Dear Editor,This letter presents a novel approach to the data-driven control of unknown nonlinear systems.By leveraging online sparse identification based on the Koopman operator,a high-dimensional linear system model...Dear Editor,This letter presents a novel approach to the data-driven control of unknown nonlinear systems.By leveraging online sparse identification based on the Koopman operator,a high-dimensional linear system model approximating the actual system is obtained online.The upper bound of the discrepancy between the identified model and the actual system is estimated using real-time prediction error,which is then utilized in the design of a tube-based robust model predictive controller.The effectiveness of the proposed approach is validated by numerical simulation.展开更多
With the rapidly increasing amount of materials data being generated in a variety of projects,efficient and accurate classification of atomistic structures is essential.A current barrier to effective database queries ...With the rapidly increasing amount of materials data being generated in a variety of projects,efficient and accurate classification of atomistic structures is essential.A current barrier to effective database queries lies in the often ambiguous,inconsistent,or completely missing classification of existing data,highlighting the need for standardized,automated,and verifiable classification methods.This work proposes a robust solution for identifying and classifying a wide spectrum of materials through an iterative technique,called symmetry-based clustering(SBC).Because SBC is not a machine learningbased method,it requires no prior training.Instead,it identifies clusters in atomistic systems by automatically recognizing common unit cells.We demonstrate the potential of SBC to provide automated,reliable classification and to reveal well-known symmetry properties of various materials.Even noisy systems are shown to be classifiable,showing the suitability of our algorithm for real-world data applications.The software implementation is provided in the open-source Python package,MatID,exploiting synergies with popular atomic-structure manipulation libraries and extending the accessibility of those libraries through the NOMAD platform.展开更多
Intelligent fluorescence detection for disease diagnosis has become a research hotspot. In the era of big data,machine learning (ML) for analyzing data and mining will be widely used in drug and biomarker detection. A...Intelligent fluorescence detection for disease diagnosis has become a research hotspot. In the era of big data,machine learning (ML) for analyzing data and mining will be widely used in drug and biomarker detection. A novel hydrogen-bonded organic framework (HOF) HOF-DBA with good luminescence properties was successfully prepared from aromatic tetracarboxylic acid (4,4’-(anthracene-9,10-diyl)dibenzoic acid) by a solvothermal method. HOF-DBA acted as a fluorescent sensor to quantitatively identify the concentration of nitrofurazone (NFZ) by photo-induced electron transfer (PET) and competitive absorption. The detection limit was lower than 0.002 μg mL^(-1),with high sensitivity and good reproducibility. HOF-DBA also exhibited highly efficient turn-up fluorescence sensing of γ-aminobutyric acid (GABA,osteoporosis biomarker) in aqueous solution and serum. In addition,a back-propagation neural network (BPNN) model based on HOF-DBA and GABA was constructed for the first time. The actual test data showed that BPNN could accurately distinguish GABA concentrations by the maximum depth likelihood method. This work provides new insights into HOF-based sensors and combines fluorescence sensing with deep ML to achieve intelligent fluorescence detection of GABA.展开更多
基金supported by the National Natural Science Foundation of China(62473020).
文摘Dear Editor,This letter presents a novel approach to the data-driven control of unknown nonlinear systems.By leveraging online sparse identification based on the Koopman operator,a high-dimensional linear system model approximating the actual system is obtained online.The upper bound of the discrepancy between the identified model and the actual system is estimated using real-time prediction error,which is then utilized in the design of a tube-based robust model predictive controller.The effectiveness of the proposed approach is validated by numerical simulation.
基金Supported by National Natural Science Foundation of China(61304079,61125306,61034002)the Open Research Project from SKLMCCS(20120106)+1 种基金the Fundamental Research Funds for the Central Universities(FRF-TP-13-018A)the China Postdoctoral Science.Foundation(201_3M_5305_27)
基金funding by the German Research Foundation(DFG)through the NFDI consortium FAIRmat,project 460197019.
文摘With the rapidly increasing amount of materials data being generated in a variety of projects,efficient and accurate classification of atomistic structures is essential.A current barrier to effective database queries lies in the often ambiguous,inconsistent,or completely missing classification of existing data,highlighting the need for standardized,automated,and verifiable classification methods.This work proposes a robust solution for identifying and classifying a wide spectrum of materials through an iterative technique,called symmetry-based clustering(SBC).Because SBC is not a machine learningbased method,it requires no prior training.Instead,it identifies clusters in atomistic systems by automatically recognizing common unit cells.We demonstrate the potential of SBC to provide automated,reliable classification and to reveal well-known symmetry properties of various materials.Even noisy systems are shown to be classifiable,showing the suitability of our algorithm for real-world data applications.The software implementation is provided in the open-source Python package,MatID,exploiting synergies with popular atomic-structure manipulation libraries and extending the accessibility of those libraries through the NOMAD platform.
基金supported by the National Natural Science Foundation of China(21971194)Major Scientific and Technological Innovation Projects of Shandong Province(2019JZZY010503)the Science&Technology Commission of Shanghai Municipality(14DZ2261100).
文摘Intelligent fluorescence detection for disease diagnosis has become a research hotspot. In the era of big data,machine learning (ML) for analyzing data and mining will be widely used in drug and biomarker detection. A novel hydrogen-bonded organic framework (HOF) HOF-DBA with good luminescence properties was successfully prepared from aromatic tetracarboxylic acid (4,4’-(anthracene-9,10-diyl)dibenzoic acid) by a solvothermal method. HOF-DBA acted as a fluorescent sensor to quantitatively identify the concentration of nitrofurazone (NFZ) by photo-induced electron transfer (PET) and competitive absorption. The detection limit was lower than 0.002 μg mL^(-1),with high sensitivity and good reproducibility. HOF-DBA also exhibited highly efficient turn-up fluorescence sensing of γ-aminobutyric acid (GABA,osteoporosis biomarker) in aqueous solution and serum. In addition,a back-propagation neural network (BPNN) model based on HOF-DBA and GABA was constructed for the first time. The actual test data showed that BPNN could accurately distinguish GABA concentrations by the maximum depth likelihood method. This work provides new insights into HOF-based sensors and combines fluorescence sensing with deep ML to achieve intelligent fluorescence detection of GABA.
