,and the time delay of the systems can be accurately identified.展开更多
Free of noble-metal and high in unit internal quantum efficiency of electroluminescence,organic molecules with thermally activated delayed fluorescence(TADF)features pose the potential to substitute metal-based phosph...Free of noble-metal and high in unit internal quantum efficiency of electroluminescence,organic molecules with thermally activated delayed fluorescence(TADF)features pose the potential to substitute metal-based phosphorescence materials and serve as the new-generation emitters for the mass production of organic light emitting diodes(OLEDs)display.Predicting the function of TADF emitters beyond classic chemical synthesis and material characterization experiments remains a great challenge.The advances in deep learning(DL)based artificial intelligence(Al)offer an exciting opportunity for screening high-performance TADF materials through efficiency evaluation.However,data-driven material screening approaches with the capacity to access the excited state properties of TADF emitters remain extremely difficult and largely unaddressed.nspired by the fundamental principle that the excited state properties of TADF molecules are strongly dependent on their D-A geometric and electronic structures,we developed the Electronic Structure-lnfused Network(ESIN)for TADF emitter screening.Designed with capacities of accurate prediction of the photoluminescence quantum yields(PLQYs)of TADF molecules based on elemental molecular geometry and orbital information and integrated with frontier molecular orbitals(FMOs)weightbased representation and modeling features,ESIN is a promising interpretable tool for emission efficiency evaluation and moleculardesign of TADF emitters.展开更多
基金Shanghai Philosophy and Social Science Program,China(No.2019BGL004)。
文摘The existence of time delay in complex industrial processes or dynamical systems is a common phenomenon and is a difficult problem to deal with in industrial control systems,as well as in the textile field.Accurate identification of the time delay can greatly improve the efficiency of the design of industrial process control systems.The time delay identification methods based on mathematical modeling require prior knowledge of the structural information of the model,especially for nonlinear systems.The neural network-based identification method can predict the time delay of the system,but cannot accurately obtain the specific parameters of the time delay.Benefit from the interpretability of machine learning,a novel method for delay identification based on an interpretable regression decision tree is proposed.Utilizing the self-explanatory analysis of the decision tree model,the parameters with the highest feature importance are obtained to identify the time delay of the system.Excellent results are gained by the simulation data of linear and nonlinear control systems,and the time delay of the systems can be accurately identified.
基金supported by the National Natural Science Foundation of China(52173282 and 21935005)the National Key R&D Program of China(2020YFA0714601)+2 种基金Guangdong Basic and Applied Basic Research Foundation(No.2022A1515140078)Jihua Laboratory(X190321TF190 and X210221TP210)Foshan Science and Technology Innovation Team Special Project(1920001000128)。
文摘Free of noble-metal and high in unit internal quantum efficiency of electroluminescence,organic molecules with thermally activated delayed fluorescence(TADF)features pose the potential to substitute metal-based phosphorescence materials and serve as the new-generation emitters for the mass production of organic light emitting diodes(OLEDs)display.Predicting the function of TADF emitters beyond classic chemical synthesis and material characterization experiments remains a great challenge.The advances in deep learning(DL)based artificial intelligence(Al)offer an exciting opportunity for screening high-performance TADF materials through efficiency evaluation.However,data-driven material screening approaches with the capacity to access the excited state properties of TADF emitters remain extremely difficult and largely unaddressed.nspired by the fundamental principle that the excited state properties of TADF molecules are strongly dependent on their D-A geometric and electronic structures,we developed the Electronic Structure-lnfused Network(ESIN)for TADF emitter screening.Designed with capacities of accurate prediction of the photoluminescence quantum yields(PLQYs)of TADF molecules based on elemental molecular geometry and orbital information and integrated with frontier molecular orbitals(FMOs)weightbased representation and modeling features,ESIN is a promising interpretable tool for emission efficiency evaluation and moleculardesign of TADF emitters.
