Schizophrenia is a devastating mental disorder affecting 20 million people worldwide.Early diagnosis is crucial for disease management and improvement in prognosis,and diagnostic biomarkerscan serveasobjective indicat...Schizophrenia is a devastating mental disorder affecting 20 million people worldwide.Early diagnosis is crucial for disease management and improvement in prognosis,and diagnostic biomarkerscan serveasobjective indicators for the early screening of the disease.Based on the observation of diminished flush responses to niacin in patients with schizophrenia Horrobin proposed anoninvasive niacin skin flush screening for schizophrenia.展开更多
Particle image velocimetry(PIV)is an essential method in experimental fluid dynamics.In recent years,the development of deep learning‐based methods has inspired new ap-proaches to tackle the PIV problem,which conside...Particle image velocimetry(PIV)is an essential method in experimental fluid dynamics.In recent years,the development of deep learning‐based methods has inspired new ap-proaches to tackle the PIV problem,which considerably improves the accuracy of PIV.However,the supervised learning of PIV is driven by large volumes of data with ground truth information.Therefore,the authors consider unsupervised PIV methods.There has been some work on unsupervised PIV,but they are not nearly as effective as supervised learning PIV.The authors try to improve the effectiveness and accuracy of unsupervised PIV by adding classical PIV methods and physical constraints.In this paper,the authors propose an unsupervised PIV method combined with the cross‐correlation method and divergence‐free constraint,which obtains better performance than other unsupervised PIV methods.The authors compare some classical PIV methods and some deep learning methods,such as LiteFlowNet,LiteFlowNet‐en,and UnLiteFlowNet with the authors’model on the synthetic dataset.Besides,the authors contrast the results of LiteFlowNet,UnLiteFlowNet and the authors’model on experimental particle images.As a result,the authors’model shows comparable performance with classical PIV methods as well as supervised PIV methods and outperforms the previous unsupervised PIV method in most flow cases.展开更多
This study considers a control problem related to the regulation of fractional-order systems controlled by fractional order proportional-integral-derivative controllers(PI^(λ)D^(μ)).The stability result of PIλDμ-b...This study considers a control problem related to the regulation of fractional-order systems controlled by fractional order proportional-integral-derivative controllers(PI^(λ)D^(μ)).The stability result of PIλDμ-based control systems is provided,such that the closed-loop systems can accomplish global stabilisation under some suitable conditions related to the system uncertainties.Finally,a simulation is demonstrated to verify the results.展开更多
基金This study was supported by National Natural Science Foundation of China(82171544,81971251,81671329,and 81871050),Science and Technology Commission of Shanghai Municipality(19441907800,16ZR1430500,19ZR1445200,17411953100,21S31903100,2018SHZDZX01,19410710800,19411969100,19411950800)。
文摘Schizophrenia is a devastating mental disorder affecting 20 million people worldwide.Early diagnosis is crucial for disease management and improvement in prognosis,and diagnostic biomarkerscan serveasobjective indicators for the early screening of the disease.Based on the observation of diminished flush responses to niacin in patients with schizophrenia Horrobin proposed anoninvasive niacin skin flush screening for schizophrenia.
基金Natural Science Foundation of Zhejiang Province,Grant/Award Number:LY21F030003National Key Research and Development Program of China,Grant/Award Number:2019YFB1705800National Natural Science Foundation of China,Grant/Award Number:61973270。
文摘Particle image velocimetry(PIV)is an essential method in experimental fluid dynamics.In recent years,the development of deep learning‐based methods has inspired new ap-proaches to tackle the PIV problem,which considerably improves the accuracy of PIV.However,the supervised learning of PIV is driven by large volumes of data with ground truth information.Therefore,the authors consider unsupervised PIV methods.There has been some work on unsupervised PIV,but they are not nearly as effective as supervised learning PIV.The authors try to improve the effectiveness and accuracy of unsupervised PIV by adding classical PIV methods and physical constraints.In this paper,the authors propose an unsupervised PIV method combined with the cross‐correlation method and divergence‐free constraint,which obtains better performance than other unsupervised PIV methods.The authors compare some classical PIV methods and some deep learning methods,such as LiteFlowNet,LiteFlowNet‐en,and UnLiteFlowNet with the authors’model on the synthetic dataset.Besides,the authors contrast the results of LiteFlowNet,UnLiteFlowNet and the authors’model on experimental particle images.As a result,the authors’model shows comparable performance with classical PIV methods as well as supervised PIV methods and outperforms the previous unsupervised PIV method in most flow cases.
基金National Natural Science Foundation of China,Grant/Award Number:61973270National Key R&D Programme of China,Grant/Award Number:2019YFB1705800Zhejiang Provincial Natural Science Foundation of China,Grant/Award Number:LY21F030003。
文摘This study considers a control problem related to the regulation of fractional-order systems controlled by fractional order proportional-integral-derivative controllers(PI^(λ)D^(μ)).The stability result of PIλDμ-based control systems is provided,such that the closed-loop systems can accomplish global stabilisation under some suitable conditions related to the system uncertainties.Finally,a simulation is demonstrated to verify the results.
