Since the high penetration of renewable energy complicates the dynamic characteristics of the AC power electronic system(ACPES),it is essential to establish an accurate dynamic model to obtain its dynamic behavior for...Since the high penetration of renewable energy complicates the dynamic characteristics of the AC power electronic system(ACPES),it is essential to establish an accurate dynamic model to obtain its dynamic behavior for ensure the safe and stable operation of the system.However,due to the no or limited internal control details,the state-space modeling method cannot be realized.It leads to the ACPES system becoming a black-box dynamic system.The dynamic modeling method based on deep neural network can simulate the dynamic behavior using port data without obtaining internal control details.However,deep neural network modeling methods are rarely systematically evaluated.In practice,the construction of neural network faces the selection of massive data and various network structure parameters.However,different sample distributions make the trained network performance quite different.Different network structure hyperparameters also mean different convergence time.Due to the lack of systematic evaluation and targeted suggestions,neural network modeling with high precision and high training speed cannot be realized quickly and conveniently in practical engineering applications.To fill this gap,this paper systematically evaluates the deep neural network from sample distribution and structural hyperparameter selection.The influence on modeling accuracy is analyzed in detail,then some modeling suggestions are presented.Simulation results under multiple operating points verify the effectiveness of the proposed method.展开更多
Objective To investigate th e anti-tumor effects of GeM10 by the natural killer(NK) cells activities and th e production of Interleukin-2 (IL-2) in peripheral blood mononuclear cells (PB MNCs). Methods Assay of hum...Objective To investigate th e anti-tumor effects of GeM10 by the natural killer(NK) cells activities and th e production of Interleukin-2 (IL-2) in peripheral blood mononuclear cells (PB MNCs). Methods Assay of human NK cells activities by dye reject ion assay in vitro and production of IL-2 in PBMNC by IL-2 bioassay with I L-2 dependent cell line CTLL2 and MTT colorometric method. Results GeM10 could significantly stimulate NK activities (60μg·mL -1 G eM10: 17.077±7.665, 120μg·mL -1 GeM10: 24.9±13.04; control: 7.72±4 .64, P< 0.05). GeM10 could up-regulate the production of IL-2 of PBMNCs in tumor patients(60μg·mL -1 GeM10: 2.965± 1.183; 120μg·mL -1 GeM10: 2.28±0.847; control: 1.792±0.823, P<0.05).Conclu si on The GeM10 not only can stimulate the NK activities but also increase the IL-2 production by PBMNCs in tumor patients. These findings indicate that the GeM10 may have promise as an anti-tumor drug and a biological response modi fier in clinic.展开更多
基金supported in part by the Science Search Foundation of Liaoning Educational Department。
文摘Since the high penetration of renewable energy complicates the dynamic characteristics of the AC power electronic system(ACPES),it is essential to establish an accurate dynamic model to obtain its dynamic behavior for ensure the safe and stable operation of the system.However,due to the no or limited internal control details,the state-space modeling method cannot be realized.It leads to the ACPES system becoming a black-box dynamic system.The dynamic modeling method based on deep neural network can simulate the dynamic behavior using port data without obtaining internal control details.However,deep neural network modeling methods are rarely systematically evaluated.In practice,the construction of neural network faces the selection of massive data and various network structure parameters.However,different sample distributions make the trained network performance quite different.Different network structure hyperparameters also mean different convergence time.Due to the lack of systematic evaluation and targeted suggestions,neural network modeling with high precision and high training speed cannot be realized quickly and conveniently in practical engineering applications.To fill this gap,this paper systematically evaluates the deep neural network from sample distribution and structural hyperparameter selection.The influence on modeling accuracy is analyzed in detail,then some modeling suggestions are presented.Simulation results under multiple operating points verify the effectiveness of the proposed method.
文摘Objective To investigate th e anti-tumor effects of GeM10 by the natural killer(NK) cells activities and th e production of Interleukin-2 (IL-2) in peripheral blood mononuclear cells (PB MNCs). Methods Assay of human NK cells activities by dye reject ion assay in vitro and production of IL-2 in PBMNC by IL-2 bioassay with I L-2 dependent cell line CTLL2 and MTT colorometric method. Results GeM10 could significantly stimulate NK activities (60μg·mL -1 G eM10: 17.077±7.665, 120μg·mL -1 GeM10: 24.9±13.04; control: 7.72±4 .64, P< 0.05). GeM10 could up-regulate the production of IL-2 of PBMNCs in tumor patients(60μg·mL -1 GeM10: 2.965± 1.183; 120μg·mL -1 GeM10: 2.28±0.847; control: 1.792±0.823, P<0.05).Conclu si on The GeM10 not only can stimulate the NK activities but also increase the IL-2 production by PBMNCs in tumor patients. These findings indicate that the GeM10 may have promise as an anti-tumor drug and a biological response modi fier in clinic.
基金supported by the Natural Science Foundation Projects in Chongqing(CSTC,2009BB5403)Education Research Project of Chongqing Science and Technology Board (KJ090321)Medical Science and Technology Research Projects of Health Bureau of Chongqing, P.R. China(2009-2-216)
