Dilute solution behavior of chitosan was studied in formic acid, acetic acid, lactic acid andhydrochloric acid aqueous solution under different pH values. The reduced viscosities, η_(sp)/C,ofchitosan solutions were d...Dilute solution behavior of chitosan was studied in formic acid, acetic acid, lactic acid andhydrochloric acid aqueous solution under different pH values. The reduced viscosities, η_(sp)/C,ofchitosan solutions were dependent on the properties of acid and pH value of solvents. For a givenchitosan concentration, η^(sp)/C decreased with the increase of acid concentration, or decreasing pHof solvent, indicating shielding effect of excessive acid similar to adding salt into solution. Thestabilities of dilute chitosan solution in formic acid and lactic acid were better than that in acetic acid and hvdrochloric acid.展开更多
In this paper, for the unbalanced Feistel network which employs diffusion matrices in a switching way, we study the fixed number of its differential active S-boxes. Firstly we obtain some lower bounds of the different...In this paper, for the unbalanced Feistel network which employs diffusion matrices in a switching way, we study the fixed number of its differential active S-boxes. Firstly we obtain some lower bounds of the differential active S-boxes for m, 2m and 3m rounds of Feistel structure, respectively. By concatenating these rounds, a fixed number of differential active S-boxes for arbitrary round number is derived. Our results imply that the unbalanced Feistel network using DSM is more secure than the traditional structure.展开更多
Partial Differential Equations(PDEs)are model candidates of soft sensing for aero-engine health management units.The existing Physics-Informed Neural Networks(PINNs)have made achievements.However,unmeasurable aero-eng...Partial Differential Equations(PDEs)are model candidates of soft sensing for aero-engine health management units.The existing Physics-Informed Neural Networks(PINNs)have made achievements.However,unmeasurable aero-engine driving sources lead to unknown PDE driving terms,which weaken PINNs feasibility.To this end,Physically Informed Hierarchical Learning followed by Recurrent-Prediction Term(PIHL-RPT)is proposed.First,PIHL is proposed for learning nonhomogeneous PDE solutions,in which two networks NetU and NetG are constructed.NetU is for learning solutions satisfying PDEs;NetG is for learning driving terms to regularize NetU training.Then,we propose a hierarchical learning strategy to optimize and couple NetU and NetG,which are integrated into a data-physics-hybrid loss function.Besides,we prove PIHL-RPT can iteratively generate a series of networks converging to a function,which can approximate a solution to well-posed PDE.Furthermore,RPT is proposed for prediction improvement of PIHL,in which network NetU-RP is constructed to compensate for information loss caused by data sampling and driving sources’immeasurability.Finally,artificial datasets and practical vibration process datasets from our wear experiment platform are used to verify the feasibility and effectiveness of PIHL-RPT based soft sensing.Meanwhile,comparisons with relevant methods,discussions,and PIHL-RPT based health monitoring example are given.展开更多
In this paper, we investigate stochastic asymptotic stability of the zero solution for certain third-order nonlinear stochastic delay differential equations by constructing Lyapunov functionals.
The differences of food culture play an important role in cross-cultural communication.Learn the cultural rooted causes of food culture between Chinese and Western countries,will promote mutual understanding between p...The differences of food culture play an important role in cross-cultural communication.Learn the cultural rooted causes of food culture between Chinese and Western countries,will promote mutual understanding between people and enjoy different feelings different foods brings,enhance cultural exchange,complement and integration.展开更多
基金The project is supported by the National Natural Science Foundation of China
文摘Dilute solution behavior of chitosan was studied in formic acid, acetic acid, lactic acid andhydrochloric acid aqueous solution under different pH values. The reduced viscosities, η_(sp)/C,ofchitosan solutions were dependent on the properties of acid and pH value of solvents. For a givenchitosan concentration, η^(sp)/C decreased with the increase of acid concentration, or decreasing pHof solvent, indicating shielding effect of excessive acid similar to adding salt into solution. Thestabilities of dilute chitosan solution in formic acid and lactic acid were better than that in acetic acid and hvdrochloric acid.
基金Supported by the National Natural Science Foundation of China(11204379)Innovation Scientists and Technicians Troop Construction Projects of Henan Province(104100510025)
文摘In this paper, for the unbalanced Feistel network which employs diffusion matrices in a switching way, we study the fixed number of its differential active S-boxes. Firstly we obtain some lower bounds of the differential active S-boxes for m, 2m and 3m rounds of Feistel structure, respectively. By concatenating these rounds, a fixed number of differential active S-boxes for arbitrary round number is derived. Our results imply that the unbalanced Feistel network using DSM is more secure than the traditional structure.
基金supported in part by the National Science and Technology Major Project of China(No.2019-I-0019-0018)the National Natural Science Foundation of China(Nos.61890920,61890921,12302065 and 12172073).
文摘Partial Differential Equations(PDEs)are model candidates of soft sensing for aero-engine health management units.The existing Physics-Informed Neural Networks(PINNs)have made achievements.However,unmeasurable aero-engine driving sources lead to unknown PDE driving terms,which weaken PINNs feasibility.To this end,Physically Informed Hierarchical Learning followed by Recurrent-Prediction Term(PIHL-RPT)is proposed.First,PIHL is proposed for learning nonhomogeneous PDE solutions,in which two networks NetU and NetG are constructed.NetU is for learning solutions satisfying PDEs;NetG is for learning driving terms to regularize NetU training.Then,we propose a hierarchical learning strategy to optimize and couple NetU and NetG,which are integrated into a data-physics-hybrid loss function.Besides,we prove PIHL-RPT can iteratively generate a series of networks converging to a function,which can approximate a solution to well-posed PDE.Furthermore,RPT is proposed for prediction improvement of PIHL,in which network NetU-RP is constructed to compensate for information loss caused by data sampling and driving sources’immeasurability.Finally,artificial datasets and practical vibration process datasets from our wear experiment platform are used to verify the feasibility and effectiveness of PIHL-RPT based soft sensing.Meanwhile,comparisons with relevant methods,discussions,and PIHL-RPT based health monitoring example are given.
文摘In this paper, we investigate stochastic asymptotic stability of the zero solution for certain third-order nonlinear stochastic delay differential equations by constructing Lyapunov functionals.
文摘The differences of food culture play an important role in cross-cultural communication.Learn the cultural rooted causes of food culture between Chinese and Western countries,will promote mutual understanding between people and enjoy different feelings different foods brings,enhance cultural exchange,complement and integration.
